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It is of significant importance to provide network-wide Quality of Service (QoS) for a wide range of event detection applications in wireless sensor networks. This paper investigates the important problem of QoS provision to abnormal event detection. For event detection applications, there are two key performance metrics, i.e., detection probability and detection latency. This paper considers both metrics, aiming to provide statistical QoS for abnormal event detection. It is, however, a challenging issue because of stringent resource constraints of sensor nodes and unpredictable randomness of physical events. We propose a probabilistic approach to statistical QoS provision for event detection in sensor networks. We propose a distributed algorithm that iteratively determines the active probability of each sensor node. The probability is kept small for energy efficiency but sufficiently large to provide the required detection QoS. Our approach is flexible and can detection QoS customized by applications. Comprehensive simulation experiments have been conducted, which demonstrate that our approach is able to provide the required detection QoS for event detection and achieves considerably longer the system lifetime compared with other competing schemes. [ABSTRACT FROM AUTHOR]

Duty cycling is a fundamental approach to conserving energy in sensor networks; however, it brings challenges to event detection in the sense that an event may be undetected or undergo a certain delay before it is detected, in particular when sensors are low duty-cycled. We investigate the fundamental relationship between event detection and energy efficiency. We quantify event detection performance by deriving the closed forms of detection delay and detectability with a relatively simple model. We also characterize the intrinsic tradeoff that exists between detection performance and system lifetime, which helps flexible design decisions for sensor networks. In addition, we propose a fully localized algorithm called CAS to cooperatively determine sensor wakeups. Without relying on location information, the distributed algorithm is easy to implement and scalable to network density and scale. Theoretical bounds of event detection are also studied to facilitate comparative study. Comprehensive experiments are conducted and results demonstrate that the proposed algorithm significantly improves event detection performance in terms of detection latency and detection probability. It reduces as high as 31% of detection delay and increases as much as 25% of detectability compared with the random independent scheme. [ABSTRACT FROM AUTHOR]