, pp.70-74 http://dx.doi.org/10.14257/astl.2018.149.15 Bee Inspired and Fuzzy Optimized AODV Routing Protocol B. Jahnavi, G. Virajita, M. Rajeshwari and N. Ch. S. N. Iyengar Department of Information Technology, Sreenidhi Institute of Science and Technology, Hyderabad 501 301, India srimannarayanach@sreenidhi.edu.in Abstract. Troubleshooting a chain of networks is getting to be harder, yet organizations and authorities depend up on basic instruments. In computer networks, the proposed framework is used for automatic dynamic analysis. While transferring data, if there is any physical or software problems with the path, it leads to loss of data. With the help of the proposed system, there will be no loss of data and can minimize the cost of transferring. A BEE-Swarm Intelligence algorithm is used for computing a small number of test packets which is used to test all the packet processing rules and also "Fuzzy and Bee- MANET algorithms" are useful for pointing out the faulty rules and perform the end-to-end testing in the network path. Keywords: BEE-MANET, AODV, Fuzzy logic, Swarm Intelligence. 1 Introduction Wireless network is a type of network in which systems can communicate with one another without any connection between them. Swarm Intelligence is an Artificial Intelligence technique, which is based on collective behavior of multiple agents. These actions are similar to the actions of creatures in nature like bee and ants. BEE- MANET is one such existing SI based routing protocol that combines the interactive behavior of group of bees searching for food and common directions of a computer network. This protocol is dynamic, scalable, reduces the number of hops between source and destination nodes and reducing the number of control packets. The route taken can be selected more efficiently ny taking considerations into various QoS parameters. Fuzzy logic can be used to implement these parameters while searching for a route and control the outcome to improve the QoS. 2 Routing Protocols There are various routing protocols that have been proposed for wireless Ad-hoc networks. They are on demand routing protocols and protocols that require routing table at each node or table-driven routing protocol. In On demand routing protocol a ISSN: 2287-1233 ASTL Copyright 2018 SERSC
route is established between the source and destination and this route is alive till the data is completely transferred between the systems. In table-driven routing protocol, routing tables are placed at each node and the new routes getting updated in the routing table present at each node as the nodes move dynamically. The routing algorithms that are based on the behavior of insects as follows: 2.1 Bee Ad hoc Routing Protocol Bee Ad hoc is a multi path routing algorithm for MANET which works on the principle that is used by the bees. This implements the scout bees for route discovery and forager bees for packet delivery along with the waggle dance of the bees to discover the most optimal path. This algorithm employs a simple architecture of packing floor, dance floor, entrance as shown in figure below. The entrance acts as an interface to the lower MAC layer and packing floor is an entrance to the upper transport layer. For a route that is inactive scout bees works on the principle of broadcast to discover the route to the destination. The Bee Ad hoc protocol was developed by employing a mechanism to deliver data through different routes rather one route. Fig. 1. Architecture of the Bee Ad Hoc Protocol 2.2 Bee MANET routing protocol The Bee MANET protocol was proposed as an improvement to the Bee Ad hoc routing protocol with the aim of reducing the number of control packets in the network. This protocol uses forward and backward agents for route discovery and achieved it using an accumulator. While the routing process is similar to that of the Bee Ad hoc network where the major difference is that broadcasting the accumulator instead of individual scout agents during the route discovery process. The accumulator consists of buffer for scout agents that are arriving from the neighboring nodes and this is created at every node in the network. Copyright 2018 SERSC 71
3 The Proposed Fuzzy Model The fuzzy model was selected because of his variant advantages like flexibility, tolerance to imprecise data and ability to be built on the top of the experience of experts over the system. 3.1 Fuzzy Inputs In order to decide which route to be selected following parameters which are collected by the scout bee while traversing back to the destination are considered Latency: It is the amount of time taken by the scout bee to traverse back to the destination. The time begins when it is broadcasted from the source. Fig. 2. Membership Graphic for the Latency Fuzzy Variable with Three Fuzzy Numbers Low, Medium and High Hop Count: It is the number of inermediate nodes between the source and destination. The parameters of this are very high, high, medium, low, very low. Fig. 3. Membership Graphic for the Hop Count Variable with Five Fuzzy Numbers Very Low, Low, Medium, High, and Very High 72 Copyright 2018 SERSC
Node Energy: It is the amount of power level of node. The greater the node enrgy, the greater the route getting selected. 3.2 Fuzzy Inference Based on these input parameters, Fuzzy logic rules are formulated to decide a route is to be selected or not. These rules depends upon the priority given for the application that the routing protocol is put into use.the rules are expressed in Mamdani form, where latency, stability and status of the route are considered as linguistic variables that is equal to four input variables and one output varaiable. De-Fuzzification method used is centroid method. MATLAB FIS editor is to select hop count, node energy and stability and Mamdani Inference system to get route selected as our output. Fig. 4. Architecture of the Fuzzy Optimized Bee Inspired Routing Protocol 4 Future Scope We have proposed a routing protocol based on fuzzy roles, Bee swarm intelligence and AODV to recognize the number of agents. AODV is more effective and responsive as quick adaptation to conclude number of mobile agents. The fuzzy logic and Bee inspired protocol helps in reducing the uncertainty in the networks, to identify the faulty locations and improved QoS through reduced delay, increased throughput and better reliability. Choosing an efficient path reduced the hop counts and establishes a reliable and strong connection between the source and destination and produces the effective communication path. Future work may include the implementation of the proposed scheme using a suitable tools for simulating network and applying fuzzy logic. This also concludes that AODV is more powerful and delay would also be minimized as manually configured work to evaluate mobile agents would be reduced. Copyright 2018 SERSC 73
References 1. Elizabeth M. Royer, A Review of Current Routing Protocols for Ad Hoc Mobile Wireless Networks, IEEE Personal Communications. 2. Shivanajay Marwaha, Chen Khong Tham, Dipti Srinivasan, A novel routing protocol using mobile agents and reactive route discovery for ad hoc wireless networks. 3. D. Chaudhary, Bee inspired routing protocols for Mobile Ad hoc network, Journal of Emerging Technologies in Web Intelligence, vol. 2, no. 2(2016). A. A. Bhaskaran, S. Ramesh, R. D. Caytiles, N. Ch. S. Iyengar, Fuzzy Optimized Bee Inspired Routing Protocol for Improved Qos in Mobile Ad hoc Networks, Advanced Science and Technology Letters, vol. 135, CES-CUBE, (2016). 74 Copyright 2018 SERSC