Using Mobile Agents (MAs) every conventional distributed system work can be performed efficiently, robustly and easily within a single and general framework. Despite many benefits, Mobile Agents have a number of issues like fault tolerance, security , routing etc. Among these issues this paper emphasizes on routing of MAs. This paper defines types of itineraries based on their knowledge and based on number of Mobile Agents used to perform optimum itinerary. It describes disadvantages of single mobile agent itinerary planning(SIPs) and different challenges faced by multi mobile Agent itinerary planning(MIPs). The objective of this paper is to bring out a comparative analysis of the existing Itinerary planning algorithms.

Itinerary planning, Mobile Agent, Mobile Agent routing

[1]. M. Mitchell, “An Introduction to Genetic Algorithms”, MIT Press, 1998.
[2]. Qi, H., Wang, F.: Optimal Itinerary Analysis for Mobile Agents in Ad Hoc Wireless Sensor Networks, Proceedings of the13th International Conference on Wireless Communications (Wireless’2001), pp. 147-153, (2001).
[3]. Wu,Q.,Rao,N.,Barhen,J.,Iyengar,S.,Vaishnavi,V.,Qi,H.,C hakrabarty,K.:OnComputing Mobile Agent Routes for Data Fusion in Distributed Sensor Networks. IEEE Transactions on Knowledge and Data Engineering, 16(6), pp. 740-753, June 2004.
[4]. Bean, N., & Costa, A. (2005). An analytic modelling approach for network routing algorithms that use “ant-like” mobile agents. Computer Networks, 49(2), 243-268.
[5]. Chen, M., Kwon, T., Yuan, Y., Leung, V.C.: ‘Mobile agent based wireless sensor networks’, J. Comput., 2006, 1, (1), pp. 14 – 21
[6]. Gavalas,D.,Pantziou,G.,Konstantopoulos,C.,Mamalis,B.: NewTechniquesforIncremen- tal Data Fusion in Distributed Sensor Networks, In Proceedings of the 11th Panhellenic Con- ference on Informatics (PCI’2007), pp. 599-608, (2007).
[7]. Chen, M., Kwon, T., Yuan, Y., Choi, Y., Leug, V.C.M.: Mobile Agent-Based Directed Diffusion in Wireless Sensor Networks, EURASIP Journal on Advances in Signal Processing Volume 2007, Hindawi Publishing Corporation (2007).
[8]. Chen, M., Gonzlez, S., Zhang, Y., Leung, V.C.: ‘Multi-agent itinerary planning for sensor networks’. Proc. IEEE 2009 Int. Conf. Heterogeneous Networking for Quality, Reliability, Security and Robustness (QShine 2009), Las Palmas de Gran Canaria, Spain, 2009
[9]. Chen, M., Leung, V., Mao, S., Kwon, T., Li, M.: ‘Energy-efficient itinerary planning for mobile agents in wireless sensor networks’. Proc. IEEE 2009 Int. Conf. Communications (ICC 2009), Bresden, Germany, 2009
[10]. Gavalas, D., Mpitziopoulos, A., Pantziou, G., Konstantopoulos, C.: ‘An approach for near-optimal distributed data fusion in wireless sensor networks’, Springer Wirel. Netw., 2009, 16, (5), pp. 1407–1425
[11]. Mpitziopoulos, A., Gavalas, D., Konstantopoulos, C., Pantziou, G.: ‘CBID: a scalable method for distributed data aggregation in WSNs’, Hindawi Int. J. Distrib. Sens. Netw., 2010, 2010, Article ID 206517, DOI: 10.1155/2010/206517
[12]. Chen, M., Cai, W., Gonzalez, S., Leung, V.C.: ‘Balanced itinerary planning for multiple mobile agents in wireless sensor networks’. Proc. Second Int. Conf. Ad Hoc Networks (ADHOCNETS2010), Victoria, BC, Canada, 2010
[13]. Konstantopoulos, C., Mpitziopoulos, A., Gavalas, D., Pantziou, G.: ‘Effective determination of mobile agent itineraries for data aggregation on sensor networks’, IEEE Trans. Knowl. Data Eng., 2010, 22, (12), pp. 1679– 1693
[14]. Jia, Y.X., Mickelson, J., Simpson, T., Ye, Q.: ‘Routing with localized clusters for wireless sensor networks’, J. Internet Technol., 2010, 11, (4), pp. 483 – 490
[15]. Cai, W., Chen, M., Hara, T., Shu, L.: ‘GA-MIP: genetic algorithm based multiple mobile agents itinerary planning in wireless sensor network’. Proc. Fifth Int. Wireless Internet Conf. (WICON), Singapore, 2010 

[16]. Chen, M., Gonzalez, S., Leung, V.: ‘Directional source grouping for multi-agent itinerary planning in wireless sensor networks’. Proc. Int. Conf. ICT Convergence (ICTC), Jeju Isaland, Korea, 2010.
[17]. Cai, W., Chen, M., Hara, T., Shu, L., & Kwon, T. (2011). A genetic algorithm approach to multi-agent itinerary planning in wireless sensor networks. Mobile Networks and Applications, 16(6), 782-793.
[18]. K. Lingaraj, Rajashree. V. Biradar, V. C. Patil. "A survey on mobile-agent middleware design principals and itinerary planning algorithms", 2015 International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT), 2015.
[19]. Prapulla, S. B., Chandra, J., Mudakavi, M. B., Shobha, G., & Thanuja, T. C. (2016, October). Multi Mobile Agent itinerary planning using Farthest Node First Nearest Node Next (FNFNNN) technique. In Computation System and Information Technology for Sustainable Solutions (CSITSS), International Conference on (pp. 105-111). IEEE