Energy Efficient Clustering Protocol for Green IoT using Improved Tunicate Swarm Intelligence for Benchmark Texas Data

Abstract

The sixth generation (6G) is expected to be a crucial technology that will enable the pervasive and seamless connectivity of a large count of Internet-of-Things (IoT) devices. The need to alleviate the worry of lowering energy consumption, i.e., green communication, is fueled by the extraordinarily fast data rate, low end-to-end delay, and high mobility of IoT devices. Sensor-enabled IoT relies heavily on green communication. Energy is at the centre of the smart IoT application that allows the sensors to function. The sensors' ability to work efficiently is hampered by rapid energy loss. To prevent the quicker loss of energy from sensors in the IoT, an energy-efficient system is necessary. Since heuristic approaches are not suited for such issues and may turn into NP-hard problems in the case of dense area sensor networks, meta-heuristic approaches are most successful in solving these kinds of problems with near-optimal solutions. Extending the longevity as well as stability of WSNs via energy efficiency is a difficult task. Clustering has increased energy efficiency by selecting Cluster Heads (CHs), but its application is yet difficult. The areas where CHs are desirable are initially sought in traditional CH selection algorithms. This paper plans to design and develop a new framework of Green IoT communication via a new, improved meta-heuristic model. Here, a novel energy-aware objective function is derived based on the constraints like “distance, delay, energy, transmission energy, reception energy, and data aggregation energy”. The Adaptive Random Variable-based Tunicate Swarm Algorithm (ARV-TSA) is adopted for performing the novel green communication in IoT. The experimentation is performed for the parametric data from Texas for “temperature sensor, humidity sensor, Thermistor, ultrasonic sensor with minimum voltage, maximum voltage, minimum current, maximum current etc”. The simulation findings have demonstrated a substantial increase in energy conservation and network stability period improvement when compared to identical methods.