Abstract

In wireless sensor networks (WSNs), collecting data with mobile sinks is an effective way to solve the “energy hole problem”. However, most of existing algorithms of mobile sinks ignore the load balance of rendezvous nodes, which will significantly shorten the network lifetime. Moreover, most mobile sinks are usually required to visit locations of sensor nodes without taking advantage of their communication ranges. Therefore, this paper proposes an energy-efficient trajectory planning algorithm (EETP) based on multi-objective particle swarm optimization (MOPSO) to shorten the trajectory length of the mobile sink and balance the load of rendezvous nodes. EETP aims to reduce the delay in data delivery and prolong the network lifetime. To shorten the trajectory length of the mobile sink, we design a mechanism to select potential visiting points within communication overlapping ranges of sensor nodes, rather than locations of sensor nodes. Additionally, according to trajectory characteristics of the mobile sink, we design an effective trajectory encoding method that can generate a trajectory containing an unfixed number of visiting points. The simulation results show that the proposed EETP is superior to existing WRP, CB and the MOPSO-based algorithm, in terms of delay in data delivery, network lifetime and energy consumption.

Highlights

  • The Internet of Things (IoT) has been widely used in environmental monitoring [1], industrial control [2], health care [3] and other fields

  • multi-objective particle swarm optimization (MOPSO) is applied to obtain the set of visiting points and plan the trajectory of the mobile sink, which can shorten the trajectory and reduce the delay in data delivery

  • efficient trajectory planning algorithm (EETP) consists of two phases: selection of potential visiting points and trajectory planning

Read more

Summary

INTRODUCTION

The Internet of Things (IoT) has been widely used in environmental monitoring [1], industrial control [2], health care [3] and other fields. We need a trade-off between the trajectory length of the mobile sink, the number of RNs and the load balance of RNs. In other words, the data collection with the mobile sink is a multi-objective and complex problem. MOPSO is applied to obtain the set of visiting points and plan the trajectory of the mobile sink, which can shorten the trajectory and reduce the delay in data delivery.

RELATED WORKS
THE PROPOSED ALGORITHM
18: The same pi is deleted 19: return Set of potential visiting points P
26: Select a suitable solution from Pareto optimal set
PERFORMANCE EVALUATION
CONCLUSION AND FUTURE WORK
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call