Research On Software Defined Wireless Sensor Networks

With the continuous development of the applications of wireless sensor network,since WSN ' s inherent characteristics such as extreme applications specific,limited node resources,the original network architecture hindered the further development of its own.In recent years,with the emergence of software defined network paradigm,researchers combined software defined network model and the model of the wireless sensor network to produce a new network architecture,which is software defined wireless sensor networks.The software defined wireless sensor network enhanced wireless sensor networks manageability,configurability and reusability,and provides a more powerful platform that support to the wireless sensor networks new application.The new network architecture makes the original computing model and optimization method no longer conform to the development of wireless sensor networks.How to design the new optimization models to solve the inherent problems in wireless sensor networks according to the characteristics of software defined wireless sensor network becomes particularly important.Considering the characteristics of sensor networks and based on the model of software defined wireless sensor networks,the task allocation and nodes scheduling,multi target localization and mobility management in wireless sensor networks are studied in this dissertation.The main research contents and contributions of this dissertation include:1)Build a task allocation and nodes scheduling model suitable for large-scale nodes in software defined wireless sensor networks.The model introduces a cooperative sensing framework,which takes full account of the resource constraints of sensors,the schedulability of the tasks,and the task's demand of the sensing quality.In order to minimize the energy consumption of network and realize the load balancing of nodes energy consumption,a reasonable global optimization algorithm for task allocation and nodes scheduling is proposed.At the same time,considering the dynamic characteristics of wireless sensor networks,according to the changes in the network's operation,the corresponding local optimization algorithm is designed,so that the scheduling strategy has better real-time performance.2)Build a multi target localization model based on compressed sensing for software defined wireless sensor networks and design a position reconstruction algorithm based on discrete artificial bee colony to solve the model.The algorithm has the characteristics of less required parameters,low computational complexity and fast convergence speed.The model reduces the size of data needed to transfer and mitigate the pressure of network communication.Meanwhile,the influence of noise is taken into account,it has certain practical value.3)Build a multi mobile sink data collecting model in software defined wireless sensor networks in order to ensure the availability of the network and reduce the energy consumption of the network.In this model several concerned factors such as resident time of mobile nodes and data transfer rate are to be considered as decisive disciplines for the plan path of mobile node,and proposed a more general multi-objective data colleting strategies based on multi-mobile nodes;this proposed data collecting technique is abstracted as a model of time variable multiple traveling salesman problem.Belonging to a discrete optimal problem,the proposed model is solved by with a proposed hybrid genetic algorithm which is suitable for the controller to determine the paths of the multi-mobile nodes.The convergence analysis of the proposed algorithm is given.4)In order to further shorten the path of mobile sink nodes and reduce the delay of data collecting,the influence of the communication range of nodes is considered,and the path planning problem is modeled as a multi traveling salesman with neighborhood problem.To solve this hybrid optimization model,an efficient approximate algorithm is proposed.The real-time performance of the path planning is improved.Finally,the dissertation provides the upper bound of the algorithm and its the oretical proof.