Key Technologies Of Distributed Data Management In Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are data-centric networks. It is one of their key missions to manage and process sensed data for WSNs with higher efficiency. Compared with traditional wireless networks, nodes in WSN are very energy constrained and often not rechargeable due to its special working environment, which directly affect the lifetime of the whole network. Meanwhile, data management in WSN is very different from that in traditional distributed databases. Therefore, we must design an energy efficient, reliable and simple data management algorithm fitting the features of WSN nodes. However, current complicated WSN data management optimization technologies can not be implemented in large scale WSNs, because sensor nodes are limited in battery energy, storage and computation capacity. As a result, there is a large gap between researches of data management in WSN and real implementations.Aiming at the inherent characteristics of wireless sensor networks and the limitation of current work, this dissertation takes the efficient management of data in wireless sensor networks as the goal and studies some key technologies of distributed data management of wireless sensor network comprehensively. The main contributions of this dissertation can be summarized in the following four aspects.(?) Data CollectionRegarding the problem of collecting all data generated by the network, we propose an energy efficient and energy balancing of data gathering protocol based on adaptive Sink node movement(EEBDG). The protocol leverages a movement strategy based on greedy algorithm and the side reversing technique, so that the Sink node always moves towards areas with large data flow or little remaining energy. We made a breakthrough by solving the problem of too large position update overhead in traditional WSNs that require the Sink node to broadcast its latest position. EEBDG can actually prolong the lifetime of the WSN networks by energy saving and energy balancing. It works best with WSNs which have nodes limited in storage and are sensitive to data collection delay.(?) Top-k QueryRegarding the basic query problem in WSN, i.e., Top-k query, where users only need the first k data generated by the network under some rule, we propose Distributed Data Table Query (DDT-Q) strategy. Based on cross-layer optimization policy, DDT-Q makes use of data distribution table to disseminate queries only to the nodes which may influence the final results and avoids flooding the network with query requests. Thus the query dissemination overhead is reduced. Meanwhile, the data transmission overhead is also reduced, because we avoid transmitting data which will not affect the final results. The communication bandwidth is assigned according to the transmission data volume, which leads to less delay and energy consumption. DDT-Q is simple and easy to implement, and can be deployed in large-scale WSNs.(?) Segmented Linked Data Storage and Query ProtocolAs for the problem of target trajectory query in target tracking oriented WSNs without Sink nodes, we propose Segmented Linked Data Storage and Query (SLDSQ) protocol to support user segmented trajectory query mode. SLDSQ uses a storage strategy combining local storage, data-centric storage and colabrative storage. The tracking data are stored in the nodes generating the tracking data. The relationship among storage nodes is stored as a chain. Segmented indexing storage mode is used to support multi-user queries of targets and trajectories without much delay.(?) Querying Protocol in Multi-Target Tracking ApplicationsTo solve the problem of target and trajectory query in multi-target tracking applications, we propose the querying protocol CRIQ that integrate random query and index query. Based on the temporal and spatial correlation of targets' entrance into the monitored area, piggybacking during exchanges of tracking information is used to diffuse the target information, implement multi-point memory in the WSN without extra overhead and, thus, reduce random query overhead. Meanwhile we appoint distributed indexing nodes, which register the information of targets and their trajectories. The query strategy integrating random query and index query can reduce energy consumption and query delay, as well as to ensure the reliability of query results and avoid query hot spot problems of indexing nodes.