consist of hundreds of thousands of small and cost effective sensor nodes. Sensor nodes are used to sense the environmental or physiological parameters like temperature, pressure, etc. For the connectivity of the sensor nodes, they use wireless transceiver to send and receive the inter-node signals. Sensor nodes, because connect their selves wirelessly, use routing process to route the packet to make them reach from source to destination. These sensor nodes run on batteries and they carry a limited battery life. Clustering is the process of creating virtual sub-groups of the sensor nodes, which helps the sensor nodes to lower routing computations and to lower the size routing data. There is a wide space available for the research on energy efficient clustering algorithms for the WSNs. LEACH, PEGASIS and HEED are the popular energy efficient clustering protocols for WSNs. In this research, we are working on the development of a hybrid model using LEACH based energy efficient and K-means based quick clustering algorithms to produce a new cluster scheme for WSNs with dynamic selection of the number of the clusters automatically. In the proposed method, finding an optimum "k" value is performed by Elbow method and clustering is done by k-means algorithm, hence routing protocol LEACH which is a traditional energy efficient protocol takes the work ahead of sending data from the cluster heads to the base station. The results of simulation show that at the end of some certain part of running the proposed algorithm, at some point the marginal gain will drop dramatically and gives an angle in the graph. The correct "k" i.e. number of clusters is chosen at this point, hence the "elbow criterion".

With the current progress in communication technology, Wireless Sensor Network (WSN)s are extensively setup for plethora of applications such as defense, intelligent transportation systems, healthcare and many more. The limitation of sensor node is in terms of resources namely storage, power, processing potential and communication range. For the sensor network to be operational an efficient routing algorithm to forward the arriving packets is essential The data transferred by the nodes will be redundant in most of the scenarios. Selecting the optimal route for data transmission is one of the vital factors from the perspective view point of a routing protocol In order to eliminate the redundancy of data and to enhance the QoS parameters, Statistical Ad-hoc Ondemand Distance Vector (S-AODV) routing protocol is proposed in this paper. This new protocol is analyzed with respect to each layer by taking QoS metrics and statistical parameters in to consideration. A comparative study reveals that the proposed protocol performs better than the present AODV protocol.

Wireless sensor system includes hundreds to thousands of sensor nodes that helps in collecting different information including temperature, sound, area, etc. It’s generally difficult to recharge or change the sensor nodes which may have confined battery capacity. Energy efficiency is therefore a key problem in sustaining the network. Certainly one of the most used alternatives to make WSNs energy-efficient is to cluster the networks. Various clustering techniques are accustomed to effectively optimize or enhance the energy of sensor nodes. In this paper we have examined the various methods of clustering which are centralized, distributed and hybrid utilized in Sensor Networks. This paper also presents a comparative study of various clustering algorithms and the issues of clustering in WSNs.

Wireless sensor networks consist of low power lightweight sensor nodes that are often placed remotely for applications like wildlife monitoring, rainforest monitoring, military surveillance, forest fire detection etc. Here energy is a critical issue as nodes cannot be recharged and/or replaced frequently. In this paper we propose an energy efficient hierarchical routing protocol for Wireless Sensor Networks to increase network lifetime. Here the cluster head is selected based on the residual energy, distance of the node from the base station, how recently the node was selected as cluster-head etc. Along with this, the proposed solution can also detect malicious nodes in the network and can prevent them from becoming cluster heads. The simulation results show that proposed solution provides good performance in terms of extending network lifetime, uniform selection of nodes as cluster head that eliminates the probability of a single node from draining away its power by its repetitive selection as a cluster head.

Wireless sensor networks (WSN) consists of dedicated sensors, which monitor and record various physical and environmental conditions like temperature, pollution levels, humidity etc. WSN is compatible with several applications related to environmental and healthcare monitoring. The sensor nodes have a limited battery life and are deployed in hostile environments. Recharging or replacement of the batteries in the sensor nodes are very difficult after deployment in inaccessible areas where energy is an important factor for continuous network operation. Energy efficiency is a major concern in the wireless sensor networks as it is important for maintaining network operation. In this paper, an energy efficient clustering algorithm based energy centroid and energy threshold has been proposed for wireless sensor networks. Here each cluster is designed to own 25% of the sensor nodes using distance centroid algorithm. Cluster head selection is based on the energy centroid of each cluster and energy threshold of the sensor nodes. Communication between the sink node and cluster head uses distance of separation as a parameter for reducing the energy consumption. The result obtained shows an average increase of 53% in energy conservation and network lifetime compared to Leach-B, Park Approach, EECPK-means Approach and MPST Approach.

Wireless sensor networks (WSN) are one of the significant technologies due to their diverse applications such as health care monitoring, smart phones, military, disaster management, and other surveillance systems. Sensor nodes are usually deployed in large number that work independently in unattended harsh environments. Due to constraint resources, typically the scarce battery power, these wireless nodes are grouped into clusters for energy efficient communication. In clustering hierarchical schemes have achieved great interest for minimizing energy consumption. Hierarchical schemes are generally categorized as cluster-based and grid-based approaches. In cluster-based approaches, nodes are grouped into clusters, where a resourceful sensor node is nominated as a cluster head (CH) while in grid-based approach the network is divided into confined virtual grids usually performed by the base station. This paper highlights and discusses the design challenges for cluster-based schemes, the important cluster formation parameters, and classification of hierarchical clustering protocols. Moreover, existing cluster-based and grid-based techniques are evaluated by considering certain parameters to help users in selecting appropriate technique. Furthermore, a detailed summary of these protocols is presented with their advantages, disadvantages, and applicability in particular cases.

Wireless sensor network is system in which the sensor nodes are interconnected with each other wirelessly to monitor physical or environmental conditions like humidity, temperature and pressure. Wireless sensor network is used in many applications like medical applications, industrial applications, environmental and earth sensing. But some of the problems stated in wireless sensor networks are network lifetime, power consumption and security. In order to overcome these problems cluster network model is used. Clustering is a process of grouping of sensor nodes to increase the scalability of the system. Many protocols are used for the clustering of sensor nodes. In this paper, we study about the clustering protocols like LEACH, HEED, K-means and K-medoids and performance of these protocols.

Wireless sensor network is a set of sensors randomly distributed to collect data and to send it to base station, these sensors present a limited batteries, so one of the key parameters of these networks is energy consumption. Many researchers proposed protocols to reduce energy of sensors and increase life time of network. In this paper we propose a new method for the Cluster Head Selection with multihop Balanced Clustering Routing protocol to compare two methods for the select of CH through a multi hop Balanced Clustering Routing Protocol, this protocol is based on k-means clustering and chooses between centroid method and score calculating method to select the cluster head. The protocol is also compared to other algorithms, the simulation results show that CHSM-MBC improved residual energy of sensors, increase the number of live nodes and improved lifetime of the network.

Wireless sensor network consists of number of sensor nodes clustered together to collect various environmental or other physiological data like temperature, pressure, seismic graph, sound, location, etc. WSNs are used in various fields like healthcare, monitoring systems, military, etc. Sensor nodes run on battery without direct power supply. Hence, energy efficiency becomes the major issue in WSNs to make WSNs run for longer. Because these wireless sensor nodes run on batteries and they carry a limited battery life. Routing process and Sensing process consumes the battery power. Sensing process is the primary process of sensor nodes, hence, cannot make changes in this process. Routing process is the secondary process of the nodes, and is having a great possibility of improvements. In the existing approach, k-mean was used for the clustering algorithm for the cluster head selection. In this paper, we have proposed an effective and efficient cluster head selection method using k-Medoids to solve the stated problem, especially for large WSNs consisting of thousands or millions of nodes. k-Medoids is more efficient and correct than kMeans for large clusters. Both of K-Means and k-Medoids utilize expectation maximization (EM) strategy to converge to a minimum error condition. While k-Medoids require the cluster centers to be centroids, in k-Means the centers could be anywhere in the sample space. k-Medoid is more robust to outliners than k-Means therefore results in more quality clustering. It is also computationally more complex. Computer simulation will be performed in the NS2 environment and the proposed approach will be compared with LEAH and HEED.

Wireless sensor network (WSN) is the group of sensor nodes which attempts to monitor and share the data. Secured and reliable routing in the WSN is the most arduous task which needs to be performed with more concern for the stable output. In the existing work clustering based symbiotic organism search method is used to share the data with assured reliability and packet delivery ratio. However, this approach lacks performance due to the intruder, and it is resolved in the proposed method by introducing the novel method namely hybridized pareto-glowworm swarm optimization and authenticated data communication. In this paper, cluster head selection is made by considering multiple quality of service parameters such mobility and energy whereas in the previous research only energy is considered. To select the optimal cluster head with multiple objectives, a pareto optimal method is hybridized with the GSO algorithm for the multi-objective fitness evaluation. Here formation of clusters is purely based on the selected cluster Head. After clustering, data is gathered from the multiple sensor nodes which are aggregated to reduce the bandwidth utilization and forwarded to the base station. To ensure the security level in this work authentication between the cluster head and the base station is done and then data is transmitted. The experimental results concluded that the proposed method has a better result than the existing methods.

Wireless sensor network (WSN) is a group of sensor nodes that provides effective long haul data transmission. It can be used for various large scale applications such as security, health-care, industry automation, agriculture, environment and habitat monitoring etc. This work focuses on a comparative analysis for improving the lifetime of Wireless Sensor Network (WSN) and creating an energy efficient communication in WSN. As clustering techniques provides efficient way to reduce energy consumption, this analysis gives various clustering algorithms like to maximize the lifetime of WSN. The energy and life time of Low-Energy Adaptive clustering Hierarchy (LEACH), K-means, Fuzzy C-means algorithms are analyzed based on their characteristics for different scenarios. The simulation results shows that Fuzzy C-Means algorithm has competing performance than LEACH.

Wireless sensor network (WSN) consists of several tiny devices that are dispersed randomly for gathering network field. Clustering mechanism divides the WSN into different sub-regions called clusters. Individual cluster is consisting of cluster head (CH) and member nodes. The main research challenges behind clustering mechanism are to optimize network overheads with efficient data delivery. Sensor nodes are operated by batteries and practically it is not feasible to replace them during sensing the environment so energy should be effectively utilized among sensors for improving overall network performance. This research paper presents an improved energy aware cluster based data routing (i-ECBR) scheme, by dividing the network regions into uniform sized square partitions and localized CH election mechanism. In addition, consistent end-to-end data routing is performed for improving data dissemination. Simulation results illustrate that our proposed scheme outperforms than existing work in terms of different performance metrics.

Wireless sensor network (WSN) consists of hundred to thousands sensor nodes to gathered the information from physical environment. Different clustering based algorithms have been proposed to improve network lifetime and energy efficiency. Practically it is not feasible to recharge the battery of sensor nodes when they are sensing the data. In such situation energy is crucial resource and it should be improved for life span of WSN. Cluster head (CH) has an important role in hierarchical energy efficient routing protocols because it receives data from nodes and sends towards base station (BS) or sink node. This paper presents a grid based cluster head selection (GBCHS) mechanism by dividing the network field into MXN uniform size partitions that aims to minimize the energy dissipation of sensor nodes and enhancing network lifetime. Simulation experiments have been performed in network simulator (NS2) that show our proposed GBCHS approach outperformed than standard clustering hierarchy LEACH protocol.

Wireless Sensor Networks are becoming a worldwide sensational topic with recent advances in wireless communications and digital electronics. It serves as the backbone for controlling real-life applications. It consists of group of sensor nodes that sense the information from an event area and passes it to the base station which reacts according to the environment. There are number of cluster based routing protocols, in which a region is divided into number of clusters and within each cluster, a cluster head is elected based on some parameter. So, a novel selection method for the cluster head having efficiency in energy is based on Flower Pollination Algorithm (FPA) is proposed in this paper. The performance of our proposed scheme is being analyzed and is compared with the already existing protocols like LEACH, C-LEACH and K-Means in terms of energy efficiency, number of alive nodes, packet drop ratio and energy dissipation etc

Wireless Sensor Network (WSN) is very effective for research community in recent years in demand and adaptive applications. The nodes of WSN are limited with their energy which requires the energy to be utilized in efficient manner. The factor of energy depletion would be reduced by grouping the nodes to support routing and data collection. Different algorithms recommended towards the energy management in different conditions. This research work proposes an Energy Efficient Cluster based Multilevel Hierarchical Routing (EECMHR) technique to support effective data collection in WSN. The method splits the entire network region into number of regions and clusters where each cluster has single Cluster Head (CH). The nodes of WSN are grouped in hierarchical level which performs routing through the CH identified which is rotated at each timestamp according to energy conditions. EECMHR approach has been simulated by using the Network Simulator 2 under different scenarios, and its performance is compared with existing clustering algorithms.

WSNs are utilized in number of fields like healthcare, weather, military, etc. These sensor nodes are battery running devices. Hence, the energy becomes the major issue. A number of researches are being conducted to increase their battery life. Sensing process is the primary process of Sensor nodes. A number of WSN processes are having great possibilities of improvement: 1) Clustering, 2) Routing 3) Routing Data Management. In this paper, we are improving an existing clustering algorithm which is based on k-medoids clustering algorithm. In this paper, we are proposing an effective and efficient cluster head selection method using kMedoids to solve the stated problem, especially for large WSNs consisting of thousands or millions of nodes. k-Medoids is more efficient and correct than k-Means for large clusters. Both of K-Means and k-Medoids utilize expectation maximization (EM) strategy to converge to a minimum error condition. While k-Medoids require the cluster centers to be centroids, in k-Means the centers could be anywhere in the sample space. kMedoid is more robust to outliners than k-Means therefore results in more quality clustering. It is also computationally more complex. Computer simulation will be performed in the NS2 environment and the proposed approach will be compared with LEAH and HEED. INTRODUCTION In the last half century, the computers have exponentially increased in processing power and at the same time decreased in both size. These rapid developments led to large market in which computer based devices are being used more and more in our society’s daily activities. In the last years, computers are reduced in size and becoming so small and so cheap, that single-purpose computers with embedded sensors are almost practical from both economical and theoretical points of view. Wireless sensor networks are the computers which are beginning to become a reality, and therefore some of the long overlooked limitations have become an important area of research. FIGURE 1: Wireless Sensor Network Architecture [LINK:http://www.google.com/url?q=http://www.fi.muni.cz /usr/staudek/vyuka/PA151/07_wpan_zb.ps.pdf&ust=139944 0775533106&usg=AFQjCNHj2reTSqQ0xQ7lDoxx2rvc-

Usually, Wireless Sensor Network operates in a wide range of untrustworthy environments that are deployed in an ad-hoc approach. Although different cluster-based schemes have been proposed for improving network lifetime, however, most of the existing solutions incur probabilistic methods, which result in non-uniform energy consumption and imbalanced load distribution. In addition, end-to-end route discovery is non-optimized in terms of the limited resources of sensor nodes, which leads to frequent route discoveries and network overheads. In this research paper, we present Weighted Energy-Efficient Clustering with Robust Routing (WECRR) protocol that maintains balanced energy consumption and improves network-wide data delivery performance. The contributions of our proposed WECRR protocol are: Firstly, WECRR initiates a deterministic approach to avoid the uncertainties in Cluster Heads election mechanism and performs bounded clustering mechanism. Secondly, it provides multi-level optimized routing decision by making use of multi-facet attributes. At the end, it provides a route maintenance strategy, upon encounters any faulty or exhausted nodes on primary route, which results in reducing re-transmissions and route breakages. Simulation results reveal improved performance of WECRR protocol than compared to existing work.

Track maintenance is the primary concern for metro railways. Currently, tracks are inspected manually which consumes a lot of time, labor and power. Condition monitoring using Wireless Sensor Network can reduce maintenance time through automated monitoring by detecting faults before they escalate. Vibration estimating sensors are laid along the length of tracks which will have a vast amount of data to be communicated where senders and receivers are sensors, trains and sink. Thus, we have used cluster based routing with data aggregation to reduce communication overhead and cluster based fault detection technique to handle cluster head failure as part of network setup and then implemented our proposed track fault detection algorithm in this network. Our proposed track fault detection algorithm provides better results in terms of total energy consumed and total time taken to detect and update train regarding track fault location.

Today, e-commerce is growing and developing in direct proportion with the development of the internet. With these developments in e-commerce and the changing marketing world, businesses have started to produce new solutions in this sector. The establishment of virtual store warehouses in certain points of the city and the delivery of orders to customers via drone vehicles are the most important of these solutions. The aim of the research is to present and test a research model for determining the location and priorities of the virtual store warehouses (Virtual Store Drone Warehouse) that provide retail product transportation with drone in metropolises. Naturally, distance and density data are used in the analysis of such problems. In the study, London, which is one of the biggest metropolises of the world, is taken as an example. Depending on the model of the research, in the first stage, the points indicating the density in the metropolis were determined. In the next stage, the location data (latitude-longitude) of these points in London Zone-2 was collected via Google maps. In the next stage of the model, the location data of the densely populated areas were grouped using x-means clustering algorithm, one of the data mining techniques. Then, the performance evaluation of the clusters was carried out using TOPSIS, one of the Multi-Criteria Decision-Making (MCDM) methods

This paper studies the secrecy performance of wireless power transfer (WPT)-based multi-hop transmissions in wireless sensors networks (WSNs), where legitimate nodes harvest energy from multiple power beacons (PBs) to support the multi-hop secure data transmission to a destination in the presence of an eavesdropper. Specifically, the PBs not only transfer radio frequency energy to the legitimate nodes but also act as friendly jammers to protect data transmission. To improve secrecy performance, we propose two secure scheduling schemes, named minimum node selection (MNS) scheme and optimal node selection (ONS) scheme. We then evaluate the performance of the proposed schemes in terms of the exact closed-form for secrecy outage probability (SOP) and asymptotic SOP. The developed analyses are verified by Monte-Carlo simulations. The numerical results show that the ONS scheme outperforms the MNS scheme emerging as an effective protocol for secure multi-hop transmission in WSNs. Furthermore, the effects of the number of PBs, number of hops, time switching ratio, and the secure target data rate on the system performance are also investigated.