Due to shortage of power source, energy is the significant concern part in Wireless Sensor Systems. Wireless Sensor Networks (WSNs) are currently used in numerous kinds of WSN simulators have been established to visually illustrate the coverage, energy consumption status and expected lifetime of WSN sensors. Most of the Researchers have proposed and develop routing algorithm to increase the network lifetime. Among them clustering is the best technique and it is well known for accomplishing energy effectiveness in WSNs. In this paper, Energy Efficient Multi-hop Routing Clustering Protocol (EEMRCP) based on Fuzzy K-Means and Centralized Mid-point Algorithm (FKM-CMA) is proposed for network lifetime improvement. An existing and frequently used algorithm is K-means algorithm in WSN. The main limitation of this K-means algorithm is random initial centroid selection. To improve K-means algorithm, a Midpoint method in initial centroid selection is used. This is named as FKM-CMA. There are two main considerations for cluster head selection in proposed work, one is it's residual energy, and another one is Euclidean distance used in basic Fuzzy K-means algorithm. Finally, Multi-hop communication is performed for transmitting the packets from CHs to Base Station depending on the distance between them. Furthermore, stimulated outcomes showed that the total network performance of the proposed approach is improved than the other existing approaches.

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<p>Wireless Sensor Networks (WSNs) can extant the individual profits and suppleness with regard to low-power and economical quick deployment for numerous applications. WSNs are widely utilized in medical health care, environmental monitoring, emergencies and remote control areas. Introducing of mobile nodes in clusters is a traditional approach, to assemble the data from sensor nodes and forward to the Base station. Energy efficiency and lifetime improvements are key research areas from past few decades. In this research, to solve the energy limitation to upsurge the network lifetime, Energy efficient trust node based routing protocol is proposed. An experimental validation of framework is focused on Packet Delivery Ratio, network lifetime, throughput, energy consumption and network loss among all other challenges. This protocol assigns some high energy nodes as trusted nodes, and it decides the mobility of data collector.  The energy of mobile nodes, and sensor nodes can save up to a great extent by collecting data from trusted nodes based on their trustworthiness and energy efficiency.  The simulation outcome of our evaluation shows an improvement in all these parameters than existing clustering and Routing algorithms.<strong></strong></p>

Nowadays, Wireless Sensor Networks (WSNs) are pondered as an exploration subject. Currently, progress in electronic communications has directed to multipurpose Sensor Nodes (SNs) with less price and power consumption. Energy efficiency is a major concern in WSNs as the sensor nodes are battery-operated devices. Clustering based techniques are implemented through data aggregation to make equal energy consumption among SNs for energy efficient data transmission. The existing clustering techniques make use of distinct Harmony Search Algorithm (HSA), Low-Energy Adaptive Clustering Hierarchy (LEACH) and Particle Swarm Optimization (PSO) algorithms. However, these algorithms have exploration exploitation trade-off and local search constraint individually. In order to obtain a global search with faster convergence, Efficient Energy Clustering Protocol (EECP) based on Genetic Algorithm (GA) is recently proposed to detect their immediate neighbors, balance energy consumption load among data transmission routes and energy efficient cluster head selection. The proposed algorithm exhibited high search efficiency and dynamic capability that improves lifetime of SNs. The presentation of the proposed algorithm was assessed using throughput, packet delivery ratio, energy consumption and end to end delay. The proposed algorithm showed an improvement in energy consumption and throughput by 95 and 90 Mbps respectively than existing clustering algorithm.