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Mobile agents-based data aggregation in WSNs: benchmarking itinerary planning approaches

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Abstract

Data aggregation represents one of the most challenging and well-studied subjects in the Wireless Sensor Networks (WSN) literature. The energy constraints of sensor nodes call for energy-efficient data aggregation methods so as to prolong network lifetime. Among other approaches, Mobile Agents (MAs) have been proposed to improve the performance of data aggregation in WSNs. In such approaches, the itineraries followed by travelling agents largely determine the overall performance of the data aggregation tasks. Along this line, several heuristics have been proposed to perform efficient itinerary planning for MAs. However, a direct comparison of the proposed algorithms is not straightforward, as they are typically performed on the ground of different parameter instances and assumptions about the underlying network and nodes capabilities. This article provides a critical review and qualitative evaluation of the most prominent itinerary planning algorithms. More importantly, having implemented and simulated a set of eleven (11) itinerary planning algorithms, we compare their performance upon a common parameter space, making realistic network-level assumptions.

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Notes

  1. http://castalia.research.nicta.com.

  2. In highly dense networks though, we can expect that the number of hops required for data transfer among nodes u and v will approximate k = d(u,v)/r, hence, the MA migration cost will be proportional to k.

  3. The impact factor \(G_{ij}\) between two nodes i and j is given by the following equation, with \(H_{j}^{i}\) denoting the estimated hop count between nodes: \(G_{ij} = e^{{ - \frac{{\left( {H_{j}^{i} - 1} \right)^{2} }}{{2\cdot\sigma^{2} }}}}\).

  4. https://github.com/ivenetis/Castalia-3.2-MA.

  5. In the VRP the objective is to design the optimal set of routes for fleet of vehicles in order to serve a given set of customers.

  6. In the MLP, visits to customers are scheduled so as to minimize the average time the customers wait before being visited.

  7. In the OP the objective is to determine a path, limited in length that visits some vertices and maximizes the sum of the collected scores.

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Acknowledgement

This research has been co-financed by the European Union (European Social Fund—ESF) and Greek national funds through the Operational Program “Education and Lifelong Learning” of the National Strategic Reference Framework (NSRF)—Research Funding Program: Archimedes III. Investing in knowledge society through the European Social Fund.

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Authors and Affiliations

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

    Ioannis E. Venetis & Grammati E. Pantziou

  2. Department of Product and Systems Design Engineering, University of the Aegean, Mytilene, Greece

    Damianos Gavalas

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

    Charalampos Konstantopoulos

Authors
  1. Ioannis E. Venetis
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  2. Damianos Gavalas
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  3. Grammati E. Pantziou
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Correspondence to Damianos Gavalas.

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Venetis, I.E., Gavalas, D., Pantziou, G.E. et al. Mobile agents-based data aggregation in WSNs: benchmarking itinerary planning approaches. Wireless Netw 24, 2111–2132 (2018). https://doi.org/10.1007/s11276-017-1460-y

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