Bio inspired WSN routing algorithm for IOT

Abstract

Efficient routing is an important issue for the design of wireless sensor network (WSN) protocols to overcome several challenges and to meet hardware and resource constraints. WSNs are integrated into 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 so are not energy efficient other than adaptable and scalable and do not accommodate node mobility and further research would be needed to address issues such as QoS for real-time applications. Since the communications parameters must be taken into account by the mechanisms that mediate in the correspondence procedure, for instance routing, the vital communication parameters, for example, the mobility of the nodes, their constrained energy, the node residual energy and route length are needed to be considered to improve communications within IoT. Ant Colony Optimization (ACO) based algorithms have the capability of self-organizing and emphasize the design of optimal algorithms and autonomous distributed systems that are robust, adaptable and scalable. However, most of the existing ACO based routing algorithms are designed on the assumption that the sensor nodes and the sinks are stationary and do not consider the overhead of mobility and do not take into account the above mentioned communication parameters and the current node energy is not considered, which will lead to premature death of some nodes. The reason behind is that the energy factor is not considered in the probability formula of traditional ACO algorithm to find the shortest and optimal path by means of the most pheromone path, which will lead to the node energy of the network on the path decrease sharply and shorten the lifetime of the entire network. Moreover, the frequent updates of the sink nodes and the sensor nodes for position changes for mobility and propagation of information for topology changes through the network may drain the energy of the sensor nodes excessively in such energy constrained environment. To overcome the existing problem of accommodating node mobility, reducing initialization time for ant based routing algorithm and to maintain scalability in WSN for time critical applications in the context of IoT, a novel ACO based WSN routing algorithm for IoT has been proposed and analyzed in this thesis. The energy factor is added and the average mobility of the nodes is added in the probability formula of an ant colony algorithm which is the distinguishing feature for the new ant algorithm compared to previous work and formulation of mathematical model is provided. A fitness function, route evaluation index, is provided for optimal path selection taking into account the existing energy of the nodes and route length. The proposed algorithm has been simulated and verified using MATLAB and evaluated against the traditional ACO and EICAntS algorithm from the benchmark paper. The evaluation results of the new ACO based routing algorithm achieved faster convergence for the initialization delay, about 30% improvement to determine the best cost route, almost 40% reduced end to end delay and almost 50% less consumed energy even with the increasing number of nodes, compared with the other two algorithms and demonstrated an improvement in conservation of energy, delay, throughput and lifetime. It improves communications within IoT in addition to its improved network performance.