The Secure GPS Tracking Data for transportation in Distributed Environments
DOI:
https://doi.org/10.21015/vtse.v13i4.2271Abstract
Efficient and secure tracking of the public transport has become a critical issue in smart cities, especially in urban areas like Karachi which has high population. Current systems are usually characterized by slowness, unreliability in data transmission and susceptibility to security risks including services by unauthorized users and information alteration. In an attempt to solve these problems, this paper introduces a Secure Transporting Tracking Method one of the (SSTM) which is a new AI-based system of real-time GPS tracking of the public transport in the distributed environment. The SSTM incorporates a secure transmit of GPS data, AIs, location prediction, and efficient vehicle-passenger matching to make it more accurate, less time-consuming, and data integrity-insured. The model has been applied to and tested in several places all around Karachi, such as Gulberg, Nipa, Gulshan-e-Iqbal and Sachal. The outcomes of simulations prove that SSTM is faster than the current approaches, such as the traditional GPS and chatbot-enhanced ones, in processing, encryption/decryption, and tracking. The research brings in a safe, large scale, and smart transport tracking platform specific to smart cities with the possible use in real-time fleet management and secure passenger safety systems.
References
O. D. Jimoh et al., “A vehicle tracking system using greedy forwarding algorithms for public transportation in urban arterial,” IEEE Access, vol. 8, pp. 191706–191725, 2020.
Z. Ning et al., “Blockchain-enabled intelligent transportation systems: A distributed crowdsensing framework,” IEEE Transactions on Mobile Computing, vol. 21, no. 12, pp. 4201–4217, 2021. DOI: https://doi.org/10.1109/TMC.2021.3079984
R. Kumar et al., “A privacy-preserving secure framework using blockchain-enabled deep learning in cooperative intelligent transport systems,” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 9, pp. 16492–16503, 2021. DOI: https://doi.org/10.1109/TITS.2021.3098636
J. Srinivas et al., “Designing secure user authentication protocol for big data collection in IoT-based intelligent transportation systems,” IEEE Internet of Things Journal, vol. 8, no. 9, pp. 7727–7744, 2020. DOI: https://doi.org/10.1109/JIOT.2020.3040938
O. D. Jimoh et al., “A vehicle tracking system using greedy forwarding algorithms for public transportation in urban arterial,” IEEE Access, vol. 8, pp. 191706–191725, 2020. DOI: https://doi.org/10.1109/ACCESS.2020.3031488
R. Verma, B. K. Singh, and F. Zahidi, “Management of GPS tracking systems in transportation,” in Intelligent Transportation System and Advanced Technology. Singapore: Springer, 2024, pp. 251–263. DOI: https://doi.org/10.1007/978-981-97-0515-3_11
P. Sadeghian, J. Håkansson, and X. Zhao, “Review and evaluation of methods in transport mode detection based on GPS tracking data,” Journal of Traffic and Transportation Engineering, vol. 8, no. 4, pp. 467–482, 2021. DOI: https://doi.org/10.1016/j.jtte.2021.04.004
V. Patil, S. B. Parikh, and P. K. Atrey, “GeoSecure-O: A method for secure distance calculation for travel mode detection using outsourced GPS trajectory data,” in Proceedings of the IEEE International Conference on Multimedia Big Data (BigMM), 2019. DOI: https://doi.org/10.1109/BigMM.2019.00015
M. Abinaya and R. U. Devi, “Intelligent vehicle control using wireless embedded systems based on GSM and GPS technology,” vol. 3, pp. 244–258, 2014.
M. N. M. Bhutta and M. Ahmad, “Secure identification, traceability, and real-time tracking of agricultural food supply during transportation using the Internet of Things,” IEEE Access, vol. 9, pp. 65660–65675, 2021. DOI: https://doi.org/10.1109/ACCESS.2021.3076373
O. Elijah et al., “Transforming urban mobility with Internet of Things: Public bus fleet tracking using proximity-based Bluetooth beacons,” Frontiers in the Internet of Things, vol. 2, p. 1255995, 2023. DOI: https://doi.org/10.3389/friot.2023.1255995
M. Ahmad, M. Bilal, A. Jolfaei, and R. M. Mehmood, “Mobility-aware blockchain-enabled offloading and scheduling in vehicular fog cloud computing,” IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 7, pp. 4212–4223, 2021. DOI: https://doi.org/10.1109/TITS.2021.3056461
T. M. Grønli et al., “IoT workload offloading for efficient intelligent transport systems in federated ACNN-integrated edge–cloud networks,” Journal of Cloud Computing, vol. 13, no. 1, p. 79, 2024. DOI: https://doi.org/10.1186/s13677-024-00640-w
T. M. Grønli, G. Muhammad, and P. Tiwari, “Evolutionary meta-heuristic offloading and scheduling schemes for industrial cyber–physical systems,” IEEE Systems Journal, vol. 18, no. 2, pp. 826–835, 2024. DOI: https://doi.org/10.1109/JSYST.2023.3347523
M. S. Memon et al., “Cost-efficient mobility offloading and task scheduling for microservices IoVT applications in container-based fog–cloud networks,” Cluster Computing, vol. 25, no. 3, pp. 2061–2083, 2022. DOI: https://doi.org/10.1007/s10586-021-03333-0
X. Li, “Transient fault-aware application partitioning and computational offloading in microservices-based mobile cloudlet networks,” Computing, vol. 102, no. 1, pp. 105–139, 2020. DOI: https://doi.org/10.1007/s00607-019-00733-4
M. Elhoseny, M. A. Mohammed, and M. M. Jaber, “SFDWA: Secure and fault-tolerant delay-aware workload assignment in edge computing for Internet of Drone Things,” Wireless Communications and Mobile Computing, vol. 2022, pp. 1–12, 2022. DOI: https://doi.org/10.1155/2022/5667012
T. M. Grønli, H. Wu, M. Younas, and G. Ghinea, “A novel homomorphic blockchain scheme for intelligent transport services in fog/cloud and IoT networks,” IEEE Transactions on Intelligent Transportation Systems, 2024.
A. Mohammed et al., “Energy-efficient distributed federated learning offloading and scheduling for healthcare systems in blockchain-based networks,” Internet of Things, vol. 22, p. 100815, 2023. DOI: https://doi.org/10.1016/j.iot.2023.100815
A. Lakhan, “Metaverse-assisted healthcare body sensor network architecture,” in Proceedings of the IEEE International Conference on Body Sensor Networks (BSN), Oct. 2024, pp. 1–4. DOI: https://doi.org/10.1109/BSN63547.2024.10780550
M. A. Mohammed et al., “Delay-optimal schemes for Internet of Things applications in heterogeneous edge cloud computing networks,” Sensors, vol. 22, no. 16, p. 5937, 2022. DOI: https://doi.org/10.3390/s22165937
F. A. Khan and Q. H. Abbasi, “Dynamic content- and failure-aware task offloading in heterogeneous mobile cloud networks,” in Proceedings of the Advances in Emerging Computing Technologies (AECT), 2020, pp. 1–6. DOI: https://doi.org/10.1109/AECT47998.2020.9194161
M. A. Mohammed, A. N. Rashid, S. Kadry, and K. H. Abdulkareem, “Deadline-aware and energy-efficient scheduling for fine-grained tasks in mobile edge computing,” International Journal of Web and Grid Services, vol. 18, no. 2, pp. 168–193, 2022. DOI: https://doi.org/10.1504/IJWGS.2022.121935
M. A. Dootio et al., “Multi-layer latency-aware workload assignment for e-transport IoT applications in mobile sensor–cloudlet–cloud networks,” Electronics, vol. 10, no. 14, p. 1719, 2021. DOI: https://doi.org/10.3390/electronics10141719
A. Lakhan and T. M. Grønli, “Sustainable AI-assisted energy-efficient edge–cloud systems for industrial IoT applications,” in Intelligent Urban Mobility. Springer, 2025, pp. 315–326. DOI: https://doi.org/10.1016/B978-0-443-34160-1.00005-5
M. A. Mohammed, S. Kozlov, and J. J. P. C. Rodrigues, “Mobile–fog–cloud-assisted deep reinforcement learning and blockchain-enabled IoMT system for healthcare workflows,” Transactions on Emerging Telecommunications Technologies, vol. 35, no. 4, p. e4363, 2024.
M. A. Mohammed et al., “Efficient deep reinforcement learning-aware resource allocation in SDN-enabled fog paradigms,” Automated Software Engineering, vol. 29, no. 1, p. 20, 2022. DOI: https://doi.org/10.1007/s10515-021-00318-6
T. M. Grønli, A. Lakhan, and M. Younas, “Federated learning-enabled green edge computing systems for IIoT applications,” in Proceedings of the Mobile Web and Intelligent Information Systems, 2024, pp. 19–31. DOI: https://doi.org/10.1007/978-3-031-68005-2_2
M. A. Mohammed et al., “Federated learning-enabled intelligent reflecting surfaces in fog–cloud cellular networks,” PeerJ Computer Science, vol. 7, p. e758, 2021. DOI: https://doi.org/10.7717/peerj-cs.758
Q. U. A. Mastoi et al., “Hybrid workload-enabled secure healthcare monitoring in distributed fog–cloud networks,” Electronics, vol. 10, no. 16, p. 1974, 2021. DOI: https://doi.org/10.3390/electronics10161974
A. H. Sodhro et al., “A lightweight secure adaptive approach for Internet of Medical Things healthcare applications in edge–cloud networks,” Sensors, vol. 22, no. 6, p. 2379, 2022. DOI: https://doi.org/10.3390/s22062379
A. A. A. Lateef et al., “Computer and information sciences,” Journal of King Saud University – Computer and Information Sciences, vol. 35, p. 101747, 2023.
M. A. Mohammed et al., “Fuzzy decision-based energy evolutionary systems for sustainable transport in ubiquitous fog networks,” Human-centric Computing and Information Sciences, vol. 13, 2023.
Q. U. A. Mastoi et al., “Deep neural network-based application partitioning and scheduling for IoT-assisted mobile fog–cloud healthcare systems,” Enterprise Information Systems, vol. 16, no. 7, p. 1883122, 2022.
M. A. Mohammed et al., “Restricted Boltzmann machine-assisted secure serverless edge systems for the Internet of Medical Things,” IEEE Journal of Biomedical and Health Informatics, vol. 27, no. 2, pp. 673–683, 2022. DOI: https://doi.org/10.1109/JBHI.2022.3178660
F. H. Khoso et al., “Hybrid runtime offloading and resource allocation in mobile-assisted cloudlet-based cloud networks,” Journal of Information and Communication Technology, vol. 14, no. 2, 2021.
A. Lakhan et al., “Dynamic application partitioning and secure task scheduling for biosensor healthcare workloads in mobile edge clouds,” Electronics, vol. 10, no. 22, p. 2797, 2021. DOI: https://doi.org/10.3390/electronics10222797
M. A. Mohammed et al., “Federated learning-aware multi-objective modeling and blockchain-enabled systems for IIoT applications,” Computers and Electrical Engineering, vol. 100, p. 107839, 2022. DOI: https://doi.org/10.1016/j.compeleceng.2022.107839
M. A. Mohammed et al., “BEDS: Blockchain energy-efficient IoE sensor data scheduling for smart home and vehicular applications,” Applied Energy, vol. 369, p. 123535, 2024. DOI: https://doi.org/10.1016/j.apenergy.2024.123535
Z. A. A. Alyasseri et al., “Sustainable secure blockchain-assisted AIoT and green multi-constraint supply chain systems,” IEEE Internet of Things Journal, 2025.
A. Lakhan et al., “Cost-efficient service selection and execution in blockchain-enabled serverless networks for Internet of Medical Things,” 2021.
A. A. A. Lateef et al., “Secure fault-tolerant industrial IoHT frameworks based on digital twin federated fog–cloud networks,” Journal of King Saud University – Computer and Information Sciences, vol. 35, no. 9, p. 101747, 2023. DOI: https://doi.org/10.1016/j.jksuci.2023.101747
M. Elhoseny et al., “Underwater sensor multi-parameter scheduling for heterogeneous computing nodes,” ACM Transactions on Sensor Networks, vol. 18, no. 3, pp. 1–23, 2022. DOI: https://doi.org/10.1145/3476513
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