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Procuring usage of the public transportation system enhances the promising effect of limiting the number of own vehicles usage in the contemporary world. The present research advocates a new paradigm of the Intelligent Transportation System (ITS) in the near future, to rescue fossil fuel and to maintain a healthy environment for the current generat...
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Citations
... With the emergence of big data and its applications [1][2][3][4][5], cloud computing is becoming increasingly important and popular for the Smart Device Client (SDC). Since sensitive user information can be easily hacked, such information is encrypted on the SDC side and then outsourced to the cloud. ...
Outsourcing data to remote cloud providers is becoming increasingly popular amongst organizations and individuals. A semi-trusted server uses Searchable Symmetric Encryption (SSE) to keep the search information under acceptable leakage levels whilst searching an encrypted database. A dynamic SSE (DSSE) scheme enables the adding and removing of documents by performing update queries, where some information is leaked to the server each time a record is added or removed. The complexity of structures and cryptographic primitives in most existing DSSE schemes makes them inefficient, in terms of storage, and query requests generate overhead costs on the Smart Device Client (SDC) side. Achieving constant storage cost for SDCs enhances the viability, efficiency, and easy user experience of smart devices, promoting their widespread adoption in various applications while upholding robust privacy and security standards. DSSE schemes must address two important privacy requirements: forward and backward privacy. Due to the increasing number of keywords, the cost of storage on the client side is also increasing at a linear rate. This article introduces an innovative, secure, and lightweight Dynamic Searchable Symmetric Encryp-tion (DSSE) scheme, ensuring Type-II backward and forward privacy without incurring ongoing storage costs and high-cost query generation for the SDC. The proposed scheme, based on an inverted index structure, merges the hash table with linked nodes, linking encrypted keywords in all hash tables. Achieving a one-time O(1) storage cost without keyword counters on the SDC side, the scheme enhances security by generating a fresh key for each update. Experimental results show low-cost query generation on the SDC side (6,460 nanoseconds), making it compatible with resource-limited devices. The scheme out-performs existing ones, reducing server-side search costs significantly.
... These OBUs are powerful nodes with various sensors, communica-tion modules, and computing capabilities, enabling the wireless exchange of traffic information between vehicles and roadside infrastructure, thereby enhancing road safety and transportation network efficiency [16]- [18]. The integration of RSUs and OBUs in ITS has attracted attention due to their potential to enhance road safety, improve traffic management, and alleviate road accidents [19]- [21].Furthermore, the deployment of RSUs and OBUs facilitates the monitoring and management of traffic flow, contributing to the development of advanced traffic management systems [22] [23], [24]. The collaboration between RSUs and OBUs also enables the implementation of intelligent upgrades in transportation infrastructure, optimizing communication environments and deployment methods [25], [26]. ...
As Intelligent Transport Systems (ITS) continue to evolve, the quest for improving road safety and transportation efficiency has gained renewed emphasis. One of the pivotal aspects in this endeavor is the detection and analysis of driver behavior. Recognizing signs of fatigue, distraction, or inattentiveness is critical in enhancing road safety and optimizing traffic flow. In this paper, we present a pioneering approach to driver behavior detection within the realm of ITS using deep learning models in the Cyber-Physical Systems (CPS) framework. Our research focuses on the discernment of critical behaviors such as eye closure, open-eye state, yawning, and non-yawning instances. With an unwavering commitment to road safety and transportation efficiency, we’ve harnessed the power of deep learning to design, develop, and train an exceptionally accurate model. Through rigorous evaluation, we achieved an impressive 94% accuracy. Our findings unveil the potential of CPS-based solutions for real-time driver behavior monitoring, providing a foundation for safer roadways and more streamlined traffic management. The proposed deep learning model offers robust and accurate predictions, enabling timely responses to various driving conditions. This research significantly advances the field of driver behavior analysis within the context of intelligent transportation systems, with broad implications for road safety and traffic management.
... Zhou, Z. and Roncoli, C. [20] introduced a ridesharing approach based on user requests in a congested network with optimal vehicle assignment to reduce costs. Here, consider factors like time constraints, waiting time, and the vehicle's capacity was considered for the user request processing. ...
... • The failure to include the optimization technique for reduction of the cost required for travelling and the complexity in solving the non-linear optimization raises the computation overhead and hence limits the performance of the method [20]. ...
Demand-based public bus service meets the need of passengers with less money, time, and resources by reducing the number of private vehicles on the road. In contrast, dynamic real-time demand-based routing faces challenges like elevated travel time due to the requested assignment based on the paths and vehicle availability. Hence, this research introduces a novel framework named Passenger Influence Bus Service-Intelligent Public Transport System (PIBS-IPTS) for efficient routing of available vehicles based on the demand of passengers. For this, optimal paths are elected from the known routes of the general vehicle through the Cuckoo Search (CS) optimization algorithm. Then efficient route prediction is employed by the Artificial Neural Network (ANN) for passenger flow. Here, the unavailability of the passenger request, such as source location or Destination locations, or the unavailability of both locations is updated while employing the path generation process. The path generation process ensures the reduction of request drops generated by the passenger, which elevates the usage of the general bus service. Here, for the optimal selection of routes from the identified routing paths, a multi-objective function based on traffic density, route condition, and route mobility is employed for the selection of a near-optimal global solution. The method's performance is analyzed using MAE, RMSE, and MAPE and obtained the best values of 0.69, 0.72, and 0.74, respectively.
... Among others, these technological fields include transportation, agriculture, and healthcare. Advanced transportation solutions not only improve the safety and efficiency of personal vehicles [18,19] but also significantly affect public transportation [20], emergency services (e.g., ambulance [21]), the transportation of goods [22], and many more. Additionally, such transformation of the transportation ecosystem may have an immensely positive impact on the environment (e.g., reduction of harmful emissions and more efficient usage of available resources) [23]. ...
The digitization and general industrial development of Montenegro is a great challenge for engineering and science due to its special characteristics. As the accession of Montenegro to the European Union has been an ongoing agenda for over a decade now, and the accession of the country is expected by 2025, adapting the interconnectivity and smart automation of Industry 4.0 plays an essential role in reducing the current gap between Montenegro and EU member states. In this paper, we investigate the present and potential future digitization efforts in the fields of Cooperative Intelligent Transport Systems (C-ITS), agriculture, and healthcare in Montenegro. Our work takes into consideration the characteristics of the country and analyzes the considerations and implications regarding the deployment of state-of-the-art technologies in the investigated fields.
... However, in the process of building a smart city, different subjects have different goals and key links that need to be solved. Table 2 gives a detailed explanation [7]. Emphasize the transformation of information forms and thetransformation of transmission ways, so that information canspread faster, wider and more accurately. ...
In this paper, a computer vision-based cashew nut grading system has been designed and implemented for classifying different grades of cashew nuts using combined features and machine learning approaches. The important task in the cashew nut grading system is to classify the whole and split down cashew nuts. Since these cashew nuts look very similar from the top view, it is a challenging task to classify the whole cashew nut and split down cashew nuts. Hence, a single-view image of cashew nut has been captured by placing a camera with a distance of 17[Formula: see text]cm (from the right side of the conveyor belt). The captured red, blue and green images are normalized and converted into hue, saturation and value color space. S channel from HSV image is used for segmentation process using Otsu threshold technique. The total numbers of features extracted are 275 and the features are texture (180), color (90), and shape (5). The constrained optimization-based feature selection method is used and 30 features are selected for further process. The Support Vector Machine (SVM) classifier is used for the classification, and the results obtained from different kernel functions are computed and compared. The 8-layer convolutional neural network (CNN) has been developed in this work for classification and to analyze the performance and accuracy. The accuracy of different machine learning classifiers like SVM 1-1, SVM 1-All and CNN model is also evaluated and compared. The overall accuracy obtained by SVM 1-All with kernel function radial basis for classification is 98.93%.
Agriculture is the country's mainstay. Plant diseases reduce production and thus product prices. Clearly, prices of edible and non-edible goods rose dramatically after the outbreak. We can save plants and correct pricing inconsistencies using automated disease detection. Using light detection and range (LIDAR) to identify plant diseases lets farmers handle dense volumes with minimal human intervention. To address the limitations of passive systems like climate, light variations, viewing angle, and canopy architecture, LIDAR sensors are used. The DSRC was used to receive an alert signal from the cloud server and convey it to farmers in real-time via cluster heads. For each concept, we evaluate its strengths and weaknesses, as well as the potential for future research. This research work aims to improve the way deep neural networks identify plant diseases. Google Net, Inceptionv3, Res Net 50, and Improved Vgg19 are evaluated before Biased CNN. Finally, our proposed Biased CNN (B-CNN) methodology boosted farmers' production by 93% per area.
