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Sensor networks face a number of challenges when deployed in unpredictable environments under dynamic, quickly changeable demands, and when shared by many partners, which is often the case in military and security applications. To partially address these challenges, we present a novel target tracking algorithm that can be deployed on various sensor nodes and invoked dynamically when needed by the presence of targets. We also demonstrate that an auction-based mechanism can be used to provide efficient and localized wireless sensor network congestion management for bursty traffic of abstract services based just on user-assigned priorities to different services and the quality of information provided by the services. We present results from using this auction mechanism to resolve congestion caused by packets from competing target tracking missions. [ABSTRACT FROM PUBLISHER]

In many applications of sensor networks, the sink needs to keep track of the history of sensed data of a monitored region for scientific analysis or supporting historical queries. We call these historical data a time series of value distributions or snapshots. Obviously, to build the time series snapshots by requiring all of the sensors to transmit their data to the sink periodically is not energy efficient. In this paper, we introduce the idea of gradient boundary and propose the Gradient Boundary Detection (GBD) algorithm to construct these time series snapshots of a monitored region. In GBD, a monitored region is partitioned into a set of subregions and all sensed data in one subregion are within a predefined value range, namely, the gradient interval. Sensors located on the boundaries of the subregions are required to transmit the data to the sink and, then, the sink recovers all subregions to construct snapshots of the monitored area. In this process, only the boundary sensors transmit their data and, therefore, energy consumption is greatly reduced. The simulation results show that GBD is able to build snapshots with a comparable accuracy and has up to 40 percent energy savings compared with the existing approaches for large gradient intervals. [ABSTRACT FROM AUTHOR]

Event detection is a crucial task for wireless sensor network applications, especially environment monitoring. Existing approaches for event detection are mainly based on some predefined threshold values and, thus, are often inaccurate and incapable of capturing complex events. For example, in coal mine monitoring scenarios, gas leakage or water osmosis can hardly be described by the overrun of specified attribute thresholds but some complex pattern in the full-scale view of the environmental data. To address this issue, we propose a nonthreshold-based approach for the real 3D sensor monitoring environment. We employ energy-efficient methods to collect a time series of data maps from the sensor network and detect complex events through matching the gathered data to spatiotemporal data patterns. Finally, we conduct trace-driven simulations to prove the efficacy and efficiency of this approach on detecting events of complex phenomena from real-life records. [ABSTRACT FROM AUTHOR]

A wireless sensor network (WSNET) can support various types of queries. The energy resource of sensors constrains the total number of query responses, called query capacity, received by the sink. There are four problems in the existing approaches for energy-efficient query processing in WSNETs: 1. the fact that sensors near the sink drain their energy much faster than distant sensors has been overlooked, 2. routing trees (RT) are rooted at the sink, and therefore, aggregative queries are less energy-efficient, 3. data reception cost has been ignored, and 4. flooding is used in query distribution or RT construction. In this paper, we propose a Broadcasting-Based query Scheme (BBS) to address the above problems. BBS reduces the energy depletion rate of sensors near the sink, builds different localized RTs for different query types, and eliminates the flooding cost of query distribution. Compared to the existing approaches, simulation studies show that BBS produces significant improvement in the query capacity for non-holistic queries (10%-100% capacity improvement) and holistic queries (up to an order of magnitude of capacity improvement). [ABSTRACT FROM AUTHOR]