Mobility causes network structures to change. In PSNs where underlying network structure is changing rapidly, we are interested in studying how information dissemination can be enhanced in a sparse disconnected network where nodes lack the global knowledge about the network. We use beamforming to study the enhancement in the information dissemination process. In order to identify potential beamformers and nodes to which beams should be directed we use the concept of stability. We first predict the stability of a node in the dynamic network using truncated levy walk nature of jump lengths of human mobility and then use this measure to identify beamforming nodes and the nodes to which the beams are directed. We also develop our algorithm such that it does not require any global knowledge about the network and works in a distributed manner. We also show the effect of various parameters such as number of sources, number of packets, mobility parameters, antenna parameters, type of stability used and density of the network on information dissemination in the network. We validate our findings with three validation model, no beamforming, beamforming using different stability measure and when no stability measure is associated but same number of node beamform and the selection of the beamforming nodes is random. Our simulation results show that information dissemination can be enhanced using our algorithm over other models.
Many mobility models have been proposed that capture the characteristics of human mobility. A comprehensive survey of mobility properties associated with humans can be found in @cite_11 @cite_6 , while a clear difference between different human mobility models can also be found in @cite_11 . Most of the traditional models on human mobility use spatial properties of human mobility in the model. However, it was brought up that human mobility also has temporal characteristics and follows truncated power law distribution. Truncated Power Law is characterized by a probability distribution that has the properties of a power law with an exponential cutoff, eq. where proportionality constant is equal to @math , @math is the power law decay exponent constant while @math is the cutoff value. Truncated power law distribution means that the distribution starts as a power law and ends as an exponential curve. The exponential cutoff is because @math r_ g @math @math @math @math @math r_ g $. We next provide a brief overview of antenna models and beamforming in section .
@cite_6 @cite_11
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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. Mobile ad hoc networks enable communications between clouds of mobile devices without the need for a preexisting infrastructure. One of their most interesting evolutions are opportunistic networks, whose goal is to also enable communication in disconnected environments, where the general absence of an end-to-end path between the sender and the receiver impairs communication when legacy MANET networking protocols are used. The key idea of OppNets is that the mobility of nodes helps the delivery of messages, because it may connect, asynchronously in time, otherwise disconnected subnetworks. This is especially true for networks whose nodes are mobile devices (e.g., smartphones and tablets) carried by human users, which is the typical OppNets scenario. In such a network where the movements of the communicating devices mirror those of their owners, finding a route between two disconnected devices implies uncovering habits in human movements and patterns in their connectivity (frequencies of meetings, average duration of a contact, etc.), and exploiting them to predict future encounters. Therefore, there is a challenge in studying human mobility, specifically in its application to OppNets research. In this article we review the state of the art in the field of human mobility analysis and present a survey of mobility models. We start by reviewing the most considerable findings regarding the nature of human movements, which we classify along the spatial, temporal, and social dimensions of mobility. We discuss the shortcomings of the existing knowledge about human movements and extend it with the notion of predictability and patterns. We then survey existing approaches to mobility modeling and fit them into a taxonomy that provides the basis for a discussion on open problems and further directions for research on modeling human mobility.