Distributed Clustering Based on Node Density and Distance in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are special type of network with sensing and monitoring the physical parameters with the property of autonomous in nature. To implement this autonomy and network management the common method used is hierarchical clustering. Hierarchical clustering helps for ease access to data collection and forwarding the same to the base station. The proposed Distributed Self-organizing Load Balancing Clustering Algorithm (DSLBCA) for WSNs designed considering the parameters of neighbor distance, residual energy, and node density.  The validity of the DSLBCA has been shown by comparing the network lifetime and energy dissipation with Low Energy Adaptive Clustering Hierarchy (LEACH), and Hybrid Energy Efficient Distributed Clustering (HEED). The proposed algorithm shows improved result in enhancing the life time of the network in both stationary and mobile environment

Energy Management of Wireless Sensor Networks by Using Games Theory

Wireless Sensor Networks (WSNs) because of their limitation in energy consumption in their nodes, high rate of energy consumption and inability interchangeable battery, are always in danger of extiction. The highest energy consumption in these networks is done when communicating of the nodes with the base station (BS) and sending information, which its reason is the limitation of BS in receiving and saving the incoming message. Now, if we will be able to increase the lifespan of  these networks. So, a lot of algorithms and models has been suggested to reduce the energy consumption of WSNs. In this study, we have tried to present a new model in order to nodes cooperation in communicating with the BS and also reducing the number of messages by using games theory and exclusively enter market game (EMG)

A RELIABLE ROUTING MECHANISM WITH ENERGY-EFFICIENT NODE SELECTION FOR DATA TRANSMISSION USING A GENETIC ALGORITHM IN WIRELESS SENSOR NETWORK

Energy-efficient and reliable data routing is critical in Wireless Sensor Networks (WSNs) application scenarios. Due to oscillations in wireless links in adverse environmental conditions, sensed data may not be sent to a sink node. As a result of wireless connectivity fluctuations, packet loss may occur. However, retransmission-based approaches are used to improve reliable data delivery. These approaches need a high quantity of data transfers for reliable data collection. Energy usage and packet delivery delays increase as a result of an increase in data transmissions. An energy-efficient data collection approach based on a genetic algorithm has been suggested in this paper to determine the most energy-efficient and reliable data routing in wireless sensor networks. The proposed algorithm reduced the number of data transmissions, energy consumption, and delay in network packet delivery. However, increased network lifetime. Furthermore, simulation results demonstrated the efficacy of the proposed method, considering the parameters energy consumption, network lifetime, number of data transmissions, and average delivery delay

Green Computing in Sensors Enabled Internet of Things: Neuro Fuzzy Logic Based Load Balancing

Energy is a precious resource in sensors enabled Internet of Things (IoT). Unequal load on sensors deplete their energy quickly that may interrupt operations of the network. Further, single artificial intelligence technique is not to be enough to fulfill the problem of load balancing and minimize energy consumption because of integration of ubiquitous nature of smart sensors enabled IoT. In this paper, we present an adaptive neuro fuzzy clustering algorithm (ANFCA) to balance the load evenly among sensors. We synthesize fuzzy logic and neural network to counterbalance the selection of optimal number of cluster heads and evenly distribution of load among sensors. We develop fuzzy rules, sets, and membership functions of adaptive neuro fuzzy inference system to decide whether a sensor play the role of cluster head. The proposed ANFCA outperforms the state of the art algorithms in terms of node death percentage, number of alive nodes, average energy consumption, and standard deviation of residual energy