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