A fuzzy-based routing protocol for metropolitan-area mobile adhoc networks

In metropolitan-area mobile adhoc networks (M-MANET), such as in vehicular networks, nodes are not connected continuously. Some nodes are power limited, such as smartphones and laptops, and some nodes have high mobility. This harsh environment imposes networking challenges among the nodes. Routing is one of the critical networking problems that needs to be resolved. Using inexact and only locally measurable parameters, we need to find good packet routes that maximize their delivery probability and minimize the delivery costs. This motivates us to use fuzzy-logic as an inference system to solve the routing problem. In this paper, we propose a fuzzy-based routing protocol for M-MANET (FRPM). The protocol works in a hop-by-hop manner, which uses some locally measurable parameters to produce a decision either to select the contacting node as the next hop or not. We conducted simulation experiments using life scenarios of vehicles and pedestrians roaming in a downtown area of a city. We compared the proposed protocol, FRPM, with two practical protocols and a theoretical optimal one. Results show that FRPM, in addition to its ease of use, update and maintain, achieves better delivery ratio with less delivery cost and less energy consumption than the practical protocols.

[1]  Anders Lindgren,et al.  Probabilistic routing in intermittently connected networks , 2003, MOCO.

[2]  Zhensheng Zhang,et al.  Routing in intermittently connected mobile ad hoc networks and delay tolerant networks: overview and challenges , 2006, IEEE Communications Surveys & Tutorials.

[3]  Amiya Nayak,et al.  Choosing the objective of optimal routing protocols in Delay Tolerant networks , 2010, 2010 International Computer Engineering Conference (ICENCO).

[4]  T. Spyropoulos,et al.  Efficient Routing in Intermittently Connected Mobile Networks: The Multiple-Copy Case , 2008, IEEE/ACM Transactions on Networking.

[5]  Brian Gallagher,et al.  MaxProp: Routing for Vehicle-Based Disruption-Tolerant Networks , 2006, Proceedings IEEE INFOCOM 2006. 25TH IEEE International Conference on Computer Communications.

[6]  Kaoru Hirota,et al.  Fuzzy Inference Based Vehicle to Vehicle Network Connectivity Model to Support Optimization Routing Protocol for Vehicular Ad-Hoc Network (VANET) , 2014, J. Adv. Comput. Intell. Intell. Informatics.

[7]  Chih-Heng Ke,et al.  Fuzzy-assisted social-based routing for urban vehicular environments , 2011, EURASIP J. Wirel. Commun. Netw..

[8]  G. Alandjani,et al.  Fuzzy routing in ad hoc networks , 2003, Conference Proceedings of the 2003 IEEE International Performance, Computing, and Communications Conference, 2003..

[9]  Haifeng Jiang,et al.  Fuzzy-Logic-Based Energy Optimized Routing for Wireless Sensor Networks , 2013, Int. J. Distributed Sens. Networks.

[10]  K. Psounis,et al.  Efficient Routing in Intermittently Connected Mobile Networks: The Single-Copy Case , 2008, IEEE/ACM Transactions on Networking.

[11]  Sagar Naik,et al.  SGBR: A Routing Protocol for Delay Tolerant Networks Using Social Grouping , 2013, IEEE Transactions on Parallel and Distributed Systems.

[12]  Joel J. P. C. Rodrigues,et al.  Enhanced fuzzy logic‐based spray and wait routing protocol for delay tolerant networks , 2016, Int. J. Commun. Syst..

[13]  Amin Vahdat,et al.  Epidemic Routing for Partially-Connected Ad Hoc Networks , 2009 .

[14]  Yi Pan,et al.  An adaptive genetic fuzzy multi-path routing protocol for wireless ad-hoc networks , 2005, Sixth International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing and First ACIS International Workshop on Self-Assembling Wireless Network.

[15]  Chun-Hua Chen,et al.  A fuzzy logic-based routing for Delay-Tolerant heterogeneous Networks , 2009, 2009 IEEE International Conference on Granular Computing.