Fog Layer Research Articles

Editorial for special issue on security, trust, and privacy in internet of things: Challenges and solutions

Editorial for special issue on security, trust, and privacy in internet of things: Challenges and solutions

Energy efficient cloud-assisted IoT-enabled architectural paradigm for drought prediction

Natural hazards like droughts give hard hit to economies, hydrological circle and human lives. Climatic fluctuations have exacerbate the chances of such calamities in future. The consequences can be abbreviated to some degree by predicting it and doing forward planning for such situations. Several drought indices evaluate the intensity of droughts but are incapable of covering most of the important drought evoking factors and lacks universality. The presented paper proposes an energy conserving cloud-assisted system for drought severity evaluation and prediction. The framework intelligently deploys sensor only to the locations that does not add redundant information. An energy conserving sleep scheduling algorithm is applied at fog layer that economize the power consumption but maintaining maximum accuracy of the system at the same time. Spatio-temporal analysis of wide spectrum of drought inciting factors by kernel based methods give the current and future degree of severity at the cloud layer. Kernel K-means clustering and Support Vector Regression are used to evaluate the situation for current and predict it for subsequent time periods respectively. Experimental results prove the system efficiency in assessing and predicting droughts with energy saving at the sensor node level.

Improvement of numerical weather prediction model analysis during fog conditions through the assimilation of ground-based microwave radiometer observations: a 1D-Var study

Abstract. This paper investigates the potential benefit of ground-based microwave radiometers (MWRs) to improve the initial state (analysis) of current numerical weather prediction (NWP) systems during fog conditions. To this end, temperature, humidity and liquid water path (LWP) retrievals have been performed by directly assimilating brightness temperatures using a one-dimensional variational technique (1D-Var). This study focuses on a fog-dedicated field-experiment performed over winter 2016–2017 in France. In situ measurements from a 120 m tower and radiosoundings are used to assess the improvement brought by the 1D-Var analysis to the background. A sensitivity study demonstrates the importance of the cross-correlations between temperature and specific humidity in the background-error-covariance matrix as well as the bias correction applied on MWR raw measurements. With the optimal 1D-Var configuration, root-mean-square errors smaller than 1.5 K (respectively 0.8 K) for temperature and 1 g kg−1 (respectively 0.5 g kg−1) for humidity are obtained up to 6 km altitude (respectively within the fog layer up to 250 m). A thin radiative fog case study has shown that the assimilation of MWR observations was able to correct large temperature errors of the AROME (Application of Research to Operations at MEsoscale) model as well as vertical and temporal errors observed in the fog life cycle. A statistical evaluation through the whole period has demonstrated that the largest impact when assimilating MWR observations is obtained on the temperature and LWP fields, while it is neutral to slightly positive for the specific humidity. Most of the temperature improvement is observed during false alarms when the AROME forecasts tend to significantly overestimate the temperature cooling. During missed fog profiles, 1D-Var analyses were found to increase the atmospheric stability within the first 100 m above the surface compared to the initial background profile. Concerning the LWP, the RMSE with respect to MWR statistical regressions is decreased from 101 g m−2 in the background to 27 g m−2 in the 1D-Var analysis. These encouraging results led to the deployment of eight MWRs during the international SOFOG3D (SOuth FOGs 3D experiment for fog processes study) experiment conducted by Météo-France.

WITHDRAWN: Energy-aware load balancing in fog cloud computing

Abstract With the introduction of fog computing, storage, computing and networking take places near to edge devices rather than in centralized data-centres of cloud computing. Fog computing works as an assistant of cloud computing because it could not work alone, for efficient work fog computing combined with cloud computing. More the number of fog nodes in the network more is that hardware requirement, which increases the demand for energy consumption at the fog layer. For efficient processing of tasks at the fog layer, tasks need to be assigned equally to all the nodes in the fog layer. Load balancing helps to reduce energy consumption in the fog-cloud computing environment. This article provides an energy-aware load balancing technique for scientific workflows in fog-cloud computing environment. Also, a load-balancing algorithm proposed for fog environment. iFogSim used for simulation results. The results also compared with existing methods. Load balancing at the fog layer help in proper utilization of resources, which helps to reduce latency and enhance the quality of service. The articles conclude by providing future directions.

DEFT: Decentralized Multiuser Computation Offloading in a Fog-Enabled IoV Environment

In this paper, we propose a mechanism – DEFT– to facilitate mobile Internet of Vehicles (IoV) user entities (UEs) with unit tasks to cooperate among one another and offload their tasks to nearby fog nodes (FNs)/roadside units (RSUs) in a decentralized and fair manner. While existing literature depends on offloading schemes based on centralized systems, DEFT provides near real-time computation offloading in a fog-enabled IoV using a two-level game-theoretic approach. Further, this approach allows the UEs to make their own decisions in a dynamic environment. In the first level, FNs play an Oligopoly game for determining the price of their services based on their computation capability and then broadcast the price to the UEs. Further, based on the price transmitted by the FNs, UEs play a non-cooperative Stackelberg game to map the offloading among the user and fog layer. We also ensure that no UE consumes all of the resources in an FN. Additionally, we prove our objective function's convexity and show that the proposed game always attains the Nash Equilibrium. Through extensive real-world emulations, we observe that DEFT minimizes the latency and power consumption, and improves throughput compared to the existing schemes. In the presence of 40, 80, and 120 UEs, the overhead for computation in the fog layer is minimized by $70-75$ % compared to the local computation overhead.

Intelligent Fog-Enabled Smart Healthcare System for Wearable Physiological Parameter Detection

Wearable technology plays a key role in smart healthcare applications. Detection and analysis of the physiological data from wearable devices is an essential process in smart healthcare. Physiological data analysis is performed in fog computing to abridge the excess latency introduced by cloud computing. However, the latency for the emergency health status and overloading in fog environment becomes key challenges for smart healthcare. This paper resolves these problems by presenting a novel tri-fog health architecture for physiological parameter detection. The overall system is built upon three layers as wearable layer, intelligent fog layer, and cloud layer. In the first layer, data from the wearable of patients are subjected to fault detection at personal data assistant (PDA). To eliminate fault data, we present the rapid kernel principal component analysis (RK-PCA) algorithm. Then, the faultless data is validated, whether it is duplicate or not, by the data on-looker node in the second layer. To remove data redundancy, we propose a new fuzzy assisted objective optimization by ratio analysis (FaMOORA) algorithm. To timely predict the user’s health status, we enable the two-level health hidden Markov model (2L-2HMM) that finds the user’s health status from temporal variations in data collected from wearable devices. Finally, the user’s health status is detected in the fog layer with the assist of a hybrid machine learning algorithm, namely SpikQ-Net, based on the three major categories of attributes such as behavioral, biomedical, and environment. Upon the user’s health status, the immediate action is taken by both cloud and fog layers. To ensure lower response time and timely service, we also present an optimal health off procedure with the aid of the multi-objective spotted hyena optimization (MoSHO) algorithm. The health off method allows offloading between overloaded and underloaded fog nodes. The proposed tri-fog health model is validated by a thorough simulation performed in the iFogSim tool. It shows better achievements in latency (reduced up to 3 ms), execution time (reduced up to 1.7 ms), detection accuracy (improved up to 97%), and system stability (improved up to 96%).

Distributed composition of complex event services in IoT network

In this paper, a distributed solution is presented for the composition of complex event services in the Internet of Things (IoT) environments. The composition of objects services in the IoT environment requires scalable and adaptable methods due to the dynamics of the network. The dynamics accommodate both the large volume of objects and the tolerance of object displacement and link failure. The composition of complex event services is more scalable and reusable. The composition of these services is done in several steps. First, the user’s goals are transformed into an event schema through a backward-chaining mechanism. Then, the event schema is matched with complex event services in the fog layer. Finally, this event schema is used to access the required services. Besides, heuristic functions are also provided to control network traffic, increase execution consistency, and reduce service failure rates. An extensive evaluation of various metrics has been done. The results show that the proposed solution is scalable and adaptable in a hierarchical IoT network with mobile objects.

Cloud-Manufacturing-Based Condition Monitoring Platform With 5G and Standard Information Model

Condition monitoring (CM) is the escort of smart manufacturing, which guarantees safety, precision, and efficiency of production process. However, traditional CM capacities with limited storage and computing resources cannot follow the explosive growth of manufacturing data. Empowered by the emerging Internet of Things, cyber–physical system, and cloud computing, cloud manufacturing (CMfg) characterizes a new service-oriented technology, which is reshaping the CM paradigm into an agile, scalable, and interoperable mode. In this article, a CMfg-based CM platform for smart manufacturing is proposed. A 5G wireless communication network-based edge computing system is presented to achieve data acquisition, feature extraction, and emergency response with ultrareliability and low-latency communication. In order to deal with the issue of data management and analysis, a generic open platform communications unified architecture information model oriented to the workshop is proposed in the fog layer. The cloud monitoring platform is constructed encompassing message delivery, data storage, status visualization, and training of artificial intelligence models. Finally, a CMfg-based CM system and a CMfg-based tool wear estimation system are outlined as two industrial cases. The experiments verify the feasibility and effectiveness of the proposed platform.

A Low-Cost Two-Tier Fog Computing Testbed for Streaming IoT-Based Applications

In this article, we propose a comprehensive multilayer (IoT-device, edge, fog, and cloud) streaming analytics platform with two-tier fog layer, which comprises streaming and analytics tiers. The approach, functionally validated on a testbed of clusters of low-cost off-the-shelf components, demonstrates that both real-time streaming processing and large-scale data analytics are feasible, with even completely avoiding the use of cloud layer. The fog layer uses advanced streaming and analytics paradigms, such as Apache Kafka, Apache Spark, and Hadoop, to support the effective development of applications that use streaming, long-term data storage and analytics. As such, the platform is suitable for and is affordable for “private” users. Furthermore, we add to the platform intelligent edge layer that abstracts and can simulate the operation of the IoT-device layer in real time or can be directly connected to the IoT-device layer. The experiments on a real-life IoT-based smart parking application show that our two-tier approach is cost effective and scalable, and can handle simultaneously streaming processing and analytics tasks successfully.

Active resilient control for two‐dimensional systems under denial‐of‐service attacks

SummaryAs the backbone of Industrial 4.0, cyber‐physical systems (CPSs) are widely applicable in industrial processes. In this paper, a resilient control is designed for a class of two‐dimensional (2D) CPSs with the occurrence of Denial‐of‐Service (DoS) attacks. Firstly, we model the physical layer of CPSs as a 2D Fornasini‐Marchesini plant and set up the stability issue as preliminaries. Next, for CPSs suffering from DoS attacks, under which the controller operating in the fog layer is attacked by adversaries, sufficient conditions are established to guarantee the exponential stability of the underlying systems, and the corresponding switched control is designed to make a prescribed system performance is satisfied. Finally, an example is given to illustrate the effectiveness and potential of the proposed design methods.

The Performance of IoT Malware Detection Technique Using Feature Selection and Feature Reduction in Fog Layer

The rapid increase in the number of devices connected to internet (IoT) lead to expansion in the attacks that targeting these devices. One of these dangers attacks is malware which embedded with IoT devices that makes the detection for such malware is extremely challenging. The machine learning is one of the most effective techniques that used to detect different types of attacks in IoT environment. This technique includes three main stages: feature extraction, feature selection, and classification. The feature selection is the most important stage in ML technique because it contributes to minimizing the size of features which significantly accelerate the detection system. In this stage, most researchers trend to use one of three methods; feature selection, feature reduction and hybrid between feature selection and reduction. The present research aims to present a comparative study between the effect of using feature selection method and feature reduction method on the performance of the IoT malware detection system. The results showed that the proposed technique could achieved an accuracy about 97% when using feature selection method only. These results emphasize that feature selection method is more efficient than the feature reduction method in detection IoT malware.

Characteristics of turbulence and dissipation mechanism in a polluted radiation–advection fog life cycle in Tianjin

Characteristics of turbulence and dissipation mechanism during a polluted advection–radiation fog life cycle occurring on 26–27 November, 2018 are investigated, using the measurements at a 255-m meteorological tower located in Tianjin. The increase in relative humidity during fog formation phase is attributed to the radiative cooling and advection, indicating that the fog type is advection–radiation fog. The dispersion of PM2.5 at all heights can be attributed to the strong winds, while dispersion of PM2.5 at surface is inhibited and delayed by the thick fog. The four phases during fog life cycle are all linked to the turbulence intensity. Weak turbulence is crucial for fog formation, and the threshold σw2 range is between 0.01 and 0.10 m2 s−2. The fog dissipation at high elevations is due to strong cold and dry advection. Downdraughts at high elevations lead dry air to sink down into the lower fog layer and aid the erosion of the fog layer at medium elevations. However, the collapse of fog bank at surface is mainly due to the strong turbulence. The theory that there is a threshold relationship between turbulence and fog dissipation is verified through case analysis. However, there are two main application conditions: one is that only radiative cooling, turbulent mixing, and gravitational settling are involved in the dissipation phase of fog. The second is that the dissipation time should be at night or dawn (radiative cooling should be positive), and the fog layer collapses due to strong turbulent mixing induced by nocturnal LLJ or synoptic system.

A Systematic Survey on Fog steered IoT: Architecture, Prevalent Threats and Trust Models

Internet of Things (IoT) is considered as a scheme that consists of smart objects furnished with sensors, processing and networking technologies amalgamated to work together and provide an ecosystem in which smart services are delivered to end-users. Fog computing is the name given to a next level computing that extends cloud services closer to the end-devices. The main idea behind the introduction of this concept is the reduction of latency that exists in a typical IoT-cloud scenario. But, if on one side it accelerates the computing jobs; on the off-side, it increases the attack surface due to the presence of fog layer in between the two existing layers. The data gets computed near to the end-user, thus making it more vulnerable. Hence, it may be said that the threats that may not even exist in a cloud environment come into the picture at fog level. In this paper, we intend to thoroughly discuss various fog level architectures with the threats prevalent at this layer through systematic literature review (SLR). This article aims to classify systematically and statistically, analyse the prevalent attacks that occur in IoT-Fog scheme that are published between 2012- 2020. When two or more devices share information, trust plays a pre-eminent role. So, the authors have also considered ‘trust' in this study. The effect of trust on the different pillars of security is critically examined. Also, it is found that most of the researchers are emphasizing to prioritize trust in Fog- IoT scenario as it is a point of paramount significance.

IOT and context‐aware learning‐based optimal neural network model for real‐time health monitoring

AbstractRecently, numerous context‐aware approaches are established to provide physiological information regarding the wellness and healthcare of each individual. While monitoring the health condition of the patient there occur delays in transferring data to the cloud. So to overcome such types of delays, numerous IoT sensors are developed to monitor, track, and sense the activities of the elder persons. This paper proposes four‐module architecture comprises of IoT module (IoT‐M), data pre‐processing module (DP‐M), context‐aware module (CA‐M) as well as decision‐making module (DM‐M) for storing and processing numerous cumulative sensor data. Here, an IoT comprises a concrete or substantial hardware ecological unit, that is, sensors and actuators. On the other hand, the context‐aware computational approach comprises of insubstantial software ecological unit in understanding and processing the context directly into an accomplishment through various IoT devices. The initial module or the IoT‐M comprises of sensors whereas in the DP‐M phase includes data collection phase, data storage phase, and data redundancy phase. The third phase or the CA‐M comprises two different types of layers namely fog layer, cloud layer. In addition to this, a context‐aware learning phase is also enumerated. In the final phase or the DM‐P phase, the feature extraction and classification is done by Back‐Propagation Neural Network along with the Adaptive grasshopper optimization algorithm so as to obtain a best optimal solution. Thus, an alarm or a notification is sent to the medical practitioner regarding the health condition of the patient with very less response time, high accuracy, and a high scalability rate. The evaluation results and discussions are made by comparing our proposed approach with several other approaches and the evaluation results reveal that the proposed framework provides better results with high accuracy, scalability, network latency, and low response time.

Research on Trusted Computing Scheme Based on Block Chain and Internet of Things

The popularity of blockchain, Bitcoin and Ethereum in 2017 can be described as an empty alley. However, the reason why such a popular technology has strong vitality must be to find the most suitable application. In view of the security and privacy, lack of resources, network transmission delay and other issues in the current Internet of Things system, analyze the advantages brought by the introduction of blockchain technology, and compare the architecture of the blockchain Internet of Things with the traditional Internet of Things system. Aiming at the problems existing in the application of blockchain IoT, a blockchain IoT architecture based on edge computing is proposed. In this architecture, the data collected by the edge device is filtered and transmitted to the fog node of the fog layer through the multi-interface base station, and the fog node reports the data processing result to the distributed cloud layer based on the blockchain. The fog layer provides positioning, and the cloud layer provides wide-area monitoring. It provides large-scale event detection, behavior analysis and long-term pattern recognition through distributed computing and storage, and combines blockchain technology to provide scalable, reliable and highly available Internet of Things services. Therefore, the proposed architecture has important reference significance for subsequent computer technology application research based on blockchain IoT technology.

Virtualized Fog Network with Load Balancing for IoT based Fog-to-Cloud

Fog Computing is a new concept made by Cisco to provide same functionalities of Cloud Computing but near to Things to enhance performance such as reduce delay and response time. Packet loss may occur on single Fog server over a huge number of messages from Things because of several factors like limited bandwidth and capacity of queues in server. In this paper, Internet of Things based Fog-to-Cloud architecture is proposed to solve the problem of packet loss on Fog server using Load Balancing and virtualization. The architecture consists of 5 layers, namely: Things, gateway, Fog, Cloud, and application. Fog layer is virtualized to specified number of Fog servers using Graphical Network Simulator-3 and VirtualBox on local physical server. Server Load Balancing router is configured to distribute the huge traffic in Weighted Round Robin technique using Message Queue Telemetry Transport protocol. Then, maximum message from Fog layer are selected and sent to Cloud layer and the rest of messages are deleted within 1 hour using our proposed Data-in-Motion technique for storage, processing, and monitoring of messages. Thus, improving the performance of the Fog layer for storage and processing of messages, as well as reducing the packet loss to half and increasing throughput to 4 times than using single Fog server.

XFogSim: A Distributed Fog Resource Management Framework for Sustainable IoT Services

Streaming large amounts of data to cloud data centers cause network congestion resulting in high network and energy consumption. The concept of fog computing is introduced to reduce workload from backbone networks and support delay-sensitive Internet of Things (IoT) applications. The concept places compute, storage, and network services closer to the source of the requests. In general fog-based simulators are used for better understanding and optimum fog resource allocation. Unfortunately, most of the simulators lack core features like network delay, latency, packet error rate, energy consumption, and distributed fog node management. In this paper, we propose a fog simulation framework termed as xFogSim to support latency-sensitive applications at the fog layer with multi-objective optimization to trade-off cost, availability, and performance among the fog federation. Moreover, during peak load, the framework provides locality-aware distributed broker node management that enables borrowing resources from nearby fog locations to meet service and energy requirements. The results show that the framework is lightweight, configurable, and scalable, capable of handling a large number of user requests using dynamic resource provisioning across the fog federation.

Adaptive deep convolutional neural network‐based secure integration of fog to cloud supported Internet of Things for health monitoring system

AbstractIn recent years, the healthcare monitoring system plays a significant role in providing early intervention for the people who are under risk. Several advanced technologies including the Internet of Things (IoT) are becoming accessible nowadays due to its high level and ubiquitous monitoring. The IoT also enables a structured and competent technique in handling the healthcare of the patients based on remote patient monitoring and mobile health. In addition to this, the deep learning approaches are employed in health‐based applications so as to achieve promising and satisfactory performances for a sizeable amount of data. While monitoring the health condition of the patients, there occur delays in data transferring to the cloud. So, to overcome such shortcomings, the proposed approach of this paper utilizes four different phases including data acquisition phase (DAP), fog to cloud phase (FCP), decision‐making phase (DMP), and execution phase (EP) in transferring the data to the cloud via the fog layer. The initial phase or DAP phase comprises of data storage and collection. The second phase or the FCP phase comprises of two different types of layers namely fog layer and the cloud layer along with that, secure integration of FCP is described. The third phase or the DMP phase involves feture extraction and classification. Here, Adaptive Deep Convolution Neural Network along with the Levy Flightbased Grey Wolf Optimization (LFGWO) are employed in the classification process to obtain the best optimal solution. The final phase or the EP phase provides notification or information to the doctor or the practitioner soon after the detection of any abnormality case. The experimental results are made by comparing various approaches with the proposed approach and the analysis reveals that the proposed approach provides better results with high accuracy, efficiency, response time with less computational cost in healthcare monitoring applications.

MFP: an approach to delay and energy-efficient module placement in IoT applications based on multi-fog

One of the challenges of using fog computing in IoT systems is the efficient placement of resources in IoT applications. This paper presents a resource placement method for fog-based IoT systems to reduce their latency and energy consumption. Given the limited processing power of fog nodes, only a limited number of modules can be run on these nodes. In fog-cloud systems, placing the modules on fog nodes instead of the cloud layer can be expected to reduce system latency. Therefore, to achieve enhanced latency and energy consumption, this paper introduces a multi-zone fog layer architecture where each zone is a multi-fog. The core idea of ​​the proposal is to use the idle processing capacity of fog nodes in each zone through the maximal placement of modules on these nodes. The paper also presents an algorithm called MFP for carrying out this placement. To evaluate the proposed algorithm, it was simulated in iFogSim for two scenarios with different topologies. The simulation results showed that the proposed scheme offers 16.81% lower latency and 17.75% lower energy consumption than the existing schemes.

FogAuthChain: A secure location-based authentication scheme in fog computing environments using Blockchain

Fog computing is an emerging computing paradigm which expands cloud-based computing services near the network edge. With this new computing paradigm, new challenges arise in terms of security and privacy. These concerns are due to the distributed ownership of Fog devices. Because of the large scale distributed nature of devices at the Fog layer, secure authentication for communication among these devices is a major challenge. The traditional authentication methods (password-based, certificate-based and biometric-based) are not directly applicable due to the unique architecture and characteristics of the Fog. Moreover, the traditional authentication methods consume significantly more computation power and incur high latency, and this does not meet the key requirements of the Fog. To fill this gap, this article proposes a secure decentralised location-based device to device (D2D) authentication model in which Fog devices can mutually authenticate each other at the Fog layer by using Blockchain. We considered an Ethereum Blockchain platform for the Fog device registration, authentication, attestation and data storage. We presented the overall system architecture, various participants and their transactions and message interaction between the participants. We validated the proposed model by comparing it with the existing method; results showed that the proposed authentication mechanism was efficient and secure. From the performance evaluation, it was found that the proposed method is computationally efficient and secure in a highly distributed Fog network.