Efficient and Scalable Ant Colony Optimization based WSN Routing Protocol for IoT

Abstract

IoT integrates and connects intelligent devices or objects with varied architectures and resources. The number of IoT devices is growing exponentially. Due to the massive wave of IoT objects, their diversity and heterogeneity among their architectures, the existing communication protocols for wireless networks become ineffective in the context of IoT. Wireless Sensor Network (WSN) has the potential to be integrated to the internet of things (IoT). The issues of the routing of WSNs impose nearly similar prerequisites for IoT routing technique. Most of the traditional routing protocols are not appropriate for WSNs and IoT because of resource constraints, computational overhead and environmental interference and do not take into account the different factors affecting energy parameter and do not accommodate node mobility. Routing algorithms must ensure the data transmission in an efficient way, having proper knowledge of the IoT system. For this reason, many intelligent systems have been utilized to design routing algorithms to handle the network's dynamic state. In this paper, an ant colony optimization (ACO) based WSN routing algorithm for IoT has been proposed and analyzed to enhance scalability, to accommodate node mobility and to minimize initialization delay for time critical applications in the context of IoT to find the optimal path of data transmission, improvising efficient IoT communications. The proposed routing algorithm is simulated using MATLAB for performance evaluations. The evaluation results have recorded an improvement in conservation of energy, of almost 50% less consumed energy even with an increase in the number of nodes, by comparing with an existing routing technique based on ant system, a current routing protocol for IoT and the conventional ACO algorithm.

Generative Service Provisioning for IoT Devices Using Line Graph Structure

“…Deep RL determines a specific subgraph from the original graph. We compared the proposed generative service provisioning with ant colony optimization (ACO), a swarm intelligence-based algorithm [14]. The ACO algorithm can identify a service subgraph.…”

“…A swarm intelligence-based algorithm develops the collective behavior of life groups. Swarm intelligence has been applied to many domains and routing problems [14,17,18]. ACO (Ant Colony Optimization) is an effective Swarm Intelligence-based algorithm that uses the concept of ants using pheromones to guide the other ants.…”

“…ACO (Ant Colony Optimization) is an effective Swarm Intelligence-based algorithm that uses the concept of ants using pheromones to guide the other ants. The ACO-based algorithm was used to address routing problems in wireless sensor networks [14]. In the literature, the trailing ant can obtain promising perspectives from the leading ant using pheromones.…”

“…If trials with different levels of pheromones are left to determine routes to the prey, the pheromone-based solution addresses the presence of desired resources in the service subgraph. We modify the method in the literature [14] for comparison. In the proposed system model, the ant travels through the device until sufficient resources are accumulated.…”

A Multi-Zone Based Hierarchical Protocol for Routing in Wireless Sensor Networks

“…The bio-inspired routing introduced advancement in the field of WSNs. These protocols are based on characteristics and behavior of the environment (4) (5). The multihop dynamic routing is recent technique of routing in WSNs.…”