Wireless Sensor Network Charging Strategy With Energy Limited Mobile Charging Device

Wireless sensor network nodes are often deployed in these areas where their natural environment is severe or they are inaccessible to people, such as desert, underwater and so on. Most of these areas are not suit to rig wired equipment. In the condition of current technological level, sensor node works more relying on battery, which means sensor network, inevitably, will die if it cannot obtain energy supplement. Taking the approach of replacing batteries of nodes in large scale network by people is uneconomical and unpractical, and the traditional way of drawing power from surroundings has great limitations; however, the emerging wireless charging technology has offered a new better solution for the problem of extending lifespan of wireless sensor network.Wireless nodes with wireless sensors equipped with wireless energy collection device and mobile wireless charging device (MWC) together constitute chargeable wireless sensor network. MWC can free move in the cover range and charge nodes. MWC has to take reasonable charging measures in order to extend lifespan of wireless sensor network. The thesis treats two dimensional wireless sensor network as a subject, targeting at minimizing the total mileage MWC traveled when it at least once completes servicing all of the nodes in the network. Moreover, the thesis has designed the AQ-LMTD algorithm based on Ant-Q algorithm to solve previous problem. Simulates the experiment of charging nodes for 30 periods in 12 different network environments on a platform. The result shows AQ-LMTD algorithm is far superior than Greedy algorithm no matter in the rate of success or in total mileage traveled by:AQ-LMTD's success rate is 48.3% higher than Greedy's and AQ-LMTD's total mileage is 36.3% less than Greedy's.Furthermore, considering the total energy which charges nodes and offers MVC traveling are limited, this thesis assumes the energy driving MVC traveling and charging for nodes both deriving from the same source, called the gross energy of MWC. Also, this thesis targets at minimizing the gross energy MWC consumed after at least once completing servicing all of the nodes within the network, and builds models in the situation where the energy of MWC and energy in the network are under maximum constraint. Meanwhile, the thesis has put forward a charging strategy of equalization which aims at trying to balance the life cycle of all nodes in the network by charging and reasonably distributes energy for charging and for traveling while avoiding nodes premature death. Thus, MMES-LME algorithm which is based on improved Ant-Q algorithm and equalization strategy is proposed to solve target problem. MMES-LME algorithm is far superior than AQ-LME algorithm and Greedy algorithm not only in the rate of success but in total energy consumed by MWC through simulation: MMES-LME's success rate is 1.5% higher than AQ-LME's and is 57.5% higher than Greedy's. MMES-LME's energy consumed is 25.5% less than AQ-LME's and is 45.7% less than Greedy's.