Reliable traffic violation detection is essential for improving road and pedestrian safety. Existing monocular vision-based methods often rely on pixel-level object representations, which limit spatial reasoning and real-world applicability. This paper presents an end-to-end, real-time traffic violation detection framework that operates in world coordinates, enabling spatially consistent violation analysis. The framework integrates transformer-based object detection with camera parameter estimation from image–world correspondences using a pinhole camera model, achieving a measurement error of approximately 20 cm and a speed estimation MAE of 0.467 m/s. The complete framework achieves 44 FPS, demonstrating practicality for real-time roadside monitoring.

Wireless sensor networks (WSNs) are mainly data-driven networks adopted to improve the Internet of Things (IoT) in terms of data throughput, energy efficiency, and self-management. Improving the data lifespan of WSN impacts the performance of the IoT. Achieving data reliability in applications of WSNs deployed in harsh environments is challenging due to the extreme constraints in resources of sensor nodes (SNs). Motivated by the inexpensive infrastructure of WSNs, a number of distributed storage systems have been proposed focusing on achieving data survivability rather than network reliability. In this article, we focus on data storage at the things layer (wireless sensors). We evaluate the performance of a number of distributed data storage systems (DDSSs) over WSN running over the ZigBee MAC protocol. Based on our findings, we introduce a new efficient-energy data dissemination scheme called data survivability with energy efficiency (DSwEE) that outperforms the existing schemes. We compare DSwEE against two prominent protocols in data storage, namely, decentralized erasure code for data survivability (DEC-DS) and decentralized erasure code encode-and-disseminate (DEC-EaD). Results show that DSwEE achieves better performance than both DEC-DS and DEC-EaD in terms of the energy consumption and data recoverability for localized failures, which improves the lifespan of the network.

Recently, the civilian applications of Unmanned Aerial Vehicles (UAVs) are gaining more interest in several domains. Due to operational costs, safety concerns, and legal regulations, Agent-Based Simulation (ABS) is commonly used to design models and conduct tests. This has resulted in numerous research works addressing ABS in civilian UAV applications. This paper aims to provide a comprehensive overview of the ABS contribution in civilian UAV applications by conducting a Systematic Literature Review (SLR) on the relevant research in the previous ten years. Following the SLR methodology, this objective is broken down into several research questions aiming to (i) understand the evolution of ABS use in civilian UAV applications and identify the related hot research topics, (ii) identify the underlying artificial intelligence systems used in the literature, (iii) understand how and when ABS is integrated in broader and more complex internet of things & ubiquitous computing environments, and (iv) identity the communication technologies, tools, and evaluation techniques used to design, implement, and test the proposed ABS models. From the SLR results, key research directions are highlighted including problems related to autonomy, explainability, security, flight duration, integration within smart cities, regulations, and validation & verification of the UAV behavior.

In energy constraint networks, the utilization of limited node battery is very crucial to enhance the network lifespan. The imbalanced node battery dissipation greatly effects the performance of the network. In this paper, we propose QLearning based energy-efficient and balanced data gathering routing protocol (QL-EEBDG). The effectiveness of a forwarder node is computed based on; residual energy of the source node and group energies of the neighbour nodes. The consideration of energy parameters provides complete control on the forwarder node selection and ensures efficient energy consumptions in the network. Still, due to topology changes, void node occurs which is avoided through adjacent node technique (QL-EEBDG-ADN). This scheme finds an alternate route via neighbor nodes to provide continuous communication among the network nodes. Simulations are performed to validate the effectiveness of proposed schemes against existing scheme based on energy tax, network lifetime.

This paper presents a decisive threshold termed as the buffer-threshold to control the selection probability of source-relay (SR) or relay-destination (RD) links for the buffer aided cooperative wireless networks. The weights of the links are reassigned using buffer-threshold and a link with the maximum weight is activated. The proposed scheme is termed as Buffer-Threshold based relay selection scheme (BTRS). The relations of the outage probability (OP) and the average delay are calculated by the Markov Modelling of the buffers. Theoretical results are analyzed for different cases of the buffer-threshold and validated by the Monte-carlo simulations. For the performance evaluation BTRS is compared with the max link relay selection (MLRS) scheme and the max weight relay selection (MWRS) scheme and outperforms its counterparts in terms of the average delay.

Carpooling for commuting can save cost and helps in reducing pollution. An automatic Web based Global CarPooling Matching Service (GCPMS) for matching commuting trips has been designed. The service supports carpooling candidates by supplying advice during their exploration for potential partners. Such services collect data about the candidates, and base their advice for each pair of trips to be combined, on an estimate of the probability for successful negotiation between the candidates to carpool. The probability values are calculated by a learning mechanism using, on one hand, the registered person and trip characteristics, and on the other hand, the negotiation feedback. The problem of maximizing the expected value of carpooling negotiation success was formulated and was proved to be NP-hard. In addition, the network characteristics for a realistic case have been analyzed. The carpooling network was established using results predicted by the operational FEATHERS activity based model for Flanders (Belgium).

In the near future vehicles will be equipped with embedded communication capabilities in order to perform context-sensitive tasks such as traffic flow control and incident avoidance. In large scale vehicular networks it is of vital importance that the information exchanged between the vehicles meets a certain level of quality so that they can make informed automated decisions. In this paper we define Quality of Context (QoC) and Peer Reputation (PR) for nodes in vehicular networks and propose ways to apply them for efficient communication. Thus, we provide a two-fold solution in which on the one hand we focus on the quality of the information and on the other hand we aim at determining the reputation of the nodes involved in the communication. This helps to eliminate the use of erroneous, ambiguous and imprecise information provided by unknown entities. Our simulated experiments show that our mechanism significantly reduces network traffic usage, eases out the decision making process and guarantees a minimum level of quality.