Research On Environment Adaptive Application Reconfiguration In Wireless Sensor Networks

With the rapid development of sensor techniques, potential applications of sensor networks span a wide spectrum. The flexibility and adaptation of applications provided by sensor networks have been paid more and more attention in recent years. Due to the ability of supporting cooperation among complex tasks, the large scale sensor networks which can self-adapt the change of environment and application requirement is becoming a hot issue. However, due to individual sensor's limited resource and varied/unpredictable environment, we cannot deploy all the applications onto sensor nodes at one time. Hence, how to provide flexible and varied application tasks in resource-limited sensor networks is one of the most important issues which limit the development of sensor networks. Aiming at energy efficiency and environment self-adaptation, this thesis studies some fundamental issues of application reconfiguration in sensor networks, such as application reconfiguration model, code transmission paradigm, routing algorithm, transmission protocol, and dynamic reconfiguration decision scheme. The main contributions of this thesis are as follows:(1) Considering environmental correlation of application reconfiguration in sensor networks, we propose an environmentally adaptive application reconfiguration (EAAR) model using knowledge-based reasoning, thus provide flexible and varied applications in the resource-limited sensor networks. Combined with the distributed feature of sensor networks, we design the execution process of application reconfiguration triggered by the sensor node actively.(2) To achieve energy-efficient code transmission in the EAAR model, we analyze and compare the energy consumption relation of code transmission with the PULL and PUSH paradigms, and present a cluster-based hybrid code transmission (CHCT) for the cluster-based sensor networks. In this hybrid paradigm, cluster heads acquire codes from sink with the PULL paradigm, and cluster members transfer codes with the PUSH paradigm.(3) Based on cluster-based hybrid code transmission (CHCT), we propose a hierachical routing scheme in sensor networks. Cluster heads transmit code by a multicast tree, and cluster members adopt the flooding method. Based on this scheme, we propose a minimum diameter multicast tree algorithm (MDMT) to construct the multicast tree for cluster heads. For the different reliability requirements of sensor nodes, we design a hybrid error recovery scheme. The above routing algorithm and error recovery protocol can conserve energy while guaranteeing the code transmission reliability.(4) For decision making of the environment self-adaptation, we model the decision making in the EAAR model using the Markov decision process. We propose a dynamic decision making framework which combines rule-based reasoning with reinforcement learning. Aiming at energy constraint and environmental self-adaptation, we design a novel Q-learning based reconfiguration decision making algorithm (QLRDM) to adjust the state transfer probability of rules, thus the decision making of sensor node can self-adapt to environmental changes.(5) To testify the feasibility and effectiveness of our methods and find out the practical problems, we implement a sensor network prototype based on our EAAR model, code transmission and reconfiguration decision making schemes. We also present a hierachical architecture for this prototype, and select a mobile code middleware. Moreover, we discuss the implementation of reconfiguration decision making and dynamic module loading.