Multi-Vehicle Speed Estimation Algorithm Based on Real-Time Inter-Frame Tracking Technique

Inappropriate vehicle speeding remains a central factor that causes road accidents claiming millions of lives every year. This challenge has raised concerns for vehicle speed estimation as an attempt to promote speed enforcement methods. Traditionally, radar and lidar systems have widely been used for this purpose, despite their several shortfalls: cosine error effects, need for direct line-of-sight, and inability to simultaneously and accurately measure speed from multiple vehicles. The current work proposes an algorithm and a multi-vehicle speed estimation system in a multi-lane road environment to address multi-vehicle speed estimation shortfalls. The proposed solution exploits image processing and computer vision techniques to flag vehicles with inappropriate speeding patterns. A series of experiments showed that the developed system generates more accurate results than those given by the lidar system. In essence, the proposed system can estimate the speed of up to six vehicles concurrently. It can produce an average percentage error of 2.7% relative to the actual speed measured by a speedometer. This error is 5.4% lower than that demonstrated by the lidar system, emphasizing that the proposed system may be a more suitable approach to traffic laws enforcement. Keywords: Computer vision; image processing; inter-frame differencing; vehicle tracking; road acciden

Design and Implementation of Distributed Identity and Access Management Framework for Internet of Things (IoT) Enabled Distribution Automation

The smart grid and Internet of Things (IoT) technologies play vital roles in improving the quality of services offered in traditional electrical grid. They open a room for the introduction of new services like distribution automation (DA) that has a significant advantage to both utility companies and final consumers. DA integrates sensors, actuators, intelligent electrical devices (IED) and information and communication technologies to monitor and control electrical grid. However, the integration of these technologies poses security threats to the electrical grid like Denial of Service (DoS) attacks, false data injection attacks, and masquerading attacks like system node impersonation that can transmit wrong readings, resulting in false alarm reports and hence leading to incorrect node actuation. To overcome these challenges, researchers have proposed a centralized public key infrastructure (PKI) with bridged certificate authority (CA) which is prone to DoS attacks. Moreover, the proposed blockchain based distributed identity and access management (DIAM) in IoT domain at the global scale is adding communicational and computational overheads. Also. It is imposing new security threats to the DA system by integrating it with online services like IoTEX and IoTA. For those reasons, this study proposes a DIAM security scheme to secure IoT-enabled distribution automation. The scheme divides areas into clusters and each cluster has a device registry and a registry controller. The registry controller is a command line tool to access and manage a device registry. The results show that the scheme can prevent impersonated and non-legitimate system nodes and users from accessing the system by imposing role-based access control (RBAC) at the cluster level. Keywords: Distributed Identity and Access Management; Electrical Secondary Distribution Network; Internet of Things; IoT Enabled Distribution Automation; Smart Grid Securit

Deep learning approaches for fault detection and classifications in the electrical secondary distribution network: Methods comparison and recurrent neural network accuracy comparison

The electrical power system comprises of several complex interrelated and dynamic elements, that are usually susceptible to electrical faults. Due to their critical impacts, faults on the electrical power system in the secondary distribution network should be immediately detected, classified, and urgently cleared. Several studies have endeavored to determine appropriate methods for electrical power systems faults detection and classifications using a mathematical approach, expert systems, and normal artificial neural network-integrated with Supervisory Control and Data Acquisition (SCADA) and Phasor Measurement Units (PMU) systems as the sensing element. However, limited studies have explored the application of deep learning approaches in fault detection and classifications. In this study, several deep learning approaches were compared including Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Feed Forward Neural Network (FFNN), and Artificial Neural Network (ANN) to determine the appropriate approach for implementation. The simulation results have shown that the RNN deep learning approach is efficient in detecting and classifying faults in the electrical secondary distribution network, whilst the accuracy increases as the complexity increases. The study takes advantage of the developments in sensors and the Internet of Things (IoT) technologies to capture and preprocess data along with the secondary distribution network. The research used the challenge-driven education approach where Tanzania Electric Supply Company Limited (TANESCO) was the case study and source of the training data

Performance Evaluation of Magnetic Wireless Sensor Networks Algorithm for Traffic Flow Monitoring in Chaotic Cities

Traffic flow monitoring involves the capturing and dissemination of real-time traffic flow information for a road network. When a vehicle, a ferromagnetic object, travels along a road, it disturbs the ambient Earth’s magnetic field, causing its distortion. The resulting distortion carries vehicle signature containing traffic flow related information such as speed, count, direction, and classification. To extract such information in chaotic cities, a novel algorithm based on the resulting magnetic field distortion was developed using nonintrusive sensor localization. The algorithm extracts traffic flow information from resulting magnetic field distortions sensed by magnetic wireless sensor nodes located on the sides of the road. The model magnetic wireless sensor networks algorithm for local Earth’s magnetic field performance was evaluated through simulation using Dar es Salaam City traffic flow conditions. Simulation results for vehicular detection and count showed 93% and 87% success rates during normal and congested traffic states, respectively. Travel Time Index (TTI) was used as a congestion indicator, where different levels of congestion were evaluated depending on the traffic state with a performance of 87% and 88% success rates during normal and congested traffic flow, respectively

Enhanced Magnetic Wireless Sensor Network Algorithm for Traffic Flow Monitoring in Low-Speed Congested Traffic

Traffic flow monitoring using magnetic wireless sensor networks in chaotic cities of developing countries represents an emergent technology. One of the challenges facing such deployment is the development of effective detection signal-processing algorithm in low-speed congested traffic based on the Earth’s magnetic fields. The proposed algorithm is the performance improvement of the previous algorithm known as the Scanning and Decision Algorithm (SDA). The novel algorithm based on the moving-average model includes an addition of a two-pass moving-average filter to improve the signal-to-noise ratio after analog-to-digital conversion. The improved mathematical capabilities enable us to capture additional features of vehicular direction and classification. Other outputs of the model include vehicular detection, count, speed, and travel time index (TTI). The performance evaluation of a proposed algorithm is conducted through on-site real-time experiments at the designated road segment. The results indicated that the roadside magnetic sensor improved vehicular detection, count, travel time index, and classification during low-speed congested traffic state