Average Intra-cluster Distance Research Articles

An Energy-Efficient improved Grey Wolf Optimization Algorithm-Based Cluster Head and Shamir Secrets Sharing-Based WSNs with Secure Data Transfer

Introduction: Due to its self-configurability, ease of maintenance, and scalability capabilities, WSNs (Wireless Sensor Networks) have intrigued plenty of interest in a variety of fields. To move data within the network, WSNs are set up with more nodes. The security of SNs (sensing nodes), which are vulnerable to malevolent attackers since they are network nodes, is a crucial element of an IoT (Internet of Things)-based WSN. This study's primary objective is to provide safe routing and mutual authentication with IoT-based WSNs. Methods: The basic GWO algorithm's imbalances between explorations and mining, lack of population heterogeneity, and early convergences are all issues that this paper addresses by selecting energy-efficient CHs (cluster Heads) using EECIGWO algorithm, an upgraded version of the GWO, is used. Mean distances within clusters, well-spaced residual energies, and equilibrium of CHs are all factors that influence the choices of CHs. The average intra-cluster distances, sink distances, residual energies, and CHs balances are some of the criteria used to choose CHs. Results and Discussion: The proposed EECHIGWO-based clustering protocol's average throughput, dead node counts, energy consumption, and operation round counts have all been evaluated. Additionally, mutual authentication between the nodes is provided through SSS (Shamir Secret Sharing) mechanism. PDR (Packet Delivery Ratio) analysis is used to assess how well the EECHIGWO-IOT-WSNs are performing. Conclusion: The suggested proposed approach is assessed against existing methods like HHH-SS (Hybrid Harris Hawk and Salp Swarm), ESR (Energy-efficient and Secure Routing) protocol, and LWTS (Light Weight Trust Sensing) approaches in terms of AEED (Average End-to-End Delay), network overheads, and PLR (Packet Loss Ratio).

Estimation of Genetic Divergence in Garden Pea (Pisum sativum L.) Germplasm

Genetic diversity among thirty garden pea genotypes was worked out using Mahalanobis D2 statistics. Based on genetic distance, genotypes were grouped into ten clusters of the ten clusters, cluster I was the largest, having 11 genotypes, followed by cluster IV, cluster VIII, cluster III, cluster V and clusters II, VI, VII, IX and X, with one genotype in each. The average intra-cluster distance was maximum in cluster VIII (487.04) and minimum in cluster V (125.72). The maximum inter-cluster distance was observed between cluster VIII and cluster X (3439.67), indicating the genotypes in these clusters are more diverged and they can be used as parents in the hybridization program to obtain superior segregants. Cluster means for various traits revealed that cluster VIII was found superior for plant height at 60 DAS, number of branches per plant at 60 DAS, and number of pods per plant. Thus, genotypes from this cluster could be used as divergent parents to improve these particular characters. Pod yield per plant contributed the maximum (38.16 %) to the genetic diversity among the characters studied.

Divergence Studies in Chilli Genotypes (Capsicum annuum L.)

A study on genetic diversity was conducted at Experimental Farm of Regional Horticultural Research and Training Station Dhaulakuan, District Sirmour (HP), Dr. YS Parmar University of Horticulture and Forestry, Nauni, Solan, Himachal Pradesh, India during kharif season, 2018 to assess the genetic diversity in 21 genotypes of chilli (Capsicum annuum L.) and to identify suitable donors for successful breeding programme in this crop.The twenty one (21) chilli genotypes were evaluated for seven horticultural traits viz., days to 50% flowering, days to first green fruit picking, fruit length (cm), fruit diameter (cm), number of fruits plant-1, fruit weight at edible maturity (g), green fruits yield plant-1 (g). The experiment was laid out in RCBD with three replications at spacing of 45×45 cm2. By estimating D2 values in all possible combinations of the genotypes, the 21 genotypes of chilli were grouped into 6 clusters based on green fruit characters, revealing the genetic diversity among the parents. Cluster IV had maximum (6) andCluster I with only one genotype. Average intra-cluster distance was maximum in cluster VI (19.56). The inter cluster distance was recorded maximum between cluster I and VI (207.49). Therefore, hybridization between the genotypes from cluster I and VI (green) can be utilized for getting superior recombinants/ transgressive segregants in segregating generations of chilli.

Energy-Efficient Cluster Head Selection in Wireless Sensor Networks Using an Improved Grey Wolf Optimization Algorithm

The internet of things (IoT) and industrial IoT (IIoT) play a major role in today’s world of intelligent networks, and they essentially use a wireless sensor network (WSN) as a perception layer to collect the intended data. This data is processed as information and send to cloud servers through a base station, the challenge here is the consumption of minimum energy for processing and communication. The dynamic formation of cluster heads and energy aware clustering schemes help in improving the lifetime of WSNs. In recent years, grey wolf optimization (GWO) became the most popular feature selection optimizing, swarm intelligent, and robust metaheuristics algorithm that gives competitive results with impressive characteristics. In spite of several studies in the literature to enhance the performance of the GWO algorithm, there is a need for further improvements in terms of feature selection, accuracy, and execution time. In this paper, we have proposed an energy-efficient cluster head selection using an improved version of the GWO (EECHIGWO) algorithm to alleviate the imbalance between exploitation and exploration, lack of population diversity, and premature convergence of the basic GWO algorithm. The primary goal of this paper is to enhance the energy efficiency, average throughput, network stability, and the network lifetime in WSNs with an optimal selection of cluster heads using the EECHIGWO algorithm. It considers sink distance, residual energy, cluster head balancing factor, and average intra-cluster distance as the parameters in selecting the cluster head. The proposed EECHIGWO-based clustering protocol has been tested in terms of the number of dead nodes, energy consumption, number of operating rounds, and the average throughput. The simulation results have confirmed the optimal selection of cluster heads with minimum energy consumption, resolved premature convergence, and enhanced the network lifetime by using minimum energy levels in WSNs. Using the proposed algorithm, there is an improvement in network stability of 169.29%, 19.03%, 253.73%, 307.89%, and 333.51% compared to the SSMOECHS, FGWSTERP, LEACH-PRO, HMGWO, and FIGWO protocols, respectively.

Energy and Environment-Aware Path Planning in Wireless Sensor Networks with Mobile Sink.

With the advances in sensing technologies, sensor networks became the core of several different networks, including the Internet of Things (IoT) and drone networks. This led to the use of sensor networks in many critical applications including military, health care, and commercial applications. In addition, sensors might be mobile or stationary. Stationary sensors, once deployed, will not move; however, mobile nodes can move from one place to another. In most current applications, mobile sensors are used to collect data from stationary sensors. This raises many energy consumption challenges, including sensor networks' energy consumption, urgent messages transfer for real-time analysis, and path planning. Moreover, sensors in sensor networks are usually exposed to environmental parameters and left unattended. These issues, up to our knowledge, are not deeply covered in the current research. This paper develops a complete framework to solve these challenges. It introduces novel path planning techniques considering areas' priority, environmental parameters, and urgent messages. Consequently, a novel energy-efficient and reliable clustering algorithm is proposed considering the residual energy of the sensor nodes, the quality of wireless links, and the distance parameter representing the average intra-cluster distance. Moreover, it proposes a real-time, energy-efficient, reliable and environment-aware routing, taking into account the environmental data, link quality, delay, hop count, nodes' residual energy, and load balancing. Furthermore, for the benefit of the sensor networks research community, all proposed algorithms are formed in integer linear programming (ILP) for optimal solutions. All proposed techniques are evaluated and compared to six recent algorithms. The results showed that the proposed framework outperforms the recent algorithms.

The Study of Genetic Divergence in Rice (Oryza Sativa L.) Genotypes with Grain Yield and Different Morpho-Physiological Traits using Mahalanobis’ D2 Analysis

The current study attempted to study genetic divergence in rice (Oryza sativa L.) genotypes with grain yield and different morpho-physiological traits among 18 rice varieties using Mahalanobis' D2 analysis. Keeping this in mind, the performance of the studied genotypes, as well as the association between various morpho-physiological and yield contributing traits among the genotypes, were evaluated in the field. The experiments were conducted from July to December 2018 at the Agronomy Field, Department of Agronomy and Agricultural Extension, Faculty of Agriculture, Rajshahi University. In terms of grain yield and other morpho-physiological traits, the genotypes differed significantly. The genotypes were classified into five clusters using Mahalonobis' D2 statistics and Tocher's method (Table 3). Cluster IV was the smallest of the clusters. Cluster l is the largest, with six genotypes. Clusters II and lV each contained three and two genotypes. The pattern of genotype distribution among various clusters reflected the significant genetic variability present in the genotypes, which may be due to adaptation of these genotypes to specific environmental conditions. Genotypes from different clusters are expected (inter-cluster). The intra (bold) and inter-cluster distances from D2 analysis (Table 4). Cluster I has the greatest intra cluster distance (0.956), while Cluster IV has the smallest (0.3583). The greatest inter-cluster distance is observed between cluster IV and (8.190), while the smallest inter-cluster distance is observed between cluster II and IV (2.924). In both vectors, the average inter-cluster distances were greater than the average intra-cluster distances, indicating the presence of greater genetic diversity among genotypes from different clusters than those from the same cluster. These findings indicated that these five traits contributed the most to the divergence among the eighteen rice genotypes. The results revealed that the clusters were remarkably distinct for the majority of the traits, indicating proper clustering. The most effective tiller, grain panicle, effective grain panicle, and days to maturity were found in Cluster V. The intra-cluster mean for grain yield was the lowest in this cluster. Cluster III had the lowest grain yield per acre. This was the cluster with the lowest initial grain yield. The intra-cluster mean for effective tiller and days to maturity was highest in this cluster. Cluster II had the smallest grain yield and the second smallest thousand grain weight. Cluster V had the shortest panicle length intra-cluster.

Cluster analysis of Corchorus capsularis jute based on agro-morphological characters to isolate high-yielding genotypes for breeding purposes

White jute (Corchorus capsularis) is an important bast fiber producing cash crop. Its narrow genetic base due to its self-pollination nature as well as genetic incompatibility in respect of interspecific hybridization cause low morphological variability. The searching for good genotypes and their use in intraspecific hybridization is the best option to increase the yield potentiality of this crop. In this research experiment, 95 white jute genotypes including 93 accessions and two pre-released varieties (CVL-1 and BJRI Deshi Pat 9) were investigated for higher fibre yield. The experiment was laid out in an augmented design at Jute Agricultural Experiment Station, Manikgonj during 2019. Plants were harvested at 120 days age and morphological data like plant height (m), base diameter (mm), green weight with leaves (g plant-1), fiber yield (g plant-1) and stick yield (g plant-1) were recorded carefully. From the descriptive analyses of the recorded data, good fibre yields were found in A-725 (20 g plant-1) and A-2232 (16.60 g plant-1) followed by A-2224, A-248, A-07, A-675, A-2264, A-1292, A-247, A-469, A-2226, A-674, A-02, A-232, A-10, A-684 than the control varieties BJRI Deshi Pat-9 (10.80 g plant-1) and CVL-1 (9.20 g plant-1). From the hierarchical cluster analysis, the average inter-cluster distances were found higher than the average intra-cluster distances, which shows the presence of wide genetic diversity among the genotypes of different clusters than those of the same cluster. These higher fibre yielding accessions with wide genetic diversity would be used as breeding materials for varietal improvement through hybridization and selection approaches.

Assessing the genetic diversity for yield traits in rice (Oryza sativa L.) genotypes using multivariate analysis under controlled and water stress conditions

The genetic diversity of yield and yield attributing characteristics was explored in this research. In the topical study, fifty-two rice genotypes including four checks were used under three environmental conditions i.e. irrigated (IR), rainfed (RF) and terminal stage drought (TSD) conditions. The prevalence of genetic divergence was evaluated using clustering and Principal component analysis (PCA) was used to determine the relative contribution of various traits. To fulfill the aim of the study, fifty-two genotypes were grouped into three distinct and non-overlapping clusters among these 3 clusters, cluster-I was the largest with the highest number of genotypes i.e. 47, 49 and 49 under IR, RF and TSD conditions, respectively. The highest average intra-cluster distance was observed in cluster-I, also the genotypes showed high variability under all three conditions. The highest inter-cluster distance between the cluster-II and cluster-III (IR and TSD) and cluster-I and cluster-II (RF) was observed, indicated that genotypes from the group should be considered for direct use as parents in hybridization programme to produce high yield. Only five of the 13 principal components (PCs) have been considered in the study based on the Eigen values and variability criteria. From the complex matrix it was revealed that the first-PC accounted for the highest variability. Genotypes which fall under a common PC were observed to be the most important factor for grain yield.

Morpho-anatomical variability, principal component analysis and Euclidean clustering of tossa jute (Corchorus olitorius L.)

Jute, the second most important natural fibre crop after cotton in the world. Bangladesh occupied the first position for jute fibre production and exportation after India among south-east Asian countries. Tossa jute is cultivated more (80–85%) here among the commercial cultivation of jute and allied fibre crops. It produces quality fibre but yield is low due to stress susceptibility compared to capsularis jute. The self-pollination nature and sexual incompatibility between two jute species (C. capsularis and C. olitorius) cause narrower genetic base and restrict genetic exchange. So, intra specific hybridization using diversified jute germplasms or genetic change will be the best option to the jute breeders for genetic improvement of existing cultivars. Hence, 20 tossa jute genotypes were investigated through field based morphological study (RCBD) and laboratory oriented anatomical study (CRD) for yield and yield attributing morpho-anatomical variability at BJRI during March, 2019 to July, 2020. Among 20 tossa jute genotypes, Acc. 1318, O-043-7-9(G), Acc. 4311, Acc. 1306, O-0411-10-4, O-049-1-3, O-0512-6-2, Acc. 4160 showed good results for yield and yield related morphological traits at 120 days after sowing and stem anatomical traits at 100 days after sowing. Higher significant genotypic correlation coefficient values were recorded than phenotypic associations among all the morphological traits. Plant height, fresh weight, fibre yield content gave high heritability (>80%) in broad sense along with possibility of more genetic advance in percentage. The average inter-cluster distances were higher than the average intra-cluster distances, which shows the presence of wide genetic diversity among the genotypes of different clusters than those of the same cluster. The first three principal components, whose Eigen values are greater than one, accounted for 99.96% of the total variation among the studied characters. The information obtained from this investigation is useful in planning further breeding programme for tossa jute improvement.

Genetic divergence analysis in basmati rice genotypes (Oryza sativa L.) for agro-morphological and quality traits using multivariate approach

Genetic diversity of 30 basmati rice genotypes was analysed based on nine agro-morphological and six quality traits. The results of the principal component analysis showed that the first six principal components explained 89.16% of the total variation in the experimental material. These 30 basmati genotypes were grouped into six clusters which indicated the presence of sufficient diversity among the tested genotypes. Cluster I and cluster III was found to be the largest comprising of 10 genotypes each followed by cluster II having seven genotypes. The highest average intra-cluster distance was observed in cluster III. Highest inter-cluster distance (D2) was observed between clusters III and VI followed by clusters III and V, III and IV & clusters II and III. Cluster II showed high mean value for grain yield per plant, 1000 grain weight, and grain length. The characters viz., amylose content, grain length after cooking, spikelet per panicle, grain width, grain length and 1000 grain weight contributed maximum towards total genetic divergence, suggest their suitability of selection.

Forming Suitable Groups in MCSCL Environments

Given the peculiarities of mobile computer-supported collaborative learning (MCSCL) environments, forming suitable groups in such learning environments represents a hard and time-consuming task. This is because many conditions related to mobile learners, devices, and environment should be considered. Unlike the existing solutions, the present paper shows a grouping approach that allows a customizable formation of (1) homogeneous groups, (2) heterogeneous groups, and (3) mixed groups. The proposed solution does not only help instructors to dynamically form appropriate MCSCL groups, but it also allows to continually control the learners' learning, psychological, and social developments. To assess the effectiveness of the proposed solution, three metrics were used: (1) comparison between the characteristics of the existing group formation tools, (2) average intra-cluster distance of each grouping algorithm, and (3) an experimental evaluation in a real world environment. The obtained results show a great superiority of the proposed solution compared to the existing ones.

An Energy Efficient e-Healthcare Framework Supported by Novel EO-μGA (Extremal Optimization Tuned Micro-Genetic Algorithm)

The edge/fog computing has the potential to gear up the healthcare industry by providing better and faster health services to the patients. In healthcare systems where every second is crucial, the edge computing can be helpful to reduce the time between data capture and analytics in a powerful manner. In edge computing, the network edge devices are configured in such a manner that they can handle critical analysis and make necessary decisions instead of sending the captured health data directly to the cloud. However, lifetime of the edge network is a critical factor and thus an energy efficient network architecture has to be designed to achieve the above mentioned goal. In this regard, this research presents a new extremal optimization tuned micro genetic algorithm (EO-μGA) based clustering technique for the sake of efficient routing and prolonging network lifetime by saving the battery power of network edge devices. Moreover, a novel fitness function with a set of relevant criteria of edge devices such as energy factor, average intra-cluster distance, average distance to cluster leader over data analytics center, average sleeping time, and computational load has been considered for the selection of the cluster leader which will be responsible for managing intra-cluster and inter-cluster data communication. The simulation results show that the proposed EO-μGA based clustering model offers a higher network lifetime and a least amount of transmission energy consumption per node as compared to various state of the art optimization algorithms.

Moth flame optimization algorithm based on decomposition for placement of relay nodes in WSNs

Metaheuristic algorithms have popularly been used to solve a wide range of complex engineering optimization problems. In order to solve the problems with two or more objectives multi-objective algorithms (MOAs) are used. Application of MOAs to solve multiple conflicting objectives yields a Pareto-optimal solution set. In this paper, we propose a multi-objective decomposition-based moth flame optimization (MOMFO/D) algorithm, that decomposes the objectives into multiple single objectives which are optimized simultaneously. The algorithm is used to solve the relay node placement problem, that is modeled as a bi-objective problem with the goal of minimization of average intra-cluster distance and average hop-count to improve the network lifetime. The Pareto-optimal fronts obtained through the simulations are evaluated using three distinct quality indicators namely the Inverted Generational Distance, Spacing Metric and Maximum Spread in order to evaluate the performance. The obtained results considered over a number of runs are compared with other existing optimizers in the literature such as multi-objective non-dominated sorted moth flame optimizer, and multi-objective evolutionary algorithm based on decomposition. The results demonstrate the superiority in the performance of the proposed algorithm over others. The statistical analysis of the experimental work has been carried out by conducting Friedman’s and Quade test.

Genetic analysis of quantitative traits in eggplant (Solanum melongena L.)

Genetic diversity among 26 eggplant accessions was assessed using Mahalanobis D2 statistics. Thirteen quantitative traits were tested for Principal Component analysis (PCA) for expression of variability. The PCA revealed that the first six PCs with eigenvalues >0.1 contributed for 89.1 per cent of total variation. The genotypes were classified into six clusters on the basis of genotypic variability. Cluster I was the largest comprising 13 genotypes. Highest inter-cluster distance was between clusters IV and VI (1328.6) and the least between cluster I and II (368.4), indicating their genetic relatedness. The average intra-cluster distance between the genotypes was maximum for cluster III (267.5). Fifty two per cent of total variation for morphological traits originated from first two principal components. Leaf length (L), leaf breadth, fruit diameter (D), fruit L/D ratio, diameter of pistil scar and plant spread remained key traits for maximum contribution to genetic diversity. Thus, hybridization among these genotypes can generate desirable transgressive segregates.

Studies on genetic divergence in rice germplasm (Oryza sativa L.)

Genetic divergence of 50 rice genotypes was studied for thirteen quantitative plant growth and yield related traits was estimated using Mahalanobis’s statistics (D2) analysis. The genotypes were grouped into 8 clusters. The cluster VII was the largest with 12 genotypes followed by cluster III with 8 genotypes, cluster V & VI with 7 genotypes each, while cluster I & IV with 6 genotypes each, cluster VIII with 3 genotypes and cluster II with 1 genotype of rice. The inter cluster distances were higher than the average intra-cluster distance reflecting wider genetic diversity among the genotypes of different groups than those of the same cluster. The highest inter-cluster distance was observed between cluster V & VI1 whereas the highest intra-cluster distance was found in the cluster VI indicated that the highly divergent types existed in these clusters. This study concluded that the genotypes UR-28 followed by UR-3 and DRR-11 were the best genotypes for grain yield as for as characters concerned for harvest index, no. of spikelet per panicle, no. of tillers per plant. The present study registered high GCV along with high genetic advance for no. of panicles per plant, no. of tillers per plant. So these genotypes with grain yield traits should be given top priority during selection of the genetically divergent parents for further utilization as parents in breeding programme.

Genetic diversity analysis in tossa jute (Corchorus olitorius L.) germplasm lines

An experiment was conducted to examine the magnitude of genetic diversity and characters contributing to genetic diversity among 81 tossa jute genotypes. Cluster analysis based on Euclidean squared distances and ward’s method, the genotypes were grouped into ten distinct clusters. Analysis reveals that stick weight contributes maximum to the phenotypic diversity (65.52%) followed by green weight (13.64%) and fibre yield (10.10%). Among the clusters Cluster IX recorded highest mean fibre yield (19.91g) followed by Cluster VII (18.94g) and these clusters also recorded high mean values for plant height, basal diameter, green weight and stick weight. The highest inter- cluster distance was 186.80 (between clusters II and X) followed by 161.26 (between clusters IV and X), indicating the wide genetic diversity among these clusters. The highest intra-cluster distance was observed in cluster II (20.34) and the lowest in cluster X (3.17). The average inter-cluster distances were higher than the average intra-cluster distances, which shows the presence of wide genetic diversity among the genotypes of different clusters than those of the same cluster. The first two principal components, whose Eigen values are greater than one, accounted for 74% of the total variation among the five characters. The information obtained from diversity analysis is useful in planning further breeding programme for tossa jute improvement.

Augmented Session Similarity Based Framework for Measuring Web User Concern from Web Server Logs

In this paper, an augmented sessions similarity based framework is proposed to measure web user concern from web server logs. This proposed framework will consider the best usage similarity between two web sessions based on accessed page relevance and URL based syntactic structure of website within the session. The proposed framework is implemented using K-medoids clustering algorithms with independent and combined similarity measures. The clusters qualities are evaluated by measuring average intra-cluster and inter-cluster distances. The experimental results show that combined augmented session dissimilarity metric outperformed the independent augmented session dissimilarity measures in terms of cluster validity measures.

A Grey Wolf Optimizer Based Automatic Clustering Algorithm for Satellite Image Segmentation

Grey Wolf Optimizer has recently emerged as an efficient meta-heuristic optimization technique. It has good capability in exploitation for unimodal problems, superior exploration ability for multimodal problems, and also works fine for composite functions avoiding local minimas. This paper proposes an application of Grey Wolf Optimizer (GWO) algorithm for satellite image segmentation. The original GWO has been suitably modified to work as an automatic clustering algorithm. This algorithm has been applied on two satellite images. It is computationally efficient and its accuracy is superior in many cases in terms of Davies-Bouldin (DB) index, average inter-cluster distance and average intra-cluster distance.

Probing the Heterogeneity of Protein Kinase Activation in Cells by Super-resolution Microscopy.

Heterogeneity of mitogen-activated protein kinase (MAPK) activation in genetically identical cells, which occurs in response to epidermal growth factor receptor (EGFR) signaling, remains poorly understood. MAPK cascades integrate signals emanating from different EGFR spatial locations, including the plasma membrane and endocytic compartment. We previously hypothesized that in EGF-stimulated cells the MAPK phosphorylation (pMAPK) level and activity are largely determined by the spatial organization of the EGFR clusters within the cell. For experimental testing of this hypothesis, we used super-resolution microscopy to define EGFR clusters by receptor numbers (N) and average intracluster distances (d). From these data, we predicted the extent of pMAPK with 85% accuracy on a cell-to-cell basis with control data returning 54% accuracy (P < 0.001). For comparison, the prediction accuracy was only 61% (P = 0.382) when the diffraction-limited averaged fluorescence intensity/cluster was used. Large clusters (N ≥ 3) with d > 50 nm were most predictive for pMAPK level in cells. Electron microscopy revealed that these large clusters were primarily localized to the limiting membrane of multivesicular bodies (MVB). Many tighter packed dimers/multimers (d < 50 nm) were found on intraluminal vesicles within MVBs, where they were unlikely to activate MAPK because of the physical separation. Our results suggest that cell-to-cell differences in N and d contain crucial information to predict EGFR-activated cellular pMAPK levels and explain pMAPK heterogeneity in isogenic cells.

Towards Energy Efficient k-MEANS Based Clustering Scheme for Wireless Sensor Networks

Clustering is one of the effective methods which are applied in the wireless sensor networks to preserve the precious battery power of sensor nodes. Various clustering approaches have been proposed in the literature and LEACH is one of clustering and routing schemes which can be used to manage the sensor network. In this paper, we present an improved version of LEACH protocol which uses Kmeans clustering algorithm for selecting better nodes for clusterhead role. Our proposed solution tries to create symmetric clusters and by reducing the average intra-cluster communications distance, improves the network lifetime. It also, considers the sink location when the sink node is positioned far away and selects nodes as clusterhead which will consume less energy for handling cluster members. Thus, because, sensed data are transmitted to the closer clusterheads, our proposed clustering solution can prolong the network lifetime.