Integrating models with domain-specific modeling languages Model integration is inescapable: any non-trivial system will be too large to fit sensibly in a single model. The model will have to be split, maybe into different aspects or languages, different modeler roles and tasks, different phases of the software development life cycle, etc. In Domain-Specific Modeling, the possibilities to integrate models are fundamentally better than with general-purpose languages as the company has full access to the language definitions. We describe and compare different ways to integrate DSM models, based on real world experience of what has been shown to work in practice on industrial scales. To model any non-trivial software system inevitably requires the integration of multiple models ('model' here means a single graph, often represented as a diagram.). A large domain further requires the use of multiple modeling languages and representational views. Splitting the system over different languages and models can be done in various ways: modularization of the system; different aspects of the system; different people and their roles in development; different phases of the development lifecycle. In all cases there is a need and expectation that individual models and their elements can be linked and integrated with other models. This reintegrates the separate models into a cohesive whole, allows modelers to see and reuse work done by others, and allows the checking of system-wide properties. This paper examines approaches for integrating modeling languages. We focus solely on Domain-Specific Modeling (DSM) languages, as opposed to General-Purpose Languages (GPLs). This difference is important since with DSM the companies have full control of the individual languages and how they can be integrated. Since there is plenty of work focusing on the technical side of integration (such as model transformation languages and metamodeling languages), we focus on integration needs and approaches coming from the higher, tool-independent level of language engineer work in practice. Where possible we refer to industrial experiences from public cases including Porsche [1] , Polar Electro [2] and Panasonic [7] .. We start by introducing an example domain for model integration: embedded UI applications. We use this to compare and contrast the two basic integration paradigms, string matching vs. direct reference. We then identify and describe different ways to integrate models, first looking at those that can be accomplished independently of the modeling language, e.g. to allow modelers with different roles to use the same model in their own ways, and then at ways of integrating the modeling languages themselves to provide the best integration on the model level. In DSM, language engineers have full control over the languages, and can thus decide on an appropriate model integration approach for their situation. Sometimes it can be possible to integrate several areas of interests, e.g. persistency, navigation, layout, data, into a single modeling language, whereas at other times the use of different languages, and explicit integration among the models is preferred. In any case, there will always be multiple model diagrams to integrate, whether by elements having subdiagrams, or elements being reused or referenced in several diagrams.. We have described some of the main model integration approaches by analyzing language integration cases based on our consulting work in various industries, including automotive, telecom, and consumer electronics. The approaches described have been illustrated by extending a common base example model.. The range of integration approaches described probably reflects our experience with a repository-based tool, which makes linking and reusing across models and languages easy. While all of the approaches could in theory also be implemented in a tool based on separate files, e.g. XML files with string matching, the amount of work necessary to provide good support to modelers with those tools may be prohibitive in some cases.. Whatever the tool, avoiding the need to split and recombine data is a useful tactic where possible. A single language integrating multiple aspects, tool support that can present different views for different users, and multiple representations and notations of a single language are all approaches that can achieve this.. Where it is necessary or desirable to split information over multiple models, possibly of different languages, they can be kept integrated by shared elements, relationships between models, and even by creating metamodels from models on the fly. It is important that there are several possibilities for integration and that tools applied do not limit the language engineer's, and later modelers', choices on how best to integrate models. The splitting and string matching of 3.1 can also be a deliberate policy, as in [9] . The wider questions of string matching vs. direct references and model versioning are covered in more detail in [3] .. To our knowledge, out of the box no other tool offers multiple simultaneously editable representational paradigms of the same model, as in 4.1. Extending a model as in 4.2 is accomplished in [8] by storing the extra information in external XML files.. A similar approach to the common language concepts of 5.3 is the use of "gateway metaclasses" in [8] . However, in that case whole elements are reused by copying, not by reference; to avoid that duplication it is suggested to have one end of the reference just use the element's name, with matching based on identical names. The ModelBus add-on to Microsoft DSL Tools has a similar approach.

O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Spam filter evaluation with imprecise ground truth When trained and evaluated on accurately labeled datasets, online email spam filters are remarkably effective, achieving error rates an order of magnitude better than classifiers in similar applications. But labels acquired from user feedback or third-party adjudication exhibit higher error rates than the best filters -even filters trained using the same source of labels. It is appropriate to use naturally occuring labels -including errors -as training data in evaluating spam filters. Erroneous labels are problematic, however, when used as ground truth to measure filter effectiveness. Any measurement of the filter's error rate will be augmented and perhaps masked by the label error rate. Using two natural sources of labels, we demonstrate automatic and semi-automatic methods that reduce the influence of labeling errors on evaluation, yielding substantially more precise measurements of true filter error rates. When trained and evaluated on accurately labeled datasets, online email spam filters achieve remarkably good performance. Gradient descent logistic regression [14] , for example, yields an overall error rate of less than 0.5% when evaluated on the trec05p-1 corpus [10] using the TREC 1 Spam Track methodology [11] . This result betters by an order of magnitude those reported for similar applications of classifiers (cf. [28] ). But is it good enough? Can it be improved? Can it be demonstrated beyond the laboratory? Imprecise knowledge of ground truth -the correct label for each filtered message -presents a substantial impediment to addressing these questions.. The TREC methodology assumes a chronological sequence of accurately labeled email messages. The labels serve two distinct purposes in evaluation: as training examples for the filter, and as ground truth against which to measure filter error. Label errors therefore compromise both the filter's learning and the accuracy of the measured error.. The TREC datasets were carefully labeled by the organizers, adhering to a prescribed definition of spam: A fully automatic method fuses the results of candidate filters to yield a pseudo-gold labeling that is used as ground truth in evaluating email spam filters. The pseudo-gold labels exhibit a lower error rate than labels obtained from natural sources including user labels and exhaustive adjudication by experts. Using the labeling as ground truth for error measure (95% c. evaluation results in lower estimated filter error rates across the board, according to standard measures. This effect is consistent with the hypothesis that the these new estimates are more precise in absolute terms. There is some possibility that the pseudo-gold labels to some extent represent "group think" of the subject filters, which all rely exclusively on the text of the message for classification, albeit using different algorithms and feature engineering. This concern is similar to that raised with respect to the pooling method for IR evaluation. This concern applies mainly to the magnitude of reported error rates, as differential comparison is insensitive to it. Differential comparison, which requires some adjudication, may be used to demonstrate that the pseudo-gold standard has a lower error rate than other available labels. And, were a radical new filter to discover errors in the labels, this situation could be discoverable by differential comparison. It is important to note that differential comparison does not require that the adjudicator be more accurate than the labels, just that the adjudicator be more accurate than chance. Differential comparison may also be used to rank filters directly; the rankings achieved by tournament ranking, even with noisy adjudication, are quite powerful, although they yield no quantitative estimate of filter error rates.. We call into question Sculley and Cormack's claim [25] that spam filters perform more poorly with natural than with random training label noise. Our results are consistent with the hypothesis that the filters perform about as well on both, and that the reported results for the best-performing filter -SVM with a low C parameter -were substantially confounded by errors in the evaluation labels. Other approaches designed to mitigate label noise, including slowed learning rates and bagging, generally improved performance as expected. Our overall conclusion is that noise-tolerant spam filters perform not quite as well as the best filters with clean data, but not nearly as poorly as previously reported.. (1-AUC)(%) (95% c.l.) filter trec labels nat labels logbagm 0.14 (0. 13 Spam filtering as a research area is relatively new but nevertheless quite diverse. The TREC Spam Track is the largest and most realistic laboratory evaluation to date. In the three years it ran, ten test corpora with a total of 721,461 messages were used to test filters submitted by thirty-five participants. The results of more than one thousand experimental runs may be found in the summaries and appendices of the TREC proceedings [11, 6, 7] .. The core task at TREC -the immediate feedback task -simulates the on-line deployment of a spam filter with idealized user feedback. The task, along with methods and evaluation measures embodied in an open-source toolkit [19] , was developed by Cormack and Lynam [12] .. This on-line evaluation strategy differs from traditional batch evaluation in that the messages are ordered and not explicitly partitioned into training and test examples. Every example is potentially available for training once the filter has rendered a verdict. The difference in methodology and results for state-of-the art filters has been studied by Cormack and Bratko [9] . The best results in these tests have been achieved by on-line support vector machines [26] , logistic regression [14] , and compression models [3] , along with a number of open-source "Bayesian" filters modeled after the work of Graham and Robinson [15, 24] , notably Bogofilter [22] and OSBF-Lua [2] . The best-performing approach demonstrated using TREC methods and datasets is the fusion all filters submitted for evaluation at TREC [20] .. Variants of the task, including delayed feedback, partial feedback, and active learning explore more realistic models of user feedback; in particular, the impact of tardy, incomplete or restricted presentation of training examples after classification. The CEAS Live Challenge [1] integrates the TREC methodology with a live data stream, so as to be able to compare laboratory and in vivo filter deployment. Two new laboratory corpora were created; one captured in real time, and the other sampled from email delivered to clients of a large email service provider.. Sculley and Cormack [25] explore the effect of synthetic and natural label noise on filter training and conclude that both substantially compromise filter performance, natural noise more substantially so. Several approaches to mitigating the effect are found to improve filter performance, but not to anywhere near that achieved with noise-free training labels. A regularized version of an SVM classifier which performed well on synthetic noise yielded mediocre performance on natural noise. The effect of label noise on the measurement of filter error is not considered.. Labeling noise has been studied from the perspective of its impact on the accuracy machine learning algorithms, with the methodology of injecting artificial noise into a an initially noise-free dataset. Brodley and Friedl [4] proposed a data cleaning methodology whereby instances misclassified with high confidence by a learner (or ones for which there is a significant disagreement within an ensemble of learners) are removed from the training collection. They showed that models created over such cleaned data tend to be more accurate and demonstrated that ensemble based models are more resilient to class noise, at least when the level of noise is moderate. In addition to cleaning methods relying on inconsistent instance removal, alternatives based on label correction [25] and instance weighting [23] have also been considered.. While there exists a substantial body of published worked dedicated to different filtering and feature extraction techniques, our work is not so much to improve on these techniques as to improve the evaluation methodology; in particular, the ability to discriminate among and to measure the absolute performance of the best reported approaches for spam filtering, and for training label noise mitigation.. Lam amd Stork [18] discuss the problem of evaluating classifiers by means of test data with noisy labels. Given an estimate of the label noise and the assumption that label noise and filter error are uncorrelated, a precise estimate of filter error may be achieved, given enough examples. If, on the other hand, there may be correlation, wide bounds apply.. The issue of noisy and incomplete relevance judgements has been considered extensively within the context of the laboratory evaluation of information retrieval systems (cf. [5] ). Although inter-adjudicator disagreement is large, system rankings are not particularly sensitive to it. Incompleteness is commonly resolved by the "pooling method," in which the top-ranked documents from each system are adjudicated as relevant or not, and all other documents are assumed to be irrelevant. More efficient methods select for adjudication more documents from top-performing systems, or documents most likely to discriminate among the filters under test; these methods are the subject of ongoing research interest. IR evaluation research is typically concerned only with ranking systems by relative performance on a particular corpus, as opposed to a calibrated effectiveness measure. IR evaluation further differs from spam filter evaluation in that relevant documents are extremely rare, and error rates (e.g. 1-recall, 1-precision) are quite large -of the order of 50% in a typical evaluation.

B O O O O O O O O O O O O O O O B B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O B B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B B O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O B O O O O O O O O O O O O O O B O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O B O O O O B O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B B O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O


Selective Message Forwarding in Delay Tolerant Networks Abstract-It is challenging to deliver messages in a network where no instant end-to-end path exists, so called Delay-Tolerant Network (DTN). Node encounters are used for message forwarding. In this paper, we propose a DTN routing protocol SMART. SMART utilizes the travel companions of the destinations (i.e. nodes that frequently meet the destination) to increase the delivery opportunities while limiting message overhead to a bounded number. Our approach differs from related work in that it does not propagate node encounter history nor the delivery probabilities derived from the encounter history. In SMART, a message source injects a fixed number of message copies into the network to forward the message to a companion of the destination, which only forwards the message to a fixed number of the destination's companions. Our analysis and simulation results show that SMART has a higher delivery ratio and a smaller delivery latency than the schemes that only use controlled opportunistically-forwarding mechanism and has a significantly smaller routing overhead than a pure flooding scheme. 1) The Configuration of f 1 : f 1 is one of the important parameters of SMART, which controls the maximum number of message copies injected into the network and affects the number of companion carriers reached at the end of the first phase of SMART. Here we use simulations to study how to configure f 1 so that a message will be able to reach a reasonable number of companion carriers at the end of the first phase of SMART. In addition, through simulations we study the latency for a message to reach a companion of the destination. Fig. 8 shows that on average it takes about 5 unit time for a message to reach a companion of the destination. Since the message propagation relies on the message exchanges among nodes, we use 100 seconds as an unit time to capture the average time for exchanging messages between two nodes. Also we let T (a parameter used in CV computation) equal to the simulation time. At the beginning, no companions are reached since it takes some time to spread the message to the nodes. And nodes need some time to accumulate the encounter history. As the time goes on, the dramatic increase of the number of companions reached by the Epidemic routing reflects that the message overhead of Epidemic increases exponentially. Since SMART uses controlled opportunisticallyforwarding mechanism, the number of companions reached by the message stabilizes after all copies of the message are forwarded. 2) Performance vs. Traffic Load: In this set of simulations, we vary traffic load (i.e. number of messages sent during the simulation) and measure the aforementioned metrics for the above three routing protocols. Fig. 9 shows the delivery rate when changing message traffic load. The simulation results demonstrate that SMART outperforms Spray protocol and delivers more than 90% messages. The Epidemic routing delivers all the messages since it floods a message to all nodes. The performance of the Spray degrades as traffic load increases because each message has relatively less time to be transmitted when message traffic load increases given that the meeting time between nodes remains the same. But for SMART, the meeting time between nodes has a less significant influence on delivery rate since companions frequently meet each other, thereby having more time to deliver messages to each other. In addition, since message source and destination are randomly selected, the message traffics are more evenly distributed to all nodes when message traffic load increases, which contributes to the slight improvement of SMART's delivery rate. Fig. 10 of the delivery latency demonstrates that SMART has much smaller delivery latency than the Spray. The reason is that SMART exploits companions to deliver the messages while the Spray does not. Because the companions of the destination are more likely to meet the destination, it takes a shorter time for them to deliver the message to the destination. Epidemic routing scheme delivers messages faster than SMART because a node forwards the messages to every node it meets. In this way, the messages are quickly forwarded to the destination. Fig. 11 shows that the routing overhead of Epidemic is much larger than that of SMART and Spray. Epidemic incurs huge overhead because it relies on flooding to deliver messages. SMART and Spray use controlled opportunisticallyforwarding mechanism to forward messages so the number of message transmissions has an upper bound, which is a small constant. It is worth noting that the overhead of SMART and Spray are almost identical when f 1 = 4. This is because in SMART after a message reaches a companion, the companion will only forward the message to other companions, which reduces overhead. 3) Performance vs. Mobility: DTN network is a sparse network, which is disconnected most of the time. So message deliveries rely on nodes' mobility. In different DTN scenarios, nodes' mobility may vary. A node's mobility influences the number of locations it may visit during the given simulation time. Hence nodes' mobility influences the meeting probability of the nodes. In addition, mobility affects meeting time, i.e. how long two nodes stay in each other's radio range. Higher mobility leads to shorter meeting time.. In this set of simulations, we measure the influences of the mobility on the performance of the above three schemes. We alter a node's mobility by changing its average dwelling time at each location. If we want a node to become more mobile, we decrease its average dwelling time. In the following set of simulations, we vary each node's average dwelling time at each location from 1 to 5 (normalized against the largest average dwelling time) and fix the message traffic as 256 messages. Fig. 12 demonstrates that when nodes become more mobile, all three routing schemes achieve higher delivery rate. From Fig. 12 , we can see that the positive effects on the delivery rate brought by the increase of meeting probability outweighs the negative effects brought by the changes of nodes' meeting time. Moreover, when nodes become more mobile, the delivery rate of SMART and Spray increasingly approaches that of Epidemic routing. So when nodes have high mobility, we can use SMART to achieve a high delivery ratio with a small routing overhead. Fig. 14 shows that the delivery latency of all three routing schemes decreases when nodes become more mobile. The reduced latency is because nodes visit more locations in a given time so they have more opportunities to meet and meet each other more quickly. From Fig.13 , we know that SMART and Spray maintain a very small routing overhead when mobility changes. The paper has presented a scheme named SMART that exploits controlled opportunistically-forwarding and a node's companions to achieve efficient message delivery. SMART protocol can achieve high delivery ratio while keeping a low routing overhead. In SMART, a message source injects a fixed number of message copies into the network to forward the message to the companions of the destination, which only forward the message to a limited number of the destination's companions until the message is delivered to the destination.. The paper has presented both analytic results and simulation results comparing SMART with the Spray routing scheme and the Epidemic routing scheme. The analytic results demonstrate that SMART has a higher delivery rate and a smaller delivery latency than Spray. And the simulation results show that SMART has a significantly smaller routing overhead than Epidemic while maintaining a comparable delivery rate and delivery latency. From the simulation results, we find that the routing overhead of SMART is almost identical to that of Spray but SMART has a higher delivery rate and a smaller delivery latency. In section I, we have categorized the methodologies of existing DTN routing schemes into 3 types: opportunistically forwarding messages, predicting good forwarders, and meeting the destinations by schedule. In this section we overview the state-of-the-art DTN routing protocols based on their methodologies.

O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Mechanisms for collaboration: A design and evaluation framework for multi-user interfaces Multi-user interfaces are said to provide "natural" interaction in supporting collaboration, compared to individual and noncolocated technologies. We identify three mechanisms accounting for the success of such interfaces: high awareness of others' actions and intentions, high control over the interface, and high availability of background information. We challenge the idea that interaction over such interfaces is necessarily "natural" and argue that everyday interaction involves constraints on awareness, control, and availability. These constraints help people interact more smoothly. We draw from social developmental psychology to characterize the design of multi-user interfaces in terms of how constraints on these mechanisms can be best used to promote collaboration. We use this framework of mechanisms and constraints to explain the successes and failures of existing designs, then apply it to three case studies of design, and finally derive from them a set of questions to consider when designing and analysing multi-user interfaces for collaboration. Multi-user interfaces have become pervasive in the last few years, with a number of commercial and customized systems now available. These include Diamond Touch Dietz and Leigh [2001] , Microsoft's Surface, Smart Table and tangibles with multi-user interfaces such as the Reactable Jorda [2003] . The lightweight and parallel action of touching, the mobility of users, and the increased ability for natural expressions of behavior such as gesture and posture extend the possibilities for communication and collaboration. User studies have shown how groups of people new to such interfaces find enjoyment in sharing and assembling sets of digital images for a variety of collaborative tasks [Huang and Mynatt 2003; Ryall et al. 2004; Scott et al. 2003b; Shen et al. 2002] . Commercially available multitouch tabletops such as the Smart Table are being promoted in terms of how they can help people work together: "young students are drawn to its surface, where work and play come together in hands-on, collaborative activities" (www.elementarytechnology.co.uk). The claim is that these new kinds of shareable interfaces provide more opportunities for flexible forms of collaboration compared, for example, with single-user PCs and mouse input, through allowing colocated users to interact smoothly and simultaneously with digital content.. It has long been recognized that group work can have benefits ("many hands make light work") but also that "too many cooks spoil the broth": that is, group work does not necessarily gain from the simple sum of each individual's contribution, but there can be "process loss" due to lack of coordination [Steiner 1972] . The advent of Single Display Groupware (SDG) led to insightful analyses of how such technologies might engender interference, for example, parallel working and conflicts [Stewart et al. 1999] , followed by technical developments such as identity-differentiating widgets [Ryall et al., 2005] , encouraging designers to think about how identity information might be used to support interleaving of actions by different users. The further development of shareable interface technology has led to a wealth of ideas about the benefits of these new capabilities. For example, Kharrufa et al. [2009, page 9] list the special features of shared interfaces as "encouraging externalisation, . . . [providing a] rich set of cognitive tools, providing structure to a task, designing for different ability levels and supporting reflection" through structure and logging tools. Rogers and Rodden [2003] propose responses to a cogent set of uncertainties and tensions that arise in shared displays while Scott et al. [2003a] cite the need to support natural interpersonal interactions, smooth transitions, and simultaneous actions with tabletops. However, such guidelines, invaluable though they are, tend to give us the what rather than the how and why. The latter require a more psychological perspective, explaining how it is that people manage to coordinate interactions in everyday social interactions. An especially illuminating way to do this is to look at the emergence of collaboration, a thriving area in developmental psychology. Tomasello [2009] premises the distinctive human ability to collaborate on our evolved disposition for shared intentionality, which is based on attention, background knowledge, and a drive to cooperate. In this article we translate these properties into three mechanisms of interaction that can be used to inform the design of shareable interfaces. We can understand how best to design and use such interfaces if we are clear about what it is that gives them their apparently natural and motivating properties in supporting interaction, and what makes them an apparently superior means of collaboration, compared, for example, to paper, remote sharing, or individual devices. We therefore draw on examples from our own research with young children learning to collaborate and with groups who have difficulties with collaboration, to produce a framework that recognizes some of the challenges of collaboration that may be hidden when studying only competent collaboration, and when observing users who recover quickly and silently from breakdowns of collaboration [Easterbrook 1996] .. New multi-user interfaces represent a qualitative shift in supporting collaborative groupwork: the freedom of input enables gesturing, speaking, and touching. These can all be seen, heard, and experienced by others whereas mouse clicking and key pressing are individual, private acts. While such actions may become largely invisible to those executing them, as they are so familiar, their enaction, in contrast, remains visible to others. Developers of surface technologies and tangibles have been inventive in logging and presenting information about users' interactions, in the hope of improving people's interactions, for example, making them more equitable (e.g., diMicco et al. [2007] and Bachour et al. [2008] . Surfaces, tangibles, and shareable public displays also enable simultaneous control by multiple users. These technologies therefore provide striking new opportunities for awareness (gesture, body orientation, and more so-called "natural" means of communication) for making salient in displays the availability of information supported by the public space provided, and for equitable simultaneous control, such as "entry points" [Rogers et al. 2008 ] to the technology. In this typology, collaboration might look easy. But if it is so natural, why is it that not every such application is a success? For example, why does increasing awareness of spoken contributions sometimes lead to lower productivity and lack of equitable participation [di Micco et al. 2007 ]? Why might users show less equal contributions with multitouch applications than with single-touch ]? And do the advantages of such interfaces compensate for the loss of feedback or trace of interactions provided in traditional GUIs [Norman 2010] ?. The answers to these questions are needed to understand, support, and explain the effective design of new user interfaces for multiple users (e.g., with tabletops, public displays, tangibles) that can capitalize on the mechanisms we have identified before to facilitate new forms of collaboration, for example, in decision-making, brainstorming, planning, learning, and creating. In what ways can an interface exploit people's implicit social behaviors for good social effect? Can they be designed to make it more obvious as to how to behave and give appropriate cues? Is it possible to make more salient the cues that can lead to improved understanding, explication of intentions, and focus of attention? Can people become more aware of what is good collaboration and conduct through interacting with a given interface? Our goal is to provide a principled way for researchers and designers to be able to make sense of the emerging empirical literature on the benefits of multi-user interfaces, and to enable them to make predictions about their value for collaboration. We present the mechanism's framework to inform the design of interfaces for collaboration through considering social-psychological properties of shared intentionality. These properties can explain why a multitouch surface that enables simultaneous interaction may be less effective at facilitating collaboration than a single-touch interface, why gesture-based interactions might be better than mouse-based or tangible interactions for collaborative learning, and why apparently minor increases in salience of users' actions can have big effects on the connectedness of social interaction.. We first define the three mechanisms (awareness, control, and availability of information) based on the psychological literature on processes of social development, and we illustrate them in the light of existing literature on tabletops designed for collaborative work. We then explain the crucial role of constraints on these mechanisms, using the psychological literature on social development to show how constraints support smooth social interaction, and identifying three sources from which constraints can arise, again using examples from the literature on tabletops. We next present three case studies illustrating how the framework of mechanisms and constraints can be used to design for and evaluate collaboration. We finish by showing how the framework could be used by designers of collaborative applications on multi-user interfaces, for a wide range of audiences, from family groups at leisure, through children working collaboratively in formal settings, to remedial work supporting people with social, sensory, and language impairments. The mechanisms and constraints framework is intended to support the design and evaluation of multi-user interfaces to support collaboration across a range of devices, users and settings. We proposed three mechanisms of behavior that can support or obstruct collaborative working: awareness of one's own and others' gestures, actions, movements, and mental states, control over interactions with the interface, and availability of the background information and expectations that users bring with them. All of these involve mutuality: not just an individual's awareness or control, but each person's recognition of the other's beliefs and intentions. Each of the mechanisms can be subject to higher or lower levels of constraint, and these constraints can arise from three different sources: the constraints imposed by physical capabilities, by what is allowed in the technology, and by the social rules, often implicit, that underpin users' approaches to the technology. We presented three case studies to illustrate the ways in which constraints on these mechanisms can work for or against better collaboration. In each case, varying the technological constraints in these mechanisms provides hard evidence of how collaboration is affected. There is no single ideal level of constraint: it depends on the aims of the designers, the interactions of the three mechanisms, and the composition of the group. We offer the framework to designers and evaluators as a way of generating ideas, codifying observations, and reflecting on how to support collaborative work and play.

O O B O O O O O O B I B I O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O B O O O O O O O O O O O O O O O O B I O O O O B O O O O O O O O O B O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O B B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O B O O O O O O O O B O O O O O O O O O O O O B O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O B O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O B O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O B O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O B I O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O B O O O O O O B O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Stability and Performance of Overlay Multicast Systems Employing Forward Error Correction The two main sources of impairment in overlay multicast systems are packet losses and node churn. Yet, little is known about their effects on the data distribution performance. In this paper we develop an analytical model of a large class of peer-to-peer streaming architectures based on decomposition and non-linear recurrence relations. We analyze the stability properties of these systems using fixed-point analysis. We derive bounds on the probability that nodes in the overlay receive an arbitrary packet of the stream. Based on the model, we explain the effects of the overlay's size, node heterogeneity, loss correlations and node churn on the overlay's performance. Our findings lead us to the definition of an overlay structure with improved stability properties. We show how and under what conditions overlays can benefit from the use of error control solutions, prioritization and taxation schemes. Based on our results, we identify the components that are needed to achieve good data distribution performance in multi-tree-based overlay multicast. The peer-to-peer paradigm has proved to be an efficient means both for file distribution, and for lookup services without the need for expensive infrastructure. Peer-to-peer multicast streaming overlays could serve content providers as a cheap and efficient alternative to commercial content distribution networks for delivering live media to a large number of spectators. In peer-to-peer multicast, peers are organized or organize themselves into an application layer overlay and distribute the data among themselves. The main advantages are that the multicast is easy to deploy and it reduces the load of the content provider, since the distribution cost in terms of bandwidth and processing power is shared by the nodes of the overlay.. A large number of overlay multicast architectures has been proposed by the research community ( [1] [2] [3] [4] [5] [6] [7] and references therein), and a number of large scale commercial deployments of peer-to-peer streaming systems were also recorded [8, 9] . There is however not much analytical understanding of the data distribution performance of these systems, such as the packet reception probability of the participating nodes. Most of the results in the literature are based on simulations, and focus on metrics like the depth of the overlay, the amount of control overhead and the link stress. There is a lack of understanding of how the parameters of the overlay (e.g., the error control solutions employed) and the environmental dynamics (e.g., the number of nodes, node churn and losses due to network failures) affect the end-to-end delays and the packet reception probability.. The goals of this paper are twofold. First, to give an understanding of how and why the above factors and the policies proposed in the literature influence the data distribution performance of overlay multicast. Second, to give a tool for system designers to evaluate the performance of their proposals, and give guidelines on how to achieve good performance.. We consider overlay multicast systems based on multiple distribution trees and the push model, such as the ones in [2] [3] [4] [5] [6] [10] [11] [12] . Multiple trees offer two advantages: they ensure graceful quality degradation in dynamic overlays, in which peers can leave during the streaming session and they enable nodes to contribute to the overlay with fractions of the stream bandwidth. The higher the number of trees, the smaller the fractions, so that nodes' output capacities can be better utilized. With multipath transmission, parts of the stream reach the peers through independent overlay paths. Consequently a node receives large part of the streaming data even if some of its parent peers stop forwarding.. The contributions of the paper are the following. (i) We present a model to describe the probability that a peer in the overlay possesses an arbitrary packet of the data stream. We describe the model applied to multi-tree based overlay multicast, but the modeling approach can generally be applied to multicast data distribution employing FEC in multi-hop environments. For example, in overlays that employ the pull model, data reach the nodes via spanning trees of the overlay graph, and hence we believe that some of our results can be applied to pull based overlays by adapting the packet loss model. (ii) We show that node churn can be treated as a form of packet losses. (iii) Based on the model, we show how factors, such as the overlay's size, heterogeneous loss probabilities, heterogeneous input and output capacities and loss correlations influence the data distribution performance of the overlays. (iv) We explain how the parameters of the overlay, such as the number of distribution trees, the error control schemes employed, the prioritization and taxation schemes affect the performance. (v) Based on our findings we propose a tree structure that improves the scalability of the overlay with respect to the number of nodes.. The rest of the paper is organized as follows. We review related work in Section 2. In Section 3 we give a description of the considered overlays. We develop the analytical model of the overlay in Section 4 and derive asymptotic bounds on the system performance in Section 5. We describe the simulation methodology used to validate the model in Section 6. We evaluate the effects of packet losses in Section 7 and apply the model to node churn in Section 8. We conclude our work in Section 9. Peer-to-peer streaming systems utilizing a single transmission tree were analyzed in [13] . The authors derived results on the number of levels as a function of the upload capacities of the peers, and evaluated the probability of blocking arriving nodes in the overlay. The first models that describe the data distribution performance of multi-tree-based overlay multicast were proposed in [14, 15] and showed that these systems exhibit a phase-transition when using FEC. The model we present here generalizes the above models, and makes it possible to evaluate the effects of node heterogeneity, churn and different overlay management policies. The effect of the forwarding capacity on multi-tree-based overlays was investigated in [16] using a queuing theoretic approach, and in [17] based on a fluid model.. The effect of node dynamics on the connectivity of peers was evaluated in [18] for peer-to-peer file sharing systems. The authors derived results for the time to isolation and the probability of isolation for various node lifetime distributions. The authors in [19] proposed master equations to model the evolution of the number of neighbors of the peers in an overlay, they did not provide however any closed form solution. To the best of our knowledge, ours is the first work to propose a general framework for modeling the data distribution performance of multi-tree based peer-to-peer streaming systems employing FEC in a dynamic environment.

B O O O B I O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O B I O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O B I O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O B O O O O


Design and optimization of distributed sensing coverage in wireless sensor networks For many sensor network applications, such as military surveillance, it is necessary to provide full sensing coverage to a security-sensitive area while, at the same time, minimizing energy consumption and extending system lifetime by leveraging the redundant deployment of sensor nodes. In this paper, we propose a surveillance service for sensor networks based on a distributed energy-efficient sensing coverage protocol. In the protocol, each node is able to dynamically decide a schedule for itself to guarantee a certain degree-of-coverage (DOC) with average energy consumption inversely proportional to the node density. Several optimizations and extensions are proposed to enhance the basic design with a better load-balance feature and a longer network lifetime. We consider and address the impact of the target size and the unbalanced initial energy capacity of individual nodes to the network lifetime. Several practical issues such as the localization error, irregular sensing range, and unreliable communication links are addressed as well. Simulation shows that our protocol extends system lift-time significantly with low energy consumption. It outperforms other state-of-the-art schemes by as much as 50% reduction in energy consumption and as much as 130% increase in the half-life of the network. Wireless sensor networks have emerged as a new information-gathering paradigm based on the collaborative effort of a large number of sensing nodes. In such networks, nodes deployed in a remote environment must self-configure without any a priori information about the network topology or global view. Nodes act in response to environmental events and relay collected and possibly aggregated information through the dynamically formed multihop wireless network in accordance with desired system functionality. These networks can form the basis for many types of applications such as military surveillance [He et al. 2004b ], habitat monitoring [Szewczyk et al. 2004] , infrastructure protection [Xu et al. 2004] , and scientific exploration [Tolle et al. 2005] .. Low-cost deployment is one acclaimed advantage of sensor networks, which implies that the resources available to individual nodes are severely limited. Limited processor bandwidth and small memory are two arguable constraints in sensor networks, which will disappear with the development of fabrication techniques. However, the energy constraint is unlikely to be relieved quickly because of slow progress in developing battery capacity. Moreover, the unattended nature of sensor nodes and hazardous sensing environments preclude battery replacement as a feasible solution. On the other hand, the surveillance nature of sensor network applications requires a long lifetime; therefore, it is a very important research issue to provide a form of energy-efficient surveillance service for a geographic area. This paper addresses the problem of providing a surveillance service for sensor networks that is adaptable to the system requirements. We first propose the main design that provides full coverage and then extend it into a differentiated version. Four major goals are achieved in this paper. First, our new sensing coverage algorithm outperforms other state-of-the-art solutions in terms of energy conservation, energy balance, and communication overhead when it runs in full coverage mode. Second, differentiated surveillance for sensor networks can be supported easily with minor changes to the basic design. Third, this paper proposes three additional optimizations, taking the size/shape of targets, the unbalanced initial energy capacity of individual nodes, and reference distribution into account to enhance the energy conservation and balance of the sensor network further. Fourth, we investigate the impact of the localization error, irregular sensing range, and unreliable communication links on our sensing performance.. The remainder of this paper is organized as follows: Section 2 discusses previous research related to the sensing coverage problem found in sensor networks. Section 3 describes the design of our sensing coverage protocol without differentiation. Section 4 extends the design to provide differentiated surveillance. Section 5 extends the design to consider the size and shape of targets. Section 6 enhances the design with a technique called reference point scattering. Section 7 gives a brief description of baselines to which we compare our work, including two other state-of-the-art sensing coverage protocols. Section 8 provides a detailed performance evaluation and comparison. Section 9 investigates the implication of reality issues such as the localization error, irregular sensing range, and unreliable communication links. We conclude the paper in Section 10. In this paper, we introduce an adaptable sensing coverage mechanism for differentiated surveillance in sensor networks. Unlike previous schemes, our scheme guarantees not only full sensing coverage to a certain geographic area, but also the degree of coverage up to the limit imposed by the density of nodes available. The distinguishing advantages of our scheme are a much longer network half-life through energy conservation and balancing among sensor nodes, and a small communication overhead required to establish a working duty sched- ule among nodes. Several optimizations and extensions are proposed to provide even better performance in terms of network lifetime and energy balance. In addition, with the enhancement considering the target size and shape, we could gain more benefits in energy conservation. With the enhancement considering the initial energy discrepancy among nodes, we achieve a better energy balance among sensor nodes and, hence, a longer network half-life. Simulations show that our protocol accomplishes differentiated surveillance with low-energy consumption and significantly outperforms other state-of-art schemes in terms of energy consumption and half-life of the network. A set of research issues needs to be addressed before surveillance-based applications, such as military tracking and environmental monitoring, become technically feasible and economically practical. Recently, several schemes are proposed to address the sensing coverage problem in sensor networks. In Tian and Georganas [2003] , full surveillance coverage is support by a node-scheduling scheme based on off-duty eligibility rules, which allows nodes to turn themselves off as long as the neighboring nodes can cover the area for them. This rule guarantees 100% sensing coverage as long as no void exists. However, this rule underestimates the area that the neighbor nodes can cover, which leads to excess energy consumption. The coordinated sleep algorithm proposed in Hsin and Liu [2004] improves the aforementioned algorithm by providing continuous coverage and compares the approach with a random sleep algorithm. In Ye et al. [2003] , surveillance coverage is achieved by a probing mechanism. In this solution, after a sleeping node wakes up, it broadcasts a probing message within a certain range and waits for a reply. If no rely is received within a timeout, it takes the responsibility of surveillance until it depletes its energy. In this solution, the probing range and wakeup rate can be adjusted to affect the degree of coverage indirectly. However, this probing-based approach has no guarantee on sensing coverage and blind points can occur. Co-Grid [Xing et al. 2004 ] is a distributed protocol for sensing coverage maintenance based on coordinating fusion groups on virtual grids. It maintains probabilistic detection guarantees based on probabilistic detection models and data fusion among multiple nodes. Our solution is different from these solutions in the sense that it can not only guarantee sensing coverage to a certain geographic area, but it can also adapt the degree of coverage to that area, up to the limitation imposed by the number of sensor nodes present.. Energy balancing is another research issue addressed in this paper. Tian and Georganas [2003] and Ye et al. [2002] only consider the metric in terms of the total amount of energy consumed, regardless of the distribution of the energy among the nodes. We argue that unbalanced energy dissipation causes some nodes to die much faster than others do, therefore, the half-life of the network is dramatically reduced in the unbalanced approach. Research has addressed the energy balance issue from different aspects of sensor networks. SPEED [He et al. 2003b] balances the traffic by nondeterministically forwarding the packet through multiple routes. GAF [Xu et al. 2001] performs leader rotation among the nodes inside a virtual grid, in order to balance energy consumption. Research on network topology control such as ASCENT [Cerpa and Estrin 2002] , SPAN [Chen et al. 2001] , LEACH [Heinzelman et al. 2000] , and GAF [Xu et al. 2001 ] addresses the problem of providing communication coverage within an energy conservation context. These have a similar flavor as the surveillance coverage problem. For example, LEACH [Heinzelman et al. 2000 ] partitions a network into clusters and randomly rotates the cluster leader in order to evenly distribute the energy consumption among the sensors. SPAN [Chen et al. 2001] is another randomized algorithm where nodes make local decisions on whether to sleep or to join a backbone network in order to reduce energy consumption. The major difference between those and this work is that communication coverage considers only connectivity between the nodes. In contrast, surveillance (sensing) coverage addresses the coverage problem to every physical point in the terrain. As a result, new scheduling is required to force some nodes to stay awake for surveillance purposes, even though they are not participating in data forwarding.. It also helps network communication to provide a full sensing coverage. The impact of full sensing coverage of a sensor network on greedy forwarding algorithms is studied in Xing et al. [2006] . An upper bound of network dilation is derived so that the sum of lengths of transmission hops along the routing paths is bounded, which leads to high communication efficiency. Geometric analysis is conducted on the relationship between sensing coverage and communication connectivity in Xing et al. [2005] . A coverage configuration protocol (CCP) is proposed for energy-efficient sensing coverage and is integrated with SPAN for connectivity. Compared with CCP, which requires broadcasts of join/withdraw signals for each state rotation, the schedule of each node in our approach is generated at the beginning of the network operation and does not require more communication at each rotation. In addition, our protocol provides more flexible control over degree of coverage for the system. Work/sleep schedules also attract research attention in the area of powerconserving tracking applications of wireless sensor networks. Several publications [Gui and Mohapatra 2004; Ren et al. 2005; Cao et al. 2005 ] address various aspects, such as theoretical analysis and algorithm designs, for tracking applications. These approaches are based on a partial sensing coverage and their main interests are on the impact of the incomplete sensing coverage on the tracking performance metrics.. Besides the aforementioned work in communication and sensing coverage that conserves energy, work in energy conservation for general sensor networks has been considered at various levels of the communication stack. From the bottom to the top, special hardware [Min et al. 2000 ] is designed with multiple energy dissipation settings. MAC layer protocols developed for energy savings mostly take advantage of overhearing and scheduling to allow nodes to sleep while they are not transmitting or receiving messages [Guo et al. 2001; Heinzelman et al. 2000] . At the network and routing layers, solutions are diversified. Data-placement schemes [Bhattacharya et al. 2003 ] minimize energy along the transmission path through data caching. In Ramanathan and Rosales-Hain [2000] , Ramanathan et al. adjust communication range dynamically, based on the node density to conserve energy consumed in transmission.. •

B O O O O B I O O B I O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O B I O O O O O O O O O O O O O O O O B I O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O B I O O O B I O O O O B O O B I O O O O O O O B O O O O O O O O B O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O B I O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O B I O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O B I O O O B O O O O O O O O B I O O O O O B I O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Identification of piecewise linear uniform motion blur Abstract-A single image based motion blur identification scheme is proposed for uniform motion blurs that consist of more than one linear motion component. The proposed scheme is composed of three modules that are a motion direction estimator, a motion length estimator and a motion combination selector. In order to identify the motion directions, the proposed scheme is based on a trial restoration by using forward ramp motion blur in horizontal and vertical directions and an analysis of directional information in frequency domain via a Radon transform. Autocorrelation functions of image derivatives in several directions are employed for estimation of the motion lengths. A proper combination of motion vectors is identified based on an analysis of local autocorrelation functions of non-flat component of restoration results. A simulation example is given to demonstrate the performance of the proposed method. Due to an imperfection of a recording process, in many practical situations, a recorded image is a degraded version of a desired original scene. For a noisy linear shift-invariant blur condition, the degradation process can be modeled in a spatial domain as g(x, y) = h(x, y) * f (x, y) + n(x, y),. where f (x, y) and g(x, y) are original and blurred images, respectively; n(x, y) is an additive noise; and h(x, y) is called a point spread function (PSF) of the blur. One of well-known types of the image degradation is a motion blur. The motion blur is caused when there are relative motions between a camera and objects during exposure time. Blur identification is an important first step for several image restoration approaches. In order to identify the motion blur, many approaches have been proposed [1] - [11] ; The identification methods can be grouped into two categories. The first category includes the methods that are specific for identifying the motion blur. The methods can be based on an analysis of zero-crossing pattern of the blurred image in transform domain [1] , [2] , an autocorrelation function of an image derivative [7] , a trial restoration [8] , [10] , or human visual-motion sensing [9] . However, the existing identification methods for the motion blur such as [1] , [2] , [7] - [10] did not consider the case of the motion blurs which are composed of multiple linear motion components. Since, in some practical situations, the motion blur may not be a linear motion vector in a single direction but may be considered as a piecewise linear motion that has multiple directional components. On the other hand, the second category can be the methods proposed for identification of general types of blur such as auto-regressive (AR) model and a maximum likelihood (ML) based methods [3] - [6] or vector quantization (VQ) based methods [11] . Although the ML-based approaches or VQ-based approach can be employed for the cases of multiple components, there are some practical limitations. ML-based approaches need a good prior estimation of the blur extent and computation cost is high. In the case of VQ-based approaches, there is a difficulty to select the proper candidates of blur operator for blur identification codebook.. In this paper, a single image based blur identification scheme for piecewise linear uniform motion blur (PLUMB) is proposed. The blur parameters are extracted from the blurred image itself without using any a priori knowledge. The proposed scheme includes modules of a motion direction estimation, a motion length estimation, and a motion combination identification. The proposed scheme can provide a possibility to identify the uniform motion blur with more than one component. A mathematical model for the PLUMB is described in Section II. Details of the proposed scheme are presented in Section III. Some experimental results are demonstrated in Section IV. Finally, Section V gives conclusions of this paper and directions for future work. The scheme for identifying the blur operator for piecewise linear uniform motion blur by using only blurred image itself was proposed. The proposed scheme consists of three stagesthe motion direction estimation stage, the motion length estimation stage, and the motion combination identification stage. The simulation result demonstrated that the proposed scheme can estimate the PSF from blurred image. However there are still some limitations of the number of motion components or the difference between direction of each components. For the future work, the performance of the proposed scheme should be improved in the cases of there is a large number of components, or the direction of component is too close to each others or in the same direction.

O O O O O B I O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O B I O O O O O O O O O O O O O O O B I O O O O O O O B I O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O B I O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O