Enhanced Service Discovery Model for Wireless Mesh Networks

Abstract

Wireless mesh networks (WMNs) are the only cost-effective networks that support seamless connectivity, wide area network (WAN) coverage, and mobility features. However, the rapid increase in the number of users on these networks has brought an upsurge in competition for available resources and services. Consequently, factors such as link congestion, data collisions, link interferences, etc. are likely to occur during service discovery on these networks. This further degrades their quality of service (QoS). Therefore, the quick and timely discovery of these services becomes an essential parameter in optimizing the performance of service discovery on WMNs. In this paper, we present the design and implementation of an enhanced service discovery model that solves the performance bottleneck incurred by service discovery on WMNs. The proposed model integrates the particle swarm optimization (PSO) and ant colony optimization (ACO) algorithms to improve QoS. We use the PSO algorithm to assign different priorities to services on the network. On the other hand, we use the ACO algorithm to effectively establish the most cost-effective path whenever each transmitter has to be searched to identify whether it possesses the requested service(s). Furthermore, we design and implement the link congestion reduction (LCR) algorithm to define the number of service receivers to be granted access to services simultaneously. We simulate, test, and evaluate the proposed model in Network Simulator 2 (NS2), against ant colony-based multi constraints, QoS-aware service selection (QSS), and FLEXIble Mesh Service Discovery (FLEXI-MSD) models. The results show an average service discovery throughput of 80%, service availability of 96%, service discovery delay of 1.8 s, and success probability of service selection of 89%.

Enhanced Dynamic Bandwidth Allocation Algorithm for Intelligent Home Networks

“…In [12] the researchers presented some experimental evaluations in terms of performance of protocols such as Link State Routing (LSR), Dynamic Source Routing (DSR), Ad hoc On-demand Distance Vector (AODV), and the varied swarm intelligence routing protocols. As discussed in [13], the objective was to evaluate the performance of these routing protocols in Vehicular Ad hoc Networks (VANETs) and was achieved through extensive simulation experiments. To have a successful analysis of these protocols, performance metrics such as throughput, latency, delivery ratio, and delivery cost were most considered.…”

“…(iii) Alignment wherein each follows the general heading of the flock in a swarm. On the other hand, the research in [13] stated that this algorithm simulates search randomly in the design space to obtain the maximum value of the objective. The research further stated that it was implemented on two paradigms, namely, one globallyoriented (GBEST) and one locally-oriented (LBEST).…”

Provisioning of Fog Computing over Named-Data Networking in Dynamic Wireless Mesh Systems

“…Conventionally, fog computing relies on an IP-based architecture, such as in [ 1 , 8 , 9 ], and requires the domain name resolution system (DNS) for resource discovery. Although several approaches are enabling this functionality in wireless mesh systems [ 10 , 11 ], the performance of these is still far from optimal due to the intermittent nature of wireless links.…”