Abstract

A phenomenal increase in the number of wireless devices has led to the evolution of several interesting and challenging research problems in opportunistic networks. For example, the random waypoint mobility model, an early, popular effort to model mobility, involves generating random movement patterns. Previous research efforts, however, validate that movement patterns are not random; instead, human mobility is predictable to some extent. Since the performance of a routing protocol in an opportunistic network is greatly improved if the movement patterns of mobile users can be somewhat predicted in advance, several research attempts have been made to understand human mobility. The solutions developed use our understanding of movement patterns to predict the future contact probability for mobile nodes. In this work, we summarize the changing trends in modeling human mobility as random movements to the current research efforts that model human walks in a more predictable manner. Mobility patterns significantly affect the performance of a routing protocol. Thus, the changing trend in modeling mobility has led to several changes in developing routing protocols for opportunistic networks. For example, the simplest opportunistic routing protocol forwards a received packet to a randomly selected neighbor. With predictable mobility, however, routing protocols can use the expected contact information between a pair of mobile nodes in making forwarding decisions. In this work, we also describe the previous and current research efforts in developing routing protocols for opportunistic networks.

Highlights

  • Opportunistic networks are infrastructure-less networks composed of pervasive wireless devices that use their contacts for data delivery

  • Modeling mobility is of interest, as it helps predict the contact pattern for a pair of mobile nodes. (Throughout this paper, we use the terms mobile nodes and mobile users interchangeably.) For example, given the movement patterns of mobile nodes, we can estimate the amount of time remaining before a pair of mobile nodes will contact each other again

  • While validation results show that Truncated Levy Walk (TLW) generates synthetic traces with power-law distributed intercontact time (ICT), our analysis shows that the movement of the mobile nodes does not appropriately represent the social behavior among humans

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Summary

Introduction

Opportunistic networks are infrastructure-less networks composed of pervasive wireless devices that use their contacts for data delivery. Since mobile nodes in opportunistic networks exchange data when they contact each other, predicting the movement patterns of mobile nodes helps improve the routing protocol performance. A growing interest in predicting human walk patterns has led to the development of several new mobility models. Developing mobility models that can generate synthetic traces to mimic real human walk patterns is of high interest. A mobility model developed using datasets collected from real scenarios is called a trace-based mobility model. The data (processed in Step 2) is analyzed to identify specific patterns that may be present in human walks. The analysis of various traces (collected from real scenarios) has revealed that human walks are not random and can be predicted to some extent.

Synthetic Mobility Models
Trace-Based Mobility Models
Changing Trends in Opportunistic Network Routing
Conclusions and Challenges for Future Research
Conclusions
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