Abstract
Computing the distributed average consensus in Wireless Sensor Networks (WSNs) is investigated in this article. This problem, which is both natural and important, plays a significant role in various application fields such as mobile agents and fleet vehicle coordination, network synchronization, distributed voting and decision, load balancing of divisible loads in distributed computing network systems, and so on. By and large, the average consensus' objective is to have all nodes in the network converged to the average value of the initial nodes' measurements based only on local nodes' information states. In this paper, we introduce a fully distributed algorithm to average the sensed data within the network itself. The network may be large since we never broadcast over all its nodes. Unlike earlier works, when a node detects a load (scalar value) imbalance in its closed neighborhoods during the average process, instead of sending parsimonious amount of load values from highly loaded nodes to less loaded ones, we move a large amount of load values by involving parallel atomic transactions between mutually exclusive pairs of neighbors. This improves the global convergence time speedup with low-cost communication and minimal energy consumption. First, we give the convergence proof of the distributed consensus process, and next we provide some experimental results based on NS3 framework to assess the behavior of the proposed algorithm.