Wireless sensor deployment for collaborative sensing with mobile phones

Ruan, Zheng; Ngai, Edith C.-H.; Liu, Jiangchuan

doi:10.1016/j.comnet.2011.06.017

Cited by 4 publications

(3 citation statements)

References 32 publications

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“…Other concepts that are more closely related to our work were proposed by Reddy et al [11], Ruan et al [12] and Cardone et al [13]. Similar to our work, their approaches choose a subset of mobile devices to which a sensing query is distributed.…”

Section: Related Workmentioning

confidence: 64%

Efficient Distribution of Sensing Queries in Public Sensing Systems

Baier

Dürr

Rothermel

2013

2013 IEEE 10th International Conference on Mobile Ad-Hoc and Sensor Systems

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The advent of mobile phones paved the way for a new paradigm for gathering sensor data termed "Public Sensing" (PS). PS uses built-in sensors of mobile devices to opportunistically gather sensor data. For instance, the microphones that are available in a crowd of mobile phones can be used to capture sound samples, which can be used to construct a city noise map.A great challenge of PS is to reduce the energy consumption of mobile devices since otherwise users might not be willing to participate. One crucial part in the overall power consumption is the energy required for the communication between the mobile devices and the infrastructure. In particular, the communication required for sending sensing queries to mobile devices has been largely neglected in the related work so far.Therefore, in this paper, we address the problem of minimizing communication costs for the distribution of sensing queries. While existing systems simply broadcast sensing queries to all devices, we use a selective strategy by addressing only a subset of devices. In order not to negatively affect the quality of sensing w.r.t. completeness, this subset is carefully chosen based on a probabilistic sensing model that defines the probability of mobile devices to successfully perform a given sensing query.Our evaluations show that with our optimized sensing query distribution, the energy consumption can be reduced by more than 70% without significantly reducing the quality of sensing.

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Section: Related Workmentioning

confidence: 64%

Efficient Distribution of Sensing Queries in Public Sensing Systems

Baier

Dürr

Rothermel

2013

2013 IEEE 10th International Conference on Mobile Ad-Hoc and Sensor Systems

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“…The critical issue is to reduce this prediction error e i . Clearly it can be achieved with a better prediction model; however, individual nodes are limited in their ability of sensory measurements and collaborative schemes usually prove better [16], [17]. Therefore, we need online method to improve error control, which is achieved through the network error control described in next section.…”

Section: Determining the Sensing Probability Boundmentioning

confidence: 97%

Collaborative Scheduling in Dynamic Environments Using Error Inference

Zhang¹,

Gü

et al. 2014

IEEE Trans. Parallel Distrib. Syst.

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Due to the limited power constraint in sensors, dynamic scheduling with data quality management is strongly preferred in the practical deployment of long-term wireless sensor network applications. We could reduce energy consumption by turning off (i.e., duty cycling) sensor, however, at the cost of low-sensing fidelity due to sensing gaps introduced. Typical techniques treat data quality management as an isolated process for individual nodes. And existing techniques have investigated how to collaboratively reduce the sensing gap in space and time domain; however, none of them provides a rigorous approach to confine sensing error is within desirable bound when seeking to optimize the tradeoff between energy consumption and accuracy of predictions. In this paper, we propose and evaluate a scheduling algorithm based on error inference between collaborative sensor pairs, called CIES. Within a node, we use a sensing probability bound to control tolerable sensing error. Within a neighborhood, nodes can trigger additional sensing activities of other nodes when inferred sensing error has aggregately exceeded the tolerance. The main objective of this work is to develop a generic scheduling mechanism for collaborative sensors to achieve the error-bounded scheduling control in monitoring applications. We conducted simulations to investigate system performance using historical soil temperature data in WisconsinMinnesota area. The simulation results demonstrate that the system error is confined within the specified error tolerance bounds and that a maximum of 60 percent of the energy savings can be achieved, when the CIES is compared to several fixed probability sensing schemes such as eSense. And further simulation results show the CIES scheme can achieve an improved performance when comparing the metric of a prediction error with baseline schemes. We further validated the simulation and algorithms by constructing a lab test bench to emulate actual environment monitoring applications. The results show that our approach is effective and efficient in tracking the dramatic temperature shift in dynamic environments.

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“…For example, the sensors are assumed to be deployed randomly in the battlefield environment in order to detect the force distribution of opponent armies. Over the past few years, the deployment problem has been paid considerable research attention .…”

Section: Introductionmentioning

confidence: 99%

Deterministic deployment based on information coverage in wireless sensor networks

Zheng

Huang

Wang

et al. 2012

Wireless Communications

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In this study, a deterministic deployment problem in wireless sensor networks is examined. On the basis of information coverage, we study equilateral triangle and square deployment strategies, and we provide the maximum distance between sensors in order to reach the required detection probability for any point in the monitoring field. First, we provide a model of the signal attenuation. On the basis of the detected signal from the K sensors, the best linear and unbiased estimation is used to estimate the signal parameter with the corresponding error. For the equilateral triangle deployment, the maximum distance between sensors is computed and provided when the received signal data from two or three sensors is used. Similarly, we have computed and supplied the maximum distance between sensors in the square deployment. Simulations are performed to show the relationship between the number of sensors and the detection probability. The simulation results show that it is not a good choice to improve the detection probability with a larger number of sensors.

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Wireless sensor deployment for collaborative sensing with mobile phones

Cited by 4 publications

References 32 publications

Efficient Distribution of Sensing Queries in Public Sensing Systems

Efficient Distribution of Sensing Queries in Public Sensing Systems

Collaborative Scheduling in Dynamic Environments Using Error Inference

Deterministic deployment based on information coverage in wireless sensor networks

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