Street lighting is a ubiquitous utility, but sustaining its operation presents a heavy financial and environmental burden.Many schemes have been proposed which selectively dim lights to improve energy efficiency, but little consideration has been given to the usefulness of the resultant street lighting system. This paper proposes a real-time adaptive lighting scheme, which detects the presence of vehicles and pedestrians and dynamically adjusts their brightness to the optimal level. This improves the energy efficiency of street lighting and its usefulness; a streetlight utility model is presented to evaluate this. The proposed scheme is simulated using an environment modelling a road network, its users, and a networked communication system -and considers a real streetlight topology from a residential area. The proposed scheme achieves similar or improved utility to existing schemes, while consuming as little as 1-2% of the energy required by conventional and state-of-the-art techniques.

Abstract-Streetlights place a heavy demand on electricity usage, providing significant financial and environmental burdens. Consequently, initiatives to reduce energy consumption have been proposed, usually by turning off or dimming the streetlight. In this paper, we propose an adaptive lighting scheme based on traffic sensing, which adaptively adjusts streetlight brightness based on current traffic conditions. The algorithm has been validated through simulation using the SUMO and OMNeT++ tools and, for two different geographical locations, the energy consumption evaluated with respect to traffic speed and volume. The simulation results presented indicate that the proposed lighting scheme can consume up to 30% less energy when compared to the state-of-the-art.

Abstract-WiFi is a cost effective technology of choice for network extension in the rural areas. The telecentre network can reach further to the nearby villages within 10km radius by the use of long range WiFi relay points. The challenges encountered will be the self-sustainability of the network. It should be highly energy efficient and to be powered by the very limited energy sources available in the rural environment. In this case, a modular solar based power supply system has been investigated and enhanced to achieve longer operating hours for equipment installed in the middle of the tropical rainforest and on top of a mountain. The overall design of the self-sustainable long range WiFi network model and the end-user wireless terminal shall also meet the conditions of the rural as well as the living pattern of the local people. The proposed network model has been successfully deployed in a remote village in Borneo, named Bario, connecting six nearby villages to the telecentre for Internet access.Index Terms-Long range WiFi, self-sustainable network, rural connectivity, energy efficient solar power supply

Abstract-Sustaining the operation of street lights incurs substantial financial and environmental cost. Consequently, adaptive lighting systems have been proposed incorporating adhoc networking, sensing, and data processing, in order to better manage the street lights and their energy demands. Evaluating the efficiency and effectiveness of these complex systems requires the modelling of vehicles, road networks, algorithms, and communication systems, yet tools are not available to permit this. This paper proposes StreetlightSim, a novel simulation environment combining OMNeT++ and SUMO tools to model both traffic patterns and adaptive networked street lights. StreetlightSim's models are illustrated through the simulation of a simple example, and a more complex scenario is used to show the potential of the tool and the obtainable results. StreetlightSim has been made open-source, and hence is available to the community.

Street lighting can enhance the safety and security of residential and commercial areas. However, its installation and operation is expensive: cables must be installed, and power is drawn from the grid which is typically dominated by nonrenewable sources. A potential solution is the use of solar energy to power individual street lights locally. However, with limited energy storage and variable solar availability, existing lighting control strategies are unsuitable for this application. This paper describes the extension of an existing gridpowered street light management scheme, which responds to vehicles and pedestrians by dynamically changing the brightness of street lights in their vicinity, setting an optimal pattern of lighting. The proposed scheme, TALiSMaN-Green, achieves energy-neutral solar-powered operation. It maintains a consistent level of usefulness of street lights across a complete overnight period, regardless of the amount of energy stored at the beginning of the night. Unlike existing schemes, which may run out of energy during the night, it learns the dynamics of traffic volumes and sunrise times and budgets energy accordingly.