A Fuzzy Approach to Trust Management in Fog Computing

With the continuous evolution of technology, the prevalence of the Internet of Things (IoT) has grown significantly. However, with this rise comesa number of challenges for integrated Cloud Computing (CC), including security, performance, latency, and network issues. Fortunately, Fog Computing is a solution that addresses these concerns by bringing CC closer to IoT devices. Essentially, fog serves as a data hub that processes and stores information locally on the fog node, rather than sending it to a cloud server. This results in faster response times and higher- quality services compared to those offered by traditional cloud servers. Fog Computing can optimize service delivery for multiple IoT clients by allowing the administrationof services and resource provisioning outside of CC, nearer to devices, or at specific locations for Service Level Agreements (SLAs). It's important to note that Fog Computing is not intended to replace CC, but rather to complement itas a critical component. In this paper, we explore various computing paradigms, examine the features and architecture of Fog Computing, analyze its relationship with IoT, and assess differentalgorithms used in Fog Computing systems. Furthermore, we tackle the distinctive challenges that emerge with Fog Computing as an intermediate layer between IoT sensors/devices and data centers. Keywords: Fog Computing, Internet of Things(IOT), Cloud Computing (CC).

A Review on Fog Computing: Architecture, Fog with IoT, Algorithms and Research Challenges

With the emergence of Internet of Things (IoT) technology, a huge amount of data is generated, which is costly to transfer to the cloud data centers in terms of security, bandwidth, and latency. Fog computing is an efficient paradigm for locally processing and manipulating IoT-generated data. It is difficult to configure the fog nodes to provide all of the services required by the end devices because of the static configuration, poor processing, and storage capacities. To enhance fog nodes' capabilities, it is essential to reconfigure them to accommodate a broader range and variety of hosted services. In this study, we focus on the placement of fog services and their dynamic reconfiguration in response to the end-device requests. Due to its growing successes and popularity in the IoT era, the Decision Tree (DT) machine learning model is implemented to predict the occurrence of requests and events in advance. The DT model enables the fog nodes to predict requests for a specific service in advance and reconfigure the fog node accordingly. The performance of the proposed model is evaluated in terms of high throughput, minimized energy consumption, and dynamic fog node smart switching. The simulation results demonstrate a notable increase in the fog node hit ratios, scaling up to 99% for the majority of services concurrently with a substantial reduction in miss ratios. Furthermore, the energy consumption is greatly reduced by over 50% as compared to a static node.

Future Internet of Things (IoT) will be a connected network of sensors enabling applications such as industrial automation and autonomous driving. To manage such a large number of applications, efficient computing techniques using fog nodes will be required. A major challenge in such IoT networks is to manage the resource allocation of fog computing nodes considering security and system efficiency. A secure selection of fog nodes will be needed for forwarding the tasks without interception by the eavesdropper and minimizing the task delay. However, challenges such as the secure selection of fog nodes for forwarding the tasks without interception by the eavesdropper and minimizing the task delay are critical in IoT-based fog computing. In this paper, an efficient technique is proposed that solves the formulated problem of allocation of the tasks to the fog node resources using a stable matching algorithm. The proposed technique develops preference profiles for both IoT and fog nodes based on factors such as delay and secrecy rate. Finally, Gale–Shapley matching is used for task offloading. Detailed simulation results show that the performance of the proposed technique is significantly higher than the recent techniques in the literature.

As the most notably emerging wave of Internet deployments, Internet of Things (IoTs) requires mobility support, location awareness and low latency. Fog Computing, also termed edge computing, is a promising solution for IoTs by extending the Cloud Computing paradigm to the edge of Internet. But how to locate fog nodes' sites and determine the scale of each fog node is a main challenge of Fog Computing systems, especially for time sensitive Fog Computing systems. In this paper, we try to deal with this problem by proposing an improved Fast Search and Find of Density Peaks-based fog node location strategy to locate the fog nodes' sites and determine the resources for each located fog node. To this end, we firstly formulate the fog node location of Fog Computing systems as a clustering problem with multi-constraints. Then we propose an improved Fast Search and Find of Density Peaks-based fog node location algorithm, which introduces the time sensitive feature of IoT applications and improves the Fast Search and Find of Density Peaks clustering algorithm to make this clustering algorithm more robustness and adaptability. The experiment results show that our fog node location strategy not also can avoid the NP-hard problem of the traditional server placement strategies, but also has low time complexity.

With the rapid growth of Internet of Things (IoT) applications, fog computing enables the IoT to provide efficient services by extending the cloud computing paradigm to the edge of the network. However, the existing attribute-based encryption schemes rarely focus on user and attribute revocation in fog computing and most of them still impose high computational and storage overhead on resource-limited IoT devices. In this article, we propose an efficient attribute-based encryption outsourcing scheme with user and attribute revocation for fog-enabled IoT. The proposed scheme improves the existing encryption scheme that uses the concept of attribute groups to achieve attribute revocation, making it suitable for fog computing and improving the efficiency of ciphertext update. In addition, it implements a novel method of user revocation in fog computing based on the characteristics of fog computing. In order to reduce the computing and storage burden of IoT devices, the heavy computation operations of encryption and decryption are outsourced to fog nodes and part of secret keys are stored in fog nodes. The security analysis shows that the proposed scheme is secure under the Decisional Bilinear Diffie–Hellman (DBDH) assumption. The performance analysis shows that the proposed scheme has high revocation efficiency and is efficient enough to be deployed in practical fog computing.

Fog computing is an encouraging technology in the coming generation to pipeline the breach between cloud data centers and Internet of Things (IoT) devices. Fog computing is not a counterfeit for cloud computing but a persuasive counterpart. It also accredits by utilizing the edge of the network while still rendering the possibility to interact with the cloud. Nevertheless, the features of fog computing are encountering novel security challenges. The security of end users and/or fog nodes brings a major dilemma in the implementation of real life scenario. Although there are several works investigated in the security challenges, physical layer security (PLS) in fog computing is not investigated in the above. The distinctive and evolving IoT applications necessitate new security regulations, models, and evaluations disseminated at the network edge. Notwithstanding, the achievement of the current cryptographic solutions in the customary way, many aspects, i.e., system imperfections, hacking skills, and augmented attack, has upheld the inexorableness of the detection techniques. Hence, we investigate PLS that exploits the properties of channel between end user and fog node to detect the impersonation attack in fog computing network. Moreover, it is also challenging to achieve the accurate channel constraints between end user and fog node. Therefore, we propose Q-learning algorithm to attain the optimum value of test threshold in the impersonation attack. The performance of the propose scheme validates and guarantees to detect the impersonation attack accurately in fog computing networks.

In 21st century, Internet of Things (IoT) is playing an important role in developing Smart cities. With the growth of IoT, data is growing with acceleration. As the data is growing the need to store data is also increasing. More the data, the latency will be high to store and retrieve data from the cloud. The concept of fog computing was originated to diminish the latency for accessing the data to and from the cloud. Fog computing imparts the storage, computing as well as networking services at the end point of the network. Fog nodes also have limited computational capabilities. Due to some weaknesses, fog computing and cloud computing cannot sustain alone, so both these technologies are integrated to build smart IoT infrastructure for Smart city. Fog computing have a major role and supreme responsibility in development of a Smart city. This paper discusses various applications of fog computing and their implementations in a Smart cities. It also proposes a prototype for Waste management system in a city. Fog computing can help to govern the waste collection of the city in a smart way. On the basis of our review, several open concerns and challenges of fog computing are discussed, and the directions for future researchers have also been discussed.

Internet of Things (IoT) allows billions of physical objects to be connected to collect and exchange data for offering various applications, such as environmental monitoring, infrastructure management, and home automation. On the other hand, IoT has unsupported features (e.g., low latency, location awareness, and geographic distribution) that are critical for some IoT applications, including smart traffic lights, home energy management and augmented reality. To support these features, fog computing is integrated into IoT to extend computing, storage and networking resources to the network edge. Unfortunately, it is confronted with various security and privacy risks, which raise serious concerns towards users. In this survey, we review the architecture and features of fog computing and study critical roles of fog nodes, including real-time services, transient storage, data dissemination and decentralized computation. We also examine fog-assisted IoT applications based on different roles of fog nodes. Then, we present security and privacy threats towards IoT applications and discuss the security and privacy requirements in fog computing. Further, we demonstrate potential challenges to secure fog computing and review the state-of-the-art solutions used to address security and privacy issues in fog computing for IoT applications. Finally, by defining several open research issues, it is expected to draw more attention and efforts into this new architecture.

As an extension of cloud computing, fog computing has received more attention in recent years. It can solve problems such as high latency, lack of support for mobility and location awareness in cloud computing. In the Internet of Things (IoT), a series of IoT devices can be connected to the fog nodes that assist a cloud service center to store and process a part of data in advance. Not only can it reduce the pressure of processing data, but also improve the real-time and service quality. However, data processing at fog nodes suffers from many challenging issues, such as false data injection attacks, data modification attacks, and IoT devices’ privacy violation. In this paper, based on the Paillier homomorphic encryption scheme, we use blinding factors to design a privacy-preserving data aggregation scheme in fog computing. No matter whether the fog node and the cloud control center are honest or not, the proposed scheme ensures that the injection data is from legal IoT devices and is not modified and leaked. The proposed scheme also has fault tolerance, which means that the collection of data from other devices will not be affected even if certain fog devices fail to work. In addition, security analysis and performance evaluation indicate the proposed scheme is secure and efficient.

Fog computing, which provides low-latency computing services at the network edge, is an enabler for the emerging Internet of Things (IoT) systems. However, due to the limited capacity of the fog devices, a large number of tasks will still be offloaded to the cloud for processing. This can overload the backbone network and cause excessive delays. In addition, the distribution of IoT devices is uneven. This paper groups fog nodes into a fog federation to increase the number of tasks that fog nodes can handle. The boom in fog computing has made fog pricing an important issue. But so far, no one has set a price for the federation. This article uses the Stackelberg model to simulate the interaction process between fog federation and IoT devices. First, fog devices bundle certain computing power, memory, and some other resources into a computing resource block (CRB). Then, fog devices act as a leader to release the price of CRB, and IoT devices act as a follower to determine the amount of CRB purchased. This article introduces how IoT can buy the right amount of CRB to minimize its cost and how CRB is priced to maximize the revenue of the fog federation.KeywordsFog computingCloud computingStackelbergInternet of Things

Internet of Things (IoT) is reshaping our daily lives by bridging the gaps between physical and digital world. To enable ubiquitous sensing, seamless connection and real-time processing for IoT applications, fog computing is considered as a key component in a heterogeneous IoT architecture, which deploys storage and computing resources to network edges. However, the fog-based IoT architecture can lead to various security and privacy risks, such as compromised fog nodes that may impede developments of IoT by attacking the data collection and gathering period. In this paper, we propose a novel privacy-preserving and reliable scheme for the fog-based IoT to address the data privacy and reliability challenges of the selective data aggregation service. Specifically, homomorphic proxy re-encryption and proxy re-authenticator techniques are respectively utilized to deal with the data privacy and reliability issues of the service, which supports data aggregation over selective data types for any type-driven applications. We define a new threat model to formalize the non-collusive and collusive attacks of compromised fog nodes, and it is demonstrated that the proposed scheme can prevent both non-collusive and collusive attacks in our model. In addition, performance evaluations show the efficiency of the scheme in terms of computational costs and communication overheads.

The rapid growth of Internet of Things (IoT) applications poses a great challenge to the computation capability of smart mobile equipments (SMEs). Fog Computing, as a promising technology, provides fast computing services for resource-limited SMEs in various applications. In this paper, we consider a blockchain-based fog computing network consisting of SMEs, fog nodes (FNs) and the cloud server. To optimize the delay and energy consumption of processing computation-intensive tasks, two offloading models are introduced, namely, task offloading to the device-to-device (D2D) cooperation group and to a nearby FN. Additionally, the blockchain technology is enabled to prevent malicious nodes from modifying with transaction information by maintaining a continuous tamper-proof ledger database. To reduce the delay and energy consumption of the traditional consensus mechanism, we propose the voting-based delegated proof of stake consensus mechanism, in which the FNs with the top half of votes will form a verification set, and the FNs will take turns being the manager to generate new blocks. Furthermore, to minimize the network cost, we jointly optimize task offloading decision, transmission rata allocation and computing resource allocation under various constraints. Finally, the effectiveness of the proposed scheme is demonstrated.

Internet of Things (IoT) is being applied to areas as smart-cities, home environment, agriculture, industry, etc. Developing, deploying and testing IoT projects require high investments on devices, fog nodes, cloud nodes, analytic nodes, hardware and software. New projects require high investments on devices, fog nodes, cloud nodes, analytic nodes, hardware and software before each system can be developed. In addition, the systems should be developed to test them, which implies time, effort and development costs. However, in order to decrease the cost associated to develop and test the system the IoT system can be simulated. Thus, simulating environments help to model the system, reasoning about it, and take advantage of the knowledge obtained to optimize it. Designing IoT simulation environments has been tackled focusing on low level aspects such as networks, motes and so on more than focusing on the high level concepts related to IoT environments. Additionally, the simulation users require high IoT knowledge and usually programming capabilities in order to implement the IoT environment simulation. The concepts to manage in an IoT simulation includes the common layers of an IoT environment including Edge, Fog and Cloud computing and heterogeneous technology. Model-driven development is an emerging software engineering area which aims to develop the software systems from domain models which capture at high level the domain concepts and relationships, generating from them the software artefacts by using code-generators. In this paper, a model-driven development approach has been developed to define, generate code and deploy IoT systems simulation. This approach makes it possible to design complex IoT simulation environments and deploy them without writing code. To do this, a domain metamodel, a graphical concrete syntax and a model to text transformation have been developed. The IoT simulation environment generated from each model includes the sensors, actuators, fog nodes, cloud nodes and analytical characteristics, which are deployed as microservices and Docker containers and where elements are connected by using publish-subscribe communication protocol. Additionally, two case studies, focused on smart building and agriculture IoT environments, are presented to show the simulation expressiveness.

In the fog computing (FC) models, a fog node supports application processes to calculate output data on input data from sensors and other fog nodes and sends the output data to target fog nodes which can calculate on the output data. In this paper, we consider the MPSFC (Mobile topic-based PS (publish/subscribe) FC) model where mobile fog nodes communicate with one another by publishing and subscribing messages in wireless networks. Subscription topics of a fog node denote input data on which the fog node can calculate and publication topics of a message show data carried by the message. A fog node only receives a message whose publication topics shares a common topic with the subscription topics. In the TBDT protocol proposed in our previous studies, a fog node only publishes a message of the output data to a target fog node in the communication range. Here, while a fewer number of messages are transmitted, the delivery ratio of messages in the TBDT protocol is smaller than the epidemic routing protocol. In this paper, we propose an ETBDT (Epidemic and Topic-Based Data Transmission) protocol in order to increase the delivery ratio, where mobile fog nodes forward messages to not only target nodes but also non-target nodes in the communication range. In the evaluation, we show the delivery ratio in the ETBDT protocol is larger than the TBTD protocol.