The widespread availability of mobile devices along with recent advancements in networking capabilities make opportunistic mobile social networks (MSNs) one of the most promising technologies for next-generation mobile applications. Opportunistic Mobile Social Networks supplies a new perspective of these networks that can help you enhance spontaneous interaction and communication among users that opportunistically encounter each other, without additional infrastructure support. The book explores recent developments in the theoretical, algorithmic, and application-based aspects of opportunistic MSNs. It presents the motivation behind opportunistic MSNs, describes their underpinning and key concepts, and also explores ongoing research. Supplies a systematic study of the constrained information flow problem Reviews the recent literature on social influence in complex social networks Presents a complete overview of the fundamental characteristics of link-level connectivity in opportunistic networks Explains how mobility and dynamic network structure impact the processing capacity of opportunistic MSNs for cloud applications Provides a comprehensive overview of the routing schemes proposed in opportunistic MSNs Taking an in-depth look at multicast protocols, the book explains how to provide pervasive data access to mobile users without the support of cellular or Internet infrastructures. Considering privacy and security issues, it surveys a collection of cutting-edge approaches for minimizing privacy leakage during opportunistic user profile exchange. The book concludes by introducing a framework for mobile peer rating using a multi-dimensional metric scheme based on encounter and location testing. It also explains how to develop a network emulation test bed for validating the efficient operation of opportunistic network applications and protocols in scenarios that involve both node mobility and wireless communication.

Recent advancements in mobile device technologies are revolutionizing how we socialize, interact, and connect. By connecting the virtual community with the local environment, mobile social networks (MSNs) create the opportunity for a multitude of new personalized services for mobile users. Along with that comes the need for new paradigms, mechanisms, and techniques with the capacity to autonomously manage their functioning and evolution. Currently, most books about mobile networks focus mainly on the technical point of view. Mobile Social Networking and Computing: A Multidisciplinary Integrated Perspective not only addresses the theoretical aspects of MSN and computing, but also introduces and categorizes existing applications. It supplies a multidisciplinary perspective that considers the technology, economics, social sciences, and psychology behind MSNs. In addition to fundamental theory, the book investigates the practical issues in MSN, including characteristics, inner structural relationship, incentive mechanisms, resource allocating, information diffusion, search, ranking, privacy, trust, and reputation. Introducing recently developed technologies, modes, and models, the book provides two distinct (but related) viewpoints about MSN applications: socially inspired networking technology and networking technology that uses recent advancements to enhance quality of life. The text illustrates the interaction between the macrolevel structure and the local rational behaviors (microlevel) in MSN. It summarizes currently available MSN development platforms, including Android and iOS, and introduces and categorizes existing applications related to MSN and computing. Both location-based service (LBS) and mobile social networks in proximity (MSNPs) are presented in a comprehensive manner. Highlighting key research opportunities, this much-needed reference outlines incentive mechanisms inspired by classical economics, behavioral economics, and social psychology, and, perhaps for the first time, it presents a summary of the economic and business models of MSNs.

The Mobile Ad Hoc Network (MANET) has emerged as the next frontier for wireless communications networking in both the military and commercial arena. Handbook of Mobile Ad Hoc Networks for Mobility Models introduces 40 different major mobility models along with numerous associate mobility models to be used in a variety of MANET networking environments in the ground, air, space, and/or under water mobile vehicles and/or handheld devices. These vehicles include cars, armors, ships, under-sea vehicles, manned and unmanned airborne vehicles, spacecrafts and more. This handbook also describes how each mobility pattern affects the MANET performance from physical to application layer; such as throughput capacity, delay, jitter, packet loss and packet delivery ratio, longevity of route, route overhead, reliability, and survivability. Case studies, examples, and exercises are provided throughout the book. Handbook of Mobile Ad Hoc Networks for Mobility Models is for advanced-level students and researchers concentrating on electrical engineering and computer science within wireless technology. Industry professionals working in the areas of mobile ad hoc networks, communications engineering, military establishments engaged in communications engineering, equipment manufacturers who are designing radios, mobile wireless routers, wireless local area networks, and mobile ad hoc network equipment will find this book useful as well.

Mobility Models for Next Generation Wireless Networks: Ad Hoc, Vehicular and Mesh Networks provides the reader with an overview of mobility modelling, encompassing both theoretical and practical aspects related to the challenging mobility modelling task. It also: Provides up-to-date coverage of mobility models for next generation wireless networks Offers an in-depth discussion of the most representative mobility models for major next generation wireless network application scenarios, including WLAN/mesh networks, vehicular networks, wireless sensor networks, and opportunistic networks Demonstrates the practices for designing effective protocol/applications for next generation wireless networks Includes case studies showcasing the importance of properly understanding fundamental mobility model properties in wireless network performance evaluation

With the increasing popularization of personal hand-held mobile devices, more people use them to establish network connectivity and to query and share data among themselves in the absence of network infrastructure, creating mobile social networks (MSNet). Since users are only intermittently connected to MSNets, user mobility should be exploited to bridge network partitions and forward data. Currently, data route/forward approaches for such intermittently connected networks are commonly "store-carry-and-forward" schemes, which exploit the physical user movements to carry data around the network and overcome path disconnection. And since the source and destination may be far away from each other, the delay for the destination to receive the data from the source may be long. MSNets can be viewed as one type of socially-aware delay tolerant networks (DTNs). Observed from social networks, the contact frequencies are probably different between two friends and two strangers, and this difference should be taken into consideration when designing data dissemination and query schemes in MSNets. In this book, the fundamental concepts of MSNets are introduced including the background, key features and potential applications of MSNets, while also presenting research topics, such as, MSNets as realistic social contact traces and user mobility models. Because the ultimate goal is to establish networks that allow mobile users to quickly and efficiently access interesting information, particular attention is paid to data dissemination and query schemes in subsequent sections. Combined with geography information, the concepts of community and centrality are employed from a social network perspective to propose several data dissemination and query schemes, and further use real social contact traces to evaluate their performance, demonstrating that such schemes achieve better performance when exploiting more social relationships between users.

In recent years, there has been a rapid growth of location-based social networking services, such as Foursquare and Facebook Places, which have attracted an increasing number of users and greatly enriched their urban experience. Typical location-based social networking sites allow a user to "check in" at a real-world POI (point of interest, e.g., a hotel, restaurant, theater, etc.), leave tips toward the POI, and share the check-in with their online friends. The check-in action bridges the gap between real world and online social networks, resulting in a new type of social networks, namely location-based social networks (LBSNs). Compared to traditional GPS data, location-based social networks data contains unique properties with abundant heterogeneous information to reveal human mobility, i.e., "when and where a user (who) has been to for what," corresponding to an unprecedented opportunity to better understand human mobility from spatial, temporal, social, and content aspects. The mining and understanding of human mobility can further lead to effective approaches to improve current location-based services from mobile marketing to recommender systems, providing users more convenient life experience than before. This book takes a data mining perspective to offer an overview of studying human mobility in location-based social networks and illuminate a wide range of related computational tasks. It introduces basic concepts, elaborates associated challenges, reviews state-of-the-art algorithms with illustrative examples and real-world LBSN datasets, and discusses effective evaluation methods in mining human mobility. In particular, we illustrate unique characteristics and research opportunities of LBSN data, present representative tasks of mining human mobility on location-based social networks, including capturing user mobility patterns to understand when and where a user commonly goes (location prediction), and exploiting user preferences and location profiles to investigate where and when a user wants to explore (location recommendation), along with studying a user's check-in activity in terms of why a user goes to a certain location.