POSITRON: Efficient Allocation of Smart City Multifunctional IoT Devices Aware of Computing Resources

Authors

DOI:

https://doi.org/10.5753/jisa.2024.3833

Keywords:

Resource Allocation, Policy-based Management, Multifunctional IoT Devices

Abstract

Many IoT scenarios demand continuous capture of information from multifunctional sensors and smart units, as well as sending those data to cloud centers. However, allocating tasks to these sensors is not straightforward due to the urgency and priority that each type of data collection requires depending on the needs of the urban environment. This paper presents the POSITRON scheme for managing the sensing allocation in a multifunctional IoT network from previously defined policies. The policies take into account the characteristics of the applications running on the network and the different specifications of the available devices. We implemented POSITRON in a network simulator aiming to analyze its efficiency in allocating network resources. The results point out that considering the requirements demanded by applications and the distinct characteristics of multifunctional IoT devices brings benefits to resource allocation.

Downloads

Download data is not yet available.

References

Ali, U. and Calis, C. (2019). Centralized Smart Governance Framework Based on IoT Smart City Using TTG-Classified Technique. In the 16th IEEE International Conference on Smart Cities, pages 157-160. DOI: 10.1109/HONET.2019.8908070.

Andal, C. K. and Jayapal, R. (2022). Design and implementation of IoT based intelligent energy management controller for PV/wind/battery system with cost minimization. Renewable Energy Focus, 43:255-262. DOI: 10.1016/j.ref.2022.10.004.

Bashir, H. et al. (2022). Resource allocation through logistic regression and multicriteria decision making method in IoT fog computing. Trans. on Emerging Telecom. Technologies, 33(2). DOI: 10.1002/ett.3824.

Bolettieri, S. et al. (2021). Application-aware resource allocation and data management for MEC-assisted IoT service providers. Journal of Network and Computer Applications, 181:103020. DOI: 10.1016/j.jnca.2021.103020.

Calderoni, L., Magnani, A., and Maio, D. (2019). Iot manager: An open-source iot framework for smart cities. Journal of Systems Architecture, 98:413-423. DOI: 10.1016/j.sysarc.2019.04.003.

Catlett, C. et al. (2020). Measuring cities with software-defined sensors. Journal of Social Computing, 1(1):14-27. DOI: 10.23919/JSC.2020.0003.

Catlett, C. et al. (2022). Hands-on computer science: The array of things experimental urban instrument. Computing in Science & Engineering, 24(1):57-63. DOI: 10.1109/MCSE.2021.3139405.

Clarindo, J. P., C. Castro, J. P., and D. Aguiar, C. (2021). Combining Fog and Cloud Computing to Support Spatial Analytics in Smart Cities. Journal of Information and Data Management, 12(4). DOI: 10.5753/jidm.2021.1798.

El Bouanani, S., El Kiram, M. A., Achbarou, O., and Outchakoucht, A. (2019). Pervasive-Based Access Control Model for IoT Environments. IEEE Access, 7:54575-54585. DOI: 10.1109/ACCESS.2019.2912975.

Guim, F., Metsch, T., et al. (2022). Autonomous Lifecycle Management for Resource-Efficient Workload Orchestration for Green Edge Computing. IEEE Transactions on Green Communications and Networking, 6(1):571-582. DOI: 10.1109/TGCN.2021.3127531.

Klein, T. and Anderegg, W. R. (2021). A vast increase in heat exposure in the 21st century is driven by global warming and urban population growth. Sustainable Cities and Society, 73:103098. DOI: https://doi.org/10.1016/j.scs.2021.103098.

Li, X., Zhao, L., et al. (2021). A cooperative resource allocation model for IoT applications in mobile edge computing. Computer Communications, 173:183-191. DOI: 10.1016/j.comcom.2021.04.005.

Lv, Z., Hu, B., and Lv, H. (2020). Infrastructure monitoring and operation for smart cities based on iot system. IEEE Transactions on Industrial Informatics, 16(3):1957-1962. DOI: 10.1109/TII.2019.2913535.

Mukherjee, B. K. et al. (2020). An SDN Based Distributed IoT Network with NFV Implementation for Smart Cities. In Cyber Security and Computer Science, pages 539-552, Cham. Springer International Publishing. DOI: 10.1007/978-3-030-52856-0_43.

Nikpour, M. et al. (2023). Intelligent Energy Management with IoT Framework in Smart Cities Using Intelligent Analysis: An Application of Machine Learning Methods for Complex Networks and Systems. [link].

Pedroso, C., de Moraes, Y. U., Nogueira, M., and Santos, A. (2021). Relational Consensus-Based Cooperative Task Allocation Management for IIoT-Health Networks. In 2021 IFIP/IEEE International Symposium on Integrated Network Management (IM), pages 579-585. Availabe at:[link].

Perera, A. et al. (2021). Light-based Internet of Things: Implementation of an Optically Connected Energy-autonomous Node. In 2021 IEEE Wireless Communications and Networking Conference (WCNC), pages 1-7. DOI: 10.1109/WCNC49053.2021.9417484.

Rafique, W. et al. (2020). A blockchain-based framework for information security in intelligent transportation systems. In Intelligent Technologies and Applications, pages 53-66, Singapore. Springer Singapore. DOI: 10.1007/978-981-15-5232-8_6.

Rocha, D., de Gois, A., da Silva, L. H. B., Matos, F., Santos, A., and Maciel Jr., P. D. (2022). Um Esquema para Alocação Justa de Dispositivos IoT Multifuncionais Ciente dos Recursos Computacionais. In Workshop de Gerência e Operação de Redes e Serviços (WGRS). DOI: 10.5753/wgrs.2022.223423.

Sangaiah, A. K. et al. (2020). IoT Resource Allocation and Optimization Based on Heuristic Algorithm. Sensors, 20(2). DOI: 10.3390/s20020539.

Tsai, C.-W. (2018). SEIRA: An effective algorithm for IoT resource allocation problem. Computer Communications, 119:156-166. DOI: 10.1016/j.comcom.2017.10.006.

United Nations (2019). Department of economic and social affairs, population division (2019). Technical report, World Population Prospects 2019: Highlights (ST/ESA/SER.A/423), United Nations, New York, USA. Available at[link].

Wang, Z. et al. (2022). Computation offloading and resource allocation based on distributed deep learning and software defined mobile edge computing. Computer Networks, 205:108732. DOI: 10.1016/j.comnet.2021.108732.

Xavier, T. C. et al. (2020). Collaborative resource allocation for Cloud of Things systems. Journal of Network and Computer Applications, 159:102592. DOI: 10.1016/j.jnca.2020.102592.

Xavier, T. C. et al. (2022). Managing Heterogeneous and Time-Sensitive IoT Applications through Collaborative and Energy-Aware Resource Allocation. ACM Trans. Internet Things, 3(2). DOI: 10.1145/3488248.

Zhao, L., Wang, J., et al. (2019). Optimal Edge Resource Allocation in IoT-Based Smart Cities. IEEE Network, 33(2):30-35. DOI: 10.1109/MNET.2019.1800221.

Downloads

Published

2024-07-02

How to Cite

da Silva, L. H. B., da Silva, J. L. F., Lins, R. P., Matos, F. M., dos Santos, A. L., & Maciel Jr., P. D. (2024). POSITRON: Efficient Allocation of Smart City Multifunctional IoT Devices Aware of Computing Resources. Journal of Internet Services and Applications, 15(1), 112–124. https://doi.org/10.5753/jisa.2024.3833

Issue

Section

Special Call: Best or CoUrb/SBRC 2023