Establishing Self-Healing and Seamless Connectivity among IoT Networks Using Kalman Filter

N. N. Srinidhi, J. Shreyas, E. Naresh

Abstract


The Internet of Things (IoT) is the extension of Internet connectivity into physical devices and to everyday objects. Efficient mobility support in IoT provides seamless connectivity to mobile nodes having restrained resources in terms of energy, memory and link capacity. Existing routing algorithms have less reactivity to mobility. So, in this work, a new proactive mobility support algorithm based on the Kalman Filter has been proposed. Mobile nodes are provided with a seamless connectivity by minimizing the switching numbers between point of attachment which helps in reducing signaling overhead and power consumption. The handoff trigger scheme which makes use of mobility information in order to predict handoff event occurrence is used.  Mobile nodes new attachment points and its trajectory is predicted using the Kalman-Filter. Kalman-Filter is a predictor-estimator method used for movement prediction is used in this approach. Kalman Filtering is carried out in two steps: i) Predicting and ii) Updating. Each step is investigated and coded as a function with matrix input and output. Self-healing characteristics is being considered in the proposed algorithm to prevent the network from failing and to help in efficient routing of data. Proposed approach achieves high efficiency in terms of movement prediction, energy efficiency, handoff delay and fault tolerance when compared to existing approach.

Keywords


Fault tolerance; Handoff delay; Internet of Things; Kalman-Filter; Seamless connectivity; Self-healing

Full Text:

PDF

References


N. N. Srinidhi, G. P. Sunitha, S. Raghavendra, S. M. D. Kumar, and V. Chang, “Hybrid energy-efficient and qos-aware algorithm for intelligent transportation system in iot,” International Journal of Grid and Utility Computing, vol. 11, no. 6, pp. 815-826, 2020.

M. A. Khan and K. Salah, “Iot security: Review, blockchain solutions, and open challenges,” Future Generation Computer Systems, vol. 82, pp. 395 411, 2018.

J. Ren, H. Guo, C. Xu, Y. Zhang, “Serving at the edge: A scalable iot architecture based on transparent computing,” IEEE Network, vol. 31, no. 5, pp. 96-105, 2017.

M. Bacco, L. Boero, P. Cassara, M. Colucci, A. Gotta, M. Marchese, and F. Patrone, “Iot applications and services in space information networks,” IEEE Wireless Communications, vol. 26, no. 2, pp. 31-37, 2019.

C.-M. Huang, C.-H. Shao, S.-Z. Xu, and H. Zhou, “The social internet of thing (s-iot)-based mobile group hando architecture and schemes for proximity service,” IEEE Transactions on Emerging Topics in Computing, vol. 5, no. 3, pp. 425-437, 2017.

B. Farahani, F. Firouzi, V. Chang, M. Badaroglu, N. Constant, and K. Mankodiya, “Towards fog- driven iot ehealth: Promises and challenges of iot in medicine and healthcare,” Future Generation Computer Systems, vol. 78, pp. 659-676, 2018.

A. Emami, M. Sarvi, and S. A. Bagloee, “Using kalman filter algorithm for short-term traffic flow prediction in a connected vehicle environment,” Journal of Modern Transportation, vol. 27, no. 3, pp. 222-232, 2019.

Y. Huang, W. Yu, E. Ding, and A. Garcia-Ortiz, “Epkf: Energy efficient communication schemes based on kalman filter for iot,” IEEE Internet of Things Journal, vol. 6, no. 4, pp. 6201-6211, 2019.

R. Ferrero, F. Gandino, M. Hemmatpour, “Estimation of displacement for internet of things applications with kalman filter,” Electronics, vol. 8, no. 9, pp. 985, 2019.

X. Lai, T. Yang, Z. Wang, P. Chen, “Iot implementation of kalman filter to improve accuracy of air quality monitoring and prediction,” Applied Sciences, vol. 9, no. 9, pp. 1831, 2019.

K. Cao, G. Xu, J. Zhou, T. Wei, M. Chen, S. Hu, “Qos-adaptive approximate real-time computation for mobility-aware iot lifetime optimization,” IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 38, no. 10, pp. 1799-1810, 2018

Y. Zahraoui, M. Akherraz, and A. Ma’arif, “A Comparative Study of Nonlinear Control Schemes for Induction Motor Operation Improvement,” International Journal of Robotics and Control Systems, vol. 2, no. 1, pp. 1–17, Dec. 2022, doi: 10.31763/ijrcs.v2i1.521.

Z. Ding, L. Shen, H. Chen, F. Yan, and N. Ansari, “Energy-efficient relay-selection-based dynamic routing algorithm for iot-oriented software-defined wsns,” IEEE Internet of Things Journal, vol. 7, no. 9, pp. 9050-9065, 2020.

K. Adhinugraha, W. Rahayu, T. Hara, D. Taniar, “On internet-of-things (iot) gateway coverage expansion,” Future Generation Computer Systems, vol. 107, pp. 578-587, 2020.

K. Cao, G. Xu, J. Zhou, T. Wei, M. Chen, and S. Hu, "QoS-adaptive approximate real-time computation for mobility-aware IoT lifetime optimization," IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 38, no. 10, pp. 1799-1810, 2018.

S. Shashank, A. Chhokra, H. Sun, A. Gokhale, A. Dubey, X. Koutsoukos, and G. Karsai, "URMILA: Dynamically trading-off fog and edge resources for performance and mobility-aware IoT services," Journal of Systems Architecture vol. 107, pp. 101710, 2020.

B. P. Santos, O. Goussevskaia, L. F. M. Vieira, M. A. M. Vieira, and A. A. F. Loureiro, "Mobile matrix: routing under mobility in IoT, IoMT, and social IoT," Ad Hoc Networks, vol. 78, pp. 84-98, 2018.

R. Hamidouche, Z. Aliouat, A. M. Gueroui, A. A. A. Ari, and L. Louail, "Classical and bio-inspired mobility in sensor networks for IoT applications," Journal of Network and Computer Applications vol. 121, pp. 70-88, 2018.

S. Ghosh, A. Mukherjee, S. K. Ghosh, and R. Buyya. "Mobi-iost: mobility-aware cloud-fog-edge-iot collaborative framework for time-critical applications," IEEE Transactions on Network Science and Engineering, vol. 7, no. 4, pp. 2271-2285, 2019.

B. Safaei, A. Mohammadsalehi, K. T. Khoosani, S. Zarbaf, A. M. H. Monazzah, F. Samie, L. Bauer, J. Henkel, and A. Ejlali. "Impacts of mobility models on RPL-based mobile IoT infrastructures: An evaluative comparison and survey," IEEE access, vol. 8, pp. 167779-167829, 2020.

H. Hu, Q. Wang, R. Q. Hu, and H. Zhu. "Mobility-aware offloading and resource allocation in a MEC-enabled IoT network with energy harvesting," IEEE Internet of Things Journal, vol. 8, no. 24, pp. 17541-17556, 2021.

R. Abozariba, M. K. Naeem, M. Patwary, M. Seyedebrahimi, P. Bull, and A. Aneiba. "NOMA-based resource allocation and mobility enhancement framework for IoT in next generation cellular networks," IEEE Access, vol. 7, pp. 29158-29172, 2019.

D. Wu, X. Nie, E. Asmare, D. I. Arkhipov, Z. Qin, R. Li, J. A. McCann, and K. Li, "Towards distributed SDN: Mobility management and flow scheduling in software defined urban IoT," IEEE Transactions on Parallel and Distributed Systems, vol. 31, no. 6, pp.1400-1418, 2018.

D. Xu, H. Wang, Y. Li, S. Tarkoma, D. Jin, and P. Hui, "IoT vs. human: A comparison of mobility," IEEE Transactions on Mobile Computing, 2020.

A. H. Sodhro, M. S. Obaidat, Q. H. Abbasi, P. Pace, S. Pirbhulal, A-UI-H. Yasa, G. Fortino, M. A. Imran, and M. Qaraqe, "Quality of service optimization in an IoT-driven intelligent transportation system," IEEE Wireless Communications, vol. 26, no. 6, pp. 10-17, 2019.

K. Manikannan, and V. Nagarajan. "Optimized mobility management for RPL/6LoWPAN based IoT network architecture using the firefly algorithm," Microprocessors and Microsystems, vol. 77, pp. 103193, 2020.

A. Serhani, N. Naja, and A. Jamali "AQ-Routing: mobility-, stability-aware adaptive routing protocol for data routing in MANET–IoT systems," Cluster Computing, vol. 23, no. 1, pp. 13-27, 2020.

P. Fazio, M. Mehic, and M. Voznak. "An Innovative Dynamic Mobility Sampling Scheme Based on Multi-Resolution Wavelet Analysis in IoT Networks," IEEE Internet of Things Journal, 2021.

G. Solmaz, F.-J. Wu, F. Cirillo, E. Kovacs, J. R. Santana, L. Sánchez, P. Sotres, and L. Munoz," Toward understanding crowd mobility in smart cities through the internet of things," IEEE Communications Magazine, vol. 57, no. 4, pp. 40-46, 2019.

Z. Ming, and M. Xu. "NBA: A name-based approach to device mobility in industrial IoT networks," Computer Networks, vol. 191, pp. 107973, 2021.

B. Nour, H. Ibn-Khedher, H. Moungla, H. Afifi, F. Li, K. Sharif, H. Khelifi, and M. Guizani. "Internet of things mobility over information-centric/named-data networking," IEEE Internet Computing, vol. 24, no. 1, pp. 14-24, 2019.

T. N. Gia, A. M. Rahmani, T. Westerlund, P. Liljeberg, and H. Tenhunen, "Fog computing approach for mobility support in internet-of-things systems," IEEE Access, vol. 6, pp. 36064-36082, 2018.

U. Hariharan, K. Rajkumar, T. Akilan, and A. Ponmalar, “A multi-hop protocol using advanced multi-hop Dijkstras algorithm and tree based remote vector for wireless sensor network,” Journal of Ambient Intelligence and Humanized Computing, pp. 1-19, 2021.

F. Behrendt, "Cycling the smart and sustainable city: Analyzing EC policy documents on internet of things, mobility and transport, and smart cities," Sustainability, vol. 11, no. 3, pp. 763, 2019.

Z. Zhao, M. Karimzadeh, F. Gerber, T. Braun, “Mobile crowd location prediction with hybrid features using ensemble learning,” Future Generation Computer Systems, vol. 110, pp. 556- 571, 2020.

H. He, Y. Qiao, S. Gao, J. Yang, J. Guo, “Prediction of user mobility pattern on a network trac analysis platform,” In: Proceedings of the 10th International Workshop on Mobility in the Evolving Internet Architecture, ACM, 2015, pp. 39-44.

C. Anagnostopoulos, S. Hadjiefthymiades, “Intelligent trajectory classification for improved movement prediction,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 44, no. 10, pp. 1301-1314, 2014.

A. Alabdulkarim, M. Al-Rodhaan, T. Ma, Y. Tian, “Ppsdt: A novel privacy-preserving single decision tree algorithm for clinical decision-support systems using iot devices,” Sensors, vol. 19, no. 1, pp. 142, 2019.

M. Sarkar and D. Ghose, “Sequential learning of movement prediction in dynamic environments using lstm autoencoder,” arXiv preprint, arXiv: 1810.05394, 2018.

A. I. Maarala, X. Su, J. Riekki, “Semantic reasoning for context-aware internet of things applications,” IEEE Internet of Things Journal, vol. 4, no. 2, pp. 461-473, 2016.

X. Wang, D. Le, Y. Yao, C. Xie, “Location-based mobility support for 6lowpan wireless sensor networks,” Journal of Network and Computer Applications, vol. 49, pp. 68 77, 2015.

F. Cadger, K. Curran, J. Santos, S. Moffet, “Location and mobility-aware routing for improving multimedia streaming performance in manets,” Wireless Personal Communications, vol. 86, no. 3, pp.1653 1672, 2016.




DOI: https://doi.org/10.18196/jrc.v3i5.11622

Refbacks

  • There are currently no refbacks.


Copyright (c) 2022 Srinidhi N N, Shreyas J, Dilip Kumar S M

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

 


Journal of Robotics and Control (JRC)

P-ISSN: 2715-5056 || E-ISSN: 2715-5072
Organized by Peneliti Teknologi Teknik Indonesia
Published by Universitas Muhammadiyah Yogyakarta in collaboration with Peneliti Teknologi Teknik Indonesia, Indonesia and the Department of Electrical Engineering
Website: http://journal.umy.ac.id/index.php/jrc
Email: jrcofumy@gmail.com


Kuliah Teknik Elektro Terbaik