IoT environments have introduced diverse logistic support services into our lives and communities, in areas such as education, medicine, transportation, and agriculture. However, with new technologies and services, the issue of privacy and data security has become more urgent. Moreover, the rapid changes in IoT and the capabilities of attacks have highlighted the need for an adaptive and reliable framework. In this study, we applied the proposed simulation to the proposed hybrid framework, making use of deep learning to continue monitoring IoT data; we also used the blockchain association in the framework to log, tackle, manage, and document all of the IoT sensor’s data points. Five sensors were run in a SimPy simulation environment to check and examine our framework’s capability in a real-time IoT environment; deep learning (ANN) and the blockchain technique were integrated to enhance the efficiency of detecting certain attacks (benign, part of a horizontal port scan, attack, C&C, Okiru, DDoS, and file download) and to continue logging all of the IoT sensor data, respectively. The comparison of different machine learning (ML) models showed that the DL outperformed all of them. Interestingly, the evaluation results showed a mature and moderate level of accuracy and precision and reached 97%. Moreover, the proposed framework confirmed superior performance under varied conditions like diverse attack types and network sizes comparing to other approaches. It can improve its performance over time and can detect anomalies in real-time IoT environments.

The widespread use of IoT devices has introduced new challenges, particularly related to security and privacy threats such as unauthorized data access and device breaches. The high demand for these devices, coupled with the lack of strong security and privacy systems for user data, highlights the need for more effective approaches and solutions to address these issues. In this study, we propose a framework designed to audit, test, and detect potential vulnerabilities within IoT environments and their applications. The main components of the proposed framework include a machine-learning algorithm for data classification and attack detection. The framework also introduces two additional features calculated by the edge computing layer (fog layer) to enhance the accuracy of the classification algorithm. The algorithm uses multiple classification models to improve accuracy. To assess our classification algorithm’s efficiency, we implemented it using a real IoT dataset known as the TON_IoT dataset. The results demonstrated the accuracy, efficiency, and precision of our approach in detecting and mitigating potential vulnerabilities and threats like Tamper Data, Injection, Denial of Service, and Backdoor attacks. We achieved an accuracy rate of over 98%. Our findings offer valuable recommendations to enhance security and privacy within IoT systems, and we also explore emerging trends in these areas.

Real-time robotic applications encounter the robot on board resources’ limitations. The speed of robot face recognition can be improved by incorporating cloud technology. However, the transmission of data to the cloud servers exposes the data to security and privacy attacks. Therefore, encryption algorithms need to be set up. This paper aims to study the security and performance of potential encryption algorithms and their impact on the deep-learning-based face recognition task’s accuracy. To this end, experiments are conducted for robot face recognition through various deep learning algorithms after encrypting the images of the ORL database using cryptography and image-processing based algorithms.

The Global Positioning System (GPS) is not the only way to solve connected objects’ geo-localization problems; it is also possible to use the mobile network infrastructure to geo-locate objects connected to the network, using antennas and signals designed for voice and data transfer, such as the 5th generation network. 5G is considered as a least expensive solution because there is no specific equipment to set up. As long as the object is in an area covered by the network, it connects to the nearest 5G Micro-Cell (MC). Through exchange of signals with the MC node we can locate the object. Currently, this location is very fast with less than 5 s but not very precise because it depends on the number of MC antennas of the operator in question and their distance. This paper presents a novel technique to geo-locate connected object in a covered 5G area. We exploit the 5G SS-RSRP used for signal quality measurement, to estimate the distance between two Connected Objects (COs) in move and in a dense urban area. The overall goal is to present a new concept laying on the 5G SS-RSRP signalling. The proposed solution takes into consideration the Deterministic and the Stochastic effect of the received signals which is not treated by the previous works. The accuracy is optimum even after approaching to the distance of one meter which is not reached in previous works too. Our method can also be deployed in the upcoming 5G network since it relies on 5G signals itself. This work and that of Wang are both based on RSRP and give comparable theoretical complexities therefore comparable theoretical execution times as well. However, to obtain a reliable learning Wang requires a huge amount of data which makes it difficult to get a real time aspect of this algorithm. The use of RSRP and the elimination of the learning phase will give more chance to our work to achieve desired performances. Numerical results show the appropriateness of the proposed algorithms and good location accuracy of around one meter. The Cramer Rao Lower Bound derivations shows the robustness of the proposed estimator and consolidate the work.

Is the given code the best? Or can it be improved? And if so, by how much? To answer these three questions, code cannot be seen in isolation from it’s developer as the developer factor plays an vital role in determining code quality. However, no universally accepted metric or developer stat currently exists that provides an objective indicator to a developer’s ability to produce code benchmarked against an expert developer. While traditional developer stats like rank, position, rating and experience published on Online Judges (OJs) provide various insights into a developer’s behavior and ability, they do not help us in answering the three questions. Moreover, unless code quality can be numerically quantified this may not be possible. Towards this end, we conducted an empirical study of over 72 million submissions made by 143,853 users in 1876 contests on Codeforces, a popular OJ, to analyze their code in terms of its correctness , completeness and performance efficiency (code quality characteristics listed in the ISO/IEC 25010 product quality model) measured against the given requirements regardless of the programming language. First, we investigated ways to predict code quality given a developer’s traditional stats using various ML regression models. To quantify and compare code quality, new concepts like score and contest scorecard , had to be introduced. Second, we identified causes that led to poor predictability. Our analysis helped classify user’s performance in contests based on our discovery of erratic or temperamental behavior of users during contests. Third, we formulated quality index of a developer or q-index , a new and unique developer stat to indicate the ability of a developer’s ability to produce quality code, to help improve the predictability of the ML models by mitigating the negative effect of temperamental behavior of users in contests. Among the ML models used, our results suggest that the GradientBoost regressor is the most suited ML model to predict code quality which gave us a high prediction accuracy of around 99.55%.We also demonstrated the uniqueness of q-index over traditional stats and described how it can be used in decision making.

This study looks at how travelers move between MADINA and JEDDA, using the UPPAAL Stratego tool to tackle the complexities of urban mobility. As cities grow, effective transportation planning becomes more challenging. Travelers have three options: car, bus, and train. The choices for car and bus travel are impacted by traffic conditions, which can vary between heavy and light, affecting both travel time and cost. We propose a detailed mathematical model that captures all possible scenarios related to these travel options, incorporating the uncertainties of real life. This allows us to simulate different traffic situations. By using UPPAAL Stratego, we evaluate three strategies: the Safe Strategy, which minimizes risk; the Fast Strategy, which aims to reduce travel time; and the Fast and Safe Strategy, which seeks a balance between speed and safety. This paper starts with an introduction to the Stochastic Priced Timed Games approach, highlighting its relevance in modeling dynamic travel environments. We then provide an overview of UPPAAL Stratego, showcasing its abilities in generating, optimizing, and comparing strategies. Next, we outline our mathematical model, explaining the assumptions, parameters, and data sources we used. Our simulation results illustrate how each strategy performs under different conditions, shedding light on traveler preferences and behaviors. The findings underscore the significance of accounting for traffic variability in travel planning and offer important insights for urban transportation policies aimed at improving the traveler experience and optimizing resource use. Additionally, we emphasize the theoretical contributions of our model by demonstrating its applicability to real-world scenarios and its potential to inform future research in urban mobility optimization. Ultimately, this research adds to the growing knowledge of smart transportation systems, demonstrating how formal mathematical modeling can address complex real-world challenges and inform future urban mobility strategies.