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An approach for near-optimal distributed data fusion in wireless sensor networks

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Abstract

In wireless sensor networks (WSNs), a lot of sensory traffic with redundancy is produced due to massive node density and their diverse placement. This causes the decline of scarce network resources such as bandwidth and energy, thus decreasing the lifetime of sensor network. Recently, the mobile agent (MA) paradigm has been proposed as a solution to overcome these problems. The MA approach accounts for performing data processing and making data aggregation decisions at nodes rather than bring data back to a central processor (sink). Using this approach, redundant sensory data is eliminated. In this article, we consider the problem of calculating near-optimal routes for MAs that incrementally fuse the data as they visit the nodes in a WSN. The order of visited nodes (the agent’s itinerary) affects not only the quality but also the overall cost of data fusion. Our proposed heuristic algorithm adapts methods usually applied in network design problems in the specific requirements of sensor networks. It computes an approximate solution to the problem by suggesting an appropriate number of MAs that minimizes the overall data fusion cost and constructs near-optimal itineraries for each of them. The performance gain of our algorithm over alternative approaches both in terms of cost and task completion latency is demonstrated by a quantitative evaluation and also in simulated environments through a Java-based tool.

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Notes

  1. Admittedly, the payload carried by the MA may practically remain constant as it migrates within the sensor field, given that an efficient fusion algorithm is applied (e.g. [29]). However, this scenario referred to as ‘full aggregation’ [24] only applies to a specific class of applications wherein sensory data exhibit high spatial redundancy so that MAs capability for performing progressive fusion can be exploited. In general, partial aggregation is observed for most applications [24]. On the other hand, NOID represents an itinerary scheduling method that generally applies to any data retrieval application, no matter whether spatial redundancy is present or not.

  2. If, for instance, the deployed sensors monitor the atmospheric temperature within the sensor field and the data fusion task involves collecting the mean or maximum measured temperature, then f is very small (since the MA will carry a single temperature value at a time).

  3. Post-order traversal (for each node v, visit the subtrees rooted at v, then visit v) is more efficient than pre-order (for each node v, visit v, then the subtrees rooted at v) or in-order (for each node v, visit a number of subtrees rooted at v, then visit v, then the rest of the subtrees rooted at v) traversal, as it shows to derive better total itinerary cost. Specifically, post-order traversal enables the MA to visit distant sensors first and leave sensors located close to the processing element for the end of the itinerary. Hence, the relatively ‘expensive’ migrations are performed when the MA has not yet collected many data; in the end of their itinerary, when MAs have already accumulated data from the previously visited sensors, they only have to perform short migrations. The cost efficiency of post-order against pre-order or in-order traversals has been experimentally verified through the simulator tool presented at Sect. 6.

  4. In this approach, sensors comprising each sub-tree would be sorted in decreasing order in terms of their remaining energy level. The sensors with the lowest residual energy would be visited first, when the MA does not yet carry large amounts of data. Hence, the energy requirement for transmitting the MA to the next destination sensor would be minimized; sensors with sufficient energy availability would be left for the end of the MA’s itinerary.

  5. NOID extends and adapts Esau-Williams algorithm in the specific requirements of agent itinerary planning problem. To the best of our knowledge, there is no distributed implementation of the Esau-Williams algorithm. Essentially, this algorithm builds a spanning tree whose subtrees have bounded weight. Distributed algorithms for building spanning trees exist in the literature. The simplest algorithm is to build a breadth -first search tree [25] simply by flooding a message from the root of the tree. However, these approaches, similarly to more recent approaches, [21] are associated with high message overhead, hence high energy cost in WSNs.

  6. Sun Microsystems’ SunSPOTs are equipped with an ARM920T 180 MHz processor, 4 MByte Flash memory and lifetime of 7 h to 909 days, depending on the CPU usage. Crossbow’s MICA2 motes are equipped with an ATmega128L 8 MHz CPU, program memory of 128 KB, external storage of 512 KB and 450 days lifetime (when their duty cycle is 1% on average).

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Acknowledgments

The authors of [39] are acknowledged for kindly providing us the source code of their genetic algorithm implementation. We are also grateful to the anonymous reviewers for thoroughly reviewing and helping us to improve the technical content and presentation of our paper.

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Authors and Affiliations

  1. Department of Cultural Technology and Communication, University of the Aegean, Lesvos, Greece

    Damianos Gavalas & Aristides Mpitziopoulos

  2. Department of Informatics, Technological Educational Institution of Athens, Athens, Greece

    Grammati Pantziou

  3. Department of Informatics, University of Piraeus, Piraeus, Greece

    Charalampos Konstantopoulos

Authors
  1. Damianos Gavalas
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  2. Aristides Mpitziopoulos
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  3. Grammati Pantziou
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  4. Charalampos Konstantopoulos
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Correspondence to Damianos Gavalas.

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Gavalas, D., Mpitziopoulos, A., Pantziou, G. et al. An approach for near-optimal distributed data fusion in wireless sensor networks. Wireless Netw 16, 1407–1425 (2010). https://doi.org/10.1007/s11276-009-0211-0

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