Energy-efficient Clustering Mechanism Research Articles

Intelligent Disease Diagnosis Model for Energy Aware Cluster Based IoT Healthcare Systems

In recent days, advancements in the Internet of Things (IoT) and cloud computing (CC) technologies have emerged in different application areas, particularly healthcare. The use of IoT devices in healthcare sector often generates large amount of data and also spent maximum energy for data transmission to the cloud server. Therefore, energy efficient clustering mechanism is needed to effectively reduce the energy consumption of IoT devices. At the same time, the advent of deep learning (DL) models helps to analyze the healthcare data in the cloud server for decision making. With this motivation, this paper presents an intelligent disease diagnosis model for energy aware cluster based IoT healthcare systems, called IDDM-EAC technique. The proposed IDDM-EAC technique involves a 3-stage process namely data acquisition, clustering, and disease diagnosis. In addition, the IDDM-EAC technique derives a chicken swarm optimization based energy aware clustering (CSOEAC) technique to group the IoT devices into clusters and select cluster heads (CHs). Moreover, a new coyote optimization algorithm (COA) with deep belief network (DBN), called COA-DBN technique is employed for the disease diagnostic process. The COA-DBN technique involves the design of hyperparameter optimizer using COA to optimally adjust the parameters involved in the DBN model. In order to inspect the betterment of the IDDM-EAC technique, a wide range of experiments were carried out using real time data from IoT devices and benchmark data from UCI repository. The experimental results demonstrate the promising performance with the minimal total energy consumption of 63% whereas the EEPSOC, ABC, GWO, and ACO algorithms have showcased a higher total energy consumption of 69%, 78%, 83%, and 84% correspondingly.

Water cycle algorithm based optimized clustering protocol for wireless sensor network

Wireless sensor networks (WSNs) are significantly resource restrained by their constrained power supply. Hierarchical routing such as LEACH (Low-energy adaptive clustering hierarchy) is an efficacious approach, in which clusters are structured; however, choosing an energy-aware cluster by optimum selection of cluster head (CH) is also very challenging. This paper presents an application of water cycle algorithm (WCA) on clustering hierarchy protocol i.e. LEACH protocol in wireless sensor networks. WCA is inspired by observing how rivers and streams flow to the sea. The WCA eventually lessens the cost of determining the best node as the cluster head. This novel energy efficient clustering mechanism sustains the network life-time. The performance analysis of the proposed technique is carried through and the simulation elucidates the potency of the proposed work over a variety of performances metrics.

Energy-efficient selection of cluster headers in wireless sensor networks

Clustering is vital for lengthening the lives of resource-constrained wireless sensor nodes. In this work, we propose a cluster-based energy-efficient router placement scheme for wireless sensor networks, where the K-means algorithm is used to select the initial cluster headers and then a cluster header with sufficient battery energy is selected within each cluster. The performance of the proposed scheme was evaluated in terms of the energy consumption, end-to-end delay, and packet loss. Our simulation results using the OPNET simulator revealed that the energy consumption of our proposed scheme was better than that of the low-energy adaptive clustering hierarchy, which is known to be an energy-efficient clustering mechanism. Furthermore, our scheme outperformed low-energy adaptive clustering hierarchy in terms of the end-to-end delay, throughput, and packet loss rate.

Multi-objective fractional artificial bee colony algorithm to energy aware routing protocol in wireless sensor network

Due to the promising application of collecting information from remote or inaccessible location, wireless sensor networks pose big challenge for data routing to maximize the communication with more energy efficient. Literature presents different cluster-based energy aware routing protocol for maximizing the life time of sensor nodes. Accordingly, an energy efficient clustering mechanism, based on artificial bee colony algorithm and factional calculus is proposed in this paper to maximize the network energy and life time of nodes by optimally selecting cluster-head. The hybrid optimization algorithm called, multi-objective fractional artificial bee colony is developed to control the convergence rate of ABC with the newly designed fitness function which considered three objectives like, energy consumption, distance travelled and delays to minimize the overall objective. The performance of the proposed FABC-based cluster head selection is compared with LEACH, PSO and ABC-based routing using life time, and energy. The results proved that the proposed FABC maximizes the energy as well as life time of nodes as compared with existing protocols.

A Novel Polymorphic Ant Colony -Based Clustering Mechanism for Wireless Sensor Networks

In wireless sensor networks, sensor nodes are extremely power constrained, so energy efficient clustering mechanism is mainly considered in the network topology management. A new clustering mechanism based on the polymorphic ant colony (PAC) is designed for dynamically controlling the networks clustering structure. According to different functions, the nodes of the networks are respectively defined as the queen ant, the scout ant and worker ant. Based on the calculated cost function and real-time pheromone, the queen ant restructures an optimum clustering structure. Furthermore, the worker ants and the scout ants can send or receive sensing data with optional communication path based on their pheromones. With the mechanism, the energy consumption in inter-cluster and intra-cluster communication for the worker ants and scout ants can be reduced. The simulation results demonstrate that the proposed mechanism can effectively remodel the clustering structure and improve the energy efficiency of the networks.