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The protection of original achievements including patents and copyrights is a significant and critical issue in today's knowledge-driven economy. In this article, we propose and develop a blockchain service architecture to enhance information traceability and protection effectiveness. Our proposed service architecture is enabled by the core functionalities of blockchain technology including distributed data storage, peer-to-peer transmission, consensus mechanisms, and encryption algorithms. Through this blockchain-based traceability system, we can record, secure, validate, and track original achievements’ registrations and other related transactions. The results of our simulation experiments further validate the effectiveness and robustness of the proposed traceability system design. Our experimental analysis shows that the proposed blockchain-based design can effectively enhance original achievements traceability with an embedded automatic incentive rewarding mechanism for both the creation and protection of original achievements. Moreover, our performance analysis and comparative analysis show that the proposed method has more advantages over several other methods. While our study provides an effective solution to address the existing challenges in intellectual property rights and achievements patents protection, the breadth of the blockchain concept and its applications require nuanced investigations in many different contexts. Thus, the proposed blockchain-based traceability system highlights the potential for broader applications of this disruptive technology in many other domains.

Erasure coding is widely used in distributed storage systems (DSSs) to efficiently achieve fault tolerance. However, when the original data need to be updated, erasure coding must update every encoded block, resulting in long update time and high bandwidth consumption. Exiting solutions are mainly focused on coding schemes to minimize the size of transmitted update information, while ignoring more efficient utilization of bandwidth among update racks. In this paper, we propose a parallel Cross-rack Pipelining Update scheme (CPU), which divides the update information into small-size units and transmits these units in parallel along with an update pipeline path among multiple racks. The performance of CPU is mainly determined by slice size and update path. We formulate the proposed pipelining update scheme as an optimization problem, based on a new theoretical pipelining update model. We prove the optimization problem is NP-hard and develop a heuristic algorithm to solve it based on the features of practical DSSs and our implementations, including Big chunk and Small overhead. We implement CPU and conduct experiments on Amazon EC2 under a variety of scenarios. The results show that CPU can reduce the average update time by 48.2%, compared with the state-of-the-art update schemes.

Current systems used by medical institutions for the management and transfer of Electronic Medical Records (EMRs) can be vulnerable to security and privacy threats. In addition, these systems are centralized, often lack interoperability, and give patients limited or no access to their own EMRs. In this paper, we propose a novel distributed data sharing scheme that applies the security benefits of blockchain to address these concerns. We deploy smart contracts on Ethereum blockchain and utilize a distributed storage system to alleviate the dependence on the record-generating institutions to manage and share patient records. To preserve privacy of patient records, we implement our smart contracts as a method to allow patients to verify attributes prior to granting access rights. Our proposed scheme also facilitates selective sharing of medical records among staff members that belong to different levels of a hierarchical institution. We provide extensive security, privacy, and evaluation analyses to show that our proposed scheme is both efficient and practical.

We consider the problem of private computation (PC) in a distributed storage system. In such a setting a user wishes to compute a function of $f$ messages replicated across $n$ noncolluding databases, while revealing no information about the desired function to the databases. We provide an information-theoretically accurate achievable PC rate, which is the ratio of the smallest desired amount of information and the total amount of downloaded information, for the scenario of nonlinear computation. For a large message size the rate equals the PC capacity, i.e., the maximum achievable PC rate, when the candidate functions are the $f$ independent messages and one arbitrary nonlinear function of these. When the number of messages grows, the PC rate approaches an outer bound on the PC capacity. As a special case, we consider private monomial computation (PMC) and numerically compare the achievable PMC rate to the outer bound for a finite number of messages., Comment: 5 pages, 1 figure, 1 table. Presented at the 2019 IEEE Information Theory Workshop (ITW). Figure 1 is updated as it contained incorrect data-points for $f=2$ and $g=3$. arXiv admin note: text overlap with arXiv:2003.10007

The pricing scheme plays a vital role in the demand-side management program. It allows utility, indirect control over the users energy consumption. This article proposes a new pricing structure, named as a real-time variable peak pricing scheme with a self-inbuilt feature to capture the benefit of both the existing pricing scheme, real-time pricing (RTP), and time of use (TOU), by switching its behavior between RTP and TOU as per the requirements. A home energy management model with comprehensive modeling of home appliances together with distributed storage and dispatchable generation is considered for testing the impact of the proposed pricing scheme and compared to existing RTP and TOU. The privacy and comfort of users are taken care of in the system modeling. An aggregative game approach is used to capture the interaction between users and utility. The conditions for the existence of Nash equilibrium and convergence of the proposed algorithm is investigated for all three pricing schemes considering residential users. The analysis shows the effectiveness of the proposed pricing scheme compare to the existing pricing schemes in terms of consumers energy bill and system peak reduction, complexity, and incentive to active consumers to participate in demand-side management programs.

As an emerging technology, blockchain has achieved great success in numerous application scenarios, from intelligent healthcare to smart cities. However, a long-standing bottleneck hindering its further development is the massive resource consumption attributed to the distributed storage and consensus mechanisms. This makes blockchain suffer from insufficient performance and poor scalability. In this article, we evaluate numerous representative blockchain proposals in the past decade and demonstrate that the potential of the widely adopted consensus-based scaling is seriously limited, especially in the current era when Moore’s law-based hardware evolution is about to end. We achieve this by developing an open-source benchmarking tool, called $\text{Prism} , with which we further investigate the resource-related factors that hinder the current scaling attempts. Furthermore, we discuss various topology and hardware innovations which could help to scale up blockchain. Generally speaking, this article provides blockchain researchers many valuable insights because it explores the next-generation scaling strategies by conducting a large-scale benchmarking.

IoT deployments that have limited memories lack sustained computation power and have limited connectivity to the Internet due to intermittent last-mile connectivity, particularly in rural and remote locations. For maintaining congestion-free operations, most of the collected data from these networks are discarded, instead of being transmitted remotely for further processing. In this article, we propose the paradigm Timed Loop Storage to distribute the data and use the underutilized bandwidth of local network links for sequentially queuing packets of computational data that are being operated on in parts in one of the IoT nodes. While the sequenced packets are executed sequentially on the target IoT device, the remaining packets, which are currently not being operated on, distribute and keep looping over the network links until they are required for processing. A time-synchronized packet deflection mechanism on each node handles data transfer and looping of individual packets. In our implementation, although we observe that the proposed approach requires data rates of 6 Mbps, it incurs only 45 Kb usage of primary storage systems even for sizeable data, ensuring scalability of the connected IoT devices’ temporary storage capabilities, thereby making it useful for real-life applications.

Presently many searchable encryption schemes have been proposed for cloud and fog computing, which use fog nodes (or fog servers) to partly undertake some computational tasks. However, these related schemes still retain cloud servers to undertake most computational tasks, which result in large communication costs between edge devices and cloud servers. Therefore, in this paper we propose a self-verifiable attribute-based keyword search scheme for distributed data storage (SV-KSDS) in full fog computing, where each decryption operation on the data required by a user must meet the negotiated decryption rule between fog servers. Our SV-KSDS scheme first provides attribute-based distributed data storage among fog servers through the (w,σ) threshold secret-sharing scheme, where fog servers can provide self-verifiable keyword search and data decryption for terminal users. Compared with the data storage in cloud computing, our scheme extends it to the distributed structure while providing fine-grained access control for distributed data storage through attribute-based encryption. The access control policy of our scheme is constructed on linear secret-sharing scheme, whose security is reduced to the decisional bilinear Diffie-Hellman assumption against chosen-keyword attack and the decisional q-parallel bilinear Diffie-Hellman assumption against chosen-plaintext attack in the standard model. Based on theoretical analysis and practical testing, our SV-KSDS scheme generates less computation and communication costs, which further unloads some computational tasks from terminal users to fog servers so as to reduce computing costs of terminal users.

To adapt to the increasing storage demands and varying storage redundancy requirements, practical distributed storage systems need to support storage scaling by relocating currently stored data to different storage nodes. However, the scaling process inevitably transfers substantial data traffic over the network. Thus, minimizing the bandwidth cost of the scaling process is critical in distributed settings. In this paper, we show that optimal storage scaling is achievable in erasure-coded distributed storage based on network coding, by allowing storage nodes to send encoded data during scaling. We formally prove the information-theoretically minimum scaling bandwidth for both scale-out and scale-in cases. Based on our theoretical findings, we also build a distributed storage system prototype NCScale based on Hadoop Distributed File System, so as to realize network-coding-based scaling while preserving the necessary properties for practical deployment. Experiments on Amazon EC2 show that the scaling time can be reduced by up to 50% over the state-of-the-art.

Low-latency data service is an increasingly critical challenge for data center applications. In the modern distributed storage systems, proper data placement helps reduce the data movement delay, which can contribute to the service latency reduction tremendously. Existing data placement solutions have often assumed the prior distribution of data requests or discovered it via trace analysis. However, data placement is a difficult online decision-making problem faced with dynamic network conditions and time-varying user request patterns. The conventional static model-based solutions are less effective to handle the dynamic system. With an overall consideration of data movement and analytical latency, we develop a reinforcement learning-based framework DataBot+, automatically learning the optimal placement policies. DataBot+ adopts neural networks, trained with a variant of Q-learning, whose input is the real-time data flow measurements and whose output is a value function estimating the near-future latency. For instantaneous decision making, DataBot+ is decoupled into two asynchronous production and training components, ensuring that the training delay will not introduce extra overheads to handle the data flows. Evaluation results driven by real-world traces demonstrate the effectiveness of our design.

The base station-mobile device communication traffic has dramatically increased recently due to mobile data, which in turn heavily overloaded the underlying infrastructure. To decrease Base Station (BS) interaction, intra-cell communication between local devices, known as Device-to-Device, is utilized for distributed data caching. Nevertheless, due to the continuous departure of existing nodes and the arrival of newcomers, the missing cached data may lead to permanent data loss. In this study, we propose and analyze a class of Low-Density Parity Check (LDPC) codes for distributed data caching in cellular networks. Contrary to traditional distributed storage, a novel repair algorithm for LDPC codes is proposed which is designed to exploit the minimal direct BS communication. To assess the versatility of LDPC codes and establish performance comparisons to classic coding techniques, novel theoretical and experimental evaluations are derived. Essentially, the theoretical/numerical results for repair bandwidth cost in presence of BS are presented in a distributed caching setting. Accordingly, when the gap between the cost of downloading a symbol from BS and from other local network nodes is not dramatically high, we demonstrate that LDPC codes can be considered as a viable fault-tolerance alternative in cellular systems with caching capabilities for both low and high code rates.

Distributed machine learning has attracted much attention in the last decade with the widespread use of the Internet of Things. As a generative model, Generative Adversarial Network (GAN) has excellent empirical performance. However, the distributed storage of data and the fact that data cannot be shared for privacy reasons in a federated learning setting bring new challenges to training GAN. To address this issue, we propose private FL-GAN, a differentially private GAN based on federated learning. By strategically combining the Lipschitz condition with differential privacy sensitivity, our model can generate high-quality synthetic data without sacrificing the training data’s privacy. When communication between clients becomes the main bottleneck for federated learning, we propose to use a serialized model-training paradigm, which significantly reduces communication costs. Considering the distributed data is often non-IID in reality, which poses challenges to modeling, we further propose universal private FL-GAN to approach this problem. We not only theoretically prove that our algorithms can provide strict privacy guarantees with differential privacy, but also experimentally demonstrate that our models can generate satisfactory data while protecting the privacy of the training data, even if the data is non-IID.

It is challenging for complex urban transportation networks to recommend taxi waiting spots for mobile passengers because the traditional centralized mining platform cannot address the storage and calculation problems of GPS trajectory big data, and especially the boundary identification of DBSCAN is difficult on the Spark parallel processing framework. To this end, we propose a parallel DBSCAN optimization algorithm with the silhouette coefficient and the pickup rate on Spark in this paper, named SP-DBSCAN, where users merely input one parameter to complete the distributed recommendation of the best waiting spot. Specifically, under the Hadoop distributed computing platform, a general framework of distributed modeling for waiting spot recommendation on Spark is developed to solve the distributed storage and parallel computing issues of the serial algorithm in handling data partition and clustering of large-scale traffic data on a single machine. Moreover, we put forward a parallel SP-DBSCAN algorithm on Spark to recommend the best waiting spot for passengers, where the traditional DBSCAN algorithm is optimized via the silhouette coefficient and the boarding ratio to address the parameter sensitive problem and the issue of the center of the non-convex clustering graph is solved by giving one cluster with two centroids in the clustering hotspot areas. Finally, experimental results on four groups of real-world taxi GPS trajectory data sets demonstrate that compared with C-DBSCAN and P-DBSCAN, the recognition rate of SP-DBSCAN is increased by 1.6%, 6.2%, 3.47%, and 5.8%, respectively. The empirical study indicates that the clustering region generated by our SP-DBSCAN algorithm can satisfy the requirements that passengers can ride in the hotspot area when they have not successfully hitchhiked at a specific location and turned to the next spot randomly.

With the continuous development of information technology, the amount of data in information systems continues to increase. Big data has become an inevitable trend in the development of information technology, and the problems faced by the secure storage of big data have also become problems that must be solved. On the one hand, the research in this paper meets the storage requirements of unstructured massive data. On the other hand, it applies digital certificate authentication technology to realize storage security identity authentication based on digital certificate, and solves the application problem of high-security identity authentication in distributed storage. The innovation of this paper is to use the corresponding virtual storage space mapping algorithm to realize the unified management of resource pool. And before data transmission, the digital certificate authentication method is used to realize the authority control of the storage resource pool. This paper has completed the design and implementation of the distributed secure storage system, and completed the related testing and verification work, which significantly improved the performance of data storage and solved the problem of big data secure storage.

Fog computing needs, particularly in terms of performance, availability and reliability, are increasing every day due to the rapid growth in the number of connected Internet of Things devices and, consequently, the quantitative explosion in the volume of data generated. This study aims to improve the management of collected data to maximize the performance of Fog computing. To this end, a dynamic architecture for the distributed storage of collected data in Fog environments, based on Fog collaboration, is proposed. Dynamicity is related to the Fog and sensors mobility, but distribution is related to data storage in Fog computing resources. In addition, a supplementary Fog layer and a Fog assignment table are used for a good achievement of the proposed architecture and efficient management of Fog sensor associations. The simulation experiments show that the collaborative distributed storage significantly improves the performance of the Fog in terms of data availability and end-user latency. Subsequently, the proposed solution minimizes the number of queries to the cloud.

With the rapid development of intelligent technology, people’s lives have gradually entered the era of information and intelligence. Traditional vision-based and sensor-based human motion detection methods have shortcomings such as high illumination requirements, difficulty in preserving privacy, and poor portability. The wireless sensor detection method based on a wireless network can overcome the shortcomings of these methods, so it has become a hot research direction. Human motion parameters are very important data. If leaked, it may cause unimaginable harm to individuals. Blockchain technology is an emerging technology in the field of information technology. Its decentralized nature, distributed storage, and data is not easy. The modification provides a feasible solution for us to realize the data upload. Combining the advantages of wireless sensing and blockchain technology, this paper proposes a method of wireless sensing collection of human motion parameters based on blockchain, and designs a human motion recognition system. The experiment found that under the experiments given in this article, the traditional accuracy rate of sensor data collection is 71.82%, and the accuracy rate of the wireless sensing collection method of human motion parameters based on the blockchain in this article has reached 95.31%, which verifies the effectiveness and superiority of the system designed in this article.

Blockchain, the underlying technology of Bitcoin, has been deeply studied in various fields after its development in recent years. As a typical decentralized distributed data storage system, consensus reached among all participants in a blockchain system requires a consensus mechanism to be realized. In order to make blockchain applicable to different application scenarios, different consensus mechanisms have been proposed. With the further development of blockchain applications, more and more studies have been conducted on the consensus mechanism. However, some existing consensus mechanisms still have some problems in various aspects. Therefore, this paper proposes a trade deal algorithm based on the blockchain mechanism of consensus. First of all, according to PBFT, the lack of a dynamic problem in the VPBFT voting mechanism was introduced. The node system is divided into four types with different responsibilities and gives the number of relations between nodes. When the number of nodes is changed, it can be calculated according to the quantity relation, ensuring dynamic. Second, a data anonymous transaction and authentication protocol is designed. In the protocol, when the seller sells data, the mapping relationship between the real identity and the false identity of the data owner is blinded and sent to the buyer. When the buyer wants to verify their identity, the seller’s identity can only be verified with the authentication of the blockchain. The proposed algorithm is superior to the current consensus in terms of time and energy consumption, throughput, and fault tolerance methods, which is proven through experimental tests and simulation analysis.

Redundant storage maintains the performance of distributed systems under various forms of uncertainty. This paper considers the uncertainty in node access and download service. We consider two access models under two download service models. In one access model, a user can access each node with a fixed probability, and in the other, a user can access a random fixed-size subset of nodes. We consider two download service models. In the first (small file) model, the randomness associated with the file size is negligible. In the second (large file) model, randomness is associated with both the file size and the system's operations. We focus on the service rate of the system. For a fixed redundancy level, the systems' service rate is determined by the allocation of coded chunks over the storage nodes. We consider quasi-uniform allocations, where coded content is uniformly spread among a subset of nodes. The question we address asks what the size of this subset (spreading) should be. We show that in the small file model, concentrating the coded content to a minimum-size subset is universally optimal. For the large file model, the optimal spreading depends on the system parameters. These conclusions hold for both access models.

To ensure the reliability and security of the data, the large-scale distributed storage system usually adopts the data redundancy mechanism to repair the data on the faulty nodes. Comparing with the replication-based method, the data redundancy mechanism of erasure code can effectively improve the use of storage space, but it may result in the large network overhead when recovering the data. The regenerating code is an improved erasure code, which can reduce the quantity of data transmission compared to that of the erasure code. Adopting the regenerating code to repair the data on a faulty node requires constructing an optimal repair tree to maximum the bandwidth of the bottleneck link, which is an NP-hard problem. To construct the optimal repair tree, a hybrid genetic algorithm is proposed in this paper. In particular, our proposal comprehensively considers the network topology and link bandwidth of storage nodes and designs a problem-specific cross-correlation operator, mutation operator and local search operator. In addition, we provided the mathematical proof of the global convergence with probability one with respect to the proposed hybrid genetic algorithm. Through a series of simulation experiments, the results show that our proposal is able to determine the optimal repair tree, which effectively reduces the delay of faulty node repair in distributed storage systems, and improve the repairing efficiency.

Generalized integrated interleaved (GII) codes show great potential in communication and distributed storage systems for their good error-correction capability and locality. To design GII codes with optimal code parameters, an accurate theoretical performance analysis method is essential, which also benefits the study of decoding behavior. However, the current performance analysis method does not fit simulation results very well and can only provide frame-error-rate (FER) evaluation. This letter presents an improved theoretical performance analysis method, including both bit-error-rate (BER) and FER estimations, which fit simulation results very closely. More importantly, the proposed method could predict the failure probability of each decoding round, thus facilitating a better understanding of decoding behavior. To simplify the analysis, an approximation performance evaluation method is also introduced by relaxing the constrained conditions, which still exhibits similar accuracy.

Redundancy occurs when multiple consumers of the network attempt to access the similar content online. Many state-of-the-art studies have been proposed to remove redundancy from the network and to enhance the network efficiency of Internet applications. Still there is a necessity to explore more data redundancy elimination (DRE) techniques, especially for defense-based networks to enhance the data transmission speed and to compress the irrelevant data optimally for reducing the network latency. In order to improve the optimal storage capacity of redundant data over serial hybrid network cascade database, a high-efficiency redundancy elimination method based on the distributed parallel algorithm is proposed in this paper. The distributed storage structure model for handling redundant data of serial mixed network cascade database is designed and tested in simulated environment. The extraction of features of redundant data is performed by using distributed hybrid feature mining technique. The dimensionality reduction of redundant data is also attained by devising the feature transformation method to remove the unwanted features of data. The two benchmarked techniques have been selected for comparative study and to evaluate the performance of the proposed method. The simulation results show that the proposed DRE method can significantly reduce the redundancy from the network traffic. The bandwidth utilization is improved, the duplicate data are also compressed optimally and it is proved that the proposed DRE method is viable for large networks to eliminate the redundant data.

In recent years, the electronic sharing of digital images faces a major threat to security, as the existing image transmission infrastructure is mainly based on the trust of third parties. At the same time, the available solutions are placed on the cloud based centralized data center, which is expensive, requires large storage area, and security issues regarding the transmission of data over the network. So, it is needed to develop an image management system which enables sharing and storing of digital images effectively. This paper develops novel multiple share creation schemes with block technology for secure image management (MSCCBT-SIM) systems. The MSCCBT-SIM model allows the user to create consensus with no dependencies on central authorities. It involves an MSC which involves share creation and share encryption using emperor penguin optimizer based ElGamal public key cryptosystem (EPO-EPKC). In addition, the blockchain is used as a distributed data storage mechanism to generate a ledger for permitting access to the user and prevent third party access to the encrypted shares. The application of blockchain technology and MSC techniques helps to achieve decentralization, highly reliable, inexpensive, and secure transmission and storage of digital images. In order to validate the effective performance of the MSCCBT-SIM model, a series of simulations take place and investigated the results interms of different measures. The experimental results ensured the better performance of the MSCCBT-SIM model with the superior PSNR of 57.09 dB whereas the HOCE-ECC, GO-ECC, PSO-ECC, and CS-ECC methods offered a lower PSNR of 53.08 dB, 52.91 dB, 52.65 dB, and 50.51 dB respectively.

The three-dimensional convolutional neural networks have abundant parameters and computational costs. It is urgent to compress the three-dimensional convolutional neural network. In this paper, an efficient and simple binary three-dimensional convolutional neural network architecture is proposed, in which the weight and activation are constrained to 0 or 1 instead of the common + 1 or – 1. Binary weight and activation are first applied to the three-dimensional convolutional neural networks. The proposed binary three-dimensional convolutional neural network has less computational complexity and memory consumption than standard convolution, and it is more appropriate for digital hardware design. Furthermore, an optimized convolution operation is proposed, in which case one input pixel is only required to be read once. A distributed storage approach is proposed to support the proposed convolution operation. With the proposed methods, a hardware accelerator for the binary three-dimensional convolutional neural network on the field programmable gate array platform is designed. The experimental results show that the presented accelerator is excellent in terms of computational resources and power efficiency. By jointly optimizing the algorithm and hardware, the accelerator achieves 89.2% accuracy and 384 frames per second on the KTH dataset.

Codes with availability allow us to access any code symbol simultaneously $t$ times using $t$ disjoint recovery sets in distributed data storage. Codes with availability that are studied in the literature provide availability for individual code symbols in the codeword. In this letter, we consider a new class of codes with availability. These codes provide availability $t$ for subsets of code symbols called multi-symbol sets. For these codes, an upper-bound on the minimum Hamming distance is derived. A construction for optimal codes with information multi-symbol availability is given. The optimal codes with multi-symbol availability allow us to access more symbols locally with the same locality, when compared to optimal codes with individual code symbol availability.

The rapid developments in the Internet of Medical Things (IoMT) help the smart healthcare systems to deliver more sophisticated real-time services. At the same time, IoMT also raises many privacy and security issues. Also, the heterogeneous nature of these devices makes it challenging to develop a common security standard solution. Furthermore, the existing cloud-centric IoMT healthcare systems depend on cloud computing for electrical health records (EHR) and medical services, which is not suggestible for a decentralized IoMT healthcare systems. In this article, we have proposed a blockchain-based novel architecture that provides a decentralized EHR and smart-contract-based service automation without compromising with the system security and privacy. In this architecture, we have introduced the hybrid computing paradigm with the blockchain-based distributed data storage system to overcome blockchain-based cloud-centric IoMT healthcare system drawbacks, such as high latency, high storage cost, and single point of failure. A decentralized selective ring-based access control mechanism is introduced along with device authentication and patient records anonymity algorithms to improve the proposed system’s security capabilities. We have evaluated the latency and cost effectiveness of data sharing on the proposed system using Blockchain. Also, we conducted a logical system analysis, which reveals that our architecture-based security and privacy mechanisms are capable of fulfilling the requirements of decentralized IoMT smart healthcare systems. Experimental analysis proves that our fortified-chain-based H-CPS needs insignificant storage and has a response time in the order of milliseconds as compared to traditional centralized H-CPS while providing decentralized automated access control, security, and privacy.

With the rapid development of global positioning systems, cellular networks, and relative technologies, people can request location-based services (LBSs) from the service provider using a handheld device quickly and easily. In recent years, privacy issues related to LBS services have attracted a lot of attention. In this paper, we focus on the difficult problem of k-nearest neighbor query based on cryptography. Instead of using the traditional Hilbert curve to store the information of the points of interest in the target map, we use the Z-order curve and quad-tree to store location information. In this way, it improves the efficiency of localization and is able to support distributed storage. Meanwhile, the algorithm uses the homomorphic encryption algorithm ASM-PH that supports additive and multiplicative homomorphism to keep the query secret, which can save the user’s cost by calculating the encrypted content with the LBS server or the storage devices when only the user knows the encrypted content. Experimental results show that the algorithm has high query accuracy and low user encryption and decryption cost. At the same time, we propose a privacy query scheme on distributed storage that supports the scheme. Based on distributed storage and quadratic residual theorem, this scheme is a secure and efficient LBS user privacy protection scheme.

This paper provides an overview on how content can be managed with a blockchain or other distributed ledger technology (DLT), and what challenges need to be addressed in managing this content as part of transactions. Transactions on a blockchain may require supporting documents, for example, photos, reference documents, or actual contracts. As DLTs becoming an increasingly popular method to complete transactions and share information, several issues are arising that need to be addressed, such as: Where should this electronic content in documents be stored? Will the storage system have the features and functionality to properly manage this content through the "information lifecycle", including the retention and disposition of business records based on legal and regulatory requirements? The paper presents an overview of the emerging technology involved with distributed storage systems. It presents five solutions currently available, including their designs, how they secure and store files, and whether or not these files can be deleted in order to meet record disposition requirements and regulations. The discussion points out the need for alignment between multiple stakeholders and consortium members in a distributed ledger-based community with shared ecosystem scaling objectives. The challenges of scaling include the need to protect personal and sensitive information, especially when this information should normally be disposed after a record's retention period has ended.

Most of the piggybacking designs can only effectively repair systematic nodes. In this letter, we propose a new piggybacking method for repairing parity nodes based on the existing piggybacking design. By elaborately designing piggyback functions and making full use of each parity node, the improved piggybacking design reduces the repair bandwidth of parity nodes while retaining the repair bandwidth of systematic nodes. Compared with the existing designs, the proposed method has lower complexity, fewer substripes and better comprehensive repair properties in large-scale distributed storage systems.

In this paper, we propose an approach to the distributed storage and fusion of data for collective perception in resource-limited robot swarms. We demonstrate our approach in a distributed semantic classification scenario. We consider a team of mobile robots, in which each robot runs a pre-trained classifier of known accuracy to annotate objects in the environment. We provide two main contributions: (i) a decentralized, shared data structure for efficient storage and retrieval of the semantic annotations, specifically designed for low-resource mobile robots; and (ii) a voting-based, decentralized algorithm to reduce the variance of the calculated annotations in presence of imperfect classification. We discuss theory and implementation of both contributions, and perform an extensive set of realistic simulated experiments to evaluate the performance of our approach., 8 pages, 10 figures

In social networks, discovery of user similarity is the basis of social media data analysis. It can be applied to user-based product recommendations and inference of user relationship evolution in social networks. In order to effectively describe the complex correlation and uncertainty for social network users, the accuracy of similarity discovery is improved theoretically for massive social network users. Based on the Bayesian network probability map model, network topological structure is combined with the dependency between users, and an effective method is proposed to discover similarity in social network users. To improve the scalability of the proposed method and solve the storage and computation problem of mass data, Bayesian network distributed storage and parallel reasoning algorithm is proposed based on Hadoop platform in this paper. Experimental results verify the efficiency and correctness of the algorithm.

A large scale fast-growing data generated in intelligent transportation systems (ITS) has become a ponderous burden on the coordination of heterogeneous transportation networks, which makes the traditional cloud-centric storage architecture no longer satisfy new data analytics requirements. Meanwhile, the lack of storage trust between ITS devices and edge servers could lead to security risks in the data storage process. However, a unified data distributed storage architecture for ITS with intelligent management and trustworthiness is absent in the previous works. To address these challenges, this paper proposes a distributed trustworthy storage architecture with reinforcement learning in ITS, which also promotes edge services. We adopt an intelligent storage scheme to store data dynamically with reinforcement learning based on trustworthiness and popularity, which improves resource scheduling and storage space allocation. Besides, trapdoor hashing based identity authentication protocol is proposed to secure transportation network access. Due to the interaction between cooperative devices, our proposed trust evaluation mechanism is provided with extensibility in the various ITS. Simulation results demonstrate that our proposed distributed trustworthy storage architecture outperforms the compared ones in terms of trustworthiness and efficiency.

In most storage systems, the storage nodes store data on a local filesystem. Thus, unless they have a dedicated caching layer, they benefit from the usual filesystem cache in the host’s free memory. However, in erasure-coded storage systems, caching is effective only if all the systematic fragments corresponding to an object are in the cache. In this work, we propose a new caching policy adapting traditional methods to erasure-coded storage systems. The main idea of our solution is to cache a full object rather than fragments object. A simulation-based evaluation showed that our full replica solution is able to improve the cache hit ratio and reduce the cache waste ratio compared to the traditional caching method. Moreover, experimental evaluation has been conducted. It indicates that our implementation not only validates the previous results but also shows that cache hits on full replicas have a better request response time.

The Internet of Things (IoT) is otherwise called the Web of everything. As a significant piece of the new age of shrewd data innovation, the IoT has pulled in the consideration both of specialists and designers everywhere on the world. Thinking about the restricted limit of keen items, the IoT essentially utilizes distributed computing to extend figuring and capacity assets. The gigantic information gathered by the sensor are put away in the distributed storage worker, additionally the cloud weakness will straightforwardly undermine the security and unwavering quality of the IoT. To guarantee information trustworthiness and accessibility in the cloud and IoT stockpiling framework, clients need to check the uprightness of distant information. In this exploration, proposed the Mixture Versatile Calculation (HAA) to improve the organization security and correspondence productivity without anyone else aligning the organization boundaries. HAA technique to eliminate the clock balance and clock slant among the sensor hubs. The proposed HAA empowers the hubs to arrive at an organization synchronization time by ascertaining the most un-regular numerous of their Clock Time Span (CTP). The organization is coordinated into bunches and each hub arrives at the organization synchronization time utilizing its own CTP. Reproduction results show that, the HAA calculation is more effective contrasted with the Normal Time Synchronization with Pair insightful messages (ATSP) as far as precision, correspondence overhead, and calculation over SDN and HDN organizations. And furthermore this framework is looked at by the Product characterized network (SDN) and Equipment Characterized Organization (HDN) for contrasting the presentation examination.

In this paper, the optimal planning of Distributed Energy Storage Systems (DESSs) in Active Distribution Networks (ADNs) has been addressed. As the proposed problem is mixed-integer, non-convex, and non-linear, this paper has used heuristic optimization techniques. In particular, five optimization techniques namely Genetic algorithm, Particle swarm optimization, Tabu search, Simulated annealing, and Pattern search optimization techniques have been applied to optimal planning of DESS. The problem has been formulated to consider distributed storage units’ optimal locations and sizes to be placed in an ADN while respecting the constraints of the system. The problem has been addressed on two levels. In the first level, an optimization technique is applied for DESS planning to determine the optimal solution and in the second level, the fitness value of this solution is evaluated by solving a daily AC Optimal Power Flow (OPF) problem. Simulations have been done for the IEEE 34 and IEEE 123 nodes network to demonstrate and compare the efficiency of different optimization techniques. The comparison of these optimization techniques has shown that Particle swarm optimization and Tabu search optimization (with a particular value of tabu tenure found by hit and trial) have performed better in finding the lowest DESS location corresponding to minimum fitness value as compared to other optimization techniques both for IEEE 34 nodes and IEEE 123 nodes network.

The monitoring systems of various industries have various types and different structures. There are problems of “data chimney” and “information islands”. Monitoring data is difficult to be effectively utilized and cannot provide reliable data information to support for environmental security. In this end, an environment monitoring system based on micro-service architecture is designed. The information management and automatic monitoring business systems are unified into a flexible, robust and efficient system platform to adapt to the big data analysis and the mining applications. Using Hadoop to build environment monitoring big data platform, distributed storage, selective extraction and efficient calculation of the massive environment monitoring data can be achieved. By integrating the detection and monitoring data of the ecological environment and in-depth mining it, a neural network model is established to automatically identify potential safety hazards and recommend corresponding treatment measures, so to assist in the comprehensive research and scientific decision-making of environmental safety and promote intelligent management of safety.

Features of the electric power market functioning, in comparison with common properties of modern markets, are considered. It is shown that continuous reforms, which take place in many countries, are far from reaching effective solution. Sectoral peculiarities, connected with both technological complexity of the field and economic specificity of the market participants, form very special model of the market. The main way to increase effectivity of the market, to make it more competitive and, in the same time, socially responsible, is to use widely and deeply the modern information technologies, including technologies of artificial intelligence. A technological scheme (especially targeted on features of the electric power market) has been proposed in the article. First, the technologies merge methods of collection, storage, processing, and presentation of information. The collection of market data should be automated, particularly, by the so-called intellectual avatars, which operate in the market as virtual agents. A reliable distributed storage of data, based on blockchain technology, is chosen. The big data technologies of data structuring, storage, and processing are involved. Datamining methods include effective presentation of results in forms of graphs and diagrams. The second set of technologies includes core mathematical models and “digital twins.” They are aimed for data interpolation and extrapolation, in particular, for prediction of further system dynamics. The used neural networks have option of self-learning and self-development. At last, the third set is a set of user interfaces, which provides market actors by complete and adopted information and is organized in accordance with ideas of ergonomics.

The paper presents techniques for analyzing the expected download time in distributed storage systems that employ systematic availability codes. These codes provide access to hot data through the systematic server containing the object and multiple recovery groups. When a request for an object is received, it can be replicated (forked) to the systematic server and all recovery groups. We first consider the low-traffic regime and present the close-form expression for the download time. By comparison across systems with availability, maximum distance separable (MDS), and replication codes, we demonstrate that availability codes can reduce download time in some settings but are not always optimal. In the high-traffic regime, the system consists of multiple inter-dependent Fork-Join queues, making exact analysis intractable. Accordingly, we present upper and lower bounds on the download time, and an M/G/1 queue approximation for several cases of interest. Via extensive numerical simulations, we evaluate our bounds and demonstrate that the M/G/1 queue approximation has a high degree of accuracy., Comment: Accepted for a publication in IEEE Transactions on Communications

In many areas including precise medical treatments and financial investments, analysis of heterogeneous treatment effects has become important. In this paper, we focus on identifying subgroups by combining data in a distributed storage system. We propose a distributed algorithm based on the alternating direction method of multipliers, which can well preserve privacy of subjects. This method can deal with large-scale data, and perform well in identifying subgroups if there exist sufficient samples in a whole distributed storage system but not necessarily in every computing node. Our numerical study indicates that the proposed method is promising in many interesting cases.

This work investigates the unified coding and identification of smart grid IoT devices, as more and more IoT devices in smart grid need to be managed and controlled. We combine blockchain technology with 5G MEC to realize the connection of massive power IoT devices at the edge of 5G network. Due to blockchain’s distributed storage and credibility, it is used to identify and register IoT devices in smart grid, ensuring the reliability and accuracy of smart grid IoT devices management. In this paper, we propose a hybrid blockchain mechanism based on 5G MEC smart grid, where both public blockchain and private blockchain are deployed on the MEC gateway/server. To facilitate the data searching and extracting, we endeavor to build a blockchain explorer indexed by IoT device identifier. After that, we study the typical consensus algorithms in the blockchain such as PoW, PoS, DPoS, PDFT, and discuss their feasibility in the hybrid blockchain. Finally, we analyzed and compared the performance of different consensus algorithms from the perspective of average computing time and average time to agreement.

Distributed storage systems (DESSs) are widely utilized to regulate voltages in active distribution networks with high penetration of volatile renewable energy. In this paper, the distributed multi-energy storage systems (MESSs) are integrated into the active distribution network to enhance the capability of voltage regulation by exploiting interactions among multi-energy loads. A novel distributed control strategy based on back-and-forth communication (BFC) framework is developed to optimally coordinate multiple MESSs for multi-time-step voltage regulation. To achieve the independent control of MESSs at each bus, the BFC is proposed to determine the global sensitivities of voltage violation in the whole network with respect to the power injection at each bus in only one-step iteration. These sensitivities updated in real time during BFC quantify the capability of the single MESS to mitigate voltage fluctuations across all buses, which can be used to individually inform the operation of each MESS. Distinct from traditional distributed control methods that achieve voltage regulation in a single timeslot, the proposed control strategy regulates MESSs in a multi-time-step manner to alleviate anticipated voltage violation in advance. The simulation based on a modified 11-bus radial distribution network demonstrates the effectiveness of the proposed distributed control strategy.

Although it has been commercially successful to deploy weakly consistent but highly-responsive distributed datastores, the tension between developing complex applications and obtaining only weak consistency guarantees becomes more and more severe. The almost strong consistency tradeoff aims at achieving both strong consistency and low latency in the common case. In distributed storage systems, we investigate the generic notion of almost strong consistency in terms of designing fast read algorithms while guaranteeing Probabilistic Atomicity with well-Bounded staleness (PAB). This problem has been explored in the case where only one client can write the data. However, the more general case where multiple clients can write the data has not been studied. In this article, we study the fast read algorithm for PAB in the multi-writer case. We show the bound of data staleness and the probability of atomicity violation by decomposing inconsistent reads into the read inversion and the write inversion patterns. We implement the fast read algorithm and evaluate the consistency-latency tradeoffs based on the instrumentation of Cassandra and the YCSB benchmark framework. The theoretical analysis and the experimental evaluations show that our fast read algorithm guarantees PAB, even when faced with dynamic changes in the computing environment.

Distributed storage systems provide cloud storage services by storing data on commodity storage servers. Conventionally, data are protected against failures of such commodity servers by replication. Erasure coding consumes less storage overhead than replication to tolerate the same number of failures and thus has been replacing replication in many distributed storage systems. However, with erasure coding, the overhead of reconstructing data from failures also increases significantly. Under the ever-changing workload where data accesses can be highly skewed, it is challenging to deploy erasure coding with appropriate values of parameters to achieve a well trade-off between storage overhead and reconstruction overhead. In this paper, we propose Zebra , a framework that encodes data by their demand into multiple tiers that deploy erasure codes with different values of parameters. Zebra automatically determines the number of such tiers and dynamically assigns erasure codes with optimal values of parameters into corresponding tiers. With Zebra, a flexible trade-off between storage overhead and reconstruction overhead is achieved with multiple tiers. When demand changes, Zebra adjusts itself with a marginal amount of network transfer. We demonstrate that Zebra can work with two representative families of erasure codes in distributed storage systems, Reed-Solomon codes and local reconstruction codes.

The Internet of Things (IoT) has infiltrated extensively into our lifestyles. Nevertheless, IoT privacy remains a significant obstacle, primarily because of the large size and distributed existence of IoT networks. Also, numerous safety, authentication, and maintenance problems of IoT systems have been overcome by the decentralized existence of blockchain. To obviate these privacy difficulties, the privacy challenges of IoT-based blockchain are examined systematically. Totally, 61 papers have been gained by electronic databases and based on different filters, 20 related articles were obtained and analyzed. The findings showed that the blockchain could dominate the IoT restrictions, such as data protection and privacy. It can also supply distributed storage, transparency, trust, and secure distributed IoT networks and supply a beneficial guarantee for the privacy and security of IoT users. Simultaneously, it has low scalability, high computing complexity, IoT-unsuitable latency, and high overhead bandwidth.

Cloud computing remains the world's most demanded development trend. It is one of the most important topic whose application is currently being explored. The distributed storage has been one of the apparent administrations in distributed computing. Instead of providing the engaged worker for conventionally organizated storage, the distributed storage sets various outsider individuals with the knowledge. The customer does not care about any details on various external employees and no one knows exactly where information is spared. The distributed storage supplier asserts that the information can be secured, but no one trusts in it. Security risk is information placed in the plain content configuration over cloud and across the organization. This document offers a method that allows customers to securely store and access data from the distributed storage. It also ensures that neither the distributed storage provider, apart from the verified customer, can get details. This technique ensures the safety and confidentiality of cloud-related information. Another favorite position is that if the cloud provider breaks down, the information of the client remains safe as all information is encoded. Customers must also not stress that cloud providers have illegal access to their data.

The Industry 4.0 IoT network integration with blockchain architecture is a decentralized, distributed ledger mechanism used to record multi-user transactions. Blockchain requires a data storage system designed to be secure, reliable, and fully transparent, emerged as a preferred IoT-based digital storage on WSN. Blockchain technology is being used in the paper to construct the node recognition system according to the storage of data for WSNs. The data storage process on such data must be secure and traceable in different forensics and decision making. The primary theme of the dynamic data security is therefore for rejecting exploitation of the unauthorized user and for evaluating the mechanism in tracing and evidence of system’s data operation in a dynamic manner, growth and quality features under the stochastic state of the model; (1) a mathematical method for the secured storage of data in dynamic is built through distributed node cooperation in IoT industry. (2) the ownership transition feature and the dynamic storage of data system architecture are configured, (3) the emerging distributed storage architecture for blockchain-based WSN will substantially reduce overhead storage for each node without affecting data integrity; (4) minimize the latency of data reconstruction in distributed over storage system, and propose an effective and scalable algorithm for optimizing storage latency issue. In addition to this research, the system implements verified possession of data for replacing the evidence in original digital currency for mining and to store new data blocks that will be compared to the proof system, dramatically reduces computational capacity. The proposed ODSD framework has exceptional benefits for real-time applications while maintaining the security of the dynamic storage of data.