Search

New and innovative wearable IoT devices for health monitoring systems (HMS) have been invented one after another. However, most of these devices are resource-constrained with restricted energy and computation power. The HMS data need to be processed via mobile edge computing (MEC) to improve the response time to fulfill the latency-sensitive and computation-intensive applications and to reduce bandwidth consumption. This paper presents an efficient task scheduling and resource allocation mechanism in MEC to meet these demands in contemplating emergency conditions under HMS. We propose a priority-based task-scheduling and resource-allocation (PTS-RA) mechanism that can assign different priorities to different tasks by considering the tasks' emergency levels computed with respect to the data aggregated from a patient's smart wearable devices. The mechanism can optimally determine whether a task should be processed locally at the hospital workstations (HW) or in the cloud. This is aimed to reduce the total task processing time and the bandwidth cost as much as possible. The proposed approach is to ensure that tasks related to the emergency are given higher priorities and to run first. After the tasks' computations, results are sent to the doctor to response promptly with quick decisions. The proposed PTS-RA was benchmarked against state-of-the-art algorithms concerning average latency, task scheduling efficiency, task execution time, network usage, CPU utilization, and energy consumption. The benchmarking results are promising as PTS-RA is capable to manage the emergency conditions and is meeting the latency-sensitive tasks' requirements with reduced bandwidth cost. [ABSTRACT FROM AUTHOR]

Soon after the proliferation of User-Equipments (UEs) that work with cloud computing, there was an explosion in the volume of generated data. Furthermore, emerging IoT applications, e.g. virtual reality, augmented reality, video streaming, intelligent transportation, and healthcare, require low latency and real-time data, processing and communication. Fog/Edge computing is a novel criterion in which distributed fog/edge nodes provide resources in close proximity to end devices due to their limited resources. Instead of sending large amounts of data to cloud servers, fog/edge nodes could use local resources to analyze, filter, and process data. Mobile Edge Computing (MEC) can significantly reduce processing delays and network traffic between UEs and servers, especially when user mobility is taken into account. This paper proposed a new approach to migration in fog/edge computing to reduce downtime. Our simulations demonstrate less downtime, packet loss, migration time, and latency compared to current techniques. [ABSTRACT FROM AUTHOR]

Internet of Vehicles (IoV) relies heavily on its computing capability to facilitate various vehicular applications. Since the cloud computing or mobile edge computing (MEC) only cannot satisfy the latency requirement due to the limitation of the resource coverage, the cloud–edge-end cooperative computing has become an emerging paradigm. A comprehensive IoV architecture is considered and a joint optimization problem is formulated to minimize the system function value. To optimize the resource allocation and the task offloading strategy, the simulated spring system algorithm (SSSA) is designed where the initial problem is decoupled into two sub-problems with priority. The first one is to allocate computing resources based on KKT conditions, thus the individual optimal solution is achieved. The second one is solved based on the idea of simulated spring system such that the task offloading strategy is obtained. Two sub-problems iterate mutually to update each other until finishing the binary tree traversal. Thus, the proposed solution adapts to various conditions and the computational complexity is also reduced compared with traditional methods. Simulation verifies that the proposed algorithm reduces the maximum system function value by about 31% compared with the benchmark methods and performs efficiently in various road conditions. [ABSTRACT FROM AUTHOR]

The growing technology of the fifth generation (5G) of mobile telecommunications has led to the special attention of cloud service providers (CSPs) to mobile cloud computing (MCC). Due to the limitations in processing power, storage space and energy capacity of mobile devices, cloud resources can be moved to the edge of the network to improve the quality of service (QoS). Server placement is a crucial emerging problem in both typical and edge types of MCC, different proposed methods of which are reviewed and evaluated in this paper. Proper placement of servers leads to more efficient utilization of these servers, reduces their response time and optimizes their energy consumption. A variety of techniques and approaches, including machine learning-based techniques, evolutionary models, optimization algorithms, heuristics and meta-heuristics have been employed by different server placement methods of the literature to find the optimal deployment map of servers. This paper provides a comprehensive analysis of these server placement methods in edge computing, fog computing and cloudlet, investigates their various aspects, dimensions and objectives, and evaluates their strengths and weaknesses. Furthermore, open challenges for server placement in MCC are provided, and future research directions are also explained and discussed. [ABSTRACT FROM AUTHOR]

Mobile edge computing (MEC) is used to provide IT services environment and cloud computing capabilities at the edge of the network. As the technology of unmanned aerial vehicles (UAVs) matures, the growing attempts have been made to use UAVs to replace fixed ground stations for MEC due to their flexibility. In this work, we study the multiobjective trajectory optimization for mobile edge computing system assisted by a single UAV, where the UAV is used to provide computing services for Internet of Things (IoT) devices located on the ground. A multiobjective trajectory optimization problem is formulated, which not only needs to minimize the energy consumption of the MEC system to provide computing services to all IoT devices, but also minimize the task urgency indicator by optimizing the UAV's flight trajectory. In this problem, the number and the locations of hover points (HPs) of UAVs have been taken into consideration. To solve this problem, a multiobjective trajectory optimization algorithm with a cutting and padding encoding strategy is proposed, where the cutting and padding encoding strategy is used to help optimize the population whose individuals may have different lengths. The verification experiments are carried out on a set of instances with up to 400 IoT devices and the experimental results demonstrate the promising performance of the proposed algorithm for trajectory optimization problems in a single-UAV-assisted MEC system. [ABSTRACT FROM AUTHOR]

The advancements in positioning technologies have led to the emergence of various location-based services, resulting in a drastic increase in location-based data generation, producing big-data. Location data are often linked with user privacy, as they can reveal sensitive information such as the places visited by a person. Moreover, most location-based services involve resource-constrained devices, needing lightweight data processing approaches. Due to these reasons, privacy and efficiency have been two of the primary components of location-based data processing. The existing approaches do not study both issues in the same setting. Consequently, current methods fail to provide efficient privacy preservation solutions towards location-based data stream processing. To address these issues, we investigate the effective integration of edge computing, cloud computing and differential privacy for location-based data clustering, which is an essential area in service recommendations (e.g. recommending the closest hotels to a particular location). In the proposed setup, we use local differential privacy to ensure user privacy. Next, we apply edge-based clustering on the differentially private input data using mobile edge devices. Next, the centroids of the clusters are collected at a cloud server to generate final clustering in a privacy-preserving manner. Our experiments show that the proposed approach provides maximum accuracy of 90% on lower privacy budgets (e.g. ɛ = 0.45-0.5). • Location data stream clustering requires advanced computing and storage capabilities. • Edge–cloud interplay can provide a potential solution to this problem. • The privacy of location data is another major issue that limits the effective use of cloud resources. • It is essential to look at both efficiency and privacy of location data stream clustering. • An efficient distributed edge–cloud scenario for location data clustering with differential privacy is proposed. [ABSTRACT FROM AUTHOR]

Recently, great progress has been witnessed in the application of mobile cloud computing in the field of health care such as online medical inquiries. However, due to the limitation of cognitive intelligence, QoE (Quality of Experience) is hampered by two problems, the first of which is that the traffic pressure of the core network cannot well meet the requirements of delay-sensitive emotional services, especially for users with different emergencies, while the second is that current applications cannot provide personalized service for different users. Based on the two problems, we propose an emotion-aware mobile edge computing architecture based on emotional task priority to guide the allocation of edge resources and to provide intelligent and personalized emotional services with higher QoE. Specifically, we first introduce the entities involved in the proposed architecture of emotion-aware mobile edge computing system. Next, we describe our optimal computing resource allocation strategy, including important concepts and a detailed algorithm. Finally, we build a test platform and conduct experiments, which show that the proposed architecture obtains better performance in terms of system utility compared with baseline methods. [ABSTRACT FROM AUTHOR]

• A taxonomy discussing application execution strategies in fog computing. • A comprehensive discussion about components of application execution in fog computing. • Critical analysis providing in detail information about key aspects of application execution strategies. • Identifying research gaps and highlight new research directions which needs to be addressed in the future. With tremendous advancements in smart phone industry and IoT devices, edge computing has emerged to provide computational services at edge of the network. As a result, applications can be executed in a distributive manner to meet the latency requirements of applications. Despite these advancements, these devices are still considered to have limited energy, memory and computational resources compared to stationary standalone devices. Mobile edge computing extends mobile cloud computing close to end-users, decreasing latency and bandwidth utilisation by offloading tasks to edge servers. Fog computing places nodes between cloud and edge servers. A challenging approach in these paradigms is establishing offloading decision to edge/fog server, and distributed application execution at run time for heterogeneous devices. This paper reviews execution of resource-intensive tasks on mobile devices in distributed frameworks. The objective is to identify research gaps and highlight new research directions that can be pursued in future. Application execution involves application partitioning, task allocation, task execution and task resumption. In this paper, we propose a separate taxonomy for each step in application execution, reviews state-of-the-art techniques, and analyses critical aspects of application execution in a distributed manner. It further investigates similarities and differences among these techniques based on each step of application execution. [ABSTRACT FROM AUTHOR]

In dealing with emergency logistics, it is essential to use regional and central distribution hubs efficiently, and local and international sources of supply for automotive relief supplies are analyzed in this research. Resource scheduling algorithms employed by service providers to supply and assign resources in an environment are collectively referred to as resource scheduling. Cloud computing uses computational resources pooled and made available across a network, including information storage (cloud) and processing capabilities, requiring user participation in its deployment, operation, and maintenance. The challenging characteristics of such emergency logistics resource scheduling are inconsistencies in tracking, empty miles, and delivery delays. Hence, Emergency Supplier Distribution Mobile Edge Computing (ESD-MEC) research has been designed to improve emergency logistics resource scheduling algorithms in cloud computing. With the abovementioned requirements, ESD may address the distributed scheduling issue of vehicle emergency logistics resources. In particular, the MEC employs a specialized negotiating system to manage the scheduling of resources in impacted regions using an agent-based approach to ESD management in light of the need to do so. The proposed technology helps the decision-maker schedule resources in a dynamic environment and addresses supply demands. An ESD-MEC effectively predicts the rise in emergency logistics resources with faster vehicle strategies in cloud computing. The research concludes that ESD-MEC effectively indicates emergency logistics resource scheduling in cloud computing. The experimental analysis of vehicle logistics outperforms the method in terms of performance, accuracy, prediction ratio, mean square error, and efficiency ratio. [ABSTRACT FROM AUTHOR]

• IoT system modeling is necessary to find eligible deployments of offloading systems. • IoT systems modeling helps to drive the resource and performance optimizations. • IoT systems modeling alleviate limitations such as device resource constraints. • IoT and computing systems integration support emerging IoT application requirements. The continuous increase of IoT applications leads to a vast amount of data that needs to be transmitted, stored, and processed. Many IoT applications rely on the Cloud infrastructure to handle these specific application demands. However, the integration of IoT and Cloud poses challenges such as network delays, throughput, energy consumption, reliability, etc. Therefore, a new computing concept is required to support emerging IoT applications. These new concepts include fog computing, edge computing, mobile edge computing, mobile cloud computing, and cloudlets. They use various approaches to distribute resources, processes, and services among IoT system architecture layers. The challenge is to decide which offloading system is the best for a specific use case that emphasizes the IoT system modeling issue. In this paper, a model for the formal description of IoT systems is presented. In addition, an analytical evaluation method was proposed to design these systems using the corresponding architecture, technologies, protocols, and integration model to optimize performance. The proposed approach facilitates and simplifies the selection of the corresponding model for the system architecture. This approach enables an efficient method for performance optimization based on offloading processes (load balancing). Also, this paper provides some insights into specific emerging issues and ideas to be addressed by future research. [ABSTRACT FROM AUTHOR]

The purposeful acts of bioterrorism and the emergence of new pathogens call for developing efficient biosurveillance systems that are capable of detecting health security threats and localizing their origins. For this sake, several biosurveillance systems have been developed and deployed in different regions around the world. The objectives of these systems are to efficiently detect threats and effectively manage responses. Although modern biosurveillance systems start to leverage recent advances in internet-of-things and communication technologies to detect natural biological outbreaks, it is not evident whether these systems can be generalized to detect threats related to purposeful biological attacks. As opposed to natural disease outbreaks, biological attacks can be triggered concurrently at different locations and in vital areas that are traversed by a large number of humans. In this paper, we propose an efficient framework to detect multiple concurrent threats and localize their origins based on real-time information and mobile-edge computing support. The proposed framework is organized into a hierarchical architecture. At the micro-level, monitored humans are equipped with wearable sensors that periodically capture the monitored human vital signs. To process the captured information efficiently, the monitored environment is partitioned into cells. Each cell is assigned a mobile-edge computing (MEC) component that is responsible for processing the monitoring information within its cell. The MEC components then send the processed information to a super component for final processing and to detect possible threats. The experimental results show that the proposed framework accurately detects multiple concurrent health security threats and localize their origins. [ABSTRACT FROM AUTHOR]

The increasing availability of Internet of Things (IoT) applications has led to the development of new technologies. Specifically, the deployment of edge servers close to IoT devices has strengthened the edge computing paradigm. With the collaboration of Mobile Edge Computing (MEC) and cloud computing, delay-sensitive and computation-intensive tasks can be offloaded to the edge/cloud servers to improve system performance in terms of the delay and energy consumption of IoT devices. However, there is a need to schedule the computation tasks for an efficient management. More importantly, the task scheduling strategy can face data tampering attacks to deliberately modify, destroy or manipulate the decisions. To solve the above problems, in this paper, we newly propose to integrate digital twin and blockchain into the edge networks. However, it is unclear (i) how energy and delay-aware computation should be combined, and (ii) which mining computations should be executed for a secure task scheduling. The state-of-the-art focuses on task scheduling and blockchain mining, separately. Therefore, we propose a novel blockchain-based digital twin-edge network architecture where our proposed algorithm solves these two challenges at the same execution. We design a three-layer system architecture, composed of physical entity layer, digital twin edge layer and blockchain layer. In the physical entity layer, we formulate an energy and delay-aware task scheduling problem. In the digital twin edge layer, we propose a novel Proof of Evaluation (PoE)-based secure energy and delay-aware task scheduling algorithm where optimization is executed by the genetic algorithm implementation of Warehouse Location Problem (WLP). In the blockchain layer, the best-found solutions are shared with the topology in a blockchain. Here, each block includes the hash of the previous block, a genetic algorithm-based solution, nonce value, and a hash of whole blocks in the blockchain. Thus, we aim to execute the computation tasks with an acceptable delay in an energy-efficient manner and prevent data tampering attacks against the optimal computation decisions. We validate the outcomes of our PoE-based secure digital twin-edge network model with extensive evaluations. Since the proposed model distributes the task not only to the local device but also to the MEC and cloud server for delay awareness, it reduces the delay but consumes more energy. Nevertheless, the additional energy consumption can be neglected against the delay reduction. The proposed scheme is also more scalable to compare with the conventional solution. The numerical results clearly show that the proposed model provides energy and delay awareness, maintaining both data integrity and trustworthiness at the same execution of algorithm. [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]

• Cloudlet is an important part of providing cloud services in Mobile Edge Computing (MEC) with sustainability. • The cloudlet is providing cloud services with low latency. Hence the task allocation and scheduling inside of the cloudlet is a challenging job. • Heuristic load balancing strategy, which minimizes task completion time and makespan enhances efficiency of cloud services. • The dynamic task allocation strategy is proposed, this approach is compared with the queue-based task allocation strategy. • The proposed method outperforms the standard algorithm in most of the observed cases with sustainability. Cloudlet is an important part of providing cloud services in Mobile Edge Computing (MEC) with sustainability. As the number of mobile users grows rapidly in the current era, the load in the cloudlet becomes very high. The cloudlet is considered in the middle layer for providing cloud services with low latency and energy efficiency. Hence the task allocation and scheduling inside of the cloudlet is a challenging job. In the recent past, many research works conducted without considering real-time network parameters. In this work, a heuristic load balancing strategy is designed and analyzed to minimize the task completion time and makespan, which enhances the efficiency of cloud services. The proposed method is considered a dynamic task allocation strategy to the cloudlet concerning network bandwidth, network delay, energy efficiency, and this approach is compared with the queue-based task allocation strategy. The experiment is conducted to compare the proposed method with the recently developed standard algorithms over the synthetic dataset. Experimental results of the proposed work show a significant improvement in task scheduling in terms of energy and execution cost, as well as time, compared to the existing methods. The proposed method outperforms the standard algorithm in most of the observed cases with sustainability. [ABSTRACT FROM AUTHOR]

This paper provides a detailed description of a new simulator, PFogSim, developed to facilitate testing of fog computing configurations of various sizes with fog nodes distributed over large geographical areas and interconnected over one or more network links. PFogSim allows for a multi-layered fog environment design as opposed to existing single or two-layered designs. Furthermore, it allows fog nodes and network links of different capacities, thus enabling realistic, network configurations to be tested. PFogSim fills the void in which simulators for very large, practical fog networks are lacking. © 2021 Elsevier Science. All rights reserved. • Proposed and implemented the PFogSim simulator for large-scale heterogeneous fog computing environments. • Simulates large-scale multilayered hierarchical fog environments having continuous mobility of devices, users, fog nodes. • Simulates cloud/edge/mobile computing deployments by restricting the number of fog layers and node resource configurations. • Newer version has features such as, 3D locations for devices/users/nodes, their energy consumption modeling, etc. [ABSTRACT FROM AUTHOR]

Mobile Edge Computing (MEC) enables smart mobile devices to handle computationally intensive tasks with limited resources. By offloading tasks to the edge of the network closer to mobile devices, mobile devices can provide users with intelligent services with shorter latency and lower energy consumption than cloud computing. while mobile edge computing is superior in terms of reliability and low latency, it is still not a complete replacement for cloud computing. In order to make full use of the large amount of network resources of cloud computing as well as to take advantage of parallel computing, this paper investigates the partial offloading of multiple mobile devices in the cloud-side collaboration scenario. Through ​​game theory, we first obtain the edge-to-terminal optimal offloading decision, then find the cloud-edge optimal offloading decisions for all terminal devices that choose to offload to the edge, and finally reach the goal of optimal offloading on the three sides of the cloud-edge-terminal. Based on this idea, we propose a two-stage computing offloading algorithm (TSGTA). The whole algorithm is divided into two stages, and both stages are designed to offload algorithms based on game theory. The simulation results show that under the time-delay constraint, this method has an average improvement of 32% compared with the traditional method. [ABSTRACT FROM AUTHOR]

Abstract 5G networks are anticipated to provide service for vertical industries. Mobile Edge Computing (MEC) framework is a physical infrastructure improvement to host mobile applications for the introduction of new service provisioning in 5G networks. Application Orchestration is responsible for scheduling user sessions to application instances. In this paper, we propose four scheduling algorithms to assign sessions in a system with multiple MEC clouds. Results show that scheduling based on the occupancy level of MECs is quite robust in various scenarios and can be applied with the fractional guard channel policy to provide service. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) tsunami, public embracement, and the ubiquitous adoption of smart devices are affecting virtually every industry, directly or indirectly. The success of the current and future landscape of IoT and connected devices requires service provision characterized by scalability, ubiquity, reliability, and high-performance, among others. In order to achieve this attribution, the integration of IoT and Cloud Computing (CC), known as cloud IoT, has emerged as a new paradigm providing advanced services specific to aggregating, storing, and processing data generated by IoT. While the convergence of IoT and Cloud brings opportunities, it suffers from specific limitations such as bandwidth, latency, and connectivity. The increasing need for supporting interaction between cloud and IoT led to Edge and Fog Computing (FC) in which computing and storage resources are located not only in the cloud but also at the edges near the source of data. The hierarchical and collaborative edge–fog–cloud architecture brings tremendous benefits as it enables us to distribute the intelligence and computation – including Artificial Intelligence (AI), Machine Learning (ML), and big data analytics – to achieve an optimal solution while satisfying the given constraints e.g., delay-energy tradeoff. Due to the hierarchical, cross-layer, and distributed nature of this model, achieving an osmotic and effective convergence of IoT, edge, fog, and cloud computing requires overcoming many challenges with respect to design and implementation, as well as deployment and evaluation. This paper provides a comprehensive insight into the edge-fog-cloud computing paradigm by providing a blend of discussions on all important aspects of the underlying technologies to offer opportunities for more holistic studies and to accelerate knowledge acquisition. To gain a deep understanding of edge–fog–cloud, we will begin this paper by providing an in-depth tutorial and presenting the main requirements, state-of-the-art reference architectures, building blocks, components, protocols, applications, and other similar computing paradigms, including their similarities and differences. Following this, a holistic reference architecture for edge–fog–cloud IoT is presented and the major corresponding design and deployment considerations (e.g., service models, infrastructure design, provisioning, resource allocation, offloading, service migration, performance evaluation, and security concerns) are discussed. Next, we will take a look at the role of privacy-preserving, distributed, and collaborative analytics as well as the interaction between edge, fog, and cloud computing. Finally, we will overview the main challenges in the field of edge–fog–cloud computing that need to be tackled to realize the full potential of IoT. • IoT architectures: SDN, Fog Computing, Multi-Access Edge Computing, Cloudlet, Transparent Computing, Extreme Edge, etc. • Edge–fog–cloud Interplay: benefits, challenges, and reference architecture. • Task offloading. • CNN-assisted ECG-based heart anomaly detection with RL-learning task offloading. [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]