Comparative Analysis of Algorithm for Cluster Head Selection in Wireless Sensor Network

One of the challenging issues to be studied in WSN is its energy saving so as to extend lifetime. The primary goal of node clustering is network preprocessing that is used to obtain information and limit energy consumed. To support high adaptability and better accumulation of information data, sensor nodes are often grouped into disconnected, non overlapping batches, groups of nodes called clusters. Clusters design hierarchical WSNs which incorporate adequate performance of finite reserves of sensor nodes and thus enhance network lifetime. In this paper different clustering algorithm are compared having different cluster head selection approach. Our paper presents review of different energy efficient cluster head selection algorithms in WSNs. DOI: 10.17762/ijritcc2321-8169.150312

Planeación y despliegue de la red de sensores inalámbricos requerida para la medición inteligente de energía eléctrica usando restricciones de capacidad y cobertura

The electrical energy measurement (EEM), seeks to provide quality services without neglecting the reliability of the system. Therefore, a quality service must be closely linked to the wireless communication technologies, to technify the EEM, not only reading, but also cuts, reconnections, and other additional services that the intelligent measurement infrastructure provides through wireless technologies Such as cell or WiFi, increasingly common because of the reliability they provide in real-time data transmission. Wireless infrastructures allow us to provide coverage to the fixed terminals, determined by the electric meter, and in turn manage and plan the optimal deployment of wireless sensors (SI) in finite areas, whether urban, rural or suburban. This article proposes an optimal model for planning and deploying SI for the EEM in order to guarantee reliable wireless communication links at the lowest implementation cost. Therefore, the proposed algorithm gives global solutions within a finite scenario, making this a scalable model in time able to manage the use of available links. The SIs for the EEM are inserted into the Neighborhood Area Networks (NANs) covered by the mobile communications network.La medición de energía eléctrica (MEE), busca proporcionar servicios de calidad sin descuidar la confiabilidad del sistema. Por lo tanto, un servicio de calidad debe ir estrechamente ligada a las tecnologías de comunicación inalámbrica, para tecnificar la MEE, no solo lectura, sino también cortes, reconexiones, y otros servicios adicionales que la infraestructura de medición inteligente provee a través de tecnologías inalámbricas como celular o WiFi, cada vez más comunes debido a la confiabilidad que estas brindan en la transmisión de datos en tiempo real [1]. Las infraestructuras inalámbricas nos permiten brindar cobertura a los terminales fijos, determinados por el medidor eléctrico, y a su vez gestionar y planificar el óptimo despliegue de sensores inalámbricos (SI) en áreas finitas, ya sean, urbanas, rurales o suburbanas. Este artículo propone un modelo óptimo de planeación y despliegue de SI para la MEE con la finalidad de garantizar enlaces de comunicación inalámbricos confiables al menor costo de implementación. Por lo tanto, el algoritmo propuesto da soluciones globales dentro de un escenario finito, haciendo de este un modelo escalable en el tiempo capaz de gestionar el uso de enlaces disponibles. Los SI para la MEE, son insertados en las Redes de Área Vecindaria (NAN) cubiertas por la red de comunicaciones móviles

An Enhanced Cluster-Based Routing Model for Energy-Efficient Wireless Sensor Networks

Energy efficiency is a crucial consideration in wireless sensor networks since the sensor nodes are resource-constrained, and this limited resource, if not optimally utilized, may disrupt the entire network's operations. The network must ensure that the limited energy resources are used as effectively as possible to allow for longer-term operation. The study designed and simulated an improved Genetic Algorithm-Based Energy-Efficient Routing (GABEER) algorithm to combat the issue of energy depletion in wireless sensor networks. The GABEER algorithm was designed using the Free Space Path Loss Model to determine each node's location in the sensor field according to its proximity to the base station (sink) and the First-Order Radio Energy Model to measure the energy depletion of each node to obtain the residual energy. The GABEER algorithm was coded in the C++ programming language, and the wireless sensor network was simulated using Network Simulator 3 (NS-3). The outcomes of the simulation revealed that the GABEER algorithm has the capability of increasing the performance of sensor network operations with respect to lifetime and stability period

SHRP - Secure Hybrid Routing Protocol over Hierarchical Wireless Sensor Networks

A data collection via secure routing in wireless sensor networks (WSNs) has given attention to one of security issues. WSNs pose unique security challenges due to their inherent limitations in communication and computing, which makes vulnerable to various attacks. Thus, how to gather data securely and efficiently based on routing protocol is an important issue of WSNs. In this paper, we propose a secure hybrid routing protocol, denoted by SHRP, which combines the geographic based scheme and hierarchical scheme. First of all, SHRP differentiates sensor nodes into two categories, nodes with GPS (NG) and nodes with antennas (NA), to put different roles. After proposing a new clustering scheme, which uses a new weight factor to select cluster head efficiently by using energy level, center weight and mobility after forming cluster, we propose routing scheme based on greedy forwarding. The packets in SHRP are protected based on symmetric and asymmetric cryptosystem, which provides confidentiality, integrity and authenticity. The performance analyses are done by using NS2 and show that SHRP could get better results of packet loss rate, delivery ratio, end to end delay and network lifetime compared to the well known previous schemes

Multihopping Multilevel Clustering Heterogeneity-Sensitive Optimized Routing Protocol for Wireless Sensor Networks

Effective utilization of energy resources in Wireless Sensor Networks (WSNs) has become challenging under uncertain distributed cluster-formation and single-hop intercluster communication capabilities. So, sensor nodes are forced to operate at expensive full rate transmission power level continuously during whole network operation. These challenging network environments experience unwanted phenomena of drastic energy consumption and packet drop. In this paper, we propose an adaptive immune Multihopping Multilevel Clustering (MHMLC) protocol that executes a Hybrid Clustering Algorithm (HCA) to perform optimal centralized selection of Cluster-Heads (CHs) within radius of centrally located Base Station (BS) and distributed CHs selection in the rest of network area. HCA of MHMLC also produces optimal intermediate CHs for intercluster multihop communications that develop heterogeneity-aware economical links. This hybrid cluster-formation facilitates the sensors to function at short range transmission power level that enhances link quality and avoids packet drop. The simulation environments produce fair comparison among proposed MHMLC and existing state-of-the-art routing protocols. Experimental results give significant evidence of better performance of the proposed model in terms of network lifetime, stability period, and data delivery ratio

PERFORMANCE ANALYSIS AND OPTIMIZATION OF QUERY-BASED WIRELESS SENSOR NETWORKS

This dissertation is concerned with the modeling, analysis, and optimization of large-scale, query-based wireless sensor networks (WSNs). It addresses issues related to the time sensitivity of information retrieval and dissemination, network lifetime maximization, and optimal clustering of sensor nodes in mobile WSNs. First, a queueing-theoretic framework is proposed to evaluate the performance of such networks whose nodes detect and advertise significant events that are useful for only a limited time; queries generated by sensor nodes are also time-limited. The main performance parameter is the steady state proportion of generated queries that fail to be answered on time. A scalable approximation for this parameter is first derived assuming the transmission range of sensors is unlimited. Subsequently, the proportion of failed queries is approximated using a finite transmission range. The latter approximation is remarkably accurate, even when key model assumptions related to event and query lifetime distributions and network topology are violated. Second, optimization models are proposed to maximize the lifetime of a query-based WSN by selecting the transmission range for all of the sensor nodes, the resource replication level (or time-to-live counter) and the active/sleep schedule of nodes, subject to connectivity and quality-of-service constraints. An improved lower bound is provided for the minimum transmission range needed to ensure no network nodes are isolated with high probability. The optimization models select the optimal operating parameters in each period of a finite planning horizon, and computational results indicate that the maximum lifetime can be significantly extended by adjusting the key operating parameters as sensors fail over time due to energy depletion. Finally, optimization models are proposed to maximize the demand coverage and minimize the costs of locating, and relocating, cluster heads in mobile WSNs. In these models, the locations of mobile sensor nodes evolve randomly so that each sensor must be optimally assigned to a cluster head during each period of a finite planning horizon. Additionally, these models prescribe the optimal times at which to update the sensor locations to improve coverage. Computational experiments illustrate the usefulness of dynamically updating cluster head locations and sensor location information over time