430 research outputs found

    Secure location-aware communications in energy-constrained wireless networks

    Get PDF
    Wireless ad hoc network has enabled a variety of exciting civilian, industrial and military applications over the past few years. Among the many types of wireless ad hoc networks, Wireless Sensor Networks (WSNs) has gained popularity because of the technology development for manufacturing low-cost, low-power, multi-functional motes. Compared with traditional wireless network, location-aware communication is a very common communication pattern and is required by many applications in WSNs. For instance, in the geographical routing protocol, a sensor needs to know its own and its neighbors\u27 locations to forward a packet properly to the next hop. The application-aware communications are vulnerable to many malicious attacks, ranging from passive eavesdropping to active spoofing, jamming, replaying, etc. Although research efforts have been devoted to secure communications in general, the properties of energy-constrained networks pose new technical challenges: First, the communicating nodes in the network are always unattended for long periods without physical maintenance, which makes their energy a premier resource. Second, the wireless devices usually have very limited hardware resources such as memory, computation capacity and communication range. Third, the number of nodes can be potentially of very high magnitude. Therefore, it is infeasible to utilize existing secure algorithms designed for conventional wireless networks, and innovative mechanisms should be designed in a way that can conserve power consumption, use inexpensive hardware and lightweight protocols, and accommodate with the scalability of the network. In this research, we aim at constructing a secure location-aware communication system for energy-constrained wireless network, and we take wireless sensor network as a concrete research scenario. Particularly, we identify three important problems as our research targets: (1) providing correct location estimations for sensors in presence of wormhole attacks and pollution attacks, (2) detecting location anomalies according to the application-specific requirements of the verification accuracy, and (3) preventing information leakage to eavesdroppers when using network coding for multicasting location information. Our contributions of the research are as follows: First, we propose two schemes to improve the availability and accuracy of location information of nodes. Then, we study monitoring and detection techniques and propose three lightweight schemes to detect location anomalies. Finally, we propose two network coding schemes which can effectively prevent information leakage to eavesdroppers. Simulation results demonstrate the effectiveness of our schemes in enhancing security of the system. Compared to previous works, our schemes are more lightweight in terms of hardware cost, computation overhead and communication consumptions, and thus are suitable for energy-constrained wireless networks

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

    Full text link
    [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

    Robust multiple frequency multiple power localization schemes in the presence of multiple jamming attacks

    Get PDF
    Localization of the wireless sensor network is a vital area acquiring an impressive research concern and called upon to expand more with the rising of its applications. As localization is gaining prominence in wireless sensor network, it is vulnerable to jamming attacks. Jamming attacks disrupt communication opportunity among the sender and receiver and deeply impact the localization process, leading to a huge error of the estimated sensor node position. Therefore, detection and elimination of jamming influence are absolutely indispensable. Range-based techniques especially Received Signal Strength (RSS) is facing severe impact of these attacks. This paper proposes algorithms based on Combination Multiple Frequency Multiple Power Localization (C-MFMPL) and Step Function Multiple Frequency Multiple Power Localization (SF-MFMPL). The algorithms have been tested in the presence of multiple types of jamming attacks including capture and replay, random and constant jammers over a log normal shadow fading propagation model. In order to overcome the impact of random and constant jammers, the proposed method uses two sets of frequencies shared by the implemented anchor nodes to obtain the averaged RSS readings all over the transmitted frequencies successfully. In addition, three stages of filters have been used to cope with the replayed beacons caused by the capture and replay jammers. In this paper the localization performance of the proposed algorithms for the ideal case which is defined by without the existence of the jamming attack are compared with the case of jamming attacks. The main contribution of this paper is to achieve robust localization performance in the presence of multiple jamming attacks under log normal shadow fading environment with a different simulation conditions and scenarios

    Comparative node selection-based localization technique for wireless sensor networks: A bilateration approach

    Get PDF
    Wireless sensor networks find extensive applications, such as environmental and smart city monitoring, structural health, and target location. To be useful, most sensor data must be localized. We propose a node localization technique based on bilateration comparison (BACL) for dense networks, which considers two reference nodes to determine the unknown position of a third node. The mirror positions resulted from bilateration are resolved by comparing their coordinates with the coordinates of the reference nodes. Additionally, we use network clustering to further refine the location of the nodes. We show that BACL has several advantages over Energy Aware Co-operative Localization (EACL) and Underwater Recursive Position Estimation (URPE): (1) BACL uses bilateration (needs only two reference nodes) instead of trilateration (that needs three reference nodes), (2) BACL needs reference (anchor) nodes only on the field periphery, and (3) BACL needs substantially less communication and computation. Through simulation, we show that BACL localization accuracy, as root mean square error, improves by 53% that of URPE and by 40% that of EACL. We also explore the BACL localization error when the anchor nodes are placed on one or multiple sides of a rectangular field, as a trade-off between localization accuracy and network deployment effort. Best accuracy is achieved using anchors on all field sides, but we show that localization refinement using node clustering and anchor nodes only on one side of the field has comparable localization accuracy with anchor nodes on two sides but without clustering

    Secure Data Provenance in Home Energy Monitoring Networks

    Get PDF
    Smart grid empowers home owners to efficiently manage their smart home appliances within a Home Area Network (HAN), by real time monitoring and fine-grained control. However, it offers the possibility for a malicious user to intrude into the HAN and deceive the smart metering system with fraudulent energy usage report. While most of the existing works have focused on how to prevent data tampering in HAN's communication channel, this paper looks into a relatively less studied security aspect namely data provenance. We propose a novel solution based on Shamir's secret sharing and threshold cryptography to guarantee that the reported energy usage is collected from the specific appliance as claimed at a particular location, and that it reflects the real consumption of the energy. A byproduct of the proposed security solution is a guarantee of data integrity. A prototype implementation is presented to demonstrate the feasibility and practicality of the proposed solution

    EasyLoc: RSS-Based Localization Made Easy

    Get PDF
    AbstractLocalization based on Received Signal Strength (RSS) is a key method for locating objects in Wireless Sensor Networks (WSNs). However, current RSS-based methods are ineffective at both deployment and operation design levels since they (i.) usually require a labor-intensive pre-deployment profiling operations to map the RSS to either locations or distances and (ii.) rely on heavy processing operations. These two designs problems limit the possibility of implementing the localization technique on resources constrained sensor nodes and also restrict its scalability and practical use. In this paper, we tackle the challenge of devising a self-organizing and practical RSS-based localization technique that improves on previous approaches in terms of ease of deployment, ease of implementation while still providing a reasonable accuracy. To this end, we come up with a new solution, EasyLoc, a plug-and-play and distributed RSS-based localization method that requires zero pre-deployment configuration. The idea consists in exploiting the available distance information between anchors to derive an online and anchor-specific RSS-to-distance mapping. We show that, in addition to its simplicity, EasyLoc provides location errors of 90% less than 1m and an average error of 0.48m in small environment and 1.8m in large environment
    • …
    corecore