EMS: Efficient mobile sink scheduling in wireless sensor networks

Gu, Yu; Ji, Yusheng; Li, Jie; Ren, Fuji; Zhao, Baohua

doi:10.1016/j.adhoc.2012.11.010

Cited by 22 publications

(17 citation statements)

References 14 publications

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“…Similarly, mobile sinks have been suggested to collect data from the sensors and forward the data to the base station as mobile relays [14]. Various algorithms have been proposed to control the movement of the mobile sinks in order to optimise the performance in data collection [15], [16]. Different from the work above, we consider the mobile phones carried by people with independent and uncontrollable mobility.…”

Section: Related Workmentioning

confidence: 99%

An optimisation-based approach for wireless sensor deployment in mobile sensing environments

Bijarbooneh

Flener

Ngai

et al. 2012

2012 IEEE Wireless Communications and Networking Conference (WCNC)

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Abstract-We consider a novel application in wireless sensor networks where mobile phones and wireless sensors can collaborate to collect sensing data. Although mobile phones can perform sensing at different locations, it is a challenge to provide stable sensing quality and availability over the entire area. One approach is to deploy stationary sensors at specific locations to maintain the sensing quality and availability. In this paper, we present a mathematical programming model to minimise the deployment cost by placing a minimum number of sensors at optimal locations. The problem is modelled by integer linear programming considering the sensing capabilities of both the mobile phones and wireless sensors. We evaluated the performance of our solution in terms of sensing quality, number of required sensors, and computation time. The results demonstrate that our approach satisfies the required sensing quality with optimal number of sensors in small sensing fields. It achieves near optimal solution with low computation time for large sensing fields.

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Section: Related Workmentioning

confidence: 99%

An optimisation-based approach for wireless sensor deployment in mobile sensing environments

Bijarbooneh

Flener

Ngai

et al. 2012

2012 IEEE Wireless Communications and Networking Conference (WCNC)

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“…Observing the advantages of MDC over traditional data collection approaches (e.g., via direct communication or multihop forwarding), much effort has been made to explore the MDC with a single MA [7,11,12]. For example, Sugihara and Gupta investigated the MDC with the objective of minimizing MA's travel distance in [9].…”

Section: Related Workmentioning

confidence: 99%

“…Traditional data collection approaches rely on the wireless communications between sensor nodes and the sink, excessively consuming nodes' limited energy supply and leading to their unbalanced energy consumption. Adopting mobile agents (MAs) for data collection, that is, mobility-assisted data collection (MDC), reduces and balances the communication loads of nodes (and thus their energy consumption) [5][6][7]. Also, with MAs' controllable mobility, the communications and networking become possible even in sparse networks via the store-carry-forward approach.…”

Section: Introductionmentioning

confidence: 99%

On‐Demand Mobile Data Collection in Cyber‐Physical Systems

Kong

Tao

et al. 2018

Wireless Communications and Mobile Computing

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The collection of sensory data is crucial for cyber-physical systems. Employing mobile agents (MAs) to collect data from sensors offers a new dimension to reduce and balance their energy consumption but leads to large data collection latency due to MAs' limited velocity. Most existing research effort focuses on the offline mobile data collection (MDC), where the MAs collect data from sensors based on preoptimized tours. However, the efficiency of these offline MDC solutions degrades when the data generation of sensors varies. In this paper, we investigate the on-demand MDC; that is, MAs collect data based on the real-time data collection requests from sensors. Specifically, we construct queuing models to describe the First-Come-First-Serve-based MDC with a single MA and multiple MAs, respectively, laying a theoretical foundation. We also use three examples to show how such analysis guides online MDC in practice.

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“…They suggest the sink to stay at specific sojourn points to collect data periodically with a distributed algorithm. Similarly, Gu et al [9] propose a partition-based algorithm to schedule the movement of mobile elements, which minimises the required moving speed and eliminates buffer overflow. Bisnik et al [10] further study the problem of quality coverage on event detection using mobile sensors.…”

Section: Related Workmentioning

confidence: 99%

Optimising quality of information in data collection for mobile sensor networks

Bijarbooneh

Flener

Ngai

et al. 2013

2013 IEEE/ACM 21st International Symposium on Quality of Service (IWQoS)

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Abstract-Wireless sensor networks have become increasingly popular for environmental and activity monitoring, such as temperature, pollution, parking space, traffic, and crowd monitoring. Mobile users can collect and visualise sensing data by communicating with wireless sensors along their walks using Bluetooth or NFC. They can also share the sensing data on the Internet through 3G or WiFi connectivity. Nevertheless, mobile users may not be able to collect all the data from the sensors due to limited contact times and batteries. It is crucial to collect data with a maximum amount of information from the available resources. In this paper, we tackle the problem by prioritising the sensing data to maximise the data utility considering the quality of information of the sensing data and the communication overhead. We formulate the optimisation problem and propose a greedy algorithm for clustering the sensors and scheduling the data collection. Our greedy algorithm coordinates the mobile users in the sensing field in order to avoid the collection of redundant sensing data. We evaluate the data utility and energy consumption of the proposed algorithm using real mobility traces from the North Carolina state fair. The results demonstrate that our algorithm can significantly improve data utility at low communication overhead compared with an existing algorithm.

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EMS: Efficient mobile sink scheduling in wireless sensor networks

Cited by 22 publications

References 14 publications

An optimisation-based approach for wireless sensor deployment in mobile sensing environments

An optimisation-based approach for wireless sensor deployment in mobile sensing environments

On‐Demand Mobile Data Collection in Cyber‐Physical Systems

Optimising quality of information in data collection for mobile sensor networks

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