A heterogeneous coverage method with multiple unmanned aerial vehicle assisted sink nodes(MUAVSs)for multi-objective optimization problem(MOP)is proposed,which is based on quantum wolf pack evolution algorithm(QWPEA)a...A heterogeneous coverage method with multiple unmanned aerial vehicle assisted sink nodes(MUAVSs)for multi-objective optimization problem(MOP)is proposed,which is based on quantum wolf pack evolution algorithm(QWPEA)and power law entropy(PLE)theory.The method is composed of preset move and autonomous coordination stages for satisfying non-repeated coverage,connectedness,and energy balance of sink layer critical requirements,which is actualized to cover sensors layer in large-scale outside wireless sensor networks(WSNs).Simulation results show that the performance of the proposed technique is better than the existing related coverage technique.展开更多
In a sensor network, data collected by different sensors are often correlated because they are observations of related phenomena. Efficient sensor data fusion is one of the most important issues in building real senso...In a sensor network, data collected by different sensors are often correlated because they are observations of related phenomena. Efficient sensor data fusion is one of the most important issues in building real sensor networks. To balance energy cost, how to select a cluster head is a key problem that must be addressed. In this paper, we use a compression-centric data collection algorithm for use in wireless sensor networks. Also, we propose a balanced cluster head selection algorithm in each cluster. Simulation results are used to investigate the performance of the algorithm. Compared to the exhaustive search solutions, the proposed algorithm shows a significant improvement in power consumption.展开更多
基金Supported by the National Natural Science Foundation of China(No.61571318)Key Research and Development Project of Hainan(No.ZDYF2018006)+1 种基金Independent Innovation Fund of Tianjin UniversityDoctoral Fund Funded Projects
文摘A heterogeneous coverage method with multiple unmanned aerial vehicle assisted sink nodes(MUAVSs)for multi-objective optimization problem(MOP)is proposed,which is based on quantum wolf pack evolution algorithm(QWPEA)and power law entropy(PLE)theory.The method is composed of preset move and autonomous coordination stages for satisfying non-repeated coverage,connectedness,and energy balance of sink layer critical requirements,which is actualized to cover sensors layer in large-scale outside wireless sensor networks(WSNs).Simulation results show that the performance of the proposed technique is better than the existing related coverage technique.
基金supported by the National Natural Science Foundation of China(No.60772055)
文摘In a sensor network, data collected by different sensors are often correlated because they are observations of related phenomena. Efficient sensor data fusion is one of the most important issues in building real sensor networks. To balance energy cost, how to select a cluster head is a key problem that must be addressed. In this paper, we use a compression-centric data collection algorithm for use in wireless sensor networks. Also, we propose a balanced cluster head selection algorithm in each cluster. Simulation results are used to investigate the performance of the algorithm. Compared to the exhaustive search solutions, the proposed algorithm shows a significant improvement in power consumption.
