Energy-Efficient Multi-Level and Distance-Aware Clustering Mechanism for WSNs

[EN] Most sensor networks are deployed at hostile environments to sense and gather specific information. As sensor nodes have battery constraints, therefore, the research community is trying to propose energyefficient solutions for wireless sensor networks (WSNs) to prolong the lifetime of the network. In this paper, we propose an energy-efficient multi-level and distance-aware clustering (EEMDC) mechanism for WSNs. In this mechanism, the area of the network is divided into three logical layers, which depends upon the hop-count-based distance from the base station. The simulation outcomes show that EEMDC is more energy efficient than other existing conventional approaches.This 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, and by the Polytechnic University of Valencia, through the PAID-15-11 multidisciplinary projectsMehmood, A.; Khan, S.; Shams, B.; Lloret, J. (2015). Energy-Efficient Multi-Level and Distance-Aware Clustering Mechanism for WSNs. International Journal of Communication Systems. 28(5):972-989. https://doi.org/10.1002/dac.2720S972989285Sendra, S., Lloret, J., Garcia, M., & Toledo, J. F. (2011). Power Saving and Energy Optimization Techniques for Wireless Sensor Neworks (Invited Paper). 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A Cluster-Based Architecture to Structure the Topology of Parallel Wireless Sensor Networks. Sensors, 9(12), 10513-10544. doi:10.3390/s91210513LEHSAINI, M., GUYENNET, H., & FEHAM, M. (2010). Cluster-based Energy-efficient k-Coverage for Wireless Sensor Networks. Network Protocols and Algorithms, 2(2). doi:10.5296/npa.v2i2.325Liu, G., Xu, B., & Chen, H. (2011). Decentralized estimation over noisy channels in cluster-based wireless sensor networks. International Journal of Communication Systems, 25(10), 1313-1329. doi:10.1002/dac.1308Cheng, L., Chen, C., Ma, J., & Shu, L. (2011). Contention-based geographic forwarding in asynchronous duty-cycled wireless sensor networks. International Journal of Communication Systems, 25(12), 1585-1602. doi:10.1002/dac.1325Wang, X., & Qian, H. (2011). Hierarchical and low-power IPv6 address configuration for wireless sensor networks. 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COCA: Constructing optimal clustering architecture to maximize sensor network lifetime. Computer Communications, 36(3), 256-268. doi:10.1016/j.comcom.2012.10.006Aslam N Phillips W Robertson W Sivakumar S A multi-criterion optimization technique for energy efficient cluster formation in wireless sensor networks 4th IEEE Consumer Communications and Networking Conference, (CCNC 2007) 2007 650 654Yi, S., Heo, J., Cho, Y., & Hong, J. (2007). PEACH: Power-efficient and adaptive clustering hierarchy protocol for wireless sensor networks. Computer Communications, 30(14-15), 2842-2852. doi:10.1016/j.comcom.2007.05.034Yong, Z., & Pei, Q. (2012). A Energy-Efficient Clustering Routing Algorithm Based on Distance and Residual Energy for Wireless Sensor Networks. Procedia Engineering, 29, 1882-1888. doi:10.1016/j.proeng.2012.01.231Chuan-Chi W A minimum transmission energy consumption routing protocol for user-centric wireless networks 2011 1143 1148Kumar, D., Aseri, T. C., & Patel, R. B. (2009). 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Optimized Cluster-Based Dynamic Energy-Aware Routing Protocol for Wireless Sensor Networks in Agriculture Precision

[EN] Wireless sensor networks (WSNs) are becoming one of the demanding platforms, where sensor nodes are sensing and monitoring the physical or environmental conditions and transmit the data to the base station via multihop routing. Agriculture sector also adopted these networks to promote innovations for environmental friendly farming methods, lower the management cost, and achieve scientific cultivation. Due to limited capabilities, the sensor nodes have suffered with energy issues and complex routing processes and lead to data transmission failure and delay in the sensor-based agriculture fields. Due to these limitations, the sensor nodes near the base station are always relaying on it and cause extra burden on base station or going into useless state. To address these issues, this study proposes a Gateway Clustering Energy-Efficient Centroid- (GCEEC-) based routing protocol where cluster head is selected from the centroid position and gateway nodes are selected from each cluster. Gateway node reduces the data load from cluster head nodes and forwards the data towards the base station. Simulation has performed to evaluate the proposed protocol with state-of-the-art protocols. The experimental results indicated the better performance of proposed protocol and provide more feasible WSN-based monitoring for temperature, humidity, and illumination in agriculture sector.This work has also been partially supported by the European Union through the ERANETMED (Euromediterranean Cooperation through ERANET joint activities and beyond) project ERANETMED3-227 SMARTWATIR.Qureshi, KN.; Bashir, MU.; Lloret, J.; León Fernández, A. (2020). Optimized Cluster-Based Dynamic Energy-Aware Routing Protocol for Wireless Sensor Networks in Agriculture Precision. Journal of Sensors. 2020:1-19. https://doi.org/10.1155/2020/9040395S1192020Sneha, K., Kamath, R., Balachandra, M., & Prabhu, S. (2019). New Gossiping Protocol for Routing Data in Sensor Networks for Precision Agriculture. Soft Computing and Signal Processing, 139-152. doi:10.1007/978-981-13-3393-4_15Qureshi, K. N., Abdullah, A. H., Bashir, F., Iqbal, S., & Awan, K. M. (2018). Cluster-based data dissemination, cluster head formation under sparse, and dense traffic conditions for vehicular ad hoc networks. International Journal of Communication Systems, 31(8), e3533. doi:10.1002/dac.3533Rault, T., Bouabdallah, A., & Challal, Y. (2014). Energy efficiency in wireless sensor networks: A top-down survey. Computer Networks, 67, 104-122. doi:10.1016/j.comnet.2014.03.027Feng, X., Zhang, J., Ren, C., & Guan, T. (2018). An Unequal Clustering Algorithm Concerned With Time-Delay for Internet of Things. IEEE Access, 6, 33895-33909. doi:10.1109/access.2018.2847036Savaglio, C., Pace, P., Aloi, G., Liotta, A., & Fortino, G. (2019). Lightweight Reinforcement Learning for Energy Efficient Communications in Wireless Sensor Networks. IEEE Access, 7, 29355-29364. doi:10.1109/access.2019.2902371Srbinovska, M., Gavrovski, C., Dimcev, V., Krkoleva, A., & Borozan, V. (2015). Environmental parameters monitoring in precision agriculture using wireless sensor networks. Journal of Cleaner Production, 88, 297-307. doi:10.1016/j.jclepro.2014.04.036Lloret, 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/s91210513Qureshi, K. N., Din, S., Jeon, G., & Piccialli, F. (2020). Link quality and energy utilization based preferable next hop selection routing for wireless body area networks. Computer Communications, 149, 382-392. doi:10.1016/j.comcom.2019.10.030Kumar, S. A., & Ilango, P. (2017). The Impact of Wireless Sensor Network in the Field of Precision Agriculture: A Review. Wireless Personal Communications, 98(1), 685-698. doi:10.1007/s11277-017-4890-zAnisi, M. H., Abdul-Salaam, G., & Abdullah, A. H. (2014). A survey of wireless sensor network approaches and their energy consumption for monitoring farm fields in precision agriculture. Precision Agriculture, 16(2), 216-238. doi:10.1007/s11119-014-9371-8Long, D. S., & McCallum, J. D. (2015). On-combine, multi-sensor data collection for post-harvest assessment of environmental stress in wheat. Precision Agriculture, 16(5), 492-504. doi:10.1007/s11119-015-9391-zFu, X., Fortino, G., Li, W., Pace, P., & Yang, Y. (2019). WSNs-assisted opportunistic network for low-latency message forwarding in sparse settings. Future Generation Computer Systems, 91, 223-237. doi:10.1016/j.future.2018.08.031Mehmood, A., Khan, S., Shams, B., & Lloret, J. (2013). Energy-efficient multi-level and distance-aware clustering mechanism for WSNs. International Journal of Communication Systems, 28(5), 972-989. doi:10.1002/dac.2720Pantazis, N. A., Nikolidakis, S. A., & Vergados, D. D. (2013). Energy-Efficient Routing Protocols in Wireless Sensor Networks: A Survey. 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A., & Niaz, I. A. (2017). A Balanced Energy-Consuming and Hole-Alleviating Algorithm for Wireless Sensor Networks. IEEE Access, 5, 6134-6150. doi:10.1109/access.2017.2676004Gupta, G. P., Misra, M., & Garg, K. (2014). Energy and trust aware mobile agent migration protocol for data aggregation in wireless sensor networks. Journal of Network and Computer Applications, 41, 300-311. doi:10.1016/j.jnca.2014.01.003Safa, H., Karam, M., & Moussa, B. (2014). PHAODV: Power aware heterogeneous routing protocol for MANETs. Journal of Network and Computer Applications, 46, 60-71. doi:10.1016/j.jnca.2014.07.035Liu, X. (2015). An Optimal-Distance-Based Transmission Strategy for Lifetime Maximization of Wireless Sensor Networks. IEEE Sensors Journal, 15(6), 3484-3491. doi:10.1109/jsen.2014.2372340Brar, G. S., Rani, S., Chopra, V., Malhotra, R., Song, H., & Ahmed, S. H. (2016). Energy Efficient Direction-Based PDORP Routing Protocol for WSN. IEEE Access, 4, 3182-3194. doi:10.1109/access.2016.2576475Abo-Zahhad, M., Ahmed, S. M., Sabor, N., & Sasaki, S. (2015). Mobile Sink-Based Adaptive Immune Energy-Efficient Clustering Protocol for Improving the Lifetime and Stability Period of Wireless Sensor Networks. IEEE Sensors Journal, 15(8), 4576-4586. doi:10.1109/jsen.2015.2424296Huynh, T.-T., Dinh-Duc, A.-V., & Tran, C.-H. (2016). Delay-constrained energy-efficient cluster-based multi-hop routing in wireless sensor networks. Journal of Communications and Networks, 18(4), 580-588. doi:10.1109/jcn.2016.000081Shen, J., Wang, A., Wang, C., Hung, P. C. K., & Lai, C.-F. (2017). An Efficient Centroid-Based Routing Protocol for Energy Management in WSN-Assisted IoT. IEEE Access, 5, 18469-18479. doi:10.1109/access.2017.2749606Awan, K. M., Shah, P. A., Iqbal, K., Gillani, S., Ahmad, W., & Nam, Y. (2019). Underwater Wireless Sensor Networks: A Review of Recent Issues and Challenges. 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Analyzing Energy-efficiency and Route-selection of Multi-level Hierarchal Routing Protocols in WSNs

The advent and development in the field of Wireless Sensor Networks (WSNs) in recent years has seen the growth of extremely small and low-cost sensors that possess sensing, signal processing and wireless communication capabilities. These sensors can be expended at a much lower cost and are capable of detecting conditions such as temperature, sound, security or any other system. A good protocol design should be able to scale well both in energy heterogeneous and homogeneous environment, meet the demands of different application scenarios and guarantee reliability. On this basis, we have compared six different protocols of different scenarios which are presenting their own schemes of energy minimizing, clustering and route selection in order to have more effective communication. This research is motivated to have an insight that which of the under consideration protocols suit well in which application and can be a guide-line for the design of a more robust and efficient protocol. MATLAB simulations are performed to analyze and compare the performance of LEACH, multi-level hierarchal LEACH and multihop LEACH.Comment: NGWMN with 7th IEEE Inter- national Conference on Broadband and Wireless Computing, Communication and Applications (BWCCA 2012), Victoria, Canada, 201

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 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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Journal of Network and Computer Applications, 35(4), 1317-1328. doi:10.1016/j.jnca.2012.01.016Heinzelman WR Chandrakasan A Balakrishnan H Energy-efficient communication protocol for wireless microsensor networks In proc of the 33rd Annual Hawaii International Conference on System Sciences 2000 2000Xiangning F Yulin S Improvement on LEACH protocol of wireless sensor network In proc of the 2007 International Conference on Sensor Technologies and Applications SensorComm 2007 2007Tong M Tang M LEACH-B: an improved LEACH protocol for wireless sensor network In proc of the 6th International Conference on Wireless Communications Networking and Mobile Computing WiCOM 2010 2010Mohammad El-Basioni, B. M., Abd El-kader, S. M., Eissa, H. S., & Zahra, M. M. (2011). An Optimized Energy-aware Routing Protocol for Wireless Sensor Network. Egyptian Informatics Journal, 12(2), 61-72. doi:10.1016/j.eij.2011.03.001Younis O Fahmy S Distributed clustering in ad-hoc sensor networks: a hybrid, energy-efficient approach In proc of the Twenty-third Annual Joint Conference of the IEEE Computer and Communications Societies INFOCOM 2004 2004Noack, A., & Spitz, S. (2009). Dynamic Threshold Cryptosystem without Group Manager. Network Protocols and Algorithms, 1(1). doi:10.5296/npa.v1i1.161Nasser, N., & Chen, Y. (2007). SEEM: Secure and energy-efficient multipath routing protocol for wireless sensor networks. Computer Communications, 30(11-12), 2401-2412. doi:10.1016/j.comcom.2007.04.014Alippi, C., Camplani, R., Galperti, C., & Roveri, M. (2011). A Robust, Adaptive, Solar-Powered WSN Framework for Aquatic Environmental Monitoring. 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