Nowadays, cloud computing has attracted the attention of large companies due to its high potential, flexibility, and profitability in providing multi-sources of hardware and software to serve the connected users. Given the scale of modern data centers and the dynamic nature of their resource provisioning, we need effective scheduling techniques to manage these resources while satisfying both the cloud providers and cloud users goals. Task scheduling in cloud computing is considered as NP-hard problem which cannot be easily solved by classical optimization methods. Thus, both heuristic and meta-heuristic techniques have been utilized to provide optimal or near-optimal solutions within an acceptable time frame for such problems. In this article, a summary of heuristic and meta-heuristic methods for solving the task scheduling optimization in cloud-fog systems is presented. The cost and time aware scheduling methods for both bag of tasks and workflow tasks are reviewed, discussed, and analyzed thoroughly to provide a clear vision for the readers in order to select the proper methods which fulfill their needs.
With the substantial growth of mobile applications and the emergence of cloud computing concepts, therefore mobile Cloud Computing (MCC) has been introduced as a potential mobile service technology. Mobile has limited resources, battery life, network bandwidth, storage, and processor, avoid mobile limitations by sending heavy computation to the cloud to get better performance in a short time, the operation of sending data, and get the result of computation call offloading. In this paper, a survey about offloading types is discussed that takes care of many issues such as offloading algorithms, platforms, metrics (that are used with this algorithm and its equations), mobile cloud architecture, and the advantages of using the mobile cloud. The trade-off between local execution of tasks on end-devices and remote execution on the cloud server for minimizing delay time and energy saving. In the form of a multi-objective optimization problem with a focus on reducing overall system power consumption and task execution latency, meta-heuristic algorithms are required to solve this problem which is considered as NP-hardness when the number of tasks is high. To get minimum cost (time and energy) apply partial offloading on specific jobs containing a number of tasks represented in sequences of zeros and ones for example (100111010), when each bit represents a task. The zeros mean the task will be executed in the cloud and the ones mean the task will be executed locally. The decision of processing tasks locally or remotely is important to balance resource utilization. The calculation of task completion time and energy consumption for each task determines which task from the whole job will be executed remotely (been offloaded) and which task will be executed locally. Calculate the total cost (time and energy) for the whole job and determine the minimum total cost. An optimization method based on metaheuristic methods is required to find the best solution. The genetic algorithm is suggested as a metaheuristic Algorithm for future work.
In the era of modern trends such as cloud computing, social media applications, emails, mobile applications, and URLs that lead to increased risks for defrauding authorized users, and then the attackers try to gain illegal access to accounts of users through a malicious attack. The phishing attack is one of the dangerous attacks caused to access of authorized account illegally way. The finances, business, banking, and other sensitive in states are faces by this type of attacks due to the important information they have. In this paper, we propose a secure verification scheme that can overcome the above-mentioned issues. Additionally, the proposed scheme can resist famous cyberattacks such as impersonate attacks, MITM attacks. Moreover, the proposed scheme has security features like strong verification, forward secrecy, user’s identity anomaly. The security analysis and the experimental results proved the strongest of the proposed scheme compared with other related works. Finally, our proposed scheme balanced between the performance and the security merits.
Most Internet of Vehicles (IoV) applications are delay-sensitive and require resources for data storage and tasks processing, which is very difficult to afford by vehicles. Such tasks are often offloaded to more powerful entities, like cloud and fog servers. Fog computing is decentralized infrastructure located between data source and cloud, supplies several benefits that make it a non-frivolous extension of the cloud. The high volume data which is generated by vehicles’ sensors and also the limited computation capabilities of vehicles have imposed several challenges on VANETs systems. Therefore, VANETs is integrated with fog computing to form a paradigm namely Vehicular Fog Computing (VFC) which provide low-latency services to mobile vehicles. Several studies have tackled the task offloading problem in the VFC field. However, recent studies have not carefully addressed the transmission path to the destination node and did not consider the energy consumption of vehicles. This paper aims to optimize the task offloading process in the VFC system in terms of latency and energy objectives under deadline constraint by adopting a Multi-Objective Evolutionary Algorithm (MOEA). Road Side Units (RSUs) x-Vehicles Mutli- Objective Computation offloading method (RxV-MOC) is proposed, where an elite of vehicles are utilized as fog nodes for tasks execution and all vehicles in the system are utilized for tasks transmission. The well-known Dijkstra's algorithm is adopted to find the minimum path between each two nodes. The simulation results show that the RxV-MOC has reduced significantly the energy consumption and latency for the VFC system in comparison with First-Fit algorithm, Best-Fit algorithm, and the MOC method.
Every day, a tremendous amount of image data is generated as a result of recent advances in imaging and computing technology. Several content-based image retrieval (CBIR) approaches have been introduced for searching image collections. These methods, however, involve greater computing and storage resources. Cloud servers can address this issue by offering a large amount of computational power at a low cost. However, cloud servers are not completely trustworthy, and data owners are concerned about the privacy of their personal information. In this research, we propose and implement a secure CBIR (SCBIR) strategy for searching and retrieving cipher text image databases. In the proposed scheme, the extract aggregated feature vectors to represent the related image collection and use a safe Asymmetric Scalar-Product-Preserving Encryption (ASPE) approach to encrypt these vectors while still allowing for similarity computation. To improve search time, all encrypted features are recursively clustered using the k-means method to create a tree index. The results reveal that SCBIR is faster at indexing and retrieving than earlier systems, with superior retrieval precision and scalability. In addition, our paper introduces the watermark to discover any illegal distributions of the images that are received by unlawful data users. Particularly, the cloud server integrates a unique watermark directly into the encrypted images before sending them to the data users. As a result, if an unapproved image copy is revealed, the watermark can be extracted and the unauthorized data users who spread the image can be identified. The performance of the proposed scheme is proved, while its performance is demonstrated through experimental results.
Recently, the incorporation of state-of-the-art technology such as Electronic Healthcare Records (EHRs), networks, and cloud computing has transformed the traditional healthcare system. However, security problems have arisen as a result of the integration of technology. Secure remote user authentication is a core part of the healthcare system to validate the user's identification via an unsecure communication network. Since then, several remote user authentication schemes have been presented, each with its own set of pros and limitations. As a result, security, malicious attacks and privacy concerns are considered one of the main challenges related to the healthcare system. In this paper, we propose a safe user authentication scheme for patients in the healthcare system that overcomes these flaws and confirms the security of the proposed work using scyther, a formal security tool. In the healthcare environment, our work provides an effective means to construct an environment capable of setting, registering, storing, searching, analyzing, authentication, and verifying electronic healthcare information in order to protect the information of patients. Furthermore, our suggested scheme uses symmetric encryption based on the crypto- hash function for accessing the anomaly of the patient's identity and One-Time Password (OTP). Towards the end of the study, the performance analysis results indicate a delicate balance of security and performance that is frequently lacking in previous works.
In this work, a healthcare monitoring system-based Internet of Medical Things (IoMT) is proposed, implemented, analyze it by artificial intelligence using fuzzy logic. Atmega microcontroller was used to achieve the function of the proposed work and provide the area for monitoring and Analytic(decision) to the caretakers or doctors through putting the results in the platform. In this paper, the heart rate pulse sensor and infrared temperature sensor are chosen, which give skin temperature and room temperature to provide their results to the caretaker. The decision that gives the patient is in a normal state, or the fuzzy logic does an abnormal state or risk state. The fuzzy logic is used for it accurate and fast in processing data and gives a result very closer to the reality in smart health services. IoMT enables the doctors and caretakers to monitor the patient easily at any time and any place by using their intelligent laptops, tablets, and phones. Finally, the proposed system can contribute to the construction of a wide healthcare monitoring system in the unit or in the department that follows on for the hospital. Therefore, Doctors can improve the accuracy of the diagnosis, as they receive all the patient data necessary.