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The existing agriculture system is having several components such as supply chain management, crop insurance, the shipment of goods, and it involves numerous untrusted stakeholders such as users (farmers, retailers, customers, wholesalers, etc.), agencies (bank, insurance company) and regulators (assessor, surveyors). Due to the active engrossment of the middle man (mostly human beings or agencies operated by human beings) some key issues are raised such as transparency, timeliness, traceability, security, and immutability resulting in financial loss, crop contamination, and spoilage. Secure distributed public ledger technology and decentralized computing paradigm make Blockchain an appropriate alternative to resolve these issues and to achieve profitability & trust for all its stakeholders. In this research, we intend to propose a Blockchain-based mechanism viz. AgriOnBlock which would address the issues mentioned in the agriculture sector by connecting various stakeholders through the usage of IoT devices and smart contracts in Ethereum. We further discuss implications, constraints (methodological, awareness related, regulatory, etc.), and potential for actual adoption of AgriOnBlock.

The blockchain has been considered as a new decentralized computing paradigm that has great potential to meet various computing needs. Considering a private network (such as data center) where incentive mechanisms are not required, this paper innovatively remolds the transaction-recording blockchain for decentralized data processing. In our design, workers have different processing capacity and tasks have different resource requirements. Workers first get task information from the blockchain and then process tasks locally, and next perform the proof of useful work (PoUW) consensus to compete for a scheduler, according to the number of the consumed CPU instructions in data processing. The scheduler is responsible for dispatching task information to the blockchain. A salient feature of our decentralized data processing is that workers actively select tasks, instead of passively receiving tasks as in a centralized framework. This will lead to collisions (i.e., multiple workers select the same task). To alleviate the collisions and provide the max-min fairness of data-processing, we propose a modified fair queue (called M-FQ) algorithm for the scheduler, as well as a fair task selection with collision avoidance (called Fair-CA) scheme for workers. Extensive simulations verify that our framework can well balance the fairness and the collision, while achieving as high throughput and good fairness as centralized frameworks. This study is the first attempt toward designing a general decentralized computing framework.

In recent years, it has been observed the exponential growth of the Internet of Things (IoT) in different application fields, such as manufacturing and energy industry. To effectively fuse and process the tremendous amount of IoT sensing data timely, there is an urgent need to shift from a conventional centralized computing to a decentralized computing. However, there remain some essential technical challenges to develop effective decentralized computing methods in the context of IoT applications, including 1) the timely response, sufficient privacy preservation, and high security are normally required in IoT-related applications and 2) the biases and non-independent identically distributed (IID) properties potentially presented in the IoT sensing data. To address these challenges, in this article, we propose a decentralized deep learning paradigm with privacy-preservation and fast few-shot learning (DDLPF) by exploiting federated learning, metalearning, and blockchain techniques. In the simulation section, we evaluate the performance of our proposed DDLPF paradigm in different scenarios and compare it with other existing techniques.

With the exponential growth in the number of Internet-of-Things (IoT) devices, the cloud-centric computing paradigm can hardly meet the increasingly high requirements for low latency, high bandwidth, ease of availability, and more intelligent services. Therefore, a distributed and decentralized computing architecture is imperative, where edge-centric computing, such as fog computing and mist computing, has been recently proposed. Edge-centric computing resources can be managed locally and personally rather than being administered by a remote centralized third party. However, security and privacy issues are the main challenges due to the absence of trust between the IoT devices and edge computing nodes (ECNs). A blockchain, as a decentralized, trustless, and immutable public ledger, can well solve the trust-absence issue. In this article, we first elaborate on the security and privacy issues of edge-computing-enabled IoT, and then present the key characteristics of blockchains, which make blockchains well suited for the edge-centric IoT scenarios. Furthermore, we propose a general framework for blockchain-based edge-computing-enabled IoT scenarios that specifies the step-by-step procedure of a single transaction between an IoT end and an ECN. In addition, we design a smart contract within a private blockchain network that exploits the state-of-the-art machine learning algorithm, asynchronous advantage actor–critic (A3C), to allocate the edge computing resources, which exemplifies how artificial intelligence (AI) can be combined with blockchains. We further discuss the benefits of the convergence of AI and blockchains. Finally, simulation results are presented.

Nowadays AM is a rapidly growing and emerging discipline in manufacturing, as well as AI is in informational applications. Both are related to logistical and self-referential/-copying concepts which make them scalable. What is in AM osmotic mass production spreading is in AI-related Cyber-Physical Systems (CPS) the osmotic computational approach. AI-AM self-propagatedly framed is itself an emerging field, which can be logically or systematically unified. The paper investigates firstly recent developments in the field of the AM process flow and how it is related to AI applications. The result is a list of logistical, organisational, and industrial process steps as well as modern and future AI-AM applications. The extended approach then gives prospect to a meta-perspectively embedded osmotic decentralized computing, as well as an osmotic manufacturing paradigm, which utilizes glocal functions, concerning local production as well as global distributed material and information transport nets and their connection graphs.

Fog computing (or fogging) is extending cloud computing to the edge of the network. It describes a decentralized computing structure located between the cloud and devices that produce data. Operations like computing, storing and networking between end devices and data centers of cloud computing are facilitated by Fog computing. Decentralization and flexibility are the main difference between Fog computing and Cloud computing. Advantages of Fog computing include, minimize latency, conserve network bandwidth, reduce operating costs, enhance security, improve reliability, deepen insights and boost business agility. Various applications of Fog computing are, smart cities and smart electric grids, smart transportation networks, connected cars, real-time analytics etc. Here, this paper presents a comparative study of Cloud computing and Fog computing.

Feeding all relevant inputs to a central solver is the obvious way to tackle a problem, but it's not always the only way. Decentralized methods that make do with only local communication and local computation are sometimes the best way. This episode of Drill Bits reviews an elegant protocol for self-organizing wireless networks that can also solve a seemingly impossible social networking problem. The protocol preserves privacy among participants and is so simple that it can be implemented with pencil, paper, and postcards. Example software implements both the decentralized protocol and a centralized solver.

Intelligent Transportation Systems (ITSs) have been widely deployed to provide traffic sensing data for a variety of smart traffic applications. However, the inevitable and ubiquitous missing data potentially compromises the performance of ITSs and even undermines the traffic applications. Therefore, accurate and real-time traffic data recovery is crucial to ITSs and its related services, especially for large-scale traffic networks. To leverage the characteristics in transportation networks for data recovery, we first conduct experimental explorations on a large-scale traffic dataset of an ITS and further quantify the spatiotemporal correlations of traffic data. Inspired by the observation results, we propose GTR , an edGe computing-empowered system for large-scale Traffic data recovery with low-Rank theory. GTR leverages the decentralized computing power of edge nodes to process massive traffic data from hundreds of traffic stations for accurate and real-time recovery. Specifically, we first propose a suboptimal edge node deployment algorithm with a theoretical performance guarantee, by exploiting the supermodularity in the NP-hard joint-optimization problem. Furthermore, to leverage the low-rank nature of traffic data, we transform the data recovery problem into a low-rank minimization problem, then utilize the fixed-point continuation iterative scheme to capture spatiotemporal correlations for accurate traffic recovery. Finally, the extensive trace-driven evaluations show that GTR only needs at most 5.7% extra total cost compared to the optimal deployment, while outperforming four baseline methods by 63.8% improvement in terms of traffic data recovery accuracy.

In this work, we aim to classify nodes of unstructured peer-to-peer networks with communication uncertainty, such as users of decentralized social networks. Graph Neural Networks (GNNs) are known to improve the accuracy of simple classifiers in centralized settings by leveraging naturally occurring network links, but graph convolutional layers are challenging to implement in decentralized settings when node neighbors are not constantly available. We address this problem by employing decoupled GNNs, where base classifier predictions and errors are diffused through graphs after training. For these, we deploy pre-trained and gossip-trained base classifiers and implement peer-to-peer graph diffusion under communication uncertainty. In particular, we develop an asynchronous decentralized formulation of diffusion that converges to centralized predictions in distribution and linearly with respect to communication rates. We experiment on three real-world graphs with node features and labels and simulate peer-to-peer networks with uniformly random communication frequencies; given a portion of known labels, our decentralized graph diffusion achieves comparable accuracy to centralized GNNs with minimal communication overhead (less than 3% of what gossip training already adds)., 12 pages, 4 figures, 2 tables, accepted manuscript, IEEE Access

Non-fungible tokens are one of the most important future application domains for smart contracts. Ethereum is the pioneer of a blockchain-based decentralized computing platform that has ultimately standardized these types of tokens into a well-defined interface, now known as ERC-721. Blockchain-based cryptocurrencies have received extensive attention recently. Massive data has been stored on permissionless blockchains. This paper aims to analyze blockchain and cryptocurrencies’ technical underpinnings, specifically non-fungible tokens or “crypto-collectibles,” with the help of a blockchain-based image matching game. While outlining the theoretical implications and use cases of NFTs, this paper also gives a glimpse into their possible use in the domain of human user verification to prevent misuse of public data by automated scripts. This demonstrates the interaction of the ERC-721 token with the Ethereum-based decentralized application. Further, we aim to reach a definitive conclusion on the benefits and challenges of NFTs and thus reach a solution that would be beneficial to both researchers and practitioners.

On every developed or developing world, the Land Administration System (LAS) is a salient infrastructure. Both the digital (traditional database system) and the manual methods are applied in the LAS (paper-based documentations). Both of these systems provide an authority with monopoly power that can increase unethical conduct in land administration. Again, while the Blockchain technology was initially used to maintain a financial ledger, it is possible to expand the use of this technology to incorporate any decentralized computing structures, including the automated record keeping and management framework. Recent studies have found that the new and largely untested applications of Blockchain technology in land administration remain. In this work, the implementation of a blockchain-based system is proposed to build a System for Land Administration. In the Ethereum blockchain platform, the proposed framework was simulated and showed that the framework contributed to developing a stable, secure, effective and efficient system of land administration.

Fog computing also referred as “fogging” or “fog networking” through which a decentralized computing structure is explained that is situated among the data producing devices as well as cloud. In logical locations, users are enabled for placing resources that include data as well as applications produced by them, with such flexible structure for enhancing the performance. The fog computing involves edge computing, cloud's power as well as various services which are brought closer to location where data is produced as well as processed. The terms edge computing and fog computing is utilized by various peoples interchangeably, as the primary aim of both technologies involves bringing intelligence as well as processing closer wherein data is developed. Generally, it is performed for enhancing efficiency, however, it can also be performed for compliance and security reasons. This paper explores the different concepts involved in fog computing, importance of fog computing and highlights various opportunities in the fog computing area. Paper also presents the key points that why this technology needs to be successful. Finally, it addresses various issues, challenges in fog computing that it should deal with.

Comprehensive modeling of a whole cell requires an integration of vast amounts of information on various aspects of the cell and its parts. To divide and conquer this task, we introduce Bayesian metamodeling, a general approach to modeling complex systems by integrating a collection of heterogeneous input models. Each input model can in principle be based on any type of data and can describe a different aspect of the modeled system using any mathematical representation, scale, and level of granularity. These input models are 1) converted to a standardized statistical representation relying on probabilistic graphical models, 2) coupled by modeling their mutual relations with the physical world, and 3) finally harmonized with respect to each other. To illustrate Bayesian metamodeling, we provide a proof-of-principle metamodel of glucose-stimulated insulin secretion by human pancreatic β-cells. The input models include a coarse-grained spatiotemporal simulation of insulin vesicle trafficking, docking, and exocytosis; a molecular network model of glucose-stimulated insulin secretion signaling; a network model of insulin metabolism; a structural model of glucagon-like peptide-1 receptor activation; a linear model of a pancreatic cell population; and ordinary differential equations for systemic postprandial insulin response. Metamodeling benefits from decentralized computing, while often producing a more accurate, precise, and complete model that contextualizes input models as well as resolves conflicting information. We anticipate Bayesian metamodeling will facilitate collaborative science by providing a framework for sharing expertise, resources, data, and models, as exemplified by the Pancreatic β-Cell Consortium.

With the emergence of the ever-increasing vehicular applications and booming Internet services, the requirements of low-latency and high efficient transmission among vehicles become urgent to meet, and their corresponding solutions need to be well investigated. To resolve the above challenges, we propose a fog computing-based content transmission scheme with collective filtering in edge of vehicles. We first provide a system model based on fog-based rode side units by considering location-awareness, content-caching and decentralized computing. Then, a content-caching strategy in RSUs is designed to minimize the downloading latency. Specifically, we model the moving vehicles with the two-dimensional Markov chains, and calculate the probabilities of file caching in RSUs to minimize the latency in file downloading. Each vehicle can also maintain a neighbor list to record the encounters with high similarities, and update it based on the historic and real-time contacts. Finally, we carry on the experiments based on the real-world taxi trajectories in Beijing and Shanghai, China. Simulation results demonstrate the effectiveness of our proposed method.

The deployment of traditional computing, control and monitoring paradigms in modern smart grids is characterized by several severe limitations. These could restrict their capability to manage the huge complexities and the vast penetration of distributed and renewable energy resources that are expected in future operational scenarios. In this context, the design of more scalable and more flexible solution paradigms represents a relevant issue to address. The enabling technologies and methodologies aimed at addressing these complex challenges include decentralized computing, self-organizing sensor networks, proactive control, and holistic computing frameworks. Accordingly, in this paper a selection from literature published on these topics will be analyzed and reviewed, in order to outline the most relevant research trends, and the main open problems.

Mobile applications nowadays generally consist of a frontend component running on the device, and a backend component running on the cloud that performs the larger computations. However, this usage model leads to high costs for developing the application (since a cloud infrastructure that should scale to the number of users must be maintained), and to a potentially bad user experience (if the latency is high or the users employ mobile broadband they pay for). Thus, we introduce the Drop Computing paradigm, which proposes the concept of decentralized computing over multilayered networks, combining cloud and wireless technologies over a social crowd formed between mobile and edge devices. Mobile devices and people interconnect to form ad-hoc dynamic collaborations to support the equivalent of a crowd-based edge multilayered cloud of clouds, where the capabilities of any mobile device are extended beyond the local technology barriers, to accommodate external resources available in the crowd of other devices. Thus, instead of every data or computation request going directly to the cloud, Drop Computing employs the mobile crowd formed of devices in close proximity for quicker and more efficient access. Devices in the mobile crowd are leveraged for requesting already downloaded data or performing computations, and the cloud acts as the second (or even third) option. We present a proof-of-concept implementation of Drop Computing and show, through simulations, that it is feasible for real-life usage, since it is able to drastically reduce costs while not affecting or even improving the user experience.

© 2018 Elsevier B.V. Fog computing (fog networking) is known as a decentralized computing infrastructure in which data, applications, compute as well as data storage are scattered in the most logical and efficient place among the data source (i.e., smart devices) and the cloud. It gives better services than cloud computing because it has better performance with reasonably low cost. Since the cloud computing has security and privacy issues, and fog computing is an extension of cloud computing, it is therefore obvious that fog computing will inherit those security and privacy issues from cloud computing. In this paper, we design a new secure key management and user authentication scheme for fog computing environment, called SAKA-FC. SAKA-FC is efficient as it only uses the lightweight operations, such as one-way cryptographic hash function and bitwise exclusive-OR (XOR), for the smart devices as they are resource-constrained in nature. SAKA-FC is shown to be secure with the help of the formal security analysis using the broadly accepted Real-Or-Random (ROR) model, the formal security verification using the widely-used Automated Validation of Internet Security Protocols and Applications (AVISPA) tool and also the informal security analysis. In addition, SAKA-FC is implemented for practical demonstration using the widely-used NS2 simulator.

Recently AI technologies, especially Deep Neural Network (DNN), have been widely used in the financial industry, such as stock price and movement prediction. In order to develop an AI-based solution for the global financial market, daily-based DNN model training for each stock is required to need collaboration among a large scale of entities. However, it is usually challenging due to data privacy, the cost of AI computing, and the lack of motivation to share information. This research proposes a novel Blockchain based platform, which utilizes the decentralized network, federated learning, and master-node to tackle these issues. The decentralized computing framework of federated learning, along with transfer learning, is applied to meet the data privacy requirements. Furthermore, the proposed federated learning platform with collaborative training is built on a decentralized AI computing cloud, which is highly affordable compared to centralized AI clouds. The master-node of Blockchain technology is further employed to enable the scalable global financial service, and effective rewards are applied to incentivize information sharing as well. We have applied the proposed Blockchain based platform to the stock prediction global service, which demonstrates the platform is practical and useful.

When a user starts exploring items from a new area of an e-commerce system, cross-domain recommendation techniques come into help by transferring the abundant knowledge from the user's familiar domains to this new domain. However, this solution usually requires direct information sharing between service providers on the cloud which may not always be available and brings privacy concerns. In this paper, we show that one can overcome these concerns through learning on edge devices such as smartphones and laptops. The cross-domain recommendation problem is formalized under a decentralized computing environment with multiple domain servers. And we identify two key challenges for this setting: the unavailability of direct transfer and the heterogeneity of the domain-specific user representations. We then propose to learn and maintain a decentralized user encoding on each user's personal space. The optimization follows a variational inference framework that maximizes the mutual information between the user's encoding and the domain-specific user information from all her interacted domains. Empirical studies on real-world datasets exhibit the effectiveness of our proposed framework on recommendation tasks and its superiority over domain-pairwise transfer models. The resulting system offers reduced communication cost and an efficient inference mechanism that does not depend on the number of involved domains, and it allows flexible plugin of domain-specific transfer models without significant interference on other domains.

Fog computing is a decentralized computing infrastructure in which data, compute, storage and applications are located somewhere between the data source and the cloud. It usually adopts convenient and flexible distributed services, which can realize low-cost and real-time data analysis and intelligent control. Efficient communication and fog/edge collaboration have become popular research issues. In this paper, the offloading problem of dependent tasks in fog/edge collaborative environment is studied. Dependent task is modeled as a directed acyclic graph (DAG), and the scenario that fog nodes are configured with heterogeneous multi-core servers is considered. According to task dependencies and energy consumption requirements, all subtasks executed on different edge devices are prioritized, and the Priority and Dependency-based DAG Tasks Offloading Algorithm (PDAGTO) is proposed. Simulation results have shown that, compared with the existing work, the proposed algorithm can effectively reduce the average delay and the total energy consumption of during the procedure of task offloading.

Land Administration System (LAS) is a salient infrastructure for any developed or developing country. The LAS is implemented both in digital form (traditional database system) and the manual approaches (paper-based documentations). Both of these systems provide monopoly control to an authority which may increase corrupt behavior in land administration. Again, though the Blockchain technology was originally introduced for keeping a financial ledger; the utilization of this technology can be extended to implement any decentralized computing systems including the digital record keeping and management system. Recent studies found that uses of Blockchain technology in land administration remain new and relatively untested. Therefore, in this work, a blockchain-based framework is proposed to develop a land administration system. The proposed framework was simulated in Ethereum blockchain platform; and showed that the framework contributed to develop a secure, reliable, scalable, effective, and efficient land administration system.

Blockchain technology originates from the Bitcoin system, and due to its great success it can draw awareness to other fields because of its global acceptable features like decentralization, anonymity, and audibility. Other than finance, blockchain technology has evolved in various applications like legal, supply chain, health care, etc. Though blockchain technology is a bit difficult to maintain and monitor, still a blockchain network is more secure than other technologies. In this work, we want to give some avenues of the blockchain security system and also highlight some specific security threats. The issues related to mining or fork problems have also been discussed. It is found that several unexpected fields contribute to security concerns rather than decentralized computing and any cryptographic issues.

Edge computing is a decentralized computing infrastructure in which data, calculation, storage and applications are located somewhere between the data source and the computing facilities. While the edge servers enjoy the close proximity to the end-users to provide services at reduced latency and lower energy costs, we use from limitations in computational and radio resources, which calls for smart, quality-of-service (QoS) guaranteed and efficient task scheduling methods and strategies. For addressing the edge-environment-oriented multi-workflow scheduling problem, in this paper, we propose a probabilistic-QoS-aware approach to multi-workflow scheduling over edge servers with time-varying QoS. Our proposed method leveraged a probability-mass function-based QoS aggregation model and a discrete firefly algorithm for generating the multi-workflow scheduling plans. In order to prove the effectiveness of our proposed method, we conducted an experimental case study based on varying types of workflows and a real-world dataset for edge server positions. It can be seen that our method clearly outperforms its competitors in terms of completion time, cost, and deadline validation rate.

Cloud computing is a new computational model for decentralized computing which has become very popular because of the “on-demand” service. Cloud computing is built up from cluster computing and grid computing. The cloud service helps to access our information anywhere and anytime. Many people believe that cloud computing is going to reshape Information technology Companies as a revolution. Strategies including encryption and decryption processing are taken to secure cloud computing and cloud storage, data security, and data transmission. Many companies have their cloud computing services like Toyota, Citibank, Amazon, Google, Microsoft, etc. As we all know, storage is one of the most significant expenditures on Information technology projects and companies. Traditionally, we store data in Pen drive, compact disc, hard drive, etc., but to meet this need, we started saving our data in the cloud because there are many advantages of cloud storage over traditional storage and it has also become popular in the last few years. Cloud storage is a storage in which we can store data online in the cloud. Cloud storage provides us many benefits like disaster healing, easy access, and many more. This work aims to provide a means of understanding the topic of cloud computing and cloud storage.