Multi-MC Charging Schedule Algorithm With Time Windows in Wireless Rechargeable Sensor Networks

Abstract

The limited lifespan of the traditional Wireless Sensor Networks (WSNs) has always restricted the broad application and development of WSNs. The current studies have shown that the wireless power transmission technology can effectively prolong the lifetime of WSNs. In most present studies on charging schedules, the sensor nodes will be charged once they have energy consumption, which will cause higher cost and lower networks utility. It is assumed in this paper that the sensor nodes in Wireless Rechargeable Sensor Networks (WRSNs) will be charged only after its energy is lower than a certain value. Each node has a charging time window and is charged within its respective time window. In large-scale wireless sensor networks, single mobile charger (MC) is difficult to ensure that all sensor nodes work properly. Therefore, it is propoesd in this paper that the multiple MCs which are used to replenish energy for the sensor nodes. When the average energy of all the sensor nodes falls below the upper energy threshold, each MC begins to charge the sensor nodes. The genetic algorithm has a great advantage in solving optimization problems. However, it could easily lead to inadequate search. Therefore, the genetic algorithm is improved by 2-opt strategy. And then multi-MC charging schedule algorithm with time windows based on genetic algorithm is proposed and simulated. The simulation results show that the algorithm designed in this paper can timely replenish energy for each sensor node and minimize the total charging cost.INDEX TERMS Wireless rechargeable sensor networks, charging schedule, time windows, multiple mobile chargers, energy threshold.

Q-learning-based distributed multi-charging algorithm for large-scale WRSNs

“…A. Tomar in [22] partition the network into regions, each of which is served by an MC. The criteria of the partition [29], [28] min(moving distance of MCs) [26] timely charging [22] min(charging cost) & max(charging utility) [23] min(total charging cost) [25] min(the number MCs) [24] max(# of charging node) & min(charging delay) [30] min(the number of MCs) [11] algorithm are to balance the charging workload of all MCs. The authors then leveraged Fuzzy logic to determine each MC's charging schedule, which considers various network attributes.…”

“…In [24], the authors tried to collect sensors into a so-called on-time-covered which can be charge in-time by one MC in a charging cycle. To deal with the second sub-problem, i.e., charging schedule optimization for each MC, various approaches such as using Fuzzy logic [22], genetic algorithm [25,26], game theory [27], linear programming [28].…”

“…1. Firstly, most of the charging models with multiple chargers so far leverage the centralization approach, where the base station collects all the network information (i.e., information concerning the sensors and mobile chargers) and decides the charging schedule for all mobile chargers [13,[22][23][24][25][26][28][29][30]. This approach imposes a heavy workload on the base station and may lead to a bottleneck phenomenon.…”

“…2. Secondly, the studies' charging schemes fail to consider the unique advantages of each sensor [13,22,23,25,26,[28][29][30] -some sensors can have better coverage or have better distance. Not prioritizing sensors by their capabilities will limit the network's coverage and connectivity since more important sensors can be left out, leading to a shorter network lifetime.…”

“…3. Thirdly, most of the exiting charging schemes with multiple MCs adopts a fully charging strategy, where the MC charge each sensor to full [13,[22][23][24][25][26][28][29][30]. This approach is impractical in large-scale networks since it will drastically increase the waiting time for other uncharged sensors.…”