Boundary Area Detection Of Continuous Objects In Wireless Sensor Networks

With the development and wide adoption of wireless sensor networks(WSNs)to support various domain applications,it is drawing more and more attention.Continuous objects boundary detection is one of the major application scenarios in industrial WSNs.This thesis research on detrimental continuous object in industrial WSNs,such as chemical raw material leakage,toxic gas leakage,regional radiation and pollution,etc.Leveraging industrial wireless sensor networks deployed in the geographical region,an accurate boundary detection of continuous objects is an important research challenge for supporting an appropriate reaction-making in a near real-time fashion.Meanwhile,sensor nodes are powered by batteries and replacing and recharging are impractical so that saving energy is also a crucial research problem.To address the proposed challenges,this thesis proposes a two-stage boundary area detection mechanism in industrial WSNs,where sensor nodes are duty-cycled and to be deployed in a dense fashion.The major contributions this thesis brings are summarized as follows:Firstly,this thesis constructs the two-stage sleep scheduling-based detection model.It divides the whole detection procedure into two phases and schedule nodes differently at each phase.Planarization algorithms and spatial interpolation methods adopted in this article are introduced.The energy model used for computing energy consumption is also explained.Thereafter,the occurrence of continuous objects is recognized using the initially activated sensor nodes in the first detection stage.The boundary nodes are identified and the initial boundary area of continuous objects is constructed through adopting planarization algorithms.Then,sensor nodes contained in certainboundary faces are examined in the second detection stage.Certain sensor nodes are selected to be woken up,only when their sensory data suggest that they should be more appropriate candidates of boundary sensor nodes.Consequently,the size of boundary faces is reduced,this coarse-to-fine refinement procedure iterates,until all sensor nodes contained in the boundary faces have been examined.Lastly,the prototype is implemented and experiments are conducted for evaluating the performance and efficiency of our technique.From theoretical analysis and evaluation result,we can see that our technique is feasible and energy efficient.It is shown that the proposed two-stage boundary area detection mechanism further promotes detection accuracy with low energy consumption and the network lifetime is prolonged.