sadat2307/MSciNLI huggingface dataset:
Domain: Networks
Sentence 1: We exploit unique physi­cal layer characteristics within a BAN environment, namely, the distinct received signal strength (RSS) variation behav­iors between an on-body and an o.-body communication channel.
Sentence 2: when two legitimate devices are placed on the same user s body, the RSS variation of the channel between them is much more stable than the case when one of the devices is o.-the-body, especially when the body as a whole is in motion.
Relationship: entailment
Domain: Networks
Sentence 1: After deploying our code in the TinyOS environment, we evaluate the energy and computation over­head by means of the Avrora Simulation Framework.This tool is a set of simulation and analysis tools for programs written for the AVR microcontroller produced by Atmel and the Mica2 sensor nodes.
Sentence 2: we test our code on the Mica2dot motes, which are smaller than Mica2 ones and better suited for commercial deployment.We remark that we evaluate only the FAIR inference module, not the entire framework aggregation architecture.
Relationship: entailment
Domain: Networks
Sentence 1: We compute the throughput via a renewal argument in which 1 time alternates between periods of successful transmission and pe­riods of thwarted transmissions and assume that such durations are 0.8 independent and identically distributed.
Sentence 2: we denote E(TL) as the expected lifetime of a route as determined by factors such as the node velocity and node density.
Relationship: entailment
Domain: Networks
Sentence 1: In this case, it can be observed that not all packets can be sni.ed using a single RSS queue (a single core).
Sentence 2: only 12.1 Mpps are sni.ed out of the 14.2 Mpps sent: notice that the CPU usage of the sni.ng module is 100% which pinpoints a processing bottleneck.
Relationship: entailment
Domain: Networks
Sentence 1: Note that it does not make sense to strictly partition the bu.er across .ows since the number of active .ows may be large and not all .ows bene.t equally from vFlood.
Sentence 2: we note that high RTT con­nections and low bottleneck bandwidth connections bene.t less from vFlood, and also consume more bu.er space since the discrepancy between the rates at which the sender VM produces and network drains is the most for such .ows.
Relationship: entailment
Domain: Networks
Sentence 1: Then, we study the problem of establishing a communication sub-network be­tween two selected nodes s,t .V of minimum cost in terms of energy consumption, while guaranteeing a minimum com­munication bandwidth B.
Sentence 2: we look for the minimum cost set of active interfaces among the input net­work, in such a way that s is guaranteed to exchange data with t at least with some bandwidth B.
Relationship: entailment
Domain: Networks
Sentence 1: 3.2.2 Solution 2: Adjusting utility function weights to guarantee the delay bounds (3) Motivated by the fact that the feasibility of a required bandwidth in (15) (and thus the required delay bound) depends on the choice of utility function weights, in this approach instead of adding lower bound constraints on source rates, the utility function weights are dynamically adjusted based on (14) so that the end-to­end delay is bounded at optimality conditions.
Sentence 2: the end-to-end delay constraints (3) are incorporated in the sources utility functions.
Relationship: entailment
Domain: Networks
Sentence 1: Let Nt denote the set of all game tree nodes referring to time t = 0.
Sentence 2: nt is the family of all (0 - 1­matrices specifying) content distributions over peers that may be reached along some game course up to t, when the abovemainconstraintRu1-3applies:in any roundeachpeer can forward at most one unit and can also submit at most one request for receiving.
Relationship: entailment
Domain: Networks
Sentence 1: Secondly, a non­transparent relay with the distributed scheduling mode works in exactly the same way as a base station or a low power node from the viewpoint of all the key internal radio resource management algorithms.
Sentence 2: it makes scheduler implementation simpler and allows for reusing an existent macro base station software at relays without implement­ing a complicated centralized scheduling, and also allows for faster scheduling as both macro and relay schedulers work as independent entities.
Relationship: entailment
Domain: Networks
Sentence 1: CONCLUSIONS In this paper we have evaluated the feasibility of using the centrality metrics from social network analysis to create a topology control mechanism based on a connected dominating set.
Sentence 2: for a dynamic wireless mesh network formed by user hand-held devices, we have proposed and evaluated some mechanisms to select which of those devices must act as routers, forwarding the packets received from other hand-helds to their destination.
Relationship: entailment
Domain: Networks
Sentence 1: Formally speaking, the most regular quadrilateral formed by triangle ti = DT (Z) and its adjacent triangles (at most three) for which Voronoi edge is a minimum.
Sentence 2: ||v(ti) - v(tj )||, i =j is min­ = imum, where v(ti) and v(tj ) are the Voronoi vertices cor­respond to Delaunay triangles ti and tj , respectively.
Relationship: entailment
Domain: Networks
Sentence 1: .LLyi (b,:) if xi(b, :) = 2 and xi(b, :) + 2 = 3 Constraints: The same resource block can t be assigned to two di.erent operators or at two di.erent power levels.
Sentence 2: n N L L xi(b, r) + yi(b, r)= 1, .1 = r = R, 1 = b = B i=1 i=1 The isolation constraint: A network operator should not experience signi.cant interference from another network operator.
Relationship: entailment
Domain: Networks
Sentence 1: They advocate discarding channelization and allowing all nodes to access the en­tire 802.11 spectrum based on demand [19, 37].
Sentence 2: this push towards wider bands is further enabled by the constantly lowering prices of high-speed ADC and DAC hardware [38, 31].1 today, wireless cards that spanover100MHzof spectrum canbebuilt us­ing off-the-shelf hardware components [35].
Relationship: entailment
Domain: Networks
Sentence 1: Only few constructions for pure additive spanners, i.e., for which the stretch is of the form (1,ß), are known, even in the sequential setting.
Sentence 2: sequential algorithms 3/2 that construct a (1, 2)-spanner with O(n )edges anda (1, 6)-spanner with O(n 4/3) edges were given respectively in [17] and [6].
Relationship: entailment
Domain: Networks
Sentence 1: Simulation results show that our protocols are more energy ef.cient than the corresponding residual energy based protocols and geographic routing protocols without considering the property of the energy renewal.
Sentence 2: given the same network, energy, and traf.c models, GREESs maintain higher mean residual energy of nodes and achieve better load balancing in terms of having a smaller standard deviation of residual energy among nodes.
Relationship: entailment
Domain: Networks
Sentence 1: In our measurement, we observe that ICMP messages come from 726 routers belonging to 68 ASes, and about 53% of those routers belong to 10 tier-1 ASes.
Sentence 2: 70 routers within ISP1 (i.e., 52% of ISP1 s routers visible in our measurement) generate ICMP messages.
Relationship: entailment
Domain: Networks
Sentence 1: To solve this problem, we formulate the pre-cross-connection based on flows instead of vectors.
Sentence 2: if there is a flow on an on-trail vector (u, v) and v is not the sink, then (u, v) must be pre-cross­connected to another vector (v, w) at node v, and the flow must be forwarded to (v, w).
Relationship: entailment
Domain: Networks
Sentence 1: In this case, theb+1-k remaining vectorpositions have to be .lled with at most Li,1+...+Li,n-1 < b+1 -k packets.
Sentence 2: there are more positions in the vector than elements to .ll the vector with, so we de.ne, for all n and for all b = Li,1 + ... + Li,n, Ci(b,(Li,1,...,Li,n))=0, Finally, the behaviour on the boundary n =1 follows from the de.nition of Ci(·,·)(Eq.
Relationship: entailment
Domain: Networks
Sentence 1: DOS: A TEAM GAME VIEW In this section, we treat distributed opportunistic schedul­ing, namely, joint channel probing and distributed schedul­ing, as a team game in which all links collaborate to maxi­mize the overall network throughput.
Sentence 2: building on optimal stopping theory, we cast the problem as maximiz­ing the rate of return, where the rate of return refers to the average throughput [12].
Relationship: entailment
Domain: Networks
Sentence 1: While this may seem like a simple problem to .x since there are multiple al­gorithms for group formation, an ideal solution should only introduce a small overhead to the existing WiFi-Direct pro­tocol, and attempt to be backwards compatible with devices running the original WiFi-Direct protocol.
Sentence 2: the improved group formation should still work even if one of devices in the group do not implement the new proto col.  4.
Relationship: entailment
Domain: Networks
Sentence 1: As with routing, route-based packet .ltering occurs at two time scales packet forwarding/discard based on table look-up (fast) and .lter table update (slow) and thus its forwarding/discard function can be performed close to line speed subject to generic processing overhead.
Sentence 2: the core .ltering function itself is not subject to DoS attack1 .
Relationship: entailment
Domain: Networks
Sentence 1: 4.4 Security analysis of the T -time SCU mech­anism Recall that a secure code update mechanism must provide the following security properties: Authenticity: The adversary must not be able to forge the authenticity on a code image of its own choice.
Sentence 2: no sensor node accepts a code image CI as authentic and writes it into memory unless CI was initially sent by the BS;  Integrity: Any modi.cation made by the adversary on the CI must surely be detected by the receiving sensor nodes;  DoS resilience: Considered the DoS attacks exploit­ing the expensive signature veri.cations and the online packet modi.cations, the SCU mechanism must limit the e.ect of the adversary aiming at exhausting sensor nodes resources (i.e., storage and power)6 .
Relationship: entailment
Domain: Networks
Sentence 1: nC stations, even though the most distant ones cannot correctly decode the transmitted infor­mation.
Sentence 2: nC stations can correcty receive A s signals; other nI - nC stations cannot correctly receive 1 Some stations are out of the range of a given one as to a correct information decoding, yet they disturb the given station in a non-negligible way.
Relationship: entailment
Domain: Networks
Sentence 1: While the goal of each user is to maximize its own energy e.ciency, a more sophis­ticated admission control can be performed to maximize the total network utility.
Sentence 2: out of the K users, the access point can choose those users for which the total network utility is the largest, i.e., X max u£ (26) L.{1,··· ,K} £.L P under the constraint that £.L F£*<1.
Relationship: entailment
Domain: Networks
Sentence 1: Bound by this constraint, mobile network operators control the traffic by restricting the users' communication demands (calls) uniformly so as to prevent too high a load on the network equipment from occurring and stopping services, and to give priority to emergency calls to police, ambulance or fire stations.
Sentence 2: when an event such as a football match is scheduled in advance, a restriction rate is set on the network equipment in the relevant area based on the place and time of the event and the forecast number of people attending.
Relationship: entailment
Domain: Networks
Sentence 1: To improve the accuracy of the large deviations asymptotic we used a refine- ment that introduces a constaaat in front of the exponential.
Sentence 2: the refined large deviations asymptotic is P[L~ _> U] ,-~ pLD ~ o~e-U0*(p), (17) where a = E[Lt]0* (p).
Relationship: entailment
Domain: Networks
Sentence 1: Since WiMAX networks typically operate over 10-20 MHz or wider bandwidth, there is a signi.cant amount of frequency se­lectivity over all the links [20].
Sentence 2: if the operating spectrum is subdivided into narrow sub-channels (as is done in OFDMA), there is substantial variation in the rates that can be supported over each sub-channel of a given link.
Relationship: entailment
Domain: Networks
Sentence 1: Once the normal operation of the network has been gleaned from the tra.c traces, one may assume that what is left is either statistical anomalies or mere noise.
Sentence 2: when the normal subspace has been removed, one is left with a(n- k)-subspace that can then either be treated as wholly anomalous or be further split into a e-subspace that is anomalous and a (n - k - e)-subspace of noise.
Relationship: entailment
Domain: Networks
Sentence 1: 1 (8) n l l nn P =.d ( f - fi ) i -i n=0 .-1 .-1 -q= .-1 3.3 Gradual Any breach of cooperation in TFT or GT results in either all packets being dropped or some fraction of packets being dropped.
Sentence 2: fully cooperative is not the Nash equilibrium point with TFT or GT.
Relationship: entailment
Domain: Networks
Sentence 1: In addition, as shown in Figure 18 (b), increasing the number of ISPs also helps to reduce maximum link utilization.
Sentence 2: we observe a 12% reduction when user 1 changes from subscribing (a) User latency (b) Maximum link utilization Figure 18: Performance bene.t of smart routing when the la­tency is a function of traf.c load.
Relationship: entailment
Domain: Networks
Sentence 1: As mentioned in [8], several other groups of researchers have either formulated connections between symbolic logic and feasible probabilistic computation, or developed rela­tionships between symbolic and computational models.
Sentence 2: abadi and Rogaway [1] propose a logical char­acterization of indistinguishability by passive eavesdroppers that has been studied by a number of others (e.g., [2]), and Kapron and Impagliazzo suggest a formal logic for reason­ing about probabilistic polynomial-time indistinguishability [19].
Relationship: entailment
Domain: Networks
Sentence 1: EEE speci.cations and state tran­sition schemes are di.erent for 100Base-T, 1000Base-T and 10GBase-T links.
Sentence 2: since we focus on commonly deployed 1 Gbps links, in the following subsection we de­scribe in detail how EEE 1000Base-T links behave, since they represent the only case in which the EEE standard ac­counts for the bidirectional behavior of tra.c.
Relationship: entailment
Domain: Networks
Sentence 1: Regardless of precise SDN definitions, while the worthy goal is to address ossification of the Internet, the S for software in SDN perhaps unintentionally ossifies views of the respective roles of hardware and software.
Sentence 2: it introduces an inbuilt assumption that there is relatively dumb switching hardware for high-speed packet forwarding, and relatively intelligent software running on processors for lesser-speed packet forwarding and networking control.
Relationship: entailment
Domain: Networks
Sentence 1: Interference-Constrained Wireless Coverage in a Protocol Model * Prateek R. Kapadia Om P. Damani Indian Institute of Technology, Bombay, India Indian Institute of Technology, Bombay, India prateek@it.iitb.ac.in damani@it.iitb.ac.in ABSTRACT We present an O(n log n) algorithm to compute the cover­age map of a given set of transmitters under interference constraints.
Sentence 2: we compute the set of points that lie within the transmission range of one transmitter and lie out­side the interference range of every other transmitter.
Relationship: entailment
Domain: Networks
Sentence 1: Inspired by the aforementioned applications, this work is (to the best of our knowledge) the .rst studying the energy balance property in wireless networks where the nodes are highly and dynamically mobile.
Sentence 2: in this paper we propose a new diverse mobility model which is easily parameterized and we also present a new protocol which tries to adaptively exploit the inherent node mobility in order to achieve energy balance in the network in an e.cient way.
Relationship: entailment
Domain: Networks
Sentence 1: We will implement a cognitive scenario formed by a legacy base station (modeling the primary system) and several point-to-point MIMO cognitive links (secondary system), trying to establish their point to point connections by exploiting the holes in the time-frequency and space do­main.
Sentence 2: as far as exploiting the antenna domain is concerned, we aim at demonstrating our recently proposed Reverse TDD scheme [2] showing that, when the secondary nodes have multiple antennas, they can sustain high trans­mission rates without signi.cantly impacting the primary system performance.
Relationship: entailment
Domain: Networks
Sentence 1: We call it centralized scheduling mode if the UEs in RS domain are scheduled by BS, or distributed scheduling mode if they are scheduled by RS.
Sentence 2: in centralized mode, the BS takes charge of scheduling all the UEs in the cell (including the UEs in BS domain and RS domain), and the RS is just a node for receiving and retransmitting data; in distributed mode, after allocated a part of the radio resources by the BS, the RS can schedule the UEs in RS domain using these resources, and the BS is only take charge of scheduling the rest.
Relationship: entailment
Domain: Networks
Sentence 1: We call a Hit, the event of detecting a new neighbor at a time within 60 seconds of the neighborhood predicted time.
Sentence 2: a hit occurs when a vehicle receives a mes­sage (data or beacon) for the .rst time from a speci.c node which is on the Predicted Neighbors Table and its predicted time t is within 60 seconds of the current time.
Relationship: entailment
Domain: Networks
Sentence 1: 2.2 Calculation on the number of unchanged nodes for optimum energy efficiency To efficiently consume energy using Table 1 in the setup phase of the traditional method and the proposed method, it is attempted to improve energy efficiency using the changed distance among cluster heads ( CHold and CH new ) and the number of unchanged nodes independent on the changed distance.
Sentence 2: it is the case in which the total energy dissipation in the setup phase of the 4 proposed method ( .Setupprop(step) ) is as much as that of in the step=1 3 setup phase of the traditional method ( .Setuptrad (step) ), or step=1 34 smaller than that (namely, .Setuptrad(step) =.Setupprop(step) ).
Relationship: entailment
Domain: Networks
Sentence 1: That is, it is as unlikely as a random hash function with sim­ilar output size for C(MX )= C(MY ) for two di.erent router messages MX and MY .
Sentence 2: in addition, C should be unpredictable, so that it is di.cult for the adversary to compute C(MX ) prior to its transmission to V.we want C(MX ) to be unpredictable to an adversary who knows only the value of X (we assume the adversary does not know all of MX ).
Relationship: entailment
Domain: Networks
Sentence 1: A .nal observation is that in all scenarios, except with the highest rate of interfering tra.c, the throughput obtained with CHARM and SampleRate is higher without RTS/CTS than with RTS/CTS.
Sentence 2: the loss in e.ciency caused by(occasional) poor rate selection is lower than the extra overhead of using RTS/CTS.
Relationship: entailment
Domain: Networks
Sentence 1: To this end, a new link metric was introduced to quantify the expected number of transmissions and retransmissions that are required to deliver a packet over a particu­lar link.
Sentence 2: if ps is the probability of a successful trans­mission over a link, the expected number of transmissions, ETX, to deliver a packet over that link is computed as [5]: X j ETX = 8 j(1 - ps)ps =1 (1) ps j=1 The end-to-end cost of a path is then de.ned as the sum of the ETX values of the links on the path; the routing layer simply computes routes that minimize this cost.
Relationship: entailment
Domain: Networks
Sentence 1: Our results for NS-BGP suggest another, more .exible, solution allow route re.ec­tors to announce di.erent routes to di.erent iBGP neigh­bors.
Sentence 2: a route re.ector could disseminate any client-learned route (such as the client-learned route with the closest egress point) to its route-re.ector peers, and any route (such as the route with the closest egress point) to its route-re.ector clients.
Relationship: entailment
Domain: Networks
Sentence 1: If nodes j and k are two relay candidates, then the fact that k is a better candidate than j (.j =.k)guarantees that k is elected over j.
Sentence 2: pr[Xk =Xj |.j =.k]= 1 Note that absolute priority can be obtained with a func­tion g(·) which is deterministic and monotonically decreas­ing with respect to ..
Relationship: entailment
Domain: Networks
Sentence 1: The first inequality in (2) expresses the need to have a unique code-word for every node in the network, and the second inequality ensures that a node will have at least some successful transmissions (greater than zero) in a frame.
Sentence 2: a node should be assigned a number of opportunities to transmit per frame greater than the maximum number of collisions possible (i.e., n > (n - d I ) if one wishes to have a lower bound min ) max performance greater than zero.
Relationship: entailment
Domain: Networks
Sentence 1: The self-healing communities en route are recon.gured by monitoring the hop count .eld.
Sentence 2: a node who forwards the PROBE or PROBE REP message sets its membership .ag to 2 (i.e., the forwarding mem­ber), and any node overhearing three consecutive ACKs should set its membership .ag to 1 (i.e., the non-forwarding member).
Relationship: entailment
Domain: Networks
Sentence 1: 0.6 The question as to which .ows su.er drops must be con­sistent (on the average) with the drop rates speci.ed by (4).
Sentence 2: the selection of the .ow f * where a drop oc-0.4 curs is made by drawing the .ow randomly from the set F, according to the distribution 0.2 ef s ef d e , .f .F .
Relationship: entailment
Domain: Networks
Sentence 1: Although our algorithm will be effective in a wide variety of scenarios, we pri­marily focus on dense deployments where only a small fraction of the nodes are awake each epoch; in such scenarios, nearly all the nodes have du » k. Second, the algorithm will minimize the aver­age number of awake nodes per epoch.
Sentence 2: all the nodes will have roughly the same number of awake neighbors (the distri­bution will be sharply concentrated just above k), a desirable prop­erty not ensured by existing algorithms.
Relationship: entailment
Domain: Networks
Sentence 1: These programs represent various classes of operations that IDSs commonly perform; our goal is to evaluate how our technique works on detectors of varying complexity and heterogeneous scopes.
Sentence 2: flowbytes and Httpvol model measurement scripts that  (a) Full trace (b) Filtered trace Figure 6: Thread load for full and .ltered trace compute common traf.c statistics.
Relationship: entailment
Domain: Networks
Sentence 1: 5.2 Routers The main challenge for transforming the analytical in­sights of Section 4 into speci.c algorithms for RD router operation lies in the dynamic nature of Internet tra.c.
Sentence 2: while Equations 5, 7, and 8 depend on nR and nD, the numbers of R and D .ows change over time.
Relationship: entailment
Domain: Networks
Sentence 1: r.X l.L Among them, D1(µ a ,µ ß ) denotes the rate allocation problem, while D2(µ a) is the scheduling problem.
Sentence 2: the rate al­location problem aims at .nding the rate of each source node that maximizes the aggregate utilities of all sources, subject to the con­straint that the system is stable under some scheduling policy, while the scheduling problem focuses on .nding a scheduling policy that stabilizes the system, for any rate vector of sources picked by the rate allocation problem.
Relationship: entailment