Abstract

Technological advancements in smart cities are a global trend, primarily focused on enhancing the quality of life for citizens while also monitoring urban environments. Viewing smart cities as urban surveillance platforms, there arises a pressing need for efficient analysis and storage of video streams from numerous cameras scattered across the city, owned by various stakeholders. However, surveillance systems face several internet of things (IoT) challenges, including privacy concerns, scalability issues, and substantial energy consumption. To tackle these challenges, the development of a trusted video surveillance system by integrating the internet of spatial things (IoST) with fog computing and blockchain technology was introduced. Fog nodes are strategically deployed at the network's edge, enabling the extension of cloud services closer to the data source. This deployment strategy aims to reduce latency and alleviate network congestion. At these fog nodes, the system employs a proof-of-work-based consensus algorithm, encrypted using the secure hash algorithm (SHA-256), to ensure data trust and reliability within the blockchain infrastructure supporting the surveillance system. The evaluation of our proposed blockchain-IoST surveillance system involves a comparison of two case studies: one based on fog computing and the other on traditional cloud-based blockchain-IoST implementations, conducted within the iFogSim framework. Furthermore, the system's scalability is tested under various scenarios. To assess the effectiveness of our methodology in mitigating latency, optimizing network utilization, and reducing energy consumption, we conducted comprehensive simulations. The results of our experiments clearly demonstrate the advantages of the fog-based blockchain-IoST approach. This approach significantly reduces latency and network utilization when compared to the conventional cloud-based blockchain-IoST implementation in the trusted video surveillance system. Additionally, the findings indicate that adopting the fog-based blockchain-IoST approach leads to a noteworthy reduction in energy consumption compared to the cloud-based implementation, further enhancing the sustainability and efficiency of the surveillance system.

Keyword

Video surveillance system, Internet of spatial things, Spatial blockchain, Fog based blockchain-IoST, Cloud based blockchain-IoST.

Cite this article

Alsaedi N, Jalal AS

Refference

[1][1]Tekouabou SC, Cherif W, Silkan H. Improving parking availability prediction in smart cities with IoT and ensemble-based model. Journal of King Saud University-Computer and Information Sciences. 2022; 34(3):687-97.

[2][2]Gallo P, Nguyen UQ, Pongnumkul S, Barone G. Blockchain for smart cities: Applications for IoT and video surveillance systems. Innovations in Land, Water and energy for Vietnam’s Sustainable Development. 2021:227-48.

[3][3]Jin Y, Qian Z, Yang W. UAV cluster-based video surveillance system optimization in heterogeneous communication of smart cities. IEEE Access. 2020; 8:55654-64.

[4][4]Ni J, Zhang K, Lin X, Shen X. Securing fog computing for internet of things applications: challenges and solutions. IEEE Communications Surveys & Tutorials. 2017; 20(1):601-28.

[5][5]Ghafir I, Prenosil V, Hammoudeh M, Baker T, Jabbar S, Khalid S, et al. Botdet: a system for real time botnet command and control traffic detection. IEEE Access. 2018; 6:38947-58.

[6][6]https://explodingtopics.com/blog/iot-trends. Accessed 18 September 2023.

[7][7]Kumar N, Jamwal P. Analysis of modern communication protocols for IoT applications. Karbala International Journal of Modern Science. 2021; 7(4):392-404.

[8][8]Al-Joboury IM, Al-Hemiary EH. Consensus algorithms based blockchain of things for distributed Healthcare. Iraqi Journal of Information and Communication Technology. 2020; 3(4):33-46.

[9][9]Deepak K, Badiger AN, Akshay J, Awomi KA, Deepak G, Kumar H. Blockchain-based management of video surveillance systems: a survey. In 6th international conference on advanced computing and communication systems 2020 (pp. 1256-8). IEEE.

[10][10]Awaisi KS, Abbas A, Zareei M, Khattak HA, Khan MU, Ali M, et al. Towards a fog enabled efficient car parking architecture. IEEE Access. 2019; 7:159100-11.

[11][11]Tariq N, Asim M, Al-Obeidat F, Zubair Farooqi M, Baker T, Hammoudeh M, et al. The security of big data in fog-enabled IoT applications including blockchain: a survey. Sensors. 2019; 19(8):1-33.

[12][12]Baker T, Asim M, Samwini H, Shamim N, Alani MM, Buyya R. A blockchain-based Fog-oriented lightweight framework for smart public vehicular transportation systems. Computer Networks. 2022; 203:108676.

[13][13]Chen G, Xu B, Lu M, Chen NS. Exploring blockchain technology and its potential applications for education. Smart Learning Environments. 2018; 5(1):1-10.

[14][14]Boulos MN, Wilson JT, Clauson KA. Geospatial blockchain: promises, challenges, and scenarios in health and healthcare. International Journal of Health Geographics. 2018; 17:1-10.

[15][15]Chung WJ, Cho TH. A security scheme based on blockchain and a hybrid cryptosystem to reduce packet loss in IoV. International Journal of Advanced Technology and Engineering Exploration. 2021; 8(81):945-56.

[16][16]Zhang S, Lee JH. Analysis of the main consensus protocols of blockchain. ICT Express. 2020; 6(2):93-7.

[17][17]Ul Abadin Z, Syed M. A pattern for proof of work consensus algorithm in blockchain. In 26th European Conference on Pattern Languages of Programs 2021 (pp. 1-6).

[18][18]Nakamoto S. Bitcoin: a peer-to-peer electronic cash system. Decentralized Business Review. 2008.

[19][19]Jamali J, Bahrami B, Heidari A, Allahverdizadeh P, Norouzi F. Towards the internet of things. Springer International Publishing; 2020.

[20][20]Sriman B, Ganesh Kumar S, Shamili P. Blockchain technology: consensus protocol proof of work and proof of stake. In the proceedings of intelligent computing and applications, Advances in Intelligent Systems and Computing 2021 (pp. 395-406). Springer Singapore.

[21][21]Barman N, Deepak GC, Martini MG. Blockchain for video streaming: Opportunities, challenges, and open issues. Computer. 2020; 53(7):45-56.

[22][22]Xu Z, Hu C, Mei L. Video structured description technology based intelligence analysis of surveillance videos for public security applications. Multimedia Tools and Applications. 2016; 75:12155-72.

[23][23]Chen N, Chen Y, Song S, Huang CT, Ye X. Smart urban surveillance using fog computing. In symposium on edge computing 2016 (pp. 95-96). IEEE.

[24][24]Memos VA, Psannis KE. UAV-based smart surveillance system over a wireless sensor network. IEEE Communications Standards Magazine. 2021; 5(4):68-73.

[25][25]Nguyen DC, Pathirana PN, Ding M, Seneviratne A. Blockchain for 5G and beyond networks: a state of the art survey. Journal of Network and Computer Applications. 2020; 166:102693.

[26][26]Chaer A, Salah K, Lima C, Ray PP, Sheltami T. Blockchain for 5G: opportunities and challenges. In Globecom workshops 2019 (pp. 1-6). IEEE.

[27][27]Rago A, Ventrella P, Piro G, Boggia G, Dini P. Towards an optimal management of the 5G cloud-RAN through a spatio-temporal prediction of users’ dynamics. In mediterranean communication and computer networking conference 2020 (pp. 1-4). IEEE.

[28][28]Rego A, Canovas A, Jiménez JM, Lloret J. An intelligent system for video surveillance in IoT environments. IEEE Access. 2018; 6:31580-98.

[29][29]Jeong Y, Hwang D, Kim KH. Blockchain-based management of video surveillance systems. In international conference on information networking 2019 (pp. 465-8). IEEE.

[30][30]Abdalla PA, Varol C. Testing IoT security: the case study of an IP camera. In 8th international symposium on digital forensics and security 2020 (pp. 1-5). IEEE.

[31][31]Fitwi A, Chen Y. Secure and privacy-preserving stored surveillance video sharing atop permissioned blockchain. In international conference on computer communications and networks 2021 (pp. 1-8). IEEE.

[32][32]Kosba A, Miller A, Shi E, Wen Z, Papamanthou C. Hawk: the blockchain model of cryptography and privacy-preserving smart contracts. In symposium on security and privacy 2016 (pp. 839-58). IEEE.

[33][33]Upmanyu M, Namboodiri AM, Srinathan K, Jawahar CV. Efficient privacy preserving video surveillance. In 12th international conference on computer vision 2009 (pp. 1639-46). IEEE.

[34][34]Chen J, Ruan Y, Guo L, Lu H. BCVehis: a blockchain-based service prototype of vehicle history tracking for used-car trades in China. IEEE Access. 2020; 8:214842-51.

[35][35]Chen X, Xing Z, Karki B, Li Y, Chen Z. Blockchain simulation: a web application for it education. In 11th Annual computing and communication workshop and conference 2021 (pp. 0486-91). IEEE.

[36][36]Chukwu E, Garg L. A systematic review of blockchain in healthcare: frameworks, prototypes, and implementations. IEEE Access. 2020; 8:21196-214.

[37][37]Gallo P, Pongnumkul S, Nguyen UQ. BlockSee: blockchain for IoT video surveillance in smart cities. In international conference on environment and electrical engineering and industrial and commercial power systems Europe 2018 (pp. 1-6). IEEE.

[38][38]Khan PW, Byun YC, Park N. A data verification system for CCTV surveillance cameras using blockchain technology in smart cities. Electronics. 2020; 9(3):1-21.

[39][39]Moolikagedara K, Nguyen M, Yan WQ, Li XJ. Video blockchain: a decentralized approach for secure and sustainable networks with distributed video footage from vehicle-mounted cameras in smart cities. Electronics. 2023; 12(17):1-11.

[40][40]Amofa S, Lin X, Xia Q, Xia H, Gao J. Blockchain-based health data sharing for continuous disease surveillance in smart environments. In 28th international conference on parallel and distributed systems 2023 (pp. 185-92). IEEE.

[41][41]Pane J, Verhamme KM, Shrum L, Rebollo I, Sturkenboom MC. Blockchain technology applications to postmarket surveillance of medical devices. Expert Review of Medical Devices. 2020; 17(10):1123-32.

[42][42]Na D, Park S. IoT-chain and monitoring-chain using multilevel blockchain for IoT security. Sensors. 2022; 22(21):8271.

[43][43]Lazaroiu C, Roscia M. Smart district through IoT and blockchain. In international conference on renewable energy research and applications 2017 (pp. 454-61). IEEE.

[44][44]Liu H, Tai W, Wang Y, Wang S. A blockchain-based spatial data trading framework. EURASIP Journal on Wireless Communications and Networking. 2022; 2022(1):71.

[45][45]Janarthanan S, Vijayalakshmi S, Savita, Ganesh Kumar T. Geospatial blockchain: promises, challenges, and scenarios in healthcare. Digitization of Healthcare Data Using Blockchain. 2022:25-47.

[46][46]Shirabe T. Classification of spatial properties for spatial allocation modeling. GeoInformatica. 2005; 9:269-87.

[47][47]Alsaedi N, Jalal AS. Big spatial data systems-a review. In 5th international conference on engineering technology and its applications 2022 (pp. 147-52). IEEE.

[48][48]Eldrandaly KA, Abdel-Basset M, Shawky LA. Internet of spatial things: a new reference model with insight analysis. IEEE Access. 2019; 7:19653-69.

[49][49]Li H, Xiezhang T, Yang C, Deng L, Yi P. Secure video surveillance framework in smart city. Sensors. 2021; 21(13):1-16.

[50][50]Sahib RH, Al-Shamery P, Salih E. An online e-voting system based on an adaptive ledger with singular value decomposition technique. Karbala International Journal of Modern Science. 2021; 7(4):1-22.

[51][51]Gupta H, Vahid Dastjerdi A, Ghosh SK, Buyya R. iFogSim: a toolkit for modeling and simulation of resource management techniques in the Internet of Things, Edge and Fog computing environments. Software: Practice and Experience. 2017; 47(9):1275-96.

[52][52]Mahmud R, Pallewatta S, Goudarzi M, Buyya R. Ifogsim2: an extended ifogsim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software. 2022; 190:111351.

[53][53]Castro M, Liskov B. Practical byzantine fault tolerance. In OsDI 1999 (pp. 173-86).