Wireless Sensor Networks: Passive Mobility Models

2021 ◽  
Vol 56 (5) ◽  
pp. 457-463
Author(s):  
Outazgui Saloua ◽  
Fakhri Youssef

This paper aims to present a detailed study of different mobility models applicable for Wireless Sensor Networks (WSN). Wireless Sensor Networks (WSN) have evolved dramatically in mobile networks, providing the key advantage of offering access to information without considering a user's spatial and topological characteristics. Due to the exponential advancement of the Internet and the development of small handheld devices as a source of connectivity and data sharing, the wireless network has almost exploded over the past few years. As a routing protocol for WSN in different studies, the Ad-hoc On-demand Distance-vector routing protocol (AODV) has shown better performance than different routing protocols. It offers quick adaptation to dynamic link conditions, low processing, low memory overheads, and low network utilization. To develop an optimized routing protocol, in our previous work, we had proposed an enhancement of the AODV routing protocol to increase the performance of the classic AODV protocol by optimizing the energy consumption and automatically maximizing the network lifetime. In this paper, we present a detailed study of mobility models applicable for WSN. We describe various mobility models representing mobile nodes whose movements are independent (individual mobility models) and dependent (group mobility models). Furthermore, we will focus on studying the behavior of our optimized version of AODV that we named RE-AODV with different existing mobility models so that we can, in the end, select the best mobility model. In terms of network efficiency, simulation results in this Work demonstrate that the type of mobility model used makes the difference and influences the behavior of nodes.


Author(s):  
Natarajan Meghanathan

In the first half of the chapter, the authors provide a comprehensive description of two broad categories of data gathering algorithms for wireless sensor networks: the classical energy-unaware algorithms and the modern energy-aware algorithms, as well as presented an exhaustive performance comparison of representative algorithms from both these categories. While the first half of the chapter focuses on static sink (that is located outside on the network boundary), the second half of the chapter explores the use of mobile sinks that gather data by stopping at the vicinity of the sensor nodes. As a first step, the authors investigate the performance of three different strategies to develop sink mobility models for delay and energy-efficient data gathering in static wireless sensor networks. The three strategies differ on the approach to take to determine the next stop for data gathering: randomly choosing a sensor node that is yet to be covered (Random), choose the sensor node that has the maximum number of uncovered neighbor nodes (Max-Density), and choose the sensor node that has the largest value for the product of the maximum number of uncovered neighbor nodes and the residual energy (Max-Density-Energy). Based on the simulation results, the authors recommend incorporating the random node selection-based strategy to be a better strategy for sink mobility models (with minimal deployment overhead) rather than keeping track of the number of uncovered neighbor nodes per node and the residual energy available at the nodes.


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