A Routing Protocol Based on Both of Density Variation and Distance-Aware for WSNs

A hierarchical routing algorithm for wireless sensor networks (WSNs) is discussed. We select cluster heads according to related distances and residual energy. Both effects of the number of nodes dissipated and the energy consumption act on propagation distances. In addition, the related density effects on the propagation distance. We Define comprehensive influence factor and propagation influence factor, adjust the initial probability of nodes participating in cluster heads’ election, make propagation distances of nodes gradually increase within a certain range. Simulation results show that both cluster heads and failure nodes are evenly distributed in the whole sensor network. The residual energy of nodes are balanced inter the living nodes, which extends the survival time of the network. The routing algorithm we have designed has the characteristics of better balanced energy consumption.

An Energy-Efficient Clustering Routing Protocol for Wireless Sensor Networks Based on AGNES with Balanced Energy Consumption Optimization

To further prolong the lifetime of wireless sensor network (WSN), researchers from various countries have proposed many clustering routing protocols. However, the total network energy consumption of most protocols is not well minimized and balanced. To alleviate this problem, this paper proposes an energy-efficient clustering routing protocol in WSNs. To begin with, this paper introduces a new network structure model and combines the original energy consumption model to construct a new method to determine the optimal number of clusters for the total energy consumption minimization. Based on the balanced energy consumption, then we optimize the AGglomerative NESting (AGNES) algorithm, including: (1) introduction of distance variance, (2) the dual-cluster heads (D-CHs) division of the energy balance strategy, and (3) the node dormancy mechanism. In addition, the CHs priority function is constructed based on the residual energy and position of the node. Finally, we simulated this protocol in homogeneous networks (the initial energy = 0.4 J, 0.6 J and 0.8 J) and heterogeneous networks (the initial energy = 0.4–0.8 J). Simulation results show that our proposed protocol can reduce the network energy consumption decay rate, prolong the network lifetime, and improve the network throughput in the above two networks.