Tree- [25][26][27] and rule-based [28] models have been used as mimic models.

mimic models are not guaranteed to match the behavior of the original model.


To address this apparent dichotomy between performance and interpretability [6], researchers have developed a number of attribution methods that provide estimates of the importance of input features towards a model's output for specific types of models [4,[7][8][9][10][11][12][13][14][15], and for any machine-learning model [6,16].

providing fast and accurate feature importance estimates for any machine-learning model is challenging because there exists a wide variety of intricate machine-learning models with different underlying model structures, algorithms, and decision functions, which makes it difficult to develop an optimised and unified approach to importance attribution.


Despite the population differences, however, we observe "days_visited_vrs_pre" continues to have a very significant positive association.

we consider estimation of heterogeneous treatment effects with respect to a set of features X, of an endogenous treatment T on an outcome Y with an instrument Z.


For the UVFA network, it only needs to remember experiences in a local neighborhood.

the training procedure requires much lower sample complexity.


One can pre-train the HER agent and then build map for planner.

as an off-policy algorithm, HER can work with arbitrary exploration policy.


In their methods, the agent has to learn the high-level policy as another RL problem.

we exploit the structure of our universal goal reaching problem and find the high-level policy by solving a pairwise shortest path problem in a small-scale graph, thus more data-efficient.


Given a predefined map of known nodes, edges, and weights, it runs the value iteration algorithm by ingeniously simulating the process through a convolutional neural network [19].

we construct the map based upon the learned local model.


A UVFA learns to predict the cumulative rewards between all state-goal pairs.

empirically, the value function for long-range goals is always hard to estimate and may consequently result in failed policy.


First, it takes longer time to sample enough state-goal pairs.

at the early stage, only few state-goal samples have been collected, so learning from them requires heavy extrapolation by networks, which is well known to be unreliable.


such map induces a new environment, where the action is to choose to move to another landmark.


To connect nearby landmarks, it leveraged a physical engine, which depends on sophisticated domain knowledge and limits its usage to other general RL tasks.

for a given goal, the agent can receive non-trivial rewards only when it can reach the goal.


Interestingly, both conditions can be met with designs that involve the Lyapunov function V itself.

the methods proposed in this paper can be understood as variable-stepsize discretizations, which are a popular class of methods in numerical analysis.


The real-time implementation of the closed-loop system can be tackled by considering a sample-and-hold implementation of (1) of the form with p(0) = p, where p is a sampled version of the state p. The most common approach consists of periodically sampling the state, selecting a stepsize small enough to ensure that the function V remains monotonically decreasing for the resulting system.

the result in Theorem 3.3 links the convergence rate of the discrete-time algorithm to the Lyapunov decay and the stepsize of the state-triggered implementation of the continuous-time dynamics.


Their runtime is limited by the necessary Ω(1/γ) degree for polynomial approximation of the sign function shown by Eremenko and Yuditskii [9].

to obtain nearly linear runtime, a new insight is required.


To improve upon this bound, we apply a similar approach of working with the convolution of f with a Gaussian.

instead of applying standard stochastic AGD we consider accelerated methods which build a more sophisticated model of the convolved function in parallel.


Since the convolved function can be accessed efficiently in parallel by random sampling, working with the convolved function is comparable to working with the original function in terms of query depth (up to the sampling error).

the paper achieves its depth bound by trading off the error induced by convolution with the depth improvements gained from stochastic variants of AGD.


Note that for any fixed query x, the probability (with respect to the random draw of v i ) that x is in C i is polynomially small in d. We now define the wall W as follows: it is equal to the function h outside of the correlation cones and the ball of radius , and it is extended by convexity to the rest of the unit ball.

near-optimal convergence for such methods under Lipschitz bounds on the k-th derivatives were recently given by [Gasnikov et al., 2018 (and follow from our framework).


In this section, we establish the consistency of Interaction Hard Thresholding, in the standard setting where sparse recovery is established.

this is our main result, described in Theorem 4.


Notice that IHT, if used for our quadratic problem, still suffers from quadratic space, similar to other techniques, e.g., the Lasso, basis pursuit, least angle regression [29,6,8].

[19] recently considers a variant of IHT, where for each sample, only a random subset of features is observed.


we establish convergence results under deterministic assumptions on the data and function, including restricted strong convexity (RSC) and smoothness (RSM).


Regression with interaction terms has been studied in the statistics community.

many existing results consider under the assumption of strong/weak hierarchical (SH/WH) structure: the coefficient of the interaction term x j1 x j2 is non-zero only when both coefficients of x j1 and x j2 are (or at least one of them is) non-zero.


The inclusion of such higher-order "interaction terms" in regression often provides an easy way to increase accuracy in already-high-dimensional problems.

this explodes the problem dimension from linear O(p) to quadratic O(p 2 ), and it is common to look for sparse interactions (typically via heuristics).


If one wants to make an existing domain adaptation algorithm private with respect to any pair of participants, one has to add noise to gradients computed at every step.

any entity can compute the marginal gain of a new point y by Thus, the curator needs to only share ) and h 2 is differentially private with respect to h 1 (x i ).


Thus, it is sufficient to have a significantly smaller number of iterations than that in Theorem 4.


Further, over the course of multiple releases, any participant must not be able to acquire any information about previous data points of other participants.

the key issue is that the releases of the curator must be differentially private while enabling the computation of the (non-linear) marginal gain, over all the iterations of the protocol.


Small incremental changes to the empirical distribution need to be matched incrementally.

it is sufficient to have a significantly smaller number of iterations than that in Theorem 4.


The authors used parameterized Hamiltonian flows for distribution embeddings, which limits its scalability and expressiveness.

dDE fails if the search space does not contain the target distribution, while our formulation only requires the support of the proposal distribution to cover that of the target.


More recently, dynamic dual embedding (DDE) explored a primal-dual view of MLE [15,16], while Stein implicit learning (SIL) [46,41] and kernel score estimation [60] match the landscape of the potential with that of kernel-smoothed empirical observations.

these approaches are susceptible to poor scalability (SM, MCMC-MLE), biased estimation (CD), and computational (DDE, SIL) and statistical (NCE) efficiency issues.


Our next experiment considers FML-based training for latent variable models and generative modeling tasks.

we directly benchmark FML against the VAE [37], for modeling complex distributions, such as images and natural language, for real-world applications.


In practice, this integral is approximated with averaging over a finite number of Monte Carlo samples.

using the existing finite-sample Monte Carlo estimate of Zθ¸ will lead to a biased approximation of the log-likelihood objective (see Section 2.1).


It is easy to show a similar convergence rate for an adaptation of this algorithm to our setting (see Appendix C), but we stick to our present algorithm because of its simplicity.

postShift performs the best on the fairness metric, but on all datasets except COMPAS, fairs poorly on the constraint.


We consider the fairness goal of training a classifier that yields at least as high a F-measure for the protected group as it does for the rest of the population, and impose this as a constraint.

we seek to maximize the overall F-measure subject to the constraint: Fmeasure prt Fmeasure other 0.02.


Our approach generalizes many existing algorithms (see Table 2), and makes possible new algorithms with more flexibility and tighter handling of non-linear rate constraints.

we give a new method (Algorithm 2) that can handle a wider range of performance metrics than previous surrogate methods (such as e.g. KL-divergence based metrics that only take inputs from a restricted range), and can be applied to constrained training problems without the risk of over-constraining the model because it needs to use surrogates less.


[33] handle constraints that are sums-of-ratios, but do so by solving a large number of linearly constrained sub-problems, with the number of sub-problems growing exponentially with the number of rates.

each player can do Best Response (BR), Online Gradient Descent (OGD) or Follow-The-Leader (FTL), and the game is zero-sum (ZS) or not.


Several papers [21][22][23] have made initial progress in defining scattering graph representation and studying their stability properties with respect to metric deformations of the domain.

most of these results offer bounds that depend on the graph topology and do not hold for certain graphs or when graphs are very large.


Regular scattering transforms have been proven invariant to translations and stable to perturbations (or deformations) that are close to translations.

the difference on the scattering transform of the original data and that of the perturbed data, is proportional to the size of the perturbation.


The effect of the pointwise nonlinearities is to cause a spillage of information throughout the frequency spectrum, in particular, into low-eigenvalue frequencies, which can then be discriminated in a stable fashion.

gSTs are stable and discriminative information processing architectures.


That is, by changing the eigenvalues λi on which the wavelet h(λ) is instantiated, the filter taps hi are changed, and so does the output λi in virtue of (6).

the first necessary result is to quantify the change in the output of a wavelet filter.


We begin by formulating an optimization problem whose solution is a form of optimal representation.

we seek a state representation from which we can best approximate the value function of any stationary policy for a given Markov Decision Process.


While a natural choice is to look for policies that maximize representation error, this poses the problem of how to parametrize the policies themselves.

a policy parametrized using the representation φ may not provide a sufficient degree of "adversariality".


So far we have argued that solving the RLP leads to a representation which is optimal in a meaningful sense.

solving the RLP seems computationally intractable: there are an exponential number of deterministic policies to consider (Prop.


We contrast the AVF-driven representation with one learned by predicting the value function of random deterministic policies (RP).

these policies are generated by assigning an action uniformly at random to each state.


The good empirical performance of distributional reinforcement learning (Bellemare et al., 2017) has also been attributed to representation learning effects, with recent visualizations supporting this claim (Such et al., 2019).

while there is now conclusive empirical evidence of the usefulness of auxiliary tasks, their design and justification remain on the whole ad-hoc.


we leverage this perspective to provide formal evidence regarding the usefulness of value functions as auxiliary tasks.


The seminal work by Wen and Van Roy [36] proposed an algorithm, optimistic constraint propagation (OCP), which enjoys polynomial sample complexity bounds for a family of Q-function classes, including the linear function class as a special case.

their algorithm can only deal with deterministic systems, i.e., both transition dynamics and rewards are deterministic.


More recent papers provided refined analyses that exploit benign properties of the MDP, e.g., the gap between the optimal action and the rest [28,38], which our algorithm also utilizes.

it is hard to generalize the exploration techniques in these previous works, since they all rely on the fact that the total number of states is finite.


The idea of using function approximation was proposed at least 60 years ago [27], where linear functions are used to approximate the value functions in playing checkers.

even in the most basic setting, Q-learning with linear function approximation, there is no provably efficient algorithm in the general stochastic setting.


In practice, it is impossible to get access to the underlying distributions D 1 and D 2 .

we use samples generated from these two distributions instead.


We further show this potential function is at always polynomially upper bounded by the size of the policy set.

we can conclude the size of IIh is polynomially upper bounded.


This is because of approximations when learner is computing the policy via projection, which in turn leads to errors on the teacher side when approximating Î©L r (refer to discussion in Footnote 2).

the numbers show the average reward over 10 randomly generated object-worlds.


The improvement on Sketch is notable because Sketch is the only colorless domain out of the four domains in PACS.

when tested with the other three domains, a model may learn to exploit the color information, which is usually local, to predict, but when tested with Sketch domain, the model has to learn colorless concepts to make good predictions.


He is also grateful to Salesforce Research, Facebook Research, and Amazon AI for faculty awards supporting his lab's research on robust deep learning under distribution shift.

our paper extends the setup of (Wang et al., 2019) and empirically studies the problem of developing image classifiers robust to a variety of natural shifts without leveraging any domain information at training or deployment time.


While we did not give a clear choice of which PAR to use, we note that none of the variants of PAR outperform the vanilla PAR consistently.

the vanilla PAR outperforms most comparable baselines in the vast majority of our experiments.


In addition to the superior performances we achieved through these experiments, we expected to further challenge our method at real-world scale.

we also constructed a dataset that matches the ImageNet classification validation set in classes and scales but contains only sketch-alike images.


The standard estimator for the distribution mean is the sample mean.

for distributions with unbounded support, e.g., Gaussians, the global sensitivity of the sample mean is infinite.


We provide methods for scaling noise in an instance-dependent way and demonstrate that they provide greater accuracy under average-case distributional assumptions.

we consider the basic problem of privately estimating the mean of a real distribution from i.i.d. samples.


Of course, the mean and median are closely related.

there is a subtle -but important -difference: Whereas the standard deviation provides the appropriate scale for the accuracy of an estimate of the mean, the reciprocal of the probability density around the median provides the appropriate scale for an estimate of the median [31].


If the stronger pure differential privacy guarantee is preferable, the Student's T distribution is likely best.

this has no third moment and consequently heavy tails.


the mean and median (the extreme cases of the trimmed mean) have both been studied extensively in the differential privacy literature.


Feldman and Steinke [16] use a median-of-means algorithm to privately estimate the mean, yielding a guarantee similar to Theorem 8.

their algorithm partitions the dataset into evenlysized subdatasets and computes the mean of each subdataset.


A recent line of work [15,5,26,4] has developed variants of differential privacy which permit tighter analyses of privacy loss over multiple releases of statistics as compared to both pure and approximate differential privacy.

the notion of concentrated differential privacy (CDP) [15,5] has a simple and tight composition theorem for analyzing how privacy degrades over many releases while accommodating most of the key algorithms in the differential privacy literature, including addition of Gaussian noise calibrated to global sensitivity.


The method makes use of two Q-functions to overcome the positive bias incurred by overestimation of Q-value, which is known to yield a poor performance [15,9].

these two Q-functions are parametrized by different parameters θi and are independently trained to minimize JQ(θi).


An MDP is defined by a tuple (S, A, P, r, P 0 , Î³), where S is the state space and A the action space with |A| actions.

we have shown there are many regularization functions that can lead to a sparse but multi-modal optimal policy such as trigonometric and exponential functions.


In practice, we apply neural networks to parameterize the Q-value and policy to increase expressive power.

we model the regularized Q-value function Qθ(s, a) and a tractable policy πψ(a|s).


Moreover, under our framework, many regularization terms can bring multi-modality and sparsity, which are potentially useful in reinforcement learning.

we present sufficient and necessary conditions that induce a sparse optimal policy.


When a suggested routine is unfortunately congested, an alternative routine could be provided by a multi-modal policy, which can't be provided by a deterministic policy without evoking a new computation.

in a real-life application, we hope the optimal policy to possess thee property of multi-modality.


we have presented the logical and mathematical foundations of these properties and also conducted the experimental results.


in Fig. 2 (b), we analyze the functional changes over time.

we fit the linear mapping function between neuron's firing rate and target trajectory in every 20-second temporal window with a stride of 1 second, and illustrate the distribution of the slope parameter.


Lastly, since the stratified structure contains all of the examples, it is managed mostly on disk, with a small in-memory buffer to speed up I/O operations.

this performance requires that the memory size is sufficient to hold a 2-3 multiple of the training set size.


We distinguish between two types of average potentials: the expected potential or true potential: and the average potential or empirical potential: The ultimate goal of the boosting algorithm is to minimize the expected potential, which determines the true error rate.

most boosting algorithms, including XGBoost and LightGBM, focus on minimizing the empirical potential b (S T ), and rely on the limited capacity of the weak rules to guarantee that the true potential is also small.


The value of n eff will drop to about 80.

it is achieved with no penalty in accuracy, and with a speedup of 10-100 over XGBoost in disk mode.


The agent's uncertainty over the environment is represented as a prior distribution over θ.

θ¸ will be treated as a random variable in the agent's mind.


As we will see in the following two lemmas, the structure of MDPs allows us to break down the deviation of value functions and the information gain at the level of state-action pairs.

it would be sufficient to construct confidence sets for the reward and transition functions for individual state-action pairs.


By Lemma 4, since noise is additive, it is sufficient to construct confidence sets on Y a = Î¸ a. Lemma 5.

if (1) is satisfied with reasonable values for Î“ and , a large expected regret on the left-hand side would imply that the right-hand side must be large as well, meaning that the agent should gain a lot of information about the environment.


Hoeffding's inequality [10] or empirical Bernstein's inequality [14], to find a valid threshold hα(yn).

they are not data-dependent and only use the tail information, rather than fully exploit the whole distribution knowledge.


We use similar notations for Py(·), Ey(·)  with respect to y only.


details are deferred to Section B.3 in the supplement.


As mentioned in [36], there are two disadvantages for median-of-means approach: (a) it involves an additional tuning parameter; (b) it is numerically unstable for small sample size.

we identify a class of heavy-tailed bandits (sub-Weibull bandit) where mean estimators can still achieve regret bounds of the same order as those under sub-Gaussian reward distributions.


Existing techniques for constructing confidence bounds are typically built upon various concentration inequalities, which thus lead to over-exploration.

the proof relies on a precise characterization of p-th moment of a Weibull random variable and standard symmetrization arguments.


Throughout the paper, we assume that the columns of the data matrix have `2-norm at most one 1 .

in our analysis of our differentially private covariance estimation mechanism, we will focus on bounding the Frobenius error.


In this section, we show that a private estimate of the covariance matrix C = XX > summarizes the data sufficiently well for all of these ridge regression learning tasks with only a one-time privacy cost.

we can view differentially private covariance estimation as a database sanitization scheme for ridge regression.


There was also an attempt by Jiang et al. [2016] to use Wishart-distributed noise to privately estimate a covariance matrix.

imtiaz and Sarwate [2016] proposed the same algorithm and later discovered that the algorithm was in fact not differentially private.


Recall that the Gaussian mechanism has an additional failure probability , thus the privacy guarantees we obtain are strictly better for the same value of âœ.

it is particularly striking that we consistently beat the Gaussian mechanism even for the very relaxed value of = .001.


We tried fixing the generator weights and optimizing the noise so that it generates the target image, under the assumption that sightly modifying the optimized noise would produce a variant of the original.

naively implementing this idea with BigGAN did not reconstruct the image well, as shown in the sample in Fig.  1(b).


As shown in Figure 1(g), we divide the images into a 3 X 3 grid and linearly combine the cells with the weights w produced by a CNN conditioned on the two images.

where is element-wise multiplication, and w is resized to the image size keeping the block structure.


Another way to address data scarcity is to synthesize additional training examples, for example by using off-the-shelf Generative Adversarial Networks (GANs) [3,13].

classifiers trained from GAN-generated images are typically inferior to those trained with real images, possibly because the distribution of generated images may be biased towards frequent patterns (modes) of the original image distribution [26].


Since only a few training images are available in the target domain, only scale and shift parameters of the batch normalization of G are updated in practice.

only the and of each batch normalization layer are updated in each layer, where x is the input feature from the previous layer, and E and Var indicate the mean and variance functions, respectively.


This is different from the neural network literature [45,25], where adversarial training usually provides better LRTE and significantly better test error than methods providing provable robustness guarantees.

our upper bound on the robust loss is tight and tractable and thus adversarial training should not be used as it provides only a lower bound and minimization of an upper bound makes more sense than minimization of a lower bound.


We can solve this certification problem for the robust test error exactly and efficiently by noting that the objective and the attack model 1 (âœ) is separable wrt the input dimensions.

we have to solve up to d simple one-dimensional optimization problems.


We note that our methods are primarily suitable for tabular data, but in the literature on robustness of neural networks there are no established tabular datasets to compare to.

we compare our robust boosted trees to the convolutional networks of [73,16,75,25,13] on MNIST, FMNIST, and CIFAR-10.


In terms of provable robustness (URTE), our method is competitive to many provable defenses for CNNs.

we outperform the LP-relaxation approach of [73] on all three datasets both in terms of test error and upper bounds.


This cannot be guaranteed with robust splits without pruning since the tree construction process is greedy, and some training examples are also influenced by splits at different branches.

in order to control the upper bound on the robust loss globally over the whole tree as in (8), and not just for the current subtree as in ( 9), we need a post-hoc approach that takes into account the structure of the whole tree.


In general, for an arbitrary loss L, there is no closed-form minimizer wrt w l and w r .

we can minimize such an objective using, e.g. coordinate descent.


Thus, in order to control the upper bound on the robust loss globally over the whole tree as in (8), and not just for the current subtree as in ( 9), we need a post-hoc approach that takes into account the structure of the whole tree.


Note that it is possible to find a separating hyperplane between the origin and the collection of points {F ai } if and only if the problem is in Regime 3.

the problem of inverse reinforcement learning can be viewed as a one class support vector machine (or as a two class support vector machine with the origin as the negative class) problem in this regime.


Methods such as Abbeel & Ng (2004) looked at using IRL to solve the apprenticeship learning problem by trying to find a reward function that maximizes the margin of the expert's policy.

reinforcement Learning is the process of generating an optimal policy for a given Markov Decision Process (MDP) along with a reward function.


Various algorithms to solve the IRL problem have been proposed including linear programming Ng & Russel (2000), Hybrid IRL Neu & SzepesvÃ¡ri (2007), Maximum Margin Planning Ratliff et al.  (2006), Multiplicative Weights for Apprenticeship Learning Syed et al. (2008) and Bayesian estimation Ramachandran & Amir (2007).


In our test, however, we are fitting a MV-Kumaraswamy variational posterior.

we compute gradient variance, for all methods, according to variance's more common definition.


We do not use any explicit regularization.

figure 5: Variance of the ELBO's gradient's first dimension for GRG [22], RSVI [18], IRG [4], and MVK (ours) when fitting a variational posterior to Categorical data with 100 dimensions and a Dirichlet prior.


This example qualitatively illustrates corollary 1.

as we demonstrate in section 2.2 and fig.  1, approximating sparse Dirichlet samples with the Kumaraswamy stick-breaking decomposition without accounting for the ordering dependence produces a large bias in the samples’ last dimension.


We also experimented with a non-variational autoencoder.

in the majority of experiments, that network overfit to a 1D manifold.


Overfitting the autoencoder to history will degrade feature quality on future scenarios.

we employ early stopping to be conservative with autoencoder training.


To remedy this problem, we maintain an experience replay buffer [Mnih et al., 2015] of snapshots from multiple simulations.

this experiment also allows comparisons over controllers of similar size.


As output, weights for an observer function with a descriptive latent space are learned.

recent breakthroughs have demonstrated capable computational methods for both controlling (Heess et al.  [2017], Schulman et al. [2017], Lillicrap et al. [2015]) and designing (Ha et al. [2017], Spielberg et al., Wampler and Popovic [2009]) rigid robots.


While we try to compare the same number of clusters and latent variables in experiments (since inputs from a cluster give highly dependent data) we acknowledge that each cluster (in 2D) provides six inputs to the controller.


In this paper, we address the problem of learning low-dimensional robot state while simultaneously optimizing robot control and/or material parameters.

we require a representation applicable to physical control of real-world robots.


we learn a variational autoencoder Welling, 2013, Rezende et al., 2014] that takes, as input, a state description of a robot and minimizes the reconstruction cost of said state.


Using SWAG as a transfer task for COPA sees an 8 point improvement.

gLUE provides a lightweight classification API and no restrictions on model architecture or parameter sharing, which seems to have been well-suited to recent work in this area.


From the traces of the mesh STAs, it has been observed that, in case of interference between 20 MHz and 40 MHz channels (we call this as asymmetric channel interference ) at STA-2, the packets transmitted through the 40 MHz channel gets su.ered.

sTA-2 can decode the overheard control packets from STA-3, however fails to decode the packets from STA-1 in case of an asymmetric channel interference.


Further, higher MCS levels require higher receiver sensitivity.

mCS 13 at 20 MHz channel performs best with poor signal quality.


The nodes use one of the control channels to send this information ¯ 2.

but none of the above schemes make use of network wide wavelength availability-states (which is more precise) to adapt the offset-time and hence cannot make full use of the advantages offered by JET protocol.


The duration of each simulation run is 900 seconds.

in a network with a high level of node mobility, links break very frequently.


The simulation results show that TADL is practical in a stationary network or a network with a low to medium level of mobility.

area-S is the smallest searching area that includes S and D.


At the same time, these attacks reduce the traffic load in the network reducing the level of PDR drop caused by congestions.

packet-dropping attacks can offset packet loss caused by network congestions.


each node estimates a neighbouring node trust value based on the quality of the link connecting to the neighbouring node.

the trust value assigned to node Ni actually reflects the reliability of the link linking this node and node Ni.


An interesting observation in scenarios 1, 1a, 2 and 2a is that the cost increases almost linearly with the number of subscriptions upto a certain point (number of subscriptions = 4000).

the average cost for matching a subscription is almost constant.


At time 2, when the new subscription f3 occurs, RMCEP-Online is invoked again, which in turn calls RMCEP-Aprx with the subscriptions f2 (since e is not yet placed to satisfy f2) and f3.

in each round of the algorithm, the chunk with the minimum AOC is selected, and added to the .nal solution.


On the other hand, the number of RSUs do not have a significant direct impact on the service provider s cost, and hence the provider can install or rent a much smaller number of RSUs placed suitably around the city to achieve the same cost.

note that sufficient number of RSUs are still needed to be suitably placed as all subscriptions and events are also reported to the service provider through the RSUs.


Its main aim is to provide an overall ordering of options.

in each observation, the residual energy value is measured after transmitting 10 packets because for each packet transmission, very small amount of energy is consumed.


While their results provide interesting insights into the relative impact as the load of the system changes from light-to-heavy load, the simulations also suggest that under light load correlated service times typically result in an increase in the end-to-end delays.

we typically observe a decrease both under light and heavy load.


Secondly, the traditional context-aware access control policies may look fine-grained for traditional data files, but is not fine-grained enough for sensor data, especially because sensor data collection is context aware.

the Portal app allows the smartphone user to review the sensor usage reports of all installed apps.


How does a server acknowledge receipt of a range of data?

even if our vision of any IoT device connecting to any smartphone proves too radical a departure from the status quo, the basic ideas could still be deployed in more constrained administrative domains, like a home, office, or university campus.


For example, information about the location of the peripheral or the current global time may be difficult for an IoT device to obtain, but straightforward for a smartphone.

how should gateways choose when to forward data?


At what point can a peripheral be sure its data reached the end server?

in this paper, we identify some of the key issues and begin to explore them, with the goal of raising awareness and generating discussion about both the opportunities and challenges.


Unfortunately, these models are necessarily tied to specific network types and locations.

we observe that high-fidelity information about the cellular channel is readily available from the radio-layer signaling protocols employed by high-speed cellular networks (e.g., HSPA+ and LTE).


In such a scenario, CQIC tries to match the the achievable bandwidth with the channel capacity offered by the cellular base station for the UE.

the fairness among CQIC and TCP flows to different UEs are provided by the underlying scheduling algorithm implemented at the base station.


We configure the UE in diagnostic mode, and the QXDM tool continuously queries and collects various radio and chip-level information.

we use the HS-DPCCH-INFO and HS-DECODE-STATUS log packets to retrieve the CQI and DTX information, respectively.


We build upon Google's QUIC [15] framework, which is a new transport protocol based on UDP, to implement a CQIC prototype.

we reuse the RTT estimation and reliable packet delivery modules in QUIC.


NetRadar [2] collects traces from mobile devices to acquire localized link throughput statistics; OpenCellID [5] collects cell tower information and correlates them with user locations to establish network density and coverage maps; and finally, OpenSignal [12] measures the signal strength of cell towers to estimate achievable throughputs.

none of the current approaches yet takes a holistic view along all operators and protocol levels to enable advanced modelling, e.g. through spatial statistics and machine learning based filtering methods, to build highly relevant coverage and performance estimates.


Intuitively, Nightcrawler should use the above collection of reflected TX beam directions to drive imaging.

the TX focuses its transmissions on these directions (by rotating its beam repeatedly across them in a round-robin fashion) while the RX locates and images object(s) in each direction.


For these reasons the academic community is concentrating its efforts in searching for mobile technologies that enable effectively the exchange of information between the vehicles.

it would be possible to receive and pass information to other automobiles warning them about accidents, traffic jams and easy ways where they have passed, allowing other drivers to choose alternative paths through the messages received.


In the last decade, many techniques and tools for available bandwidth estimation have been developed.

their accuracy still remains a challenge [24].


Normal has 23.9Mbps at 0m when the number of terminals is 10.

if multiple STAs transmit frame to an AP at the same time and the collision occurs, the behavior depends on the difference of received power between two frames.


This study uses Eq.(1) as the condition for activating MIMmode.

fig.2 represents the model of the assumed environment.


As mentioned in section 4.2, when the number of terminals is one, the parallel transmission case decreases because the number of collision among WLANs decreases by coexisting EIFS and DIFS.

if the number of terminals is larger than two, the number of collision in same WLAN decreases and the one among WLANs increases.


It is because the increment using binary increase cannot avoid the collision when CWmin is small.

if CW = 1, CW becomes 2 by binary increase in case of Collision/Error.


If the difference is larger than SINR, a frame with stronger received power results in Collision/Capture and the other frame with weak received power results in Collision/Error.

if the difference is smaller than SINR, both frames result in Collision/Error.


It is because the distance between WLANs is comparatively large and the capture probability becomes higher.

the collision results in Collision/Capture.


We use the PC (Toshiba dynabook SSRX2) as the sender terminal whose OS is ubuntu10.04 (the chipset and WLAN driver are AR928X and ath9k, respectively).

when Collision/Error occurs, the proposal increases CWmin drastically.


If the Capture Probability is high, the throughout improves by parallel transmission when the collisions often occur.

the modified small CW causes Collision/Capture and Parallel Transmission.


One of the most distinctive and substantial characteristics of VANETs is non-infrastructure.

there is no fixed routing, like access point (AP), or any central unit [8, 9].


In the past, several schemes have been proposed to reduce the broadcast storm problem.

they have been only validated using simple scenarios such as a highway (several lanes, without crossroads) [5,6], or with the help of road side units [7].


However, significant overheads in terms of communication and storage are incurred due to the flooding or multi-hop forwarding [10, 11]. 3) Without the network infrastructure, steady connection between nodes is difficult to be guaranteed, especially in large scale VANETs.

the scalability is difficult to be achieved in large scale VANETs [12].


If a node has more neighbors, the node resides in a more important position.

the node with larger connectivity degree should have more probability to be elected as the CH.


This VLAN could be directing all the traffic with destination address to an Akamai IP address to a dedicated cache server.

the mobile operators not only can host several MVNO in the same infrastructure but also new business models such as hosting CDN providers.


For the same reason, enhanced CDL system is also expected to use FDMA structure [3]; however typical FDMA structure, which allocates frequency resource to each UAV, cannot support multiple UAVs simultaneously in limited bandwidth.

enhanced CDL can utilize hybrid FDMA/TDMA structure to improve bandwidth efficiency [5].


Also, each UAV has different size of data traffic.

if hub performs TDMA scheduling without considering the size of RTT and data traffic, idle resource time and packet delay will occur in hub network.


As shown in (8), sum of RTT and data traffic size in PH should be similar with RTT size in PL.

in order to perform RTT/traffic based resource scheduling, the condition value Ci, which is shown in (3), should be changed as follows.


In addition, since the number of unused beams is decreased as the number of the UEs increases, the performance of the MAS with dynamic beam control reduces.

because of the inter-beam interference the performance of the MAS reduces as the number of beams increases.


Since antenna tilt angle of switched beamforming is fixed, it is easy to implement and operate.

the maximum performance of switched beamforming is relatively low than adaptive beamforming.


Since most of the millimeter-wave band is unused, it is possible to construct an ultra-wide band radio access network (RAN) in the 5G mobile communication system.

in this paper, we have also considered switched beamforming for our proposed scheme.


In [6][7], they provide simple closed-form expressions for coverage probability, which is the probability that a randomly deployed UE can achieve a target SINR in heterogeneous network with SBSs.

this result implies that the number of hidden nodes is proportional to intensity .of SBSs Figure 6: CDF of the number of hidden nodes (without consideration of UE's location) Figure 7: CDF of the number of hidden nodes (with consideration of UE s location) 5.


However, the analytical model hardly considers any hidden node problem which is affected by geographical distribution of sender and receiver nodes [9].

it is hard to use this model to analyze the performance of SBSs with WiFi RAT.


However, results obtained by Eq. (2) does not consider the location of UEs.

in addition, we also consider the distance distribution of SBSs by using Eq. (4).


However, SBSs with multi-RAT have different characteristics depending on the RAT.

in order to minimize the risks and failures in real markets, it is essential to thoroughly analyze the varying characteristics and performance of multi-RAT SBSs, before their release.


In addition, we only considered the channel for downlink transmission from SBS to UE.

we will expand our model for estimating the performance of uplink transmission from UE to SBS.


According to AT&T, 3 percent of its smartphone users generate about 40 percent of the network traffc in 2009 [3].

beside high deployment cost, even after installing additional BSs or upgrading system it may not be very easy for mobile network operators (MNOs) to determine the possible extension in network capacity.


If QcandEF is better than QprevEF, the node regards the EFS message from the previous EF as being dropped from a link error.

it selects the candidate EF as a new EF if the candidate EF was not a SF in the previous EFS period.


This enables a node that has more SFs to forward an EFS message earlier than those that have fewer SFs.

a node having more SFs in a previous EFS period has a shorter waiting time than others (the deferred time should sufficiently guarantee the queuing delay and back-off competition of the MAC layer).


We assume that if a content requester detects a DATA loss in the CCN module, it retransmits an INTEREST until it receives the DATA successfully.

as the message forwarding scheme is unreliable the content download time increases due to the INTEREST retransmission.


In addition, the proposed scheme using pseudo-broadcasting shows a slightly higher performance than when using broadcast flooding mode because, in the MAC layer, the retransmission mechanism operates when it fails to transmit so as to provide communication reliability.

nodes can receive the packet with a higher probability compare to those of broadcast flooding mode.


Although a linear program can be solved in a polynomial time, the caching LP is actually a large-scale problem since there is a large number of contents in the network.

the caching LP has a special structure, the block-angular form (see Figure 2) which can be decomposed into many subproblems.


The time required to switch the network interface from an active to idle is assumed to be negligible in this work.

it is sufficient to utilize the time duration where a network interface is turned off as a direct representation of the energy saving for such receiver.


In the 0-1 knapsack problem, we are replacing the variables v with p and w with b to avoid confusion with our defined variables.

our instance of 0-1 knapsack is defined as following: there are n items xl such that l ∈ [1, n] and xl = 1 if the item is chosen and 0 otherwise.


Comparing with traditional scheduling policies such as: groups with maximum users and firstcome first-served, the gain in service ratio by the proposed algorithm can be at least 2 and 4 times higher, respectively.

efficient utilization of the expensive and limited wireless spectrum remains an important problem, especially in the context of multimedia streaming services that consume a large portion of the wireless capacity.


To offer the flexibility of resource distribution, we are exploiting three type of transmission: unicast, multicast over an SFN, and multicast within the local coverage of a cell.

our results show that the proposed algorithm can serve up to 40X more users than the common unicast streaming approach.


During our implementation, we faced a problem related to the fact that the LTE model in OPNET (which follows the 3GPP specifications for single frequency networks) considers these settings to be static during the entire running time of a simulation.

the LTE configuration node cannot be adjusted dynamically, and its initial settings (including the MCS assigned for each MBMS bearer) cannot be changed after the deployment phase.


Dolphin generates near field assertions via acoustic communications, manipulates the sound power to restrict the communication distance, and uses full-duplex communication [8] to resist relay attacks.


For real-time detection tree-based algorithms seem more suitable than SVMs, due to their fast prediction time.

such good results regarding identification are crucial for future implementations of practical tools.


Parking enforcement officers must periodically patrol on-street parking areas and check cars one by one to identify cars that are parked over time .

the design of such a protocol must take into account constrained resources of smart objects and the mobility of drones.


After the time period expires, new private keys are generated.

if a drone is captured, information leakage is limited to the time period during which the private keys were valid.


Securing such communications requires an effective and efficient encryption key establishment protocol.


Several pairing-free CL-AKA protocols [10, 9, 26, 24] have thus been proposed.

most of those protocols were proved to be insecure and only two of them still remain secure: Sun s CL-AKA [24] and Yang s CL-AKA [26].


Since drones and smart objects are batterypowered, energy efficiency as well as security is a critical issue.

protocol executions should be completed as soon as possible to save energy.


The arms race between malware developers and defenders is endless.

it is essential to continuously track and understand the latest strategies of attackers in manipulating botnets for attacks.


Probabilistic forwarding (e.g., Crowds [?]) delinks messages from their actual senders without a preset forwarding path.

the onus of message confidentiality in Crowds is on the probabilistic forwarding nodes since those nodes have message decryption keys.


In this protocol, a constrained yet PUF-enabled device and a server compute the session key and authenticate each other.

the device authenticates itself by performing a zero knowledge proof on its PUFs generated secrets.


The priorities are considered in regards to both the sites corresponding to each distributed active participant, and also the cameras within each site.

hosseini et al [4, 3] adopted priority-based approaches towards the study of e.ciently transmitting, rendering, and displaying bulky 3D gaming information to power-limited devices given the importance of di.erent 3D objects in the gaming context.


As can be seen, the larger value of Rmax results in more bandwidth reduction, and thus quality sacrifices, for streams in C22 (corresponding to the highest priority class), while preserving more of the full-bandwidth streams in C11 (corresponding to the least priority class).

our framework does provide quality degradation in general, but considering the bandwidth savings achieved using our adaptations, it is reasonable to believe that teleimmersive users would make this sacrifice in quality in exchange for respecting their view and the network bandwidth constraints.


The experiments used a Logitech C920 HD webcam [11] mounted on an Ascending Technologies (AscTec) Firefly [3] UAV (shown in Figure 1).

802.11 was never intended to be used for fast moving and direction changing devices.


He presents a proxy server that can adapt SVC video streams according to current network conditions.

“instead of reacting on packet loss, [the] approach uses an increase in queueing delay at the router to detect phases of throughput degradation.” [9]


An alternative approach is to recreate RDMA state and resources on the newly migrated machine [31].

implementation complexity of such approaches prohibits their deployment in a distributed, general-purpose virtualized infrastructure.


However, in contrast to HyV, vRDMA uses a paravirtual communication channel to perform data operations.

vRDMA cannot completely bypass the hypervisor on data operations.


HyV allows for a more efficient memory usage because all the RDMA memory (which is DMA able) is explicitly registered through the hypervisor.

hyV has a much lower memory footprint than passthrough, and thereby allows packing more VMs on to a single physical machine.


In the paper, we provide a detailed evaluation of HyV for different RDMA technologies and operations.

the critical aspect of RNIC s is that the control paths are used to directly map device provided, connectionspecific, structures into an applications address space.


Here, the provider also expects a contiguous (host) virtual memory region, but unlike in bottom up it does not back the region itself but relies on the operating systems to do so.

the provider only pins the memory to ensure that the pages can be used for DMA transfers and extracts them for installation on the device.


In this work, we presented HyV, a virtualization framework that unleashes the full performance advantages of RDMA interconnects to virtual machines.

hyV is implemented as a plugin component for the Linux OFED RDMA software stack.


This strategy has the downsides that, when a video streaming is aborted (for instance to start streaming a new video), it is often the case that the hottest HTTP/1.1 connection is aborted to stop receiving the next video segment, in which case the next video streaming will start with a cold TCP connection and will not benefit of any prior warm up.

with HTTP/2, this case no longer happens and the streaming of a first video will benefit to the next one.


To determine the origin of this performance gain of HTTP/2, a deeper analysis is necessary.

when using the traditional adaptation (no push and bandwidth estimation is based on downloading time), the client directly selects the representation with the highest bitrate and continues using it.


HTTP/2 defines a general multiplexed transport designed mainly for HTTP messages, but that can accommodate other messaging application protocols.

we can use HTTP/2 framing layer to transport WebSocket-like messages over an HTTP/2 stream rather than over a dedicated TCP connection.


Admittedly, Halma requires multiple antenna elements at the transmitter side, entailing more space cost.

we have observed vast energy saving from Halma even with 2 transmit antenna elements, with marginal space cost.


The receiver is randomly placed within line-of-sight of the transmitter (which may adversely increase channel similarity) in an office environment.

when both channel magnitude and phase are used as signatures, on average in 95.8% of cases, an antenna's instantaneous signature remains a best-match with its other signatures.


In some sense, Halma actually augments amplitude modulation on legacy ZigBee by leveraging the symbol amplitude variation naturally provided by negligible compared with the switching period in Halma (8 samples or 4 Âµs for ZigBee).

halma's finegrained, sub-symbol-level antenna switching mechanism is feasible in practice.


Since ZigBee uses a differential demodulator to decode normal data symbols, and antenna switching occurs only per Ns = 8 samples, AIC itself is unlikely to affect the performance of the normal decoder.

the receiver decodes the normal data symbols first, separately from antenna index decoding.


The experiments are conducted in an busy office environment with 12 people, and 2 intentionally walking back and forth.

different transmit antennas symbols are distorted by the channel in different ways.


(iii) The optimal configuration may vary due to channel variation.

the receiver monitors the throughput TH(t) for current configuration.


We observe that the modulated waveform of any chip sequence is made from 4 elementary patterns, corresponding to 32 complex samples (Figure 6 shows two of such patterns).

halma employs an adaptive antenna hopping (AAH) protocol that efficiently selects the subset of antennas to optimize this tradeo., based on a model-driven framework instrumented by channel pro.le measurement.


In contrast, Halma focuses on achieving high energy efficiency.

for a ZigBee link with single RF-chain transmitter and receiver, Halma can scale link capacity with Nt at a even faster rate than SSK, which translates into enormous energy saving.


The USB adapter's TX, RX and idle power consumption all grows linearly as the number of active antennas increases.

with 3 antennas, the idle power is 1.3× that of 1-antenna case.


We placed 4 DEE R loggers, complete with modified collar, on freeranging horses in a fenced area of maximum length 150 m. This allowed us to test the effect of the animal body on contact detection.

since animals were free to range, we could test the effect of neither distance from ground nor relative horse body positions on contact detection.


The camera failed again on April 14, then we removed it.

our camera trap dataset covers ~15 days over two separate periods.


The data recorded by nodes are timestamped with the logical time (epoch) of acquisition.

the logical time is synchronized neither across nodes nor w.r.t. global (physical) time.


In this case, the contact duration affects only the radio contribution, explaining the gentle slope for Tc > 60'.

the last component of the WILDSCOPE toolset is the database where the data gathered is stored.


The link between PCO-based synchronization methods and consensus algorithms for networked oscillators is established in [32].

only limited work has been focused on extending distributed desynchronization to the multichannel case [7].


If no ticket key has been configured, a fresh one is generated for each IP address and port (but not for each virtual host).

if a ticket key is specified in the global configuration of the server, all tickets created by any virtual host can be resumed on any other.


However, non-public files cannot be served from this low-trust origin when the user wants to download data from her account, because they require access to the session cookie to prove that the user is authorized to view the file.

the dl-web.dropbox.com origin is used for the purpose of displaying files from the user's own Dropbox account while he is logged in.


On the server, each parameter is considered separately in a manually configured set of complex rules to determine the virtual host that will handle the request (see Section 3 for more detail).

most web servers will pick a fallback virtual host when the normal routing rules fail.


Many web servers employ virtual hosting to serve multiple HTTPS domains behind the same TLS server.

the same check applies to every other pair of IP address and port where this certificate is used.


Figure 2 depicts how HTTPS requests are processed by Akamai: each PoP has N custom certificates installed for N virtual hosts, and each certificate is served on a dedicated IP address.

if a client connects to IP 1, it will be given the certificate for a.com, whereas if it connects to IP 2, it will be given the certificate for *.z.com.


We have described four different exploits of virtual host confusion against major websites.

these particular exploits do not give a clear picture of the general proportion of all websites vulnerable to similar attacks.


In each subfigure, x-axis and y-axis vary the desirable data demand u and value v of the user types.

each point in the subfigures represents a unique user type.


We observe that although the resulting data load under any data cap gI does not vary much, the maximum revenue R * I (gI ) varies significantly.

when the pricing converges to the flat-rate structure as gI goes to 1, the optimal revenue decreases to a minimum value.


As we observe from the plot, when apps react to firewall-timer, and when firewall-timer is set to a high value, it substantially reduces signaling per minute for most of the apps.

for apps having long-lived background connections such as Skype and Facebook, a high value of firewall timeout reduces the signaling overhead by more than a factor of 4.


A desirable way to collect traffic data of apps is to access their packet traces in operator's network.

we do not have access to such data as it is typically not published by network operators.


As a increases from 2.4 to 3.6, most of the links become local, short-range links and the network approaches the regular multi-hop pattern that also results in high EDP.

we need to choose a value of a that achieves a compromise between these two extreme cases.


In this way we are able to bring physically far and highly interacting nodes to logical proximity so that they are easily accessible to each other.

in this context, design of small-world network-based NoC architectures [1] is a notable example.


To reduce complexity, we restrict the routing algorithm to X-Y routing (dimension-order routing) with the standard 2D mesh.

the path of a packet has at most one turn.


Through the website, users can define emergent monitoring tasks and preview the video results immediately.

the website consists of three units: setting, execution, and display.


Real-time video surveillance systems are essential in modern 2D environments has been studied in [1, 2].

in this prototype, the model of PTZ cameras is Compro IP540/IP570[6], which supports the maximum resolution of 1280 by 1024 and provides flexible configurations (e.g., 340-degree pan, 100-degree tilt, and 12x optical zoom).


For example, 480p video with a 16:9 display aspect ratio can not have a square pixel shape since (480/9) Ã— 16 is not an integer.


Video pass-through, combined with batching/prefetching, can save 52% to 61% for continuous video streaming.

contention does vary the relative contribution of different optimization mechanisms.


U-Wear offers a training-based channel estimation approach that requires the presence of a training sequence known a-priori in the transmitted packet.

u-Wear leverages the good autocorrelation property of the synchronization preamble sequence, discussed in Section 3.1.2, to estimate the CIR.


Finally, the dielectric nature of the human body also affects the coupling between on-body RF antennas and the body itself.

the gain and the radiation pattern of the antenna deteriorate because of the contact or proximity with the human body [16], while the resonant frequency and the input impedance of the antenna may shift from their nominal values.


The ADMP401 offers a mostly flat bandwidth, i.e., -3 dB roll o., between 100 Hz and 15 kHz, omnidirectional sensitivity pattern, and requires a supply voltage between 1.5 V and 3.3 V DC.

as a proof of concept, we design two prototypes that implement the U-Wear framework and operate in the nearultrasonic frequency range, i.e., 17 - 22 kHz, using COTS speakers and microphones.


However, it is not intuitive to users how much they have to rotate their wrists in order to move the pointers in an intended direction, which makes them fail to reach the target.

users have trouble moving a pointer in a diagonal direction, as it requires them to rotate their wrist in two axes simultaneously.


One of the challenges in RF phase based tracking is accurate measurement of the received signal phase.

the carrier frequency offset (CFO) between the sender and the receiver causes the phase to change over time even if the receiver is not moving.


We believe this approach is reasonable in normal situations since such idle connections will be likely to stay paused to minimize the cellular traffic usage.

we understand this is not a solution to a Denial-of-Service (DoS) attack on D2Prox, which would require more careful thoughts.


The disparity comes from the fact that the developers have to handle each case differently without the support from the transport layer.

vLC exploits D2BufMgr in D2TP by setting an appropriate deadline, and delays cellular data transfer if possible to offload to Wi-Fi.


Higher-resolution cameras capture more pixels on the transmitter screen, and thus they support smaller grids on the transmitter screen and achieve higher throughput.

these observations together justify the configuration in HiLight (Algorithm 1), which increases the for dark areas and dynamic scenes.


To address the challenge, we design strategies to filter out color intensity change associated with interfering factors, and adapt our strategy to the current scene type.

for the static scene, we leverage an audio beamforming algorithm [33, 35] to minimize the impact of interfering factors and extract the desired color intensity change encoded with data; for the dynamic scene, we identify patterns caused by the encoded change.


the high-end SLR camera supports 720 grids on the 10-in screen enabling 6.6 Kbps throughput, six times higher than that achieved by iPhone 5s in our prototype.


Among different screen types, OLED screens are the most preferable, because OLED screens do not have backlight and each pixel emits light independently.

colors on OLED screens are brighter with higher contrast, making color intensity change easier to detect.


We obtain the noise correlation matrix R via a training process for a static scene.

once the receiver detects a static scene, it buffers frames for M frame windows.


We encode data by changing the pixel transparency of the communication layer.

this is because these steps are performed upon sampled pixels of each frame.


A three-layer FatTree with K-port switches can provide up to (K/2)2 shortest paths between every pair of endpoints that locate in different pods, e.g., a FatTree with 24-port switches, contains 144 shortest paths between a pair of endpoints, hence MPTCP leverages the redundant paths in the topology by opening multiple subflows to achieve the goal of high bandwidth utilization.

opening more subflows is expensive due to the following three ways: (1) spreading data across multiple subflows leads to more resource and scheduling overheads.


The increase of CPU usage is mainly due to the increase in the number of the subflows, because it need more CPU cycles to schedule the transmissions of all the subflows.

it does not distinguish mice flows and elephant flows, and thus unnecessarily opens too many subflows for mice flows.


The trace data were then divided into time intervals with a fixed length of 100 s. To quantify the effectiveness of using past bandwidth statistics to predict future values, we measured the bandwidth similarity of two vectors with PBA.

we chose a set of intervals, the candidate prediction intervals, which ranged from 50 ms to 2.6 s in length and spaced 100 ms apart.


To generalize the model, we assume that the update interval is equal to the RTT, but we can derive similar results if the update interval is equal to the interval-arrival time of the ACKs or a fixed interval.

we set up the network topology shown in Fig. 1 with a link buffer size of 852 packets [20] and a base propagation delay of 160 ms (Dmin=0.16 s) as we measured from an actual 3G/HSPA network.


The QCC queue length adaptation algorithms can be divided into two key stages: 1) the startup phase and 2) the adaptive phase.

the startup phases of TCP Vegas and FAST TCP are identical to the TCP slow-start phase.


Not much work has been done on determining stable paths or trees that could exist for a longer time.

to the best of our knowledge, there has been no work done to determine stable sequence of a communication topology that spans (i.e., connect all the SU nodes) the entire CRAHN network of SU nodes.


In general, for k such parallel routes we would have, the probability of breakage as Pk = (P1)k where P1 is probability of breakage of single route.

the probability of communication of breakage between Source and Destination is reduced exponentially if parallel routes are used.


Namely, the solution concept for this market is related to the competitive (or, Walrasian) equilibrium [3], [18], which has been also applied in communication networks [4], and extended to graphical economies (which exhibit localities ) [16], [17].

for the problem under consideration, there do not exist explicit price variables (or, price signals), and hence we employ a different equilibrium concept: Definition 5.


Therefore, the lex-optimal allocation is an exchange equilibrium, and, additionally, any possible exchange equilibrium is also a lex-optimal allocation.

the competitive interactions of rational users embedded in a graph, lead to the same allocation point that a central designer would have selected for such a system.


This important result reveals that there is a unifying approach that solves the resource allocation problem for graph-constrained systems (or, economies), for different node behaviors.

we can apply the max-min fair criterion, that has been extensively used for load balancing in centralized communication networks (e.g., see [21] and references therein), to service exchange models with autonomous and selfish nodes.


A mixed-signal SIC architecture has been proposed in [28], where the digital TX signal is processed and upconverted to RF for cancellation.

this requires a separate upconversion path which introduces its own noise and distortion.


This work explores logically centralized inter-domain QoS routing mediators for the provisioning of inter-domain path guarantees in light of ongoing changes in the Internet ecosystem.

copyrights for third-party components of this work must be honored.


We thus propose a sample-select approach to heuristically tackle the QMRP in an online manner.

given the NP-hardness of the optimal path calculations, we employ a sample-select process, where in the first stage, a set of feasible paths is sampled (i.e., generated) in polynomial time, and subsequently one of them is selected for the actual embedding.


This is because content providers utilize content distribution networks for serving content at different user locations.

a problematic content server can only affect the performance of a subset of user locations.


we use robust regression for this baseline modeling because it can minimize the impact of extreme outlier data points on the produced model.


More and more devices in the future, such as Google Glasses, baby monitors, and new generation of home appliances, will all desire mouse functionalities, which allow users to choose from a wide variety of options and easily click on different parts of the view.

a traditional mouse, which requires a flat and smooth surface to operate, cannot satisfy many new usage scenarios.


The ITS is a critical infrastructure, and any benign or malicious fault could have far-reaching consequences.

reliability, security and survivability are of paramount importance.


This type of jammer simply emits radio signals continuously (e.g. random noise), which interferes with the signal, i.e., it decreases the signal-to-noise ratio.

one of the main objectives of these technologies is to increase safety.


We use the example of remote control attack to illustrate the game between the system owner and the attacker.

mCUs cannot directly interpret the encrypted data, on-the-fly decryption extracts the program from the encrypted code.


The larger the window, the more likely they will choose different contention time so as to avoid collisions.

this will not work for prioritized contention, since the contention time is not randomly picked but pre-defined by the alignment metrics.


On the other hand, this prioritized contention causes an extra difficulty in avoiding collisions.

when different users have similar alignment metrics, they will end up in collision since they have similar contention time.


After user selection, the AP needs to know the exact CSI to perform precoding before beamforming to users.

rBF only precodes with the randomly selected directions without CSI feedback, which causes much capacity loss.


Fig. 11 presents the average throughput under 3 scenarios.

they should coordinate with each other spontaneously to form the best user group for each UL MU-MIMO transmission.


For this case, later users cannot detect that more than one users CAs collide on the same subcarrier, and therefore will not start collision recovery.

these two kinds of collisions could be limited to a minimum using a reasonable contention window.


Due to frequency diversity, the subcarriers may have different channel gains, which makes the user selection problem more sophisticated.

for each user, one subcarrier s1 may be better aligned with a Signpost direction, whereas another subcarrier s2 may be better aligned to a different direction.


This fact indicates that having exact CSI is critical for DL MU-MIMO beamforming in practice.

recently, many experimental studies [9–18] have explored MU-MIMO in practical scenarios.


Since U3 and U4 are correlated to AP1’s selected users (U1 and U2), they may reduce total network capacity when selected by AP2.

aP2 excludes U3 and U4, and instead selects a bridge client (U6) uncorrelated to all currently selected users w.r.t.


Their design principle is to make the most of the multiple DoFs at the client side.

Kardia focus on the optimal utilization of the DoFs at the AP by employing multi-cell transmission to combat the channel hardening effect.


Multi-user MIMO (MU-MIMO) is a hallmark of the recent WLAN standard 802.11ac, with which an Mtx-antenna AP can simultaneously transmit up to Mtx data streams to different users, thus achieving a maximum degree-of-freedom (DoF) of Mtx.

exploiting MU-MIMO's full potential is a non-trivial issue in practice.


The winner's CSI is in turn leveraged to construct the next probing packet.

hence, the degree of channel orthogonality between a pair of users will also vary across subcarriers.


However, when other nodes are added to it as relays, cycles might be formed.

when paths connecting different pairs of multicast members share the same relay nodes, such node may receive redundant information, which indicates that cycles come into being.


When modified neighbor selection criteria is applied, more constraints are added to neighbor selection.

the distance between a node and its selected neighbor may increase.


The spectrum sensing approach, which is less widely used, relies on the user devices to perform spectrum sensing.

it requires the devices to be equipped with proper sensing hardware and to have enough power for sensing and signal analysis.


It is indeed a creative way to utilize the similarity relationship between locations.

in our mechanism, we consider not only the similarity relationship but also the linear dependence between locations.


Instead of explicitly aligning Pilotfish symbols to incumbent symbols, we prefer truely independent transmissions.

in our design we set Pilotfish's symbol size to be twice of the incumbent symbol, as shown in Figure 6(a).


We expect these new channels will bring significant value, because communication spectrum is quite expensive today.

at the receiving side, if we pick the FFT outputs (i.e. take samples) corresponding to these elements, the received OFDM symbol will be entirely nulled


The downside of such an IIR filter, however, is that the signal will spread out in the time domain.


Only when the SNR approachs to marginal value, e.g 5dB, the false negtive rate increases a little to 20%.

we conclude that the CCA for Pilotfish MAC is robust.


The goal of our filter design is that the signal leakage at the stop-band would be as low as noise floor (i.e., -90dBm), and thus the interference to nearby data communication can be neglected.

we recorded received signal strength indicator (RSSI) on every link, then calculated SINR in guardband using a model with three nodes: one node as guardband transmitter, one as guardband receiver and one as incumbent transmitter.


In a typical real-world scenario one may have tens or hundreds of femto-BSs inside a macrocell and hundreds or thousands of users.

femto-BSs would be grouped into clusters of nearby femto-BSs which can concurrently serve a number of users.


it can verify if a packet has already been received.


Another impact is the high number of collisions between the nodes which try to transmit.

this high load impacts not only the number of received packets within deadline, but also the energy consumption and the end-toend delay as shown in all other figures.


On the other hand, an increasing number of applications require additional guarantees, like minimal delay and reliability, for the packets they generate.

a good routing protocol needs to select best paths through several existing ones to balance energy and memory consumption and guarantee the application s constraints.


Moreover, few of them have investigated the method to estimate the quality of sensing data.

we systemically consider the participants’ willingness to take a sufficient amount of efforts in crowdsensing, and bridge the gap between quality of sensing data and rewards for contributions, by providing a quality based incentive mechanism.


This mechanism estimates the quality of sensing data, and offers each participant a reward based on her effective contribution.

lee and Hoh [13] proposed a reverse auction based dynamic pricing scheme to motivate participants to sell their sensing data with claimed bids.


Sensing, processing, and transmitting data in crowdsensing applications requires manual efforts and physical resources.

appropriate rewards are always expected to compensate the owners of task-taking mobile devices.


In addition, it shows little consideration about incentives.

although various empirical experiments [17, 20, 25, 35] demonstrate that financial and social incentives do have an impact on the performance of participants, such as engagement, compliance and quality, they fail to generalize an incentive model to adaptively guide the participants’ behavior.


We consider goodput over throughput as different protocols incur different amounts of protocol overhead, and goodput can better reflect the data transmission efficiency and delay, i.e., a higher goodput indicates better efficiency and a shorter delay [33].

to understand the protocol overhead, we further evaluate the detailed breakdown of throughput for each protocol in this section.


Recall that the transmitter in each D2D link randomly selects a channel from the set {m1,m2,...,m|M|} for data transmission, while one-hop cellular users transmit in channels {n1,n2,...,n|N|}.

there is no cross-tier interference between cellular links and D2D links.


So far, D2D communication has been mostly studied for direct downlink traffic offloading between two mobile users within proximity, such as interference management, resource allocation, performance evaluation, etc.

for a cellular user, the requested data is delivered from the buffer of a neighboring user via D2D communication, without traversing through the serving BS or the core network.


Note that in previous works [4, 7, 8], to facilitate the analysis of coverage probability and average rate for a user occupying a given channel, it assumed a large enough user density such that the channel will always be occupied in all cells, while neglecting the fact that it actually becomes more difficult for a user to obtain a channel as the user density increases.

the users may suffer from “channel outage” due to the limited frequency resources (channels) in a cell.


Social networks are the most engaging applications on mobile devices, and they are becoming the main sources for users to consume content.

content retrieval, especially for embedded links and multimedia, can often be too slow, too energy hungry or too expensive for on-the-go mobile users.


Also, notice that since WiFi is often not available when our users launch Twitter, prefetch at application launch can be costly in terms of data and battery usage.

simply prefetch at launch does not work as well.


Because existing work has carefully studied the network-condition-based prefetching scheduling using fine-grained traces [27, 21], we focus our work and the corresponding data collection on the complementary components.

to limit the collection overhead and to avoid user burn out, we decide not to collect detailed network trace in a fine-grain manner, but only log the WiFi availability, which is a major leverage for prefetching.


We achieve this goal by selecting an appropriate threshold and time-window.

only links with a click-through score higher than the threshold (based on budget §Tuning the Thresholds) will be prefetched.


Although Twitter s tweets are publicly available, when and how users access Twitter is not.

we collected a large set of usage data from Twidere1 users who agreed to provide this information to us.


We select as many as possible features in the initial set to be comprehensive.

for each individual user, features without good distinguish power introduce noise in the prediction.


The increasing popularity and ubiquity of such content consumption calls for exceptional support from mobile devices.

the current experience of content consumption is far from satisfactory in terms of access delay and network availability.


In particular, we compute average bits/sec/H z/m2 for each UHF channel and present average values of the 20 channels normalized to the achievable rate of a UHF channel that has no TV transmitters nor TV receivers.

the channel switching time is defined as the duration between when the channel switching command is sent and when the new TV programming is displayed.


Unfortunately, this technique alone would provide little benefit to WATCH due to the strong interference from TV transmitters.

we design CAT, a Constructive Addition Transmission scheme that maximizes secondary signal SINR at SU-RX after accounting for the channels from the TV transmitter to SU-RX.


Instead, we estimate , which HP U 2 does not need to use preambles of TV signals and therefore does not require x between the primary and the secondary system.

the SU network operates fully asynchronously.


However, channel changes of TV receivers cannot be detected by external techniques such as spectrum sensing.

we require active TV receivers to inform the database controller of the channel changes through primary feedback.


To date, TVWS models calculate exclusion zones (areas where secondary transmissions are not allowed/transmit power is set to zero) based on transmitting TV channels and their corresponding tower locations [9].

we propose a dynamically computed exclusion zone characterized as the union of locations where secondary user transmit power must be reduced in order to protect active TV receivers.


That is, the SU network operates fully asynchronously.

we exploit that the TV transmitter is always transmitting whereas SU-TX transmits intermittently.


The required feedback in the previous discussion considers all channels (TV channels, UHF channels) as identical.

in practice, channels are divided into two types: a physical channel which occupies 6 MHz bandwidth and a virtual channel which contains TV programming.


While the broadcasting TV signals and the secondary data are sent in the UHF band, primary feedback can be transmitted out-of-band via WiFi, cellular, or wired connections such as DSL, or in-band via a UHF feedback channel.

we propose two methods to implement primary feedback with minimum modifications to legacy TV systems: (i) Smart remote: Smartphones can control TVs via infrared, e.g., Samsung Galaxy S5 and HTC One M8.2 smartphones can be used as combined feedback and remote devices.


As in [5], the quadratic form of the internal utility function not only allows for tractable analysis, but also serves as a good second-order approximation for a broad class of concave utility functions.

ai models the maximum internal demand rate, and bi models the internal demand elasticity factor.


A randomly generated code, might be (1) catastrophic, i.e., there exists a non-zero input sequence that can produce all-zero output sequence; or (2) non-equiprobable, i.e., the output values are not uniformly distributed, which can help the adversary deploy statistical attacks to distinguish the mapping we aim to conceal.

the generated code is first validated against above properties, then its free distance d8 is computed.


In the first part of the section, we expose the limitations of the graph method in Euclidean spaces.

we show, in particular, that conflict graphs do not yield any non-trivial approximation to the Scheduling problem in terms of the number of links n.


This analysis shows that one model does not fit all flights.

the correction step runs whenever GPS data is received.


Moreover, to ensure a controlled flight, it is essential to maintain a good quality radio link from the base station to the quadcopter.

most quadcopter applications do not consider these factors and they tend to use an empirical preset measurement for the maximum distance.


Our intention is to run the controller on the quadcopter itself.

to ease the debugging and testing we run the controller in a laptop in this prototype.


a model based on the historical data can be used to provide an initial estimation or a hint.


To obtain the accuracy of the localization, an average localization error was calculated.

the number of the collected beacons is directly proportional to the speed of the UAV.


He received his PhD from University of York in 1999.

david Grace is Head of Communications and Signal Processing Research Group within the Department of Electronics at the University of York.


The rational behind GameOn was that, in many urban cities, commuters spend a large part of their day on a train or a bus.

it would be great if we availed them of the opportunity to engage in spontaneous multiplayer gaming with their fellow commuters on the same train or bus.


The next hop of a packet (bird) is defined so that it is (1) repulsed from congested nodes in the repulsion zone of the focal node, (2) attracted to non-congested nodes in the attraction zone of the focal node, (3) and on the way toward the destination.

we define the following parameters (Figure 2): Z oR of a node is the set that contains its parents and children (one hop).


We believe that for even more constrained devices, there is no way around specialized, simplified, and highly optimized implementations.

note that our goal is not to engineer the most memory-efficient network stack but to design a clean, structured, and universal network stack that can be reused for many different IoT use cases, while still being able to cope with constrained environments.


One category of stacks are ultra-minimalistic implementations, such as the work by Santos et al. [9], which – by design – are not extensible and cannot become a full-featured IP stack.

they do not meet the requirements from Section 2.


Whenever a network event happens, e.g., node failure, link failure or network partition, the event will be detected by the nearby nodes and the update will be propagated to every broker within the network domain.

the brokers can have a consistent view of the network condition to help them in deciding proper transmission strategies.


At the same time, there is a considerable amount of traffic from both B and C to D. By duplicating service D at both nodes where B and C reside, we can significantly reduce traffic footprint.

if the Broker is aware of an intradomain instance of the service, the broker contacts the device that has the instance and requests that it migrates the service to the SEG using a specific transmission module that is suitable for the underlying network topology.


Service discovery itself is also an important topic that needs further investigation: users could potentially utilise conventional methods such as search engines to discover various services.

such services themselves may become inaccessible when the network is isolated.


We observe that the correlation values between each type of centrality measure computed on the two network layers vary across the various datasets.

we observe that for SIG-COMM and Social Evolution datasets, characterized by a higher number of nodes (67 and 55, respectively), the correlation between each centrality measure assumes values close to 0, while for the other datasets the correlation varies from weak to strong.


This trend of high variability between the centrality correlation values of a given dataset can be also observed in UNICAL, LAPLAND and SASSY datasets.

the interesting result is that across al measures, we note that the degree correlation for UNICAL, UPB, LAPLAND and SASSY, assumes relatively high values, varying from moderate to strong correlation.


Understanding shopper’s behavior through physical analytics can provide crucial insights to the business owner in terms of e↵ectiveness of promotions, arrangement of products and eciency of services.

analyzing shopper’s behavior and browsing patterns is challenging.


The proposed system does not require the shopper to carry any device as the movements of the shopper is detected purely by observing the variations in CSI of WiFi.

this mapping between the states and the inferred activities is shown in Tables 1 and 2.


In addition, there are at most 2k final requests that wish to reach to top row (as each final far request must have entered the tile).

in total there are at most 3k paths that wish to reach the top side of the NE-quadrant.


As discussed in Section III-A1, the area of the AND gate is set to be considerably larger than ` the rest (indicated by the symbol).

the homomorphic operations are implemented using the libScarab cryptographic library [13].


Since there is no other algorithm implementation to be compared, our evaluation consists of the calculation of the frequency that each vehicle changed its state during the simulation.

we calculate the times that the vehicles join, leave or merge with other vehicles tested in different transmission range scenarios and with varying number of vehicles.


The network topology and the choice of parameters for the benchmark models are motivated by the considerations in previous work [5].

to isolate the true simulator performance metrics from network model effects such as routing and congestion, we consider network traffic between nearest neighbor nodes only


The most common used techniques are based on L3 and L2 forwarding.

there are trade-offs in creating a L2 network or L3 network [16].


The Space-Time Scheduler receives an input set Ji of associated jobs which contains at least one job.


(a) Along a straight line (b) Along a curve Figure 8: Traces of moving robot We evaluated the system by executing a swarm application that consists of eleven actions each of which has different spatial-temporal constraints.

section 5 shows a proof of concept while Section 6 describes the state of the art.


Already in the near future, there will be an enormous amount of wireless devices [14] ranging from deeply embedded sensors and actuators over intelligent gadgets to fully autonomously acting robots forming sensing and actuating platforms featuring a variety of hardware and software, different programming approaches and system interfaces making, especially, the cooperation and coordination a challenging task.

our approach is to consider all these devices as one emerging system (the swarm) and build a distributed swarm runtime system on top of it that hides heterogeneity and diversity, provides a common interface to the outside and decouples swarm applications and their execution in space and time.


NDN hourglass architecture's thin-waist also supports data exchange instead of service interoperability.

nDN can be well suited for Open mHealth architecture.


The key idea of H-CRAN is so called 4C, i.e., clean, centralized, cooperative and cloud, resulting in several notable advantages such as saving CAPEX and OPEX, reducing power consumption, enhancing network capacity, dynamically allocating resources based on current network loads and improving quality-of-experience of mobile subscribers.

the more complexity that a wireless communication system has, the higher possibility with which security vulnerabilities will occur.


In the wireless access network, radio signals are transmitted in the open shared medium, typically the air environment.

they are easily eavesdropped by adversaries.


The environment is special in underground coal mine, with a high incidence of accidents[3].

the monitoring in coal mine monitoring is becoming increasingly important.


In this paper, the number of base station is limited, but the positioning accuracy achieved is very impressive.

it is validated that the positioning algorithm and the model is very consistent with the requirements of location in coal mine.


The rest of the paper is organized as follows.

the system throughput and the number of selected RDs grow with the increase of p0.


However, if the decoding process of MAC header fails, no noti.cation signal will be generated and broadcasted.

the transmitter (Alice) stops transmitting and all the nodes, including clients and AP, restart the channel contention immediately.


Note that, the pt-channel contention and transmission are proceeded concurrently and transparently with AP transmission.

process for pt-channel contention and transmission should be completely collision-free for AP transmission.


We examine the effectiveness and advantages of LIPS in Section 5.

we also omit it due to the limited space.


We try to present fast algorithms for these theories, and improve the performance of our coding scheme.

fig. 9(a)-(d) plot the information throughput improvement return on the occupancy of decoding resource queue.


To support a multi-hop D2D communication (even intra-cell), the routing and scheduling algorithms must be carefully designed.

we employ the statistics of the previous flow information to calibrate the topology.


Device-to-device (D2D) communication is envisioned as a promising technique to supplement the cellular network, which can enhance the spectral efficiency, extend the coverage, and create new services.

the current D2D communication only supports one-hop communication within one cell.


For typical operating conditions, the residual self-interference is above the noise-floor, and this can make it diffcult to demodulate weak external signals.

for cognitive-radio, the goal is only to detect the presence of a primary user, and not necessarily to correctly decode the transmissions.


This allows us to reuse the receiver design from the prover for the verifier, which greatly simplified the implementation.

both verifier and receiver shared the same design and most components.


We agree with the general intuition of the principle in [9], but would reformulate it in the following way: Minimize the length of the symbol used to present a single bit as well as the processing time the transceiver needs for demodulating a single bit challenge and sending the response bit.

distinguish the two possible transmitted bit values at the receiver within as short a time as is feasible and send an answer as fast as possible.


The delay itself is not a problem regarding accurate distance measurements: As long as it is known, distances can still be computed with high precision.

a malicious prover may exploit high expected processing delays to shorten measured distance by replying early, for instance using a sophisticated receiver structure with lower receive processing delay.


Advancing only the preamble (without advancing the data pulses) also advances the sampling intervals, leading to invalid received replies.

the attacker cannot advance the preamble more than its processing advantage.


In this section, we will discuss a transceiver design for a UWB rapid-bit-exchange phase.

we will study which modulation scheme is best suited to implement rapid-bit-exchange given a fixed bandwidth.


That allows an attacker to shorten the ranging result between two honest nodes.

the attacks are possible due to the large integration window (128 ns) of the receiver.


To simplify analysis, we restrict our attention to deterministic environments.

we can assume the environment is modeled as synchronous deterministic automaton that receives the nodes' ack outputs as input, and generates their bcast inputs as its output.


For example, a 3-uniform jamming adversary may jam channel one for group one, jam channel one and six for group two, and do nothing for group three.

an n-uniform jamming adversary can make jamming decision for each node individually.


In the lemma below, we show a lower bound for winning the (c, k)-bipartite hitting game.

we first calculate the player s losing probability.


Therefore this assumption could be dropped if one is not interested in implementing an absMAC that performs local broadcast exactly in G1-e.

our absMAC implementation outputs rcv events for all bcast-messages received, which can be modified if required by other higher-level algorithms designed for absMACs and when the G1-e-neighborhood is known.


As a network module in Linux, tc does not reference Linux kernel's time as the way user application does.

tc is transparent to our virtual time system.


It is also important to note that the deployments vary from relatively simple ones such as the epilepsy case study, to complicated ones such as the incontinence and depression case studies.

in the case studies we show that using the framework we can quickly implement and deploy multimodal, largely passive behavioral monitoring systems that are useful to caregivers and medical professionals.


the modules that we have built, available in a library, are meant to be cross-platform and have been tested on a variety of platforms such as the Raspberry Pi, Beaglebone, and laptops.


We have developed software (based on the Arduino programming language [2]) uploaded on network nodes, tailored to the role and functionality of each node.

the software executed by microcontrollers undertakes the collection of information measured by sensors, data processing and information transmission to the wireless medium.


By processing it, we obtained that there were 88 smart meters, 3 of them working as switches, with 4, 3 and 2 nodes reaching the concentrator through each of them.

as in many other communications standards, PRIME specification just sets the maximum values of certain communications parameters that may influence overall network performance, thus leaving them up to the manufacturer.


Finally, we address the second question asked at the beginning this section, evaluating the reconfiguration latency of the Blueswitch design for variable number of rules.

the flow-table pipeline sends the matched output actions to per-port packet marshallers via an 1-to-N arbiter.


It does not rely on specialized drivers like the other frameworks and can be used with every NIC as long as this card is supported by Linux.

without modified drivers the NIC cannot push the packets directly to user space.


The next drop in the graph is the transition between L3-cache and non-cached RAM accesses.

even under these circumstances the model provides a good estimation if the average per-packet costs are used.


A recent survey indicated that the number of L7 appliances (e.g., middleboxes) in SDN’s infrastructures is comparable to the number of routers [29], making high layer processing in SDN highly challenging.

to empower SDN with such functions, high layer visibility and protocol parsing in the device are necessary.


We translate DCCFG into DCRG in a similar way.

the growth of DCCFGs does not linearly increase the unique leaf nodes because of the very similarities between multiple DCCFGs.


The PI-based approaches benefit from the linear-complex prior DFA on the input and achieve much faster goodput than SP-based ones.

it is also affected by the RegEx libraries.


Encrypted traffic increases continuously in today’s Internet for better security performance, which obstructs the finegrained identification on the packets.

we argue that the content parsing is still necessary and feasible for SDN.


But the opposite is not always true, x might match an ELCP of a range that does not contain x.

on top of ORange1 we present the ORange-k classiffication scheme that supports k dimensional ranges and achieves exponential with k space improvement by using cascaded instances of the ORange1 classifier.


First, an OCS does not process packet headers and requires a separate control plane to configure optical circuits before data can flow through the interconnect.

we also discuss scheduling ineffciencies of current fast OCSs and evaluate ideas to solve them.


There are huge tradeoffs between these metrics, and it is easy to achieve high values in one of them sacrificing one of the others.

latency and throughput are often conflicting goals (e.g., larger batch sizes improve throughput at the expense of latency).


Using this tree structure, the root node of each Sync Tree holds the combined hash of all the names stored under the represented collection.

we describe and evaluate a functional prototype that executes in CCNx-compatible environments.


Unlike the IP paradigm, NDN focuses on what the content is and not on where the content is located.

in the NDN architecture, a packet is delivered according to its name rather than its destination address.


If more than one consumer asks for the same data, the PIT entry of the requested name will contain the faces of all the requesting consumers.

on its way back, the Data packet will be forwarded to all the requested faces.


All other (internal) nodes of an address represent XIDs, and each node is associated with between one and four strictly prioritized outgoing edges; four being the maximum fanout supported in XIP addresses.

also, XIA zFilter cleanly interoperates with TCP/IP, as §6.1 demonstrates.