Abstract

Mobile agent (MA)-based wireless sensor networks present a good alternative to the traditional client/server paradigm. Instead of sending the data gathered by each node to the sink as in client/server, MAs migrate to the sensor nodes (SNs) to collect data, thus reducing energy consumption and bandwidth usage. For MAs, to migrate among SNs, an itinerary should be planned before the migration. Many approaches have been proposed to solve the problem of itinerary planning for MAs, but all of these approaches are based on the assumption that MAs visit all SNs. This assumption, however, is inefficient because of the increasing size of the MAs after visiting each node. Also, in case of node(s) failure, as it is often the case in WSNs, the MAs may not be able to migrate among SNs. None of the proposed approaches takes into consideration the problem of fault tolerance. In this paper, we propose multi-mobile agent itinerary planning-based energy and fault aware data aggregation in wireless sensor networks (MAEF) to plan itineraries for MAs. This can be achieved by grouping nodes in clusters and planning itineraries efficiently among cluster heads (CHs) only. What is more, an alternative itinerary is planned in case of node(s) failure. The simulation result clearly shows that our novel approach performs better than the existing ones.

Highlights

  • The recent technological advances in wireless communications and microelectromechanical systems (MEMS) have made it possible to develop a tiny, low-power, and low-cost sensor node (SN)

  • 4 The proposed approach we present our multi-mobile agent itinerary planning-based energy and fault aware data aggregation in wireless sensor networks MAEF that consists of three phases: (1) Cluster head (CH) selection and cluster construction, (2) CH-based itinerary planning, (3) Mobile agent (MA) migration and data collection

  • At first, when MAs visit the CHs for the first time, it notifies the nodes within the range of the CHs to send the collected data to the selected CHs (Fig. 4a), when the MAs arrive to the last CH in the itinerary as Fig. 4b shows, it starts collecting the data from the CHs in its way back to the sink

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Summary

Introduction

The recent technological advances in wireless communications and microelectromechanical systems (MEMS) have made it possible to develop a tiny, low-power, and low-cost sensor node (SN). Near-optimal itinerary design (NOID) algorithm [27] adapts a method called the Esau-Williams heuristic originally designed for network design problems for the constrained minimum spanning tree (CMST) issue to the specific requirements of WSNs. In NOID, multiple itinerary planning for MAs is proposed where each MA visits a group of SNs. NOID suffers from low working speed and high computational complexity. Many solutions have been proposed to solve this problem as we have surveyed but the most proposed multi-agent itinerary algorithms are either time-consuming or too complicated in practice, these approaches are based on the assumption that MAs visit all SNs and no nodes’ failure takes place in the network. We use the following equation to calculate the density impact factor [29]:

27: Vgroup 28
9: Output
Fault tolerance based on alternative itinerary planning
Results and discussion
Conclusions
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