LEACH Based Method for Prolong the Network Life

In this paper, it focuses at the communication protocols, that can have important effect on the whole energy dissipation of these types of networks. Depend on the observations that the conventional protocols of direct transmission, multi-hop routing, minimum-transmission-energy, and static clustering cannot be better for the sensor networks, it suggest the LEACH (Low-Energy Adaptive Clustering Hierarchy), to equally share the load of energy in the network among the sensors. MANET has a dynamic type of topology because of the movement of portable terminals in the network. These types of mobile terminals are battery operated and need battery resources for the purpose of communication also these types of resources are very limited. This protocol provides help to decrease the power consumption of terminals and also extends the life of battery to enhance the life time of network. This paper, point out on the energy efficient routing protocol that is LEACH (Low Energy Adaptive Clustering Hierarchy) is discovered, by the use of this protocol the performance of network is improved by decreasing the consumption of power of mobile terminals in the network

Study of different mobility models and clustering algorithms like weighted clustering algorithm (WCA) and dynamic moblity adaptive clustering algorithm (DMAC)

This project addresses issues pertaining to mobile multi-hop radio networks called mobile ad hoc networks (MANET), which plays a critical role in places where a wired backbone is neither available nor economical to deploy. Our objective was to form and maintain clusters for efficient routing, scalability and energy utilization. To map the cellular architecture into the mobile ad hoc network cluster heads are elected that form the virtual backbone for packet transmission. However, the constant movement of the nodes changes the topology of the network, which perturbs the transmission. This demands the cluster maintenance. Weighed Clustering Algorithm (WCA)[4] and Distributed and Mobility adaptive Clustering (DMAC) [1,2,3] are two better proven algorithms on which we have implemented different mobility models like Random Walk (RW), Random Way Point (RWP) and Random Direction (RD). In both the algorithms each node is assigned some weight .In WCA the weight is a function of parameters like Battery power, mobility, transmission range and degree of connectivity. DMAC is mobility adaptive, i.e. it takes the mobility of the nodes into consideration while forming the clusters. We have chosen some measuring parameters like no of clusterheads, Average cluster lifetime, and Reaffilation rate for comparing the performance of both the algorithms

Overlapping Multi-hop Clustering for Wireless Sensor Networks

Clustering is a standard approach for achieving efficient and scalable performance in wireless sensor networks. Traditionally, clustering algorithms aim at generating a number of disjoint clusters that satisfy some criteria. In this paper, we formulate a novel clustering problem that aims at generating overlapping multi-hop clusters. Overlapping clusters are useful in many sensor network applications, including inter-cluster routing, node localization, and time synchronization protocols. We also propose a randomized, distributed multi-hop clustering algorithm (KOCA) for solving the overlapping clustering problem. KOCA aims at generating connected overlapping clusters that cover the entire sensor network with a specific average overlapping degree. Through analysis and simulation experiments we show how to select the different values of the parameters to achieve the clustering process objectives. Moreover, the results show that KOCA produces approximately equal-sized clusters, which allows distributing the load evenly over different clusters. In addition, KOCA is scalable; the clustering formation terminates in a constant time regardless of the network size

M-GEAR: Gateway-Based Energy-Aware Multi-Hop Routing Protocol for WSNs

In this research work, we advise gateway based energy-efficient routing protocol (M-GEAR) for Wireless Sensor Networks (WSNs). We divide the sensor nodes into four logical regions on the basis of their location in the sensing field. We install Base Station (BS) out of the sensing area and a gateway node at the centre of the sensing area. If the distance of a sensor node from BS or gateway is less than predefined distance threshold, the node uses direct communication. We divide the rest of nodes into two equal regions whose distance is beyond the threshold distance. We select cluster heads (CHs)in each region which are independent of the other region. These CHs are selected on the basis of a probability. We compare performance of our protocol with LEACH (Low Energy Adaptive Clustering Hierarchy). Performance analysis and compared statistic results show that our proposed protocol perform well in terms of energy consumption and network lifetime.Comment: IEEE 8th International Conference on Broadband and Wireless Computing, Communication and Applications (BWCCA'13), Compiegne, Franc

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial