Study On Coordination Algorithms For Wireless Sensor And Actor Network

The wireless sensor network (WSNs), whose theory and technology development are mostly driven by application, is an emerging research direction in the area of control. In recent years, there are a large number of applications that require the coordination between sensors and higher capability devices to support not only environmental monitoring but also the proper execution of specific tasks. As a result, wireless sensor and actor networks (WSANs) which is integrated by a large quantity of sensor nodes and a few number of actor nodes has been proposed as a new extension of WSNs.WSANs is a new type of information acquisition and processing technology. In WSANs, coordination mechanisms are required among sensors and actors to gather information about the physical world and then perform appropriate actions upon the environment. In particular, new networking phenomena called sensor-actor (SA) and actor-actor (AA) coordination may occur. Currently research in WSANs face a serious challenge that is how to provide distributed information sensing and processing by means of coordination among nodes, the requirement of real-time and energy-balancing characteristic can be ensure at the same time.Most of typical protocols and algorithms for WSNs may not be well-suited for the unique features and application requirements of WSANs. Facing these limitations, based on optimization theory, swarm intelligence optimization, graph theory and other calculating methods, the coordination mechanism of SA and AA is carried out in this thesis to meet the requirement of real-time and energy-balancing characteristic. The important research results are as follows:â‘ Most of the existing collaborative algorithms had been proposed for the next relay node without considering network energy balance. Considering that in WSANs the cluster size, the cluster head number and the residual energy of the node are key indicators of energy-efficient clustering algorithms, the article proposes the CASA, a clustering algorithm based on sensor-actor coordination model, in order to make the whole network energy consumption more balanced. The algorithm establishes the energy consumption model to obtain the optimized number of cluster heads which determine the cluster size. The sensor-actor communication energy consumption is modeled as a nonlinear program to obtain optimal transmission range of sensors and number of actors. Some methods are used to deploy actors and form cluster of heterogeneous sensor and actor network. Considering a few application requirements such as low-latency, connectivity and energy-efficient, performances of the proposed approaches are validated through simulations.â‘¡Based on clustering networks strut, a real-time actor-actor coordination framework is proposed to solve"hot zone"problem. In this article, Tasks are partitioned into different task units and the cost of taking action is computed among different actors by using auction method. The actor-actor coordination is formulated as a balanced or non-balanced task assignment optimization problem to achieve more energy-balance. In addition, different task units are executed in parallel to enhance higher real-time response. The result of simulation shows that the algorithm could provide more balance in energy consumption and higher real-time performance.â‘¢A single-objective task scheduling approach based on actor-actor coordination for WSANs is proposed to solve the execution problem of ordered execution tasks collaboratively among actors. The purpose of approach is minimizing the maximum response time in the actuators subject to residual energy constraints and schedule execution period of each task operation within given time. The algorithm is based on the principle of particle swarm optimization (PSO), an evolutionary computation technique, which is simple and has fewer adjustable parameters and possesses high search efficiency. Nawaz-Enscore-Ham (NEH) as a local search algorithm employs certain probability to avoid becoming trapped in a local optimum and has been proved to be effective for a variety of situations. Simulation results have shown that the proposed hybrid approach is of high convergence speed and good performance between task response time and balancing the energy dissipation among actors.â‘£In view of the actor-actor task coordination in WSANs, a multi-objective task scheduling approach is proposed. Considering the maximum response time, energy-balanced metric and storage cost, the task assignment among actuators is formulated as a multi-objective optimization problem. A modified ideal point algorithm is used to solve the dimension problem caused by different targets. By translating the multi-object optimization problem into a single-object one, the near-optimum execution period of each task operation would be scheduled in our approach. The algorithm is based on the principle of particle swarm optimization (PSO), an evolutionary computation technique, which is simple and have fewer adjustable parameters and possesses high search efficiency. Multi-neighboring experience as a local search algorithm employs certain probability to avoid becoming trapped in a local optimum and has been proved to be effective for a variety of situations. Simulation results have shown that the proposed algorithm is effective in terms of three performances.