Analyzing Object Detection Quality Under Probabilistic Coverage in Sensor Networks

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

doi:10.1007/11499169_10

Cited by 32 publications

(34 citation statements)

References 14 publications

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“…To the best of our knowledge, no one has proposed a median search algorithm for sensor-tags. We believe the research in this paper on RFID sensors can be applied to other sensor network or security problems (Xuan et al, 2002;Ren et al, 2005;Xing et al, 2005;Wang et al, 2008). …”

Section: Related Workmentioning

confidence: 99%

Efficient median estimation for large-scale sensor RFID systems

Mustafa

Zhu

et al. 2012

IJSNET

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Abstract:We consider the median estimation problem in a large-scale sensor augmented RFID system. The large-scale deployment of RFID technology has opened the door to innovative ways to integrate RFID and sensor technology. Sensor-tags are tags that can report over 50 types of physical information to a reader. The traditional way to obtain information from sensor-tags is to query each tag. When the number of tags is large, however, it is prohibitive to query tags individually due to the high delay. In this paper, we present a probabilistic algorithm to estimate the median of a set of sensor-RFID tags without individually querying each tag. The median estimation problem is solved using binary search. Our evaluation demonstrates that the median search algorithm exhibits high accuracy and reasonable time latency. Moreover, we also design an exact algorithm for the continuous median update problem. Our algorithm can incrementally compute the exact median in less time.

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Section: Related Workmentioning

confidence: 99%

Efficient median estimation for large-scale sensor RFID systems

Mustafa

Zhu

et al. 2012

IJSNET

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“…Power management protocols. Related work on power management protocols for sensor networks include [7][8][9][10][11][12][13]. In [9], a sensor is allowed to go to sleep if and only if one of its neighbors can completely cover its sensing area.…”

Section: Related Workmentioning

confidence: 99%

“…Specifically, existing platforms (e.g., nesC/TinyOS [4]) for programming sensor networks use event-driven programming model and, hence, require the designer be responsible for stack management, buffer management, and flow control [5,6]. Therefore, to rapidly prototype and quickly evaluate protocols, the designers of existing power management protocols (e.g., [7][8][9][10][11][12][13]) implement their own simulators or model their protocols in specialized simulators (e.g., GloMoSim [14]). However, it is desirable that the designers prototype their protocols in nesC/TinyOS platform as it provides a framework for generating both simulation as well as production code from the same source.…”

Section: Introductionmentioning

confidence: 99%

A Case Study on Prototyping Power Management Protocols for Sensor Networks

Arumugam

Wang

Kulkarni

2006

Lecture Notes in Computer Science

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Abstract. Power management is an important problem in battery powered sensor networks as the sensors are required to operate for a long time (usually, several weeks to several months). One of the challenges in developing power management protocols for sensor networks is prototyping. Specifically, existing programming platforms for sensor networks (e.g., nesC/TinyOS) use an event-driven programming model and, hence, require the designers to be responsible for stack management, buffer management, flow control, etc. Therefore, the designers simplify prototyping their solutions either by implementing their own discrete event simulators or by modeling them in specialized simulators. To enable the designers to prototype power management protocols in target platform (e.g., nesC/TinyOS), in this paper, we use ProSe, a programming tool for sensor networks. ProSe enables the designers to specify their programs in simple abstract models while hiding low-level challenges of sensor networks and programming-level challenges. As a case study, in this paper, we specify a power management protocol with ProSe, automatically generate the corresponding nesC/TinyOS code, and evaluate its performance. Based on the performance results, we expect that ProSe enables the designers to rapidly prototype, quickly deploy, and easily evaluate their protocols.

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“…In our previous protocol design of the random and the synchronized schedules in [12], we focused on how to meet the requirements of detection probability, average stealth distance and energy consumption. However, worst-case object detection quality metrics, such as sufficient phase and worst-case stealth distance, are not bounded in these protocols.…”

Section: Object Detection and Tracking Problem Formulationmentioning

confidence: 99%

“…However, full sensing coverage requires that a large portion of the sensors remain awake continuously, resulting in energy inefficiency and short system lifetime. As an alternative approach, probabilistic sensing coverage has been recently proposed in [5,12]. Instead of staying awake all the time, nodes can periodically rotate between active state and sleeping state to conserve energy while meeting the object detection quality requirement.…”

Section: Introductionmentioning

confidence: 99%

Design and Analysis of Wave Sensing Scheduling Protocols for Object-Tracking Applications

Ren

Wang

et al. 2005

Distributed Computing in Sensor Systems

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Abstract. Many sensor network applications demand tightly-bounded object detection quality. To meet such stringent requirements, we develop three sensing scheduling protocols to guarantee worst-case detection quality in a sensor network while reducing sensing power consumption. Our protocols emulate a line sweeping through all points in the sensing field periodically. Nodes wake up when the sweeping line comes close, and then go to sleep when the line moves forward. In this way, any object can be detected within a certain period. We prove the correctness of the protocols and evaluate their performances by theoretical analyses and simulation.

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Analyzing Object Detection Quality Under Probabilistic Coverage in Sensor Networks

Cited by 32 publications

References 14 publications

Efficient median estimation for large-scale sensor RFID systems

Efficient median estimation for large-scale sensor RFID systems

A Case Study on Prototyping Power Management Protocols for Sensor Networks

Design and Analysis of Wave Sensing Scheduling Protocols for Object-Tracking Applications

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