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Blockchain technology and decentralized learning are attracting growing attention. Most existing methods of machine learning is in the centralized form which relies upon the third party in terms of the raw datasets and mining resources. Blockchain solves world centralization problems that keep the system secure through complex mathematical computations puzzle solved by blockchain miners. Concurrently, decentralized learning such as federated model allows the user to collaboratively access the updated prediction model without revealing the training data to the public. By doing so, it provides less power consumption, lower latency that respects the user’s privacy concern. Therefore, we study the extent to which these two technologies can be applied in the real world for faster convergence without compromising user’s security.

Cloud computing relies on sharing of resources to achieve coherence and economy of scale, similar to a utility. In existing system ABE (attribute based encryption) is used to maintain security. Attribute-based encryption is a type of public-key encryption in which the secret key of a user and the cipher-text are dependent upon attributes. In computing, data de-duplication is a specialized data compression technique for eliminating duplicate copies of repeating data. Related and somewhat synonymous terms are intelligent compression and single-instance storage. In this paper we studied the different attribute storage mechanisms as well as de-duplication system.

In the cloud platform, basic aim of task scheduling based algorithm is to reduce the makespan of the task and to minimize the load on resources. Users are working on large amount and the resources to be used are not up to the requirements. So, to fulfill global performance, cloud computing is used. Stated problem has been improved up to some extent by variant of genetic algorithm like JLGA (Job Spanning Time and Load Balancing and GA) and MPGA. Therefore, here it reviewed variant of genetic algorithm and did comparison among them. Furthermore, this paper proposed HJLGA (Hybridization of good features of JLGA and MPGA) which uses min-min algorithm to initialize population and also brings the concept of hill climbing to find the best fitted value. HJLGA satisfied the requirements of load balancing, least cost and minimum makespan of nodes.

Resource allocation and power management have become challenging tasks in cloud computing. Since workloads change over time, dynamic cloud resource allocation and power management solutions are needed, that can intrinsically adapt to the demand of the resources. This paper demonstrates the use of deep reinforcement learning algorithms for the task dispatching and power management in the simulated Alibaba cluster environment with the goal of achieving better power and latency management than traditional task scheduling algorithms. In this paper, we propose a hierarchical RL model for resource allocation and power management which obtains better results in terms of latency as well as power usage, than traditional task scheduling.

This paper provides a strategy for formation control of second-order multi-agent systems. On the basis of using graph theory to describe the topology of measurement information and communication information between multi-agents, the distributed coordinated movement formation control strategy based on graph Laplacian is studied, and obstacles that may be encountered in complex application scenarios, or there may be collisions between the main bodies, or unpredictable situations such as the disconnection of the link between the main bodies due to the limited detection radius of the sensor, respectively. The formation control method with collision avoidance and communication maintenance is proposed in this paper. The simulation result proves the effectiveness of the method.

With the development of cloud computing, cloud computing provides more and more computing services. In today's fiercely competitive environment, the service flexibility and choice provided by this highly scalable technology are becoming more and more attractive to enterprises. However, behind the rapid development of cloud computing, its security and trust issues have become increasingly unignorable. The purpose of this paper is to analyze the key technologies of cloud computing security based on the trust model (TM). This paper first proposes a subjective and objective TM based on the ideal point method for the trust problem in the cloud environment, and verifies the performance of the model through experiments. This paper proposes a decentralized TM based on blockchain, and verifies the effectiveness of the model through experiments. This paper starts from the current problems of cloud computing, and on the basis of understanding the shortcomings of traditional security technologies, proposes a key technology of cloud computing security based on the TM. The solution proposed in this paper has a minimum average time consumption of 0.12 ms in the copy + label generation stage, while the minimum average time consumption of a traditional PDP is 16.3 ms. It is obvious that this solution has great advantages.

The conventional energy system dependent on exhaustible resources which use single-way interfacing between the source and the consumer via generation, transmission, distribution and consumption is inefficient, unsustainable and non-reliable due to the high energy needs of the digital society. A new system must be defined which overcomes all the above disadvantages and is self-reliable and manageable. The objective of this paper is to compare the conventional electricity grid system with the modern (smart grid) system, to define the smart grid, its components and their impact on the future of the electrical system. There is a need to use electrical energy in a more efficient and sustainable manner. The aim is to define a system that will be cyber secured and a two-way interface between the supplier and customers. This system will work on real-time data and also integrate it with technologies, tools and techniques to identify faults and reconfigure them automatically based on proper diagnostics. Also, several new components such as distribution generators and mini-grids will be introduced which will run the system uninterruptedly during the outages. This paper gives a brief about the distribution automation and energy management system post comparing conventional grid system with the smart grid system.

The evolutionary computing (EC) domain provides variety of optimization algorithms to the computer science community. The increase of large-scale real-world optimization problems which consists of thousands of decision variable has given rise to different challenges EC algorithms (popularly known as evolutionary algorithms (EAs)). However, these challenges are addressed by the research community of EC domain integrating the parallel and distribute computing paradigms to design novel EA frameworks for solving complex large-scale optimization problems. The parallel and distributed EA frameworks have received global attention over the past two decades. This paper summarizes various commonly followed algorithm models for parallel and distributed EAs. This paper also comprehensively reviews the research attempts in the literature relating these algorithmic models with different EAs and different optimization problems.

WSNs have enthused resurgence in research on machine learning-based approaches with the intent of overcoming the physical restraints of sensors. Although resource constrained in nature, WSNs domain has tremendous potential for building powerful applications, each with its own individual characteristics and requirements. This fascinating field of WSNs although comprises of various research issues and challenges, viz. energy efficiency, localization, etc., which needs to rectified. One such prominent challenge is detection of outliers whose chore is to preclude any kind of malicious attacks in the network or lessen the noisy error prone data in millions of wireless sensor networks. To do so, the methodologies developed needs to take care of inherent limits of sensor networks so that the energy intake of motes is minimum and lifespan of the motes is maximized. Consequently, the quality of data must be thoroughly patterned as any kind of outlier in the sensed network may degrade the quality of the data and hence affect the final decision. Thus, it becomes imperious to retain the quality of the data. Numerous ML-based methodologies have been used by several researchers over the time to detect any form of outliers or anomaly present in the network. In this paper, some machine learning methodologies have been discussed which have proved their mettle in outlier detection for sensor networks. This paper presents a brief survey on outlier detection in WSNs data using various ML-based techniques.

This paper presents a tool for evaluating transport layer congestion control schemes, including various TCP variants. In real computer networks, it is difficult to obtain detailed information about the internal state and operation of network protocols without disturbing protocol behavior. This is especially true for transport layer protocols where timing plays an important role. For deep analysis of protocol mechanisms, network simulation plays an essential role. This paper demonstrates a network simulator tool channel SimCast. This simulator focuses on the congestion control methods of the transport layer. Using this special purpose simulator, the effects of network topology and loss models can be investigated in detail. This paper presents a comparison of simulation results from SimCast to the output of other network simulators and real network measurements.

Traditional data mining technology is based on data repository and related data to calculate data, and find data relationship and data model. Therefore, when processing cloud computing platform network data, it will consume a lot of storage resources and computer resources, which not only has low mining efficiency, but also has high material and software costs, which to a certain extent limits the development of cloud computing platform, traditional data processing technology has been unable to adapt to the current era of large amount of information. Based on this, this paper studies the network data processing based on cloud computing platform. This paper designs k-means clustering algorithm to process network data, and selects 32G, 16G, 8G, 4G, 2G, 1G and other data groups to test the performance of clustering algorithm for network data processing. The experimental results show that with the increase of the size of the data set, the acceleration efficiency of the algorithm will also be improved.

Software Defined Network (SDN) is the most significant model for the administration of large-scale complex systems, which may require reconfigurations occasionally. It involves the separation of the network control plane and the forwarding plane, where a control plane controls many devices. In this way, the switches forwards packets by the rules defined in the flow table by the controller. Despite the fact, SDN is a new area, it has pulled in a lot of consideration from both the scholar world and industry. This paper investigates the multi-controller placement problem from the viewpoint of cost and load balance factors. The paper proposes an algorithm named Load Balancing with ECP-LL that places controllers by using clustering. The algorithm uses Shannon entropy which avoids poor local minima problems.

Blockchain is an effective means to store data securely. Existing blockchain systems mostly adopt the equal mining mode, and all bookkeepers (entities) record ledger books on a single main chain, resulting in random data storage in the whole blockchain. Moreover, in complex or classification scenarios, data in a single main chain cannot be correlated or regularly stored, resulting in low storage efficiency. To solve these problems, this paper proposes a composite chain structure blockchain storage method based on blockchain technology. This method firstly proposes the blockchain model of composite chain structure, then constructs the private chain and alliance chain respectively, and finally realizes the adaptive data association storage in complex or classified scenarios. The experimental results show that a composite chain structure blockchain storage method based on blockchain technology proposed in this paper has great advantages in storage efficiency, storage overhead, security performance, availability and other aspects.

Warehouses and cold storage mechanisms are integral and important components for the storage of the agricultural products beyond their intended life-period. Configurability and self-adaptability can greatly enhance the functional efficiency of these systems and minimize wastage of resources. This paper explores the technical glitches faced by the cold storage system and proposes a novel design for a new and advanced automated cold storage system (ACSS). To analyze the performance of ACSS, a mathematical representation of the model is presented. Two performance metrics, namely, temperature and humidity, are considered which play a vital role in any cold storage system. This paper also proposes efficient methods to monitor and control these performance metrics of the cold storage system. The self-adaptable capabilities are provided by using proportional-integral-derivative (PID) controller whose parameter are tuned using particle swarm optimization (PSO) algorithm. The performance of the designed cold storages system is evaluated using MATLAB simulation. Also, to provide the intelligence capability, the ubiquitous Internet of Things (IoT) implementation of ACSS has been proposed in this paper along with the deployment strategy which further improves the efficacy of cold storage system due to its seamless reconfigurable and self-adaptable capabilities.

The integration of multi-energy systems is an important feature of energy internet system. The traditional energy systems, such as electric power, thermal power, gas and other energy systems, are operating separately. All kinds of energy systems have been separated and self-made in a long time. They are managed by different departments, and it is difficult to set up a centralized dispatching agency for operation management, which restricts the development of energy internet system greatly. Block chain technology has the main characteristics of decentralization and transparency. Therefore, a decentralization dispatching and operation control mechanism based on block chain technology for multi-energy fusion system is proposed in this paper. Through the establishment of decentralized dispatching agencies for operation management, it is proposed to share the information of energy supply and demand relationship between different energy systems and different regions in multiple energy systems. An optimal operation model of self-dispatching decision-making is established to obtain the results of energy self-dispatching transaction of each energy demand node. Finally, a simulation is conducted of a multi-energy system, and the calculation results verify the effectiveness and feasibility of the self-scheduling decision-making optimization model proposed in this paper.

This paper presents and investigates a cyber-physical fabrication workflow, which can respond to the deviations between built- and designed form in real-time with vision augmentation. We apply this method for large scale structures built from natural bamboo poles. Raw bamboo poles obtain evolutionarily optimized fibrous layouts ideally suitable for lightweight and sustainable building construction. Nevertheless, their intrinsically imprecise geometries pose a challenge for reliable, automated construction processes. Despite recent digital advancements, building with bamboo poles is still a labor-intensive task and restricted to building typologies where accuracy is of minor importance. The integration of structural bamboo poles with other building layers is often limited by tolerance issues at the interfaces, especially for large scale structures where deviations accumulate incrementally. To address these challenges, an adaptive fabrication process is developed, in which existing deviations can be compensated by changing the geometry of subsequent joints to iteratively correct the pose of further elements. A vision-based sensing system is employed to three-dimensionally scan the bamboo elements before and during construction. Computer vision algorithms are used to process and interpret the sensory data. The updated conditions are streamed to the computational model which computes tailor-made bending stiff joint geometries that can then be directly fabricated on-the-fly. In this paper, we contextualize our research and investigate the performance domains of the proposed workflow through initial fabrication tests. Several application scenarios are further proposed for full scale vision-augmented bamboo construction systems.

In the modern era of the world, WSNs and IoT are managing the improvements in the research of the current environments. All sciences and technologies are searching for efficient and cost-effective moves toward that reveal new strategies and ideas in the planned field. Overview of survey gives efficient and broad access to these ideas in the proposed domain. However, this inspiration as a key concern and an idea about the energy-efficient routing protocols on controlling and overseeing energy utilization of WSNs. Moreover, to minimize energy consumption and maximize the lifetime of the network, they are the key necessities to design the energy-efficient routing protocols. In this research paper, energy-efficient routing protocols of WSNs and IoT are presented with their methods and performance metrics. This paper also surveys the fog computing techniques and highlights their key features and performance metrics. This study can be valuable for researchers as the beginning stage for a fast comprehension of weaknesses and inadequacies in this field to help out advance research in the future.

In today’s environment of rapid information generation, getting precise information about an event is not only sufficient but getting that information in time (or as early as possible) is rather more important. Further, in certain situations, it may also be equally important to put a control to a sort of outbreak in the network. This calls for the processes and techniques which can be developed to make the information to flow from one source to another as per the desired necessities. Social networks, VANETs, electrical circuit networks, etc. are prime examples where channelized information flow is required to accomplish the desired task. For this, we need to map or mimic the real information flow scenario to some simulative environment or model. In this paper, basic graph theory concepts have been used to model various scenarios of these suggested networks to depict how network characteristics can play a vital role to make the information flow happen in a more systematic and optimized way so that the desired task can be accomplished. The paper takes into consideration degree distribution of the graph, PageRank scores, strongly connected components, weakly connected components, average path length, etc. and other desired characteristics that will guide node to node information flow so that various parameters remain optimized like path length, no. of nodes visited, etc. The graphical model will be mimicked using Stanford Network Analysis Platform (SNAP) to evaluate these basic measure’s characteristics and help us propose different algorithms depending upon the scenarios. Finally, the paper will be concluded with an insight that how the algorithms can be worked out to already existing framework using these network characteristics.

MTS (maintenance and test software), as an important human-computer interactive tool in DCS of nuclear power plant, plays a crucial role in configuration and management of the engineering applied data, engineering design, system debugging and operational maintenance. With the advent of information age, industrial designers pay more and more attention to interactive design, and user attaches increasing importance to the human-machine interactive experience of products, which puts forward new requirements for the human-machine interactive interface of MTS. This paper takes the human-computer interaction visual design of MTS as the core, through analyzing the human-computer interaction model and requirements of MTS, formulates the overall design scheme of the human-computer interaction system of MTS. According to the overall scheme, a set of human-computer interaction visual design method applied to MTS of DCS in nuclear power plant is proposed. The method and criterion of human-computer interaction design proposed in this paper is very important for the design human-computer interaction of nuclear power plant application software, and also points out the direction of human-computer interaction optimization of MTS mentioned in this paper.

Graph computing is more and more widely used in various fields such as spatial information network and social network. However, the existing graph computing systems have some problems like complex programming and steep learning curve. This paper introduces GRAPE, a distributed large-scale GRAPh Engine, which has the unique features of solid theoretical guarantee, ease of use, auto-parallelization and high performance. The paper also introduces several typical scenarios of graph computing, including entity resolution, link prediction, community detection and graph mining of spatial information network. In these scenarios, various problems have been encountered in the existing systems, such as failure to compute over large-scale data due to the high computation complexity, loss of accuracy due to the cropping of original data and too long execution time. In the face of these challenges, GRAPE is easy to support these computing scenarios with a series of technical improvements. With the deployment of GRAPE in Alibaba, both effectiveness and efficiency of graph computing have been greatly improved.

Task Scheduling is a prominent research topic in cloud computing. There are several objectives associated with Optimise Task Scheduling and Resource allocation as cloud computing systems are more complex than the traditional distributed system. There are several challenges such as resolving the task mapped to the node on which task to be executed. Modelling deadline or make spam parameter, task reliability, various task allocation strategies, etc. A simplified but near-optimal proposed nature-inspired algorithms are focus in this paper. In this paper, a basic idea about optimisation, reliability, and complexity is considered while designing a modern BDA solution (Big Data Application). In this paper, we focused on Dragonfly algorithm and Sea lion algorithms which are nature-inspired algorithms. These algorithms are efficient for optimisation purposes for solving task scheduling and resource allocation problem. Finally, the performance of the DA algorithm and Sea lion is compared with the Genetic Algorithm (GA) and Particle Swarm Optimisation (PSO) for modern BDA such as Hadoop Map reduce. Simulation results prove the efficacy of the suggested algorithms

Cyber physical systems (CPS) is the most extensive inventions in the field of information and computer technology. CPS utilizes the computers to perform tasks efficiently in connection to physical world. CPS can be expressed as an association of computers and networks with the physical process, also tightly integrated with internet and uses cloud for storage and computation. This paper proposes a software agent architecture for efficient job request scheduling in a cloud and improved response in a CPS environment. Intelligent agent architecture for scheduling interconnects various software agents and work without any human interference; acts on behalf of others to make the cyber physical system operate in an intelligent fashion. As it is well known that cloud is an essential paradigm widely used all over the world to handle multiple concurrent jobs and also to bring down their computational and operational expenditure, there is always a need for a highly functional scheduling architecture aimed at timely job completion, which is detailed in this paper.

Blockchain technology has gained maximum popularity in recent years as compared to the other latest technologies. It works as immutable ledger in which the transactions are done in decentralized way. It has started spreading its wings now as it is not only used in financial applications but many other companies in the industry are trying to leverage the benefits offered by blockchain in their projects. As industries are committing towards blockchain based projects, there is a need of blockchain implementation and associated development. This paper introduces the basic requirements of blockchain development and proposes a generic blockchain that can be considered as a mould for implementation in different use cases. In this paper a distributed architecture is implemented using blockchain with improved security and privacy. The expected contribution of this paper is to provide an overview for researchers and developers to build applications utilizing the blockchain as a basis.

Power system faces thousands of physical and cyber attacks which seriously threaten its security. It is noted that most defense methods are only suitable for specific cyber attacks and are not applicable to physical attacks. This paper provides a generic method regardless of different attack types through topological efforts to reduce potential loss of the power grid. In this paper, a proportional loss model is proposed depending on the different attack-defense resource allocations. The optimal allocation strategy can be converted into the solution to a \(\min \max \) problem. In order to further improve the security of the power grid, by taking the geographical feasibility into consideration, a hexagonal construction method is proposed to provide a cost-affordable and geographically-feasible solution for new power grid construction.

The load balancing is an important aspect of systematized operations in distributed environments. As cloud computing is emerging expeditiously and clients are claiming more facilities and improved results, load balancing for the cloud environment has become a very fascinating and crucial research area. Load balancing in cloud computing is defined as the method of splitting workloads and other computing properties among different computers, networks or servers in a cloud computing environment. Researchers have proposed many approaches to provide cloud load balancing with the aim to intensify the overall performance of the cloud computing. In this paper, the different cloud load balancing methods with the three most efficient approaches have been investigated. The three approaches that are reviewed in this paper are the ant colony optimization method, the honey-bee algorithm, and genetic algorithm. This research paper summarized the methods for cloud load balancing based on these three mentioned approaches.

The conceptualization of microgrid has led to various technological advances in power generation and consumption as it facilitates the integration of environment-friendly renewable energy sources. Microgrid generally comprises of several distributed energy resources interfaced with the utility grid through power converters. The system’s frequency and voltage are sustained in order that the microgrid’s operation is secure and reliable. This paper encompasses futuristic control strategies defining the functioning of the microgrid when connected to the grid and likewise in stand-alone mode. Primary level of control, secondary level of control and tertiary level of control form the three hierarchical control structures for the microgrid. A comparative analysis of various controls has also been shown in this paper.