Design and Analysis of Sensing Scheduling Algorithms under Partial Coverage for Object Detection in Sensor Networks

Ren, Shansi; Li, Qun; Wang, Haining; Xhen, Xin; Zhang, Xiaodong

doi:10.1109/tpds.2007.41

Cited by 71 publications

(25 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the effectiveness of these schemes largely relies on the assumption that sensors have circular sensing regions and deterministic sensing capability. Several recent studies [2,17,32,40] on the coverage problem have adopted probabilistic sensing models. The numerical results in [40] show that the coverage of a network can be expanded by the cooperation of sensors through data fusion.…”

Section: Related Workmentioning

confidence: 99%

Data fusion improves the coverage of wireless sensor networks

Xing

Tan

Liu

et al. 2009

Proceedings of the 15th Annual International Conference on Mobile Computing and Networking

124

View full text Add to dashboard Cite

Wireless sensor networks (WSNs) have been increasingly available for critical applications such as security surveillance and environmental monitoring. An important performance measure of such applications is sensing coverage that characterizes how well a sensing field is monitored by a network. Although advanced collaborative signal processing algorithms have been adopted by many existing WSNs, most previous analytical studies on sensing coverage are conducted based on overly simplistic sensing models (e.g., the disc model) that do not capture the stochastic nature of sensing. In this paper, we attempt to bridge this gap by exploring the fundamental limits of coverage based on stochastic data fusion models that fuse noisy measurements of multiple sensors. We derive the scaling laws between coverage, network density, and signal-to-noise ratio (SNR). We show that data fusion can significantly improve sensing coverage by exploiting the collaboration among sensors. In particular, for signal path loss exponent of k (typically between 2.0 and 5.0),, where ρ f and ρ d are the densities of uniformly deployed sensors that achieve full coverage under the fusion and disc models, respectively. Our results help understand the limitations of the previous analytical results based on the disc model and provide key insights into the design of WSNs that adopt data fusion algorithms. Our analyses are verified through extensive simulations based on both synthetic data sets and data traces collected in a real deployment for vehicle detection.

show abstract

Section: Related Workmentioning

confidence: 99%

Data fusion improves the coverage of wireless sensor networks

Xing

Tan

Liu

et al. 2009

Proceedings of the 15th Annual International Conference on Mobile Computing and Networking

124

View full text Add to dashboard Cite

show abstract

“…There are other application-driven scheduling algorithms, e.g., for minimum latency routing [29], [30], [31], target tracking [32], [33], [34], [35], event detection [36], and throughput optimization [37]. All these works support only a single mission and do not treat network lifetime as the objective or constraint.…”

Section: Related Workmentioning

confidence: 99%

Spatial-Temporal Coverage Optimization in Wireless Sensor Networks

Liu

2011

IEEE Trans. on Mobile Comput.

View full text Add to dashboard Cite

Abstract-Mission-driven sensor networks usually have special lifetime requirements. However, the density of the sensors may not be large enough to satisfy the coverage requirement while meeting the lifetime constraint at the same time. Sometimes, coverage has to be traded for network lifetime. In this paper, we study how to schedule sensors to maximize their coverage during a specified network lifetime. Unlike sensor deployment, where the goal is to maximize the spatial coverage, our objective is to maximize the spatialtemporal coverage by scheduling sensors' activity after they have been deployed. Since the optimization problem is NP-hard, we first present a centralized heuristic whose approximation factor is proved to be 1 2 , and then, propose a distributed parallel optimization protocol (POP). In POP, nodes optimize their schedules on their own but converge to local optimality without conflict with one another. Theoretical and simulation results show that POP substantially outperforms other schemes in terms of network lifetime, coverage redundancy, convergence time, and event detection probability.

show abstract

“…Background. The literature dealing with sleep-wake protocols is large, both for synchronous systems [9,16,21,3,12,14,15,11,19] and asynchronous [18,20,17,4,13,7] systems. These protocols can be classified by general objectives and by the constraints under which they must operate.…”

Section: The Greenberg-hastings Automatonmentioning

confidence: 99%

“…Some protocols [21,18,20] implement wake-sensor density control, while others [15] implement "sweep" protocols in much the same spirit as the technique proposed here but much more demanding of resources, i.e., much less of a minimalist protocol for maximizing system lifetime. Wake-sensor wave-propagation provides a flexible and systematic trade-off between energy consumption and time-todetection or entrapment.…”

Section: The Greenberg-hastings Automatonmentioning

confidence: 99%