Internet of Things (IoT) refers to a new extended network that enables to any object to be linked to the Internet in order to exchange data and to be controlled remotely. Nowadays, due to its multiple advantages, the IoT is useful in many areas like environment, water monitoring, industry, public security, medicine, and so on. For covering all spaces and operating correctly, the IoT benefits from advantages of other recent technologies, like radio frequency identification, wireless sensor networks, big data, and mobile network. However, despite of the integration of various things in one network and the exchange of data among heterogeneous sources, the security of user's data is a central question. For this reason, the authentication of interconnected objects is received as an interested importance. In 2012, Ye et al. suggested a new authentication and key exchanging protocol for Internet of things devices. However, we have proved that their protocol cannot resist to various attacks. In this paper, we propose an enhanced authentication protocol for IoT. Furthermore, we present the comparative results between our proposed scheme and other related ones.
In recent years, the monitoring systems play significant roles in our life. So, in this paper, we propose an automatic weather monitoring system that allows having dynamic and real-time climate data of a given area. The proposed system is based on the internet of things technology and embedded system. The system also includes electronic devices, sensors, and wireless technology. The main objective of this system is sensing the climate parameters, such as temperature, humidity, and existence of some gases, based on the sensors. The captured values can then be sent to remote applications or databases. Afterwards, the stored data can be visualized in graphics and tables form.
The modeling of an efficient classifier is a fundamental issue in automatic training involving a large volume of representative data. Hence, automatic classification is a major task that entails the use of training methods capable of assigning classes to data objects by using the input activities presented to learn classes. The recognition of new elements is possible based on predefined classes. Intrusion detection systems suffer from numerous vulnerabilities during analysis and classification of data activities. To overcome this problem, new analysis methods should be derived so as to implement a relevant system to monitor circulated traffic. The main objective of this study is to model and validate a heterogeneous traffic classifier capable of categorizing collected events within networks. The new model is based on a proposed machine learning algorithm that comprises an input layer, a hidden layer, and an output layer. A reliable training algorithm is proposed to optimize the weights, and a recognition algorithm is used to validate the model. Preprocessing is applied to the collected traffic prior to the analysis step. This work aims to describe the mathematical validation of a new machine learning classifier for heterogeneous traffic and anomaly detection.
Mechanization is a depollution activity, because it provides an energetic and ecological response to the problem of organic waste treatment. Through burning, biogas from mechanization reduces gas pollution from fermentation by a factor of 20. This study aims to better understand the influence of the seasons on the emitted biogas in the landfill of the city Mohammedia. The composition of the biogas that naturally emanates from the landfill has been continuously analyzed by our intelligent system, from different wells drilled in recent and old waste repositories. During the rainy season, the average production of methane, carbon dioxide, and oxygen and nitrogen are currently 56%, 32%, and 1%, respectively, compared to 51%, 31%, and 0.8%, respectively, for old waste.Hazards levels, potential fire, and explosion risks associated with biogas are lower than those of natural gases in most cases. For this reason a system is proposed to measure and monitor the biogas production of the landfill site remotely. Measurement results carried out at various sites of the landfill in the city of Mohammedia by the system show that the biogas contents present dangers and sanitary risks which are of another order.
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