SIR: A New Wireless Sensor Network Routing Protocol Based on Artificial Intelligence

Currently, Wireless Sensor Networks (WSNs) are formed by hundreds of low energy and low cost micro-electro-mechanical systems. Routing and low power consumption have become important research issues to interconnect this kind of networks. However, conventional Quality of Service routing models, are not suitable for ad hoc sensor networks, due to the dynamic nature of such systems. This paper introduces a new QoS-driven routing algorithm, named SIR: Sensor Intelligence Routing. We have designed an artificial neural network based on Kohonen self organizing features map. Every node implements this artificial neural network forming a distributed intelligence and ubiquitous computing system

Performance Evaluation of Routing Protocols in Wireless Sensor Networks

The growing field of information technology enhanced the capabilities of the wireless communication. The large usage of WSN in the various fields of the real world is scaling with the wide variety of roles for wireless sensor network performance is challenging tasks. The issues of performance in the wireless sensor networks in many literatures, yet more studies are being done on the performance because the user and application needs are keep increasing,to encounter the challenges of the performance issues are studied here by digging out the routing protocols performance in WSN. To conduct the study and analysis on performance of WSN protocols the there are various performance metrics used for the evaluation of performance in WSN. This study will be carried out to come up with the simulation experiments over the directed diffusion (DD) and LEACH routing protocols in terms of energy consumption, congestion and reliability in the wireless sensor networks (WSN) environment with the low power consumptions. The simulation experiments in this study are based on the reliability, delay and other constraints to compare the speed, reliability and electricity saving data communication in the wireless sensor networks (WSN). The discussion of the conducted simulation experiments describes the steps which are pertaining to the protocols and tradeoffs and complexity of the data traffic for the efficiency. The NS2 simulation is used for the simulation based experiments for performance of wireless sensor network (WSN) communications which is demonstrating the comparative effectiveness of the routing protocols in the recent concepts. The results of the simulation are lightening the ways for the minimization of the delay and enhancement in the reliability issues in wireless sensor networks (WSN)

Energy Efficient Protocol with Static Clustering (EEPSC) Comparing with Low Energy Adaptive Clustering Hierarchy (LEACH) Protocol

A wireless sensor network with a large number of tiny sensor nodes can be used as an effective tool for gathering data in various situations. One of the major issues in wireless sensor networks is developing an energy-efficient routing protocol which has a significant impact on the overall lifetime of the sensor network. In this paper, we propose a novel hierarchical with static clustering routing protocol called Energy-Efficient Protocol with Static Clustering (EEPSC). EEPSC, partitions the network into static clusters, eliminates the overhead of dynamic clustering and utilizes temporary-cluster-heads to distribute the energy load among high power sensor nodes; thus extends network lifetime. We have conducted simulation-based evaluations to compare the performance of EEPSC against Low-Energy Adaptive Clustering Hierarchy (LEACH). Our experiment results show that EEPSC outperforms LEACH in terms of network lifetime and power consumption minimization. Keywords—Clustering methods, energy efficiency, routing protocol, wireless sensor network

A beacon-enabled least-time and energy efficient with one-level data aggregation routing protocol for WSNs using IEEE 802.15.4

The Wireless Sensor Networks (WSNs) field of research is an interesting topic in the research community these days, because of its applicability in various fields such as civilian and medical research applications. Due to the resources and energy constraints in WSNs, routing can be considered as one of the most important issues in these networks. Every routing protocol designed for WSNs should be reliable, energy-efficient and prolong the network lifetime. This research proposes a beacon-enabled least-time and energy-efficient routing protocol with one-level data-aggregation using an IEEE 802.15.4 which is suitable for Low-Rate Wireless Personal Area Networks as WSNs, because of its low power consuming feature. The proposed protocol is compared to popular ad hoc and WSNs routing protocols i.e., Ad hoc On-Demand Distance Vector, Dynamic Source Routing, Destination-Sequenced Distance Vector routing, Directed Diffusion and Minimum Cost Forwarding. The propose work is simulated using network simulator 2. The simulation results show that the proposed protocol outperformed the routing protocols in the literature in terms of latency, throughput, average energy consumption and average network lifetime

Prolonging Wireless Sensor Network Lifetime Using Routing Protocol

Prolonging network lifetime is one of the challenging issues of Wireless Sensor Networks (WSN). Many techniques have been proposed to achieve a longer battery life for the sensor nodes. In this paper, we focus on the routing technique to improve the battery life and extend the network lifetime. Our protocol is based upon the two existing protocols, namely, LEACH (Low-Energy Adaptive Clustering Hierarchy) and PEGASIS (Power Efficient GAthering in Sensor Information Systems). By combining these two basic routing techniques, we propose a new protocol which provides an increase in network lifetime compared to the existing basic protocols

Denial of service mitigation approach for IPv6-enabled smart object networks

Denial of service (DoS) attacks can be defined as any third-party action aiming to reduce or eliminate a network's capability to perform its expected functions. Although there are several standard techniques in traditional computing that mitigate the impact of some of the most common DoS attacks, this still remains a very important open problem to the network security community. DoS attacks are even more troublesome in smart object networks because of two main reasons. First, these devices cannot support the computational overhead required to implement many of the typical counterattack strategies. Second, low traffic rates are enough to drain sensors' battery energy making the network inoperable in short times. To realize the Internet of Things vision, it is necessary to integrate the smart objects into the Internet. This integration is considered an exceptional opportunity for Internet growth but, also, a security threat, because more attacks, including DoS, can be conducted. For these reasons, the prevention of DoS attacks is considered a hot topic in the wireless sensor networks scientific community. In this paper, an approach based on 6LowPAN neighbor discovery protocol is proposed to mitigate DoS attacks initiated from the Internet, without adding additional overhead on the 6LoWPAN sensor devices.This work has been partially supported by the Instituto de Telecomunicacoes, Next Generation Networks and Applications Group (NetGNA), Portugal, and by National Funding from the FCT - Fundacao para a Ciencia e Tecnologia through the Pest-OE/EEI/LA0008/2011.Oliveira, LML.; Rodrigues, JJPC.; De Sousa, AF.; Lloret, J. (2013). Denial of service mitigation approach for IPv6-enabled smart object networks. Concurrency and Computation: Practice and Experience. 25(1):129-142. doi:10.1002/cpe.2850S129142251Gershenfeld, N., Krikorian, R., & Cohen, D. (2004). The Internet of Things. Scientific American, 291(4), 76-81. doi:10.1038/scientificamerican1004-76Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). Wireless sensor networks: a survey. Computer Networks, 38(4), 393-422. doi:10.1016/s1389-1286(01)00302-4Karl, H., & Willig, A. (2005). Protocols and Architectures for Wireless Sensor Networks. doi:10.1002/0470095121IEEE Std 802.15.4-2006 Part 15.4: wireless medium access control (MAC) and physical layer (PHY) specificationsfor low-rate wireless personal area networks (LR-WPANs) 2006ZigBee Alliance ZigBee Specification 2007WirelessHARThomepage 2012 http://www.hartcomm.org/Hui, J. W., & Culler, D. E. (2008). Extending IP to Low-Power, Wireless Personal Area Networks. IEEE Internet Computing, 12(4), 37-45. doi:10.1109/mic.2008.79Kushalnagar N Montenegro G Schumacher C IPv6 over Low-Power Wireless Personal Area Networks (6LoWPANs): Overview, Assumptions, Problem Statement, and Goals 2007Montenegro G Kushalnagar N Hui J Culler D Transmission of IPv6 Packets over IEEE 802.15.4 Networks 2007Shelby Z Thubert P Hui J Chakrabarti S Bormann C Nordmark E 6LoWPAN Neighbor Discovery 2011Zhou, L., Chao, H.-C., & Vasilakos, A. V. (2011). Joint Forensics-Scheduling Strategy for Delay-Sensitive Multimedia Applications over Heterogeneous Networks. IEEE Journal on Selected Areas in Communications, 29(7), 1358-1367. doi:10.1109/jsac.2011.110803Roman, R., & Lopez, J. (2009). Integrating wireless sensor networks and the internet: a security analysis. Internet Research, 19(2), 246-259. doi:10.1108/10662240910952373Wang, Y., Attebury, G., & Ramamurthy, B. (2006). A survey of security issues in wireless sensor networks. IEEE Communications Surveys & Tutorials, 8(2), 2-23. doi:10.1109/comst.2006.315852Xiaojiang Du, & Hsiao-Hwa Chen. (2008). Security in wireless sensor networks. IEEE Wireless Communications, 15(4), 60-66. doi:10.1109/mwc.2008.4599222Pelechrinis, K., Iliofotou, M., & Krishnamurthy, S. V. (2011). Denial of Service Attacks in Wireless Networks: The Case of Jammers. IEEE Communications Surveys & Tutorials, 13(2), 245-257. doi:10.1109/surv.2011.041110.00022Zhou, L., Wang, X., Tu, W., Muntean, G., & Geller, B. (2010). Distributed scheduling scheme for video streaming over multi-channel multi-radio multi-hop wireless networks. IEEE Journal on Selected Areas in Communications, 28(3), 409-419. doi:10.1109/jsac.2010.100412Lin, K., Lai, C.-F., Liu, X., & Guan, X. (2010). Energy Efficiency Routing with Node Compromised Resistance in Wireless Sensor Networks. Mobile Networks and Applications, 17(1), 75-89. doi:10.1007/s11036-010-0287-xLi, H., Lin, K., & Li, K. (2011). Energy-efficient and high-accuracy secure data aggregation in wireless sensor networks. Computer Communications, 34(4), 591-597. doi:10.1016/j.comcom.2010.02.026Oliveira, L. M. L., de Sousa, A. F., & Rodrigues, J. J. P. C. (2011). Routing and mobility approaches in IPv6 over LoWPAN mesh networks. International Journal of Communication Systems, 24(11), 1445-1466. doi:10.1002/dac.1228Narten T Nordmark E Simpson W Soliman H Neighbor Discovery for IP version 6 (IPv6) 2007Singh H Beebee W Nordmark E IPv6 Subnet Model: The Relationship between Links and Subnet Prefixes 2010Roman, R., Lopez, J., & Gritzalis, S. (2008). Situation awareness mechanisms for wireless sensor networks. IEEE Communications Magazine, 46(4), 102-107. doi:10.1109/mcom.2008.4481348Sakarindr, P., & Ansari, N. (2007). Security services in group communications over wireless infrastructure, mobile ad hoc, and wireless sensor networks. IEEE Wireless Communications, 14(5), 8-20. doi:10.1109/mwc.2007.4396938Tsao T Alexander R Dohler M Daza V Lozano A A Security Framework for Routing over Low Power and Lossy Networks 2009Karlof C Wagner D Secure Routing in Wireless Sensor Networks: Attacks and Countermeasures First IEEE International Workshop on Sensor Network Protocols and Applications 2003 113 127 10.1109/SNPA.2003.1203362Hui J Thubert P Compression Format for IPv6 Datagrams in 6LoWPAN Networks 2009Elaine Shi, & Perrig, A. (2004). Designing Secure Sensor Networks. IEEE Wireless Communications, 11(6), 38-43. doi:10.1109/mwc.2004.1368895Akkaya, K., & Younis, M. (2005). A survey on routing protocols for wireless sensor networks. Ad Hoc Networks, 3(3), 325-349. doi:10.1016/j.adhoc.2003.09.01

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. IEEE Communications Surveys & Tutorials, 15(2), 551-591. doi:10.1109/surv.2012.062612.00084De Farias, C. M., Pirmez, L., Fortino, G., & Guerrieri, A. (2019). A multi-sensor data fusion technique using data correlations among multiple applications. Future Generation Computer Systems, 92, 109-118. doi:10.1016/j.future.2018.09.034Rao, P. C. S., Jana, P. K., & Banka, H. (2016). A particle swarm optimization based energy efficient cluster head selection algorithm for wireless sensor networks. Wireless Networks, 23(7), 2005-2020. doi:10.1007/s11276-016-1270-7Fu, X., Fortino, G., Pace, P., Aloi, G., & Li, W. (2020). Environment-fusion multipath routing protocol for wireless sensor networks. Information Fusion, 53, 4-19. doi:10.1016/j.inffus.2019.06.001Liu, X. (2015). Atypical Hierarchical Routing Protocols for Wireless Sensor Networks: A Review. IEEE Sensors Journal, 15(10), 5372-5383. doi:10.1109/jsen.2015.2445796Jan, N., Javaid, N., Javaid, Q., Alrajeh, N., Alam, M., Khan, Z. 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. Wireless Communications and Mobile Computing, 2019, 1-20. doi:10.1155/2019/6470359Sajwan, M., Gosain, D., & Sharma, A. K. (2018). CAMP: cluster aided multi-path routing protocol for wireless sensor networks. Wireless Networks, 25(5), 2603-2620. doi:10.1007/s11276-018-1689-0Varga, A. (2010). OMNeT++. Modeling and Tools for Network Simulation, 35-59. doi:10.1007/978-3-642-12331-3_3Lartillot, O., Toiviainen, P., & Eerola, T. (2008). A Matlab Toolbox for Music Information Retrieval. Studies in Classification, Data Analysis, and Knowledge Organization, 261-268. doi:10.1007/978-3-540-78246-9_31Mathur, P., Nielsen, R. H., Prasad, N. R., & Prasad, R. (2016). Data collection using miniature aerial vehicles in wireless sensor networks. IET Wireless Sensor Systems, 6(1), 17-25. doi:10.1049/iet-wss.2014.0120Zou, T., Lin, S., Feng, Q., & Chen, Y. (2016). Energy-Efficient Control with Harvesting Predictions for Solar-Powered Wireless Sensor Networks. Sensors, 16(1), 53. doi:10.3390/s16010053Song, Y., Ma, J., Zhang, X., & Feng, Y. (2012). Design of Wireless Sensor Network-Based Greenhouse Environment Monitoring and Automatic Control System. Journal of Networks, 7(5). doi:10.4304/jnw.7.5.838-844Nikolidakis, S., Kandris, D., Vergados, D., & Douligeris, C. (2013). Energy Efficient Routing in Wireless Sensor Networks Through Balanced Clustering. Algorithms, 6(1), 29-42. doi:10.3390/a6010029Ndzi, D. L., Harun, A., Ramli, F. M., Kamarudin, M. L., Zakaria, A., Shakaff, A. Y. M., … Farook, R. S. (2014). Wireless sensor network coverage measurement and planning in mixed crop farming. Computers and Electronics in Agriculture, 105, 83-94. doi:10.1016/j.compag.2014.04.01

Opportunistic Source Coding for Data Gathering in Wireless Sensor Networks

We propose a jointly opportunistic source coding and opportunistic routing (OSCOR) protocol for correlated data gathering in wireless sensor networks. OSCOR improves data gathering efficiency by exploiting opportunistic data compression and cooperative diversity associated with wireless broadcast advantage. The design of OSCOR involves several challenging issues across different network protocol layers. At the MAC layer, sensor nodes need to coordinate wireless transmission and packet forwarding to exploit multiuser diversity in packet reception. At the network layer, in order to achieve high diversity and compression gains, routing must be based on a metric that is dependent on not only link-quality but also compression opportunities. At the application layer, sensor nodes need a distributed source coding algorithm that has low coordination overhead and does not require the source distributions to be known. OSCOR provides practical solutions to these challenges incorporating a slightly modified 802.11 MAC, a distributed source coding scheme based on network coding and Lempel-Ziv coding, and a node compression ratio dependent metric combined with a modified Dijkstra's algorithm for path selection. We evaluate the performance of OSCOR through simulations, and show that OSCOR can potentially reduce power consumption by over 30% compared with an existing greedy scheme, routing driven compression, in a 4 x 4 grid network