ENERGY-AWARE CLUSTER HEAD SELECTION IN IOT-WSN USING DEEP GENETIC OPTIMIZATION STRATEGY

The enhancement of new technology in the sensor network shows a significant result in every aspect of life such as military surveillance, hospitals, mining and hospitals etc. The nodes are scattered randomly in RoI (Region of Interest) and data is transmitted to Base Station (BS) using the multi-hop technique. The Wireless Sensor Network (WSN) become an important research field for challenging problems as energy consumption, efficient cluster head selection process, routing algorithm, network strength, packet loss, energy loss and so forth. The agenda in the paper is to enhance Residual Energy (RE) of nodes and network lifetime. The problem is solved by using an efficient clustering and Cluster Head (CH) selection process.The cluster head selection is based on the maximum node residual energy and the minimum distance from the base station. The Proposed protocol worked in two stages. The new Threshold value T(H) is calculated for the cluster head selection process in the first stage. The data fusion method based on the trust function is used to get accurate data in the second stage. The energy model is utilized to reduce the excessive energy transmission inside the network. The Proposed protocol is compared with Stable Election Protocol and achieves 44% lifetime improvement, 59\% stability improvement and 15% in survival rate respectively.

Internet of things (IoT) integrate the technologies such as sensing, communication, networking and cloud computing in wide range monitoring zone. For applications of IoT, the most appropriate monitoring network is wireless sensor networks (WSN). It is most important to develop energy efficient cluster head (CH) selection scheme to increase the network lifetime of WSNs. It is most crucial to save the energy of these sensor nodes to avoid quick battery drain. In this work, a dynamic CH selection method (DCHSM) is proposed to extend the network lifetime of the systems developed for IoT applications. At first the concept of Voronoi diagram is utilized to divide the large scale monitoring area into small clusters in order to ensure maximum coverage and then the CH is selected in two stages. The first stage of the CH selection is based on perceived probability and the second stage is based on the survival time estimation. Results depicts that the new work performs better than existing algorithms with regard to energy saving and network lifetime for the IoT systems.

Nowadays, Wireless Sensor Network had made a major contribution to surveillance, target tracking and healthcare. Due to the nature of its complex functions in sensing and monitoring the environment at a diverse area, energy efficiency is one of the primary objectives that need to be considered to prolong the network lifetime. Cluster based is one the most common use and suitable protocol in enhancing energy efficiency in WSN. However, an efficient cluster head (CH) selection mechanism is still needed to ensure that the most appropriate CH is selected. Selecting CH based on single criteria could lead to inappropriate decision. Thus, a holistic view of the CH considering multiple criteria is more promising. Four criteria were considered for the CH selection in our approach; number of neighbour nodes (NNN), residual energy (RE), initial energy (IE) and distance of nodes to base station (DTBS). In this paper, we demonstrate that the use of Analytic Hierarchy Process (AHP) helps in CH selection better and we managed to identify the distance as the important criteria in the selection of CH.

Wireless sensor network (WSN) comprises of numerous sensors deployed either directly or randomly in the region of interest. Due to the limited power of the sensors, these networks are energy-constrained and thus need efficient power utilisation. Efficient clustering and cluster head (CH) selection ensures balanced energy distribution to the sensors within the WSN and hence prolong the network lifetime. This study proposes the method for evaluating the threshold for the CH selection after each round, which increases the network lifetime and throughput significantly. The threshold for CH selection is modified considering the normalised first-order and second-order statistical parameters, such as mean average low-energy adaptive clustering hierarchy (AvgLEACH) and variance (VarLEACH) of the overall network energy. These methods have been formulated after studying the effect of the number of working nodes in each round on the threshold value selection. Apart from including energy parameter to the threshold equation, the methods of VarLEACH and AvgLEACH are augmented with a residual energy parameter that is local to the nodes and named as VarRLEACH and AvgRLEACH. The simulation results comparing all the methods suggest that the proposed method AvgRLEACH outperforms LEACH by a factor of 1.5 in delivering data to the base station and outlives the network driven by LEACH protocol by 30–40%.

Efficient clustering and cluster head (CH) selection are the critical parameters of wireless sensor networks (WSNs) for their prolonged network lifetime. However, the performances of the traditional clustering algorithms like LEACH and HEED are not satisfactory when they are implemented on a dense WSN due to their unbalanced load distribution and high contention nature. In the traditional methods, the cluster heads are selected with respect to the residual energy criteria, and often create a circular cluster shape boundary with a uniform node distribution. This causes the cluster heads to become overloaded in the high-density regions and the unutilized cluster heads gather in the sparse regions. Therefore, frequent cluster head changes occur, which is not suitable for a real-time dynamic environment. In order to avoid these issues, this proposed work develops a density-aware adaptive clustering (DAAC) protocol for optimizing the CH selection and cluster formation in a dense wireless sensor network. The residual energy information, together with the local node density and link quality, is utilized as a single cluster head detection metric in this work. The local node density information assists the proposed work to estimate the sparse and dense area in the network that results in frequent cluster head congestion. DAAC is also included with a minimum inter-CH distance constraint for CH crowding, and a multi-factor cost function is used for making the clusters by inviting the nodes by their distance and an expected transmission energy. DAAC triggers re-clustering in a dynamic manner when it finds a response in the CH energy depletion or a significant change in the load density. Unlike the traditional circular cluster boundaries, DAAC utilizes dynamic Voronoi cells (VCs) for making an interference-aware coverage in the network. This makes dense WSNs operate efficiently, by providing a hierarchical extension, on making secondary CHs in an extremely dense scenario. The proposed model is implemented in MATLAB simulation, to determine and compare its efficiency over the traditional algorithms such as LEACH and HEED, which shows a satisfactory network lifetime improvement of 20.53% and 32.51%, an average increase in packet delivery ratio by 8.14% and 25.68%, and an enhancement in total throughput packet by 140.15% and 883.51%, respectively.

An efficient cluster head selection for wireless sensor network-based smart agriculture systems

Nodes in wireless Internet of Things (IoT) sensor networks are heterogeneous in nature. This heterogeneity can come from energy and security resources available at the node level. Besides, these resources are usually limited. Efficient cluster head (CH) selection in rounds is the key to preserving energy resources of sensor nodes. However, energy and security resources are contradictory to one another. Therefore, it is challenging to ensure CH selection with appropriate security resources without decreasing energy efficiency. Coverage and energy optimization subject to a required security level can form a solution to the aforementioned trade-off. This paper proposes a security level aware CH selection algorithm in wireless sensor networks for IoT. The proposed method considers energy and security level updates for nodes and coverage provided by associated CHs. The proposed method performs CH selection in rounds and in a centralized parallel processing way, making it applicable to the IoT scenario. The proposed algorithm is compared to existing traditional and emerging CH selection algorithms that apply security mechanisms in terms of energy and security efficiencies.

The Internet of Things (IoT) has transformed various aspects of human life nowadays. In the IoT transformative paradigm, sensor nodes are enabled to connect multiple physical devices and systems over the network to collect data from remote places, namely, precision agriculture, wildlife conservation, intelligent forestry, and so on. The battery life of sensor nodes is limited, affecting the network’s lifetime, and requires continuous maintenance. Energy conservation has become a severe problem of IoT. Clustering is essential in IoT to optimize energy efficiency and network longevity. In recent years, many clustering protocols have been proposed to improve network lifetime by conserving energy. However, the network experiences an energy-hole issue due to picking an inappropriate Cluster Head (CH). CH node is designated to manage and coordinate communication among nodes in a particular cluster. The redundant data transmission is avoided to conserve energy by collecting and aggregating from other nodes in clusters. CH plays a pivotal role in achieving efficient energy optimization and network performance. To address this problem, we have proposed an osprey optimization algorithm based on energy-efficient cluster head selection (SWARAM) in a wireless sensor network-based Internet of Things to pick the best CH in the cluster. The proposed SWARAM approach consists of two phases, namely, cluster formation and CH selection. The nodes are clustered using Euclidean distance before the CH node is selected using the SWARAM technique. Simulation of the proposed SWARAM algorithm is carried out in the MATLAB2019a tool. The performance of the SWARAM algorithm compared with existing EECHS-ARO, HSWO, and EECHIGWO CH selection algorithms. The suggested SWARAM improves packet delivery ratio and network lifetime by 10% and 10%, respectively. Consequently, the overall performance of the network is improved.

Wireless sensor networks (WSN) is the key resource of perception and is widely used in the systems based on Internet of Things (IoT). The smart sensor nodes are used in applications like infrastructure monitoring, medical health care systems, etc. But these nodes are energy constraint devices. Efficient clustering and proper cluster head (CH) selection schemes are required, in order to improve energy saving of sensor nodes. In this paper, dynamic CH selection method (DCHSM) is used where CHs are selected in two phases. This algorithm improves energy saving on large scale thus can be used for IoT applications. Initially, Voronoi diagram is used to divide the monitoring area in polygonal shaped clusters. Then, CH election is performed in two phases. First class of CH is elected based on perceived probability and the second class is elected on the basis of survival time estimation. Simulation analysis show that DCHSM outperforms the conventional methods in terms of network lifetime.

An enhanced Elman spike neural network (EESNN) optimized with hybrid wild horse optimization and chameleon swarm algorithm is proposed in this manuscript for multi‐objective cluster head selection and energy aware routing in wireless sensor network (CH‐EESNN‐Hyb‐WH‐CSOA‐WSN). Initially, EESNN is used to intelligent selection of cluster head. Then, the optimal cluster head utilized the selected data transferring process with the consideration of multi‐objective fitness function based EESNN. Here, some of the multi‐objective fitness function factors are considered, like energy, delay, throughput, distance between the nodes, traffic rate and cluster density. The hybrid wild horse optimization and chameleon swarm algorithm (Hyb‐WH‐CSOA) is taken into account for the optimal route path selection with minimal delay. The proposed CH‐EESNN‐Hyb‐WH‐CSOA‐WSN method is activated in network simulator 3 (NS‐3) tool. The performance of the proposed method is examined under certain performance metrics, like count of alive nodes, drop, network lifetime, delay, throughput, energy consumption, and packet delivery ratio. Finally, the proposed method attains 98.78%, 97.21%, 99.61% lower delay, 98.78%, 99.21%, 96.78% higher delivery ratio, and 99.57%, 98.67%, 98.88% lower packet drop compared with the existing methods, like optimal secure cluster head placement through source coding techniques in wireless sensor networks (CHP‐HMC‐WSN), optimal placement of single cluster head in wireless sensor networks via clustering (CHP‐K‐Means C‐PSO‐WSN) and hybrid firefly approach along particle swarm optimization for energy efficient optimum cluster head selection in wireless sensor networks (CHP‐HFAPSO‐WSN) respectively.

An Energy Balancing LEACH Algorithm for Wireless Sensor Network

A wireless sensor network (WSN) is a collection of numerous independent sensor nodes that can sense, process, and manipulate data. WSN is grouped into clusters for energy-efficient data collection. A clustering and aggregation technique automatically extends the lifetime of a WSN by collecting data within the cluster to the cluster head, reduces the amount of data through processing, and transmitting. WSN routing protocols are also required for completing all types of operations in a Internet of things (IOT) environment, such as sensing, controlling, and transmitting packets. In this paper, a novel Fuzzy Clustering and Optimal Routing (FCOR) method is proposed in order to lessen the energy consumption, delay, and improve network lifetime and node density. The proposed FCOR method is executed in two stages. The initial stage consists of clustering and cluster head selection using modified Fuzzy c-means algorithm (MFCM). This algorithm will efficiently cluster the nodes and select the optimal cluster head. The second phase consists of optimal routing using a normalized whale optimization algorithm (NWOA), that select the optimal route and thus improve the lifetime of the nodes. The efficiency of the proposed FCOR approach has been determined using the evaluation metrics such as energy efficiency, packet delivery, and network lifetime. The experimental results reveals that the proposed FCOR model achieves less energy consumption of 67.8%, 54.4%, 60% and 6.67% than existing FRNSEER, E-ALWO, ACI-GSO and CRSH respectively.

In a harsh environment, enormous number of sensor nodes are spreaded randomly in the network. Each and every sensor will sense the physical environment and communicates the information to the processing node. Here, power source plays the vital role. To balance energy dissipation, clustering strategy is adopted in which sensor network is grouped in to a set of clusters, one node is chosen as cluster head(CH) from each cluster. Sometimes perverse cluster leader will lead to coverage problem and network life time is also get affected. To overcome this, thiessen polygon is used to group the observing region to guarantee the maximum coverage and the divided area are put in to clusters and one node is selected on the same coverage area but the most redundant nodes as first kind of cluster head. In one particular region, all redundant node died then redivide it, after that, second kind of cluster head is selected. Experimental results show that our proposed method Energy Efficient Reclustering Method(EERM) maintains the network lifetime longer when compared with existing clustering algorithms like Low Energy Adaptive Clustering Hierarchy(LEACH), Distributed Energy Efficient Clustering(DEEC) and Dynamic Cluster Head Selection Method(DCHSM).

Purpose Efficient resource utilization in wireless sensor networks is an important issue. Clustering structure has an important effect on the efficient use of energy, which is one of the most critical resources. However, it is extremely vital to choose efficient and suitable cluster head (CH) elements in these structures to harness their benefits. Selecting appropriate CHs and finding optimal coefficients for each parameter of a relevant fitness function in CHs election is a non-deterministic polynomial-time (NP-hard) problem that requires additional processing. Therefore, the purpose of this paper is to propose efficient solutions to achieve the main goal by addressing the related issues. Design/methodology/approach This paper draws inspiration from three metaheuristic-based algorithms; gray wolf optimizer (GWO), incremental GWO and expanded GWO. These methods perform various complex processes very efficiently and much faster. They consist of cluster setup and data transmission phases. The first phase focuses on clusters formation and CHs election, and the second phase tries to find routes for data transmission. The CH selection is obtained using a new fitness function. This function focuses on four parameters, i.e. energy of each node, energy of its neighbors, number of neighbors and its distance from the base station. Findings The results obtained from the proposed methods have been compared with HEEL, EESTDC, iABC and NR-LEACH algorithms and are found to be successful using various analysis parameters. Particularly, I-HEELEx-GWO method has provided the best results. Originality/value This paper proposes three new methods to elect optimal CH that prolong the networks lifetime, save energy, improve overhead along with packet delivery ratio.

Wireless Sensor Network has become a leading area of research because of its efficiency in design. A sensor is a small equipment that senses input from both the physical or environmental conditions, like pressure, heat, light, etc., and then respond to that input. The core problem faced by the WSN is high energy consumption. This will decrease the overall network lifetime. To overcome these problems, we introduce a new energy efficient threshold based routing protocol (ET-LEACH) for the wireless sensor network. This protocol is an improvement for LEACH protocol. Routing must be performed in an energy efficient manner, dynamic routing is preferred for proposed protocol. We group sensor nodes into different clusters and cluster head election is based on the node’s residual energy and a threshold value. Comparing node’s energy with a threshold value, re-election of cluster head takes place. The sensed information is send through the cluster head and reach the base station. In wireless sensor network most important is its network lifetime, this paper proposes a new strategy to increase the network lifetime and scalability. Also, it maintains a balanced energy consumption that causes efficient load balancing.