Based WSANs Actuator Nodes Optimal Placement And Fusion Algorithm Performance Analysis

Wireless sensor and actor networks (WSANs) employ a large quantity of miniaturized sensor nodes and a few number of actor nodes. Sensor nodes are small and inexpensive, usually with limited power and limited data processing capabilities, while actor nodes are more capable nodes with relatively more onboard energy supply and richer computation and communication resources. Sensors probe their surroundings and report their findings to the actor node, which processes the collected sensor data and respond to emerging events of interest. Some applications may impose a time deadline on sensed data transformation to be delivered to an actor for timely acting, which is different from wireless sensor networks. In order to provide such services, the whole monitor area is divided into several virtual areas and nodes in the same area form a cluster. Clustering of the WSANs is often pursued to give that each actor can also act as a cluster-head and takes certain actions based on the received information from the sensors within its cluster. Sensor nodes are stationary while actor nodes have mobility. Both sensor nodes and actor nodes are deployed randomly in practical applications. How to determine the location of the actor nodes or what is the best layout for the actor nodes is the important problem that attracts growing interests. The deployment of actor nodes should take two essential issues:how to array actor nodes and how many actor nodes needed. There are few research approaches about the least number of actor nodes during the recent years.The main research achievements of this article include the following three aspects:(1) Time-delay system theory, Lyapunov theory combined with Linear Matrix Inequality (LMI) techniques and free-weighting-matrices method were used to study the time-delay system stability problem. Based on the stability analysis, a new method of designing a delay-dependent feedback controller was proposed;(2) A k-MinTE and k-MaxTE clustering algorithm was proposed based on imposed delay constraints for clustering of WSANs. By the statistical analysis of simulation experiments, WSANs were divided into the sum of clusters which are clustered by k-MinTE and k-MaxTE clustering algorithm. According to the size of WSANs clusters, the minimal number of actors needed to deploy in the field of interest is available. (3) A general distributed fusion algorithm was proposed in this paper to compare with a general centralized fusion algorithm. Their features were discussed in transmission delay, node energy consumption, network lifetime and the probability of transmission failure.