Extend Network Life Research Articles

Network Coding-Aware Compressive Data Gathering for Energy-Efficient Wireless Sensor Networks

This article investigates the joint application of compressive sensing (CS) and network coding (NC) to the problem of energy-efficient data gathering in wireless sensor networks. We consider the problem of optimally constructing forwarding trees to carry compressed data to projection nodes. Each compressed dataset refers to a weighted aggregation (or sum) of sensed measurements from network sensors collected at one projection node. Projection nodes then forward their received compressed data to the sink, which subsequently recovers the original measurements. This aggregation technique, based on CS, is shown to reduce significantly the number of transmissions in the network. We observe that the presence of multiple forwarding trees gives rise to many-to-many communication patterns in sensor networks that, in turn, can be exploited to perform NC on the compressed data being forwarded on these trees. Such a technique will further reduce the number of transmissions required to gather the measurements, resulting in a better network-wide energy efficiency. This article addresses the problem of NC--aware construction of forwarding/aggregation trees. We present a mathematical model to optimally construct such forwarding trees, which encourage NC operations on the compressed data. Owing to its complexity, we further develop algorithmic methods (both centralized and distributed) for solving the problem and analyze their complexities. We show that our algorithmic methods are scalable and accurate, with worst-case optimality gap not exceeding 3.96% in the studied scenarios. We also show that, when bothNC and compressive data gathering are considered jointly, performance gains (reduction in number of transmissions) of up to 30% may be attained. Finally, we show that the proposed methods distribute the workload of data gathering throughout the network nodes uniformly, resulting in extended network life times.

Comparison of LEACH protocol withWormhole Attack and without WormholeAttack in Wireless Sensor Networks

In Wireless Sensor Networks, routing is the major concern. It comprises of small sensor nodes with limited resources. It is necessary to introduce a routing protocol to extend network life time and to reduce the power consumption in sensor nodes. LEACH is one of the most interested techniques that offer an efficient way to minimize the power consumption in sensor networks. It uses self organizing and dynamic cluster formation which makes it attractive to various routing attacks, such as Denial of Service (DoS), Black hole, Wormhole and Sybil attacks. Wormhole attack is a Denial of Service attack launched by malicious nodes. It records packets at one location and tunnels them into another location. To check the reliable operation of LEACH, implement wormhole attack and evaluated the LEACH protocol in terms of metrics like throughput, average end-to-end delay, Packet Delivery Ratio (PDR). The evaluation of LEACH with wormhole attack has been done with the help of NS2 simulator. Watchdog is a monitoring technique which detects the misbehaving nodes in the network. It can be implemented in LEACH. In Watchdog-LEACH, some nodes are considered as watchdogs and some changes are applied on LEACH protocol for intrusion detection. Watchdog-LEACH is able to protect against a wide range of attacks and it provides security, energy efficiency and memory efficiency. Comparison made on LEACH with wormhole attack and LEACH with watchdog shows that LEACH with watchdog achieves high throughput, Packet Delivery Ratio and low End to End Delay.

Reducing Energy Consumption in Wired OpenFlow-Based Networks

Due to the proliferation of mobile devices such as smart phones and tablet computers, and increase in the speed of network devices to forward data, the Internet traffic has increased manyfold compare to a decade ago. Energy consumed by network devices handling the network traffic has increased manyfold also. Researchers are not only focusing on battery oriented network such as wireless sensor and ad hoc networks on saving battery power to extend network life but are also focusing on wired network to save energy consumed by network devices. Energy consumed by wired network can be reduced by putting underutilized network devices or links to sleep. If a network device has a few links such as a backbone network device, it is better to consider underutilized device to put to sleep, but if a network device has many links such as in data centers network devices and campus networks devices, it is better to consider underutilized links to put to sleep. In this paper, we consider putting underutilized links to sleep. Off course, if all links of a network device are put to sleep, the device itself will be put to sleep. Furthermore, in this paper we consider OpenFlow-based network as it is software defined network and flexible to control network devices. Besides, OpenFlow is being standardized by big enterprises such as NEC, Google, Cisco, Microsoft etc. We evaluated our approach by simulation and compare it with node based approach to see its effectiveness.

Retracted] Differential Evolution Algorithm with Diversified Vicinity Operator for Optimal Routing and Clustering of Energy Efficient Wireless Sensor Networks

Due to large dimension of clusters and increasing size of sensor nodes, finding the optimal route and cluster for large wireless sensor networks (WSN) seems to be highly complex and cumbersome. This paper proposes a new method to determine a reasonably better solution of the clustering and routing problem with the highest concern of efficient energy consumption of the sensor nodes for extending network life time. The proposed method is based on the Differential Evolution (DE) algorithm with an improvised search operator called Diversified Vicinity Procedure (DVP), which models a trade-off between energy consumption of the cluster heads and delay in forwarding the data packets. The obtained route using the proposed method from all the gateways to the base station is comparatively lesser in overall distance with less number of data forwards. Extensive numerical experiments demonstrate the superiority of the proposed method in managing energy consumption of the WSN and the results are compared with the other algorithms reported in the literature.

ScEP: A Scalable and Energy Aware Protocol to Increase Network Lifetime in Wireless Sensor Networks

Wireless Sensor Networks are known by cooperative endeavour of large deployment of sensors with limited battery power. One of the main challenges about these networks is how to minimize the energy consumption of sensor nodes which will lead to extended network life time. In this paper, we proposed an efficient protocol based on Map-Reduce computing model and a new clustering algorithm. In the proposed protocol, the cluster heads are determined based on the remaining energies of sensor nodes and their distance to base station. Experimental results with a prototype implementation of ScEP demonstrate considerable improvement in enhancing both network lifetime and residual energies of sensor nodes compared to when the two main related work, MRKCP and LEACH is used.

K-Centers Clustering Protocol over Heterogeneous Wireless Sensor Networks

This paper introduces a K-Centers clustering protocol for heterogeneous wireless sensor networks. Energy consumption is an important issue for wireless sensor networks, and sensor nodes consume most of their energy with data delivery. The energy needed to transmit data is proportional to the distance between sensor nodes and either cluster heads or a base station. Clustering is an efficient technique for saving energy and extending network life. The authors' protocol uses a K-centers clustering algorithm to alter the network, topology and establish data routing. The result is k cluster heads which accommodate the distribution of sensor nodes and achieve minimum maximum intra-cluster distances. Their simulations show that their algorithm will outperform K-Means under many but not all conditions. The authors' always produce better minimum maximum intra-cluster distances compared to K-means.

Research on Energy-Saving Technology of Wireless Sensor Network

As the wireless sensor network node battery is limited and changing battery is difficult, we did research on feasible energy conservation design technology. Paper analyzed energy consumption and the proportion of network node in work station of dormancy, data dealing, and communication and so on. Hardware circuit was promoted through power partition, signal isolation and low energy consumption chip application. Software program was optimized through state machine combined with differential compression coding. Then the purpose of extending network life can be reached. The experiment result shows that the energy conservation design can reduce the energy consumption greatly and extend the network life 2~3 times.

Time Slots Investment of a dead node in LEACH protocol on Wireless Sensor Network

Wireless sensor network (MSN) is a wirelessly interconnected network. WSN promises a wide range of potential such as surveillance, military and civilian, to name just a few, applications. A sensor node senses the environment and delivers data to the sink. Energy saving is one of the keys to challenge network life time. LEACH protocol has been incorporated to extend network life time. This protocol forms clusters of the sensor nodes and elect one of them to become a Cluster Head (CH) to route data cluster to sink. In cluster, communication uses TDMA technique. This latter organizes transmission time (Time Slot) which corresponds to each node member of cluster. When a node dies, time slot corresponding to this node will be free. In this paper, we will present LEACH comportments after a node dies, then we will propose some ideas to invest its time slots by alive node members to maximize data reception time parameter so throughput end-to-end. This parameter is very important for real-time data. Finally, we will simulate this idea with Network Simulator (NS2) to argue for our propositions.

Optimization Protocol using Load Balancing for Hierarchical Wireless Sensor Network

Abstract The Wireless sensor network(WSN) consisting of a large number of sensors aims to gather data in a variety of environments. The sensor nodes operate on battery of limited power. so, To extend network life time is major goals of research in the WSN. In this paper, we state the key point of a energy consumption with minimum&load balancing. The proposed protocol guarantee balance of number of cluster member nodes using the node memory threshold and optimization of distribution of cluster head using the optimized clustering method. The results show that the proposed protocol could support the load balancing and high energy efficiency by distributing the clusters with a reasonable number of member nodes. The simulation results show that our schme ensure longer life time in WSN as compare with existing schemes such as LEACH and CBLM. Key Words : Clustering, Load Balancing, Energy Efficiency, Network Life Time, Wireless Sensor Network Received 6 September 2013, Revised 27 September 2013Accepted 20 October 2013Corresponding Author: Chu-Seock ChangKyungwoon University) Email: happychw@ikw.ac.krⒸ The Society of Digital Policy & Management. All rights reserved. This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.otg/licenses/by-nc/3.0), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is ISSN: 1738-1916 properly cited.

A Self-Optimizing Scheme for Energy Balanced Routing in Wireless Sensor Networks Using SensorAnt

Planning of energy-efficient protocols is critical for Wireless Sensor Networks (WSNs) because of the constraints on the sensor nodes' energy. The routing protocol should be able to provide uniform power dissipation during transmission to the sink node. In this paper, we present a self-optimization scheme for WSNs which is able to utilize and optimize the sensor nodes' resources, especially the batteries, to achieve balanced energy consumption across all sensor nodes. This method is based on the Ant Colony Optimization (ACO) metaheuristic which is adopted to enhance the paths with the best quality function. The assessment of this function depends on multi-criteria metrics such as the minimum residual battery power, hop count and average energy of both route and network. This method also distributes the traffic load of sensor nodes throughout the WSN leading to reduced energy usage, extended network life time and reduced packet loss. Simulation results show that our scheme performs much better than the Energy Efficient Ant-Based Routing (EEABR) in terms of energy consumption, balancing and efficiency.

A Clustering-Based K-Coverage Algorithm for Mobile Wireless Sensor Networks

To obtain more accurate and comprehensive information from target monitoring area in mobile wireless sensor networks (mWSN), multiple area coverage is often considered. However, it may cause coverage holes and inefficiencies in a mWSN due to sensor nodes' moving, energy loss, physical damage, errors, failures or other factors. To address this problem, a k-coverage algorithm for mobile wireless sensor network based on clustering(k-mWSN) is proposed in this paper, it not only can accurately monitor coverage holes in the target network, but also can rapidly repair and achieve uniform k-coverage . Simulation results show, by applying this algorithm to mWSNs, it can perform effectively in achieving uniform k-coverage, thus can increase coverage area and extend network life.

Research for Multi-Path Transmission and Network Coding in Wireless Sensor Network

Wireless sensor network (WSN) is a emerging, highly integrated field which involves several subjects and colligate knowledge at present .It contains a large number ,even thousands of nodes concentrating with the monitor that controls wireless communications network systems .The nodes extensively distributed have facility of breaking down because of the diversified environment and exhausting of energy .For the limit of energy and communication capacity, this paper comes up with a new kind of technique that we import the multi-path transmission combined with network coding into WSN to balance network load, reduce energy consumption integrally and extend network life cycle.

A Distributed Data Storage Method Based on Integrated Threshold

Based on the analysis and study of the data storage strategy in wireless sensor networks, this paper presents a distributed data storage method based on sleep scheduling to resolve the problems of network imbalance and storage hot spots problems.Finally, multi group analysis of simulate experiments results show that compared to other data storage method the distributed data storage method based on composite threshold have obviously advantages on the sides of overall energy consumption,data storage capacity,the number of failure node and data quality,thus have a significant effect on reducing energy consumption and extending network life cycle.

A Mac Protocol of Single Covered -Multiple Sensors Monitoring System

Abstract In cost efficient monitoring projects, the single-covered technology is usually used for the design of multiple-sensor monitoring system. This paper studied MAC protocols with star topology. After studying the MAC protocol based on scheduling and competition, and considering that the energy of the sink node is unlimited, we improved the traditional MAC protocol, get a new protocol-STAR MAC protocol. The new protocol uses frames to realize synchronization, in non-handshake. It can reduce the energy consumption of sensor nodes, extend network life cycle. Theoretical analysis and simulation results show that STAR-MAC protocol has higher energy efficiency than the traditional MAC protocol. Experiments also show that the data packet loss rate under STAR-MAC protocol is less than 1%, which can meet the requirements of applications.

English

Aggregating data of several nodes in a wireless sensor networks allows us to accurately monitor a remote environment. However, low-cost battery-powered nodes are often used to implement such networks, resulting in tight energy and communication bandwidth constraints. We have found that sensor node distances and densities are key factors in clustering. The aim of this paper is to introduce a new protocol to reduce overall power consumption, extend network life time in wireless sensor networks. In this paper, Normal Technique and Distance and Density based Clustering Algorithm (DDCSA) technique is compared on various parameters. Simulation results show a better performance of DDCSA as compared to Normal Technique

Optimal control of mobile monitoring agents in immune-inspired wireless monitoring networks

This paper studies optimal control of mobile monitoring agents in artificial-immune-system-based (AIS-based) monitoring networks. In AIS-based structural health monitoring (SHM) networks, the active structural health monitoring is performed by a group of mobile monitoring agents equipped with damage pattern recognition algorithms. The mobile monitoring agents mimic immune cells in the natural immune system and patrol a structure to detect damage patterns using their receptors (feature vectors), damage pattern recognition algorithms, and the dynamic response data of the structure. The optimal control of mobile monitoring agents includes agent generation and distribution. The generation of mobile monitoring agents is optimized to minimize the response time for the mobile monitoring agents to diagnose structural damage in a sub-network and maximize the average affinity of monitoring agents′ receptors to the damaged sensor data feature vector. The objective functions for distributing mobile monitoring agents are to increase the detection probability and extend network life by balancing energy consumption of sensor nodes in the network. The presented optimization algorithms are developed using multi-objective genetic algorithms. The impact of the algorithm parameters on the performance of the algorithm is also investigated.

Energy-Efficient Dynamic Spatial Resolution Control for Wireless Sensor Clusters

In very large-scale dense sensor network applications, more sensor nodes may be deployed than are required to provide the initial desired spatial resolution. Such over-deployment can extend network life, improve robustness, and accommodate network dynamic. To enable large deployments, tiered and clustered network structures may be adopted for scalability and manageability. This article presents a highly scalable, distributed control method to manage the activity of sensors in each cluster so that dynamic application-specific spatial resolution requirement can be achieved with minimum energy cost and with uniform participation of sensors. The method, Look-Ahead Resolution Control (LARC), utilizes a look-ahead prediction of activities of sensors in the cluster and provides feedback to nodes as part of acknowledgments to transmissions. LARC is shown to be highly responsive to system dynamics such as changes in the resolution requested, node failures or replenishment. Existing control methods not only fall short in network life and scalability, but also do not provide such responsiveness and uniform participation to ensure a diverse representation of sensed data. LARC is extended to tradeoff between energy and delay in recovery from failures of large number of nodes, by multiplexing sleeping, listening, and transmissions probabilistically in such a way that the control overhead is minimized while the delay is bounded. The article presents the control strategy for LARC along with performance studies showing near-uniform participation and near-optimal cluster lifetime.

A biologically inspired mobility control framework for energy conservation in sensor networks

AbstractThis paper presents a paradigm for extending network life by introducing energy‐aware mobility in wireless sensor networks. The concept of controlled mobility for energy saving has been motivated by a natural grouping behavior that is observed in Emperor penguins in the Antarctic region. During the winters, a group of those penguins form a closely huddled group to improve their collective heat insulation. Individual penguins within a group exhibit a local mobility pattern that ensures that each individual spends roughly equal amount of time at the periphery of the group, where heat loss is the maximum. As a result, the total heat loss of the group is thought to be evenly distributed across all the individuals. In this paper, we first draw a parallel between the heat loss of a penguin at the group periphery, and the routing energy burden of sensor nodes near the aggregating base stations in a sensor network. Then we develop a fully distributed and localized mobility control algorithm for collective extension of the network life. Experimental results demonstrate that the proposed controlled mobility paradigm can significantly extend a sensor network's operating life even in situations where the energy cost of physical node movement is modeled as high as up to four orders of magnitude larger than the energy cost for packet transmission. Copyright © 2009 John Wiley & Sons, Ltd.