Fog Cloud Research Articles

Optimal cross-layer resource allocation in fog computing: A market-based framework

The potential upsides of fog computing are undeniable, including better near-real-time response, reduced transmission costs and analysis for IoT, which will further have a significant impact on operation of factories, enterprises, organizations and even the quality of our life. However, due to the resource-constrained nature, heterogeneity and distributed nodes, how to manage the extraordinarily complex fog computing resources is necessary to improve the QoE of users. This paper investigates the rational resource allocation for fog computing from the cross-layer point of perspective, and formulates the resource allocation scenario as Cloud–Fog–User market which is composed of cloud center devices, fog nodes, and user devices. Firstly, the resource market is divided into Cloud–Fog market containing cloud layer and fog layer, and Fog–User market containing fog layer and user layer based on different scenarios separately. The paper proposes a market-based framework for fog computing networks, which enables the cloud layer and the fog layer to allocate their resources in the form of pricing, payment and supply–demand relationship, while analyzes the relationship among resource allocation participants in the market. Then the paper investigates the utility optimization models from the viewpoints of both the participants of respective markets, so as to optimize optimal payment and optimal resource allocation via convex optimization techniques, and proposes a gradient-based iterative algorithm to optimize the utilities, respectively. Finally, experimental results have displayed the performance of convex analysis results and effectiveness of the proposed algorithm.

A Methodology and Simulation-Based Toolchain for Estimating Deployment Performance of Smart Collective Services at the Edge

Research trends are pushing artificial intelligence (AI) across the Internet of Things (IoT)–edge–fog–cloud continuum to enable effective data analytics, decision making, as well as the efficient use of resources for QoS targets. Approaches for collective adaptive systems (CASs) engineering, such as aggregate computing, provide declarative programming models and tools for dealing with the uncertainty and the complexity that may arise from scale, heterogeneity, and dynamicity. Crucially, aggregate computing architecture allows for “pulverization”: applications can be decomposed into many deployable micromodules that can be spread across the ICT infrastructure, thus allowing multiple potential deployment configurations for the same application logic. This article studies the deployment architecture of aggregate-based edge services and its implications in terms of performance and cost. The goal is to provide methodological guidelines and a model-based toolchain for the generation and simulation-based evaluation of potential deployments. First, we address this subject methodologically by proposing an approach based on deployment code generators and a simulation phase whose obtained solutions are assessed with respect to their performance and costs. We then tailor this approach to aggregate computing applications deployed onto an IoT–edge–fog–cloud infrastructure, and we develop a corresponding toolchain based on Protelis and EdgeCloudSim. Finally, we evaluate the approach and tools through a case study of edge multimedia streaming, where the edge ecosystem exhibits intelligence by self-organizing into clusters to promote load balancing in large-scale dynamic settings.

Ice fog observed at cirrus temperatures at Dome C, Antarctic Plateau

Abstract. As the near-surface atmosphere over the Antarctic Plateau is cold and pristine, its physico-chemical conditions resemble to a certain extent those of the high troposphere where cirrus clouds form. In this paper, we carry out an observational analysis of two shallow fog clouds forming in situ at cirrus temperatures – that is, temperatures lower than 235 K – at Dome C, inner Antarctic Plateau. The combination of lidar profiles with temperature and humidity measurements from advanced thermo-hygrometers along a 45 m mast makes it possible to characterise the formation and development of the fog. High supersaturations with respect to ice are observed before the initiation of fog, and the values attained suggest that the nucleation process at play is the homogeneous freezing of solution aerosol droplets. This is the first time that in situ observations show that this nucleation pathway can be at the origin of an ice fog. Once nucleation occurs, the relative humidity gradually decreases down to subsaturated values with respect to ice in a few hours, owing to vapour deposition onto ice crystals and turbulent mixing. The development of fog is tightly coupled with the dynamics of the boundary layer which, in the first study case, experiences a weak diurnal cycle, while in the second case, it transits from a very stable to a weakly stable dynamical regime. Overall, this paper highlights the potential of the site of Dome C for carrying out observational studies of very cold cloud microphysical processes in natural conditions and using in situ ground-based instruments.

On the Efficient Delivery and Storage of IoT Data in Edge-Fog-Cloud Environments.

Cloud storage has become a keystone for organizations to manage large volumes of data produced by sensors at the edge as well as information produced by deep and machine learning applications. Nevertheless, the latency produced by geographic distributed systems deployed on any of the edge, the fog, or the cloud, leads to delays that are observed by end-users in the form of high response times. In this paper, we present an efficient scheme for the management and storage of Internet of Thing (IoT) data in edge–fog–cloud environments. In our proposal, entities called data containers are coupled, in a logical manner, with nano/microservices deployed on any of the edge, the fog, or the cloud. The data containers implement a hierarchical cache file system including storage levels such as in-memory, file system, and cloud services for transparently managing the input/output data operations produced by nano/microservices (e.g., a sensor hub collecting data from sensors at the edge or machine learning applications processing data at the edge). Data containers are interconnected through a secure and efficient content delivery network, which transparently and automatically performs the continuous delivery of data through the edge–fog–cloud. A prototype of our proposed scheme was implemented and evaluated in a case study based on the management of electrocardiogram sensor data. The obtained results reveal the suitability and efficiency of the proposed scheme.

Reliability Evaluation of a Cloud–Fog Computing Network Considering Transmission Mechanisms

A cloud–fog computing system is modeled as a network topology where each edge represents a transmission line, and each node represents a hub, an Internet of Things (IoT) device, a fog server, or a cloud server. Such a system with several capacities at each edge is called a stochastic-flow cloud–fog computing network (SCCN). There are three phases of transmitting data: the first is to transmit data from the IoT devices to the fog servers. The second and third phases are transmitting a part of the data from the fog servers to the cloud servers for the further operations and back to the IoT devices for the immediate operations, respectively. Based on the transmission mechanisms, two demands, including initial demands and processed demands, should be satisfied simultaneously, where an initial demand (a processed demand) is outing from an IoT device (a fog server). An algorithm is developed to evaluate reliability, the probability of successfully transmitting the data through the SCCN, by elucidating the flow relationship among the IoT devices, edge servers, and cloud servers. Furthermore, another approach to adjust the flow assignment is also proposed for the case that demand may not be an integer. A large real case is provided to validate the applicability and scalability of the proposed methodology.

An approach for data integrity authentication and protection in fog computing

The data integrity verification process in cloud has become more promising research area in several Internet of Things (IoT) applications. The traditional data verification approaches use encryption in order to preserve data. Moreover, fog computing is considered as extensively employed virtualized platform and it affords various services including storage as well as services interconnected to computing and networking between user and data center based on standard cloud computing. Moreover, fog computing is an extensive description of cloud computing. Thus, fog servers effectively decrease the latency by integrating fog servers. In this paper, novel model for data integrity authentication and protection is designed in IoT cloud-fog model. This method mainly comprises fog nodes, cloud server, IoT nodes, and key distribution center. Here, dynamic and secure key is produced based on the request to key distribution center based on hashing, Exclusive OR (XOR), homomorphic encryption and polynomial. The fog nodes are employed to encrypt the data gathered from IoT nodes as well as allocate the nearby nodes based on Artificial Bee Colony-based Fuzzy-C-Means (ABC FCM) – based partitioning approach. The proposed data integrity authentication approach in IoT fog cloud system outperformed than other existing methods with respect to detection rate, computational time and memory usage of 0.8541, 34.25 s, and 54.8 MB, respectively.

TPEL: Task possible execution level for effective scheduling in fog–cloud environment

TPEL: Task possible execution level for effective scheduling in fog–cloud environment

Hybrid heuristic algorithm for cost-efficient QoS aware task scheduling in fog–cloud environment

Fog cloud computing promotes the combination of fog and cloud nodes to satisfy the requests of data accessing from IoT (Internet of Things) devices. To reduce the data delay and enhancing the QoS (Quality of Service), effective task scheduling is one of the major requirements in fog cloud environment. Numerous approaches have been deployed by the researchers for maintaining the QoS requirements. However, the emergence of bursty traffic affects the process of task scheduling due to high service latency. Effective QoS is promoted by minimizing the costs of computation, communication and deadline violation cost. To achieve the main objective of cost minimization, the proposed work is implemented by adopting a novel model called HFSGA (Hybrid Flamingo Search with a Genetic Algorithm) for better task scheduling. Seven basic benchmark optimization test functions are used to compare the performance of HFSGA with other well-known algorithms. Also, Friedman Rank Test is performed to demonstrate the significance of the results. The implemented model presents better outcomes with respect to PDST (Percentage of deadline satisfied task), makespan and cost. When compared to the existing algorithms that include Ant colony optimization (ACO), Particle swarm optimization (PSO), Genetic Algorithm (GA), Min-CCV, Min-V and Round Robin (RR) approach, the proposed work shows better output in satisfying the task scheduling process.

A Decentralized Location-Based Reputation Management System in the IoT Using Blockchain

The Internet of Things (IoT) allows an object to connect to the Internet and observe or interact with a physical phenomenon. The communication technologies allow one IoT device to discover and communicate with another in order to exchange services, in a similar way to what humans do in their social networks. Knowing the reputation of another device is important to consider whether it is trustworthy before establishing a new connection and thus, avoid possible unexpected behaviors as a consequence. Trustworthiness, as a property of a device, can be affected by different factors including its geographical location. Hence, this research work proposes an architecture to manage reputation values of end devices in an IoT system based on the area where they are located. A cloud–fog–edge architecture is proposed, where the fog layer uses the Blockchain technology to keep the reputation management system consistent and fault tolerant across different nodes. The location-based part of the system was done by storing geographical areas in smart contracts (coined as geospatial smart contracts) and making the reputation values subject to different regions depending on the geographical location of the device. To reduce the complexity of the spatial computation, the geographical data are geocoded by either one of two different spatial indexing techniques. This work also introduced two different structures for storing geocoded areas based on either cell list or tree structure. Finally, three experiments to test the proposed architecture are presented, to deploy the architecture in IoT devices, and to compare the two geocoding techniques in smart contracts.

Resource placement strategy optimization for IoT oriented monitoring application

Cloud computing and the low power wide area network (LPWAN) network represent the key infrastructures for developing intelligent solutions based on the internet of things (IoT). However, the diversity of use cases and deployment scenarios of IoT in the different domains makes optimizing IoT-based cloud solutions a major challenge. The cloud solution’s cost increases with the increase in central processing unit (CPU) resources and energy consumption. The optimal use of edge material resources in industrial solutions will reduce the consumption of resources and thus optimize cloud infrastructure costs in terms of resources and energy consumption. The article presents the network and application architecture of an IoT monitoring solution based on cloud services. Then, we study the integration of IoT services based on application placement strategies on the fog cloud compared to the traditional centralized cloud strategy. Simulations evaluate the scenarios with the iFogSim simulator and the analyzed results compare the traditional strategy with the cloud-fog. The results show that cost and energy consumption in the cloud can be significantly reduced by processing the application at the end devices level with respect to the possible limit of CPU processing power for each IoT end device. Latency and network usage respect quality of service constraints in cloud-fog placement for this type of monitoring-oriented IoT application.

A Cloud–Fog Based Adaptive Framework for Optimal Scheduling of Energy Hubs

In the presence of energy storage in energy hubs, it is possible to efficiently coordinate energy supply and demand while taking advantage of changing energy tariffs and local renewable energy supply sources. This article proposes a novel method based on Grey wolf optimization (GWO) which breaks down the complex optimization problem into sub problems, including the storage scheduling and another part in the grid, and comes up with the respective solutions via GWO and interior-point. This methodology avoids the drawbacks of numerical ways and artificial intelligence techniques, which tend to converge only to a local minimum or require heavy computation times. Furthermore, the paper proposes a multilayer cloud computing framework for optimally scheduling energy hubs. The present study proves the approach can achieve very near global point, which is confirmed through an analytical method, as well as being rapid sufficient to permit for online implementations with receding time horizons.

Optimizing deadline violation time and energy consumption of IoT jobs in fog–cloud computing

Optimizing deadline violation time and energy consumption of IoT jobs in fog–cloud computing

A Novel Low-Latency and Energy-Efficient Task Scheduling Framework for Internet of Medical Things in an Edge Fog Cloud System.

In healthcare, there are rapid emergency response systems that necessitate real-time actions where speed and efficiency are critical; this may suffer as a result of cloud latency because of the delay caused by the cloud. Therefore, fog computing is utilized in real-time healthcare applications. There are still limitations in response time, latency, and energy consumption. Thus, a proper fog computing architecture and good task scheduling algorithms should be developed to minimize these limitations. In this study, an Energy-Efficient Internet of Medical Things to Fog Interoperability of Task Scheduling (EEIoMT) framework is proposed. This framework schedules tasks in an efficient way by ensuring that critical tasks are executed in the shortest possible time within their deadline while balancing energy consumption when processing other tasks. In our architecture, Electrocardiogram (ECG) sensors are used to monitor heart health at home in a smart city. ECG sensors send the sensed data continuously to the ESP32 microcontroller through Bluetooth (BLE) for analysis. ESP32 is also linked to the fog scheduler via Wi-Fi to send the results data of the analysis (tasks). The appropriate fog node is carefully selected to execute the task by giving each node a special weight, which is formulated on the basis of the expected amount of energy consumed and latency in executing this task and choosing the node with the lowest weight. Simulations were performed in iFogSim2. The simulation outcomes show that the suggested framework has a superior performance in reducing the usage of energy, latency, and network utilization when weighed against CHTM, LBS, and FNPA models.

Energy-Efficient Real-Time Heart Monitoring on Edge–Fog–Cloud Internet of Medical Things

The recent developments in wearable devices and the Internet of Medical Things (IoMT) allow real-time monitoring and recording of electrocardiogram (ECG) signals. However, continuous monitoring of ECG signals is challenging in low-power wearable devices due to energy and memory constraints. Therefore, in this article, we present a novel and energy-efficient methodology for continuously monitoring the heart for low-power wearable devices. The proposed methodology is composed of three different layers: 1) a noise/artifact detection layer to grade the quality of the ECG signals; 2) a normal/abnormal beat classification layer to detect the anomalies in the ECG signals; and 3) an abnormal beat classification layer to detect diseases from ECG signals. Moreover, a distributed multioutput convolutional neural network (CNN) architecture is used to decrease the energy consumption and latency between the edge–fog/cloud. Our methodology reaches an accuracy of 99.2% on the well-known MIT-BIH Arrhythmia Data Set. Evaluation on real hardware shows that our methodology is suitable for devices having a minimum RAM of 32 kb. Moreover, the proposed methodology achieves <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$7\times $ </tex-math></inline-formula> more energy efficiency compared to state-of-the-art works.

GA-IRACE: Genetic Algorithm-Based Improved Resource Aware Cost-Efficient Scheduler for Cloud Fog Computing Environment

The ever-growing number of Internet of Things (IoT) devices increases the amount of data produced on daily basis. To handle such a massive amount of data, cloud computing provides storage, processing, and analytical services. Besides this, real-time applications, i.e., online gaming, smart traffic management, and smart healthcare, cannot tolerate the high latency and bandwidth consumption. The fog computing paradigm brings the cloud services closer to the network edge to provide quality of service (QoS) to such applications. However, efficient task scheduling becomes critical for improving the performance due to the heterogeneous nature, resource-constrained, and distributed environment of fog resources. With an efficient task scheduling algorithm, the response time to application requests can be reduced along with bandwidth and cloud resource costs. This paper presents a genetic algorithm-based solution to find an efficient scheduling approach for mapping application modules in a cloud fog computing environment. Our proposed solution is based on the execution time as a fitness function to determine an efficient module scheduling on the available fog devices. The proposed approach has been evaluated and compared against baseline algorithms in terms of execution time, monetary cost, and bandwidth. Comprehensive simulation results show that the proposed approach offers a better scheduling strategy than the existing scheduler.

Real-Time Task Scheduling Algorithm for IoT-Based Applications in the Cloud–Fog Environment

IoT applications have become a pillar for enhancing the quality of life. However, the increasing amount of data generated by IoT devices places pressure on the resources of traditional cloud data centers. This prevents cloud data centers from fulfilling the requirements of IoT applications, particularly delay-sensitive applications. Fog computing is a relatively recent computing paradigm that extends cloud resources to the edge of the network. However, task scheduling in this computing paradigm is still a challenge. In this study, a semidynamic real-time task scheduling algorithm is proposed for bag-of-tasks applications in the cloud–fog environment. The proposed scheduling algorithm formulates task scheduling as a permutation-based optimization problem. A modified version of the genetic algorithm is used to provide different permutations for arrived tasks at each scheduling round. Then, the tasks are assigned, in the order defined by the best permutation, to a virtual machine, which has sufficient resources and achieves the minimum expected execution time. A conducted optimality study reveals that the proposed algorithm has a comparative performance with respect to the optimal solution. Additionally, the proposed algorithm is compared with first fit, best fit, the genetic algorithm, and the bees life algorithm in terms of makespan, total execution time, failure rate, average delay time, and elapsed run time. The experimental results show the superiority of the proposed algorithm over the other algorithms. Moreover, the proposed algorithm achieves a good balance between the makespan and the total execution cost and minimizes the task failure rate compared to the other algorithms.Graphical

Blockchain based context-aware CP-ABE schema for Internet of Medical Things security

Nowadays, the number of corona patients is increasing significantly. The relationship between the Internet of Medical Things (IoMT) and the Internet is struggling to keep up with this number of patients. The transmission of Patient Health Records (PHR) to the care of a patient through Internet plays an important role in the remote monitoring and fast detection of new contaminated patient with coronavirus. Moreover, it has generated significant security and privacy concerns for the global health care system due to tampering of control messages. This paper focuses on the application of blockchain and smart contract mechanisms to solve the shortcomings of the current health application and propose a new security schema based on context-aware CP-ABE. The proposed schema includes context-aware policies to achieve a robust authentication of identity and confidentiality of patient's healthcare data. Therefore, the proposed schema shows promising results in enhancing security and minimizing encryption time in Fog cloud environments based on proxy-fog and reinforcement of security policies.

Optimization of Fuel Cell Stack Consistency Based on Multi-Model

With the proposal of cloud computing, fog computing, and edge computing, various simulation operations are greatly guaranteed, which benefits the multi-model operations of Matlab and CFD. This paper established the 1-D flow network model and 12 cm ∗ 8 cm 3-steady-state PEMFC model. Based on the experiment, the intake flow distribution of the cathode anode of 80 cells is simulated to obtain the maximum and minimum intake flow cell. The 3-D and steady-state single-cell model is used to calculate the cell’s performance, and the performance difference between the two cells is improved by optimizing the size structure of the single cell. The results show that the best version of the cell was obtained when the values of the width and depth were 1.1 mm and 0.8 mm, and the power density difference between the two cells decreased from 5.7% to 2.1%. The voltage difference at 1000 mA·cm−2 current density decreases from 0.065 to 0.035 V after optimization. The intake flow extreme difference of the reactor improved significantly, and C v was reduced by 48.7%.

An Intelligent Healthcare Cyber Physical Framework for Encephalitis Diagnosis Based on Information Fusion and Soft-Computing Techniques.

Viral encephalitis is a contagious disease that causes life insecurity and is considered one of the major health concerns worldwide. It causes inflammation of the brain and, if left untreated, can have persistent effects on the central nervous system. Conspicuously, this paper proposes an intelligent cyber-physical healthcare framework based on the IoT–fog–cloud collaborative network, employing soft-computing technology and information fusion. The proposed framework uses IoT-based sensors, electronic medical records, and user devices for data acquisition. The fog layer, composed of numerous nodes, processes the most specific encephalitis symptom-related data to classify possible encephalitis cases in real time to issue an alarm when a significant health emergency occurs. Furthermore, the cloud layer involves a multi-step data processing scheme for in-depth data analysis. First, data obtained across multiple data generation sources are fused to obtain a more consistent, accurate, and reliable feature set. Data preprocessing and feature selection techniques are applied to the fused data for dimensionality reduction over the cloud computing platform. An adaptive neuro-fuzzy inference system is applied in the cloud to determine the risk of a disease and classify the results into one of four categories: no risk, probable risk, low risk, and acute risk. Moreover, the alerts are generated and sent to the stakeholders based on the risk factor. Finally, the computed results are stored in the cloud database for future use. For validation purposes, various experiments are performed using real-time datasets. The analysis results performed on the fog and cloud layers show higher performance than the existing models. Future research will focus on the resource allocation in the cloud layer while considering various security aspects to improve the utility of the proposed work.

Pangea: An MLOps Tool for Automatically Generating Infrastructure and Deploying Analytic Pipelines in Edge, Fog and Cloud Layers.

Development and operations (DevOps), artificial intelligence (AI), big data and edge–fog–cloud are disruptive technologies that may produce a radical transformation of the industry. Nevertheless, there are still major challenges to efficiently applying them in order to optimise productivity. Some of them are addressed in this article, concretely, with respect to the adequate management of information technology (IT) infrastructures for automated analysis processes in critical fields such as the mining industry. In this area, this paper presents a tool called Pangea aimed at automatically generating suitable execution environments for deploying analytic pipelines. These pipelines are decomposed into various steps to execute each one in the most suitable environment (edge, fog, cloud or on-premise) minimising latency and optimising the use of both hardware and software resources. Pangea is focused in three distinct objectives: (1) generating the required infrastructure if it does not previously exist; (2) provisioning it with the necessary requirements to run the pipelines (i.e., configuring each host operative system and software, install dependencies and download the code to execute); and (3) deploying the pipelines. In order to facilitate the use of the architecture, a representational state transfer application programming interface (REST API) is defined to interact with it. Therefore, in turn, a web client is proposed. Finally, it is worth noting that in addition to the production mode, a local development environment can be generated for testing and benchmarking purposes.