عباس محمد علي حسين المقرم

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.

The symptoms can be detected through the use of sensors in smartphones that produce a large amount of data that we use to detect symptoms of the disease, processing all data using sensors inside the mobile phone is almost impossible because the processing and storage capabilities are insufficient. For this reason, the data is sent to the cloud that takes Long processing time, high power consumption, delay in transmission and reception, which makes thinking about sending to the cloud useless, especially for data that needs fast processing. This means that a node is located near the end user's devices, which may be computers, smartphones, routers, etc. Its purpose is to reduce energy consumption, and data upload costs and reduce transmission and reception time. In our paper, we proposed a new architecture for a system Quick response to detecting suspected cases with the COVID-19 virus. There are two types of nodes; the first type is the sensor node, which can monitor the state of citizens such as temperature and colds, etc. The second type is the edge node, which may be computers, smartphones, routers, etc. It is intended to reduce power consumption and data download costs and reduce transmission and reception time. The application used the mobile crowdsensing by IoT protocols approach enabled by Real-time Surveillance and Response System to detect and analyze the Suspects of COVID-19 patients by using IoT under edge computing techniques (RS-SYS) proven effective for the response and diagnosis to patients in real-time. The proposed work algorithm was implemented by the Cup Carbon Simulator and the implementation of the application by MIT App Inventor compared to previous articles on the same topic.

Classical cryptography with complex computations has recently been utilized in the latest computing systems to create secret keys. However, systems can be breached by fast-measuring methods of the secret key; this approach does not offer adequate protection when depending on the computational complexity alone. The laws of physics for communication purposes are used in quantum computing, enabling new computing concepts to be introduced, particularly in cryptography and key distribution. This paper proposes a quantum computing lattice (CQL) mechanism that applies the BB84 protocol to generate a quantum key. The generated key and a one-time pad encryption method are used to encrypt the message. Then Babai’s algorithm is applied to the ciphertext to find the closet vector problem within the lattice. As a result, quantum computing concepts are used with classical encryption methods to find the closet vector problem in a lattice, providing strength encryption to generate the key. The proposed approach is demonstrated a high calculation speed when using quantum computing.

Smartphones today are playing important role in our daily life. The reason behind that is they contain many embedded sensors such as camera, microphone, GPS, etc., in addition to wireless communication technologies such as Bluetooth and Wi-Fi. The deployment of sensors in large areas with limited resources is difficult and as well as the high cost, for this reason the mobile crowdsensing (MCS) scenario appeared, which assumes that people are sensing the environment through their smart phones and sharing this data with others, where there are problems of zone with unavailable coverage of Internet and the energy consumption in smartphones. In this paper, we proposed a protocol to manage the process of forwarding data in the environment of MCS based on the Bluetooth technology, in the forwarding process between the participants until they reach to the participant who has the Internet to be uploading data to the server via Wi-Fi and based on the Internet of Things (IoT) protocols known as CoAP and MQTT, and also we presented models to estimate the direct communication time between users in different situations in terms of velocity, acceleration, distance, and direction with constant acceleration of each node during a direct communication to predict the amount of data that could be transferred during the specified time and measurement of throughput, delay from end-to-end, success and failure cases when transferring data to the server in real time and energy consumption. We used a CupCarbon simulator which used for smart city and IoT also used MATLAB to simulation the mathematical models.

Internet of Things (IoT) is a sophisticated concept of the traditional internet. In IoT, all things in our lives can be connected with the internet or with each other to exchange data and perform specific functions through the network. However, combining several devices-especially by unskilled users-may pose a number of security risks. In addition, some commonly used communication protocols in the IoT area are not secure. Security, on the other hand, increases overhead by definition, resulting in performance degradation. The Message Queuing Telemetry Transport (MQTT) protocol is a lightweight protocol and can be considered as one of the most popular IoT protocols, it is a publish/subscribe messaging transport protocol that uses a client-server architecture. MQTT is built to run over TCP protocol, thus it does not provide any level of security by default. Therefore, Transport Layer Security (TLS) can be used to ensure the security of the MQTT protocol. This paper analyzed the impact on the performance and security of the MQTT protocol in two cases. The first case, when using TLS protocol to support the security of the MQTT protocol. The second case, using the traditional MQTT without providing any level of security for the exchanged data. The results indicated that there is a tradeoff between the performance and the security when using MQTT protocol with and without the presence of TLS protocol.

Internet of Things is a sophisticated concept of the Internet, so all things in our lives can be connected to the Internet or to each other to send and receive data to perform specific functions through the network. The Mobile Crowd Sensing (MCS) technology is used by users who use a smartphone as a new direction in the development of the Internet of Things. The volunteers rely on collecting data from the environment by taking advantage of smartphone features such as (camera, temperature, GPS, Microphone, etc.).There are two challenges with MCS are Battery power consumption and the cost of data upload. This paper presented a new framework called Efficient power consumption (EPC - MCS) based on the use of one of the IoT protocols for the limited devices resources such as( power, memory, and processing ,etc.) which is the CoAP protocol after comparing its performance with the protocols (MQTT and XMPP) by simulation proved to be power efficient. This framework proposes dividing the MCS area into two areas , Global mobile node, and local mobile node, volunteers in the global area are collecting data by using their smartphone and sending data to the local area, that might be smartphone, computers or other existing devices at home or a health center. If a local nodes uploading the data to a switch, the switch will route the data to the gateway which has two choices: If there is a need to make a quick decision, should be switched to the fog layer, else uploading the data to the cloud via a Wi-Fi or a 3G connection with a piggyback. Thus, yield two advantages are Zero cost for uploading data and less energy consumption. In this paper, we presented an algorithm to route the data between the global nodes and local nodes with fog computing and without. Evaluate the performance of our framework through use cooja emulator show to be effective in efficient power consumption and data uploading, compared to previous articles in the same subject.

The proliferation of smart devices such as, smart phones, traffic light, medical devices and those in the smart home generate a large amount of data. This data is send to the cloud for the purpose of storage, processing and etc. because these devices have not enough memory or limited processing capabilities, but the transmission of data to the cloud has become an undesirable process and the reason is the consumption of more energy, the cost of uploading data , delay as well as the need for the Internet, recently appeared the term of fog , which means there are a nodes located near to the end-user devices and it may be smart phones, computers , routers or etc. and the purpose is to reduce the time of transmission and reception, reduce the energy consumption and cost of uploading data. In our paper we proposed a new architecture, it called Multi-Level Fog Computing (MLF-MCS), where there are two types of nodes, the first type is the sensor nodes , the second type is the Fog nodes , where the Fog nodes contains different information, so each Fog node can processing different data from other Fog to prevent the exit of data to the Internet and then to the cloud to control on the most important problems which are energy consumption, delay and the cost of uploading data , reducing energy consumption and delay happen because that the sensor nodes have a very few hopes to reach to the fog compared to the cloud, either the cost is reduced by keeping the data within the local network and not go out to the Internet. The algorithm was written for the proposed work and applied in Cup Carbon simulator, also we give equations for energy consumption and time delay, where show to be effective in efficient power, delay time and data uploading cost, compared to previous articles in the same subject.

Cloud computing environments are increasingly popular due to their flexibility and scalability, but they also present significant security challenges, particularly in the form of malware attacks. These malicious attacks exploit weaknesses within cloud infrastructures, which can result in serious repercussions like data breaches, unauthorized system access, and identity theft. In this paper, we introduce an innovative malware detection classifier specifically designed to overcome the shortcomings of conventional machine learning algorithms, such as K-Nearest Neighbor (KNN) and Support Vector Machine (SVM), in the unique context of cloud environments. Our proposed method relies on Log-spectral distance as a fundamental metric, which enables a more precise and effective approach to detecting malware. Through rigorous and extensive experimentation, our findings demonstrate that this novel classifier achieves an outstanding accuracy rate of 97% without the need for feature selection—surpassing the 95% accuracy attained when employing feature selection through the Mutual Information (MI) method. Additionally, our classifier outperforms both traditional machine learning (ML) and deep learning (DL) techniques, showcasing its robustness and dependability in identifying malware threats within cloud settings. The results of our study underscore the classifier's potential to serve as a crucial tool for enriching security in cloud environments. This advanced solution not only contributes to academic research but also offers practical applications for safeguarding cloud infrastructures against the continuously evolving landscape of malware threats.

The data generated by the IoT needs a powerful platform such as cloud computing for data processing. However, the cloud faces challenges when dealing with various types of resources, high delay, and cost, this represents a substantial challenge in scheduling tasks. Therefore, the need appeared to introduce the concept of fog. To address these limitations, optimization algorithms such as PSO were used. In traditional PSO, all particles in the swarm are influenced by a single global best particle (Gbest), if it becomes stuck in a local optimum, all the particles will move closer to it, thus, the PSO may easily get trapped in premature convergence. This paper proposed an adaptive cloud-fog integrated approach based on modified PSO called PSO Optimized Leader (PSO-OL). These modifications on four stages: Firstly, a method to ensure swarm diversity in the initialization phase is introduced. Secondly, to reduce the chance of the population getting trapped in a local optimum, the farthest-best particle is introduced. Third, in addition to the primary Gbest, a second Gbest represents a different good particle presented to explore multiple promising regions. Finally proposed a new crossover operator to get an optimized leader. The PSO-OL approach was evaluated and the results show the effectiveness of the enhanced leader by 40% with farthest-best, 45% with second-Gbest when compared to standard PSO, and when compared to scheduling algorithms where outperforms the other algorithms by minimizing makespan by 34%, cost by 14%, and increasing throughput by 75%, in comparison to existing load balancing and scheduling methods: RR, BLA, MPSO, ETS, and TCaS.

Electronic learning (E-learning) has been a popular trend in education in recent years. As the cornerstone of E-Learning, the learning management system (LMS) must be available 24/7 with strong internet connectivity, enabling students to engage in paperless learning. However, not all universities, especially those in rural areas, have reliable internet connectivity. This has become an issue for universities that lack reliable internet connectivity and an adequate computer laboratory since they are unable to offer an Elearning environment that simulates a classroom setting. Even though the majority of students and teachers have smartphones, without reliable internet connectivity, it will be challenging to establish a connection to the learning management system's remote server. In this paper, a mobile learning management system based on Raspberry Pi and MoodleBox has been developed to address the aforementioned E-learning problem. This prototype can be used locally as a server even without an internet connection to provide the most fundamental LMS features. Students can view the information locally from their smartphone or laptop after teachers submit it to this portable system. This prototype has been successfully installed in our lab, and the LMS is simultaneously accessible through Wi-Fi.

The holiday season brings out the crowds at shopping centers in major cities. When there are substantial savings to be obtained, this becomes even more true. People today often fill their shopping carts with a wide selection of products. Once a customer has finished shopping, he or she should head to the counter to settle the payment. It takes time for the cashier to create the bill using a barcode scanner. Because of this, there are often substantial wait times at the cash registers. This project proposes a solution for retail centers to address the issues mentioned above. This might be accomplished by installing RFID tags on all mall merchandise and retrofitting all shopping carts with a radio-frequency identification reader and a liquid crystal display. Any item placed in the shopping cart will have its barcode scanned automatically, and the trolley's digital signboard will update to show the item's name and price. When an item is removed from a shopping cart, its cost is subtracted from the grand total, and the relevant data is transmitted to the central billing unit through radio-frequency (RF) modules. Customers may save a ton of time since billing can be done right on the tram. The researchers worked on developing a shopping cart, which is smart, and this system allows for faster checkout and payment of the bill in a secure manner, and the adoption of technology in the store increases the demand for it continuously, the computer will remember the barcode data you enter and compare it to its database, from which it will get the necessary data. The database and RF model will be connected through software. Small radio-frequency (RF) electrical modules called a "transmitter" and "receiver" can be used to transmit and receive signals between any two devices. The data is sent from the transmitter module to the receiver module, which then processes the data. The group believes that the intelligent shopping cart will result in a more dependable checkout procedure and less waiting time for customers.