Fog environment proposal to reduce energy consumption on public roads in smart cities

• João Santos UFMS https://orcid.org/0009-0005-4543-7286
• Maycon Peixoto UFBA https://orcid.org/0000-0002-4851-5228
• Bruno Batista UNIFEI https://orcid.org/0000-0002-2329-0747
• Bruno Kuehne UNIFEI https://orcid.org/0000-0003-2529-225X
• Dionisio Leite UFMS https://orcid.org/0000-0003-1180-8710

Resumo

Context: Smart cities are the future in terms of good resource and people management practices. Several resources can be managed by smart cities, among them urban mobility and energy consumption stand out.Problem: The brightness of public roads consume energy resources, but they are not always necessary, as is the case with roads with no use. Thus, energy resources are wasted. This work aimed to explore the economy of energy resources by observing the flow of use of public roads.Solution: Sensors observe the roads, and the costs of the edges are sent to the fog that applies a graph algorithm to determine the paths that have movement and, thus, generate the reductions in energy consumption via switching off lamps or reducing their frequency.Information System Theory: Based on the General Systems Theory, the general system is mapped into independent systems: fog sensors (fog) and algorithm execution nodes (nodes with higher processing power). A general ecosystem is formulated making the information system based on the information generated by the observation of the sensors.Method: A simulated environment was proposed to obtain the representation of a region of interest. For this region, a graph approach based on Dijkstra was applied to consider the paths with the highest flow of accesses and, thus, propose energy consumption reductions.Results Summary: The results obtained point to a possibility of saving energy resources in the range of 60 to 79% depending on the type of lamps used, and the size of observed region. The results reinforce the need to explore intelligent resources for shared use resources management.Contributions and Impact in the IS area: Among the contributions to the area are: a manageable information system for public lighting. Use of fog for path management and energy matrix management. This work also contributes to proposing new approaches to the proposed problem, such as using the social context (via social networks) to define optimal paths.

Palavras-chave: Smart cities, Fog computing, Energetic matrix

Referências

Shristi Agarwal, Drishti Rai, and Sumran Talreja. 2022. Applications of IoT in Smart Homes and Cities. In IoT Based Smart Applications. Springer, 55–70.

Chiara Bachechi and Laura Po. 2019. Traffic analysis in a smart city. In IEEE/WIC/ACM International Conference on Web Intelligence-Companion Volume. 275–282.

Peterson Belan, Anderson S Vanin, and Edward Netzer. 2020. Computer vision System for Public Illumination Management. International Journal of Computer Applications 975 (2020), 8887.

Josimary Horta De Araujo, Etelvina Maria Marques Moreira, Carla Fernandes de Freitas, Francisco Edivaldo Brito de Castro, Andre Luiz Carneiro de Araujo, and Tecia Vieira Carvalho. 2020. Smart Cities: um estudo prospectivo sobre Internet das Coisas (IoT) aplicada ao setor de mobilidade urbana. Cadernos de Prospecção 13, 1 (2020), 138.

M. Joundi, M. El Alaoui, and A. Hayar. 2018. New Urban Mobility Algorithm Based On User Experience. In 2018 IEEE International Smart Cities Conference (ISC2). 1–4. https://doi.org/10.1109/ISC2.2018.8656955

Moabi K. Manyake and Tebello N.D. Mathaba. 2022. An Internet of Things Framework for Control and Monitoring of Smart Public Lighting Systems: A Review. In 2022 International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems (icABCD). 1–9. https://doi.org/10.1109/icABCD54961.2022.9856268

Aditee Mattoo, Kumud Saxena, Somesh Kumar, and Neha Bagwari. 2021. Design of IoT-Based Smart Illumination System in Smart Cities. In Proceedings of 3rd International Conference on Computing Informatics and Networks, Ajith Abraham, Oscar Castillo, and Deepali Virmani (Eds.). Springer Singapore, Singapore, 517–528.

Roberto Minerva, Abyi Biru, and Domenico Rotondi. 2015. Towards a definition of the Internet of Things (IoT). IEEE Internet Initiative 1, 1 (2015), 1–86.

Mohsen Mohammadzadeh and Son Phung. 2022. Toward Smart Public Lighting of Future Cities. In The Palgrave Encyclopedia of Urban and Regional Futures. Springer, 1–8.

Dr. A. Malathi Ms. M. Kokilavani. 2017. Smart Street Lighting System using IoT.

Dandu Rajendra Parkash, Prabu V. 2016. Internet of Things Based Intelligent Street Lighting System for Smart City.

Diego Julián Rodríguez Patarroyo, Iván Felipe Cely Garzón, and Cristhian Alexander Letrado Forero. 2018. Revisión del alumbrado público inteligente LED.

Juan F. De Paz, Javier Bajo, Sara Rodríguez, Gabriel Villarrubia, and Juan M. Corchado. 2016. Intelligent system for lighting control in smart cities.

M. Popa and A. Marcu. 2012. A Solution for Street Lighting in Smart Cities. 5 (2012), 91–96.

Kasa Sudheer, Duvvuru Madhurita, Amudala Chandana, Marella Thanesh, and M Karunakar Babu. 2019. INTELLIGENT STREET LIGHT SYSTEM FOR SMART CITIES.

Ana Iolanda Voda and Laura-Diana Radu. 2019. Chapter 12 - How can artificial intelligence respond to smart cities challenges? In Smart Cities: Issues and Challenges, Anna Visvizi and Miltiadis D. Lytras (Eds.). Elsevier, 199–216. https://doi.org/10.1016/B978-0-12-816639-0.00012-0

Jorge Von Atzingen, Cláudio Barbieri Da Cunha, Francisco Yastami Nakamoto, Fábio Rogério Ribeiro, and André Schardong. 2012. Análise comparativa de algoritmos eficientes para o problema de caminho mínimo. Universidade de São Paulo (USP). São Paulo. Escola Politécnica (2012).