Energy Efficient Scheme for Wireless Sensor Networks

Recent advances in wireless sensor networks have commanded many new protocols specifically designed for sensor networks where energy awareness is an important concern. This routing protocols might differ from depending on the application and the network architecture. To extend the lifetime of Wireless sensor network (WSN), an energy efficient scheme can be designed and developed via an algorithm to provide reasonable energy consumption and network for WSN. To maintain high scalability and better data aggregation, sensor nodes are often grouped into disjoint, non-overlapping subsets called clusters. Clusters create hierarchical WSNs which incorporate efficient utilization of limited resources of sensor nodes to reduce energy consumption, thus extend the lifetime of WSN. The objective of this paper is to present a state of the art survey and classification of energy efficient schemes for WSNs. Keywords: Wireless Sensor Network, clustering, energy efficient clustering, network lifetime, energy efficient algorithms, energy efficient routing, and sensor networks. DOI: 10.17762/ijritcc2321-8169.15024

A Brief Survey on Cluster based Energy Efficient Routing Protocols in IoT based Wireless Sensor Networks

The wireless sensor network (WSN) consists of a large number of randomly distributed nodes capable of detecting environmental data, converting it into a suitable format, and transmitting it to the base station. The most essential issue in WSNs is energy consumption, which is mostly dependent on the energy-efficient clustering and data transfer phases. We compared a variety of algorithms for clustering that balance the number of clusters. The cluster head selection protocol is arbitrary and incorporates energy-conscious considerations. In this survey, we compared different types of energy-efficient clustering-based protocols to determine which one is effective for lowering energy consumption, latency and extending the lifetime of wireless sensor networks (WSN) under various scenarios

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

A QoS-Based Wireless Multimedia Sensor Cluster Protocol

Wireless Sensor Networks (WSNs) provide a wireless network infrastructure for sensed data transport in environments where wired or satellite technologies cannot be used. Because the embedded hardware of the sensor nodes has been improved very much in the last years and the number of real deployments is increasing considerably, they have become a reliable option for the transmission of any type of sensed data, from few sensed measures to multimedia data. This paper proposes a new protocol that uses an ad hoc cluster based architecture which is able to adapt the logical sensor network topology to the delivered multimedia stream features, guaranteeing the quality of the communications. The proposed protocol uses the quality of service (QoS) parameters, such as bandwidth, delay, jitter, and packet loss, of each type of multimedia stream as a basis for the sensor clusters creation and organization inside the WSN, providing end-to-end QoS for each multimedia stream. We present real experiments that show the performance of the protocol for several video and audio cases when it is runningThis work has been partially supported by the "Ministerio de Ciencia e Innovacion," through the "Plan Nacional de I+D+i 2008-2011" in the "Subprograma de Proyectos de Investigacion Fundamental," Project TEC2011-27516. This work has also been partially supported by the Instituto de Telecomunicacoes, Next Generation Networks and Applications Group (NetGNA), Portugal, by the Government of Russian Federation, Grant 074-U01, and by National Funding from the Fundacao para a Ciencia e a Tecnologia (FCT) through the PEst-OE/EEI/LA0008/2013 Project.Díaz Santos, JR.; Lloret, J.; Jimenez, JM.; Rodrigues, JJPC. (2014). A QoS-Based Wireless Multimedia Sensor Cluster Protocol. International Journal of Distributed Sensor Networks. 2014:1-17. https://doi.org/10.1155/2014/480372S1172014Bri, D., Garcia, M., Lloret, J., & Dini, P. (2009). 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S., & Villalba, L. J. G. (2012). A distributed QoS mechanism for ad hoc network. International Journal of Ad Hoc and Ubiquitous Computing, 11(1), 25. doi:10.1504/ijahuc.2012.049282Çevik, T., & Zaim, A. H. (2013). A Multichannel Cross-Layer Architecture for Multimedia Sensor Networks. International Journal of Distributed Sensor Networks, 9(3), 457045. doi:10.1155/2013/457045Li, Z., Bi, J., & Chen, S. (2013). Traffic Prediction-Based Fast Rerouting Algorithm for Wireless Multimedia Sensor Networks. International Journal of Distributed Sensor Networks, 9(5), 176293. doi:10.1155/2013/176293Lloret, 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.2008.05.030Lloret, 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/s11390-008-9147-6Lehsaini, M., Guyennet, H., & Feham, M. (2010). An efficient cluster-based self-organisation algorithm for wireless sensor networks. International Journal of Sensor Networks, 7(1/2), 85. doi:10.1504/ijsnet.2010.031852Lloret, J., Garcia, M., Bri, D., & Diaz, J. (2009). A Cluster-Based Architecture to Structure the Topology of Parallel Wireless Sensor Networks. Sensors, 9(12), 10513-10544. doi:10.3390/s91210513Diaz, J. R., Lloret, J., Jimenez, J. M., & Sendra, S. (2014). MWAHCA: A Multimedia Wireless Ad Hoc Cluster Architecture. The Scientific World Journal, 2014, 1-14. doi:10.1155/2014/913046Wei, D., & Chan, H. (2006). Clustering Ad Hoc Networks: Schemes and Classifications. 2006 3rd Annual IEEE Communications Society on Sensor and Ad Hoc Communications and Networks. doi:10.1109/sahcn.2006.288583Yu, J. Y., & Chong, P. H. J. (2005). A survey of clustering schemes for mobile ad hoc networks. IEEE Communications Surveys & Tutorials, 7(1), 32-48. doi:10.1109/comst.2005.1423333Abbasi, A. A., & Younis, M. (2007). A survey on clustering algorithms for wireless sensor networks. Computer Communications, 30(14-15), 2826-2841. doi:10.1016/j.comcom.2007.05.024Boyinbode, O., Le, H., & Takizawa, M. (2011). A survey on clustering algorithms for wireless sensor networks. International Journal of Space-Based and Situated Computing, 1(2/3), 130. doi:10.1504/ijssc.2011.040339Ramachandran, L., Kapoor, M., Sarkar, A., & Aggarwal, A. (2000). Clustering algorithms for wireless ad hoc networks. Proceedings of the 4th international workshop on Discrete algorithms and methods for mobile computing and communications - DIALM ’00. doi:10.1145/345848.345860Chatterjee, M., Das, S. K., & Turgut, D. (2002). Cluster Computing, 5(2), 193-204. doi:10.1023/a:1013941929408Huang, Y.-M., Hsieh, M.-Y., & Wang, M.-S. (2007). Reliable transmission of multimedia streaming using a connection prediction scheme in cluster-based ad hoc networks. Computer Communications, 30(2), 440-452. doi:10.1016/j.comcom.2006.09.012Tang, S., & Li, W. (2006). QoS supporting and optimal energy allocation for a cluster based wireless sensor network. Computer Communications, 29(13-14), 2569-2577. doi:10.1016/j.comcom.2006.02.007Rosário, D., Costa, R., Paraense, H., Machado, K., Cerqueira, E., Braun, T., & Zhao, Z. (2012). A Hierarchical Multi-hop Multimedia Routing Protocol for Wireless Multimedia Sensor Networks. Network Protocols and Algorithms, 4(4). doi:10.5296/npa.v4i4.2121Diaz, J. R., Lloret, J., Jiménez, J. M., & Hammoumi, M. (2014). A new multimedia-oriented architecture and protocol for wireless ad hoc networks. International Journal of Ad Hoc and Ubiquitous Computing, 16(1), 14. doi:10.1504/ijahuc.2014.062486Meghanathan, N., & Mumford, P. (2013). Centralized and Distributed Algorithms for Stability-based Data Gathering in Mobile Sensor Networks. Network Protocols and Algorithms, 84. doi:10.5296/npa.v5i4.420

Multihop clustering algorithm for load balancing in wireless sensor networks

The paper presents a new cluster based routing algorithm that exploits the redundancy properties of the sensor networks in order to address the traditional problem of load balancing and energy efficiency in the WSNs.The algorithm makes use of the nodes in a sensor network of which area coverage is covered by the neighbours of the nodes and mark them as temporary cluster heads. The algorithm then forms two layers of multi hop communication. The bottom layer which involves intra cluster communication and the top layer which involves inter cluster communication involving the temporary cluster heads. Performance studies indicate that the proposed algorithm solves effectively the problem of load balancing and is also more efficient in terms of energy consumption from Leach and the enhanced version of Leach

Coverage Protocols for Wireless Sensor Networks: Review and Future Directions

The coverage problem in wireless sensor networks (WSNs) can be generally defined as a measure of how effectively a network field is monitored by its sensor nodes. This problem has attracted a lot of interest over the years and as a result, many coverage protocols were proposed. In this survey, we first propose a taxonomy for classifying coverage protocols in WSNs. Then, we classify the coverage protocols into three categories (i.e. coverage aware deployment protocols, sleep scheduling protocols for flat networks, and cluster-based sleep scheduling protocols) based on the network stage where the coverage is optimized. For each category, relevant protocols are thoroughly reviewed and classified based on the adopted coverage techniques. Finally, we discuss open issues (and recommend future directions to resolve them) associated with the design of realistic coverage protocols. Issues such as realistic sensing models, realistic energy consumption models, realistic connectivity models and sensor localization are covered