Energy-Efficient Trajectory Planning Algorithm Based on Multi-Objective PSO for the Mobile Sink in Wireless Sensor Networks

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. INDEX TERMS Mobile sink, MOPSO, load balance of rendezvous nodes, trajectory planning.

An Energy Efficient and Lifetime Ratio Improvement Methods Based on Energy Balancing

“…10 each Sensing Point has its own unique position in the network. Sensing Point6 is placed at the coordinate (2,9). Sensing Point 20 is placed at the coordinate (60,35).…”

“…When the data collection cycle triggers the mobile sink, Sensing points have to visit the target sites and collect the data, instead sensor Sensing points can transmit the data if the sink is within communication range which can improve the lifetime ratio of the network. The path of mobile sink can be reduced and flexible design can be provided so that either sensor can transmit or mobile sink can collect the data [9]. There is an overlap between region of interest and sensing area which needs to be significantly analysed to exponential increase the lifetime ratio.…”

“…The Single System network as shown in the Fig.1 has 100 Sensing points which are spread in an area of 100*100m. Sensing Point 9 has been placed at the coordinate (9,11). Sensing Point 37 has been placed at the coordinate (42,2) and Sensing Point 20 has been placed at the coordinate (79,1) and Sensing Point 52 has been placed at the coordinate (90,10).…”

Lightweight Fault Detection Strategy for Wireless Sensor Networks Based on Trend Correlation

“…For example, if the sensed data series X i (t) and X j (t) obtained by neighboring nodes i and j at time t are [1,1.1,1.2,1.1] and [1.2,1.3,1.4,1.3], we can conclude that the trend of these two nodes are perfectly correlated, and the difference in their readings is due to their different positions. In this case, we can get χ i,j (t)=1 according to (4), which perfectly characterizes the trend correlation between X i (t) and X j (t).…”

“…Each of these individual sensor nodes in a WSN has sensing and processing capability. Sensor nodes are low power devices with limited computational power, memory, battery and storage [1]- [4]. Since sensor nodes are usually deployed in hostile and harsh environments, they are susceptible to frequent and unexpected faults.…”