Fast Sensor Placement Algorithms for Fusion-Based Target Detection

Yuan, Zhaohui; Tan, Rui; Xing, Guoliang; Lu, Chenyang; Chen, Yixin; Wang, Jianping

doi:10.1109/rtss.2008.39

Cited by 39 publications

(39 citation statements)

References 18 publications

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“…This scheme suffers from the false alarming, that is the static sensor making a positive detection decision when no event is present [19]. In our designed system, the final system decision is made by the static sensor which detects the event initially as soon as enough local decisions are received to reach a majority consensus.…”

Section: Discussion On the False Alarmingmentioning

confidence: 99%

DREAM: On the reaction delay in large scale wireless networks with mobile sensors

Tang

Yuan

et al. 2010

2010 IEEE 18th International Workshop on Quality of Service (IWQoS)

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Abstract-In this work, we present a monitor and rescue system utilizing hybrid networks which is a integration of stationary sensor networks and mobile sensor networks: stationary sensor networks comprised of large numbers of small, simple, and inexpensive wireless sensors, and the mobile sensor network contains a set of mobile sensors (robots). The static sensors in our network have "monitoring" ability, i.e., any activated static sensor can detect the event as long as its sensing range intersects the event region. And the mobile sensors have "moving" and "rescuing" ability, e.g., they can move toward the event region with limited speed and further perform certain rescuing/processing operations on the event. We can consider the event as a hazard, e.g., wild fire, and the mobile sensors as fireman robots. As soon as the fire is detected by the static sensors, the fireman robots are expected to move from its initial location to the hazard region within minimum latency. We define the reaction delay of the system as the delay from the occurrence of event till at least one mobile sensor reaches the event. In order to satisfy certain reaction delay requirement while minimizing the total cost, we propose a number of deployment strategies for the stationary sensor network and mobile sensor network respectively. We further design a random wake-up scheduling for the static sensors for the sake of energy efficiency. Finally, we propose a pure distributed motion strategy for mobile sensors without reliance on localization services such as GPS, focusing on simple algorithms for distributed decision making and information propagation. We demonstrate the efficacy of our system in simulation, providing empirical results.

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Section: Discussion On the False Alarmingmentioning

confidence: 99%

DREAM: On the reaction delay in large scale wireless networks with mobile sensors

Tang

Yuan

et al. 2010

2010 IEEE 18th International Workshop on Quality of Service (IWQoS)

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“…A sensor placement scheme based on the multivariate Gaussian process model is proposed in [22], which provides most informative results after the data training period. A fast sensor placement approach for fusion-based target detection is proposed in [11], [12] to minimize the number of deployed sensors while achieving assured detection performance. Different from these previous schemes of sensor deployment, the sensor deployment approach we propose leverages on the computational results from CFD which analyzes the thermal condition of a monitored field based on theoretical thermal dynamics.…”

Section: Related Workmentioning

confidence: 99%

“…Theoretically, CSA can reach a global optimal solution by converging asymptotically to a constrained global optimum with a probability of 1. However, a limitation of CSA is that its computational complexity grows exponentially with respect to the number of variables and the solution search space [29], [11]. The execution time of the algorithm can reach up to thousands of days with hundreds of sensors to place [11].…”

Section: Lightweight Sensor Placement (Lsp) Algorithmsmentioning

confidence: 99%

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Intelligent Sensor Placement for Hot Server Detection in Data Centers

Wang

Xing

et al. 2013

IEEE Trans. Parallel Distrib. Syst.

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Abstract-Recent studies have shown that a significant portion of the total energy consumption of many data centers is caused by the inefficient operation of their cooling systems. Without effective thermal monitoring with accurate location information, the cooling systems often use unnecessarily low temperature set points to overcool the entire room, resulting in excessive energy consumption. Sensor network technology has recently been adopted for data-center thermal monitoring because of its nonintrusive nature for the already complex data center facilities and robustness to instantaneous CPU or disk activities. However, existing solutions place sensors in a simplistic way without considering the thermal dynamics in data centers, resulting in unnecessarily degraded hot server detection probability. In this paper, we first formulate the problems of sensor placement for hot server detection in a data center as constrained optimization problems in two different scenarios. We then propose a novel placement scheme based on computational fluid dynamics (CFD) to take various factors, such as cooling systems and server layout, as inputs to analyze the thermal conditions of the data center. Based on the CFD analysis in various server overheating scenarios, we apply data fusion and advanced optimization techniques to find a near-optimal sensor placement solution, such that the probability of detecting hot servers is significantly improved. Our empirical results in a real server room demonstrate the detection performance of our placement solution. Extensive simulation results in a large-scale data center with 32 racks also show that the proposed solution outperforms several commonly used placement solutions in terms of detection probability.

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“…Such a delay may be tolerable for applications which do not require timely data delivery, however, for other applications (e.g., military surveillance and hazardous monitoring), the sensed events must be received within specified time frames to meet application requirements. However, many energy management protocols such as sensing coverage [10], [11] and target tracking [12], [13], although are very effective to minimize energy consumption within the network, they rarely consider the impact of resulting node working schedules on communication delay. In this work, we take a step further and aim at bridging the gap between energy management protocols and effective communication in sensor networks.…”

Section: Bounding Communication Delaymentioning

confidence: 99%

Spatiotemporal Delay Control for Low-Duty-Cycle Sensor Networks

Gü

Lin

et al. 2009

2009 30th IEEE Real-Time Systems Symposium

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Abstract-Data delivery is a major function of sensor network applications. Many applications, such as military surveillance, require the detection of interested events to be reported to a command center within a specified time frame, and therefore impose a real-time bound on communication delay. On the other hand, to conserve energy, one of the most effective approaches is to keep sensor nodes in the dormant state as long as possible while satisfying application requirements. Obviously a node can not communicate if it is not active. Therefore, to deliver data in a timely manner for such extremely low duty-cycle sensor networks, communication needs to be carefully managed among sensor nodes.In this work, we introduce three different approaches to provide real-time guarantee of communication delay. First, we present a method for increasing duty-cycle at individual node. Then we describe a scheme on placement of sink nodes. Based on previous two methods, we discuss a hybrid approach that shows better balance between cost and efficiency on bounding communication delay. Our solution is global optimal in terms of minimizing the energy consumption for bounding pairwise endto-end delay. For many-to-one and many-to-many cases, which are NP-hard, we propose corresponding heuristic algorithms for them. To our knowledge, these are the most generic and encouraging results to date in this new research direction. We evaluate our design with an extensive simulation of 5,000 nodes as well as with a small-scale running test-bed on TinyOS/Mote platform. Results show the effectiveness of our approach and significant improvements over an existing solution.

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Fast Sensor Placement Algorithms for Fusion-Based Target Detection

Cited by 39 publications

References 18 publications

DREAM: On the reaction delay in large scale wireless networks with mobile sensors

DREAM: On the reaction delay in large scale wireless networks with mobile sensors

Intelligent Sensor Placement for Hot Server Detection in Data Centers

Spatiotemporal Delay Control for Low-Duty-Cycle Sensor Networks

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