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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.

References

  1. Almazaydeh, L., Abdelfattah, E., Al-Bzoor, M., & Al-Rahayfeh, A. (2010). Performance evaluation of routing protocols in wireless sensor networks. International Journal of Computer Science and Information Technology, 2(2), 64–73.

    Article  Google Scholar 

  2. Al-Karaki J. N., & Al-Mashaqbeh G. A. (2007). SENSORIA: A new simulation platform for wireless sensor networks. In Proceedings of the 2007 International Conference on Sensor Technologies and Applications (SensorComm 2007) (pp. 424–429).

  3. Blum, A., Chalasani, P., Coppersmith, D., Pulleyblank, B., Raghavan, P., & Sudan, M. (1994). The minimum latency problem. In Proceedings of the 26th Annual ACM Symposium on Theory of Computing (STOC’94) (pp. 163–171).

  4. Boulis A., Han, C., & Srivastava, M. (2003). Design and implementation of a framework for efficient and programmable sensor networks. In Proceedings of the 1st ACM International Conference on Mobile Systems, Applications and Services (MobiSys’03) (pp. 187–200).

  5. Chen, M., Cai, W., González, S., & Leung, V. C. M. (2010). Balanced itinerary planning for multiple mobile agents in wireless sensor networks. In Proceedings of the Second International Conference in Ad Hoc Networks (ADHOCNETS’2010) (pp. 416–428).

  6. Chen, M., González, S., Zhang, Y., & Leung, V. C. M. (2009). Multi-agent itinerary planning for wireless sensor networks. In Proceedings of the IEEE 2009 International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness (QShine’2009) (pp. 584–597).

  7. Chen, M., Kwon, T., Choi, Y. (2005). Data dissemination based on mobile agent in wireless sensor networks. In Proceedings of the 30th IEEE Conference on Local Computer Networks (LCN’05) (pp. 527–529).

  8. Chen, M., Kwon, T., Yuan, Y., Choi, Y., & Leung, V. C. M. (2007). Mobile agent-based directed diffusion in wireless sensor networks. EURASIP Journal on Applied Signal Processing, 2007(1), 219.

    Google Scholar 

  9. Chen, M., Yang, L. T., Kwon, T., Zhou, L., & Jo, M. (2011). Itinerary planning for energy-efficient agent communications in wireless sensor networks. IEEE Transactions on Vehicular Technology, 60(7), 3290–3299.

    Article  Google Scholar 

  10. Christofides, N., Mingozzi, A., & Toth, P. (1979). The vehicle routing problem. Combinatorial Optimization, 11, 315–338.

    MATH  Google Scholar 

  11. Darabkh, K. A., Ismail, S. S., Al-Shurman, M., Jafar, I. F., Alkhader, E., & Al-Mistarihi, M. F. (2012). Performance evaluation of selective and adaptive heads clustering algorithms over wireless sensor networks. Journal of Network and Computer Applications, 35(6), 2068–2080.

    Article  Google Scholar 

  12. Dave, P. M., & Dalal, P. D. (2013). Simulation & performance evaluation of routing protocols in wireless sensor network. International Journal of Advanced Research in Computer and Communication Engineering, 2(3), 1405–1413.

    Google Scholar 

  13. Del-Valle-Soto, C., Mex-Perera, C., Orozco-Lugo, A., Lara, M., Galván-Tejada, G. M., & Olmedo, O. (2014). On the MAC/Network/Energy performance evaluation of wireless sensor networks: Contrasting MPH, AODV, DSR and ZTR routing protocols. Sensors, 14(12), 22811–22847.

    Article  Google Scholar 

  14. Dong, M., Ota, K., Yang, L. T., Chang, S., Zhu, H., & Zhou, Z. (2014). Mobile agent-based energy-aware and user-centric data collection in wireless sensor networks. Computer Networks, 74, 58–70.

    Article  Google Scholar 

  15. Esau, L., & Williams, K. (1966). On teleprocessing system design, part II: A method for approximating the optimal network. IBM Systems Journal, 5(3), 142–147.

    Article  Google Scholar 

  16. Fok, C., Roman, G., & Lu, C. (2005). Mobile agent middleware for sensor networks: An application case study. In Proceedings of the 4th IEEE International Symposium on Information Processing in Sensor Networks (IPSN’2005) (pp. 382–387).

  17. Gavalas, D. (2001). Mobile software agents for network monitoring and performance management. PhD Thesis, University of Essex, UK.

  18. Gavalas, D., Mpitziopoulos, A., Pantziou, G., & Konstantopoulos, C. (2010). An approach for near-optimal distributed data fusion in wireless sensor networks. Wireless Networks, 16(5), 1407–1425.

    Article  Google Scholar 

  19. Geihs, K. (2001). Middleware challenges ahead. IEEE Computer, 34, 24–31.

    Article  Google Scholar 

  20. Gupta, G. P., Misra, M., & Garg, K. (2014). Energy and trust aware mobile agent migration protocol for data aggregation in wireless sensor networks. Journal of Network and Computer Applications, 41, 300–311.

    Article  Google Scholar 

  21. Hong, S.-H., Park, J.-M., & Gil, J.-M. (2013). Performance evaluation of a simple cluster-based aggregation and routing in wireless sensor networks. International Journal of Distributed Sensor Networks, 2013.

  22. Kabara, J., Calle, M. (2012). MAC protocols used by wireless sensor networks and a general method of performance evaluation. International Journal of Distributed Sensor Networks, 2012.

  23. Konstantopoulos, C., Mpitziopoulos, A., Gavalas, D., & Pantziou, G. (2010). Effective determination of mobile agent itineraries for data fusion tasks on sensor networks. IEEE Transactions on Knowledge and Data Engineering, 22(12), 1679–1693.

    Article  Google Scholar 

  24. Lin, S., & Kernighan, B. W. (1973). An effective heuristic algorithm for the traveling salesman problem. Operations Research, 21(2), 498–516.

    Article  MathSciNet  MATH  Google Scholar 

  25. Mercadal, E., Vidueira, C., Sreenan, C. J., & Borrell, J. (2013). Improving the Dynamism of Mobile Agent Applications in Wireless Sensor Networks through Separate Itineraries. Computer Communications, 36(9), 1011–1023.

    Article  Google Scholar 

  26. Mokdad, L., Ben-Othman, J., Yahya, B., & Niagne, S. (2014). Performance evaluation tools for QoS MAC protocol for wireless sensor networks. Ad Hoc Networks, 12, 86–99.

    Article  Google Scholar 

  27. Mpitziopoulos, A., Gavalas, D., Konstantopoulos, C., & Pantziou, G. (2009). Mobile agent middleware for autonomic data fusion in wireless sensor networks. In M. K. Denko, L. T. Yang, & Y. Zhang (Eds.), Autonomic Computing and Networking, Chapter 3 (pp. 57–81). Berlin: Springer.

    Chapter  Google Scholar 

  28. Paul, T., & Stanley, K. G. (2014). Data collection from wireless sensor networks using a hybrid mobile agent-based approach. In Proceedings of the 2014 IEEE 39th Conference on Local Computer Networks (LCN’2014) (pp. 288–295).

  29. Pham, V., & Karmouch, A. (1998). Mobile software agents: An overview. IEEE Communications Magazine, 36(7), 26–37.

    Article  Google Scholar 

  30. Qi, H., & Wang, F. (2001). Optimal itinerary analysis for mobile agents in ad hoc wireless sensor networks. In Proceedings of the 13th International Conference on Wireless Communications (Wireless’2001) (pp. 147–153).

  31. Qi, H., Iyengar, S. S., & Chakrabarty, K. (2001). Multi-resolution data integration using mobile agents in distributed sensor networks. IEEE Transactions on Systems, Man, and Cybernetics Part C: Applications and Reviews, 31(3), 383–391.

    Article  Google Scholar 

  32. Senouci, M. R., Mellouk, A., Senouci, H., & Aissani, S. (2012). Performance evaluation of network lifetime spatial-temporal distribution for WSN routing protocols. Journal of Network and Computer Applications, 35(4), 1317–1328.

    Article  Google Scholar 

  33. Shim, Y., & Kim, Y. (2014). Data aggregation with multiple sinks in information-centric wireless sensor network. In Proceedings of the 2014 International Conference on Information Networking (ICOIN’2014) (pp. 13–17).

  34. Tong, L., Zhao, Q., & Adireddy, S. (2003). Sensor networks with mobile agents. In Proceedings of the IEEE Military Communications Conference (MILCOM’03) (pp. 688–693).

  35. Tunca, C., Isik, S., Donmez, M. Y., & Ersoy, C. (2014). Distributed mobile sink routing for wireless sensor networks: A survey. IEEE Communications Surveys & Tutorials, 16(2), 877–897.

    Article  Google Scholar 

  36. Vales-Alonso, J., Egea-López, E., Martínez-Sala, A., Pavón-Mariño, P., Victoria, Bueno.-Delgado. M., & García-Haro, J. (2007). Performance evaluation of MAC transmission power control in wireless sensor networks. Computer Networks, 51(6), 1483–1498.

    Article  MATH  Google Scholar 

  37. Vansteenwegen, P., Souffriau, W., & Oudheusden, D. V. (2011). The orienteering problem: A survey. European Journal of Operational Research, 209(1), 1–10.

    Article  MathSciNet  MATH  Google Scholar 

  38. Venetis, I. E., Pantziou, G., Gavalas, D., & Konstantopoulos, C. (2014). Benchmarking mobile agent itinerary planning algorithms for data aggregation on WSNs. In Proceedings of the 6th International Conference on Ubiquitous and Future Networks (ICUFN’2014) (pp. 105–110).

  39. Wang, X., Chen, M., Kwon, T., & Chao, H. C. (2011). Multiple mobile agents’ itinerary planning in wireless sensor networks: survey and evaluation. IET Communications, 5(12), 1769–1776.

    Article  Google Scholar 

  40. Wang, J., Zhang, Y., Cheng, Z., & Zhu, X. (2016). EMIP: Energy-efficient itinerary planning for multiple mobile agents in wireless sensor network. Telecommunication Systems, 62(1), 93–100.

    Article  Google Scholar 

  41. Wu, Q., Rao, N. S., Barhen, J., Iyenger, S. S., Vaishnavi, V. K., Qi, H., et al. (2004). On computing mobile agent routes for data fusion in distributed sensor networks. IEEE Transactions on Knowledge and Data Engineering, 16(6), 740–753.

    Article  Google Scholar 

  42. Zaslavsky, A. (2004). Mobile agents: Can they assist with context awareness?. In Proceedings of the 2013 IEEE 14th International Conference on Mobile Data Management (MDM’13) (pp. 304–304).

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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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  4. Charalampos Konstantopoulos
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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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