Fog Systems Research Articles

VeriFog: A Generic Model-based Approach for Verifying Fog Systems at Design Time and Generating Deployment Configurations

Fog Computing is a paradigm decentralizing the Cloud by geographically distributing computation, storage, network resources and related services. It provides benefits such as reducing the number of bottlenecks, limiting unwanted data movements, etc. However, managing the size, complexity and heterogeneity of the Fog systems to be engineered is challenging and can quickly become costly. According to best practices in software engineering, verification tasks could be performed on a system design prior to its implementation and deployment. We propose a generic model-based approach for verifying Fog systems at design time, also enabling the automatic generation of corresponding deployment configuration files. Named VeriFog, this approach is notably based on a customizable Fog Modeling Language (FML). We experimented in practice by modeling three use cases, from three different application domains, and by considering three main types of non-functional properties to be verified. From this modeling and verification effort, we show that we are able to automatically generate usable deployment configuration files for different deployment tools. In direct collaboration with our industrial partner Smile, the approach and underlying language presented in this paper are necessary steps towards a more global model-based support for the complete life cycle of Fog systems.

Resource allocation of industry 4.0 micro-service applications across serverless fog federation

The Industry 4.0 revolution has been made possible via AI-based applications (e.g., for automation and maintenance) deployed on the serverless edge (aka fog) computing platforms at the industrial sites—where the data is generated. Nevertheless, fulfilling the fault-intolerant and real-time constraints of Industry 4.0 applications on resource-limited fog systems in remote industrial sites (e.g., offshore oil fields) that are uncertain, disaster-prone, and have no cloud access is challenging. It is this challenge that our research aims at addressing. We consider the inelastic nature of the fog systems, software architecture of the industrial applications (micro-service-based versus monolithic), and scarcity of human experts in remote sites. To enable cloud-like elasticity, our approach is to dynamically and seamlessly (i.e., without human intervention) federate nearby fog systems. Then, we develop serverless resource allocation solutions that are cognizant of the applications’ software architecture, their latency requirements, and distributed nature of the underlying infrastructure. We propose methods to seamlessly and optimally partition micro-service-based application across the federated fog. Our experimental evaluation express that not only the elasticity is overcome in a serverless manner, but also our developed application partitioning method can serve around 20% more tasks on-time than the existing methods in the literature.

Autonomic IoT Application Placement in Edge/Fog Computing

Edge/Fog computing recently emerged as a novel distributed virtualized computing paradigm, where cloud services are extended to the edge of the network, thereby increasing network capacity and reducing latencies. In fog computing, applications are composed of microservices that are mapped to edge computing and communication devices (fog nodes). A crucial component in fog computing is placement algorithms that assign microservices to fog nodes since they determine the overall system performance in terms of energy consumption, communication costs, load balancing, and others. Placement strategies devised for cloud computing are generally centralized and, therefore, not well suited for decentralized fog systems. In this paper, we consider the joint optimization of two conflicting objectives, energy consumption at fog nodes and communication costs of applications, as a game between fog nodes and applications where each agent is modeled to control one objective. We follow a Markov approximation method for the design of a fully distributed autonomic service placement strategy without central coordination or global state information. Evaluation results show that the new approach provides a more optimal solution as compared to previous autonomic placement algorithms.

Optimization of End-to-End AoI in Edge-Enabled Vehicular Fog Systems: A Dueling-DQN Approach

Optimization of End-to-End AoI in Edge-Enabled Vehicular Fog Systems: A Dueling-DQN Approach

Investigation of dynamic wind speed impacts on radiative transmittance and reflectance in the ocean-sea fog system

Frequent fog and dynamic wind speeds in the ocean significantly impact the radiation transmittance and reflectance on the sea surface, substantially challenging remote sensing and target detection in marine environments. Establishing a model to study the radiative transfer more accurately in ocean-sea fog systems under dynamic wind speed conditions is crucial. Here, a multichannel Monte Carlo (Mc-MC) model is introduced to investigate radiative transfer in ocean-fog systems, which is more realistic and has greater potential than the traditional MC (Tra-MC) approach. Addressing the challenges of dynamic wind speed, this paper introduces two specialized dynamic wind speed models. One prioritized the accurate simulation of changing sea surface wind speeds, and we investigated the transmittance and reflectance of six regions in the Pacific and Atlantic Oceans under this model. The other is proposed for investigating the effect of dynamic wind speed on radiative transfer. The experimental results indicate that an increase in the wind speed dynamic factor accelerates the rate of wind speed changes, thereby intensifying the instability of the radiative transfer transmittance and reflectance. In addition, the light around both the 1 µm and 2 µm wavelengths exhibit high transmittance, but the light surrounding 1 µm has relatively weaker stability compared to that surrounding 2 µm. These investigations provide valuable insights for infrared remote sensing, target detection, and the development of light sources suitable for marine applications.

Security Issues in Service Model of Fog Computing Environment

Fog computing is an innovative way to expand the cloud platform by providing computing resources. The platform is a cloud that has the same data, management, storage and application features, but their origins are different because they are deployed to different locations. The platform system can retrieve a large amount, work in the field, be fully loaded, and mount on a variety of hardware devices. With this utility, Fog Framework is perfect for applications and critical moments. Fog computing is similar to cloud computing, but because of its variability, creates new security and privacy challenges that go beyond what is common for fog nodes. This paper aims to understand the impact of security problems and how to overcome them, and to provide future safety guidance for those responsible for building, upgrading and maintaining fog systems.

A Validated Performance Model for Micro-services Placement in Fog Systems

A Validated Performance Model for Micro-services Placement in Fog Systems

Resource Provisioning Using Meta-Heuristic Methods for IoT Microservices With Mobility Management

The fog and edge computing paradigm provide a distributed architecture of nodes with processing capability for smart healthcare systems driven by Internet of Thing (IoT) applications. It also provides a method to reduce big data transmissions that cause latency and enhance the system’s efficiency. Resource provisioning and scheduling in edge and fog systems is a significant problem due to heterogeneity and dispersion of edge/fog/cloud resources. The goal of scheduling is to map tasks to appropriate resources, which belong to NP-hard problems, and it takes much time to find an optimal solution. Meta-heuristic methods achieve near-optimal solutions within a reasonable time. Current edge/fog resource allocation research does not sufficiently address resource allocation problems in mobility-aware microservice-based IoT applications. This paper proposes a meta-heuristic-based micro-service resource provisioning model with mobility management for smart healthcare systems. The proposed approach has been tested on an experimental set-up with a simulation of a critical real-time smart healthcare application with and without considering the mobility of the devices. It applies meta-heuristic methods such as modified genetic and flower pollination algorithms for resource management. The proposed method outperforms the existing solutions in energy consumption, network usage, cost, execution time, and latency by 17%, 20%, 22%, 17%, and 63%, respectively.

Offloading Using Traditional Optimization and Machine Learning in Federated Cloud–Edge–Fog Systems: A Survey

The huge amount of data generated by the Internet of things (IoT) devices needs the computational power and storage capacity provided by cloud, edge, and fog computing paradigms. Each of these computing paradigms has its own pros and cons. Cloud computing provides enhanced data storage and computing power but causes high communication latency. Edge and fog computing provide similar services with lower latency but limited capacity, capability, and coverage. A single computing paradigm cannot fulfill all the requirements of IoT devices and a federation between them is needed to extend their capacity, capability, and services. This federation is beneficial to both subscribers and providers and also reveals research issues in traffic offloading between clouds, edges, and fogs. Optimization has traditionally been used to solve the problem of traffic offloading. However, in such a complex federated system, traditional optimization cannot keep up with the strict latency requirements of decision-making, ranging from milliseconds to sub-seconds. Machine learning approaches, especially reinforcement learning, are consequently becoming popular because they could quickly solve offloading problems in dynamic environments with some unknown information. This study provides a novel federal classification between cloud, edge, and fog and presents a comprehensive research roadmap on offloading for different federated scenarios. We survey the relevant literature on the various optimization approaches used to solve this offloading problem and compare their salient features. We then provide a comprehensive survey on offloading in federated systems with machine learning approaches and the lessons learned as a result of these surveys. Finally, we outline several directions for future research and challenges that have to be faced in order to achieve such a federation.

Blockchain-enabled cooperative computing strategy for resource sharing in fog networks

Several heterogeneous network designs have been developed to fulfill user experience needs, driven by widely growing applications needing large data processing, such as Industry 4.0. Fog computing is one of them since it allows for the efficient integration and exploitation of ubiquitous computing resources. Willingness and service billing concerns become important in fog computing situations for computing resource sharing. The present fog systems, on the other hand, are vulnerable to malicious attacks. The integration of blockchain technology into the fog computing environment is characterized by successfully allowing consensus in an untrustworthy environment. However, creating public blockchains requires a lot of processing power, which can quickly deplete the computational resources of IoT-enabled Smart Machines (SM) in Industry 4.0. This study suggests partitioning the fog system into fog clusters (FC), with fog nodes (FN) in each cluster sharing the same access control list (ACL) that is protected by a public blockchain. Therefore, Blockchain-enabled Resource Sharing inside SM in Fog networks (B-RSSF), a novel architecture for computing resource sharing in fog networks is proposed. B-RSSF has specified physical architecture, design principles, and inner workings. To make full and effective use of ubiquitous computing resources, fog computing’s wireless features and blockchain technology are tightly integrated. In addition, an enhanced PoW consensus mechanism for reaching consensus in an untrustworthy fog environment has been discussed. Finally, the experimental results have been conducted to evaluate the performance of the proposed scheme over other existing schemes in a fog-based environment.

A new service recommendation method for agricultural industries in the fog-based Internet of Things environment using a hybrid meta-heuristic algorithm

Regardless of public perceptions of the agricultural techniques, the fact is that the current agriculture industry is more data-driven, accurate, and intelligent than before. Therefore, novel technologies such as fog computing and the Internet of Things (IoT) can provide a flexible and real-time platform to meet the data-driven requirements of the current agricultural decision-makers. Smart agriculture is a new idea since IoT sensors and fog platforms can provide information about agriculture and then operate on them based on user feedback. Also, with the rapid growth of IoT, the importance of recommender systems is increased in this domain. Therefore, the main goals of this study are to improve the accuracy of agricultural service recommendations and decrease the Mean Absolute Error (MAE) in the IoT-based fog systems using collaborative filtering and artificial Artificial Bee Colony (ABC). However, many of the current methods suffer from low accuracy of recommendations of the agricultural services. The present article suggested a collaborative filtering-based approach based on the ABC and genetic operators to design an effective recommender scheme in fog-based IoT systems. The results showed that the accuracy and MAE are optimized compared to some state-of-the-art methods. They also revealed that compared to other methods, the proposed method improves ranking score by 5.8 %, precision by 5%, recall by 5.7%, intra similarity by 13%, and hamming distance by 4.8%.

Ephemeral data handling in microservices with Tquery.

The adoption of edge and fog systems, along with the introduction of privacy-preserving regulations, compel the usage of tools for expressing complex data queries in an ephemeral way. That is, queried data should not persist. Database engines partially address this need, as they provide domain-specific languages for querying data. Unfortunately, using a database in an ephemeral setting has inessential issues related to throughput bottlenecks, scalability, dependency management, and security (e.g., query injection). Moreover, databases can impose specific data structures and data formats, which can hinder the development of microservice architectures that integrate heterogeneous systems and handle semi-structured data. In this article, we present Jolie/Tquery, the first query framework designed for ephemeral data handling in microservices. Jolie/Tquery joins the benefits of a technology-agnostic, microservice-oriented programming language, Jolie, and of one of the most widely-used query languages for semi-structured data in microservices, the MongoDB aggregation framework. To make Jolie/Tquery reliable for the users, we follow a cleanroom software engineering process. First, we define Tquery, a theory for querying semi-structured data compatible with Jolie and inspired by a consistent variant of the key operators of the MongoDB aggregation framework. Then, we describe how we implemented Jolie/Tquery following Tquery and how the Jolie type system naturally captures the syntax of Tquery and helps to preserve its invariants. To both illustrate Tquery and Jolie/Tquery, we present the use case of a medical algorithm and build our way to a microservice that implements it using Jolie/Tquery. Finally, we report microbenchmarks that validate the expectation that, in the ephemeral case, using Jolie/Tquery outperforms using an external database (MongoDB, specifically).

Secure and Efficient Semantic Extension Search over Encrypted Documents by Integrating Cloud and Fog Systems

With the explosive increase of document files, more and more data owners outsource their documents to the public cloud, which can decrease the costs of local data management systems. However, the problem of information privacy leakage in the cloud is a great challenge and it has been attracting more and more attention. In this article, we propose a secure and efficient document search scheme, named SES, based on both the cloud and fog systems. All the documents are symmetrically encrypted before being outsourced to the cloud, and an index vector is constructed based on the keywords for each document. Specifically, we integrate the position information of keywords into the TF-IDF model to generate document vectors, which are accurate and inherent summarizations about the documents. In query requests, a data user needs to provide a set of keywords, which are first extended by the Word2Vec tool and then mapped to a query vector. The extension process of keywords makes the provided keywords more comprehensive and accurate, and hence, it improves document search accuracy. To achieve the forward and backward security, both the document vectors and query vectors are appended with an ingenious vector. The relevance score between a document and a query is defined as the inner product of the document vector and the query vector. We return the most k relevant documents as the search results to the data users. To protect the contextual information stored in the document and query vectors, we encrypt the vectors by the secure kNN algorithm. To improve the search efficiency, a searchable index structure for the document set is constructed based on the Diffie–Hellman secret key negotiation algorithm. The analysis and simulation results illustrate that the proposed scheme performs well in terms of both security and search efficiency.

Securing Fog Computing with a Decentralised User Authentication Approach Based on Blockchain.

The use of low-cost sensors in IoT over high-cost devices has been considered less expensive. However, these low-cost sensors have their own limitations such as the accuracy, quality, and reliability of the data collected. Fog computing offers solutions to those limitations; nevertheless, owning to its intrinsic distributed architecture, it faces challenges in the form of security of fog devices, secure authentication and privacy. Blockchain technology has been utilised to offer solutions for the authentication and security challenges in fog systems. This paper proposes an authentication system that utilises the characteristics and advantages of blockchain and smart contracts to authenticate users securely. The implemented system uses the email address, username, Ethereum address, password and data from a biometric reader to register and authenticate users. Experiments showed that the proposed method is secure and achieved performance improvement when compared to existing methods. The comparison of results with state-of-the-art showed that the proposed authentication system consumed up to 30% fewer resources in transaction and execution cost; however, there was an increase of up to 30% in miner fees.

클라우드 환경에서 블록체인을 이용한 포그 기반 IoT 서비스 상호운용 시스템

사물 클라우드(CoT, Cloud of Things)는 사물인터넷(IoT, Internet of Things) 애플리케이션에 클라우드 서비스가 지원하는 무제한 저장기능과 처리능력을 제공할 수 있다. 그러나, 중앙 집중식 사물 클라우드에서는 병목 문제, 사물 클라우드 네트워크의 중단으로 이어질 수 있는 단일 장애 지점을 발생시킬 수 있다. 본 논문에서는 중앙 집중식 사물 클라우드의 문제를 해결하고 서로 다른 서비스 도메인 간 상호운용을 위하여, 분산 포그 컴퓨팅과 블록체인 기술을 적용한 IoT 서비스 상호운용 시스템을 제안한다. 분산 포그를 사용하여 IoT 장치와 지역적으로 가까운 거리에 위치한 포그 시스템에서 실시간 데이터 처리 및 서비스를 제공하고, 블록체인의 스마트 컨트렉트와 분산 원장을 이용하여 각 포그간에 서비스 상호운용이 가능하도록 한다. 제안 시스템은 클라우드로부터 서비스를 위임받은 분산 포그에서 가까운 지역 내 서비스를 제공하며, 포그 간에도 클라우드를 거치지 않고, 다른 포그의 서비스를 접근할 수 있다. 또한, 블록체인 네트워크상에서 클라우드와 포그 노드들은 서비스 권한 토큰 발행 정보를 공유함으로써 토큰에 대한 무결성을 보장하고 포그 노드들 간 신뢰할 수 있는 서비스 상호운용이 수행될 수 있다.Cloud of Things (CoT) can provide IoT applications with unlimited storage functions and processing power supported by cloud services. However, in a centralized cloud of things, it can create a single point of failure that can lead to bottleneck problems, outages of the CoT network. In this paper, to solve the problem of centralized cloud of things and interoperate between different service domains, we propose an IoT service interoperability system using distributed fog computing and blockchain technology. Distributed fog is used to provide real-time data processing and services in fog systems located at a geographically close distance to IoT devices, and to enable service interoperability between each fog using smart contracts and distributed ledgers of the blockchain. The proposed system provides services within a region close to the distributed fog entrusted with the service from the cloud, and it is possible to access the services of other fogs without going through the cloud even between fogs. In addition, by sharing a service right token issuance information between the cloud and fog nodes using a blockchain network, the integrity of the token can be guaranteed and reliable service interoperability between fog nodes can be performed.

Towards Building Cyberphysical Systems with Agile Architecture

The current stage of technology development is characterized by an increase in the complexity of the created anthropogenic systems, a constant expansion of the scope of information technologies, an increase in the intelligence level of the created systems, and the appearance of new paradigms for building information-oriented systems such as cyber-physical systems, the Internet of things, and cloud and fog systems. Modern information-oriented systems very often have dynamic structure, implement complex adaptive behavior, and can be considered as systems with agile architecture. The article discusses one of the possible approaches for building cyberphysical systems with agile architecture on fog platforms. The idea of the proposed approach is to accumulate knowledge about the current state of the observed cyberphysical systems in the form of knowledge graphs. As a model, it is proposed to use multilevel relatively finite state operating automaton at the upper level and knowledge graphs at the lower level. A distinctive feature of the developed approach is that models that describe the current state of the observed system can be built automatically.

A Strategy-Based Formal Approach for Fog Systems Analysis

Fog systems are a new emergent technology having a wide range of architectures and pronounced needs making their design complex. Consequently, the design of fog systems is crucial, including service portability and interoperability between the various elements of a system being the most essential aspects of fog computing. This article presents a fog system cross-layer architecture as a first step of such a design to provide a graphical and conceptual description. Then, a BiAgents* (Bigraphical Agents) formal model is defined to provide a rigorous description of physical, virtual, and behavioural aspects of Fog systems. Besides, this formalisation is implemented and executed under a Maude strategy system. The proposed approach is illustrated through a case study: an airport terminal Luggage Inspection System (LIS) while checking the correctness of its relevant properties: the portability of data and their interoperability. The integration of the Maude strategies in the rewriting of Fog system states made it possible to guide the execution of the model and its analysis.

SIMAD: Secure Intelligent Method for IoT-Fog Environments Attacks Detection

The Internet of Thing IoT paradigm has emerged in numerous domains and it has achieved an exponential progress. Nevertheless, alongside this advancement, IoT networks are facing an ever-increasing rate of security risks because of the continuous and rapid changes in network environments. In order to overcome these security challenges, the fog system has delivered a powerful environment that provides additional resources for a more improved data security. However, because of the emerging of various breaches, several attacks are ceaselessly emerging in IoT and Fog environment. Consequently, the new emerging applications in IoT-Fog environment still require novel, distributed, and intelligent security models, controls, and decisions. In addition, the ever-evolving hacking techniques and methods and the expanded risks surfaces have demonstrated the importance of attacks detection systems. This proves that even advanced solutions face difficulties in discovering and recognizing these small variations of attacks. In fact, to address the above problems, Artificial Intelligence (AI) methods could be applied on the millions of terabytes of collected information to enhance and optimize the processes of IoT and fog systems. In this respect, this research is designed to adopt a new security scheme supported by an advanced machine learning algorithm to ensure an intelligent distributed attacks detection and a monitoring process that detects malicious attacks and updates threats signature databases in IoT-Fog environments. We evaluated the performance of our distributed approach with the application of certain machine learning mechanisms. The experiments show that the proposed scheme, applied with the Random Forest (RF) is more efficient and provides better accuracy (99.50%), better scalability, and lower false alert rates. In this regard, the distribution character of our method brings about faster detection and better learning.

Effects of altitude on soil properties in coastal fog ecosystems in Morro Moreno National Park, Antofagasta, Chile

AbstractRare Pacific coast fog ecosystems are under threat from climate change and local factors. Although coastal fog is known to affect soil properties and microbial diversity, few studies on the Pacific coast have examined the specific microbiomes associated with these ecosystems. We evaluated the effects of coastal fog on the physicochemical, mineralogical, and microbiological properties of bare soils (vegetation‐free) and bulk soils (surrounding plant roots) from different altitudes in the Morro Moreno National Park (MMNP) in the Antofagasta region, Chile. We found that the temperature and relative humidity of the soil varied temporally (daily) and spatially (with altitude). We detected that soil organic matter and organic phosphorus content tended to increase with altitude, whereas the pH, electrical conductivity, and total phosphorus decreased. Our results showed that coastal fog could induce physical weathering below 300 masl and chemical weathering at the intermediate altitudes of ~400–600 masl. The biodiversity of bacteria and fungi increased considerably above 400 masl. Actinobacteria and Bacteroidetes dominated the bacteria in bare soil, and Bacteroidetes dominated the bulk soil communities at all altitudes. Ascomycota and Basidiomycota dominated the fungal community in both soil types. Moreover, the electrical conductivity and CaO content appear to be more closely associated with microbial communities from lower altitudes. In addition, the organic carbon content, humidity, and weathering index 2 (WI2) isolated some communities at 500 and 600 masl. The microbiological diversity reported in this work reflects the variable and different microbial niches comprising the MMNP environment. Linkages between soil property and microbial variations with altitude within this Northern Chilean coastal fog ecosystem were elucidated. This novel scientific knowledge contributes to global network strategies for fog ecosystem conservation, which aim to preserve the microbial niches and diversity in such soils.Highlights We studied physicochemical, mineralogical and microbiological properties in a fog ecosystem soils. The study provides valuable information from rare and unique Pacific coast fog systems. Soil properties and microbial diversity showed specific correlations, and dependence on altitude. This baseline could contribute to the creation of conservation strategies of these unique ecosystems.

The Universal Fog Proxy: A Third-party Authentication Solution for Federated Fog Systems with Multiple Protocols

Fog computing is suitable for latency constrained applications useful to end users and IoT devices in smart cities, factories, and homes. A federation among fogs is beneficial for subscribers and providers in terms of enhanced capability, capacity, coverage, and services. To realize such a federation, a third-party authentication mechanism among fog providers is required, so that a subscriber of a fog can access the services provided by the other fogs without having to create new accounts. In this article, we propose a transparent and standard-compliant universal fog proxy that provides third-party authentication among OpenID Connect (OIDC), 802.1x, and Protocol for Carrying Authentication for Network Access (PANA) without requiring a new protocol. The proxy consists of virtual counterparts of the entities involved in these protocols so that it provides transparency. For example, when a fog using OIDC receives an authentication request, the proxy relays and behaves as a virtual Identity Provider (vIdP) for the fog using OIDC and a virtual supplicant for the fog using 802.1x. We applied our solution to nine scenarios across OIDC, 802.1x, and PANA. Experimental results show that the proxy takes 4–52 percent of the total authentication time of 0.128-3.504s for nine scenarios, with a larger percentage in scenarios involving OIDC due to multiple re-directions among virtual components. The scenarios involving 802.1x take a considerably lon-ger time, though a low percentage (4–12 percent) by the proxy, as the spanning tree protocol in an 802.1x switch takes about one second to converge when adding a new device to the network.