Trade-offs between mobility and density for coverage in wireless sensor networks

Srinivasan, Wei Wang Vikram; Chua, Kee-Chaing

doi:10.1145/1287853.1287860

Cited by 75 publications

(24 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several projects proposed to eliminate coverage holes in a sensing field by relocating mobile sensors [17,3,14]. Although such an approach improves the sensing coverage of a network deployment, it does not dynamically improve the network's performance after targets of interest appear.…”

Section: Related Workmentioning

confidence: 99%

“…In practice, the precision of a sensor is determined by the bandwidth of its ADC converter. Detection probabilities of the two phases, P D1 and P D2 , are given by (14) and (15), respectively. E i (j, S) is the energy measured by sensor i during the j th sampling period under movement schedule S. According to definition (17), E i (j, S) is equal to the integral of power over T if sensor i moves from x to x − vT in S. Otherwise, it is equal to the product of T and power measured at x, which is the position of sensor i after the last move that occurs before time instance jT or its initial position x 0 i if it has not moved.…”

Section: E I (J S) Is the Energy Sampled By Sensor I During Intervalmentioning

confidence: 99%

“…For each value of η 1 , P D1 and P F1 can be computed according to (14) and (12). Furthermore, unique P F2 can be determined as the minimum value that satisfies constraint (7).…”

Section: The Solving Proceduresmentioning

confidence: 99%

See 2 more Smart Citations

Mobility-Assisted Spatiotemporal Detection in Wireless Sensor Networks

Xing

Wang

Shen

et al. 2008

2008 the 28th International Conference on Distributed Computing Systems

View full text Add to dashboard Cite

show abstract

Section: Related Workmentioning

confidence: 99%

Section: E I (J S) Is the Energy Sampled By Sensor I During Intervalmentioning

confidence: 99%

See 1 more Smart Citation

Mobility-Assisted Spatiotemporal Detection in Wireless Sensor Networks

Xing

Wang

Shen

et al. 2008

2008 the 28th International Conference on Distributed Computing Systems

View full text Add to dashboard Cite

show abstract

“…Several projects proposed to eliminate coverage holes in a sensing field by relocating mobile sensors [8], [9], [10]. Although such an approach improves the sensing coverage of the initial network deployment, it does not dynamically improve the network's performance after targets of interest appear.…”

Section: Related Workmentioning

confidence: 99%

Collaborative Target Detection in Wireless Sensor Networks with Reactive Mobility

Tan

Xing

Wang

et al. 2008

2008 16th Interntional Workshop on Quality of Service

View full text Add to dashboard Cite

Abstract-Recent years have witnessed the deployments of wireless sensor networks in a class of mission-critical applications such as object detection and tracking. These applications often impose stringent QoS requirements including high detection probability, low false alarm rate and bounded detection delay. Although a dense all-static network may initially meet these QoS requirements, it does not adapt to unpredictable dynamics in network conditions (e.g., coverage holes caused by death of nodes) or physical environments (e.g., changed spatial distribution of events). This paper exploits reactive mobility to improve the target detection performance of wireless sensor networks. In our approach, mobile sensors collaborate with static sensors and move reactively to achieve the required detection performance. Specifically, mobile sensors initially remain stationary and are directed to move toward a possible target only when a detection consensus is reached by a group of sensors. The accuracy of final detection result is then improved as the measurements of mobile sensors have higher signal-to-noise ratios after the movement. We develop a sensor movement scheduling algorithm that achieves near-optimal system detection performance within a given detection delay bound. The effectiveness of our approach is validated by extensive simulations using the real data traces collected by 23 sensor nodes.

show abstract

“…In the literature, Wang et al [18] proposed a relocation algorithm to move the mobile sensor to heal the holes and minimize the energy consumption.…”

Section: Introductionmentioning

confidence: 99%

Adaptive approximation algorithms for hole healing in hybrid wireless sensor networks

Shen

Nguyen

Thai

2013

2013 Proceedings IEEE INFOCOM

View full text Add to dashboard Cite

Region coverage and network connectivity are among the most important problems for the quality of service in wireless sensor networks. Unfortunately, due to the sensor failures and hostile environments, such as active volcanic regions or battle fields, the emergence of coverage holes and disconnections among sensors is unavoidable. One way to handle this problem is to deploy mobile sensors in the network, which is called hybrid sensor networks, so that these mobile sensors can be relocated to heal the holes or maintain the network connectivity. However, because of the low-power of mobile sensors, it is extremely challenging to design a fast and effective movement schedule for mobile sensors to (1) maintain both the region coverage and network connectivity at any time, and (2) minimize the moving energy consumption.In this paper, we develop an adaptive algorithm, AHCH algorithm, to adaptively heal the holes with the guarantee of network connectivity without recomputing from scratch. By comparing AHCH algorithm with the optimal solution at each time-slot, we show its expected adaptive approximation ratio as O(log |M |) with |M | mobile sensors in some special cases. In more general cases, we extend our AHCH algorithm to InAHCH and GenAHCH algorithms, handling insufficient mobile sensors as well as disconnected regions, along with the proof of their corresponding theoretical adaptive approximation ratios. The experimental evaluation shows the effectiveness of our proposed algorithms with respect to both low energy consumption and hole healing latency.

show abstract

Trade-offs between mobility and density for coverage in wireless sensor networks

Cited by 75 publications

References 19 publications

Mobility-Assisted Spatiotemporal Detection in Wireless Sensor Networks

Mobility-Assisted Spatiotemporal Detection in Wireless Sensor Networks

Collaborative Target Detection in Wireless Sensor Networks with Reactive Mobility

Adaptive approximation algorithms for hole healing in hybrid wireless sensor networks

Contact Info

Product

Resources

About