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In the IoT-based systems, the fog computing allows the fog nodes to offload and process tasks requested from IoT-enabled devices in a distributed manner instead of the centralized cloud servers to reduce the response delay. However, achieving such a benefit is still challenging in the systems with high rate of requests, which imply long queues of tasks in the fog nodes, thus exposing probably an inefficiency in terms of latency to offload the tasks. In addition, a complicated heterogeneous degree in the fog environment introduces an additional issue that many of single fogs can not process heavy tasks due to lack of available resources or limited computing capabilities. To cope with the situation, this article introduces FRATO (Fog Resource aware Adaptive Task Offloading) - a framework for the IoT-fog-cloud systems to offer the minimal service provisioning delay through an adaptive task offloading mechanism. Fundamentally, FRATO is based on the fog resource to select flexibly the optimal offloading policy, which in particular includes a collaborative task offloading solution based on the data fragment concept. In addition, two distributed fog resource allocation algorithms, namely TPRA and MaxRU are developed to deploy the optimized offloading solutions efficiently in cases of resource competition. Through the extensive simulation analysis, the FRATO-based service provisioning approaches show potential advantages in reducing the average delay significantly in the systems with high rate of service requests and heterogeneous fog environment compared with the existing solutions.

Fog computing is used to process a large amount of data produced by terminal devices where fog nodes are the closest acquirers to the terminal devices. In fog computing, the processed data may be tampered with or illegally captured by some malicious nodes while the data is transferred or stored. So, when some applications need to require data process with high security, fog computing must provide a security approach to secure and check the final results. In this paper, we propose a secure data sequence query framework based on multiple fog servers, where we use multiple fog servers to store and provide data hashing values to verify corresponding data sequence. In the proposed scheme, the cloud server needs to pre-designate some Merkle hashing tree topologies to the fog network, then the fog server directly acquires related data from leaf-nodes (fog nodes) according to one of the pre-designated Merkle hashing tree topologies; at the same time, an actual Merkle hashing tree is constructed according to the selected Merkle hashing tree topology in the fog network. In fog computing, the related fog nodes participate in computing and transferring the hashing values on data, then the corresponding root node uses the (t; n) threshold secret sharing scheme to compute and distribute the sub-secrets of the root value to other fog servers. Therefore, when a user requests a data sequence from a fog server, the fog server may immediately respond to the request and the other fog servers can provide the sub-secrets of hashing root value to verify the corresponding data sequence. Our proposed framework can ensure that users may obtain required data fast, accurately and reliably. Additionally, based on the data query security requirements of fog computing, we analyze the security of our proposed scheme. Our proposed scheme not only guarantees the reliability of data but also effectively protects data against various attacks.

Internet of Things (IoT) systems are expected to generate a massive amount of data that needs to be processed. Given the large scale and geo-distributed nature of such systems, fog computing along with publish/subscribe (pub/sub) messaging has been proposed as possible solutions for coping with processing at scale. However, it is still unclear how practitioners can leverage the benefits of fog computing, e.g., how to optimally place data processing operators and pub/sub brokers. Moreover, current IoT systems typically rely on pub/sub brokers at the cloud, which might diminish the benefits offered by edge or fog processing as the communication between IoT operators has to be mediated by the brokers located in the cloud. To address this shortcoming, we propose to place the IoT application operators and the pub/sub brokers jointly on a network of nodes spanning from edge to the cloud considering various factors such as network topology or the locations of the IoT sensors and the consumers of the IoT applications. Different than the prior works, we specifically consider pub/sub brokers and their unique characteristics in the placement decision. First, we formulate the placement of operators and brokers jointly across edge, fog, and the cloud as a cost minimization problem. Next, we design two low-complexity heuristics. Our simulation results corroborate the argument that a placement in the cloud is usually a good option for IoT use cases, but also reveal the gap to the optimal solution in scenarios with heavier clustering of producers and consumers of sensor data. Studying the optimality gap shows that in such a setting heuristic solutions usually stay under a stretch factor of 2, with a worst case factor of 2.5 for a tabu-based solution and 2.85 for a greedy and a fixed placement in the cloud.

Network delays cause disturbance and reduction in the Quality-of-Service (QoS) for Internet-of-Things (IoT) while end-users are running critical real-time services. In parallel, federated fogs are not effective when formed without considering the performance perceived by the end-users. This article presents a novel architecture for the federated fog concept and proposes an adaptive and intelligent federation formation approach using Genetic Algorithm and Machine Learning models. Fog federations serve as a solution for fog providers to offer the required QoS they serve. Such a concept allows efficient distribution of load among multiple fog providers that share their resources. Throughout this process, the issue of QoS deterioration, due to local overloads, is relatively solved. Hence, the end users can enjoy a delay-free experience when using real-time applications. Real data is used to evaluate the proposed architecture and formation mechanism. The results show a notable improvement in the throughput as well as a decrease in the response time for the services requested.

We propose a time-of-flight measurement algorithm for depth and intensity that is robust to fog. The key idea of the algorithm is to compensate for the scattering effects of fog by using multiple time-gating and assigning one time-gated exposure for scattering property estimation. Once the property is estimated, the depth and intensity can be reconstructed from the rest of the exposures via a physics-based model. Several experiments with artificial fog show that our method can measure depth and intensity irrespective of the traits of the fog. We also confirm the effectiveness of our method in real fog through an outdoor experiment.

A microgrid is a flexible and localized grid that can disconnect from the main grid and operate independently on the traditional and/or renewable resource. It also can work with the main grid, and even provide power to the main grid when it has excess power to help relieve the pressure of energy. The flow of power between main grid and microgrid is monitored by smart meter, which kept the end user’s privacy information and his/her energy usage in real time. Hence, ensuring the privacy and security of these information is a considerable challenge. In this paper, we investigate the relationship between microgrids and the main grid, and design a quantum-resistant lightweight authentication and key agreement (AKA) protocol based on fog computing. The proposed protocol provides the mutual authentication and generates a session key between a smart meter and a fog node. More importantly, it can resist against quantum computing attacks, as the security of our scheme relies on the hardness of the learning parity with noise (LPN) problem, an NP-complete problem. Finally, we evaluate the performance of our proposed protocol and prove that our protocol is more lightweight and efficient.

Internet of things (IoT) is one of key pillars in fifth generation (5G) and beyond 5G (B5G) networks. It is estimated to have 42 billion IoT devices by the year 2025. Currently, carbon emissions and electronic waste (e-waste) are significant challenges in the information & communication technologies (ICT) sector. The aim of this article is to provide insights on green IoT (GIoT) applications, practices, awareness, and challenges to a generalist of wireless communications. We garner various efficient enablers, architectures, environmental impacts, technologies, energy models, and strategies, so that a reader can find a wider range of GIoT knowledge. In this article, various energy efficient hardware design principles, data-centers, and software based data traffic management techniques are discussed as enablers of GIoTs. Energy models of IoT devices are presented in terms of data communication, actuation process, static power dissipation and generated power by harvesting techniques for optimal power budgeting. In addition, this article presents various effective behavioral change models and strategies to create awareness about energy conservation among users and service providers of IoTs. Fog/Edge computing offers a platform that extends cloud services at the edge of network and hence reduces latency, alleviates power consumption, offers improved mobility, bandwidth, data privacy, and security. Therefore, we present the energy consumption model of a fog-based service under various scenarios. Problems related to ever increasing data in IoT networks can be solved by integrating artificial intelligence (AI) along with machine learning (ML) models in IoT networks. Therefore, this article provides insights on role of the ML in the GIoT. We also present how legislative policies support adoption of recycling process by various stakeholders. In addition, this article is presenting future research goals towards energy efficient hardware design principles and a need of coordination between policy makers, IoT devices manufacturers along with service providers.

This paper proposes a novel and efficient real time network architecture, named RT-TelSurg, for one of the most appealing tactile Internet applications, i.e., Telesurgery. In telesurgery, the patient’s vital signs and status and the required robotic commands during the surgery should be received on time. Otherwise, the life of the patient or the safety of the operation is endangered. Hence, transmitted packets should meet their respective relative deadlines. Software-defined networking is a relatively new architecture for computer and telecommunications networks in which the network control plane is separated from the data plane. One way to achieve real time telesurgery is to employ cloud and fog networks using SDN as infrastructure. By using a real time cloud controller in the SDN as the core and a fog controller on the edge of the network on the master (physician) side, one may satisfy the acceptable level of the timing constraints of the telesurgery. Accordingly, based on the presented architecture, we develop the statistical performance model of our proposed approach, RT-TelSurg. This statistical queuing theory model is designed for critical and non-critical states of the network. Based on these states, network resources are allocated to telesurgery data in both modes, and a computational bottleneck is detected. RT-TelSurg is evaluated according to real time and surgery efficiency parameters. The results show that the average deadline hit ratio is 98.2% in different conditions, which is quite acceptable for telesurgery applications.

In this paper, we provide the first software-based implementation of multiple-input multiple-output (MIMO) free space optical (FSO) link with the adaptive switching based on the software defined radio developed by GNU Radio software system, which emulates the real-time capability of the proposed scheme. We propose a switching mechanism to independently configure each transmitter and receiver, based on the channel state information provided at the transmitter via a feedback link and evaluate the link performance under atmospheric conditions such as fog and turbulence. We also validate the advantages of mitigating both the turbulence and fog in the proposed multiple input multiple output FSO system by means of numerical simulations and the developed GNU Radio software platform. We demonstrate that the proposed system can effectively operate in heavy fog with bit error rates (BER) within the range of 10−8 to $\sim 10^{-7}$ over link spans of 100, 200 and 300 m. Additionally the system has been shown to mitigate moderate turbulence over a 100 m link span with BER of $\sim 10^{-7}$ .

Este trabajo proporciona una visión general del paradigma Internet de las Cosas (IoT) apuntando a las aplicaciones, arquitecturas, protocolos, tecnologías, y problemas pendientes y oportunidades para la investigación, tal como puede encontrarse en literatura reciente. El trabajo comienza proveyendo marcos históricos y conceptuales breves. A continuación, se profundiza en temas teóricos como las soluciones verticales, arquitecturas propuestas, protocolos específicos y tecnologías comerciales. También se revisan problemas concernientes al marco legal, todavía por desarrollarse. El postulado básico de IoT es la colaboración entre sensores inteligentes para realizar tareas innovadoras sin intervención humana. Sin embargo, avances recientes indican que serán posibles aplicaciones más potentes combinando IoT con cierto grado de inteligencia, previamente reservado a la nube. El objetivo principal de este informe es proporcionar un marco de trabajo inicial que permita a los investigadores iniciarse rápidamente en la materia, y al mismo tiempo, enfatizar la importancia de la identificación y desarrollo de aplicaciones. El trabajo concluye recomendando especial atención al modelado de sistemas IoT, a los protocolos de aplicación de tiempo real, y a la computación fog.

The Galapagos Archipelago is a semiarid zone scarce of superficial water resources. Due to its geographic location and the presence of oceanic currents, these islands are covered by fog during seven months along the year which could represent a potential source of water supply. In this study, the feasibility of fog collection for domestic rural use and irrigation was investigated using two Standard Fog Collectors (SFC) of 50% and 35% shade coefficient of 1 m2 and a cylindrical fog net (CFN) of 0.15 m2 at 600 m of elevation in the windward side of San Cristobal Island (Galapagos). The methodology applied consists in the quantification of the monthly and annual water balance at different altitudes in an average and dry years, using the orographic gradients of rainfall, evapotranspiration and cloud water interception. The latter was used to estimate the fog water availability to satisfy any water deficit. Fog water interception was estimated using a geometric model and the use of different climatic variables. On the other hand, water demand in the rural zone for domestic consumption and livestock was estimated with the quota method, and the water demand for agriculture was estimated through the water balance. Results show that the amount of fog captured reaches 7.9, 5.9 and 3.4 mm/d with 50-SFC, 35-SFC and CFN, respectively. Comparing these results with other locations in the world, fog water collection in Galapagos is above the average. A fog gauge system of 50-SFC was designed at different elevations to cover the 25% and 15% of the water deficit in the average and dry year, respectively.

The growth of IoT is proven with the massive amount of data generated in 2015, and expected to be even more in the years to come. Relying on the cloud to meet the expanding volume, variety, and velocity of data that the IoT generates may not be feasible. In the last two years, fog computing has become a considerably important research topic in an attempt to reduce the burden on cloud and solve cloud's inability to meet the IoT latency requirement. However, fog environment is different than in cloud since fog environment is far more distributed. Due to the dynamic nature of fog, backups such as redundant power supply would deem unnecessary, and relying on just one Internet Service Provider for their fog device would be sufficient. If obstacles arise in this fog environment, factors such as latency, availability or reliability would in turn be unstable. Fogs become harder to trust, and this issue is more complicated and challenging in comparison to the conventional cloud. This implies that trustworthiness in fog is an imperative issue that needs to be addressed. With the help of a broker, managing trust in a distributive environment can be tackled. Acting as an intermediary, a broker helps in facilitating negotiation between two parties. Although the brokering concept has been around for a long time and is widely used in the cloud, it is a new concept in fog computing. As of late, there are several research studies that incorporates broker in fog where these brokers focus towards pricing management. However to the best of our knowledge there is no literature on broker-based trust evaluation in fog service allocation. This is the first work that proposes broker-based trust evaluation framework that focuses on identifying a trustworthy fog to fulfi ll the user requests. In this paper, fuzzy logic is used as the basis for the evaluation while considering the availability and cost of fog. We propose Request Matching algorithm to identify a user request, and Fuzzy-based Filtering algorithm to match the request with one of the predefi ned sets created and managed by the broker. In this paper, we present a use case that illustrates how fuzzy logic works in determining the trustworthiness of a fog. Our findings suggest that the algorithms can successfully provide users a trustworthy fog that matches their requirement.

Objective It is unknown how sex affects the prevalence of freezing of gait (FOG). We conducted a systematic review and meta-analysis to establish the sex-specific prevalence of FOG in persons with Parkinson’s disease (PD). In addition, we investigated whether men and women were represented accurately in intervention trials targeting FOG. Methods We queried the EMBASE and PubMed databases and identified 2637 articles. Of these, 16 epidemiological studies were included in the meta-analysis, and 51 intervention studies were included in the comparative analysis. Results In total, 5702 persons were included in the final meta-analysis of epidemiological studies. The pooled estimate of overall FOG prevalence was 43% [95% CI 33–53%]. We found no difference in FOG prevalence between men [44% (34–54%)] and women [42% (31–52%)] with PD. However, women were markedly underrepresented in intervention trials targeting FOG, with an average proportion of only 29.6% of women in trial populations. The percentage of women included in trials was similar across intervention types but differed greatly across geographical regions. Conclusion Sex is not a predictor of FOG. This could aid clinicians in counseling persons with PD about FOG. Importantly, a global effort is needed to include more women into clinical trials. Given the skewed distribution of men and women included in intervention trials targeting FOG, caution might be warranted when extrapolating results from FOG trials to women.

Cloud computing, despite its inherent advantages (e.g., resource efficiency), still faces several challenges. The wide area network used to connect the cloud to end users could cause high latency, which may not be tolerable for some applications, especially Internet-of-Things (IoT) applications. Fog computing can reduce this latency by extending the traditional cloud architecture to the edge of the network and by enabling the deployment of some application components on fog nodes. Application providers use Platform-as-a-Service (PaaS) to provision (i.e., develop, deploy, manage, and orchestrate) applications in cloud. However, existing PaaS solutions (including IoT PaaS) usually focus on cloud and do not enable provisioning of applications with components spanning cloud and fog. Provisioning such applications requires novel functions, such as application graph generation, that are absent from existing PaaS. Furthermore, several functions offered by existing PaaS (e.g., publication/discovery) need to be significantly extended in order to fit in a hybrid cloud/fog environment. In this article, we propose a novel architecture for PaaS for hybrid cloud/fog system. It is IoT use case driven, and its applications’ components are implemented as virtual network functions (VNFs) with execution sequences modeled as graphs with substructures, such as selection and loops. It automates the provisioning of applications with components spanning cloud and fog. In addition, it enables the discovery of existing cloud and fog nodes and generates application graphs. A proof of concept is built based on Cloudify open source. Feasibility is demonstrated by evaluating its performance when the PaaS modules and application components are placed in clouds and fogs in different geographical locations.

With the increasing number of IoT devices, fog computing has emerged, providing processing resources at the edge for the tremendous amount of sensed data and IoT computation. The advantage of the fog gets eliminated if it is not present near IoT devices. Fogs nowadays are pre-configured in specific locations with pre-defined services, which limit their diverse availabilities and dynamic service update. In this paper, we address the aforementioned problem by benefiting from the containerization and micro-service technologies to build our on-demand fog framework with the help of the volunteering devices. Our approach overcomes the current limitations by providing available fog devices with the ability to have services deployed on the fly. Volunteering devices form a resource capacity for building the fog computing infrastructure. Moreover, our framework leverages intelligent container placement scheme that produces efficient volunteers’ selection and distribution of services. An Evolutionary Memetic Algorithm (MA) is elaborated to solve our multi-objective container placement optimization problem. Real life and simulated experiments demonstrate various improvements over existing approaches interpreted by the relevance and efficiency of (1) forming volunteering fog devices near users with maximum time availability and shortest distance, and (2) deploying services on the fly on selected fogs with improved QoS.

Nowadays, several universities and institutions make profit from the information technologies to enhance and develop their educational strategies and attract more learners. Therefore, distance learning (e-learning) and learning-on-the-go are technologies adopted by universities and service providers to afford more flexible education system. In fact, e-learning is gaining popularity worldwide and the number of learners enrolled in on-line courses is growing. This trend is explained mainly by the opportunities provided by Cloud Computing. In the cloud based educational context, the security factor in sharing the educational content is important and poses several security challenges, such as fine-grained access control and security preservation of content learning. Moreover, there is emergence of the new concept of User-Fog-Cloud architecture to bring closer the services to the client. In this paper, a new fog computing e-learning scheme is provided. Specifically, the proposed solution extends learning content from the cloud to the edge of the network. It can improve the efficiency of learning data analysis, reduces the encryption burden in terms of computation cost on user's devices by offloading part of encryption cost to fog servers and provides fine grained access control to learning content by encrypting the course and the exam with different cryptographic techniques like IBBE and CP-ABE. Further, we present a profile matching mechanism that helps teachers to find colleagues within their vicinity in an efficient and secure way. Security analysis shows that our scheme can achieve data confidentiality, fine-grained access control, collusion resistance and unforgeability. Performance evaluations demonstrate the efficiency of our solution, especially in terms of encryption computation costs.

The Internet of Things (IoT) has emerged as a promising paradigm to enhance the living standard of human life by employing varied smart devices including smart phones, smart watches, sensors, on-board units and other networking equipment. However, these devices consume a considerable amount of energy to perform their operations that has a significant impact on the environment, product cost and life of the device. Given this fact, energy-efficient solutions for smart environments have gained great attention from researchers and the industrial community. In this context, a novel fog-based multi-level energy-efficient framework for IoT-enabled smart environments has been proposed. To achieve this, the proposed framework adds additional two layers in the existing IoT-fog-cloud architecture - sensors-based energy-efficient hardware layer and policy layer, to monitor the energy consumption and to enable the energy-aware decision making. Initially, the main sources of energy consumption in an IoT-enabled smart environment are identified. Further, the energy requirements of a device to perform a specific task are estimated. Moreover, the alternative devices to perform the same task using less energy are searched out. Finally, a device or a set of devices, to process the job consuming lower energy while ensuring the job requirements, is selected. To validate the proposed framework, four case studies are considered - smart parking, smart hospital specifically ICU, smart agriculture and smart airport. Simulations are conducted using iFogsim toolkit and results show that a significant amount of energy can be conserved by employing the proposed framework.

The Internet of Things paradigm enables a new set of smart end-user applications. The Cloud-Fog-Mist-Internet of Things infrastructure provides communication, compute, and storage support for these applications. However, this complex, heterogeneous, and distributed landscape requires orchestration and management mechanisms in order to guarantee their proper functioning. One particular factor to manage is the capacity to provide service resilience even in the presence of failures in components of the substrate infrastructure. This research proposes a set of mechanisms to formalize, orchestrate, and embed a batch of service requests for chained Virtual Functions to fulfill the specific requirements of applications while enhancing their availability and ultimately their resilience. In detail, this work introduces a formal grammar to describe customized Service Chains, allowing the definition of replicas for different Virtual Functions, and an Integer Linear Programming model for Virtual Function embedding that prioritizes the use of nodes with higher availability. Additionally, an alternative heuristic is presented to handle more complex scenarios by taking advantage of the multi-tier scenario comprising the Cloud-Fog-Mist-Internet of Things. Simulation results for the embedding mechanisms show that it is possible to increase the resilience of chained Virtual Functions, while balancing the load of the infrastructure nodes.

Internet of Things (IoT) is now serving as a vehicle to a huge number of applications, resulting in innovative and smart solutions in many fields. While providing abundant benefits which improve the quality of our lives, the IoT environment has also created new challenges, especially to users’ privacy. Many IoT applications use location-based services (LBS), where service provider (SP) trust cannot be taken for granted. Sensitive information available to SPs could be used to cause considerable loss or damage to users’ property or even endanger their lives. There are several methods to preserve the privacy of users’ data from SPs but they all suffer from one or more anomalies. This research presents a new method, known as the Swapping of Peers and Fogs (SPF), to protect users’ privacy from SPs. By exploiting the features of fogs and smart dummies, the SPF approach offers remarkable improvements to the level of protection of users’ identity, which can be used to extract personal information. The SPF method does not compromise accuracy and, by using a pair of caches and fogs, provides greater efficiency for applications as compared to the existing approaches. To demonstrate the effectiveness and efficiency of the proposed method, detailed comparisons with current methods are presented via simulations based on different scenarios. Finally, an application of the SPF method to connected street systems in smart cities is also discussed.

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link. Heat recovery from combined sewers has a significant potential for practical renewable energy provision as sources of heat demand and sewer pipes are spread across urban areas. Sewers are continuously recharged with relatively hot wastewater, as well as interacting with heat sources from surrounding air and soil. However, the potential effects of modifying sewage temperature on in-sewer processes have received little attention. The deposition of Fats, Oils and Greases (FOGs) and hydrogen sulphide formation are biochemical processes and are thus influenced by temperature. This paper utilises a case study approach to simulate anticipated temperature reductions in a sewer network due to heat recovery. A laboratory investigation into the formation of FOG deposits at temperatures varying between 5°C and 20°C provided mixed results, with only a weak temperature influence, highlighting the need for more research to fully understand the influence of the wastewater composition as well as temperature on FOG deposit formation. A separate modelling investigation into the formation of hydrogen sulphide when inflow temperature is varied between 5°C and 20°C showed considerable reductions in hydrogen sulphide formation. Hence, heat extraction from sewers could be a promising method for managing some in-sewer processes, combined with traditional methods such as chemical dosing.

Fog (cloud) and drizzle microphysics were measured from July 2015 to September 2016 at the Cloud Physics Observation Site (CPOS), located at a mountainous region in Korea. In total 110 cloud/fog events were observed during this period. In detail, fogs at various stages, i.e., newly generated, developing, or dissipating stage were observed, along with some advected clouds with or without precipitation. Based on elaborate analyses of these extensive observation data, we propose a new visibility parameterization method. First, we compare previously developed visibility parameterization methods. Second, we propose a new visibility parameterization method that can be applied to typical fog and fog with precipitation. Uniquely to this new parameterization, the effective fog (cloud) droplet diameter data are utilized, along with liquid water content and droplet concentration that were utilized in previous studies. Third, we validate the accuracies of the previous and newly proposed visibility parameterization methods. The newly proposed visibility parameterization method is the most accurate (with a critical success index of 0.76), and it exhibits better performance than the direct Mie calculation method regardless of the values of effective droplet diameter.

With the emergence of fog and edge computing, new possibilities arise regarding the data-driven management of citizens’ mobility in smart cities. Internet of Things (IoT) analytics refers to the use of these technologies, data, and analytical models to describe the current status of the city traffic, to predict its evolution over the coming hours, and to make decisions that increase the efficiency of the transportation system. It involves many challenges such as how to deal and manage real and huge amounts of data, and improving security, privacy, scalability, reliability, and quality of services in the cloud and vehicular network. In this paper, we review the state of the art of IoT in intelligent transportation systems (ITS), identify challenges posed by cloud, fog, and edge computing in ITS, and develop a methodology based on agile optimization algorithms for solving a dynamic ride-sharing problem (DRSP) in the context of edge/fog computing. These algorithms allow us to process, in real time, the data gathered from IoT systems in order to optimize automatic decisions in the city transportation system, including: optimizing the vehicle routing, recommending customized transportation modes to the citizens, generating efficient ride-sharing and car-sharing strategies, create optimal charging station for electric vehicles and different services within urban and interurban areas. A numerical example considering a DRSP is provided, in which the potential of employing edge/fog computing, open data, and agile algorithms is illustrated.

Cloud Computing is a well-established paradigm for building service-centric systems. However, ultra-low latency, high bandwidth, security, and real-time analytics are limitations in Cloud Computing when analysing and providing results for a large amount of data. Fog and Edge Computing offer solutions to the limitations of Cloud Computing. The number of agricultural domain applications that use the combination of Cloud, Fog, and Edge is increasing in the last few decades. This article aims to provide a systematic literature review of current works that have been done in Cloud, Fog, and Edge Computing applications in the smart agriculture domain between 2015 and up-to-date. The key objective of this review is to identify all relevant research on new computing paradigms with smart agriculture and propose a new architecture model with the combinations of Cloud–Fog–Edge. Furthermore, it also analyses and examines the agricultural application domains, research approaches, and the application of used combinations. Moreover, this survey discusses the components used in the architecture models and briefly explores the communication protocols used to interact from one layer to another. Finally, the challenges of smart agriculture and future research directions are briefly pointed out in this article.

In the IoT-based systems, the integration of fog computing allows the fog nodes to offload and process tasks requested from IoT-enabled devices in a distributed manner to reduce the response delay. However, achieving such a benefit is still challenging in the heterogeneous fog systems in which long task queues of powerful fogs can contribute to an average long delay of task execution. To handle the conflict of resource request for task processing this paper proposes a distributed fog resource allocation algorithms, namely MaxRU (Maximum Resource Allocation). Through the simulation analysis, MaxRU show potential advantages in reducing the average delay in the heterogeneous fog environment compared with the existing solutions.

Forest fire monitoring is very much needed for protecting the forest from any kind of disaster or anomaly leading to the destruction of the forest. Now, with the advent of Internet of Things (IoT), a good amount of research has been done on energy consumption, coverage, and other issues. These works did not focus on forest fire management. The IoT-enabled environment is made up of low power lossy networks (LLNs). For improving the performance of routing protocol in forest fire management, energy-efficient routing protocol for low power lossy networks (E-RPL) was developed where residual power was used as an objective function towards calculating the rank of the parent node to form the destination-oriented directed acyclic graph (DODAG). The challenge in E-RPL is the scalability of the network resulting in a long end-to-end delay and less packet delivery. Additionally, the energy of sensor nodes increased with different transmission range. So, for obviating the above-mentioned drawbacks in E-RPL, compressed data aggregation and energy-based RPL routing (CAA-ERPL) is proposed. The CAA-ERPL is compared with E-RPL, and the performance is analyzed resulting in reduced packet transfer delay, less energy consumption, and increased packet delivery ratio for 10, 20, 30, 40, and 50 nodes. This has been evaluated using a Contiki Cooja simulator.

We compare conventional intensity imaging against different modes of polarimetric imaging by evaluating the image contrast of images taken in a controlled foggy environment. A small-scale fog chamber has been designed and constructed to create the necessary controlled foggy environment. A division-of-focal-plane camera of linear polarization and a linearly polarized light source has been used for performing the experiments with polarized light. In order to evaluate the image contrast of the different imaging modes, the Michelson contrast of samples of different materials relative to their background has been calculated. The higher the image contrast, the easier it is to detect and segment the targets of interest that are surrounded by fog. It has been quantitatively demonstrated that polarimetric images present an improvement in contrast compared to conventional intensity images in the situations studied. This research was funded by MICINN project PID2020-119484RB-I00, and by AGAUR Grants 2020FI_B1 00185 and 2020FI_B2 00068, supported by the Secretaria d’Universitats i Recerca de la Generalitat de Catalunya and the Fons Social Europeu (FSE).

Industrial winter fogs posed an almost existential threat to filmmaking in Britain during the first decades of the twentieth century, disrupting outdoor or location filming, penetrating studio buildings and delaying production schedules and increasing costs. The problem was exacerbated by many British studios being located in, or on the outskirts of London, a city famous for its ‘pea soupers’. This article, having outlined the nature and extent of the problem, explores the ways in which the British film industry responded to the fog. Some studios ceased operations in the winter, whilst some producers relocated to less meteorologically-challenging climes. Two other responses, however, allowed for year-round production in Britain: the installation of specially designed fog-dispersal plant, and the construction of new studios outside the London ‘fog-zone.’ Using fog to explore the impact of climate on the siting, design and equipping of British film studios, the article is intended to encourage recognition of the ways in which the spaces and infrastructure of film production develop in relation to specific local, regional or national level factors, and stimulate discussion of the relationship between filmmakers, studios and the natural (or, in the case of the London fog, anthropogenic) environments within which they operate.

Fog computing is meant to bring the cloud resource closer to the edge of the Internet so that devices can access the back end services much faster. Additionally, the services hosted at the fogs can be customized to fit the local needs. Because fogs are dispersed throughout the network, each installation will have limited resources. This makes resource management a very critical issue. In this paper, we present a two-step resource management approach for fog computing. The first step decides the allocation of the devices to the fogs. The fogs are allocated in a two-tiered manner. That is, for each device, a home fog and a pool of backup fogs are allocated. In the second step, the requests from the devices are distributed to the allocated fogs or the cloud. Using simulation studies, we compared the performance of the proposed request distribution algorithm against existing ones. The results indicate that our request distribution algorithm outperforms existing ones.

Internet of things (IoT) improves the development and operation of smart electrical grids (SEGs). Overcoming the cloud challenges, 2-tier architecture is replaced by 3-tier one for including a fog computing tier that acts as a bridge between the IoT devices embedded in SEG and cloud. Load forecasting is an essential process for the electrical system operation and planning as it provides intelligence to energy management. This paper completes the electrical load forecasting (ELF) strategy introduced (Rabie et al. in Cluster Comput 22(1):241–270, 2019). ELF consists of two phases which are; (1) data pre-processing Phase (DP2) and (2) load prediction phase (LP2). Both phases can be performed in the cloud tier on the stored data which is sent from fogs to cloud at cloud servers. DP2 aims to perform feature selection and outlier rejection using data mining techniques. The main contribution of this paper focuses on LP2 providing multi-ensemble load prediction (MELP) method which can be learned by the filtered data from DP2 to give fast and accurate predictions. MELP can deal with big electrical data based on Map-Reduce method. It mainly consists of two levels which are; (1) local ensemble level (LEL) in map phase and (2) global ensemble level (GEL) in reduce phase. In LEL, the ensemble classification principle is applied at every device in map phase. In GEL, the perfect and final decision for load prediction is taken in reduce phase based on global judger (GJ) method from many local predictions which are the results of all devices in map phase. The conducted experimental results have shown that the proposed MELP outperforms recent prediction methods in terms of accuracy, precision, recall, F1-measure, and run time. It is concluded that the proposed MELP method can deal with big electrical data. It has a good impact in maximizing system reliability, resilience, and stability as it introduces fast and accurate load predictions.

To enable and support smart environments, a recent ICT trend promotes pushing computation from the remote Cloud as close to data sources as possible, resulting in the emergence of the Fog and Edge computing paradigms. Together with Cloud computing, they represent a stacked architecture, in which raw datasets are first pre-processed locally at the Edge and then vertically offloaded to the Fog and/or the Cloud. However, as hardware is becoming increasingly powerful, Edge devices are seen as candidates for offering data processing capabilities, able to pool and share computing resources to achieve better performance at a lower network latency—a pattern that can be also applied to Fog nodes. In these circumstances, it is important to enable efficient, intelligent, and balanced allocation of resources, as well as their further orchestration, in an elastic and transparent manner. To address such a requirement, this article proposes an OpenStack-based middleware platform through which resource containers at the Edge, Fog, and Cloud levels can be discovered, combined, and provisioned to end users and applications, thereby facilitating and orchestrating offloading processes. As demonstrated through a proof of concept on an intelligent surveillance system, by converging the Edge, Fog, and Cloud, the proposed architecture has the potential to enable faster data processing, as compared to processing at the Edge, Fog, or Cloud levels separately. This also allows architects to combine different offloading patterns in a flexible and fine-grained manner, thus providing new workload engineering patterns. Measurements demonstrated the effectiveness of such patterns, even outperforming edge clusters.

In this paper, we design and implement a distributed Internet of Things (IoT) framework called IoT-guard, for an intelligent, resource-efficient, and real-time security management system. The system, consisting of edge-fog computational layers, will aid in crime prevention and predict crime events in a smart home environment (SHE). The IoT-guard will detect and confirm crime events in real-time, using Artificial Intelligence (AI) and an event-driven approach to send crime data to protective services and police units enabling immediate action while conserving resources, such as energy, bandwidth (BW), and memory and Central Processing Unit (CPU) usage. In this study, we implement an IoT-guard laboratory testbed prototype and perform evaluations on its efficiency for real-time security application. The outcomes show better performance by the proposed system in terms of resource efficiency, agility, and scalability over the traditional IoT surveillance systems and state-of-the-art (SoA) approaches.

The progress in network technology and hardware in conjunction with the Internet-of-Things (IoTs) has provided the comfortability for an easier human life at present. Apart from the so-called “smart” environments, including smart homes, smart city, smart agriculture, IoTs have been included recently in e-healthcare systems as well for real-time diagnosis and medical consultancy. To enhance the capabilities of IoT-based healthcare systems, fog layers have been employed and that have shown its worth by providing fast response time and low latency. However, such development is posing a severe challenge in preserving the privacy of the users which further addresses the security/privacy issues to some extent. Being in an infant stage, such technology has invariably run into fewer privacy controls. Therefore, our present work is about an e-healthcare framework dealing with electronic medical records (EMRs) which preserves the privacy issues. Moreover, we have experimented the proposed work with respect to response time and delay and have compared with recent works. The results show that the proposed work is efficient in providing privacy along with standard network parameters.

Machine type communication (MTC) is a fundamental technology to realize the concept of fully connected world in fifth generation (5G) Internet of Things (IoT). The massive roll out of the MTC devices is a serious challenge for cellular networks from operational and management perspective, including massive access and network congestion. Many proposals have been put forward by the research community to cater to the nuisance of massive MTC (mMTC) access in cellular networks. Recently, data aggregation has attracted a lot of research attention owing to its robust ability to resolve the above-mentioned challenges. In this paper, we review the recent development in data aggregation techniques, including their application scenarios, design, and limitations. Commencing with the application scenarios and current challenges in the MTC network, the classification of various proposed solutions for massive access along with the family of data aggregation techniques is discussed in detail. By doing so, it provides an insight about the future design trends that can propel the current research efforts to curb the mMTC access in a cellular network.

The content of fats, oils, and greases (FOG) in wastewater, as a result of food preparation, both in homes and in different commercial and industrial activities, is a growing problem. In addition to the blockages generated in the sanitary networks, it also represents a difficulty for the performance of wastewater treatment plants (WWTP), increasing energy and maintenance costs and worsening the performance of downstream treatment processes. The pretreatment stage of these facilities is responsible for removing most of the FOG to avoid these problems. However, so far, optimization has been limited to the correct design and initial installation dimensioning. Proper management of this initial stage is left to the experience of the operators to adjust the process when changes occur in the characteristics of the wastewater inlet. The main difficulty is the large number of factors influencing these changes. In this work, a prediction model of the FOG content in the inlet water is presented. The model is capable of correctly predicting 98.45% of the cases in training and 72.73% in testing, with a relative error of 10%. It was developed using random forest (RF) and the good results obtained (R2 = 0.9348 and RMSE = 0.089 in test) will make it possible to improve operations in this initial stage. The good features of this machine learning algorithm had not been used, so far, in the modeling of pretreatment parameters. This novel approach will result in a global improvement in the performance of this type of facility allowing early adoption of adjustments to the pretreatment process to remove the maximum amount of FOG.

As one of the most common adverse weather phenomena, haze has caused detrimental effects on many computer vision systems. To eliminate the effect of haze, in the field of image processing, image dehazing has been studied intensively, and many advanced dehazing algorithms have been proposed. Physical model-based and deep learning-based methods are two competitive methods for single image dehazing, but it is still a challenging problem to achieve fidelity and effectively dehazing simultaneously in real hazy scenes. In this work, a mixed iterative model is proposed, which combines a physical model-based method with a learning-based method to restore high-quality clear images, and it has good performance in maintaining natural attributes and completely removing haze. Unlike previous studies, we first divide the image into different regions according to the density of haze to accurately calculate the atmospheric light for restoring haze-free images. Then, dark channel prior and DehazeNet are used to jointly estimate the transmission to promote the final clear haze-free image that is more similar to the real scene. Finally, a numerical iterative strategy is employed to further optimize the atmospheric light and transmission. Extensive experiments demonstrate that our method outperforms existing state-of-the-art methods on synthetic datasets and real-world datasets. Moreover, to indicate the universality of the proposed method, we further apply it to the remote sensing datasets, which can also produce visually satisfactory results.

[EN] Internet of Things (IoT) is a new buzzword in information technology where real-world physical objects are made smart by integrating them with internet-enabled technologies. The things can sense information around them, communicate the sensed information over some protocol and employ the information to solve real-life problems. In IoT, several technologies are integrated under a common umbrella so that they can connect and exchange data over a network protocol. A huge amount of data is generated from diverse geographical locations with the consequent urge for fast aggregation of overall sensed information, leading to an increase in the need to store and process such data in a more efficient and effective manner. The traditional fields of embedded systems, WSN, real-time analytics, automation system, machine learning and others all contribute to enabling the IoT. This article is focused on discussing the various IoT technologies, protocols and their application and usage in our daily life. It also summarizes the current state-of-the-art IoT architecture in various spheres conventionally and all related terminologies that will give the forthcoming researchers a glimpse of IoT as a whole.

Currently, in CD6, a new line of research devoted to polarimetric imaging is being developed. The use of polarization adds new information that is not reachable using other conventional imaging modes. In addition, recent literature explores the capability of using polarized illumination and detection to extend the range and enhance image contrast in turbid media such as fog, dust or smoke. Different computational and experimental tools are presented, in order to study polarized light behavior when propagating through foggy media. This work was supported by the Secretary of Universities and Research from the Generalitat of Catalunya and the European Social Fund under the grant 2019FI-B-00543 and partially supported by the Spanish Ministry of Economy and Competitiveness (MINECO) under the project FIS2017-89850R.

Encompassing Cloud-IoT paradigms, Osmotic Computing features the management of IoT microelements (MELs), i.e. computation units (e.g. services, programs) that are available in different versions to compose adaptive IoT applications. New techniques are then needed to decide where to place MELs and which of the available versions to deploy, considering both infrastructure resource availability and Quality of Service (e.g. latency, costs) of the whole application. In this article, we tackle such an open challenge by proposing a declarative methodology in support of the placement of osmotic MELs.

International audience; Due to recent advancements and the success of versatile mobile applications, more and more services around the globe are being moved to the cloud. As a result, its limitations have become more evident. The major issues that cloud-based applications face include large latency, bottlenecks because of central processing, compromised security, and lack of offline processing. The drawbacks of cloud computing are reduced by fog and edge computing, where data are processed near the places where it is generated—at network edges or fog nodes—most importantly in a distributed way. Smart agriculture is an approach based on the Internet of Things (IoT) where cloud computing is not an option as the internet is usually not available at remote sites. In addition, pure edge computing also is not practical, as most sensor nodes are very small and they do not have enough computing power. Intermediate fog computing also is not a good choice, as fixed fog nodes (getaway nodes) do not work well with high node fluctuation caused by bad weather or harsh conditions. Considering these issues and limitations, we have proposed the idea of flying edge computing where an unmanned aerial vehicle (UAV) acts as an edge-computing machine. This can be an ideal solution for smart agriculture, given the size and remoteness of many agricultural areas. This technique can be called “wind or breeze computing” because the data are “blown” or moved by the current of computing. The Flying-Edge offers fast deployment of edge facilities in challenging locations and it can be a major step to accomplish the goal of IoT-based smart agriculture.

Autonomous vehicles are regarded as future transport mechanisms that drive the vehicles without the need of drivers. The photonic-based radar technology is a promising candidate for delivering attractive applications to autonomous vehicles such as self-parking assistance, navigation, recognition of traffic environment, etc. Alternatively, microwave radars are not able to meet the demand of next-generation autonomous vehicles due to its limited bandwidth availability. Moreover, the performance of microwave radars is limited by atmospheric fluctuation which causes severe attenuation at higher frequencies. In this work, we have developed coherent-based frequency-modulated photonic radar to detect target locations with longer distance. Furthermore, the performance of the proposed photonic radar is investigated under the impact of various atmospheric weather conditions, particularly fog and rain. The reported results show the achievement of significant signal to noise ratio (SNR) and received power of reflected echoes from the target for the proposed photonic radar under the influence of bad weather conditions. Moreover, a conventional radar is designed to establish the effectiveness of the proposed photonic radar by considering similar parameters such as frequency and sweep time.

Although smart devices markets are increasing their sales figures, their computing capabilities are not sufficient to provide good-enough-quality services. This paper proposes a solution to organize the devices within the Cloud-Edge Continuum in such a way that each one, as an autonomous individual –Agent–, processes events/data on its embedded compute resources while offering its computing capacity to the rest of the infrastructure in a Function-as-a-Service manner. Unlike other FaaS solutions, the described approach proposes to transparently convert the logic of such functions into task-based workflows backing on task-based programming models; thus, agents hosting the execution of the method generate the corresponding workflow and offloading part of the workload onto other agents to improve the overall service performance. On our prototype, the function-to-workflow transformation is performed by COMPSs; thus, developers can efficiently code applications of any of the three envisaged computing scenarios – sense-process-actuate, streaming and batch processing – throughout the whole Cloud-Edge Continuum without struggling with different frameworks specifically designed for each of them. This work has been supported by the Spanish Government (PID2019-107255GB), by Generalitat de Catalunya (contract 2014-SGR-1051), and by the European Commission through the Horizon 2020 Research and Innovation program under Grant Agreement No. 101016577 (AI-SPRINT project).

In this work we propose a Disaster Management System on 5G ultra Reliable Low Latency Networks that targets unprecedented reliability levels as well as low latency. In fact, referring to the 5G vision a Structural Health Monitoring system can be considered depending on the operational scenario: in the case of data collection and processing from sensors in monitored buildings, considering the high number of sensors installed, it can refer to the massive Machine Type Communications context. Vice versa, during a seismic event or just after it, the use case requires high reliability connectivity and, sometimes, low latency. Those features refer to the ultra Reliable Low Latency context. It seems interesting to evaluate and experiment the ability of 5G network to dynamically adapt to the changing scenario that this use case can provide. Moreover this work presents an innovative 5G architecture for Earthquake Early Warning that uses Structural Health Monitoring systems to detect a seismic event and to propagate a message reporting the event detection to all the buildings that may be damaged by the event.

Despite standards such as International Standards Association (ISA-95) and Manufacturing Enterprise Solutions Association (MESA) International guiding operational process, asset automation and control for some time, the integration challenge remains for Industrial Internet of Things (IIoT) that the strategies of converging Information Technology (IT) and Operational Technology (OT) domains remain disparate. IT systems prioritize data governance and reliability to ensure the needs of enterprise users are met while OT systems prioritize human safety and asset reliability to ensure that the production process remain safe and effective. These differing priorities lead to greater complexities in the design of convergent systems necessary to progress Industrial Internet of Things, coined more recently as Industry 4.0. The gaps are between IoT computing layers and alignment to the ISA-95 automation layers plus the evolving constructs of Industry 4.0 and how in the future un/trusted data moves seamlessly between converged OT and IT domains. In doing so, the information governance links between technology, organizational processes, and people (TOP) across the ISA-95 and Manufacturing Operations Management (MOM) System stacks must be considered to provide true secure convergence of the OT and IT domains. This paper explores and builds upon the growing industrial manufacturing OT/IT/IoT convergence literature [1, 31], extending exploration of the role of TOP governance factors to RAMI4.0, suggesting a single OT/IT strategy for organizations to achieve Industry 4.0 initiatives. The case study research method provides insights into emerging industry and standards convergence practices. Applying these standards and aligning the interdependence between OT and IT strategies with TOP constructs, are explored in the context of achieving Industry 4.0 objectives robustly and resiliently through applying a holistic Data Infrastructure Platform (DIP) in process-centric organizations.

Fog Computing is expected to decentralize clouds to improve users quality of service, in technologies like the Internet of Things, by pushing some computing onto mini-clouds (called Fogs) located close to end users. To enhance M&S with this concept, we have developed a complete Fog/Cloud collaboration methods to conduct simulation experiments: Users manipulate their experiments though nearby Fogs servers while M&S resources are dynamically discovered and allocated throughout the Fogs/Cloud. We have already built those methods using privately owned clouds. However, it was difficult in practice to study those methods scalability using real system setups. As a result, we present here the simulation model that we developed to mimic this real system in order to study the proposed collaboration methods on large scale. This model was validated with reference to the real system. The results have clearly shown the scalability of those proposed methods at the structure and coordination levels.

Amazing progress of various inter-related technologies, such as communications, automatic control, and embedded systems has changed the traditional vehicle model pushing it forward towards new paradigms: Internet of Vehicles, Connected Vehicles, Vehicular Clouds/Fogs, Fog Computing, Smart Vehicles, Intelligent IoV etc. However, implementing such paradigms is very challenging due to their extreme complexity, which prevents them to be a more substantial part of our daily life, at the full potential of the technology involved. To tackle such complexity, new approaches are necessary and, therefore, we propose here a user-centric architecture of a smart connected vehicle that can be used also for smart surrounding environments and that provides for autonomic, pervasive, and fog computing. Its current implementation in a connected vehicle is also shown.

Materialization in Internet-of-Things (IoT) has exponentially raised the usage of smart devices by the individuals or business organizations. Fog computing was introduced for serving the raising needs of IoT applications locally with minimal delay and cost. Based on the Quality of Service (QoS) requirements like data requirements, rate of data updating, and accessing authority of IoT applications, their requests may be processed on the locally available fog nodes at low cost or forwarded to the globally available rentedcloud-nodes for processing at higher cost. Hence, there is a key need of optimizing the information-centric IoT architecture for classifying the tasks of IoT applications and scheduling them on to the most suitable fog or cloud nodes for processing. The proposed CS_IcIoTA identifies the application needs and classifies them into diverse categories. The scheduler assigns the tasks from these categories either to the local fog nodes or to the remotely available rented-cloud-nodes for execution based on the current resource requirements of tasks. If the demanded computing or storage resources by the tasks is huge and if that is not attainable at fogs then cloud nodes are preferred otherwise local fog nodes are used. Three cloud nodes, four fog nodes and three IoT application domains with a sum of 1500 tasks are considered for the experimental analysis and performance evaluation. Simulation results states that the proposed CS_IcIoTA minimizes the average makespan time and service-cost up to 11.45%, and 10.60% respectively. Proposed CS_IcIoTA also maximizes the average fog node utilization up to 77.83%.

The huge volume of IoT data generated by emerging IoT end devices have triggered the prosperous development of Fog computing in the past years, mainly due to their real-time requirements. Fog computing aims at forming the idle edge devices that are in the vicinity of IoT end devices as instantaneous small-scale Fog networks (Fogs), so as to provide one-hop services to satisfy the real-time requirement. Since Fogs may consist of only wireless nodes, only wired nodes or both of them, it is significant to map IoT tasks with diverse QoS requirements to appropriate types of Fogs, in order to optimize the overall Fog performance in terms of the OPEX cost and transmission latency. Regarding this, we propose an integer linear programming (ILP) model to optimally map the IoT tasks to different Fogs and/or Cloud, taking into consideration of the task mobility and real-time requirements. Numerical results show that the real-time and mobility requirements have significant impact on the OPEX cost of the integrated Cloud-Fog (iCloudFog) framework.

Visibility is a critical factor for transportation, even if we refer to air, water, or ground transportation. The biggest trend in the automotive industry is autonomous driving, the number of autonomous vehicles will increase exponentially, prompting changes in the industry and user segment. Unfortunately, these vehicles still have some drawbacks and one, always in attention and topical, will be treated in this paper&mdash, visibility distance issue in bad weather conditions, particularly in fog. The way and the speed with which vehicles will determine objects, obstacles, pedestrians, or traffic signs, especially in bad visibility, will determine how the vehicle will behave. In this paper, a new experimental set up is featured, for analyzing the effect of the fog when the laser and LIDAR (Light Detection And Ranging) radiation are used in visibility distance estimation on public roads. While using our experimental set up, in the laboratory, the information offered by these measurement systems (laser and LIDAR) are evaluated and compared with results offered by human observers in the same fog conditions. The goal is to validate and unitarily apply the results regarding visibility distance, based on information arrives from different systems that are able to estimate this parameter (in foggy weather conditions). Finally, will be notifying the drivers in case of unexpected situations. It is a combination of stationary and of moving systems. The stationary system will be installed on highways or express roads in areas prone to fog, while the moving systems are, or can be, directly installed on the vehicles (autonomous but also non-autonomous).

The central wavelength stability of the erbium-doped fiber source (EDFS) is one of the most significant parameters to provide the accurate operation of high-accuracy fiber-optic gyroscopes (FOGs). In this paper, the method of stabilization of the EDFS as a part of the FOG is presented. The main advantage of this method is the possibility of quick and simple matching of temperature-compensating spectral filters by changing the pump ratio without modifications of the optical scheme. It allows to obtain the world-class stability of the central wavelength of the EDFS as a component of spectral selectivity devices and provides the constant value of the optical power of the output light. The proposed method makes it possible to compensate the spectral characteristics of FOG, achieving the stability of output light central wavelength about 0.7 ppm/°C. Also, a method of real-time stabilization of the central wavelength of the output light (OL) with constant optical power is proposed. The temperature stability of the EDFS central wavelength as a component of the FOG was obtained at 0.32 ppm/°C.

The proliferation of the Internet of Things (IoT) caused new application needs to emerge as rapid response ability is missing in the current IoT end-devices. Therefore, Fog Computing has been proposed to be an edge component for the IoT networks as a remedy to this problem. In recent times, cyber-attacks are on the rise, especially towards infrastructure-less networks, such as IoT. Many botnet attack variants (Mirai, Torii, etc.) have shown that the tiny microdevices at the lower spectrum of the network are becoming a valued participant of a botnet, for further executing more sophisticated attacks against infrastructural networks. As such, the fog devices also need to be secured against cyber-attacks, not only software-wise, but also from hardware alterations and manipulations. Hence, this article first highlights the importance and benefits of fog computing for IoT networks, then investigates the means of providing hardware security to these devices with an enriched literature review, including but not limited to Hardware Security Module, Physically Unclonable Function, System on a Chip, and Tamper Resistant Memory.