Energy cooperation for sustainable IoT services within smart cities

Abstract

In this paper, we consider energy cooperation in an Internet of Things (IoT) smart city scenario. We assume the presence of interconnecting energy harvesting IoT gateways (GWs), that are endowed with energy harvesting capabilities and whose role is to collect and aggregate data from field sensing devices. Energy cooperation complements and balances the energetic needs to those devices that are neither connected to the power grid, nor satisfactorily served by energy harvesting due to the instability of ambient energy arrivals. The proposed solution entail energy transfers from energy rich gateways to energy scarce ones, i.e., those which are not connected to the power grid. To identify the optimal energy transfer/allocation scheme, we formulate a convex optimization problem that finds the optimal solution for heterogeneous smart systems. With this energy allocation technique, the gateways are unlikely to run out of energy during operation and the gap between energy offer and demand among interconnected gateways is kept to a minimum. We also quantify the performance of the proposed energy transfer policies as a function of network parameters, including: the amount of traffic generated by sensing devices, the number of smart services in the system, and the number of gateways that are connected to the power grid.

A Literature Study: The Concept of Smart City between Municipal vs. Regency (Between Efficiency, Habitability, and Local Wisdom)

“…Some of the conveniences offered cannot be separated from the role of technology, especially ICT to the Internet of Things [25]. Various applications have been developed to support smart cities, such as the development of intelligent systems for optimization of energy allocations [26], [27] monitoring landfills capable of reducing urban waste management costs [28], electronic public service-based complaints services [29] lighting control with remote control systems [30], [31], intelligent control of city traffic lights [32], [33], [34], analysis and development of intelligent systems in the field of transportation [35], [36], [ 37], [38] as well as the development of a city bike rental sharing system that is able to minimize city noise [39].…”

A Game Theoretic Approach for Cost-Effective Management of Energy Harvesting Smart Grids

“…In a sense, it also comprises the participants (energy prosumers), and the whole market. One way to cope with a distributed system consisting of many heterogeneous components is to apply multi-objective optimization, choosing a solution from the resulting Pareto frontier, in which no objective can be improved without sacrificing at least another one [4], [9].…”