Clustering, as an essential part in an hierarchy protocol that can prolong the network lifetime, is influenced by the cluster head selection and clustering scheme. A new clustering algorithm called clustering by fast search and finding of density peaks (CFSFDP) based on local density and distance is implementable and efficient. In this paper, we combine this clustering algorithm with a hierarchy protocol in wireless sensor networks (WSNs). However, energy consumption in each round is unbalanced only considering these two variables during the clustering phase, which leads to the early death of the first node. In order to solve this problem, we take residual energy into consideration in our improved CFSFDP-E (energy) algorithm so as to ultimately balance the energy consumption of the network. We analyze different forms of energy and choose a dynamic threshold for each round in the CFSFDP-E algorithm. Simulation results demonstrate that the proposed approach can not only postpone the death of the first node by almost 50% compared to LEACH, but that it also outperforms several related protocols with respect to energy efficiency.
Clustering is a popular method to achieve energy efficiency and scalable performance in large-scale sensor networks. Many clustering algorithms were proposed to use energy efficiently, extend network life span, and improve data transfer. Clustered routing for selfish sensors is a recently proposed algorithm based on game theory. In clustered routing for selfish sensors, the sensor nodes campaign to be cluster heads in order to achieve equilibrium probability. However, this algorithm needs global information for the computation of probability and disregard the uneven energy dissipation from different nodes that serve as cluster heads, thereby causing some nodes to die quickly. Therefore, an energy-efficient clustering algorithm based on game theory is proposed in this study. In the cluster head selection phase, each node competes as potential cluster head by joining a localized clustering game, and a potential cluster head is selected to be a real cluster head through a properly designed probability method. Simulation results show that the life span of wireless sensor networks extended by our algorithm becomes longer than those extended by low-energy adaptive clustering hierarchy and clustered routing for selfish sensors when proper parameters are used.
Node placement is one of the basic problems in a Wireless Sensor Network (WSN). During the operation of a WSN, sensor nodes may fail or die suddenly, which may lead to a coverage hole. To solve this problem, the node placement needs to be re-optimized. The dimensions of node placement optimization are high because of the large node number. In view of this defect, a regional optimization dynamic algorithm is put forward. In this paper, the regional optimization problem of node placement is modeled, and a regional optimization dynamic algorithm with a mixed strategy for node placement (MRDA) is proposed. Simulation experiments are carried out for the proposed algorithm and other comparison algorithms. Results of experiments show that the proposed algorithm can greatly reduce the dimensions and narrow the search range, with a significant improvement in the search performance and convergence speed.
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