Fairness-Oriented Semichaotic Genetic Algorithm-Based Channel Assignment Technique for Node Starvation Problem in Wireless Mesh Networks

Abstract

Wireless mesh networks (WMNs) have emerged as a scalable, reliable, and agile wireless network that supports many types of innovative technologies such as the Internet of Things (IoT), Wireless Sensor Networks (WSN), and Internet of Vehicles (IoV). Due to the limited number of orthogonal channels, interference between channels adversely affects the fair distribution of bandwidth among mesh clients, causing node starvation in terms of insufficient bandwidth distribution, which impedes the adoption of WMN as an efficient access technology. Therefore, a fair channel assignment is crucial for the mesh clients to utilize the available resources. However, the node starvation problem due to unfair channel distribution has been vastly overlooked during channel assignment by the extant research. Instead, existing channel assignment algorithms equally distribute the interference reduction on the links to achieve fairness which neither guarantees a fair distribution of the network bandwidth nor eliminates node starvation. In addition, the metaheuristic-based solutions such as genetic algorithm, which is commonly used for WMN, use randomness in creating initial population and selecting the new generation usually leading the search to local minima. To this end, this study proposes a Fairness-Oriented Semichaotic Genetic Algorithm-Based Channel Assignment Technique (FA-SCGA-CAA) to solve node starvation problem in wireless mesh networks. FA-SCGA-CAA maximizes link fairness while minimizing link interference using a genetic algorithm (GA) with a novel nonlinear fairness-oriented fitness function. The primary chromosome with powerful genes is created based on multicriterion links ranking channel assignment algorithm. Such a chromosome was used with a proposed semichaotic technique to create a strong population that directs the search towards the global minima effectively and efficiently. The proposed semichaotic technique was also used during the mutation and parent selection of the new genes. Extensive experiments were conducted to evaluate the proposed algorithm. A comparison with related work shows that the proposed FA-SCGA-CAA reduced the potential node starvation by 22% and improved network capacity utilization by 23%. It can be concluded that the proposed FA-SCGA-CAA is reliable to maintain high node-level fairness while maximizing the utilization of the network resources, which is the ultimate goal of many wireless networks.

Gateway selection and placement for Multi-radio multi-channel Wireless Mesh Networks with Garter Snake Optimization algorithm

“…This list, however, is used to calculate gateway costs in preparation for future sub-network allocation. Despite the importance of the path state between wireless routers and gateway nodes, some studies [17,19,20] only considered tra c load. In a self-organizing, self-con guring wireless mesh network (SRSC WMN), gateway load has been proposed as a criterion for selecting the best gateway.…”

“…Since each node may communicate directly with many of its neighbors, the network can be easily expanded by adding additional nodes. Because data may be diverted to operational nodes in a mesh network, it is more resilient to disturbances and outages [19,20].…”

“…A wireless mesh network's coverage area and throughput may be easily increased by adding additional nodes, which can be done with relative ease. The mesh network's decentralized architecture and e cient resource utilization will allow it to handle more tra c and more devices as the network grows [29,30].…”

“…As described in references [21] and [12], several models were employed to evaluate the capacity of wireless networks. In [20] analyzed the capacity of random multi-hop multi-radio multi-channel wireless networks. In [17] used the protocol interference model to investigate the issues of ow routing and transmission scheduling within a single-channel wireless network.…”

Information Fusion in Autonomous Vehicle Using Artificial Neural Group Key Synchronization

“…Nowadays, data amalgamation in MITS has shown to be extremely effective in making the transport systems easier and safer. By minimizing traffic jams, offering smart transport techniques, drastically decreasing the frequency of road accidents, and eventually achieving autonomous vehicles, the data-based technologies implemented in MITS promise to profoundly transform a person's driving experiences [1][2][3].…”