Variable Power Energy Efficient Clustering for Wireless Sensor Networks 1

Abstract : Wireless sensor networks (WSN) are inheriting many application areas like environment observation, target tracking, border monitoring and battle field surveillance. To alleviate the problem of energy utilization and extending the lifetime of wireless sensor nodes, one approach is employing an effective clustering mechanism. In this paper variable power energy efficient clustering (VEEC) mechanism for wireless sensor networks has been proposed. It is a well distributed, energy efficient clustering algorithm which employs relay nodes, variable transmission power and single message transmission per node for setting up the cluster. The proposed scheme is compared with two existing distributed clustering algorithms LEACH and HEED. Simulation results clearly show an excellent improvement in average communication energy and the total energy of the wireless sensor system. Simulation study also shows the reduction in node death rate and prolongation in network lifetime compared to the two existing algorithms

Implementation of DEEC Protocol Using Optimization Technique in Wireless Sensor Technology

Wireless sensor networks are employed in several applications like military, medical, household and environmental. In these applications energy factor is the determining factor in the performance of wireless sensor networks. In wireless sensor network, clustering is used as an effective technique to achieve scalability, self-organization, power saving, channel access, routing etc. Lifetime of sensor nodes determines the lifetime of the network and is crucial for the sensing capability. Clustering is the key technique used to extend the lifetime of a sensor network and also reduce energy consumption etc,. Energy-efficient clustering protocols should be designed for the characteristic of heterogeneous wireless sensor networks[1]. DEEC which is named as distributed energy efficient clustering protocol is selected as clustering protocol[1]. In DEEC, the cluster heads are elected by a probability based on the ratio between residual energy of each node and the average energy of the network. Since in DEEC, the lifetime of sensors as well as network degrades very quickly. Hence in order to increase the network lifetime a new algorithm is proposed. This technique balances the cluster by using some backup nodes. The backup high energy and high processing power nodes replace the cluster head after the cluster reaches to its threshold limit. This approach will increase the network lifetime and will provide high throughput

A Secure and Low-Energy Zone-based Wireless Sensor Networks Routing Protocol for Pollution Monitoring

[EN] Sensor networks can be used in many sorts of environments. The increase of pollution and carbon footprint are nowadays an important environmental problem. The use of sensors and sensor networks can help to make an early detection in order to mitigate their effect over the medium. The deployment of wireless sensor networks (WSNs) requires high-energy efficiency and secures mechanisms to ensure the data veracity. Moreover, when WSNs are deployed in harsh environments, it is very difficult to recharge or replace the sensor's batteries. For this reason, the increase of network lifetime is highly desired. WSNs also work in unattended environments, which is vulnerable to different sort of attacks. Therefore, both energy efficiency and security must be considered in the development of routing protocols for WSNs. In this paper, we present a novel Secure and Low-energy Zone-based Routing Protocol (SeLeZoR) where the nodes of the WSN are split into zones and each zone is separated into clusters. Each cluster is controlled by a cluster head. Firstly, the information is securely sent to the zone-head using a secret key; then, the zone-head sends the data to the base station using the secure and energy efficient mechanism. This paper demonstrates that SeLeZoR achieves better energy efficiency and security levels than existing routing protocols for WSNs.Mehmood, A.; Lloret, J.; Sendra, S. (2016). A Secure and Low-Energy Zone-based Wireless Sensor Networks Routing Protocol for Pollution Monitoring. Wireless Communications and Mobile Computing. 16(17):2869-2883. https://doi.org/10.1002/wcm.2734S286928831617Sendra S Deployment of efficient wireless sensor nodes for monitoring in rural, indoor and underwater environments 2013Javaid, N., Qureshi, T. N., Khan, A. H., Iqbal, A., Akhtar, E., & Ishfaq, M. 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HEEL: A new clustering method to improve wireless sensor network lifetime

In wireless sensor networks, some resources such as memory and energy are limited. In recent years, there has been an increasing interest in improving network lifetime. Node energy plays an important role in the network lifetime. Along with this remarkable growth in wireless sensor networks, however, there is an increasing concern over network lifetime. The principal purpose of this study is to develop an understanding of the effects of other parameters on selecting a cluster head. The methodological approach taken in this study is a mixed methodology typically based on the node's energy. The authors have operated four parameters to select the cluster head: Node energy, the energy of the node's neighbours, number of hops and number of links to neighbours. Each of these parameters has an impact in selecting the cluster head. They accurately observed hop size, energy of each sensor node, average energy of sensor neighbours, links to sensor nodes (HEEL) has better improvements in comparison of Node ranked Low Energy Adaptive Clustering Hierarchy (Nr-LEACH), Modified Low Energy Adaptive Clustering Hierarchy (ModLEACH), Low Energy Adaptive Clustering Hierarchy-B (LEACH-B), Low Energy Adaptive Clustering Hierarchy (LEACH), Power-Efficient Gathering in Sensor Information System (PEGASIS), energy-aware clustering scheme with transmission power control for sensor networks (EACLE) and hybrid energy efficient distributed clustering (HEED) algorithms in possible case of network lifetime and throughput

MEPA: A New Protocol for Energy-Efficient, Distributed Clustering in Wireless Sensor Networks

Abstract-Clustering is an effective approach to hierarchically organizing network topology for efficient data aggregation in wireless sensor networks. Distributed protocols with simple local computations to accomplish a desired global goal, offer a good prospect for achieving energy efficiency. This paper presents MEPA -an energy-efficient distributed clustering protocol using simple and local message-passing rules. Our proposed clustering protocol combines both node residual energy and network topology features to recursively elect a near-optimal set of cluster heads. Simulation results show that MEPA can produce a set of cluster heads with compelling characteristics, and effectively prolong the network lifetime

Outlier Detection in Wireless Sensor Networks Using Distributed Principal Component Analysis

Detecting anomalies is an important challenge for intrusion detection and fault diagnosis in wireless sensor networks (WSNs). To address the problem of outlier detection in wireless sensor networks, in this paper we present a PCA-based centralized approach and a DPCA-based distributed energy-efficient approach for detecting outliers in sensed data in a WSN. The outliers in sensed data can be caused due to compromised or malfunctioning nodes. In the distributed approach, we use distributed principal component analysis (DPCA) and fixed-width clustering (FWC) in order to establish a global normal pattern and to detect outlier. The process of establishing the global normal pattern is distributed among all sensor nodes. We also use weighted coefficients and a forgetting curve to periodically update the established normal profile. We demonstrate that the proposed distributed approach achieves comparable accuracy compared to the centralized approach, while the communication overhead in the network and energy consumption is significantly reduced

Novel reliable and dynamic energy-aware routing protocol for large scale wireless sensor networks

Wireless sensor networks (WSN) are made up of an important number of sensors, called nodes, distributed in random way in a concerned monitoring area. All sensor nodes in the network are mounted with limited energy sources, which makes energy harvesting on top of the list of issues in WSN. A poor communication architecture can result in excessive consumption, reducing the network lifetime and throughput. Centralizing data collection and the introduction of gateways (GTs), to help cluster heads (CHs), improved WSN life time significantly. However, in vast regions, misplacement and poor distribution of GTs wastes a huge amount of energy and decreases network’s performances. In this work, we describe a reliable and dynamic with energy-awareness routing (RDEAR) protocol that provides a new GT’s election approach taking into consideration CHs density, transmission distance and energy. Applied on 20 different networks, RDEAR reduced the overall energy consumption, increased stability zone and network life time as well as other compared metrics. Our proposed approach increased network’s throughput up to 75.92% , 67.7% and 9.78% compared to the low energy adaptive clustering hierarchy (LEACH), distributed energy efficient clustering (DEEC) and static multihop routing (SMR), protocols, respectively

Improved Estimation Performance of Sensor in Wireless Sensor Network Using Suboptimal Technique

ABSTRACT--This paper presents a novel network lifetime extension technique. In order to collect information more efficiently, wireless sensor networks (WSNs) are partitioned into clusters. Clustering provides an effective way to prolong the lifetime of WSNs. Current clustering approaches often use two methods: selecting cluster heads with more residual energy, and rotating cluster head periodically, to distribute the energy consumption among nodes in each cluster and extend the network lifetime. However, most of the previous algorithms have not considered the expected residual energy, only consider the estimation performance. In this paper we propose a probabilistic based transmission using clustering algorithm. Probabilistic transmission control at which is to minimize the mean squared error of estimation by increasing the packet transmission success probability of only sensors having high observation SNR. These newly available sensors are partitioned into several sensor sets select the cluster head to maintain the same estimation performance. The simulation results show that the proposed approach is more efficient than other distributed algorithms. It is believed that the technique presented in this paper could be further applied to large-scale wireless sensor networks

Enhanced group-based wireless ad-hoc sensor network protocol

[EN] Communication is the major energy consumption source in wireless ad-hoc sensor networks. Thus, an efficient tradeoff between the energy cost of the communication and network's performance is a key challenge in conceiving a wireless ad-hoc sensor network. In this article, we propose an improved group-based architecture for wireless ad-hoc sensor networks. An optimized group forming procedure and an efficient communication operation are introduced. In order to validate the proposed approach, we suggest a group-based strategy to monitor pharmaceutical drugs during transportation. Real measurements of temperature and vibration were performed to validate the effectiveness of our approach.Khedher, M.; Lloret, J.; Douik, A. (2016). Enhanced group-based wireless ad-hoc sensor network protocol. International Journal of Distributed Sensor Networks. 12(7):1-18. https://doi.org/10.1177/1550147716659427S118127Dargie, W., & Poellabauer, C. (2010). Fundamentals of Wireless Sensor Networks. doi:10.1002/9780470666388Singh, S. P., & Sharma, S. C. (2015). A Survey on Cluster Based Routing Protocols in Wireless Sensor Networks. Procedia Computer Science, 45, 687-695. doi:10.1016/j.procs.2015.03.133Liao, Y., Qi, H., & Li, W. (2013). Load-Balanced Clustering Algorithm With Distributed Self-Organization for Wireless Sensor Networks. IEEE Sensors Journal, 13(5), 1498-1506. doi:10.1109/jsen.2012.2227704Peng, I.-H., & Chen, Y.-W. (2013). Energy consumption bounds analysis and its applications for grid based wireless sensor networks. Journal of Network and Computer Applications, 36(1), 444-451. doi:10.1016/j.jnca.2012.04.014Lloret, J., Garcia, M., Tomás, J., & Boronat, F. (2008). GBP-WAHSN: A Group-Based Protocol for Large Wireless Ad Hoc and Sensor Networks. Journal of Computer Science and Technology, 23(3), 461-480. doi:10.1007/s11390-008-9147-6Lloret, J., García, M., Boronat, F., & Tomás, J. (s. f.). MANET Protocols Performance in Group-based Networks. IFIP International Federation for Information Processing, 161-172. doi:10.1007/978-0-387-84839-6_13Lloret, J., Garcia, M., & Tomas, J. (s. f.). Improving Mobile and Ad-hoc Networks performance using Group-Based Topologies. Wireless Sensor and Actor Networks II, 209-220. doi:10.1007/978-0-387-09441-0_18Lloret, J., Palau, C., Boronat, F., & Tomas, J. (2008). Improving networks using group-based topologies. Computer Communications, 31(14), 3438-3450. doi:10.1016/j.comcom.2008.05.030Garcia, M., Sendra, S., Lloret, J., & Canovas, A. (2011). Saving energy and improving communications using cooperative group-based Wireless Sensor Networks. Telecommunication Systems, 52(4), 2489-2502. doi:10.1007/s11235-011-9568-3Garcia, M., & Lloret, J. (2009). A Cooperative Group-Based Sensor Network for Environmental Monitoring. Cooperative Design, Visualization, and Engineering, 276-279. doi:10.1007/978-3-642-04265-2_41Shaikh, R. A., Jameel, H., d’ Auriol, B. J., Heejo Lee, Sungyoung Lee, & Young-Jae Song. (2009). Group-Based Trust Management Scheme for Clustered Wireless Sensor Networks. IEEE Transactions on Parallel and Distributed Systems, 20(11), 1698-1712. doi:10.1109/tpds.2008.258Chen, Y.-S., Hsu, C.-S., & Lee, H.-K. (2014). An Enhanced Group Mobility Protocol for 6LoWPAN-Based Wireless Body Area Networks. IEEE Sensors Journal, 14(3), 797-807. doi:10.1109/jsen.2013.2287895Yao-Chung Chang, Zhi-Sheng Lin, & Jiann-Liang Chen. (2006). Cluster based self-organization management protocols for wireless sensor networks. IEEE Transactions on Consumer Electronics, 52(1), 75-80. doi:10.1109/tce.2006.1605028Fazio, P., De Rango, F., Sottile, C., & Santamaria, A. F. (2013). Routing Optimization in Vehicular Networks: A New Approach Based on Multiobjective Metrics and Minimum Spanning Tree. International Journal of Distributed Sensor Networks, 9(11), 598675. doi:10.1155/2013/598675Saravanan, M., & Madheswaran, M. (2014). A Hybrid Optimized Weighted Minimum Spanning Tree for the Shortest Intrapath Selection in Wireless Sensor Network. Mathematical Problems in Engineering, 2014, 1-8. doi:10.1155/2014/71342