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Integration of electric vehicles (EVs) into the smart grid has attracted considerable interest from researchers, governments, and private companies alike. Such integration may bring problems if not conducted well, but EVs can be also used by utilities and other industry stakeholders to enable the smart grid. This paper presents a systematic literature review of the topic and offer a research framework to guide future research and enrich the body of knowledge. The systematic literature review presented in this paper does not contain all the material available on this subject. It does, however, include most of the key publications readily available in a power-utility or technical-reference library together with some of the earlier papers in the field (the anchor papers). For this review, we selected appropriate digital sources (digital libraries and indexing systems; IEEE Xplore and Web of Science), determined the search terms, and conducted a broad automated search. This article also details the components of the research theme—EV integration into the smart grid—as well as its accompanying use cases. The analysis of the relevant papers indicated four types of key research concerns: power-grid, power-system, and smart-grid reliability and the impacts of changes on them. These results can help guide future research to further smart-grid development. Future research can also expand the reach of this research to address its limitations in scope and depth. [ABSTRACT FROM AUTHOR]

The knowledge graph with relational abundant information has been widely used as the basic data support for the retrieval platforms. Image and text descriptions added to the knowledge graph enrich the node information, which accounts for the advantage of the multi-modal knowledge graph. In the field of cross-modal retrieval platforms, multi-modal knowledge graphs can help to improve retrieval accuracy and efficiency because of the abundant relational information provided by knowledge graphs. The representation learning method is significant to the application of multi-modal knowledge graphs. This paper proposes a distributed collaborative vector retrieval platform (DCRL-KG) using the multimodal knowledge graph VisualSem as the foundation to achieve efficient and high-precision multimodal data retrieval. Firstly, use distributed technology to classify and store the data in the knowledge graph to improve retrieval efficiency. Secondly, this paper uses BabelNet to expand the knowledge graph through multiple filtering processes and increase the diversification of information. Finally, this paper builds a variety of retrieval models to achieve the fusion of retrieval results through linear combination methods to achieve high-precision language retrieval and image retrieval. The paper uses sentence retrieval and image retrieval experiments to prove that the platform can optimize the storage structure of the multimodal knowledge graph and have good performance in multi-modal space. [ABSTRACT FROM AUTHOR]

Recently, research on a distributed storage system that efficiently manages a large amount of data has been actively conducted following data production and demand increase. Physical expansion limits exist for traditional standalone storage systems, such as I/O and file system capacity. However, the existing distributed storage system does not consider where data is consumed and is more focused on data dissemination and optimizing the lookup cost of data location. And this leads to system performance degradation due to low locality occurring in a Wide Area Network (WAN) environment with high network latency. This problem hinders deploying distributed storage systems to multiple data centers over WAN. It lowers the scalability of distributed storage systems to accommodate data storage needs. This paper proposes a method for distributing data in a WAN environment considering network latency and data locality to solve this problem and increase overall system performance. The proposed distributed storage method monitors data utilization and locality to classify data temperature as hot, warm, and cold. With assigned data temperature, the proposed algorithm adaptively selects the appropriate data center and places data accordingly to overcome the excess latency from the WAN environment, leading to overall system performance degradation. This paper also conducts simulations to evaluate the proposed and existing distributed storage methods. The result shows that our proposed method reduced latency by 38% compared to the existing method. Therefore, the proposed method in this paper can be used in large-scale distributed storage systems over a WAN environment to improve latency and performance compared to existing methods, such as consistent hashing. [ABSTRACT FROM AUTHOR]

Distributed storage systems play a pivotal role in modern data-intensive applications, with Hadoop distributed file system (HDFS) being a prominent example. However, optimizing the efficiency of such systems remains a complex challenge. This research paper presents a novel approach to enhance the efficiency of distributed storage by leveraging multi-agent systems (MAS). Our research is centered on enhancing the efficiency of the HDFS by incorporating intelligent agents that can dynamically assign storage tasks to nodes based on their performance characteristics. Utilizing a decentralized decision-making framework, the suggested approach based on MAS considers the real-time performance of nodes and allocates storage tasks adaptively. This strategy aims to alleviate performance bottlenecks and minimize data transfer latency. Through extensive experimental evaluation, we demonstrate the effectiveness of our approach in improving HDFS performance in terms of data storage, retrieval, and overall system efficiency. The results reveal significant reductions in job execution times and enhanced resource utilization, there by offering a promising avenue for enhancing the efficiency of distributed storage systems. [ABSTRACT FROM AUTHOR]

In distributed storage systems, the utilization of locally repairable codes offers the potential to reduce the complexity and bandwidth required for repairs. This paper focuses on a scenario where each information symbol is associated with several distinct repair sets, each of which includes a single parity check symbol. By leveraging various combinatorial designs, such as resolvable balanced incomplete block designs and resolvable group divisible designs, we establish regular packings and resolvable packings for all alphabet sizes greater than a specified constant. These packings enable the construction of optimal locally repairable codes with multiple repair sets. Specifically, in the case where r = 4 , we successfully construct optimal locally repairable codes with multiple repair sets, provided that the code dimension k is greater than or equal to 277. [ABSTRACT FROM AUTHOR]

Wireless body area networks (WBANs) are an upcoming technology for achieving effective healthcare. The security and privacy of patient-related data are two essential aspects of WBAN system security. Storing data on a single server is simple but may lead to a single point of failure (whether a typical failure or a failure due to security attacks). In this paper, enhanced security and privacy of patient information and E-health systems based on distributed storage systems (DSSs) are developed using public-key cryptography to store patient information. The storage of data and the security requirements (such as confidentiality, reliability, authentication, and dynamic integrity) are simultaneously distributed among individual nodes throughout the network. The patient data are encrypted using the redundant residue number system (RRNS) technique, which depends on a library of moduli in the encrypting process to generate residues. Three algorithms were implemented to decode the RRNS: the Chinese Remainder Theorem (CRT), Base Extension (BEX) with mixed radix conversion, and the new Chinese Remainder Theorem I (CRT I). The results of a system prototype implementation showed that whenever the value of the moduli was a big prime number, the coding efficiency became large, and the time to send a message decreased by 2 s. The performance of the algorithms was expressed in terms of execution time and cyclomatic complexity (CC). The results showed that the CRT I was 1 s faster than the other decoding algorithms and took around 2.5 s to decode the RRNS. The CRT had a low CC of 15, whereas the MRC and CRT I had a CC of 45 and 40, respectively. The main operation phases of the proposed system are described. [ABSTRACT FROM AUTHOR]

Distributed storage can store data in multiple devices or servers to improve data security. However, in today’s explosive growth of network data, traditional distributed storage scheme is faced with some severe challenges such as insufficient performance, data tampering, and data lose. A distributed storage scheme based on blockchain has been proposed to improve security and efficiency of traditional distributed storage. Under this scheme, the following improvements have been made in this paper. This paper first analyzes the problems faced by distributed storage. Then proposed to build a new distributed storage blockchain scheme with sharding blockchain. The proposed scheme realizes the partitioning of the network and nodes by means of blockchain sharding technology, which can improve the efficiency of data verification between nodes. In addition, this paper uses polynomial commitment to construct a new verifiable secret share scheme called PolyVSS. This new scheme is one of the foundations for building our improved distributed storage blockchain scheme. Compared with the previous scheme, our new scheme does not require a trusted third party and has some new features such as homomorphic and batch opening. The security of VSS can be further improved. Experimental comparisons show that the proposed scheme significantly reduces storage and communication costs. [ABSTRACT FROM AUTHOR]

A new family of scalable codes with locality and availability for information repair in data storage systems for e-health applications was presented recently. The construction was based on a graph of the [7, 3-4] Simplex code. In this paper it is shown that the construction can be generalized via tessellation in a Euclidian plane. The codes obtained have new interesting recoverability properties. They can in some cases repair damage to many storage nodes in multiple connected graphs via sequential decoding, which is similar to healing wounds in biological systems. The advantages of the original codes, namely the availability, functionality, efficiency and high data accessibility, will be preserved also in these new codes. The computational complexity and communication costs of their incrementation will remain constant and modest. These codes could be adapted to disaster recovery because it is straightforward to place the nodes so that the graph is easily mapped on a real structure in space. [ABSTRACT FROM AUTHOR]

Coding-based distributed storage systems (DSS) are employed in many diverse heterogeneous settings, e.g., cloud storage data centers, peer-to-peer systems, wireless sensor networks, fog/edge computing system, to provide better throughput, latency, reliability, scalability, load adaptation, geographical migration and fault tolerance with respect to traditional monolithic enterprise storage systems. Despite the undoubted advantages offered by coding, reliability and security are jeopardized by a pollution attack that can easily disrupt the entire system and degrade performance. In this paper we take an abstract view of a DSS and we investigate by means of mathematical modeling what are the availability, robustness, and timeliness of heterogeneous, coding-based DSS when storage nodes (SN) are unreliable and can be malicious. To this end, we focus on a class of allocations of coded fragments to SNs that we call feasible allocations; the model takes into account both reliability and reactivity of SNs. We define robust availability and timeliness of feasible allocations that we use to characterize the overall performance and robustness of the DSS in a reference scenario. Our analysis reveals that code redundancy is a double-edged sword in a DSS where malicious SNs come into play and that there exists an optimal value of code redundancy regardless all system parameters that maximizes the number of malicious SNs that can be tolerated to achieve maximum DSS performance. We also found that larger codes are preferred over short ones as they yield superior DSS performance in the presence of malicious SNs. Furthermore, when multiple feasible allocations yield the highest DSS performance timeliness can be used as a guide for the choice. Finally, heterogeneity plays a role in determining the timeliness of the maximally spread allocations in the case of targeted attacks. [ABSTRACT FROM AUTHOR]

A block code is called a locally recoverable code (LRC for short) with $(r,\delta)$ -locality, if subject to any $\delta -1$ erasure failures, every symbol in the encoding can still be recovered by accessing at most $r$ other symbols. Recently, it was discovered by several authors that a $q$ -ary optimal $(r,\delta)$ -LRC, i.e., an LRC achieving the generalized Singleton-type bound, can have length much bigger than $q+1$. This is quite different from the classical $q$ -ary MDS codes where it is conjectured that the code length is upper bounded by $q+1$ (or $q+2$ for some special cases). In this paper, we further investigate constructions of optimal $(r,\delta)$ -LRCs along the line of using parity-check matrices. Inspired by classical Reed-Solomon codes and the work in Jin (2019), we equip parity-check matrices with the Vandermonde structure. It turns out that a parity-check matrix with the Vandermonde structure that produces an optimal LRC must obey certain disjoint property for subsets of $\mathbb {F}_{q}$. We manage to show the existence of these disjoint subsets. Thus, this yields an optimal $(r,\delta)$ -LRC with code length $\Omega \left({q^{\frac {\delta }{2}\left({1+\frac {1}{\lfloor \frac {d-1}{\delta }\rfloor -1}}\right)}}\right)\vphantom {\bigg)_{j}}$ , where $d$ is the minimum distance. In particular, to our surprise, for $\delta =2$ this disjoint condition is equivalent to a well-studied problem in extremal graph theory. With the help of extremal graph theory, we succeed to improve all of the best known results in Guruswami et al. (2018) for $d\geq 7$. In addition, for $d=6$ , we are able to remove the constraint required in Jin (2019) that $q$ is even. [ABSTRACT FROM AUTHOR]

As the distributed ledger technology underlying cryptocurrencies such as Bitcoin and Ethereum, blockchain has empowered various industries, such as supply chain management, healthcare, government services, e-voting, etc. However, the ever-growing ledger on each node has been the main bottleneck for blockchain scalability as the network scale expands, which worsens in blockchain-enabled IoT scenarios with resource-limited devices. With the support of the Reed–Solomon (RS) code, the Cuckoo-Store (CS), a ledger storage optimization engine, is proposed in this paper to dramatically decrease the storage burden on each node by encoding the ledger as data segments with redundancy and distributing them to multiple nodes. These distributed data segments can be collected and decoded using RS code to recover the original ledger. Furthermore, the Cuckoo filter (CF) is used to guarantee the integrity of the encoded segments, which helps detect the forged segments and facilitates the process of ledger recovery. Theoretical analysis and simulation results show that the CS engine can decrease the storage in each node by more than 94%, and the original ledger can be recovered efficiently with acceptable communication overheads. [ABSTRACT FROM AUTHOR]

In a traditional distributed storage system, a source can be restored perfectly when a certain subset of servers is contacted. The coding is independent of the contents of the source. This paper considers instead a lossy source coding version of this problem where the more servers that are contacted, the higher the quality of the restored source. An example could be video stored on distributed storage. In information theory, this is called the multiple description problem, where the distortion depends on the number of descriptions received. The problem considered in this paper is how to restore the system operation when one of the servers fail and a new server replaces it, that is, repair. The requirement is that the distortions in the restored system should be no more than in the original system. The question is how many extra bits are needed for repair. We find an achievable rate and show that this is optimal in certain cases. One conclusion is that it is necessary to design the multiple description codes with repair in mind; just using an existing multiple description code results in unnecessary high repair rates. [ABSTRACT FROM AUTHOR]

In this paper, we consider the update bandwidth in distributed storage systems (DSSs). The update bandwidth, which measures the transmission efficiency of the update process in DSSs, is defined as the average amount of data symbols transferred in the network when the data symbols stored in a node are updated. This paper contains the following contributions. First, we establish the closed-form expression of the minimum update bandwidth attainable by irregular array codes. Second, after defining a class of irregular array codes, called Minimum Update Bandwidth (MUB) codes, which achieve the minimum update bandwidth of irregular array codes, we determine the smallest code redundancy attainable by MUB codes. Third, the code parameters, with which the minimum code redundancy of irregular array codes and the smallest code redundancy of MUB codes can be equal, are identified, which allows us to define MR-MUB codes as a class of irregular array codes that simultaneously achieve the minimum code redundancy and the minimum update bandwidth. Fourth, we introduce explicit code constructions of MR-MUB codes and MUB codes with the smallest code redundancy. Fifth, we establish a lower bound of the update complexity of MR-MUB codes, which can be used to prove that the minimum update complexity of irregular array codes may not be achieved by MR-MUB codes. Last, we construct a class of $(n = k + 2, k)$ vertical maximum-distance separable (MDS) array codes that can achieve all of the minimum code redundancy, the minimum update bandwidth and the optimal repair bandwidth of irregular array codes. [ABSTRACT FROM AUTHOR]

In this paper, a new repair scheme for a modified construction of MDS codes is studied. The obtained repair scheme has optimal bandwidth for multiple failed nodes under the cooperative repair model. In addition, the repair scheme has relatively low access property, where the number of data accessed is less than two times the optimal value. [ABSTRACT FROM AUTHOR]

Blaum–Roth Codes are binary maximum distance separable (MDS) array codes over the binary quotient ring F 2 [ x ] / (M p (x)) , where M p (x) = 1 + x + ⋯ + x p − 1 , and p is a prime number. Two existing all-erasure decoding methods for Blaum–Roth codes are the syndrome-based decoding method and the interpolation-based decoding method. In this paper, we propose a modified syndrome-based decoding method and a modified interpolation-based decoding method that have lower decoding complexity than the syndrome-based decoding method and the interpolation-based decoding method, respectively. Moreover, we present a fast decoding method for Blaum–Roth codes based on the LU decomposition of the Vandermonde matrix that has a lower decoding complexity than the two modified decoding methods for most of the parameters. [ABSTRACT FROM AUTHOR]

Distributed data management requires data partitioning and deployment at the data storage level, and data querying requires the configuration and integration of query subresults at each site. The data partitioning strategy is closely related to the overhead of the distributed system. It is necessary to determine the appropriate data partitioning strategy and update strategy according to the application. This paper proposes a widely distributed storage and processing scheme for a distributed linear order partition (DLOP) based on time stamps. This scheme proposes two kinds of partition strategy based on the characteristics of an "equivalent division" of a linear order partition (LOP), namely, partitioning based on time interval equilibrium and partitioning based on query expectation. Each site in the distributed system is uniformly configured with an index-based data query mechanism to complete the distributed management of data. The corresponding experiments verify the practicability and efficiency of the proposed storage strategy and show that the proposed method is effective for the self-scalability of the data scale and reduces the cluster hardware configuration requirements. [ABSTRACT FROM AUTHOR]

In wireless sensor networks, sensor nodes are vulnerable due to the challenging environment and limited energy. Once a sensor node fails, the stored sensed data will be lost. In this paper, we develop a distributed algorithm for solving this problem based on the fountain codes and the minimum spanning tree. Similar to previous works, we adopt the Luby Transform (LT) codes, the first practical scheme of fountain codes, in our algorithm. However, different from the previous schemes, our algorithm deploys the minimum spanning tree instead of the random walk. Due to the fact that all nodes in the wireless sensor networks do not have the location information of other nodes, we use the Gallager-Humble-Spira (GHS) algorithm to generate a distributed minimum spanning tree. Data packets can be transmitted through the tree and easily reach every node in the network. We obtain the performance of the algorithm through simulations, which show that our algorithm consumes less number of the transmissions compared with the existing algorithms using the random walk. [ABSTRACT FROM AUTHOR]

In the distributed storage system, when data need to be recovered after node failure, the erasure code redundancy method occupies less storage space than the multi-copy method. At present, the repair mechanism using erasure code to reconstruct the failed node only considers the improvement of link bandwidth on the repair rate and does not consider the impact of the selection of data providing node-set on the repair performance. A single node fault data reconstruction method based on the Software Defined Network (SDN) using the erasure code method is designed to solve the above problems. This method collects the network link-state through SDN, establishes a multi-attribute decision-making model of the data providing node-set based on the node performance, and determines the data providing nodes participating in providing data through the ideal point method. Then, the data recovery problem of a single fault node is modeled as the optimization problem of an optimal repair tree, and a hybrid genetic algorithm is designed to solve it. The experimental results show that under the same erasure code scale, after selecting the nodes of the data providing node-set, compared with the traditional tree topology and star topology, the repair delay distribution of the designed single fault node repair method for a distributed storage system is reduced by 15% and 45% respectively, and the repair flow is close to the star topology, which is reduced by 40% compared with the traditional tree repair. [ABSTRACT FROM AUTHOR]

Hadoop is a framework for storing and processing huge amounts of data. With HDFS, large data sets can be managed on commodity hardware. MapReduce is a programming model for processing vast amounts of data in parallel. Mapping and reducing can be performed by using the MapReduce programming framework. A very large amount of data is transferred from Mapper to Reducer without any filtering or recursion, resulting in overdrawn bandwidth. In this paper, we introduce an algorithm called Inner MAPping Combiner (IMapC) for the map phase. This algorithm in the Mapper combines the values of recurring keys. In order to test the efficiency of the algorithm, different approaches were tested. According to the test, MapReduce programs that are implemented with the Default Combiner (DC) of IMapC will be 70% more efficient than those that are implemented without one. To make computations significantly faster, this work can be combined with MapReduce. [ABSTRACT FROM AUTHOR]

The problem of Multi-user Blind $X$ -secure $T$ -colluding Symmetric Private Information Retrieval from Maximum Distance Separable (MDS) coded storage system with $B$ Byzantine and $U$ unresponsive servers (U-B-MDS-MB-XTSPIR) is studied in this paper. Specifically, a database consisting of multiple files, each labeled by $M$ indices, is stored at the distributed system with $N$ servers according to $(N,K+X)$ MDS codes over $\mathbb {F}_{q}$ such that any group of up to $X$ colluding servers learn nothing about the data files. There are $M$ users, in which each user $m,m=1,\ldots,M$ privately selects an index $\theta _{m}$ and wishes to jointly retrieve the file specified by the $M$ users’ indices $(\theta _{1},\ldots,\theta _{M})$ from the storage system, while keeping its index $\theta _{m}$ private from any $T_{m}$ colluding servers, where there exists $B$ Byzantine servers that can send arbitrary responses maliciously to confuse the users retrieving the desired file and $U$ unresponsive servers that will not respond any message at all. In addition, each user must not learn information about the other users’ indices and the database more than the desired file. An U-B-MDS-MB-XTSPIR scheme is constructed based on Lagrange encoding. The scheme achieves a retrieval rate of $1-\frac {K+X+T_{1}+\ldots +T_{M}+2B-1}{N-U}$ with secrecy rate $\frac {K+X+T_{1}+\ldots +T_{M}-1}{ N-(K+X+T_{1}+\ldots +T_{M}+2B+U-1)}$ on the finite field of size $q\geq N+\max \{K, N-(K+X+T_{1}+\ldots +T_{M}+2B+U-1)\}$ for any number of files. [ABSTRACT FROM AUTHOR]

Fractional repetition (FR) codes are a class of repair efficient erasure codes that can recover a failed storage node with both optimal repair bandwidth and complexity. In this paper, we study the minimum distance of FR codes, which is the smallest number of nodes whose failure leads to the unrecoverable loss of the stored file. We derive a new upper bound on the minimum distance of FR codes, which is tighter than the Singleton bound and a Singleton-like bound that takes locality into account. Based on regular graphs and combinatorial designs, several families of FR codes with optimal minimum distance are obtained. [ABSTRACT FROM AUTHOR]

Abstract Integration of electric vehicles (EVs) into the smart grid has attracted considerable interest from researchers, governments, and private companies alike. Such integration may bring problems if not conducted well, but EVs can be also used by utilities and other industry stakeholders to enable the smart grid. This paper presents a systematic literature review of the topic and offer a research framework to guide future research and enrich the body of knowledge. The systematic literature review presented in this paper does not contain all the material available on this subject. It does, however, include most of the key publications readily available in a power-utility or technical-reference library together with some of the earlier papers in the field (the anchor papers). For this review, we selected appropriate digital sources (digital libraries and indexing systems; IEEE Xplore and Web of Science), determined the search terms, and conducted a broad automated search. This article also details the components of the research theme—EV integration into the smart grid—as well as its accompanying use cases. The analysis of the relevant papers indicated four types of key research concerns: power-grid, power-system, and smart-grid reliability and the impacts of changes on them. These results can help guide future research to further smart-grid development. Future research can also expand the reach of this research to address its limitations in scope and depth.

The Singleton-type bound that offers an upper limit on the minimum Hamming distance for locally repairable codes (LRCs) has been studied by a lot of literatures. However, the work to determine tight Singleton-type bound for LRCs under all parameters is still incomplete. This paper focuses on the tightness of Singleton-type bound for general LRCs (linear and nonlinear). By exploring how the structure of repair sets affects the minimum Hamming distance, some properties for minimum Hamming distance of LRCs are derived. Through these properties, the following results are obtained: 1. The necessary conditions for LRCs whose minimum Hamming distance reaches the previous Singleton-type bound are provided. Due to these necessary conditions, the previous Singleton-type bound is proved to be unreachable under a number of parameters, and the tight Singleton-type bounds are given under some of these parameters. 2. Under a class of parameters where the previous Singleton-type bound for linear LRCs has been proved unreachable in the literatures, we improve Singleton-type bound and show that the new Singleton-type bound is tight. In addition, based on the existing LRC constructions, the previous Singleton-type bound for LRCs is shown to be tight under some other parameters. Finally, for an importance case of LRCs (i.e., every repair set is a 1-erasure correcting code), the Singleton-type bound under three classes of parameters is further improved. [ABSTRACT FROM AUTHOR]

In the field of distributed medical data storage, relayed data is usually kept centrally on the central node, which may lead to an increased risk of a single point of failure and security challenges from malicious tampering. While introducing backup nodes can partially alleviate these issues, the efficiency of synchronization and switching between nodes remains constrained. In addition, nodes storing relayed data can reach a consensus to modify the forwarded data, which lacks trustworthiness. Given the problems faced by traditional distributed storage in the field of medical data, combined with the characteristics of blockchain technology, we propose a distributed medical data storage model based on blockchain technology, named DDS (Decentralized Distributed Storage), which is intended to be applied in medical data management. In the DDS model, we embed relay data into blocks for storage and ensure the integrity and security of relay data for medical data by introducing redundant blockchain storage and collaborative verification mechanisms. The model is divided into two key phases: the storage phase of relay data of medical data and the verification phase. In the storage phase, the doctor's signature and the associated copy location data are sent to multiple validation nodes, after which the relay data block of medical data is generated and written to the blockchain. In the validation phase, the node first verifies whether the local relay data blockchain state is consistent with the global state. If it is not, a state synchronization operation is performed. Next, the validation node retrieves the local relay data blockchain to verify the integrity of the medical data relay data. From the theoretical analysis and experimental results, the DDS model can ensure the traceability and integrity of relay medical data and has good concurrent processing performance and less impact on the efficiency of medical data storage. [ABSTRACT FROM AUTHOR]

Powered by edge servers (also called as edge nodes) which are close to the data source, distributed edge AI processes the huge amounts of data generated by Internet of Things (IoT) devices, extracting value for users. In edge computing, massive data are stored in several distributed edge nodes with heterogeneous capabilities. Intelligent applications running on one edge node may need data from other edge nodes. An efficient data indexing mechanism can rapidly locate the edge node where the data is kept, supporting latency-sensitive intelligent applications. The existing indexing methods in edge computing assume that all edge nodes are the same in capability and the number of edge nodes is constant. This paper proposes CREIM, a coordinate-based efficient indexing mechanism for intelligent IoT systems in heterogeneous edge computing. CREIM achieves fair load balancing on edge nodes with heterogeneous capabilities. The indexing mechanism deals well with the horizontal scaling of edge nodes. Besides, CREIM addresses a fast lookup with one overlay hop, providing low latency data retrieval for edge intelligent applications. In the experiments, CREIM is applied in a realistic network simulated by the mininet and the routing forwarding is supported by the P4 switch. The experiments are constructed by combining real location datasets of Shanghai Telecoms base stations with the real-collected requests of end-devices. The experimental results demonstrate that CREIM achieves a near-optimal latency of index-lookup, adapts the heterogeneous capabilities among edge nodes and reduces the cost of increasing/decreasing edge nodes by 56.36% compared with the state-of-the-art method. • We propose an indexing mechanism called CREIM for IoT systems, which achieves fast lookup and fair load balance. • We provide an important function in CREIM to deal with the dynamic variation of edge nodes. • We construct experiments based on real-world datasets and we compare our method with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

For high-rate linear systematic maximum distance separable (MDS) codes, most early constructions could initially optimally repair all the systematic nodes but not all the parity nodes. Fortunately, this issue was first solved by Li et al. in (IEEE Trans. Inform. Theory, 64(9), 6257-6267, 2018), where a transformation that can convert any nonbinary MDS array code into another one with desired properties was proposed. However, the transformation does not work for binary MDS array codes. In this paper, we address this issue by proposing another generic transformation that can convert any $[n, k]$ binary MDS array code into a new one, which endows any $r=n-k\ge 2$ chosen nodes with optimal repair bandwidth and optimal rebuilding access properties, and at the same time, preserves the normalized repair bandwidth/rebuilding access for the remaining $k$ nodes under some conditions. As two immediate applications, we show that 1) by applying the transformation multiple times, any binary MDS array code can be converted into one with optimal rebuilding access for all nodes, 2) any binary MDS array code with optimal repair bandwidth or optimal rebuilding access for the systematic nodes can be converted into one with the corresponding optimality property for all nodes. [ABSTRACT FROM AUTHOR]

We construct maximally recoverable codes (corresponding to partial MDS codes) which are based on linearized Reed-Solomon codes. The new codes have a smaller field size requirement compared with known constructions. For certain asymptotic regimes, the constructed codes have order-optimal alphabet size, asymptotically matching the known lower bound. [ABSTRACT FROM AUTHOR]

The increased digitalization of society raises concerns regarding data protection and user privacy, and criticism on how the companies handle user data without being transparent and without providing adequate mechanisms for users to control how their own data is being processed or shared. To address this problem and open the way for a secure and efficient society, where the privacy of citizens is paramount, the identity concept and proof of identity mechanisms need to be redesigned from the ground up. In this paper we discuss how the emerging Web3 technologies like distributed ledger technology (DLT), blockchain, smart contracts, decentralized storage systems, and crypto wallets can be leveraged to design and implement a decentralized digital identity system based on decentralized identifiers (DID) and self-sovereign identities (SSI). Such a system puts the users in full control over their own data while also providing a solid backbone for building interoperable systems that are secure, scalable, and efficient. We propose different architectures for the decentralized identity infrastructure and storage layer, and also discuss the mapping of these architectures on cloud platforms. The main goal is to provide an architectural blueprint for a scalable, secure, privacy-preserving and trusted system.

As the modern computing market experiences a surge in demand for efficient data-management solutions, challenges posed by centralized storage systems become more pronounced, especially with the proliferation of Internet of Things devices. Centralized storage, although cost-effective, faces issues of scalability, performance bottlenecks, and security vulnerabilities. With decentralized storage, data are distributed across nodes, offering redundancy, data availability, and enhanced security. Unfortunately, decentralized storage introduces its own challenges, such as complex data retrieval processes, potential inconsistencies in data versions, and difficulties in ensuring data privacy and integrity in a distributed setup. Effectively managing these challenges calls for innovative techniques. In response, this paper introduces a decentralized storage system that melds cloud-native concepts with blockchain technology. The proposed design delivers enhanced scalability, data security, and privacy. When operating on a containerized edge infrastructure, this storage system provides higher data-transfer speeds than the interplanetary file system. This research thus blends the advantages of cloud-native frameworks with the security mechanisms of blockchain, crafting a storage system that addresses the present-day challenges of data management in decentralized settings.

In a traditional distributed storage system, a source can be restored perfectly when a certain subset of servers is contacted. The coding is independent of the contents of the source. This paper considers instead a lossy source coding version of this problem where the more servers that are contacted, the higher the quality of the restored source. An example could be video stored on distributed storage. In information theory, this is called the multiple description problem, where the distortion depends on the number of descriptions received. The problem considered in this paper is how to restore the system operation when one of the servers fail and a new server replaces it, that is, repair. The requirement is that the distortions in the restored system should be no more than in the original system. The question is how many extra bits are needed for repair. We find an achievable rate and show that this is optimal in certain cases. One conclusion is that it is necessary to design the multiple description codes with repair in mind; just using an existing multiple description code results in unnecessary high repair rates.

In distributed storage systems, data are stored across multiple storage nodes which are unreliable and prone to failure. While erasure coding is more efficient than simple replication in terms of storage overhead and reliability, classic erasure codes like Reed-Solomon codes require a large repair bandwidth when repairing a failed node. Therefore, reducing both the storage overhead and repair bandwidth under a given fault tolerance is desired, however, it is not possible to minimize both. In 2007, Dimarkis et al. characterized the storage-bandwidth trade-off under functional repair. While exact repair is preferred in practical systems, it was shown that only two extremal points and a line segment are achievable under exact repair. Up to now, the storage-bandwidth trade-off under exact repair remains unresolved for general parameters. Nevertheless, constructing codes with exact repair between the two extremal points is still of great interest, however, very few such constructions have been reported in the literature. In this paper, we present explicit code constructions based on block designs, which can be viewed as a generalization of a previous work by Tian et al. Such a generalization leads to two new codes, i.e., an $(n, k=n-1, d=n-1)$ storage code based on regular mandatory representation designs (MRDs) and an $(n, k=n-2, d\ge n-2)$ storage code based on 3-designs. It is shown that the new storage codes have a better performance than the ones by Tian et al. in terms of the sub-packetization level and storage-bandwidth trade-off. In addition, the new $(n, k=n-2, d)$ storage code supports two repair degrees, i.e., $d\in \{n-2,n-1\}$ .

Blockchain has the potential to reconfigure the contemporary economic, legal, political and cultural landscape, causing a flood of research on this topic. However, limited efforts have been made to conduct retrospective research to appraise the blockchain studies in the recent period, easily leading to a neglect of new technological trends. Consequently, the present research designs a quantitative- and qualitative-analysis procedure to review the latest research status. Adopting a four-step workflow, six research hotspots (i.e., the specific application areas of blockchain technology, the integration of blockchain and other technologies, the driving factors of blockchain, the values of blockchain technology, the types of blockchain and the core technologies of blockchain) and five research frontiers (i.e., entrepreneurship, contract, industrial internet, data management and distributed ledger technology) were detected using quantitative analysis. Furthermore, three other topics (i.e., the Internet of things, access control and trust) and two research gaps (i.e., the true effect of blockchain technology on firms' operational efficiency and the regulation of the "dark sides" of blockchain technology) were also identified, using qualitative analysis. Finally, the evolutionary paths were qualitatively analyzed, and then three phases of blockchain research were summarized. The conclusions are able to provide a more comprehensive enlightenment regarding blockchain's research hotspots, research frontiers, evolutionary paths and research gaps in the recent period, from 2015 to 2021, and to provide a reference for future research. [ABSTRACT FROM AUTHOR]

As Internet of Things (IoT)-based systems become more prevalent in the era of data-driven intelligence, they are prone to some unprecedented challenges in terms of data security and systems scalability in an era of context-sensitive data. The current advances in IoT-driven data sensing and sharing rely on third-party sources of information (TTPs) that gather data from one party, then transmit it to the other. As a result of TTPs' involvement, such IoT systems suffer from many issues including but not limited to security, transparency, trust, and immutability as a result of the involvement of the company. Moreover, a multitude of technical impediments, such as the computation and storage poverty of IoTs, privacy concerns, and energy efficiency, enhances the challenges for IoTs. To address these issues of IoT security, we propose a blockchain-enabled open IoT data-sharing framework based on the potential of the interplanetary file system (IPFS). We have used a case study-based approach to evaluate the proposed solution. It is submitted that the proposed scenario is implemented by building smart contracts in Solidity and deploying them on the local Ethereum test network, using the Solidity programming language. With the implementation of smart contracts on the blockchain for access roles in IoT data sensing, the proposed solution advocates for a blockchain-based approach to data security for IoT systems that makes use of smart contracts for access roles. [ABSTRACT FROM AUTHOR]

[Display omitted] • Novel cost – benefit model for bi-directional functionality of eVTOLs. • Optimization model using Genetic Algorithms is presented for archipelagos. • Canary Islands Case Study shows total net profit of eVTOL aircraft implementation. • The increase in renewable energy is 15% and emission reduction of around 4.6% • Profitable number of eVTOL aircraft per island is calculated. This research paper presents a novel approach for cost-benefit analysis for the bi-directional functionality of eVTOL (Electric Vertical Take-Off and Landing) aircraft charging. eVTOL aircrafts represents an emerging aviation market that seeks to revolutionize mobility, both intra-city and inter-city, through an air transportation system for passengers based on safety, efficiency and accessibility on demand. In this research, an optimization based on genetic algorithms has been used to find the number of eVTOL vehicles which makes vehicle-to-grid services profitable for each of the electrical systems considered. This research discusses the impact that vehicle-to-grid eVTOL aircraft would cause, changing the current demand curve peaks and the economic benefits for eVTOL end users, power plant companies and system operator. The results show that there is an increase in the penetration of renewable energy sources, causing a reduction of approximately 4.6% in global emissions. In addition, the benefits obtained by the stakeholders considered in the system is greater than zero. The methodology considered in this research offers a new perspective for electrical systems to predict the demand of eVTOL aircraft, in specific territories, such as in the presented case study in the Archipelago of Canary Islands. [ABSTRACT FROM AUTHOR]

Traditional centralized cloud storage has difficulties in realizing the secure storage and sharing of speech and other multimedia data, as well as realizing fine-grained access control and privacy protection for speech data. To address this problem, we propose a distributed storage scheme for encryption speech data based on blockchain and inter planetary file system (IPFS). Our scheme is based on the characteristics of blockchain anti-tampering, decentralization and traceability. It is designed to ensure the security and controllability of private sensitive speech data of data users without the use of centralized cloud storage architecture. First, the ciphertext policy hierarchical attribute-based encryption (CP-HABE) scheme is used to encrypt the speech data. The encrypted speech data is then stored in the IPFS to achieve distributed storage. Second, a distributed and trusted access control policy is implemented by deploying an access control protocol Ethereum smart contract related to speech attributes. Finally, the proposed scheme is deployed and tested in the Linux environment, and the smart contract is deployed on the Ethereum test chain. Through experimental comparison with different storage schemes, analysis of the operational properties of our scheme and the evaluation of the quality index of the encryption scheme, the proposed scheme is shown to reliable, secure and scalable. [ABSTRACT FROM AUTHOR]

COVID-19 has become a very transmissible disease that has had a worldwide impact, resulting in a huge number of infections and fatalities. Testing is critical to the pandemic's successful response because it helps detect illnesses and so attenuate (isolate/cure) them and now vaccination is a life-safer innovation against the pandemic which helps to make the immunity system stronger and fight against this infection. Patient-sensitive information, on the other hand, is now held in a centralized or third-party storage paradigm, according to COVID-19. One of the most difficult aspects of using a centralized storage strategy is maintaining patient privacy and system transparency. The application of blockchain technology to support health initiatives that can minimize the spread of COVID-19 infections in the context of accessibility of the system and for verification of digital passports. Only by combining blockchain technology with advanced cryptographic algorithms can a secure and privacy-preserving solution to COVID-19 be provided. In this article, we investigate the issue and propose a blockchain-based solution incorporating conscience identity, encryption, and decentralized storage via interplanetary file systems (IPFS). For COVID-19 test takers and vaccination takers, our solution includes digital health passports (DHP) as a certification of test or vaccination. We explain smart contracts constructed and tested with Ethereum to preserve a DHP for test and vaccine takers, allowing for a prompt and trustworthy response from the necessary medical authorities. We use an immutable trustworthy blockchain to minimize medical facility response times, relieve the transmission of incorrect information, and stop the illness from spreading via DHP. We give a detailed explanation of the proposed solution's system model, development, and assessment in terms of cost and security. Finally, we put the suggested framework to the test by deploying a smart contract prototype on the Ethereum TESTNET network in a Windows environment. The study's findings revealed that the suggested method is effective and feasible. [ABSTRACT FROM AUTHOR]

An $(n,r,h,a,q)$ -Local Reconstruction Code (LRC) is a linear code over $\mathbb {F}_{q}$ of length $n$ , whose codeword symbols are partitioned into $n/r$ local groups each of size $r$. Each local group satisfies ‘ $a$ ’ local parity checks to recover from ‘ $a$ ’ erasures in that local group and there are further $h$ global parity checks to provide fault tolerance from more global erasure patterns. Such an LRC is Maximally Recoverable (MR), if it offers the best blend of locality and global erasure resilience—namely it can correct all erasure patterns whose recovery is information-theoretically feasible given the locality structure (these are precisely patterns with up to ‘ $a$ ’ erasures in each local group and an additional $h$ erasures anywhere in the codeword). Random constructions can easily show the existence of MR LRCs over very large fields, but a major algebraic challenge is to construct MR LRCs, or even show their existence, over smaller fields, as well as understand inherent lower bounds on their field size. We give an explicit construction of $(n,r,h,a,q)$ -MR LRCs with field size $q$ bounded by $\left ({O\left ({\max \{r,n/r\}}\right)}\right)^{\min \{h,r-a\}}$. This significantly improves upon known constructions in many practically relevant parameter ranges. Moreover, it matches the lower bound from Gopi et al. (2020) in an interesting range of parameters where $r=\Theta (\sqrt {n})$ , $r-a=\Theta (\sqrt {n})$ and $h$ is a fixed constant with $h \leqslant a+2$ , achieving the optimal field size of $\Theta _{h}(n^{h/2})$. Our construction is based on the theory of skew polynomials. We believe skew polynomials should have further applications in coding and complexity theory; as a small illustration we show how to capture algebraic results underlying list decoding folded Reed-Solomon and multiplicity codes in a unified way within this theory. [ABSTRACT FROM AUTHOR]

We present two lower bounds on sub-packetization level $\alpha $ of MSR codes with parameters $(n, k, d=n-1, \alpha)$ where $n$ is the block length, $d$ is the number of helper nodes contacted during single-node repair, $\alpha $ the sub-packetization level and $k\alpha $ the scalar dimension. The first bound we present is for any MSR code and is given by $\alpha \ge e^{\frac {(k-1)(r-1)}{2r^{2}}}$. The second bound we present is for the case of optimal-access MSR codes and the bound is given by $\alpha \ge \min \left\{{ r^{\frac {n-1}{r}}, r^{k-1} }\right\}$. There exist optimal-access MSR constructions that achieve the second sub-packetization level bound with an equality making this bound tight. We also prove that for an optimal-access MSR code to have optimal sub-packetization level under the constraint that the $\beta $ scalar symbol indices we access from a given helper node is dependent only on the index of the failed node, it is necessary that the support of the parity-check matrix be the same as the support structure of the existing MSR constructions in literature such as the Clay code. [ABSTRACT FROM AUTHOR]

About fifty years ago, the world's first fully automated system for trading securities was introduced by Instinet in the US. Since then the world of trading has been revolutionised by the introduction of electronic markets and automatic order execution. Nowadays, financial institutions exploit the associated flow of daily data using more and more advanced analytics to gain valuable insight on the markets and inform their investment decisions. In particular, time series of Open High Low Close prices and Volume data are of special interest as they allow identifying trading patterns useful for forecasting both stock prices and volumes. Traditionally, relational databases have been used to store this data; however, the ever-growing volume of this data, the adoption of the hybrid cloud model, and the availability of novel non-relational databases which claim to be more scalable and fault tolerant raise the question whether relational databases are still the most appropriate. In this study, we define a set of criteria to evaluate performance of a variety of databases on a hybrid cloud environment. There, we conduct experiments using standard and custom workloads. Results show that migration to a MongoDB database would be most beneficial in terms of cost, storage space, and throughput. In addition, organisations wishing to take advantage of autoscaling and the maintenance power of the cloud should opt for a cloud native solution. [ABSTRACT FROM AUTHOR]

In this paper, we present a distributed classification technique for big data by efficiently using distributed storage architecture and data processing units of a cluster. While handling such large data, the existing approaches consider specific data partitioning techniques which demand complete data be processed before partitioning. This leads to an excessive overhead of high computation and data communication. The proposed method does not require any pre-structured data partitioning technique and is also adaptive to big data mining tools. We hypothesize that an effective aggregation of the information generated from data partitions by subprocesses of the complete learning process can lead to accurate prediction results while reducing the overall time complexity. We build three SVM based classifiers, namely one phase voting SVM (1PVSVM), two phase voting SVM (2PVSVM), and similarity based SVM (SIMSVM). Each of these classifiers utilizes the support vectors as the local information to construct the synthesized learner for efficiently reducing the training time and ensuring minimal communication between processing units. In this context, an extensive empirical analysis demonstrates the effectiveness of our classifiers when compared to other existing approaches on several benchmark datasets. However, among existing methods and three of our proposed (1PVSVM, 2PVSIM, and SIMSVM) methods, SIMSVM is the most efficient. Considering MNIST dataset, SIMSVM achieves an average speedup ratio of 0.78 and minimum scalability of 73% when the data size is scaled up to 10 times. It also retains high accuracy (99%) similar to centralized approaches.