Abstract

Energy shortage obstructs the applications of the wireless rechargeable sensor network (WRSN). With the development of the wireless energy transfer technology, the mobile wireless charging vehicle (WCV) becomes a promising solution to solve that problem. However, the importance of different sensor nodes in the data transmission and uneven energy consumptions are often ignored. In this paper, the charging strategy of the WCV is studied in the WRSN considering these two phenomena. According to the importance of the sensor node, which is associated with the distance to the base station, we divide sensor nodes into two types: sensor nodes in ring 0 and sensor nodes in outer ring. We propose a novel charging model, the WCV adopts different charging strategies for different sensor nodes. To make the charging more efficient, the WCV charges sensor nodes one by one in ring 0 first, and then charges multiple sensor nodes simultaneously in outer ring. To estimate the lifetime of the network, a new metric named as the normalized dead time is proposed. Maximizing the lifetime of the network is modeled as minimizing the sum normalized dead time, and an efficient algorithm is proposed to minimize the sum normalized dead time through searching the optimal charging timeslots sequences. Then, through reassigning charging timeslots of sensor nodes, the proposed minimum travel cost algorithm minimizes the travel distance of the WCV and guarantee the lifetime of the network. We further deploy a cluster head node which has larger battery capacity in each cluster and can charge other sensor nodes within a limited distance. An algorithm is proposed to pre-distribute energy of the cluster head node. At last, the performance of proposed algorithms is verified by MATLAB. The results indicate that the performance of the WRSN can be improved by our proposed algorithms.

Highlights

  • The wireless sensor network (WSN) often consists of a mass of sensor nodes [1]–[3]

  • In this paper, considering the importance of different sensor nodes in the data transmission, the uneven energy consumption and the number of different sensor nodes simultaneously, we propose a new charging scheme

  • We summarize the problems considered in our work as follows. (i) What is the optimal charging timeslots sequence of sensor nodes which makes the sum normalized dead time be minimized under the new charging model? (ii) What is the optimal travel tour of the wireless charging vehicle (WCV), which can make the travel cost be minimized? (iii) How to schedule the cluster head node (CN) to replenish energy to sensor nodes? It is worth pointing out that problems mentioned above have not been solved by current studies

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Summary

INTRODUCTION

The wireless sensor network (WSN) often consists of a mass of sensor nodes [1]–[3]. There are many applications of the WSN, like military reconnaissance, smart home, environmental monitoring, etc. [4], [5]. In this paper, considering the importance of different sensor nodes in the data transmission, the uneven energy consumption and the number of different sensor nodes simultaneously, we propose a new charging scheme. Through solving the problem of finding the charging timeslots sequence of sensor nodes, the sum normalized dead time of sensor nodes in the network can be minimized. 1) Considering the importance of different sensor nodes in the data transmission, the uneven energy consumptions and the number of sensor nodes in different area simultaneously, we propose a new charging model. 2) Under the new charging model, we formulate two optimization problems, (i) Minimizing the sum normalized dead time and (ii) Minimizing the traveling cost of the WCV while the lifetime of sensor nodes can be guaranteed, respectively.

SYSTEM MODEL AND PROBLEM FORMULATION
NETWORK MODEL
THE DATA FLOW ROUTING AND THE ENERGY CONSUMPTION
RECHARGING MODEL
PROBLEM DEFINITION
ALGORITHM FOR THE TRAVEL COST OF THE WCV MINIMIZATION PROBLEM
ALGORITHM
ALGORITHM ANALYSIS Theorem 3
ALGORITHM FOR ENERGY DISTRIBUTION OF SENSOR NODES IN CLUSTERS
ALGORITHM ANALYSIS
PERFORMANCE EVALUATION
PARAMETER SETTING
Findings
CONCLUSION
Full Text
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