Traditional target detection algorithms have difficulty to adapt complex environmental changes and have limited applicable scenarios. However, the deep-learning-based target detection model can automatically learn with strong generalization capability. In this article, we choose a single-stage deep-learning-based target detection model for research based on the model’s real-time processing requirements and to improve the accuracy and the robustness of target detection in remote sensing images. In addition, we improve the YOLOv4 network and present a new approach. First, we propose a classification setting of the nonmaximum suppression threshold to increase the accuracy without affecting the speed. Second, we study the anchor frame allocation problem in YOLOv4 and propose two allocation schemes. The proposed anchor frame scheme also improves the detection performance, and experimental results on the DOTA dataset validate their effectiveness.

These days, with the emerging developments in wireless communication technologies, such as 6G and 5G and the Internet of Things (IoT) sensors, the usage of E-Transport applications has been increasing progressively. These applications are E-Bus, E-Taxi, self-autonomous car, E-Train and E-Ambulance, and latency-sensitive workloads executed in the distributed cloud network. Nonetheless, many delays present in cloudlet-based cloud networks, such as communication delay, round-trip delay and migration during the workload in the cloudlet-based cloud network. However, the distributed execution of workloads at different computing nodes during the assignment is a challenging task. This paper proposes a novel Multi-layer Latency (e.g., communication delay, round-trip delay and migration delay) Aware Workload Assignment Strategy (MLAWAS) to allocate the workload of E-Transport applications into optimal computing nodes. MLAWAS consists of different components, such as the Q-Learning aware assignment and the Iterative method, which distribute workload in a dynamic environment where runtime changes of overloading and overheating remain controlled. The migration of workload and VM migration are also part of MLAWAS. The goal is to minimize the average response time of applications. Simulation results demonstrate that MLAWAS earns the minimum average response time as compared with the two other existing strategies.

The intelligent transportation system is currently an active research area, and vehicle re-identification (Re-Id) is a fundamental task to implement it. It determines whether the given vehicle image obtained from one camera has already appeared over a camera network or not. There are many possible practical applications where the vehicle Re-Id system can be employed, such as intelligent vehicle parking, suspicious vehicle tracking, vehicle incident detection, vehicle counting, and automatic toll collection. This task becomes more challenging because of intra-class similarity, viewpoint changes, and inconsistent environmental conditions. In this paper, we propose a novel approach that re-identifies a vehicle in two steps: first we shortlist the vehicle from a gallery set on the basis of appearance, and then in the second step we verify the shortlisted vehicle’s license plates with a query image to identify the targeted vehicle. In our model, the global channel extracts the feature vector from the whole vehicle image, and the local region channel extracts more discriminative and salient features from different regions. In addition to this, we jointly incorporate attributes like model, type, and color, etc. Lastly, we use a siamese neural network to verify license plates to reach the exact vehicle. Extensive experimental results on the benchmark dataset VeRi-776 demonstrate the effectiveness of the proposed model as compared to various state-of-the-art methods.

The Internet of Medical Things (IoMT) workflow applications have been rapidly growing in practice. These internet-based applications can run on the distributed healthcare sensing system, which combines mobile computing, edge computing and cloud computing. Offloading and scheduling are the required methods in the distributed network. However, a security issue exists and it is hard to run different types of tasks (e.g., security, delay-sensitive, and delay-tolerant tasks) of IoMT applications on heterogeneous computing nodes. This work proposes a new healthcare architecture for workflow applications based on heterogeneous computing nodes layers: an application layer, management layer, and resource layer. The goal is to minimize the makespan of all applications. Based on these layers, the work proposes a secure offloading-efficient task scheduling (SEOS) algorithm framework, which includes the deadline division method, task sequencing rules, homomorphic security scheme, initial scheduling, and the variable neighbourhood searching method. The performance evaluation results show that the proposed plans outperform all existing baseline approaches for healthcare applications in terms of makespan.

Due to growth of vehicles in urban cities, such problems are increases traffic control systems, security and crime investigation, intelligent parking and electronic toll collection. Moreover, Logistics access management system (LMS)is also a big problem in urban cities and all the issues are challenging and demands to develop effective and efficient approach for vehicle classification. Mostly, traditional vehicle classification uses hand craft feature extraction method like SIFT, Surf, HoG etc. However, these approaches are not efficient in results. This paper presents the efficient framework for vehicle classification by using Inception-v3 model for image vector features extraction that is most advance deep learning neural network, and this model is not used before for vehicle classification. After that, different classification algorithms are implemented on the feature vector. The classification conducted on three vehicle datasets and results demonstration is helpful for researchers to choose dataset with algorithm performance.