Wireless synchronisation for low cost wireless sensor networks using DCF77

Wireless Sensor Networks (WSN) consist out of multiple end nodes containing sensors and one or more coordinator nodes which poll and command the end nodes. WSN can prove very efficient in distributed energy data acquisition, e.g. for phasor or power measurements. These types of measurements however require relatively tight synchronisation, which is sometimes difficult to achieve for low-cost WSN. This paper explores the possibility of a low-cost wireless synchronization system using the DCF77 long wave time signal to achieve sub-millisecond synchronisation accuracy. The results are compared to conventional GPS based synchronisation. As a practical example, the implementation of the described synchronisation method is proposed for a non-contact electrical phase identifier, which uses synchronised current measurements to distinguishing between the different phases in an unmarked electrical distribution grid

Energy Consumption Of Visual Sensor Networks: Impact Of Spatio-Temporal Coverage

Wireless visual sensor networks (VSNs) are expected to play a major role in future IEEE 802.15.4 personal area networks (PAN) under recently-established collision-free medium access control (MAC) protocols, such as the IEEE 802.15.4e-2012 MAC. In such environments, the VSN energy consumption is affected by the number of camera sensors deployed (spatial coverage), as well as the number of captured video frames out of which each node processes and transmits data (temporal coverage). In this paper, we explore this aspect for uniformly-formed VSNs, i.e., networks comprising identical wireless visual sensor nodes connected to a collection node via a balanced cluster-tree topology, with each node producing independent identically-distributed bitstream sizes after processing the video frames captured within each network activation interval. We derive analytic results for the energy-optimal spatio-temporal coverage parameters of such VSNs under a-priori known bounds for the number of frames to process per sensor and the number of nodes to deploy within each tier of the VSN. Our results are parametric to the probability density function characterizing the bitstream size produced by each node and the energy consumption rates of the system of interest. Experimental results reveal that our analytic results are always within 7% of the energy consumption measurements for a wide range of settings. In addition, results obtained via a multimedia subsystem show that the optimal spatio-temporal settings derived by the proposed framework allow for substantial reduction of energy consumption in comparison to ad-hoc settings. As such, our analytic modeling is useful for early-stage studies of possible VSN deployments under collision-free MAC protocols prior to costly and time-consuming experiments in the field.Comment: to appear in IEEE Transactions on Circuits and Systems for Video Technology, 201

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

Cooperative Detection and Network Coding in Wireless Networks

In cooperative communication systems, multiple terminals in wireless networks share their antennas and resources for information exchange and processing. Recently, cooperative communications have been shown to achieve significant performance improvements in terms of transmission reliability, coverage area extension, and network throughput, with respect to existing classical communication systems. This dissertation is focused on two important applications of cooperative communications, namely: (i) cooperative distributed detection in wireless sensor networks, and (ii) many-to-many communications via cooperative space-time network coding. The first application of cooperative communications presented in this dissertation is concerned with the analysis and modeling of the deployment of cooperative relay nodes in wireless sensor networks. Particularly, in dense wireless sensor networks, sensor nodes continuously observe and collect measurements of a physical phenomenon. Such observations can be highly correlated, depending on the spatial separation between the sensor nodes as well as how the physical properties of the phenomenon are evolving over time. This unique characteristic of wireless sensor networks can be effectively exploited with cooperative communications and relays deployment such that the distributed detection performance is significantly improved as well as the energy efficiency. In particular, this dissertation studies the Amplify-and-Forward (AF) relays deployment as a function of the correlation of the observations and analyzes the achievable spatial diversity gains as compared with the classical wireless sensor networks. Moreover, it is demonstrated that the gains of cooperation can be further leveraged to alleviate bandwidth utilization inefficiencies in current sensor networks. Specifically, the deployment of cognitive AF cooperative relays to exploit empty/under-utilized time-slots and the resulting energy savings are studied, quantified and compared. The multiple terminal communication and information exchange form the second application of cooperative communications in this dissertation. Specifically, the novel concept of Space-Time-Network Coding (STNC) that is concerned with formulation of the many-to-many cooperative communications over Decode-and-Forward (DF) nodes is studied and analyzed. Moreover, the exact theoretical analysis as well as upper-bounds on the network symbol error rate performance are derived. In addition, the tradeoff between the number of communicating nodes and the timing synchronization errors is analyzed and provided as a network design guideline. With STNC, it is illustrated that cooperative diversity gains are fully exploited per node and significant performance improvements are achieved. It is concluded that the STNC scheme serves as a potential many-to-many cooperative communications scheme and that its scope goes much further beyond the generic source-relay-destination communications

Social Network Analysis Based Localization Technique with Clustered Closeness Centrality for 3D Wireless Sensor Networks

[EN] In this paper, we proposed a new wireless localization technique based on the ideology of social network analysis (SNA), to study the different properties of networks as a graph. Centrality is a main concept in SNA, so we propose using closeness centrality (CC) as a measurement to denote the importance of the node inside the network due to its geo-location to others. The node with highest degree of CC is chosen as a cluster heads, then each cluster head can form its trilateration process to collect data from its cluster. The selection of closest cluster based on CC values, and the unknown node's location can be estimated through the trilateration process. To form a perfect trilateration, the cluster head chooses three anchor nodes. The proposed algorithm provides high accuracy even in different network topologies like concave shape, O shape, and C shape as compared to existing received signal strength indicator (RSSI) techniques. Matlab simulation results based on practical radio propagation data sets showed a localization error of 0.32 m with standard deviation of 0.26 m.This work was fully supported by the Vice Chancellor Doctoral Scholarship at Auckland University of Technology, New Zealand.Ahmad, T.; Li, XJ.; Seet, B.; Cano, J. (2020). Social Network Analysis Based Localization Technique with Clustered Closeness Centrality for 3D Wireless Sensor Networks. Electronics. 9(5):1-19. https://doi.org/10.3390/electronics9050738S11995Zhou, B., Yao, X., Yang, L., Yang, S., Wu, S., Kim, Y., & Ai, L. (2019). Accurate Rigid Body Localization Using DoA Measurements from a Single Base Station. Electronics, 8(6), 622. doi:10.3390/electronics8060622Ahmad, T., Li, X., & Seet, B.-C. (2017). Parametric Loop Division for 3D Localization in Wireless Sensor Networks. Sensors, 17(7), 1697. doi:10.3390/s17071697Kaur, A., Kumar, P., & Gupta, G. P. (2019). A weighted centroid localization algorithm for randomly deployed wireless sensor networks. Journal of King Saud University - Computer and Information Sciences, 31(1), 82-91. doi:10.1016/j.jksuci.2017.01.007Khelifi, F., Bradai, A., Benslimane, A., Rawat, P., & Atri, M. (2018). A Survey of Localization Systems in Internet of Things. Mobile Networks and Applications, 24(3), 761-785. doi:10.1007/s11036-018-1090-3Sanchez-Iborra, R., G. Liaño, I., Simoes, C., Couñago, E., & Skarmeta, A. (2018). Tracking and Monitoring System Based on LoRa Technology for Lightweight Boats. Electronics, 8(1), 15. doi:10.3390/electronics8010015Sayed, A. H., Tarighat, A., & Khajehnouri, N. (2005). Network-based wireless location: challenges faced in developing techniques for accurate wireless location information. IEEE Signal Processing Magazine, 22(4), 24-40. doi:10.1109/msp.2005.1458275Maşazade, E., Ruixin Niu, Varshney, P. K., & Keskinoz, M. (2010). Energy Aware Iterative Source Localization for Wireless Sensor Networks. IEEE Transactions on Signal Processing, 58(9), 4824-4835. doi:10.1109/tsp.2010.2051433Yang, X., Kong, Q., & Xie, X. (2009). One-Dimensional Localization Algorithm Based on Signal Strength Ratio. International Journal of Distributed Sensor Networks, 5(1), 79-79. doi:10.1080/15501320802571822Xie, S., Wang, T., Hao, X., Yang, M., Zhu, Y., & Li, Y. (2019). Localization and Frequency Identification of Large-Range Wide-Band Electromagnetic Interference Sources in Electromagnetic Imaging System. Electronics, 8(5), 499. doi:10.3390/electronics8050499Zhu, X., Wu, X., & Chen, G. (2013). Relative localization for wireless sensor networks with linear topology. Computer Communications, 36(15-16), 1581-1591. doi:10.1016/j.comcom.2013.07.007Meng, W., Xiao, W., & Xie, L. (2011). An Efficient EM Algorithm for Energy-Based Multisource Localization in Wireless Sensor Networks. IEEE Transactions on Instrumentation and Measurement, 60(3), 1017-1027. doi:10.1109/tim.2010.2047035Lim, H., & Hou, J. C. (2009). Distributed localization for anisotropic sensor networks. ACM Transactions on Sensor Networks, 5(2), 1-26. doi:10.1145/1498915.1498917Xiaohong Sheng, & Yu-Hen Hu. (2005). Maximum likelihood multiple-source localization using acoustic energy measurements with wireless sensor networks. IEEE Transactions on Signal Processing, 53(1), 44-53. doi:10.1109/tsp.2004.838930Yun Wang, Xiaodong Wang, Demin Wang, & Agrawal, D. P. (2009). Range-Free Localization Using Expected Hop Progress in Wireless Sensor Networks. IEEE Transactions on Parallel and Distributed Systems, 20(10), 1540-1552. doi:10.1109/tpds.2008.239Huang, H., & Zheng, Y. R. (2018). Node localization with AoA assistance in multi-hop underwater sensor networks. Ad Hoc Networks, 78, 32-41. doi:10.1016/j.adhoc.2018.05.005Zàruba, G. V., Huber, M., Kamangar, F. A., & Chlamtac, I. (2006). Indoor location tracking using RSSI readings from a single Wi-Fi access point. Wireless Networks, 13(2), 221-235. doi:10.1007/s11276-006-5064-1Singh, M., & Khilar, P. M. (2015). An analytical geometric range free localization scheme based on mobile beacon points in wireless sensor network. Wireless Networks, 22(8), 2537-2550. doi:10.1007/s11276-015-1116-8Yiqiang Chen, Qiang Yang, Jie Yin, & Xiaoyong Chai. (2006). Power-efficient access-point selection for indoor location estimation. IEEE Transactions on Knowledge and Data Engineering, 18(7), 877-888. doi:10.1109/tkde.2006.112Alzoubi, K., Li, X.-Y., Wang, Y., Wan, P.-J., & Frieder, O. (2003). Geometric spanners for wireless ad hoc networks. IEEE Transactions on Parallel and Distributed Systems, 14(4), 408-421. doi:10.1109/tpds.2003.1195412Safa, H. (2014). A novel localization algorithm for large scale wireless sensor networks. Computer Communications, 45, 32-46. doi:10.1016/j.comcom.2014.03.020Kaemarungsi, K., & Krishnamurthy, P. (2012). Analysis of WLAN’s received signal strength indication for indoor location fingerprinting. Pervasive and Mobile Computing, 8(2), 292-316. doi:10.1016/j.pmcj.2011.09.003Patwari, N., Hero, A. O., Perkins, M., Correal, N. S., & O’Dea, R. J. (2003). Relative location estimation in wireless sensor networks. IEEE Transactions on Signal Processing, 51(8), 2137-2148. doi:10.1109/tsp.2003.814469Niculescu, D. (2003). Telecommunication Systems, 22(1/4), 267-280. doi:10.1023/a:1023403323460Mahyar, H., Hasheminezhad, R., Ghalebi K., E., Nazemian, A., Grosu, R., Movaghar, A., & Rabiee, H. R. (2018). Compressive sensing of high betweenness centrality nodes in networks. Physica A: Statistical Mechanics and its Applications, 497, 166-184. doi:10.1016/j.physa.2017.12.145Plets, D., Bastiaens, S., Martens, L., & Joseph, W. (2019). An Analysis of the Impact of LED Tilt on Visible Light Positioning Accuracy. Electronics, 8(4), 389. doi:10.3390/electronics8040389RSSI Datasethttps://github.com/pspachos/RSSI-DatasetAhmad, T., Li, X. J., & Seet, B.-C. (2019). Noise Reduction Scheme for Parametric Loop Division 3D Wireless Localization Algorithm Based on Extended Kalman Filtering. Journal of Sensor and Actuator Networks, 8(2), 24. doi:10.3390/jsan8020024Benson, S. J., Ye, Y., & Zhang, X. (2000). Solving Large-Scale Sparse Semidefinite Programs for Combinatorial Optimization. SIAM Journal on Optimization, 10(2), 443-461. doi:10.1137/s105262349732800

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 survey of localization in wireless sensor network

Localization is one of the key techniques in wireless sensor network. The location estimation methods can be classified into target/source localization and node self-localization. In target localization, we mainly introduce the energy-based method. Then we investigate the node self-localization methods. Since the widespread adoption of the wireless sensor network, the localization methods are different in various applications. And there are several challenges in some special scenarios. In this paper, we present a comprehensive survey of these challenges: localization in non-line-of-sight, node selection criteria for localization in energy-constrained network, scheduling the sensor node to optimize the tradeoff between localization performance and energy consumption, cooperative node localization, and localization algorithm in heterogeneous network. Finally, we introduce the evaluation criteria for localization in wireless sensor network

Compress sensing algorithm for estimation of signals in sensor networks

ARTICULO DE INVESTIGACION IDEXADO EN JCR CON FACTOR DE IMPACTO 2.4In this research, we present a data recovery scheme for wireless sensor networks. In some sensor networks, each node must be able to recover the complete information of the network, which leads to the problem of the high cost of energy in communication and storage of information. We proposed a modified gossip algorithm for acquire distributed measurements and communicate the information across all nodes of the network using compressive sampling and Gossip algorithms to compact the data to be stored and transmitted through a network. The experimental results on synthetic data show that the proposed method reconstruct better the signal and in less iterations than with a similar method using a thresholding algorithm