Maximizing the lifetime of wireless sensor networks(WSNs) is an important and challenging research problem. Properly scheduling the movements of mobile sinks to balance the energy consumption of wireless sensor networ...Maximizing the lifetime of wireless sensor networks(WSNs) is an important and challenging research problem. Properly scheduling the movements of mobile sinks to balance the energy consumption of wireless sensor network is one of the most effective approaches to prolong the lifetime of wireless sensor networks. However, the existing mobile sink scheduling methods either require a great amount of computational time or lack effectiveness in finding high-quality scheduling solutions. To address the above issues, this paper proposes a novel hyperheuristic framework, which can automatically construct high-level heuristics to schedule the sink movements and prolong the network lifetime. In the proposed framework, a set of low-level heuristics are defined as building blocks to construct high-level heuristics and a set of random networks with different features are designed for training. Further, a genetic programming algorithm is adopted to automatically evolve promising high-level heuristics based on the building blocks and the training networks. By using the genetic programming to evolve more effective heuristics and applying these heuristics in a greedy scheme, our proposed hyper-heuristic framework can prolong the network lifetime competitively with other methods, with small time consumption. A series of comprehensive experiments, including both static and dynamic networks,are designed. The simulation results have demonstrated that the proposed method can offer a very promising performance in terms of network lifetime and response time.展开更多
This paper proposed distributed strategies for the joint control of power and data rates in a wireless sensor network. By adapting a linear state-space model to describe the network dynamics, the power controller with...This paper proposed distributed strategies for the joint control of power and data rates in a wireless sensor network. By adapting a linear state-space model to describe the network dynamics, the power controller with static output feedback is designed in the case that the transmission signal are not always available and the estimation of the unmeasured states constitutes a crucial task in the network. The existence of the power controller is formulated as the feasibility of the convex optimization problem, which can be solved via a linear matrix inequality （LMI） approach. The proposed algorithm also caters to the uncertainties in the network dynamics. Numerical examples are given to illustrate the effectiveness of the proposed methods.展开更多
基金supported by the National Natural Science Foundation of China(61602181,61876025)Program for Guangdong Introducing Innovative and Entrepreneurial Teams(2017ZT07X183)+2 种基金Guangdong Natural Science Foundation Research Team(2018B030312003)the Guangdong–Hong Kong Joint Innovation Platform(2018B050502006)the Fundamental Research Funds for the Central Universities(D2191200)
文摘Maximizing the lifetime of wireless sensor networks(WSNs) is an important and challenging research problem. Properly scheduling the movements of mobile sinks to balance the energy consumption of wireless sensor network is one of the most effective approaches to prolong the lifetime of wireless sensor networks. However, the existing mobile sink scheduling methods either require a great amount of computational time or lack effectiveness in finding high-quality scheduling solutions. To address the above issues, this paper proposes a novel hyperheuristic framework, which can automatically construct high-level heuristics to schedule the sink movements and prolong the network lifetime. In the proposed framework, a set of low-level heuristics are defined as building blocks to construct high-level heuristics and a set of random networks with different features are designed for training. Further, a genetic programming algorithm is adopted to automatically evolve promising high-level heuristics based on the building blocks and the training networks. By using the genetic programming to evolve more effective heuristics and applying these heuristics in a greedy scheme, our proposed hyper-heuristic framework can prolong the network lifetime competitively with other methods, with small time consumption. A series of comprehensive experiments, including both static and dynamic networks,are designed. The simulation results have demonstrated that the proposed method can offer a very promising performance in terms of network lifetime and response time.
基金supported by the National Natural Science Foundation of China (Nos. 60704021, 61074039)the Natural Science Foundation of Zhejiang Province of China (No. Y1100845)
文摘This paper proposed distributed strategies for the joint control of power and data rates in a wireless sensor network. By adapting a linear state-space model to describe the network dynamics, the power controller with static output feedback is designed in the case that the transmission signal are not always available and the estimation of the unmeasured states constitutes a crucial task in the network. The existence of the power controller is formulated as the feasibility of the convex optimization problem, which can be solved via a linear matrix inequality （LMI） approach. The proposed algorithm also caters to the uncertainties in the network dynamics. Numerical examples are given to illustrate the effectiveness of the proposed methods.
