A fuzzy-logic control based probabilistic broadcasting technique for mobile ad hoc networks

Abstract

An ad-hoc network consists of hosts (mobile or static) and each node can directly communicate with its neighbours lying within the nodes transmission range. In a mobile ad hoc network the most common operation is broadcasting. Broadcasting means sending a message from one given node to all the nodes in the network. In MANET, broadcasting results in a network with redundancy, contention, collision which is often called "Broadcast Storm Problem". There are number of broadcasting schemes that have been developed and the main objective of broadcasting scheme is to avoid the broadcast storm problem and provide better reachability with minimum overheads. Existing probabilistic broadcast schemes have focused on pure broadcast scenarios with investigations on only a single node parameter either node density or node location, which results in their limited performance. In this paper, the probability of broadcast is determined by considering node density, node location and node remaining battery power. The proposed probabilistic broadcasting scheme takes into account three node parameters to generate the probability to rebroadcast. As the probability to rebroadcast is calculated based on the multiple node parameter, there is a need of an optimization technique. The Fuzzy Logic optimization technique has the ability to simultaneously consider multiple criteria and to model human experience in the form of simple rules. The developed Fuzzy Based probabilistic broadcasting scheme reduces redundant rebroadcasting by minimizing the number of rebroadcasting nodes. To evaluate the performance of developed algorithm it has been implemented on Qualnet with fuzzy logic embedded on it.

On demand knowledge‐based memory‐aided rebroadcast algorithm for multimedia voice data transmission in MANET

“…The nodes with more number of neighbors are given more priority in the broadcasting method. The RC value of a node is calculated by using Figure Residual Coverage Computation where residual coverage request (RCReq) and a residual coverage reply (RCRep) messages are exchanged between neighboring nodes …”

“…After the calculation of Residual Coverage, the node's locations are determined by using equation . The node location “ N L ” is determined based on the transmitting node given in equation . When the source node broadcasting multimedia voice message to their neighbors is divided into two categories, they are the primary leaf nodes and the secondary leaf nodes.…”

“…The RC value of a node is calculated by using Figure 5 Residual Coverage Computation where residual coverage request (RCReq) and a residual coverage reply (RCRep) messages are exchanged between neighboring nodes. 26,27 Steps involved in RC computation are given below:…”

“…The node location "N L " is determined based on the transmitting node given in equation (1). 26 When the source node broadcasting multimedia voice message to their neighbors is divided into two categories, they are the primary leaf nodes and the secondary leaf nodes. The secondary leaf nodes are the node at the end of the coverage area of the transmitting node.…”

“…Therefore, the expected minimum, average and maximum number of neighbors for a given topology scenario are related as n min < n < n max . 26 Table 2 shows the recommended analyses on the node's behavior for different network conditions…”