Routing in Wireless Mesh Networks based on Termites' Intelligence

Routing in Wireless Mesh Networks based on Termites' Intelligence

Sharad Sharma (Department of Electronics & Communications Engineering, Maharishi Markandeshwar University, Mullana, Ambala, Haryana, India) and Asha Malik (Department of Electronics & Communications Engineering, Panipat Institute of Engineering and Technology, Panipat, Haryana, India)
Copyright: © 2017 |Pages: 21
DOI: 10.4018/IJAMC.2017040101
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Abstract

Wireless Mesh Network (WMN) is envisaged to be key component of next generation wireless networks which can effectively cope with the ever increasing fast and vast growing need to access data and avail services over the network. These networks provide infrastructure less high speed internet access to the end users and have a cutting edge over the existing networks. Routing, being most critical issue in their implementation. The dynamic network conditions impose setbacks in the selection of optimal path. There is exigent requirement to tackle these routing issues in context of these networks. In this paper, the authors apply a nature inspired soft computing based meta heuristic technique called Termite Colony Optimization in WMN to find an optimal route based on the link cost. TCO approach is inspired by the emergent behaviour exhibited by the natural termite colony swarms for mound building. Experimentally TCO shows faster converges over some existing algorithms.
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I. Introduction

WMN is multi hop wireless network being a superset of the existing wireless network topologies. It incorporates the features of dynamically self-forming and organising, rapidly deployable, self-configurable, balancing healing and self-aware network. The nodes (static and dynamic) are the main structural and functional units of these networks. They can individually sense similar capability nodes within their radio range and establish connection among themselves to form networks. The nodes are comprised of mesh routers and mesh clients. A node forwards packet between any two nodes within vicinity of its radio range and serves the purpose of both host as well as routers.

WMN has a hybrid architecture providing advantage of low upfront cost, robustness, ease of maintenance with reliable service coverage and connectivity anytime anywhere with compatible devices. Based upon the functionality of the nodes primarily WMNs can be classified as: (1) Infrastructure mesh, (2) Client Mesh, and (3) Hybrid mesh. Infrastructure mesh forms infrastructure mesh for the client nodes with mesh routers having gateway functionality. Client WMNs provide peer to peer networks and the client nodes perform dual functions of self-configuring and routing. Hybrid WMN is a combination of both infrastructure and client meshing. The infrastructure meshing provides connectivity to other networks while mesh clients can directly mesh with other clients simultaneously accessing the network through mesh routers. This classification along with overview of these networks and open research challenging issues at various layers are mentioned by (Akyildiz, Wang, & Wang, 2005).

In WMNs compatibility and interoperability with the existing wireless networks is a principle characteristics and enable significant applications by connecting with Wi-Fi, wireless sensors, WiMAX, etc. technologies thus provide dedicated routing, configurations to the end users for offering better scalability as compared to the existing networks.

Routing is one of the most challenging issues in multi hop wireless networks that significantly affect network performance. As the topology of WMNs is highly dynamic, so to cope with routing issues in literature many routing metrics and protocols are formulated by research fraternity. In combination with these, routing algorithms are designed that take these metrics in consideration to find optimal path. The routing algorithms must work in a decentralized, self-configured, self-organized and self-healing manner in order to make network fast enough to converge and feasible for large WMNs.

The routing metric values reflect the cost of using a particular path with respect to some optimization objectives that account for network performance indicator. A value is assigned to each path or route and that is used by the routing algorithm to select optimal path out of the subset of paths discovered by the routing protocol found in due time. The categorization of performance parameters in WMNs can be done as per node; per flow; inter flow and network wide parameters. Routing metrics provide high flexibility in selection of paths to the routing algorithms.

Routing metrics are broadly categorised in different types depending on the topology, signal strength, active probing, mobility and energy awareness. Hop count, Per Hop Round Trip Time (RTT), Per Hop Packet Pair Delay (Pkt Pair), are most commonly used metrics in wireless networks. A comparison of routing metrics for static multi-hop wireless networks is presented by (Draves, Padhye & Zill, 2004). In WMNs various routing metrics and protocols like Expected Transmission Count (ETX), Expected transmission time(ETT), Weighted Cumulative Expected Transmission Time (WCETT), Metric of Interference and Channel Switching (MIC), Modified ETX (mETX) and Effective Number of Transmission (ENT) are compared on the basis of characteristics like data rate, packet size, quality awareness, Intra-flow interference and medium instability is done by (Campista, Esposito, Moraes, Cosla, Duarte, Passos, Albuquerque, Saade and Rubinstein, 2008).Expected Transmission on a Path (ETOP) (Jakllari, Eidenbenz, Hengartner, Krishnamurthy, & Faloutsos, 2008),Bottleneck Link Capacity (BLC) (Liu&Liao, 2006), interference aware routing metric iAWARE (Subramanian,Buddhikot,&Miller,2006), Min-Max Battery Cost Routing (MMBCR)(Singh,Woo,&Raghavendra,1998), Conditional Max-Min Battery Capacity Routing (CMMBCR) (Toh,2001).

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