CUBOID-BASED WIRELESS SENSOR NETWORK LOCALIZATION ALGORITHM

CUBOID-BASED WIRELESS SENSOR NETWORK LOCALIZATION ALGORITHM

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  International Journal ofAdvanced Smart Sensor Network Systems (IJASSN), Vol 12, No.1/2/3, July 2022 DOI:10.5121/ijassn.2022.12301 1 CUBOID-BASED WIRELESS SENSOR NETWORK LOCALIZATION ALGORITHM Aranya Waan Department of Computer Engineering, Chiang Mai University, Thailand ABSTRACT Localization is one of the key technologies in wireless sensor networks (WSNs), since it provides fundamental support for many location-aware protocols and applications. Constraints on cost and power consumption make it infeasible to equip each sensor node in the network with a global position system (GPS) unit, especially for large-scale WSNs. A promising method to localize unknown nodes is to use anchor nodes, which are equipped with GPS units among unknown nodes and broadcast their current locations to help nearby unknown nodes with localization. In this paper we can proposed a novel algorithm of cuboid localization with the help of central point precision method. Simulation shows that the results are far better then existing cuboid methods and gain accuracy of up to 83% with a localization error of 1.6m and standard deviation of 2.7. KEYWORDS Network Protocols, Wireless Network, Mobile Network, Virus, Localization 1. INTRODUCTION Wireless Sensor Network (WSN) is a network of multi-hop self-organizing wireless communication system. WSN consist of a set of physically small and cheap micro-sensor nodes deployed in a given monitoring area (region), namely, in two-dimensional (2D) or three- dimensional (3D) environments, to fulfill tasks such as surveillance, biological detection, home care, object tracking, etc., [1-4]. The monitoring information is sent to sink nodes via multi-hop communication [5]. The sink collects the sensing data from the sensor nodes and then processes this information as required by the specific applications [6,7]. In WSNs, determining unknown nodes’ locations is a critical task since it provides fundamental support for many location-aware protocols and applications, such as location-based routing protocols, where the location information is critical for sensor nodes to make optimal routing decisions [8,9]. The problem of localization is the process of finding location information of the sensor nodes in a given coordinate system. To localize a WSN in the global coordinate system, some special nodes should be aware of their positions in advance from Global Position System (GPS), which are called anchors (beacons). Other nodes, which are usually called unknown nodes, calculate their positions by using special localization algorithms [10-12]. The performance of the WSNs localization algorithm depends on different network parameters such as the number of anchors, the communication range, the network topology, the density of nodes and so on. Sensor nodes localization usually consists of two steps: (i) distance and angle measurements between neighboring nodes, and (ii) geometric calculation based on measured distances and angles.
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  International Journal ofAdvanced Smart Sensor Network Systems (IJASSN), Vol 12, No.1/2/3, July 2022 2 Based on the distance and angle measurement techniques used, localization algorithms can be classified into range-based localization algorithms and range-free localization algorithms. 2. RELATED WORK Many techniques have been proposed to determine the locations of the nodes in WSNs. A general overview of state-of-the-art localization methods is available in [13]. In this section, existing works under both the range-based and range free context are reviewed. 2.1. Range - Based Methods The measuring technologies in the range-based category consist of the received signal strength indicator (RSSI) method, the time of arrival (TOA) method, the time difference of arrival (TDOA) method and the angle of arrival (AOA) method. The RSSI method applies a known mathematical model describing the path loss attenuation with distance [14]. The receiving node measures the strength of received radio frequency signal, which is compared with the original transmit signal power to obtain the propagation loss of the communication path. This propagation loss can be converted to the distance between two nodes using the empirical model and theoretical formulas. As a result, the locations of the nodes can be determined using classic geometric relationships. However, any obstacles between nodes lead to decrease the RSSI, which considered as error leads to longer distance. Moreover, multipath in signal reception leads to error in distance measurement by constructive or distractive interference. In the TOA method, the propagation time of radio frequency signal can be measured from the transmitting node to the receiving node. 2.2. Range - Free Method Range-free methods are proposed as a cost-effective alternative to range-based methods. They depend on the connectivity between nodes and anchors. Moreover, range-free methods avoid costly hardware by exploiting inter-node communication and the sensing range of the node to estimate node locations. On the other hand, range free methods accept more localization error as a tradeoff. In the centroid localization algorithm (CLA) [15], all nodes calculate their locations as the centroid of all received anchor’s locations. However, the inherent bias of the CLA is not considered. In recent years, weighted methods are proposed to improve the centroid algorithm. The DV-Hop algorithm can find the locations of the nodes using fewer number of anchors [16- 18]. Instead of one hop broadcast, the anchors are flooded in the entire network maintaining the hop count and distances from anchors in each hop. 3. PRELIMINARIES & MOTIVATION 3.1. System Model For the cuboid generation centre of gravity plays an important role having equal effect with neighbouring beacon nodes. This algorithm allows to all unknown nodes having one beacon nodes and a triangulation formation. For lower localization error all the nodes are deployed randomly and uniformly. For uniform distribution of beacons consider an array of beacons node broadcasting signals to all unknown nodes with its location and id.
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  International Journal ofAdvanced Smart Sensor Network Systems (IJASSN), Vol 12, No.1/2/3, July 2022 3 𝐵𝑖 = (𝐵𝑖 + 𝐵𝑖+1 + 𝐵𝑖+2) 3 ⁄ (1) The localization error is quite high so we need to reform some model which can satisfied the following relation. 𝐸[𝑒𝑢𝑙]𝑖 = √(𝐵𝑖−1 − 𝐵𝑖 )2 (2) 𝐸[𝑑𝑖𝑠𝑝] = (𝐵𝑖−1 − 𝐵𝑖 )2 (3) 3.2. Motivation The existing localization methods suffers high localization error, energy consumption and computation resources. The range based methods show more accuracy compared to range free methods. In contrast, the range based methods have the following problem: 1) Received signal strength indicator (RSSI): the RSSI suffers from multi path problem, which creates error in distance measurement by constructive interference (i.e. shorter distance measured) or distractive interference (i.e. longer distance measured). Moreover, RSSI suffers from attenuating obstacles, which gives wrong distance estimation and may leads to miss angle calculation if no other method is used for angle measurement. The RSSI models are also considered for image, VANET and RFID tag localization [19-22] 2) Time of arrival (TOA): the TOA suffers from miss synchronization among WSN nodes. In contrast, to improve the synchronization each node must be equipped with advanced clock generator that is resource consumption and leads to higher cost. 3) Time difference of arrival (TDOA): the TDOA shows promising idea but lack implementation because it needs two different types of signals. Each signal mush has its own receiver, which increase the node size, cost and energy consumption. If TDOA can be used with the same signal, it will be much better and cheaper. 4) Angle of arrival (AOA): the AOA is the best way to measure the angles between nodes. However, attaching an antenna array with each node will increase the node size, cost and energy consumption. Moreover, to transmit a packet per each neighbouring node gives high energy consumption and delay. In order to improve the localization performance by minimizing the localization error and decrease the nodes energy consumption, cost and size, this paper targets shifting the complexity of the localization system to anchors. This make the sensor nodes cheaper, more energy saving and has low required resources. In other words, we aim to benefit from the anchors advanced capabilities and power to achieve higher accuracy (i.e. less than 0.2 m) and cheaper nodes [23- 25]. 4. ALGORITHM DESIGN The entire localization algorithm will be as follows. 1. The beacon nodes broadcast its id and location to all the nodes in a network to be localized. 2. The beacon nodes further select three more nodes to form the triangulation. All the triangles are not overlapped 3. The distance from nodes to each beacon is computed using RSSI. 4. The RSSI data set is recorded in a table with further used in error computation.
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  International Journal ofAdvanced Smart Sensor Network Systems (IJASSN), Vol 12, No.1/2/3, July 2022 4 5. Compute the spherical area to calculate the network boundary. If there is no node in a boundary, its already localized or node failure. The algorithm of the entire process is explained here. Input: beacon-centroid pair [𝐵, 𝐶𝐺𝑖], random approximation point [𝐸𝑖] If 𝑬[𝒅𝒊𝒔𝒑] > 0 𝐵𝑖 = upper bounding 𝐶𝐺𝑖 = lower bounding Else 𝐵𝑖 = lower bound point (𝐿𝑃) 𝐶𝐺𝑖 = upper bound point (𝑈𝑃) 2. Computation of network area (a) = 𝐶𝐺𝑖 𝐸𝑢𝑖 2 3. Consider a 3d region 3. onward 3D interplanetary 𝑆𝑆𝐹 = 𝐿𝐸𝑃 + CE contrary 3D interplanetary 𝑆𝑆𝑅 = 𝐸𝑈𝑃 - CE 4. Repeat 5. Euclidian distance [𝐶𝐸𝑖] and [𝑆𝑆𝑗𝐹] Compute Euclidian distance amid [𝐶𝐸𝑖] and [𝑆𝑆𝑗𝐹] 6. Is 𝐷𝑅𝑗 𝐸𝑢𝑖 is minimum 7. Until 𝐷𝐹𝑗 𝐸𝑢𝑖 and 𝐷𝑅𝑗 𝐸𝑢𝑖 is minimum 8. Calculate 𝑆𝑗𝐹 𝑆𝑗𝑅 from 𝑆𝐹 and 𝑆𝑅 at 𝐷𝐹𝑗 𝐸𝑢𝑖 and 𝐷𝑅𝑗 𝐸𝑢𝑖 9. The system self-calibration and arrival time estimation. 9. Predictable opinion: 𝑃𝐶𝑖𝐸 = 𝐸𝑃𝑓𝐹 ∗ 𝐸𝑆𝑗𝐹 + 𝑃𝐸𝑆𝑓𝑅 ∗ 𝐸𝑆𝑗𝑅 10. distance computation RSSI 11. RSSI in a table form. 12. Centroid formula for distance Output: distance: localization error. 5. SIMULATION As the algorithm is dynamic nature, we choose the area of (1000 x1000 x 1000)m in a 3d space. Initially we choose 40 beacons and 50 unknown nodes to compute the localization error. The localization error also checks by increasing the number of beacons nodes. We found that the error is continuously decreasing by increasing the number of nodes. The error plot is shown in fig 1.
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  International Journal ofAdvanced Smart Sensor Network Systems (IJASSN), Vol 12, No.1/2/3, July 2022 5 Figure 1. Localization error along with each distance in meter 6. CONCLUSION Localization is misery from accuracy and error distance. In this proposed method we proposed a new localization algorithm which is free from network coverage, and noisy data element. The density of beacon nodes also plays an important role in decreasing the localization error. Future works encompasses study of accuracy in noisy environment, uniform random distribution analysis and reduction of localization error. In particular, we also consider lower bounding and upper bounding in localization error. REFERENCES [1] Ahmad, T., Li, X. J., & Seet, B. C. (2017). Parametric loop division for 3D localization in wireless sensor networks. Sensors, 17(7), 1697. [2] V. Rajendran, K. Obraczka, and J. J. Garcia-Luna-Aceves, “Energy-Efficient, Collision-Free Medium Access Control for Wireless Sensor Networks,” Proc. ACM SenSys „03, Los Angeles, CA, Nov. 2003, pp. 181–92. [3] L. Bao and J. J. Garcia-Luna-Aceves, “A New Approach to Channel Access Scheduling for Ad Hoc Networks,” 7th Ann. Int‟l. Conf. Mobile Comp. and Net., 2001, pp. 210– 21. [4] Ahmad, Tanveer, Xue Jun Li, and Boon-Chong Seet. "A self-calibrated centroid localization algorithm for indoor ZigBee WSNs." In 2016 8th IEEE International Conference on Communication Software and Networks (ICCSN), pp. 455- 461. IEEE, 2016. [5] Ahmad, Tanveer, Xue Jun Li, and Boon-Chong Seet. "3D localization based on parametric loop division and subdivision surfaces for wireless sensor networks." In 2016 25th Wireless and Optical Communication Conference (WOCC), pp. 1-6. IEEE, 2016. [6] Y. C. Tay, K. Jamieson, and H. Balakrishnan, “Collision Minimizing CSMA and Its Applications to Wireless Sensor Networks,” IEEE JSAC, vol. 22, no. 6, Aug. 2004, pp. 1048–57. [7] Ahmad, Tanveer, Xue Jun Li, and Boon-Chong Seet. "3D localization using social network analysis for wireless sensor networks." In 2018 IEEE 3rd international conference on communication and information systems (ICCIS), pp. 88-92. IEEE, 2018. [8] Chen, Kai, Zhong-hua Wang, Mei Lin, and Min Yu. "An improved DV-Hop localization algorithm for wireless sensor networks." (2010): 255-259. [9] zeng Wang, Ji, and Hongxu Jin. "Improvement on APIT localization algorithms for wireless sensor networks." In 2009 International Conference on Networks Security, Wireless Communications and Trusted Computing, vol. 1, pp. 719-723. IEEE, 2009. [10] Ahmad, Tanveer, Xue Jun Li, and Boon-Chong Seet. "Noise Reduction Scheme for Parametric Loop Division 3D Wireless Localization Algorithm Based on Extended Kalman Filtering." Journal of Sensor and Actuator Networks 8, no. 2 (2019): 24.
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  International Journal ofAdvanced Smart Sensor Network Systems (IJASSN), Vol 12, No.1/2/3, July 2022 6 [11] Ahmad, Tanveer, Xue Jun Li, and Boon-Chong Seet. "Fuzzy-Logic Based Localization for Mobile Sensor Networks." In 2019 2nd International Conference on Communication, Computing and Digital systems (CCODE), pp. 43-47. IEEE, 2019. [12] G. Lu, B. Krishnamachari, and C. S. Raghavendra, “An Adaptive Energy-Efficient and Low-Latency MAC for Data Gathering in Wireless Sensor Networks,” Proc. 18th Int‟l. Parallel and Distrib. Processing Symp., Apr. 2004, p. 224. [13] Ahmad, Tanveer, Xue Jun Li, Boon-Chong Seet, and Juan Carlos Cano. "Social Network Analysis Based Localization Technique with Clustered Closeness Centrality for 3D Wireless Sensor Networks." Electronics 9, no. 5 (2020): 738. [14] hakila, R., and B. Paramasivan. "An improved range based localization using Whale Optimization Algorithm in underwater wireless sensor network." Journal of Ambient Intelligence and Humanized Computing (2020): 1-11. [15] Pala, S., Palliyani, S., Himdi, M., Lafond, O., & Kurup, D. G. (2020). Localization of unknown electromagnetic source using 3D-antenna arrays. International Journal of Microwave and Wireless Technologies, 12(1), 86-94. [16] R. Niu and P. Varshney, “Target location estimation in wireless sensor networks using binary data,” in Proceedings of the 38th International Conference on Information Sciences and Systems, pp. 17–19, Princeton, NJ, USA, March 2004. [17] Ahmad, T. (2019). 3D Localization Techniques for Wireless Sensor Networks (Doctoral dissertation, Auckland University of Technology). [18] Ahmad, T., Li, X. J., Wenchao, J., & Ghaffar, A. (2020, September). Frugal Sensing: A Novel approach of Mobile Sensor Network Localization based on Fuzzy-Logic. In Proceedings of the ACM MobiArch 2020 The 15th Workshop on Mobility in the Evolving Internet Architecture (pp. 8-15). [19] Murtaza, Marryam, Muhammad Sharif, Musarrat AbdullahYasmin, and Tanveer Ahmad. "Facial expression detection using six facial expressions hexagon (sfeh) model." In 2019 IEEE 9th annual computing and communication workshop and conference (CCWC), pp. 0190-0195. IEEE, 2019. [20] Ahmad, Tanveer. "An improved accelerated frame slotted ALOHA (AFSA) algorithm for tag collision in RFID." arXiv preprint arXiv:1405.6217 (2014). [21] Saleem, Muhammad Asim, Zhou Shijie, Muhammad Umer Sarwar, Tanveer Ahmad, Amarah Maqbool, Casper Shikali Shivachi, and Maham Tariq. "Deep learning-based dynamic stable cluster head selection in VANET." Journal of Advanced Transportation 2021 (2021). [22] Ahmad, Tanveer, Imran Khan, Azeem Irshad, Shafiq Ahmad, Ahmed T. Soliman, Akber Abid Gardezi, Muhammad Shafiq, and Jin-Ghoo Choi. "Spark Spectrum Allocation for D2D Communication in Cellular Networks." CMC-COMPUTERS MATERIALS & CONTINUA 70, no. 3 (2022): 6381-6394. [23] Y. Wang, X. Wang, D. Wang, and D. P. Agrawal, “Rangefree localization using expected hop progress in wireless sensor networks,” IEEE Transactions on Parallel and Distributed Systems, vol. 20, no. 10, pp. 1540–1552, 2009. [24] H. Xu, Y. Tu, W. Xiao, Y. Mao, and T. Shen, “An archimedes curve-based mobile anchor node localization algorithm in wireless sensor networks,” in Proceedings of the 8th World Congress on Intelligent Control and Automation (WCICA ‟10), pp. 6993–6997, Jinan, China, July 2010. [25] J. Lee, W. Chung, and E. Kim, “Robust DV-Hop algorithm for localization in wireless sensor network,” in Proceedings of the International Conference on Control, Automation and Systems, pp. 2506–2509, Gyeonggi-do, South Korea, October 2010.