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The study systematically reviews the integration of Fog and Edge Computing within Learning Analytics to enhance data privacy and security in educational settings that use cloud computing. Employing the PRISMA methodology, we analyze current literature from Web of Science and Scopus databases to examine how these decentralized computing technologies can mitigate the risks associated with centralized cloud storage by processing data closer to its source. Our findings highlight the significant potential of Fog and Edge Computing to transform Learning Analytics by enabling real-time, context-aware data analysis that supports personalized learning while ensuring stringent data privacy. This approach challenges conventional data management practices, advocating for privacy by design in developing new strategies and frameworks. The research underscores the need for collaborative efforts in establishing standards and guidelines for secure and effective technology use in education, pointing towards the necessity of addressing technical, operational, and ethical challenges to maximize the benefits of fog and edge computing in Learning Analytics. • Fog and Edge computing improve data privacy for Learning Analytics and minimizes dependence on centralized cloud storage. • Nearer analytical technologies to users promotes a personalized and secure learning environment. • Future research should focus on creating secure architectures and policies to leverage Fog and Edge Computing in education. [ABSTRACT FROM AUTHOR]

Edge computing is an emerging computing paradigm, which extends the functions of cloud center to the edge of the networks, and brings great convenience to solving problems of delay and bandwidth in the traditional cloud computing paradigm. In edge computing architecture, one-to-many communication is a important communication mode that supports the edge nodes to send one message to multiple terminal devices in one broadcast report. To build a secure one-to-many communication, several multi-receiver signcryption (MRSC) schemes have been brought forward to ensure the security of broadcast messages. However, the existing MRSC schemes need the heavy computation and communication cost, and is not suitable for IoT terminal devices with limited resources. Besides, most of the existing MRSC schemes are vulnerable to the security and privacy leakage, and fail in achieving the decryption fairness. To solve these problems, based on the implicit certificate (IC) cryptosystem and polynomial interpolation evaluation, this paper proposes an provably secure multi-receiver IC-based signcryption (MRICSC) scheme for one-to-many communication in edge computing. The security analysis evidences the proposed MRICSC scheme can ensure the security. With the experimental results indicating that the proposed MRICSC scheme achieves the better performance than existing schemes. [Display omitted] [ABSTRACT FROM AUTHOR]

The advent of technologies like IoT and 5G brought a new computing paradigm called cloud computing into the world. Cloud computing has become the main platform for data warehousing and processing. However, storing data into the cloud has its own set of challenges and security concerns. Moreover, with increasing data being generated from each device; the traditional cloud computing paradigm has become ineffective in addressing problems like high latency, bandwidth limitation, and resource limitation. So, new computational paradigms like edge and fog computing are proposed to solve the former's issues at the device itself or near the device. Both these paradigms provide computing and memory storage options close to the device itself. Despite their advantages, no system is perfect. In this paper, I list down the different privacy and security problems in all three computing paradigms and their proposed solutions. [ABSTRACT FROM AUTHOR]

Vehicular networking allows vehicles with sensing capabilities to carry out communication from vehicle to vehicle or with accessible roadside units. To attain effective communication in vehicular networking, we have proposed a hybrid technological solution in terms of vehicular edge computing (VEC) utilizing resources, cloud and edge server environment. The proposed architecture supports a frequent communication between a vehicle and a legitimate edge server without any trusted party communication. Both security and privacy are two key challenges in edge based vehicular communications due to the adoption of a real-time environment that is required to respond to hundreds of moving vehicles. This paper presents an edge computing-based framework for vehicular communication to ensure security, message integrity, and privacy. The presented framework attains all security attributes in the presence of active as well as passive adversaries. The formal security proof ensures its correctness and robustness. The analysis indicates that it is secure and efficient, being based on new edge computing framework as compared to general cloud computing framework. [ABSTRACT FROM AUTHOR]

Electrocardiogram(ECG) monitoring is a critical and intricate task in cardiac healthcare. While large models supported by the remote cloud servers with abundant computational resources offers a feasible solution for this task, which still face challenges related to processing costs, privacy risks, and response time. Efforts have been made to integrate edge computing as a supplementary solution, but the current collaborative computing strategy for ECG monitoring is static and inefficient. To address these shortcomings, this study proposes an innovative confidence constraint cloud–edge collaborative computing(3CE2C) strategy. Firstly, the model implementation processes are illustrated, including an ultra-lightweight model for the edge node and a large model for the cloud server. To enhance classification performance, the personalized strategy is employed, resulting in an accuracy improvement from 0.9849 to 0.9929 in the model-cloud. For edge implementation, the optimal input length and model quantization both are explored to reduce the energy consumption. Based on the given confidence constraint, the models dynamically collaborate, with the low-confidence samples uploaded to the cloud server. This approach can achieve accuracy comparable to cloud computing, transmitting only about 17% low-confidence samples, the accuracy ratio(r A c c) is 0.9985. In addition, the method is validated in SVDB, where 3CE2C outperforms state-of-the art framework with the same uploaded sample ratio, resulting in a 1.54% improvement in classification accuracy. In conclusion, the proposed method provides a practical solution in real-time arrhythmia detection applications. • Proposing a confidence constraint cloud–edge collaborative computing strategy. • Improving the arrhythmia classification performance with a personalized strategy. • Implementing an ultra-lightweight model at edge with minimal performance loss. • Achieving accuracy similar to cloud computing, with uploading only 17% samples. • Demonstrating the superiority of the proposed framework compared to other studies. [ABSTRACT FROM AUTHOR]

The huge prospects of the internet of things (IoT) have led to an ever-growing demand for computing power by IoT users to enable various applications. Multi-access edge computing (MEC) research and development has rapidly gained attention during the last decade. The ability to deploy edge servers at different points across a content delivery network that can offer communication and computing services close to mobile user devices is one of the main factors driving the evolution of MEC. Furthermore, MEC has been considered a potentially transformational approach for fifth-generation (5 G) and beyond 5 G (B5G) networks, as well as a potential improvement to conventional cloud computing. Unmanned aerial vehicles (UAVs) can be used as effective aerial platforms to offer reliable and ubiquitous connections in wireless communication networks due to their distinctive qualities, such as high cruising altitude, on-demand deployment, and three-dimensional (3D) maneuverability. The number of research studies published in this area has dramatically increased due to the growing interest in UAV-enabled MEC. Although UAV-enabled MEC systems have been well studied, the existing models are becoming increasingly heterogeneous and scattered without harmony. This paper provides a comprehensive analysis of the literature on UAV-enabled MEC systems with a special focus on the system modeling, and optimization techniques for five identified domains, such as energy efficiency, resource allocation, trajectory control, latency, and security. For each domain, we have highlighted the recent advances, critical findings, and the advantages and disadvantages. Additionally, the identified proposed techniques were analyzed and discussed, with emphasize on the constraints and performance metrics. We also discuss a general system model for each highlighted domain. Moreover, the lessons are also derived from the study on system optimization and system modeling techniques identified in this paper. Then we discuss open issues related to UAV-enabled MEC systems in each highlighted domain, including problem formulation and optimization techniques. Finally, this paper lay out directions for future research to solve the aforementioned problems associated with UAV-enabled MEC systems. [ABSTRACT FROM AUTHOR]

To supervise massive generated data by the Internet of Things (IoT) efficiently, we face two issues that should be addressed which are: (1) heterogeneity or satisfying diversity among IoT devices, and (2) privacy-preserving or preventing unintentional disclosure of sensitive data. Through observation, we found that existing solutions apply one common privacy-preserving rule for all devices while they address the heterogeneity issue separately that lead to unappealing performance. In this paper, we propose a framework for addressing the heterogeneity issue and privacy-preserving of IoT devices at the network edge using a novel proposed ontology data model. Besides, it leverages the proposed ontology to obtain a privacy-preserving method by frequently changing the privacy-preserving behaviors of IoT devices. Through simulation, we show that our solution overhead is less than 9 percent in the worst situation so that it is affordable to most IoT devices in one of its applications that is smart city. [ABSTRACT FROM AUTHOR]

Due to technological advancement and deployment of handheld devices, mobile computing devices have gained the user attention to save resource usage, service access, and application use. In practice, it can be deployed widely to experience massive data storage and less computation cost. However, wireless channel access is vulnerable and insecure to prevent various potential attacks such as replay, redirection, etc. As a consequence, a seamless secure anonymous authentication scheme (S-SAAS) is proposed to create a secure session in cloud-based mobile edge computing. Since IoT, cloud computing, big-data and mobile technologies are converged to permit the group-based communication systems, it is highly demanded to strengthen the authentication process in order to reduce the security risk and vulnerabilities. Moreover, the experimental analysis proves that the proposed S-SAAS scheme consumes less signaling congestion and routing control overhead to achieve better service connection rate. [ABSTRACT FROM AUTHOR]

In recent decades, artificial intelligence techniques have been adopted for many real-time applications. The Internet of Things (IoT) network comprises many sensing devices and physical objects for information gathering and further transmission. In addition to being sent to the receiving nodes, the collected data also needs to be received promptly. Also, many solutions have been proposed for IoT-based embedded systems using edge computing but they are not fully protected against unidentified communication threats. In such circumstances, such systems decrease the trust ratio, and communication performance is compromised. In this research, we describe an optimization model based on IoT-edged technology that incorporates cloud computational intelligence. Furthermore, edge nodes employ artificial intelligence algorithms to provide the optimal outcome for selecting trustworthy forwarded data and lengthen the connected time for smart devices. Firstly, the edge devices extract useful information from the IoT nodes, and accordingly, it provides a decision module based on optimization computing. Secondly, utilizing cryptographic approaches, edge technology secures the multi-layers of the IoT system and ensures data privacy with integrity. Finally, the proposed model is tested and verified for its performance than other related studies in terms of energy consumption, packet delivery ratio, and data delay. [ABSTRACT FROM AUTHOR]

• New cybersecurity countermeasure for the threats in computational cloud and IoT is proposed. • A secure authorization model for IoT architectures is proposed. • The proposed model is implemented using Amazon Web Services IoT platforms. • It can be used to manage the user's authorization securely on many IoT components in different applications. • Power and delay results are inversely proportional to the number of edges and directly proportional to clients number. Internet of Things (IoT) is getting more attention and popularity these days as they make the place smarter and efficient. Every IoT-enabled device is communicating with one another either directly or through a gateway to the internet. Data transmission brings security challenges and concerns to retain data integrity. In the Healthcare field, it is important to ensure medical information integrity, confidentiality, and anonymity. With the increasing need to provide healthcare access in urban and rural areas, and the lack of broadband infrastructure, and the fund to build hospitals, new satellite medical centers or units suffer the challenge of accessibility and data transmission. Yet, there is a need to communicate with the main medical units for full access and provide" anytime, anywhere" access to patient records. It is vital to secure all devices and components involved in an IoT setup as it involves pervasive data collection and dissemination. In this paper, we present a latency and energy-efficient secure IoT model for healthcare systems. We illustrate the model by using Mobile Cloud Computing Simulator (MCCSim) with varying parameters to discuss the impact of security measures on power and bandwidth requirements. We found that data encryption and decryption doubled the delay time and power consumption at the cloud compared to the edge layer as we increase the packet rates. [ABSTRACT FROM AUTHOR]