On the Construction of Data Aggregation Tree with Maximized Lifetime in Wireless Sensor Networks

Scheduling of data aggregation trees using Local Heuristics to enhance network lifetime in sensor networks

Limited energy supply (battery-powered) is a crucial problem in wireless sensor networks (WSNs). Sensor node placement schemes and routing protocols are mostly proposed to address this problem. In this paper, we first present how to place sensor nodes by use of a minimal number of them to maximize the coverage area when the communication radius of the sensor node is different from the sensing radius, which results in the application of regular topology to WSNs deployment. With nodes placed at an equal distance and equipped with an equal power supply, the problem of unbalanced energy consumption in 2-D regular topologies becomes more severe and much more difficult to tackle than that in 1-D chains, though the latter is known as an already quite hard problem. We address this problem and propose an adaptive data collection scheme by employing different communication radii for nodes in different locations to balance the energy consumption in WSNs. In order to achieve the ultimate goal of maximizing network lifetime in grid-based WSNs, we give a mathematical formulation, which shows the problem of maximizing network lifetime is a nonlinear programming problem and NP-hard even in the 1-D case. We discuss several heuristic solutions and show that the halving shift data collection scheme is the best solution among them. We also generalize the maximizing network lifetime problem to the randomly-deployed WSNs, which shows the significance of our mathematical formulation for this crucial problem in WSNs.

Wireless sensor network is an emerging technology capable of having low-cost and low-power and battery-powered sensor node. At this point the protocol concentric clustered power-efficient gathering in sensor information systems and clustered general self organized tree based energy-balance routing protocol energy efficient protocol are used to increase the energy and to extend network lifetime. For these achievement of clustering techniques, optimization technique particle swarm optimization is used for data transmission through base station. If the base station is inaccessible region from the cluster-head, energy consumption will be augmented and it can shrink the lifetime of wireless sensor network. To conquer these, multi-hop gateway node is implemented with both protocol to achieve maximum lifetime and energy efficiency of wireless sensor network. To improve the quality-of-service (QoS) placing more number of gateways is required. So we propose a new algorithm nearer optimal algorithm which recursively computes least possible adequate dominating sets, at the time constantly preserving QoS requirements across iterations. In this paper comparison is done in terms of throughput, packets delivery ratio and number of nodes alive.

In wireless sensor networks, sensor nodes have limited battery power. Data aggregation is an important technique in a wireless sensor network to reduce the energy consumption by eliminating the redundant data and delivering useful data to the user. Data aggregation technique is used to gather the data sensed by the sensor nodes in the network and aggregates the sensed data in an energy-efficient way so that overall network lifetime can be increased. Cluster, tree, chain, and grid are some of the architectures for data aggregation in wireless sensor networks. In this paper, authors present an analysis-based survey of data aggregation protocols for tree-based architecture in wireless sensor networks. The authors analyze each algorithm on the basis of performance measurements such as network lifetime, energy consumption, and node distance. Authors also compare these algorithms on the basis of the preferred parameter and a key feature of each algorithm. In the fifth section of the paper, authors described the proposed approach to construct the data aggregation tree to maximize the network lifetime. In the proposed approach, authors used distance parameter to construct minimum spanning tree, and other parameters such as load and energy are preferred in a balanced manner to reduce the energy consumption and to maximize the network lifetime.

Division of the entire wireless sensor network geographic area into number of equal sized regions by applying virtual grid and strategy to appoint head node of the cluster in each region improves stability, consistency, and lifetime of the wireless sensor network. Applying the same size of grid to the different size of wireless sensor networks will not give consistent performance. In this paper, we are presenting a protocol which will dynamically determine the size of the virtual grid, which will give optimal performance of any geographic sized wireless sensor network. The protocol also allows to place sink node to any location and not at some fixed specific location. Protocol discussed in this paper can also support multiple sink nodes. Protocol dynamically takes decision to transmit the data directly to the sink node or by using multi-hop transmission by considering distances between sensor nodes internally and sink node(s), which will optimize energy efficiency of the sensor nodes and increases the lifetime of wireless sensor network.

We analyze the impact of the structure of the Data Gathering (DG) trees on node lifetime (round of first node failure) and network lifetime (minimum number of rounds by which the network gets either disconnected due to node failures or the fraction of coverage loss reaches a threshold) in wireless sensor networks through extensive simulations. The two categories of DG trees studied are: the Bottleneck Node Weight-Based (BNW-DG) trees and Bottleneck Link Weight-Based (BLW-DG) trees. The BNW-DG trees incur a smaller diameter and a significantly larger fraction of nodes as leaf nodes: thus, protecting a majority of the nodes in the network from simultaneously being exhausted of the energy resources (contributing to a significantly larger network lifetime); nevertheless the nodes that serve as intermediate nodes in the first few instances of the BNW-DG trees are bound to lose their energy more quickly than the other nodes, leading to a smaller node lifetime compared to that of the BLW-DG trees (that incur a larger diameter and a relatively lower fraction of nodes as leaf nodes).

Wireless Sensor Networks (WSN) concentrate on the essential problem of increasing the network lifetime of WSN, which are bordered by low capacity batteries1,3 One of the fundamental egresses in WSN is maximizing the network lifetime by mitigating the Hotspot problem. It is the problem of isolation of sink node due to energy diminution of nodes most closelipped to sink1. In WSN nodes which are most adjacent to the Base Station (BS) need to relay many packets while compare with nodes far away from the sink. The outcome of this approach is these nodes deplete their energy very quickly. It results in a network partitioning and reduces the network lifetime. This paper proposes and evaluates a technique for maximizing the lifetime of WSN by preventing sink isolation due to hotspot problem. The clustering mechanism can be used to reduce the energy depletion of node near to the sink node. The cluster is formed based on distance attribute. This mechanism provides the scalability and avoids the redundancy through data collection operation. This paper will analyze the energy consumption of proposed technique using various distance measures. *Author for correspondence Indian Journal of Science and Technology, Vol 7(S4), 92–98, April 2014 ISSN (Print) : 0974-6846 ISSN (Online) : 0974-5645 Increase the Lifetime of WSN by Preventing Sink Isolation using Supercluster Formation R. Devika1 , B. Santhi2 and T. Sivasubramanian3 1,2School of Computing, SASTRA University, Thanjavur, India; srdevika.cse@sastra.edu, b.shanthi@sastra.edu 3 Kings College of Engineering, Punalkulam, Pudhukkotai District, India; shivamiru@gmail.com Keywords: Clustering Mechanism, Data Aggregation Operation, Hotspot Problem, Network Lifetime, Sink

The lifetime of wireless sensor networks (WSNs) are considered one of the most challenges that face the topology control of WSNs. Topology control of WSNs is a technique to optimize the connections between nodes to reduce the interference between them, save energy and extend network lifetime. In this paper proposed an algorithm based on Whale Optimization Algorithm (WOA) called WOTC, the paper provides a discrete version of the WOA, where the position of each Whale is calculate and represented in a binary format. The proposed fitness function is designed to consider two main target; a minimization in numbers of active nodes, and low energy consumption within these nodes to overcome challenges that face topology control to prolong the WSNs lifetime, the simulations were carried out using Attaraya a simulator. Consequently, the results showed that the final topology obtained by WOTC is better than A3 topology depending on the number of neighbors and their energies for active nodes, use a graph traversal function to ensure that all nodes which selected in network are covered in the best topology selection.

Wireless sensor networks offer a lot of potential to improve our ability to see and manage the physical world, but the issue of power consumption in these networks has emerged as a key factor in their dependability and in many use cases for networks. End-to-end quality parameters for wireless sensors must be guaranteed, and these networks' support for quality of service is crucial. The lifespan of each network in wireless sensor networks, which is closely correlated with the energy use of the network sensors, is one of the most crucial challenges. One of the most difficult tasks in these types of networks is to extend the network's lifetime. The most efficient strategy for reducing power consumption in wireless sensor networks is clustering, which is presented in this study. This is the most efficient approach for increasing the scalability and lowering the energy consumption of wireless sensor networks. The simulation results demonstrate that the suggested algorithm may significantly extend the lifetime of the wireless sensor network and decrease its energy consumption.

Maximization of lifetime of wireless sensor network (WSN) is an emerging area of research in present scenario. Many authors are performing their research work, so that they could achieve lower energy consumption for the sensor network which leads to increase in lifetime of network. Our work focuses on sink mobility in a fixed trajectory within a wireless sensor network while sensed data are required to be collected. We divide the whole sensor network into two different regions as direct communication area (DCA) and multi-hop communication area (MCA). Sensors node that lies in DCA is at one hop count distance from the sink node trajectory while sensors within MCA are at more than one hop count from the trajectory. We considered all the sensors that are within DCA as subsink nodes. During sink mobility, whenever a subsink is closer to the mobile sink node, then it starts transmitting its data to the sink node. But those sensors that are within MCA needs to search an appropriate sink node for sending its sensed data to the subsink so that on further stage, that subsink could provide those data to the sink node whenever it is in nearest proximity of that subsink. Basically, a sink node is the ultimate destination for the data while a subsink node acts as a relay node for the nodes that are within MCA. So our work is to find out the appropriate subsink node from a given set of subsinks. For doing so, we used location-aided routing on global positioning system (GPS)-enabled sensors and sink node. Our work is validated through simulation experiments using NS-2.

It is important to prolong the network lifetime in Wireless Sensor Networks. There are two approaches to increase the network lifetime, which are balancing energy consumption and reducing the number of non-working nodes. This paper presents two Cluster head Selection algorithms, one is based on Ensuring target Area and the other is based on Affinity Propagation. We propose a method which makes the energy uniformly distributed throughout the network nodes. In particular, we analyse the effect of the powerful nodes on the network lifetime and cluster characteristics. Simulation results show that the two proposed algorithms distribute non-working nodes evenly, such that the target area is well covered by the remaining nodes. Our proposed algorithms balance the traffic load and energy distributions, and prolong the network lifetime effectively compared with the existing schemes.

Sensor technology is developing rapidly and up to date. The lifetime of the Wireless Sensor Network (WSN) has also attracted many researchers, and the location of the Base Station (BS) plays a crucial role in prolonging the lifetime. The energy consumption in the WSN occurs during transmission of data and selection of cluster-head nodes. A reasonable location of the BS prolongs the lifetime of the WSN. This study proposes a Levy Flight Edge Regeneration Black Hole algorithm (LEBH) to speed up convergence and enhance optimization capabilities. The performance of LEBH and other heuristic algorithms are compared on CEC 2013. The result shows that the LEBH outperforms other heuristics in most cases. In this study, the energy consumption and WSN models are simulated. Subsequently, the proposed LEBH is combined with relocation technology to change the location of the BS to prolong the lifetime. Simulation results show LEBH-BS prolongs the lifetime of the WSN better than random-base and static-base stations and other heuristic algorithms in most cases.

CMSTR: A Constrained Minimum Spanning Tree Based Routing Protocol for Wireless Sensor Networks

Using the mobile sink for data collection from all sensors is the most highly effective way to prolong the network lifetime of wireless sensor networks(WSNs). Many studies focused on the anchors selection, aiming to reduce the path length of mobile sink and prolong the network lifetime. However, the appropriateness and the number of the selected anchors, which significantly impact the network lifetime of the entire WSNs, still can be improved. This paper proposes a Dynamic Anchor Points Mechanism for data collection, called DAM, aiming to select appropriate anchors under the path length constraint for prolonging the network lifetime. The experimental results show that the proposed DAM outperforms existing data collection mechanisms in term of network lifetime and fairness index.

Wireless Sensor Networks (WSNs) are a collection of a large number of small sensors capable of sensing the environment. In spite of limited resources in WSNs, they are employed in various applications and a large researches are done to extend their performance. In addition to decreasing energy consumption, some strategies should be employed to balance network load and consequently balance the energy consumption of these nodes and ensure a maximum network lifetime. In this paper, with the goal of reducing energy consumption and extending lifetime, a regular network is considered, then we formalize the network lifetime as an optimization programming. By using of load balancing technique, it will increase node lifetime. However, solving this problem is complex and time consuming, so we propose a genetic algorithm. We compare optimal solution and genetic algorithm and conclude through the results that combining load balancing with energy consumption improve network lifetime.