Mobile Element Scheduling for Efficient Data Collection in Wireless Sensor Networks with Dynamic Deadlines

Somasundara, Arun; Ramamoorthy, Aditya; Srivastava, Mani

doi:10.1109/real.2004.31

Cited by 254 publications

(194 citation statements)

References 9 publications

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“…On the other hand, the problem of tour selection for mobile elements was formulated as the Label-Covering Problem in [9], which was also proved to be NP-hard. An heuristic algorithm using dynamic programming was presented there to solve the problem, which was shown through simulation to be able to achieve better results than [15], [18], [19] and is considered as the best known heuristic algorithm.…”

Section: Related Workmentioning

confidence: 99%

Reducing data collection latency in Wireless sensor networks with mobile elements

Pan

2011

2011 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)

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Abstract-The introduction of mobile elements has created a new dimension to reduce and balance energy consumption in wireless sensor networks, however, data collection latency may become higher. Thus the scheduling of mobile elements, i.e., how they traverse through the sensing field and when they collect data from which sensor, is of ultimate importance and has attracted increasing attention from the research community. Formulated as the Traveling Salesman Problem with Neighborhoods (TSPN) and due to its NP-hardness, so far only approximation and heuristic algorithms have appeared in the literature, but the former only have theoretical value due to their large approximation factors. In this paper, following a progressive optimization approach, we first propose a combine-skip-substitute (css) scheme, which is shown to outperform the best known heuristic algorithm. We then take the advantage and reality of multi-rate wireless communications into account to further reduce the data collection latency with the extended css scheme. Besides the correctness and complexity analysis of the proposed schemes, we also show their performance and potentials through extensive simulation.

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Section: Related Workmentioning

confidence: 99%

Reducing data collection latency in Wireless sensor networks with mobile elements

Pan

2011

2011 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)

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“…Nam [28] builds a timeparameterized sensing task model for real-time tracking. Somasundara [29] proposes a mobile agent scheduling algorithm to collect the buffered sensor data, before the buffer overflow occurs at individual sensor nodes. The Lightning protocol [30] localizes the acoustic source with a bounded delay regardless of the node density.…”

Section: Performance For Hybrid Schemementioning

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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“…Somasundara et al [4], [5] studied the problem of choosing the path of a data mule that traverses at a constant speed through a sensor field with sensors generating data at a given rate. Their formulation requires the data mule to visit the exact location of each sensor to collect data.…”

Section: Related Workmentioning

confidence: 99%

“…For instance, the assumption of no remote communication (as in [4], [5]) is easily expressed by setting the communication range to zero in the path selection subproblem. The constant speed assumption (as in [14], [15]) and variable speed assumption (as in [6], [13]) are handled in the speed control subproblem.…”

Section: Problem Framework For Optimal Control Of Data Mulementioning

confidence: 99%

Optimizing Energy-Latency Trade-Off in Sensor Networks with Controlled Mobility

Sugihara

Gupta

2009

Ieee Infocom 2009

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Abstract-We consider the problem of planning path and speed of a "data mule" in a sensor network. This problem is encountered in various situations, such as modeling the motion of a data-collecting UAV (Unmanned Aerial Vehicle) in a field of sensors for structural health monitoring. Our specific context here is use of a data mule as an alternative or supplement to multihop forwarding in a sensor network. While a data mule can reduce the energy consumption at each sensor node, it increases the latency from the time the data is generated at a node to the time the base station receives it. In this paper, we introduce the "data mule scheduling" or DMS framework that enables data mule motion planning to minimize the data delivery latency. The DMS framework is general; it can express many previously proposed problem formulations and problem settings related to data mules. We design algorithms for DMS and extend to the more general case of combined data mule and multihop forwarding to enable a flexible trade-off between energy consumption and data delivery latency. Using DMS, we can calculate the optimal way for node-to-node forwarding and data mule motion plan. Our implementation and simulation results using ns2 show nearly monotonic decrease of data delivery latency when each node can use more energy, thus vastly increasing the flexibility in the energy-latency trade-off for sensor network communications.

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Mobile Element Scheduling for Efficient Data Collection in Wireless Sensor Networks with Dynamic Deadlines

Cited by 254 publications

References 9 publications

Reducing data collection latency in Wireless sensor networks with mobile elements

Reducing data collection latency in Wireless sensor networks with mobile elements

Spatiotemporal Delay Control for Low-Duty-Cycle Sensor Networks

Optimizing Energy-Latency Trade-Off in Sensor Networks with Controlled Mobility

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