Analysis of data aggregation methods and related issues in Wireless Sensor Networks

• Autorzy: Nels S. Ninisha , Singh J. Amar Pratap
• Języki publikacji: EN

Abstrakty

EN

Data aggregation is the process aimed at reducing the transmission count of packets being transmitted in the framework of in-network data processing. It is the data transmission model that takes the information transmitted from different nodes and generates a single data packet after finding and eliminating the redundant packets. Accordingly, this process decreases the transmission count and makes it possible to consume less energy. The major issues in data aggregation mechanism are related to reduction of latency and to energy balancing. Moreover, it is very complex to resolve the issue of packet loss, which is the failure of one or more transmitted packets to arrive at their destination due to the bad and/or congested channel conditions. The present survey involves a collection of 50 research papers dealing with the data aggregation models in wireless sensor networks (WSN). Various data aggregation methods, like the cluster-based approach, structure-free method, tree-based approach, in-network methods, and energy based aggregation model are considered in this survey, regarding the application and the energy usage involved. On the basis of the survey, the issues and drawbacks faced by the respective methodologies are highlighted. In addition, the paper presents simple statistics of the studies considered with respect to the performance measures, simulation tools, publication year, and classification of methods. The future dimensions of the respective research are supposed to be based on the challenges identified in this survey.

Słowa kluczowe

EN

data aggregation; wireless sensor network; WSN; clustering; cluster head; CH; cluster-based data aggregation

• Wydawca: Systems Research Institute, Polish Academy of Sciences
• Czasopismo: Control and Cybernetics
• Rocznik: 2020
• Tom: Vol. 49, No. 4
• Strony: 419--446
• Opis fizyczny: Bibliogr. 54 poz., rys., tab.

Twórcy

autor

Nels S. Ninisha

• Department of Computer Science and Engineering, Noorul Islam Centre for Higher Education, Kumaracoil, Thuckalay, Tamil Nadu, India

autor

Singh J. Amar Pratap

• Department of Computer Science and Engineering, Noorul Islam Centre for Higher Education, Kumaracoil, Thuckalay, Tamil Nadu, India

Bibliografia

• Abdelhafidh, M., Fourati, M., Fourati, L.C., Mnaouer, A.B. and Zid, M. (2018) Lifetime maximization for pipeline monitoring based on data aggregation and bio-inspired clustering algorithm. In: IEEE 14th International Wireless Communications & Mobile Computing Conference (IWCMC), 666-671.
• Acharya, S. and Tripathy, C.R. (2020) A reliable fault-tolerant ANFIS model based data aggregation scheme forWireless Sensor Networks. Journal of King Saud University-Computer and Information Sciences, 32(6), 741–753.
• Arul, V.H., Sivakumar, V.G., Marimuthu, R. and Chakraborty, B. (2019) An Approach for Speech Enhancement Using Deep Convolutional Neural Network. Multimedia Research (MR), 2(2): 37-44.
• Ashtikar, R., Javale, D. and Wakchaure, S. (2017) Energy Efficient & Secured Data Routing Through Aggregation Node in WSN. In: IEEE International Conference on Computing, Communication, Control and Automation (ICCUBEA), 1-6.
• Atoui, I., Ahmad, A., Medlej, M., Makhoul, A., Tawbe, S. and Hijazi, A. (2016) Tree-based data aggregation approach in wireless sensor network using fitting functions. In: IEEE Sixth international conference on digital information processing and communications (ICDIPC), 146-150.
• Balakrishnan, C., Vijayalakshmi, E. and Vinayagasundaram, B. (2016) An enhanced iterative filtering technique for data aggregation in WSN. In: IEEE International Conference on Information Communication and Embedded Systems (ICICES), 1-6.
• Chen, Q., Gao, H., Cheng, S., Li, J. and Cai, Z. (2017) Distributed non-structure based data aggregation for duty-cycle wireless sensor networks. In: IEEE INFOCOM 2017-IEEE Conference on Computer Communications, 1-9.
• Choudhari, E., Bodhe, K.D. and Mundada, S.M. (2017) Secure data aggregation in WSN using iterative filtering algorithm. In: IEEE International Conference on Innovative Mechanisms for Industry Applications (ICIMIA), 1-5.
• Conti, L., Bartolozzi, S., Racanelli, V., Guerri, F.S. and Iacobelli, S. (2018) Alarm guard systems for the prevention of damage produced by ungulates in a chestnut grove of Middle Italy. Agronomy Research, 16(3).
• Devi, V.S., Ravi, T. and Priya, S.B. (2020) Cluster Based Data Aggregation Scheme for Latency and Packet Loss Reduction in WSN. Computer Communications 149, 36-43.
• Dhand, G. and Tyagi, S.S. (2016) Data aggregation techniques in WSN: Survey. Procedia Computer Science, 92, 378-384.
• Hua, P., Liu, X., Yu, J., Dang, N. and Zhang, X. (2018) Energy-efficient adaptive slice-based secure data aggregation scheme in WSN. Procedia Computer Science, 129, 188-193.
• Jain, K. and Bhola, D.A. (2018) Data Aggregation Design Goals for Monitoring Data in Wireless Sensor Networks. Journal of Network Security Computer Networks 4(3): 1-9.
• Jasim, A.A., Idris, M.Y.I.B., Azzuhri, S.R.B., Issa, N.R., Noor, N. B.M., Kakarla, J. and Amiri, I.S. (2019) Secure and Energy-Efficient Data Aggregation Method Based on an Access Control Model. IEEE Access 7, 164327-164343.
• Jothiprakasam, S. and Muthial, C. (2018) A method to enhance life-time in data aggregation for multi-hop wireless sensor networks. AEU-International Journal of Electronics and Communications, 85, 183-191.
• Kadlikoppa, P., Umarji, I. and Patil, S. (2017) Data Aggregation & Transfer in Data Centric Network Using Spin Protocol in WSN. International Journal on Recent and Innovation Trends in Computing and Communication, 5(8): 142-149.
• Kamalesh, S. and Ganesh Kumar, P. (2017) Data aggregation in wireless sensor network using SVM-based failure detection and loss recovery. Journal of Experimental & Theoretical Artificial Intelligence, 29(1): 133-147.
• Kamble, S. and Dhope, T. (2016) Reliable routing data aggregation using efficient clustering in WSN. In: IEEE International Conference on Advanced Communication Control and Computing Technologies (ICAC-CCT), 246-250.
• Krishnan, A.M. and Kumar, P.G. (2016) An effective clustering approach with data aggregation using multiple mobile sinks for heterogeneousWSN. Wireless Personal Communications, 90(2): 423-434.
• Kumar, N.K. and Bharathi, A. (2020) Real time energy efficient data aggregation and scheduling scheme for WSN using ATL. Computer Communications, 151, 202-207.
• Lakshmi, M., Velmani, P. and Rani, P.A.J. (2019) Design and Implementation of Priority Hop Based Energy Efficient Cluster Routing Algorithm for WSN Data Aggregation. International Journal of Applied Engineering Research, 14(11): 2698-2703.
• Latha, A., Prasanna, S., Hemalatha, S. and Sivakumar, B. (2019) A harmonized trust assisted energy efficient data aggregation scheme for distributed sensor networks. Cognitive Systems Research, 56, 14-22.
• Manishankar, S., Ranjitha, P.R. and Kumar, T.M. (2017) Energy efficient data aggregation in sensor network using multiple sink data node. In: IEEE International Conference on Communication and Signal Processing (ICCSP), 0448-0452.
• Mantri, D.S., Prasad, N.R. and Prasad, R. (2016) Mobility and heterogeneity aware cluster-based data aggregation for wireless sensor network. Wireless Personal Communications, 86(2): 975-993.
• Metan, J. and Murthy, K.N. (2018) FSDA: Framework for Secure Data Aggregation in Wireless Sensor Network for Enhancing Key Management. International Journal of Electrical & Computer Engineering, 8(6).
• Mohanty, P. and Kabat, M.R. (2016) Energy efficient structure-free data aggregation and delivery in WSN. Egyptian Informatics Journal, 17(3): 273-284.
• Nayaka, R.J. and Biradar, R.C. (2017) Data aggregation and routing scheme for smart city public utility services using WSN. In: IEEE Second International Conference on Electrical, Computer and Communication Technologies (ICECCT), 1-8.
• Nguyen, N.T., Liu, B.H., Pham, V.T. and Liou, T.Y. (2018) An efficient minimum-latency collision-free scheduling algorithm for data aggregation in wireless sensor networks. IEEE Systems Journal, 12(3): 2214-2225.
• Otoum, S., Kantarci, B. and Mouftah, H. (2018) Adaptively supervised and intrusion-aware data aggregation for wireless sensor clusters in critical infrastructures. In: IEEE International Conference on Communications (ICC), 1-6.
• Prabhavathi, S., Subramanyam, A. and Rao, A.A. (2016) Energy efficient dynamic reconfiguration of routing agents forWSN data aggregation. In: Emerging Research in Computing, Information, Communication and Applications, Springer, New Delhi, 291-301.
• Preetha, M. and Sivakumar, K. (2018) An Energy Efficient Sleep Scheduling Protocol for Data Aggregation in WSN. Taga Journal, 14, 1748–0345.
• Ren, M., Li, J., Guo, L., Li, X. and Fan, W. (2017) Distributed data aggregation scheduling in multi-channel and multi-power wireless sensor networks. IEEE Access, 5, 27887-27896.
• Roy, N.R. and Chandra, P. (2019) EEDAC-WSN: Energy Efficient Data Aggregation in Clustered WSN. In: IEEE International Conference on Automation, Computational and Technology Management (ICACTM), 586-592.
• Sankaralingam, S.K., Nagarajan, N.S. and Narmadha, A.S. (2020) Energy aware decision stump linear programming boosting node classification based data aggregation in WSN. Computer 3Communications, 155, 130–142.
• Sasirekha, S. and Swamynathan, S. (2017) Cluster-chain mobile agent routing algorithm for efficient data aggregation in wireless sensor network. Journal of Communications and Networks, 19(4): 392-401.
• Shobana, M., Sabitha, R. and Karthik, S. (2020a) An enhanced soft computing-based formulation for secure data aggregation and efficient data processing in large-scale wireless sensor network. Soft Computing, 24, 1-12.
• Shobana, M., Sabitha, R. and Karthik, S. (2020b) Cluster-based systematic data aggregation model (CSDAM) for real-time data processing in large-scale WSN. Wireless Personal Communications, 117, 2865–2883
• SriVenkateswaran, C. and Sivakumar, D. (2019) Secure cluster-based data aggregation in wireless sensor networks with aid of ECC. International Journal of Business Information Systems, 31(2): 153-169.
• Subedi, S., Lee, S. and Lee, J. (2018) A New LEACH Algorithm for the Data Aggregation to Improve the Energy Efficiency inWSN. International Journal of Internet, Broadcasting and Communication, 10(2): 68-73.
• Ullah, I. and Youn, H.Y. (2019) A novel data aggregation scheme based on self-organized map for WSN. The Journal of Supercomputing, 75(7): 3975-3996.
• Ullah, I. and Youn, H.Y. (2020) Efficient data aggregation with node clustering and extreme learning machine for WSN. The Journal of Supercomputing, 76, 10009–10035.
• Wan, R., Xiong, N., Hu, Q., Wang, H. and Shang, J. (2019) Similarity-aware data aggregation using fuzzy c-means approach for wireless sensor networks. EURASIP Journal on Wireless Communications and Networking, 1, 59.
• Wang, X., Zhou, Q. and Cheng, C.T. (2019) A UAV-assisted topology-aware data aggregation protocol in WSN. Physical Communication, 34, 48-57.
• Wang, X., Zhou, Q. and Tong, J. (2019) V-Matrix-Based Scalable Data Aggregation Scheme in WSN. IEEE Access, 7, 56081-56094.
• Wang, Z., Liu, Z., Wang, F., Chen, L. and Shang, W. (2019) Energy minimum encrypted data aggregation scheme for WSN in smart grid. In: AIIPCC’19 Proceedings of the International Conference on Artificial Intelligence, Information Processing and Cloud Computing. Article no. 73, 1-6.
• Waykar, S. (2010) Dynamic routing protocol for ad-hoc network. In: ICWET 2010 at TECT Mumbai. Kluwer Academic Publishers.
• Yestemirova, G. and Saginbekov, S. (2018) Efficient data aggregation in wireless sensor networks with multiple sinks. In: IEEE 32nd International Conference on Advanced Information Networking and Applications (AINA), 115-119.
• Yoon, I., Kim, H. and Noh, D.K. (2017) Adaptive data aggregation and compression to improve energy utilization in solar-powered wireless sensor networks. Sensors, 17(6): 1226.
• Zhang, K., Huang, H., Wang, Y. and Wang, R. (2017) Link-based privacy-preserving data aggregation scheme in wireless sensor networks. In: International Conference on Industrial IoT Technologies and Applications, Springer, Cham. 119-129.
• Zhang, J., Hu, P., Xie, F., Long, J. and He, A. (2018) An energy efficient and reliable in-network data aggregation scheme for WSN. IEEE Access, 6, 71857-71870.
• Zhang, J., Lin, Z., Tsai, P.W. and Xu, L. (2020) Entropy-driven data aggregation method for energy-efficient wireless sensor networks. Information Fusion, 56, 103-113.
• Zhong, H., Shao, L., Cui, J. and Xu, Y. (2018) An efficient and secure recoverable data aggregation scheme for heterogeneous wireless sensor networks. Journal of Parallel and Distributed Computing, 111, 1-12.
• Zhou, L., Ge, C., Hu, S. and Su, C. (2019a) Energy-Efficient and Privacy-Preserving Data Aggregation Algorithm for Wireless Sensor Networks. IEEE Internet of Things Journal, 7 (5), 3948–3957.
• Zhou, Q., Qin, X., Liu, G., Cheng, H. and Zhao, H. (2019b) An Efficient Privacy and Integrity Preserving Data Aggregation Scheme for Multiple Applications in Wireless Sensor Networks. In: IEEE International Conference on Smart Internet of Things (SmartIoT), 291-297.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).