Traffic congestion is still a challenge faced by most countries of the world. However, it can be solved most effectively by integrating modern technologies such as Internet of Things (IoT), fog computing, cloud computing, data analytics, and so on, into a framework that exploits the strengths of these technologies to address specific problems faced in traffic management. Unfortunately, no such framework that addresses the reliability, flexibility, and efficiency issues of smart-traffic management exists. Therefore, this paper proposes a comprehensive framework to achieve a reliable, flexible, and efficient solution for the problem of traffic congestion. The proposed framework has four layers. The first layer, namely, the sensing layer, uses multiple data sources to ensure a reliable and accurate measurement of the traffic status of the streets, and forwards these data to the second layer. The second layer, namely, the fog layer, consumes these data to make efficient decisions and also forwards them to the third layer. The third layer, the cloud layer, permanently stores these data for analytics and knowledge discoveries. Finally, the fourth layer, the services layer, provides assistant services for traffic management. We also discuss the functional model of the framework and the technologies that can be used at each level of the model. We propose a smart-traffic light algorithm at level 1 for the efficient management of congestion at intersections, tweet-classification and image-processing algorithms at level 2 for reliable and accurate decision-making, and support services at level 4 of the functional model. We also evaluated the proposed smart-traffic light algorithm for its efficiency, and the tweet classification and image-processing algorithms for their accuracy.
Social media has become one of the most important sources of news in our lives, but the process of validating news and limiting rumors remains an open research issue. Many researchers have suggested using Blockchain to solve this problem, but it has traditionally failed due to the large volume of data and users in such environments. In this paper, we propose to modify the structure of the Blockchain while preserving its main characteristics. We achieve this by integrating customize blockchain with the Text Mining (TM) algorithm to create a modified Light Weight chain (LWC). LWC will speed up the verification process, which is carried out through proof of good history where the nodes will have the weights according to their previous posts. Moreover, the LWC will be compatible with different applications such as verifying the authenticity of news or legal religious ruling (fatwas). In this research, we have implemented a simple model to simulate the proposed LWC for the detection of fake news and preserving the characteristics and features of the traditional Blockchain. The results on experimental data reflect the effectiveness of the proposed algorithm in establishing the chain.
The intelligent transportation system has made a huge leap in the level of human services, which has had a positive impact on the quality of life of users. On the other hand, these services are becoming a new source of risk due to the use of data collected from vehicles, on which intelligent systems rely to create automatic contextual adaptation. Most of the popular privacy protection methods, such as Dummy and obfuscation, cannot be used with many services because of their impact on the accuracy of the service provided itself, they depend on changing the number of vehicles or their physical locations. This research presents a new approach based on the shuffling Nicknames of vehicles. It fully maintains the quality of the service and prevents tracking users permanently, penetrating their privacy, revealing their whereabouts, or discovering additional details about the nature of their behavior and movements. Our approach is based on creating a central Nicknames Pool in the cloud as well as distributed sub-pools in fog nodes to avoid intelligent delays and overloading of the central architecture. Finally, we will prove by simulation and discussion by examples the superiority of the proposed approach and its ability to adapt to new services and provide an effective level of protection. In the comparison, we will rely on the well-known privacy criteria: Entropy, Ubiquity, and Performance.
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