LAUSR

The street lighting, which is a ubiquitous utility in cities, plays an important role in improving comfort and safety of cities, as well as brings a heavy financial burden for governments to sustain its operation. Hence, the Intelligence Street Lighting system (ISLs) based on the Internet of Things (IoT) technology, committed to improving the quality of life of citizens by minimizing electrical energy consumption, has attracted the attention of the governments and researchers recently. To achieve energy saving, the approach of adjusting streetlights’ illuminance according to the traffic parameters obtained by additional sensors, has been proposed in many existing works. However, adding new sensors will increase the cost of ISLs, thereby restricting its application and promotion. The paper proposes a novel traffic-adaptive street lighting scheme (TaSLC), whose traffic parameters are extracted from the signatures of received signal strengths (RSS) stem from the behaviors of vehicles and pedestrians moving on the roads, and dimming streetlights accordingly. Therefore, the proposed TaSLC can improve the performance of ISLs without any additional cost. Finally, the feasibility and performance of TaSLC have been evaluated in a real testbed, the experimental results show that the accuracy of TaSLC in detecting road users is up to 95% and the electric energy consumed by TaSLC is only 10.5% of that consumed by existing methods.