A hybrid approach for energy‐efficient routing in IoT using duty cycling and improved ant colony

Abstract

The promising technology of IoT in the fourth industrial revolution connects everything to the Internet in the digital world of the current era. Though the gigantic connectivity among things through the blend of multifarious technologies offers potential opportunities, it also increases the overhead to optimise the energy consumption in IoT networks. Energy optimisation has become the major concern in the IoT realm because of the continuous sensing of the constrained sensor nodes and data transmission to longer distances. The state‐of‐the‐art studies do not take into account the management of duty‐cycling and the process of efficient route discovery hand in hand. Also, the prevalent studies focus on the management of the sensor node's duty cycling (DC) solely instead of adaptive DC scheduling of the communication unit of the sensor node that consumes more energy during transmission. Therefore, keeping in view the existing tribulations regarding energy consumption, the authors attempt to devise an energy‐efficient routing approach using On‐Demand Duty Cycling and Ant‐Colony optimisation (DC‐ACO) for IoT. The ACO‐based routing approach is applicable in IoT networks because the ants’ environment is conceptualised as a distributed set of interconnected graph nodes. The proposed approach poses the empirical notion to manage the energy consumption of the IoT network by considering the key performance indicators (KPI) like energy consumption, packet delivery rate, average residual energy, mobility factor, distance, throughput, and network lifespan to accomplish the tangible outputs. The proposed approach is modelled using Data flow Diagrams (DFDs) and algorithms supported by results. Experimental results show significant improvement in relative throughput, network lifetime, and energy efficiency by 45%, 78%, and 68%, respectively, after simulating for successive iterations.

An Insight on Optimization Techniques for Uncertain and Reliable Routing in Wireless Body Area Networks

“…The brightness level of each firefly Attractiveness Induces the mating process between two fireflies Randomization Movement of fireflies towards the best possible solution Absorption Controls the decrease in the range of light intensity Artificial Bee Colony Algorithm Artificial Bee Colony (ABC) is a nature-inspired meta-heuristic algorithm derived from the intelligent behavior of honey bees in search of food. In ABC honey bees are classified into three categories based on their role in bee-hive [17].…”