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Vehicular ad hoc networks (VANET) are an emergent technology with a promising future. VANETs are quite different from mobile ad hoc networks (MANETs) in terms of characteristics, challenges, system architecture, and application. One of the significant challenges of the VANET is the security from two essential points of view, the prediction and prevention attackers. In this article, we proposed an operating system that combined both software-defined network (SDN) and self-organizing map (SOM) for the 5G-based VANET system. The proposed system will be a new combination of the SDN- and a self-organizing map (SOM)-based network solution to enhance security in the two dimensions, detecting and preventing attacks. This article analysis the vulnerability of the network performance with taken into account the distributed denial of service (DDoS) attacks. Then, the security of the proposed system has been analyzed and checked with existing of the DDoS. The simulation results presented in this article show that, under general conditions of networks, the proposed system can enhance the networks performance compared to the existing work.

The vehicular ad hoc network, combined with the distribution of roadside units (RSUs), play a significant role in reducing accidents and traffic jams by sending warning and emergency messages to drivers in real-time. However, communication is not always available owing to the lack of RSUs, reliability of data access and control, to avoid such problems we have proposed a new strategy for the deployment of least possible number of RSUs by developing a support protocol in case of loss of communication that relies on smartphones. The simulation results show that the proposed distribution method clearly reduces the number of RSUs required (compared to existing methods), and contributes towards increased coverage, reduced costs, and less missing data.

Smart mobile pharmacy has become a necessary requirement in improving the quality of healthy life in the last five years. Determining the type of medication and calculating the dose for each patient according to the patient's condition, monitoring the patient at the post and how to take the doses on time have a great effect on improving the patient's condition as soon as possible. In this paper, a system of monitoring, monitoring, and guidance based on potency management have been designed to contain a cloud data storage platform and its implementation to solve the above problem. The system is achieved by connecting each item (Medicine bottle) to a device (RFID) to verify that the patient has arrived in the bottle the correct medicine and is equipped with suppression of the treatment of the drug contains the sensors of weight to be sent all information to the control unit to be over the Internet through the routing portal. All data is stored in the first two data folders, which are static and the second is mobile. Synchronization between the two stations is performed to update the information and will be executed on a server that uses an MQTT protocol to register and integrate messages on the platform. The results of the experiment show the speed of guidance and reduce cost and reduce energy.

In urban-traffic scenarios, massive mobile terminals can provide more comprehensive and accurate environmental sensing data with various built-in sensors. Humans can also collect environmental information as task executors. Therefore, facing different sensing requirements from environmental monitoring, intelligent transportation, urban management, and other fields, if we make good use of the sensing abilities of mobile terminals and humans, we can provide more supporting data for them. This paper proposes a multitasking-oriented participant-recruitment algorithm for vehicular crowd sensing based on the Combine-S algorithm that could merge-common sensing requirements in the time and space dimensions to solve the problem of overlapping requirements among multiple tasks. Combine-S could also be integrated with a greedy recruitment algorithm to improve the sensing effect and reduce sensing costs. We used the OMNet++ simulators that are used for simulating urban transportation and IoTKeywords-Genetic Algorithm, NSL-KDD, Intrusion Detection system, Fast Learning Network.

The vehicular ad hoc network (V ANET), combined with the distribution of roadside units (RSUs), play a significant role in reducing accidents and traffic jams by sending warning and emergency messages to drivers in real-time. However, communication is not always available owing to the lack of RSUs. Therefore, in this paper, we have proposed a new strategy for the deployment of the least possible number of RSUs by developing a support protocol in case of loss of communication that relies on smartphones until the connection returns. The simulation results show that the proposed distribution method clearly reduces the number of RSUs required (compared to existing methods), and contributes towards increased coverage, reduced costs, and less missing data.

In today’s digital environment, devices are able to interconnect and react to contextual data more than ever before: artificial intelligence is beginning to coordinate how data collected from sensors and de-vices within the network is analysed, and device ecosystems are replacing standalone devices to deliver solutions to the user. In this paper, the researcher explores current implementations of IoT that have led to positive outcomes for the user; but also, the challenges that remain in today’s applications. Moreover, ex-ploring these current barriers may be able to infer future applications capable of being deployed on a global scale

In this study we have some estimators for exponential distribution in simulation study. These estimators were classical estimator like maximum likelihood estimator (MLE) and robust estimators that is called MAD/MED robust estimator. These methods are used to estimate the parameter of exponential distribution  . We applied the simulation in MATLAB and then compared the results that we have them. The results of the study proved that the method of MAD/MED robust is better than the classical method MSE in the event that the data regular (without pollution) or contaminated.