Motivated by enormous highway-lighting energy consumption, smart lighting development is crucial to better manage available resources. While existing literature focused on ensuring cost-effective lighting, an equally important requirement, namely the visual comfort of motorists, is almost disregarded. This article proposes a novel Internet of Things-enabled system that can be intelligently controlled according to the traffic demand. Cooperative relay-network architecture is the central element that leverages upon placement of cyber-enabled lampposts to allow for sensing-exchanging highway traffic information. Data accumulation is exploited to automate adaptive switching on/off the lighting and provide backtracking detection of faulty lampposts. From the service provider’s perspective, we envision to deploy low-cost highly durable sensing and network components to significantly cut down the operating cost. From the road user’s perspective, the relay-network is envisaged to provide seamless driving experience where sufficient lighting is always perceived along the road. A critical analysis quantitatively evaluates the seamless driving experience considering car arrival rate, outage probability, and device malfunction probability. A road occupancy-based cost estimation analysis demonstrates the effective cost reduction of the proposal compared to existing systems. Furthermore, the performance of chosen communication modules under different setups is assessed through simulation, suggesting appropriate protocol for different highway traffic conditions.

While recent works on investigating renewable energy sources for powering the highway offer promising solutions for sustainable environments, they are often impeded by unequal distribution of sources across the region due to variations in solar exposure and road intensity that electromagnetically and mechanically generate the energy. By exploiting viable gathering of massive renewable energy data using the Internet of Things (IoT), this paper proposes a framework for improved highway-energy management based on the unmanned aerial vehicle-assisted wireless energy re-distribution of the harvested renewable energy. Combining both massive low-rate sensing with high-speed 6G-envisioned transmission for data aggregation, the IoT architecture is of multi-scale, consisting of: i) global data exchange and analytics for energy mapping, re-distribution planning and forecasting, and ii) local data sensing and processing at individual highway lampposts for micro-energy management. The feasibility of the networked energy system is analyzed via analytical cost-reliability analyses. The cost analysis demonstrates the cost-effectiveness through the lowest Requirement of Energy and Cost of Energy for the setup and maintenance. The reliability analysis reveals the energy plus (E $+$ ) feature of the system in certain conditions with enhanced reliability in adverse weathers that impact energy generation. With multi-scale data connectivity to intelligently manage standalone renewable energy, this work puts forward a viable idea of 6G use cases with massively networked energy sensors with a vision of achieving super-connected and intelligence-equipped highways.

Image corner detection is a principal task for successful pattern recognition and identification. Finding corners of a shape usually involve two steps, firstly, shape segmentation and, secondly, locating corners in its boundary. This paper presents a robust method for detecting shape corner points using enhanced curvature properties. Numerous applications like image retrieval, object recognition, and motion tracking uses these corner points, which contain essential information about their location or position in a particular shape. Six existing shape detectors and descriptors, i.e., the Harris, SUSAN, Harris-Laplace, CSS, SIFT, and global and local curvature properties (GLCP), have been compared with the proposed shape detection method yielding its performance. The binary image dataset has been utilised as a benchmark to test the experiment. The experiment exhibits a significant number of true corners of the shape and outperforms the existing methods in terms of accuracy and reliability.