(BQ) Part 2 book Mobile AD hoc networking has contents Data dissemination in opportunistic networks, task farming in crowd computing, mobility models, topology, and simulations in VANET, experimental work on VANET,...and other contents.
12 DATA DISSEMINATION IN OPPORTUNISTIC NETWORKS Chiara Boldrini and Andrea Passarella ABSTRACT Among the alternatives to pure general-purpose MANETs, one of the most promising approach is that of opportunistic networks [2] Differently from MANETs, opportunistic networks are designed to work properly even when the nodes of the network move More specifically, opportunistic networks reverse the approach of MANETs, and what was before an accident to avoid (the mobility of nodes) now becomes an opportunity for communications In fact, in an opportunistic network messages are exchanged between nodes when they come into contact, creating a multi-hop path from the source to the destination of the message One of the most appealing applications to build upon an opportunistic network is data dissemination Conceptually, data dissemination systems can be seen as variations of the publish/subscribe paradigm: publisher nodes generate content items and inject them into the network, subscriber nodes declare their interest in receiving certain types of content (e.g., sport news, radio podcast, blog entries, etc.) and strive to get it in some ways Nodes can usually be publishers and subscribers at the same time The main difference between message forwarding and content dissemination is that the source and destination of a message are typically well known when routing a message (and clearly listed in the header of the message itself), while, in content dissemination, content generators and content consumers might well be unaware of each other Publish/subscribe systems have gained new momentum thanks to the Web 2.0 User Generated Content (UCG) paradigm, with users generating their own content Mobile Ad Hoc Networking: Cutting Edge Directions, Second Edition Edited by Stefano Basagni, Marco Conti, Silvia Giordano, and Ivan Stojmenovic © 2013 by The Institute of Electrical and Electronics Engineers, Inc Published 2013 by John Wiley & Sons, Inc 453 454 DATA DISSEMINATION IN OPPORTUNISTIC NETWORKS and uploading it on popular platforms like Blogger, Youtube, or Flickr The application of the UGC paradigm to opportunistic networks is particularly appealing A future of users generating content items on the fly while moving, and distributing this content to the users in their proximity, can be realistically envisioned for the next years In order to make this future a reality, new strategies for disseminating content items must be designed, while at the same time accounting for a wise usage of network resources, which can be easily saturated in this scenario In this chapter we discuss the challenges connected with content dissemination in an opportunistic network and the solutions proposed in the literature We classify current proposals that address the problem of content dissemination into six main categories, based on the specific problem targeted and the type of solution proposed Then, we present and discuss the work that we believe best summarizes the main features of each category 12.1 INTRODUCTION Opportunistic networks represent one of the most interesting evolution of traditional Mobile Ad Hoc NETworks (MANET) The typical MANET scenario comprises mobile users with their wireless-enabled mobile devices that cooperate in an ad hoc fashion to support communication without relying on any preexisting networking infrastructure Specifically, in MANETs the nodes of the network become active entities and also become a substitute to routers, switches, and so on, in forwarding messages Thus, messages are delivered following a multihop path over the nodes of the MANET itself Despite the huge research activity that they have generated, MANETs were far from being widely adopted The main drawback of MANETs was their lack of realism in research approach [1] From a practical standpoint, real small-scale implementations have been long disregarded, and real users have not been involved in the MANET evaluation From a research standpoint, MANET results were mined by excessively unrealistic assumptions The most significant among these is the intolerance to temporary network partitions, which actually may be very common in a network where users move and where communication devices are expected to run out of battery, or to be out of reach, very often Among the alternatives to pure general-purpose MANETs, one of the most promising approach is that of opportunistic networks [2] Differently from MANETs, opportunistic networks are designed to work properly even when the nodes of the network move More specifically, opportunistic networks reverse the approach of MANETs, and what was before an accident to avoid (the mobility of nodes) now becomes an opportunity for communications In fact, in an opportunistic network, messages are exchanged between nodes when they come into contact, creating a multihop path from the source to the destination of the message The exploitation of direct contacts between nodes for message forwarding introduces, as a side effect, additional delays in the message delivery process In fact, user mobility cannot be engineered: Node contacts are usually neither controllable nor scheduled, and networking protocols can only wait for them to occur For this reason, opportunistic networks fall into the category of delay-tolerant networks [3] For many common applications, this INTRODUCTION 455 additional latency may be a tolerable price for the ubiquity provided by the opportunistic network Web 2.0 content sharing services, for example, are already delay-tolerant in their nature, because they rely on an asynchronous communication paradigm One of the most appealing applications to build on top of an opportunistic network is data dissemination Conceptually, data dissemination systems can be seen as variations of the publish/subscribe paradigm [4]: Publisher nodes generate contents and inject them into the network, and subscriber nodes declare their interest in receiving certain types of content (e.g., sport news, radio podcast, blog entries, etc.) and strive to get it in some ways Nodes can usually be publishers and subscribers at the same time The main difference between message forwarding and content dissemination is that the source and destination of a message are typically well known when routing it (and clearly stated in the header of the message itself), while, in content dissemination, content generators and content consumers might well be unaware of each other Publish/subscribe systems have gained new momentum thanks to the Web 2.0 User Generated Content (UCG) paradigm, with users generating their own content and uploading it on popular platforms like Blogger, Youtube, or Flickr The application of the UGC paradigm to opportunistic networks is particularly appealing A future of users generating content items on the fly while moving, as well as distributing this content to the users in their proximity, can be realistically envisioned for the next years In order to make this future a reality, new strategies for disseminating content items must be designed, while at the same time accounting for a wise usage of network resources, which can be easily saturated in this scenario 12.1.1 Motivation and Taxonomy In this chapter we discuss the challenges connected with content dissemination in an opportunistic network and the solutions proposed in the literature We classify current proposals that address the problem of content dissemination into six main categories, based on the specific problem targeted and the type of solution proposed For each category, we present and discuss the work that we believe best summarizes the approach proposed We start in Section 12.2 by discussing the initial work on the area which ignited the research on this topic To the best of our knowledge, the PodNet Project [5] was the first initiative to explicitly address the problem of disseminating content in a network made up of users’ mobile devices in an opportunistic fashion Within the PodNet project, heuristics were defined in order to drive the selection of content items to be cached based on the popularity of the content itself Such heuristics enforced a cooperative caching among nodes and were shown to clearly outperform the simple strategy in which each node only keeps the content it is directly interested in The second category of solutions is based on the exploitation of the social characteristics of user behavior (Section 12.3) In this case, heuristics are proposed that take into account the social dimension of users—that is, the fact that people belonging to the same community tend to spend significant time together and to be willing to cooperate with each other We take ContentPlace [6] as representative of this approach because it was one of the first fully-fledged solutions to incorporate the idea of communities with a systematic approach to data dissemination 456 DATA DISSEMINATION IN OPPORTUNISTIC NETWORKS The third category of content dissemination approaches brings the ideas of publish/subscribe overlays into the realm of opportunistic networks (Section 12.4) Publish/subscribe systems are based on content-centric overlays in which broker nodes bring together the needs of both content publishers and subscribers by matching the content generated by publishers with the interests of subscribers and by delivering the content to them How the publish/subscribe ideas can be adapted to an opportunistic environment is well exemplified by Yoneki et al [7], in which the pub/sub overlay is built exploiting the knowledge on the social behavior of users Protocols belonging to the fourth category, discussed in Section 12.5, reverse the approach of heuristic-based protocols as they depart from the local optimization problems at the basis of heuristic approaches in order to find a global, optimal solution to the content dissemination problem Such a global solution, typically unfeasible in practice in real scenarios, is then approximated using a local, distributed strategy To the best of our knowledge, the work by Reich and Chaintreau [8] has been the first to provide a comprehensive analysis of a global optimization problem applied to content dissemination in opportunistic networks The fifth category (Section 12.6) is characterized by the exploitation of a broadband wireless infrastructure in conjunction with the opportunistic network of user devices The idea here is to partially relieve the burden of disseminating content from the infrastructure by exploiting opportunistic content dissemination among users We choose the work by Whitbeck et al [9] as representative of this category because of the tight interaction that it proposes between the infrastructure and the opportunistic network Finally, the sixth category hosts proposals that tackle the dissemination problem using an analogy with unstructured p2p systems (Section 12.7) To the best of our knowledge, the work by Zhou et al [10] is one of the most significant in this area, which formulates the dissemination problem by means of p2p universal swarms and provides solid theoretical results regarding the advantage of cooperative strategies against greedy approaches 12.2 INITIAL IDEAS: PODNET Initial research efforts on content dissemination in opportunistic networks were made within the PodNet project [5] The aim of the PodNet project is to develop a content distribution system that builds up from the mobile users that opportunistically participate to the network The scenario considered by PodNet is that of one or more Access Points that are able to retrieve content from the Internet and to send it to those nodes that are in radio range Coverage provided by the Access Points might be limited, thus content items are also disseminated by the mobile users of the network in an opportunistic fashion In addition, content may also be generated by the users themselves, according to the Web 2.0 User Generated Content paradigm 12.2.1 Content Organization PodNet borrows the representation of content as a set of channels from Web syndication [11,12] Syndication provides a structured way for making content available 457 INITIAL IDEAS: PODNET NODE A NODE B Request for discovery channel Discovery channel returned Requests for subscribed channels Entries returned Requests for entries to be stored in the public cache Entries returned Figure 12.1 PodNet message exchange on the Internet Content items are organized into channels, based on the information they carry Each item is an entry of the channel For example, a blog can be classified as a channel, and each new post becomes an entry of the channel Upon generation of a new channel, the content producer generates a feed (which is an XML file) that lists the available entries for the channel Depending on the type of content that it generates, each channel can have different requirements For breaking news channels, the freshness of the entries is most important On the contrary, music channel entries remain interesting for months or years after their original publication Entries are exchanged by nodes during pairwise contacts Besides the user-defined channels described above, a discovery channel is defined in the PodNet system as a control channel that lists the channels cached by the node itself Consider two nodes that establish a contact (Figure 12.1) The one that is interested in retrieving new content items asks the other for its discovery channel By reading the information provided through the discovery channel, the node will be informed of the entries available on the peering node and it will make decisions about which entries to download 12.2.2 Content-Centric Dissemination Strategies In the simplest case, nodes only download and store those content items that are interesting to them There is no intentional content dissemination in this case, but the net result is that content items happen to travel across the network based on the interests of users This can be considered a baseline, unintentional content dissemination process Lenders et al [13] evaluate the performance improvement that is introduced when nodes not only download the entries they are interested to, but also cooperate with other nodes by making available the unused portion of their cache to entries that might be of interest to other nodes Cache is thus split into two spaces: a private space, reserved to content items of subscribed channels, and a public cache (Figure 12.2) 458 DATA DISSEMINATION IN OPPORTUNISTIC NETWORKS Private Cache Channel Entry Entry Channel Entry Entry Channel Entry Entry Channel Entry Entry Channel Entry Entry Channel Entry Entry Public Cache Figure 12.2 Cache and content organization More specifically, Lenders et al investigate the problem of which entries should be hosted in the public cache in order to best serve the other nodes The popularity of channels among mobile users is assumed to be different: There are channels that have more subscribers, other channels that have less subscribers Statistics on the popularity of channels among encountered nodes are collected by sharing personal interests when encountering other peers The popularity of channels is at the basis of the PodNet dissemination strategies In fact, each policy in PodNet is a heuristic that is a function of the channel popularity Four strategies are defined With the Most Solicited strategy, entries of the most popular channels are requested first On the contrary, entries belonging to the least popular channels are requested first when using the Least Solicited strategy A probabilistic approach is used by the Inverse proportional strategy, in which entries are requested with a probability that is inversely proportional to the popularity of the channel they belong to Finally, with the Uniform strategy, items are requested at random and channel popularity is not taken into consideration 12.2.3 Performance Results Lenders et al [13] propose two metrics for the evaluation of the performance of content dissemination The freshness metric measures the age of entries at the time a subscriber receives them This metric is important, for example, when considering the dissemination of news items Freshness can be computed for individual channels, as well as for the aggregate of all channels The latter gives a global picture of the overall capability of the protocol to deliver fresh items, whereas the former allows the authors to evaluate the behavior of the policy depending on the specific channel popularity This is important because a good overall freshness could be obtained by just disseminating those channels that are more popular, while letting the others starve Finally, dissemination policies are also ranked based on their fairness value INITIAL IDEAS: PODNET 459 Fairness is measured in terms of the max–min fairness A strategy is fair according to the max–min fairness if the performance of a channel cannot be increased anymore without affecting the performance of a channel with a lower performance As far as simulations are concerned, crucial is the way that user preferences are modeled A Zipf-like distribution of requests for cached content has been highlighted in the context of Web caching [14], and Internet RSS feeds not seem to be an exception [15] Thus, the distribution of channel popularity is often assumed to follow a Zipf’s law also in the context of opportunistic networks When a Zipf’s law is used, the frequency fn at which the channel ranked nth in popularity is requested is given by fn ∼ n1α [16] Parameter α is positive by definition and allows for a fine tuning of the Zipf distribution More specifically, the greater the α value, the more uneven the frequency of requests across channels with different popularity Lenders et al [17] evaluate the content dissemination policies by means of simulations using the random waypoint model to represent user mobility This model reproduces a very mixed network, where anybody can meet with anybody Despite their simplicity, the heuristics defined for PodNet show the improvement provided by cooperative caching In fact, in all scenarios considered, the intentional dissemination strategies discussed above increase the freshness of the content seen by users and, at the same time, are more fair with respect to the baseline unintentional content dissemination Among the proposed policies, the one that surprisingly performs best overall is the Uniform policy The reason is that the dissemination policies defined by Lenders et al affect only the public cache The caching process for the private cache is entirely driven by the subscriptions—that is, by the interests of the user owning the device Given that most popular channels have more subscribers, the fraction of private caches allocated to most popular channel is greater than the fraction occupied by least popular channel Thus, intuitively, the Uniform strategy for the public cache helps to increase the diversity of content items and to give a chance also to least popular channel, eventually increasing the fairness 12.2.4 Take-Home Messages PodNet has been the first work to tackle the problem of content dissemination in opportunistic networks Two are the main contributions of PodNet First, PodNet has clearly shown the advantage of cooperative caching with respect to a greedy behaviour of the users Second, borrowing the approach of Internet Podcasting, PodNet has introduced a way for classifying items into channels, to which users express their interests with subscriptions The main limitation of PodNet is that the policies defined by Lenders et al [13] only focus on one of the two actors of content dissemination—that is, on the channels PodNet heuristics are totally content-centric: Each policy is exclusively a function of the popularity of the channel and individual user preferences or different user capabilities to disseminate messages are not considered at all These strategies might work when users are well mixed and homogeneous, and items can easily travel from one side of the other of the network However, when node movements are heterogeneous and communities of nodes tend to cluster together, this approach can be quite limited [6] 460 12.3 DATA DISSEMINATION IN OPPORTUNISTIC NETWORKS SOCIAL-AWARE SCHEMES The simple heuristics defined by PodNet highlighted the opportunities offered by cooperative caching for disseminating public content items in opportunistic networks PodNet heuristics were totally content-centric: Each policy is exclusively a function of the popularity of the channel, and individual user preferences or different user capabilities to disseminate messages are not considered at all Thus, later works have focused on the definition of more elaborate heuristics that could better exploit user diversity in order to improve content dissemination We refer to these heuristics as user-centric, in contrast with PodNet content-centric strategies Opportunistic networks, especially as far as content dissemination is concerned, are intrinsically networks of people, and sociality is something that peculiarly distinguishes such kind of networks with respect to others Thus, one of the principal directions of user-centric heuristics is that of social awareness People are social in the sense that their movements are influenced by their relationships with other users [18] This suggests that it is possible, based on the analysis of social relations or mobility patterns, to identify those nodes that are better fit to cache certain content items People are also social in the sense that their interests for specific content might be correlated with the interests of those who are socially close to them [19], and also in the sense that the degree to which they are willing to cooperate with others might depend on the kind of social relationships that they share [20] The class of socialaware dissemination protocols aims to exploit all these aspects in order to improve the performance of the content dissemination The social-aware ContentPlace dissemination system [6,21] proposes a general framework for designing content dissemination policies The building block of this framework is the utility function that is defined in order to quantify, using a heuristic approach, the advantage of caching a certain content item or not The utility function is then used to solve, in a distributed way, an optimization problem Thus, when two nodes come into contact, they exchange a summary vector that lists the items in each other’s cache, and then the items to be stored are selected among these listed items If the available memory on mobile devices were infinite, the best strategy would be to cache whatever content is found However, memory is a limited resource, as well as battery Thus, the best dissemination strategy is the one that maximizes the benefit for the system without breaking existing resource constraints This is equivalent to solving a multiconstrained knapsack problem like the one in equation (12.1), where k denotes the kth item that the node can select, Uk its utility, cjk the percentage consumption of resource j related to fetching and storing item k, m the number of considered resources, and xk the problem’s variables (xk = corresponds to not caching the item, xk = to caching it) ⎧ ⎪ ⎨ max s.t ⎪ ⎩ k U k xk k cjk xk ≤1 xk ∈ {0, 1} j = 1, , m ∀k (12.1) 461 SOCIAL-AWARE SCHEMES When the number of managed resources (m) is not big (which is quite reasonable), solving this problem is very fast from a computational standpoint [22] Such a solution is therefore suitable to be implemented in resource constrained mobile devices 12.3.1 Social-Aware Utility The main strength of ContentPlace lies in the social-aware definition of the utility Uk that it provides ContentPlace builds upon a community detection algorithm (like the one proposed by Hui et al [23]) that is able to identify the social communities the user belongs to Users belonging to the same community have strong social relationships with each other In general, users can belong to more than one community (a working community, a family community, etc.), each of which is a “home” community for that user Users can also have relationships outside their home communities (“acquainted” communities) ContentPlace assumes that people movements are governed by their social relationships and by the fact that communities are also bound to particular places (i.e., the community of office colleagues is bound to the office location) Therefore, users will spend their time in the places their home communities are bound to, and they will also visit places of acquainted communities Different communities will have, in general, different interests (Figure 12.3a) Therefore, the utility of the same data object will be different for different communities Given that communities represent the sets of nodes with which the user interacts most, intuitively, caching items that are popular within these communities will increase the probability that such items will be actually delivered to people that are interested in them (Figure 12.3b) Once the communities have been identified, ContentPlace splits the utility function into as many components as the number of communities the user belongs to Thus, dropping subscript k in equation (12.1), the utility can be written as follows: U= (12.2) ωi ui i (a) (b) Figure 12.3 ContentPlace at a glance: (a) Nodes declare their interests (b) Nodes remember the interests of their communities and, based on these interests, while moving around, they fetch data to be brought back to their communities 462 DATA DISSEMINATION IN OPPORTUNISTIC NETWORKS where ui is the utility component associated with the ith community, and ωi measures user’s willingness to cooperate with the ith community Thus, each component ui measures the gain that caching a certain content item provides to community i The advantage of this approach is that, by tuning parameter ωi , each user is able to cooperate with each community in a targeted manner, without wasting its resources For example, ωi could be taken as proportional to the social strength of the relationship between the user and nodes in the ith community Following the approach of Web caching literature [24], for each community i the utility ui is defined as a function of the access probability (pac,i ), the availability (pav,i ), and the size s of a given content item [equation (12.3)] ui = pac,i · fc (pav,i ) s (12.3) The access probability pac,i is a measure of how many users of community i are expected to be interested in a given content item, and thus to issue requests for it The availability pav,i , instead, quantifies the penetration of the content item in the community and can be measured as the fraction of nodes that share a copy of the item The utility increases as the access probability increases, while it decreases with the availability of the content (thus, fc must be a monotonically decreasing function) In fact, when an item is quite available in the network, the marginal gain of replicating it once more is low, and so is the utility that it provides to the system 12.3.1.1 Parameter Estimation ContentPlace estimates the access probability pac,i and the availability pav,i for each content item via online estimation This estimation is based on the information collected during meeting between pairs of nodes More specifically, when two nodes meet, they exchange a summary of the state of their buffers From this summary, each node is able to keep track of how often a given content item has been seen on other nodes’ caches during a time period T , and based on this information, it computes a sample pˆ av for the availability of that item More specifically, each node keeps an estimate of the availability of a given content item for each community i it belongs to (pˆ av,i ) Keeping separate the statistics for each community allows the node to make targeted decision for each community The estimate of the availability is then updated using the exponential weighted moving average method: pav,i ← αpav,i + (1 − α)pˆ av,i Similarly, a sample pˆ ac,i of the access probability related to community i is obtained for each time period T by tracking the interests advertised by encountered nodes that belong to community i, and such sample is then used to update the estimate pac,i as described above 12.3.2 Social-Aware Dissemination Strategies Based on the relation between the user and its communities, and based on the current position of the user, ContentPlace defines and evaluates the following dissemination policies REFERENCES 851 126 Tritech, Micron Data Modem [Online] Available: http://www.tritech.co.uk 127 R Otnes, T Jenserud, J E Voldhaug, and C Soldberg A roadmap to ubiquitous underwater acoustic communications and networking In Proceedings of Underwater Acoustic Measurement: Technologies and Results, Nafplion, Crete, Greece, June 2009, pp 1–8 128 L Freitag, M Grund, S 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underwater vehicle team formation and steering algorithms In Proceedings of IEEE Conf on Sensor, Mesh and Ad Hoc Communications and Networks (SECON), Boston, MA, June 2010, pp 1–3 149 T Melodia, S Batalama, D Pados, W Su, J Atkinson UW-Buffalo: An Underwater Acoustic Testbed at the University at Buffalo [Online] Available: http://www.eng buffalo.edu/wnesl/underwater testbed.html INDEX Absorption coefficient, 808 Fisher and Simmons formula, 810 loss, 809 Thorp’s formula, 809 Access control, 539, 581, 676 Acknowledgment, 521, 522 ackPBSM, 525 Acoustic modems, 781, 787, 789, 792, 795–797, 835, 837 Ad hoc – large scale deployment, 546 Advanced safety vehicles (ASVs), 593 Analog to digital converter (ADC), 780, 782–783, 789 Anchor paging controller (APC), 589 Anchors, 534 Anonymous keys, 677 ANSWER, 665 Anti-dos, 674 Application categories (ACs), 605 Applications in DTN content search, 341–344 email, 324–326 floating content, 352–357 web, 336–339 XMPP, 326–328 Application mechanisms application semantics, 323 proxies and gateways, 331–334 security, 323, 328–331 transport protocols, 322 user interfaces, 334–335 Application scenarios underground mining, 318–320, 346–349 urban environments, 320–322 AquaNode, 775, 786–789, 795, 798 Arbitration Inter Frame Space (AIFS), 605 Artificial ant colonies, 754 Artificial light, 710 Attacker, 668 Attenuation, 809, 810 Authentication, 674, 676 Autonomous underwater vehicle (AUV), 770–771, 773 Availability, 669 AWS, 651 Background traffic (BK), 605 Backoff timer (BT), 601 bandwidth efficiency in UAN, 815, 819, 820, bandwidth in UAN, 808, 818, 822 Basic Service Set (BSS), 605 Beacon, 522 Best effort traffic (BE), 605 Bit error rate (BER) in UAN, 815, 822 Black burst, 523, 531 Bluetooth in UAN, 786–787, 789, 796 BMV, 670 BPAB, 531 Mobile Ad Hoc Networking: Cutting Edge Directions, Second Edition Edited by Stefano Basagni, Marco Conti, Silvia Giordano, and Ivan Stojmenovic © 2013 by The Institute of Electrical and Electronics Engineers, Inc Published 2013 by John Wiley & Sons, Inc 853 854 Broadband Wireless Access (BWA), 37–43 Broadcasting, 520 CA (Certification Authority), 667 CaaS (Cooperation as a Service), 656 Cabernet, 580 CafNet, 580 Calibration, 220, 223 Capacitor, 710 CAR (Connectivity-Aware Routing), 534 Car following, 547 Carmeq GmbH, 592 CarTel Project, 579 CarTorrent protocol, 589 CBRF (Connection-Based Restricted Forwarding), 533 CC - Cloud computing, 650 CCA (cooperative collision avoidance), 527 CCH interval (CCHI), 604 Cell-based model, 548 Channel access time, 602 Channel coherence time, 813 Channel equalization, 816 Channel impulse response, 813 Charging efficiency, 706 Chromophoric dissolved organic matter (CDOM), 787, 790–792, 794 Clusterhead (CH), 610 Clustering, 500–501 Fielder clustering, 501 K-Clique, 501 Code-division multiple access (CDMA), 606 CodeTorrent, 589 Cognitive radio vehicular Ad Hoc networks applications, 628–629 architecture, 629–630 characteristics, 622–628 definition, 621 security, 637 simulation tools, 638–640 Coherence bandwidth, 815 Coherent modulation, 815 Collision avoidance systems (CCA), 601 Community structure, 441 Confidentiality, 669 Congestion, 528 Connected dominating set, 525 Connectivity, 523, 547, 565, 422 almost connected networks, 423 INDEX connectivity islands, 424 sparse networks, 424 Contact aggregation, 440 Contact graph, 439 contact aggregation, 440 unweighted graph, 441 weighted graph, 440–441 ContentPlace, 460 dissemination strategies, 462 knapsack problem, 460 performance results, 463 utility function, 461 Content sharing, 589 Contention-based forwarding (CBF) protocol, 591 Contention window (CW), 607 Control channel (CCH), 605 Conversion efficiency, 713 Covariance, 771–773, 775–777, 790–796 CSMA/CA, 607 CTP, 580 CVeT testbed, 589 Crowd computing, 491–494, 498, 508, 510–512 Data center for VC, 658 Data isolation for VC, 674 Data leak for VC, 670 DEAR (Distance-aware epidemic routing), 534 Dedicated short-range communications (DSRC), 578, 590–591, 595, 602 Decentralized control algorithm, controller, 771, 773, 775–778, 789 Decentralized depth controller, 787, 789–792, 793–797 Decentralized gradient-descent, 773, 775–786, 789–792 Decision feedback equalizer (DFE), 816 Delay tolerant networks (DTNs), 134, 317, 578, 582 applications, 135 bundle layer, 135 hierarchical identity-based cryptography (HIBC), 136–137 security issues, 136 Denial of service in VANETs, 670 Depth adjustment, 771–772, 774, 786–787, 789, 793, 796–800 855 INDEX Depth first search, 538 DieselNet, 581 Differentially coherent, 815 Digital signatures, 675 Direct-sequence code division multiple access (DS-CDMA), 819, 826 Direct-sequence spread-spectrum (DSSS), 818 Discharging efficiency, 706, 708 Discharging rate, 719 Dissemination of data in VANETs, 520 Dissociative node (DN), 615 Distributed computation, 491, 493–494, 496, 498, 501, 510–512 Diversity gain, 821, 827 D-minCost, 536 DMV, 672 Doppler spread, 807, 813, 814, 816, 820 DOT (Department of transportation), 649 DPP, 529 DSRC, 521, 646 Duty-cycle, 704, 722, 723, 726, 727 DV-CAST, 533 Dynamic voltage and frequency scaling (DVFS), 718, 720 EB3 (Elastic book store), 651 EC2 (Elastic compute cloud), 651 EDR (Event data recorder), 648 Effective communication range, 568 Electric field, 709 Electromagnetic, 709, 710 Electrostatic, 709, 710 EMDOR, 528 Energy availability, 703, 715, 717, 721 Energy buffer, 705, 726 Energy consumption, 707, 720, 728 Energy harvested, 707, 708, 714, 715, 721 Energy harvester, 705, 714 Energy harvesting-based wireless sensor networks (EHWSNs), 703, 704, 715, 717 Energy harvesting techniques acoustic noise, 712, 713 biochemical, 712 electromagnetic, 710, 713 electrostatic, 710, 713 mechanical, 709 photovoltaic, 710, 713 piezoelectric, 709, 713 pyroelectric, 711, 713 resonant, 711 RF, 711, 713 thermal, 710 thermoelectric, 710, 713 wind, 712, 713 wireless, 711 Energy neutral operation (ENO), 722 Energy neutrality, 720 Energy overflow, 720 Energy prediction models, 714–715 ETH predictor, 715 exponentially weighted moving-average (EWMA), 714 pro-energy, 715 weather conditioned moving average (WCMA), 714 Energy source, 718 Energy storage, 705, 706, 708, 718, 719, 721, 724 Energy types, 709 Enhanced Distributed Channel Access (EDCA), 605 Entropy, 773 Environmental source, 704 Epidemic routing, 534 Event-driven messages, 600 Experimental platforms, 194, 834 APE, 194 DOME, 195 MAC layer emulators, 198 mobile emulab, 195 physical layer emulators, 197 QuRiNet, 195 Faraday’s law, 710 Fairness, 282, 602 max-min, 282 proportional, 282 FCC (Federal Communications Commission), 646 Feedback-based path selection, 280, 307 gateway-driven solutions, 307 GLWB, 307 load balancing, 280, 307 online algorithms, 307 residual capacity, 308 856 Field-programmable gate array (FPGA), 781–782, 797 FleetNet project, 591 Flooding, 534 Focused coverage, 745 FRAM, 784–785 Frequency-division multiple access (FDMA), 606, 822 Frequency-shift keying (FSK), 814 Friis free space model, 561 Gaussian, 773, 777–778, 790, 792–793 Genetic algorithm, 757 Geocasting, 520, 525, 527, 593 Geometric spreading cylindrical, 808 spherical, 808 Geometric spreading loss, 808 GeOpps, 538 Global optimization for impatient users, 469 delay-utility function, 470 optimal solution, 472 performance results, 473 query counting replication (QCR) scheme, 472 social welfare, 471 Global positioning system (GPS), 602 GrooveNet, 557 Guard interval (GI), 606 GyTAR, 538 HERO protocol, 586 Highway capacity manual (HCM), 565 HOV, 661 IaaS (infrastructure as a service), 651 IEEE 1609 WAVE Standards, 603 IEEE 1609.1, 603 IEEE 1609.2, 603 IEEE 1609.3, 603 IEEE 1609.4, 603, 626 IEEE-802.11e, 605 IEEE 802.11s, 44–48 medium access control (MAC), 46–48 routing, 45–46 IEEE 802.11n, 48–49 INDEX IEEE 802.11p, 50–53, 564, 605, 621, 629 GeoNetworking, 51–53 medium access control (MAC), 51 physical layer, 50–51 IEEE 802.11z, 49 IEEE 802.15.5, 53–57 address allocation, 56 mobility, 57 network formation, 55 routing, 56–57 IEEE 802.16d (mesh), 37–40 centralized mode, 37–39 distributed mode, 39–40 IEEE 802.16j, 40–43 non-transparent relaying, 42–43 transparent relaying, 41–42 IEEE 802.21, 65–67 IEEE 802.22, 621 IETF IPv6 over Low-power WPAN (6LoWPAN) Working Group, 60–61 IETF Routing Over Low-power and Lossy networks (ROLL) Working Group, 61 ILD (inductive loop detectors), 649 Induction, 710 Inductive coupling resonant, 711 strong, 712 weak, 712 Informational messages, 601 Integer division theorem, 611 Integrated simulator, 546, 557 Integrity, 669 Intelligent transportation system (ITS), 579, 599 Intercarrier interference (ICI), 820 Inter-contact times (ICTs), 584, Interference model, 244 physical model, 245 protocol model, 244 Intermittent connectivity, 524 Intersymbol interference (ISI), 813, 814, 815, 820 IT (information technology), 651 ITS (Intelligent transportation systems), 545, 646 Journey (path over time), 571 857 INDEX Key assignment, 678 Key revocation, 679 Key verification, 679 Lag time, 824 Layered architecture, 833 Leakage, 706, 707, 710, 721 Limited mobility, 769–771, 775, 786, 789–790 Location security, 683 Log-normal path loss, 562, 566 Mac layer, 212 back-off procedure, 214 bugs and errors, 213 implementation accuracy, 213 manufacturer idiosyncrasies, 213 standard interpretation, 213 transmission rate, 214 Magnetic field, 710 Mathematical programming of Mesh resource management column generation, 250, 256, 267 cooperation graph, 263 formulation, 246–247, 249–250, 255–256, 258–259, 262–263, 269 path generation, 256 superlink, 264–265 supernode, 264 MapReduce, 492, 498, 510–511 Minimum-cost path selection techniques, 280 optimization-based path selection techniques, 279 taxonomy, 279 MaxProp protocol, 582 MCTRP, 615 Mechanical energy harvesting, 709, 710 Medium access control (MAC) for WSNs energy adaptive (EA-MAC), 723, 725 multi-tier probabilistic polling (MTTP), 724, 725 on-demand (ODMAC), 722, 725 probabilistic polling (PP-MAC), 723, 725 Medium access control (MAC) for VANETs, 600–638 Medium access control (MAC) for UW-ASNs ALOHA-based, 822, 823 CDMA-based, 822, 826 CSMA-based, 822, 824 FDMA-based, 822, 823 TDMA-based, 822, 823 Mesh networks architectures, 155–156, 166–167 bash, 164–166 end-user mobility definitions and challenges, 156–158 IEEE 802.21, 168–169 iMesh, 162 macromobility support, 168–181 micromobility support, 158–181 properties, 162–163 SMesh, 172–178 SyncScan, 158–159 WiOptiMo, 178–181 Message aggregation, 683 Message content, 524 MHVB (Multihop vehicular broadcast), 528 Microcontroller, 705 Microscopic traffic applet, 554 Military applications communication, 79 coordination, 81 distributed sensing, 81 UAVs, 81, 90 Mission (WSN appropriate task) assignment, 721 critical, 722 leader, 721 MobEyes protocol, 589 MOBIL, 549 Mobile ad hoc networks (MANETs) architecture, challenges, 25–26 cross-layer architectures, 10–14 cross-layer issues, 9–10 enabling technologies, energy-efficiency, IETF MANET working group, middleware, MobileMAN, 12–13 routing and forwarding protocols, 7–8 security and cooperation, 10 socioeconomic aspects, 16 testbeds, 15 Mobility, 510, 512, 445 858 Mobility management in Cellular/4G Networks, 588 Mobility model analysis in VANETs, 578, 586 Mobility model in VANETs, 546–547 Mobility simulator, 546, 551 Modular sensor network, 779 Modulation coherent, 815 multicarrier, 820 noncoherent, 814 spatial, 814 Mote, 774 MOVE, 556 Multi-application platform, 784 Multicarrier modulation, 820 Multi-hop ad hoc networking mesh networks, 17–18, 154–157 opportunistic networks, 19–21 sensor networks, 23–24 vehicular ad hoc networks (VANETs), 22–23 Multipath, 807, 813, 814 Multipath delay spread, 815, 816 Multiple access interference (MAI), 827 Multiple access techniques, 822 Multiple frequency-shift keying (MFSK), 815 Multiple-input-multiple-output (MIMO), 821, 827 Multiplexing gain, 821, 827 Mutual information, 773 Multi-lane traffic, 549 NaaS (Network as a Service), 654 Nakagami fading, 563, 566 NCTUns, 557 Neighbour elimination, 522, 526 Neighbour knowledge, 521 Neponset river, 790, 794 Network connectivity applications interplanetary internet, 81 municipal systems, 84 rural communication, 83 one laptop per child project, 83 SARI project, 83 Kiosknet, 83 network layer, 829 Network simulator, 546 INDEX NHTSA (National Highway Traffic Safety Administration), 648 Node deregistration, 688 Node registration, 686 Noise in UW-A, 807, 811 Noncoherent modulation, 814 NOW project, 592 NS-2 in VANETs, 546, 557, 566 NS-3 in VANETs, 557 Objective function, 773, 776, 778, 792, 794–795 Omnet++ in VANETs, 546, 557 On-board unit (OBU), 684 OPERA, 529 OPNET in VANETs, 557 Opportunistic computing, 24–25 Opportunistic networks, 491, 493, 500, 511 contact time analysis, 370–374 data dissemination, 453 ContentPlace, 459 global optimization for impatient users, 469 PodNet, 455 Pub/sub overlay, 465 Push-and-Track, 475 taxonomy, 455 universal swarms for data dissemination, 479 delay-capacity tradeoff, 396–397 delay-energy tradeoff, 413–414 delay-load balance tradeoff, 398–399 experimental data, 363–366, 392–393, 396 flight length analysis, 378–380 inter-contact time analysis, 361–372 IRTF Delay-Tolerant Networking Research Group (DTNRG), 20 Levy walk and Levy flight, 384, 407–408 mobile data offloading, 409–412 mobility models, 360–414 pause time analysis, 377–378 programming, 491–492, 510–512 social structure, 402–406 socially aware task farming, 510–511 spatial context distribution, 386–389 Opportunistic routing, 538, 578, 586, 419 BubbleRap, 442, 467 delegation forwarding, 442, 444 859 INDEX encounter-based routing (EBR), 447 epidemic routing immunization, 430 epidemic routing, 429–430 flooding, 428–430 gossiping, 430 hybrid routing, 444–447 limited-time flooding, 430 minimum estimated expected delay (MEED), 443 minimum expected delay (MED), 443 ORWAR, 444 PRoPHET, 403–405, 437, 443, 445, 446, 510 randomized flooding, 430 RAPID, 444 redundancy-based routing, 422, 428 coding based schemes, 433 controlled replication, 431 SimBet, 442, 446, 510 SLEF, 430 space-time path, 428 spray and focus, 446 spray and wait, 431 store-carry-forward, 421, 428 two-hop routing, 430 utility-based routing, 436 contact-based utility, 436 non-contact-based utility, 444 Optical modem, 787, 789, 796–797 Optimization-based path selection, 279, 280 elastic demands, 283 LMMBA, 284 MMBA, 284 fixed demands, 285 flow bandwidth allocation vector, 281 link bandwidth allocation vector, 281 network utility maximization, 284 saturated flows, 289 MaLB, 289 SWARM, 290 Stochastic demands, 288 throughput maximization, 279, 281 traffic awareness, 291 unknown demands, 286 Optimum velocity model, 548 Orthogonal frequency-division multiplexing (OFDM), 820 Overhead in IEEE 802.11p, 605 Overlay in VANETs, 539 P2P, 646 PaaS (Platform as a Service), 651 PARAMICS, 551 Park-and-plug, 658 Path loss, 807, 808 Pay-as-you-go, 652 Peercast, 614 Periodic messages, 600 Personal Area Networks (PAN), 53–65 Personal Content Dissemination Applications, 96 BlueTorrent, 97 Haggle Project, 97 WiFi Direct, 97 PKI (Public Key Infrastructure), 667 Phase-shift keying (PSK), 815 Photovoltaic, 710 Physical layer, 813 Physical layer modeling, 199 adjacent co-channel interference, 205 bonnmotion, 220 carrier sense, 204 capture effect, 205 cooperative transmission, 263 directional antenna, 261 interference, 204, 208 mobility, 209 reception model, 204 radio propagation model, 203, 206 free space propagation, 206 two-ray ground propagation, 206 Rayleigh propagation, 206 Ricean propagation, 207 shadowing, 206, 207 transmission preamble, 203 Physical layer modeling in MESH, 243 multiple channels, 243, 257–258 power control, 252 rate adaptation, 252 Piezoelectric, 709, 710 Planned evacuation, 663 PodNet, 455 channels, 456 dissemination strategies, 457 performance results, 458 Power density, 713 Power management , 705, 779, 783, 787 PP-CSMA, 613 860 Prediction error, 714 interval, 715 model, 713, 719, 722 Pressure, 709 Privacy breach, 670 Propagation delay, 807, 822 Proxy MIPv6, 65 Psychophysical model, 548 Pub/sub overlay, 465 broker election, 467 brokers, 464 closeness centrality, 467 community detection, 466 performance results, 468 Pure ALOHA, 823 Purely phase-coherent, 815 Push-and-track, 475 objective function, 475 performance results, 477 The offloading concept, 475 when-strategies, 476 whom-strategies, 476 Puzzle check, 676 Pyroelectric, 711 Quadrature amplitude modulation (QAM), 815 Quadrature phase shift keying (QPSK), 820 Quality of Service (QoS), 276, 605 definition, 277 hard QoS, 278 relative QoS, 278 routing, 276 soft QoS, 278 QUALNET in VANETs, 557 QuickWiFi, 580 Radio transceiver, 705 Random access, 611 Range, 807, 808 RAPID protocol, 583 Rayleigh fading, 563 RBVT (Road-based vehicular traffic routing), 538 Receiver consensus, 529 Recharge cycle, 708 Rechargeable batteries, 706, 708 Reinforcement learning, 623 INDEX Re-keying, 678 Reliability, 525, 526, 602 Repudiation, 670 Resource aggregation, 683 RF, 711 Rician fading, 563 Ring founder node (RFN), 615–616 Road-side infrastructure, 646 Road-side unit (RSU), 585, 602 Robot dispatch, 764 Robot-assisted wireless sensor networks, 737 Round trip time (RTT), 813 Routing metrics, 280, 291 ALARM, 298 C2WB, 299 CATT, 298 Configurability, 306 CWB, 299 DARM, 304 design principles, 292 EDR, 304 EETT, 298 EFW, 296 ELP, 297 ETN, 295 ETP, 290, 298 ETT, 295 ETX, 295 full-path scope, 300 GARM, 296 IAR, 297 iAWARE, 302 iETT, 301 ILA, 302 INX, 298 LAETT, 299 link and path measurements, 292, 396 link-restricted scope, 294 MCR, 302 MD, 296 mETX, 295 MIC, 300 MIND, 303 MTM, 295 neighborhood-restricted scope, 298 PEF, 305 RARE, 297 stability, 306 INDEX WCETT, 300 WCETT-LB, 301 WEED, 303 Routing in VANETs, 520, 529, 534 RSS (received signal strength), 526 RSU, 684 Routing protocols (energy efficient), 725 D-APOLLO, 726, 727 DEHAR, 726 E-WME, 727 EHOR, 726 GREES-L, 727, 728 GREES-M, 727, 728 HESS, 725, 726 R-MF, 727 Routing protocols in MESH, 276 Routing protocols for UW-A, 829 geographical, 829 location-based, 830–831 non-location-based routing protocols, 831–833 proactive, 829 reactive, 829 Routing protocols in validation, 217 RPL, 61–64 S3 (Simple Storage Service), 651 SaaS (Software as a Service), 652 SADV, 537 Safe distance model, 548 Safety information dissemination, 592 Sanitization, 674 SCH interval (SCHI), 604 Search and rescue applications, 89–90 area exploration, 90 stigmergy, 91 Ants algorithm, 91 Brick & Mortar, 92 UAV, 90 Self-discharge, 706, 707, 708 Semi-dissociative node (SDN), 615 Sensor maintenance, 762 placement, 740, 748 relocation, 751 Sensor networks architectures, 182 FLEXOR, 184–186 MIPv6, 182 861 mobile phone sensing, 24–25 mobile sinks, 182–184 modular design, 185 mules, 182–183 opportunistic sensing, 24 participatory sensing, 24 IEEE 802.14.5, 182 Service channels (SCHs), 602 ShanghaiGrid, 584 Skewed degree distribution, 441 Signal-to-noise ratio (SNR), 812 Simulation-testbed interoperability, 226 Simulation tools, 192 GloMoSim, 193, 219, 221, 222 ns-2, 193, 219, 221, 222 ns-3, 193, 219, 221, 222 Opnet, 193, 219, 221, 222 Omnet++, 194, 219, 221, 222 QualNet, 193, 219, 221, 222 Jist, 194, 219, 221, 222 Single-input-single-output (SISO), 827 SINR (signal-to-interference-noise ratio), 561 Slotted p-persistence, 526 SmartAHS, 554 Small world, 441 SNR, 561 Social graph, 439 contact aggregation, 440 weighted graph, 440 Social networks, 494, 500, 511–512 centrality, 494, 503, 511 community, 494, 500–503, 510–512 Solar, 710, 713, 714, 715, 716 Spatial modulation, 820 Spectrum management cognitive cycle, 623 control channel, 626 primary User, 622 public safety communication, 628 software defined radio, 622, 634 spectrum access, 634 spectrum horizon, 634 spectrum selection, 634 spectrum sensing mobile sensing, 636–637 sensing techniques, 632 spectrum cooperation, 633–634 spectrum database, 632 spectrum measurements, 631 862 Spoofing, 670 Standards Stimulus-response model, 547 Store-carry-forward, 571, 578 SUMO, 555, 569 Spreading factor, 808 Super-capacitor, 706, 707, 721, 722, 725 SWANS, 557 SWANS++, 557 Sybil attack, 674 Symmetric algorithms, 675 Sync interval (SI), 603 Taking turns, 615 Tampering, 670 Task allocation, 717, 721 aperiodic, 717, 718 deadline, 717, 718, 719, 720 dependent, 717 execution time, 718, 719, 720 independent, 717 multi-version, 717 network, 717, 718 node, 717, 718 non-preemptive, 717, 719 periodic, 717, 718, 719, 720 power requirement, 718, 719 preemptive, 717, 718, 719 reward, 718, 719 running speed, 718, 720 scheduling, 717, 718, 720 utility, 720 virtual, 719 Task farming, 491, 493, 498–507, 510–511 Task scheduling algorithms adaptive scheduling DVFS (AS-DVFS), 720 earliest deadline with energy guarantee (EDeg), 719 energy-aware DVFS (EA-DVFS), 720 harvesting-aware DVFS (HA-DVFS), 720 lazy scheduling algorithm (LSA), 718 multi-version scheduling algorithm (MVSA), 719 smooth to average method (STAM), 718 smooth to full utilization (STFU), 718 INDEX TBD (Trajectory-Based Data Forwarding), 536 TEMIC Speech Dialog Systems GmbH, 591 Thermal, 710, 711 Thermocouple, 711 Thermoelectric, 710, 711 Thermopile, 710 Throughput, 602 Throwbox, 581 Time delay, 528, 536 Time-Division Multiple Access (TDMA), 248, 606, 819 TO-GO, 533 Token Holder node (THN), 615 TOPO, 538 TraCI, 558 TRADE, 532 Traffic condition perception, 586 Trajectory-based model, 548 TraNS, 557 Transmission loss, 808 Trust management, 683 TSIS-CORSIM, 551 Turbine, 712, 716 Turbo equalizer, 817 Two-ray ground reflection model, 562 UART, 781–783, 785 UDG (uniform disk graph), 560 Unattended wireless sensor networks (UWSNs), 125 authentication, 129 backward secrecy, 128 data survivability, 126 forward secrecy, 128 self-key healing, 128 smart adversary, 128–129 Underwater acoustic (UW-A) channel, 807 horizontal, 813, 815 impulse response, 813 overspread, 814 underspread, 814 vertical, 813, 816 communications, 807 deep water, 808, 826 shallow water, 808, 826 propagation speed, 813 INDEX Underwater acoustic sensor networks (UW-ASNs), 822 Underwater robot see: autonomous underwater vehicle Underwater sensor network, 771–775, 789–790 Universal swarms for data dissemination, 479 individual swarm, 479 BARON heuristic, 481 stability region, 480 Unplanned evacuation, 663 Urban air quality sensing, 588 Validation, 220, 223 V2I (vehicles to the infrastructure), 518, 577, 650 V2V (vehicles to vehicle), 518, 577, 650 VADD (vehicle-assisted data delivery), 536 VANET applications, 519 VanetMobiSim, 555 VC (vehicular cloud), 647, 653 VDEB (Vehicle density-based emergency broadcasting), 533 Vehicle tracking, 585 Vehicular ad hoc networks (VANETs) advantages and problems, 137 alert messages, 138 announcement messages, 138 dedicated short-range communications (DSRC), 137 design goals and challenges, 139 identity-based group signatures, 143 message aggregation, 140–141 on-board units (OBUs), 137 privacy, 139, 141 roadside units (RSUs), 137 scalability, 139, 140 security, 139, 141 Sybil attack, 142 Vehicular network applications, 93 coordination, 94 PATH program, 94 driving safety support systems (DSSS), 93 DSRC, 93 intelligent transport systems, 95 CarTel, 96 traffic avoidance, 96 CATE, 96 863 notification systems, 94 epidemic dissemination, 94 publish/subscribe, 94 FeetNet, 95 CarTalk, 95 DieselNet, 95 CarView, 95 GeOpps, 95 vehicle to vehicle, 93 Vehicular networking, 646, 649 Vehicular self-organized MAC (VeSOMAC), 607 Vehicular surveillance and sensing system (VS3 ), 587 Veins, 557 VeMAC, 608 Verification, 220, 223 VGSIM, 557 vibration for mechanical energy harvesting, 709, 710 Video traffic (VI), 605 ViFi protocol, 583 Virtualization, 657, 685 VISSIM, 551 VM (virtual machines), 669 Voice traffic (VO), 605 Voltage, 707 Water filling, 529 WAVE, 563 Weather, 714, 715, 716 WiFi monitoring, 581 Winch, 775, 786–787, 788, 792, 798 Wind, 712, 713, 714, 716 Wireless access in vehicular environments (WAVE), 603 Wireless Ad Hoc networks features, 109 security challenges, 108 Wireless local access (WLAN), 43–53 Wireless Mesh networks (WMNs) architecture, 242, 275–276 backbone, 130 countermeasures, 132 gateway, 277 interference, 276 Mesh routers, 130 MIMO, 290 optimization 864 Wireless Mesh networks (Continued) channel assignment, 257 link activation, 245–246 routing, 254–255 scheduling, 247, 254 routing, 276–277 security challenges, 132 traffic types, 277 Wireless signal propagation model, 547, 561 Wireless sensor network applications, 84 animal monitoring, 88 ZebraNet, 88 WildSensing, 89 body and health monitoring, 85 environmental monitoring, 87 industrial monitoring, 86 internet of things, 86 smart homes, 85 INDEX Wireless sensor networks (WSNs) security attacks taxonomy, 112 Black and Sink Hole attack, 117 data integrity, 123 desynchronization, 121 eavesdropping, 122 flooding, 121 IEEE 802.15.4, 110 jamming, 111–113 privacy, 121 Sybil attack, 119 tampering, 114 trafic analysis, 123 wormhole attack, 118 ZigBee, 111 ZigBee, 57–59 mobility, 59 routing, 58 ieee digital and mobile-cp_ieee digital and mobile-cp@2011-05-06T15;32;42.qxd 1/29/2013 2:32 PM Page IEEE PRESS SERIES ON DIGITAL AND MOBILE COMMUNICATION John B Anderson, Series Editor University of Lund Wireless Video Communications: Second to Third Generation and Beyond Lajos Hanzo, Peter Cherriman, and Jurgen Streit Wireless Communications in the 2lst Century Mansoor Sharif, Shigeaki Ogose, and Takeshi Hattori Introduction to WLLs: Application and Deployment for Fixed and Broadband Services Raj Pandya Trellis and Turbo Coding Christian Schlegel and Lance Perez Theory of Code Division Multiple Access Communication Kamil Sh Zigangirov Digital Transmission Engineering, Second Edition John B Anderson Wireless Broadband: Conflict and Convergence Vern Fotheringham and Shamla Chetan Wireless LAN Radios: System Definition to Transistor Design Arya Behzad Millimeter Wave Communication Systems Kao-Cheng Huang and Zhaocheng Wang 10 Channel Equalization for Wireless Communications: From Concepts to Detailed Mathematics Gregory E Bottomley 11 Handbook of Position Location: Theory, Practice, and Advances Edited by Seyed (Reza) Zekavat and R Michael Buehrer 12 Digital Filters: Principle and Applications with MATLAB Fred J Taylor 13 Resource Allocation in Uplink OFDMA Wireless Systems: Optimal Solutions and Practical Implementations Elias E Yaacoub and Zaher Dawy 14 Non-Gaussian Statistical Communication Theory David Middleton 15 Frequency Stabilization: Introduction and Applications Venceslav F Kroupa 16 Mobile Ad Hoc Networking: Cutting Edge Directions, Second Edition Stefano Basagni, Marco Conti, Silvia Giordano, and Ivan Stojmenovic Mobile Ad Hoc Networking: Cutting Edge Directions, Second Edition Edited by Stefano Basagni, Marco Conti, Silvia Giordano, and Ivan Stojmenovic © 2013 by The Institute of Electrical and Electronics Engineers, Inc Published 2013 by John Wiley & Sons, Inc ... category 12. 1 INTRODUCTION Opportunistic networks represent one of the most interesting evolution of traditional Mobile Ad Hoc NETworks (MANET) The typical MANET scenario comprises mobile users... according to the Web 2. 0 User Generated Content paradigm 12. 2.1 Content Organization PodNet borrows the representation of content as a set of channels from Web syndication [11, 12] Syndication provides... it will make decisions about which entries to download 12. 2 .2 Content-Centric Dissemination Strategies In the simplest case, nodes only download and store those content items that are interesting