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The IDN Hebat program is an important tool for schools to track and analyze student achievement data. However, with the targeted activities in the IDN program, challenges arise in managing and measuring achievement data efficiently. The research aims to develop a Web Cloud-based data management system for IDN Hisbat achievements at IDN Akhwat School by utilizing Google Looker Studio and the Naive Bayes Algorithm. The data source used in this study is by applying a classification dataset obtained from student achievement information data in the Great IDN Program. The results of this analysis show that the highest accuracy of teaching achievement fell on the status of exceeding the target with a percentage of 89%, and the highest class that placed the status above the target was class 9A with an average percentage of 35%. In addition, the results from this analysis can help coordinators and schools in planning more effective and strategic programs in the future. Overall, this study provides important benefits in improving the quality of teaching and student coaching, as well as supporting data-driven decision-making. This study is expected to enhance the efficiency, accuracy, and effectiveness of managing student achievement, while also supporting the attainment of optimal educational goals for each student to achieve extraordinary results. [ABSTRACT FROM AUTHOR]

Developing a simple and efficient attack detection system for ensuring the security of cloud systems against cyberthreats is a crucial and demanding process in the present time. In traditional work, various machine-learning-based detection methodologies have been developed for securing the cloud network. However, those methodologies face the complications of overfitting, complex system design, difficulty understanding, and higher time consumption. Hence, the proposed work contributes to the design and development of an effective security model for detecting cyberthreats from cloud systems. The proposed framework encompasses the modules of preprocessing and normalization, feature extraction, optimization, and prediction. An improved principal component analysis (IPCA) model is used to extract the relevant features from the normalized dataset. Then, a hybrid grasshopper–crow search optimization (GSCSO) is employed to choose the relevant features for training and testing operations. Finally, an isolated heuristic neural network (IHNN) algorithm is used to predict whether the data flow is normal or intrusive. Popular and publicly available datasets such as NSL-KDD, BoT-IoT, KDD Cup'99, and CICIDS 2017 are used for implementing the detection system. For validation, the different performance indicators, such as detection accuracy (AC) and F1-score, are measured and compared with the proposed GSCSO-IHNN system. On average, the GSCO-IHNN system achieved 99.5% ACC and 0.999 F1 scores on these datasets. The results of the performance study show that the GSCSO-IHNN method outperforms the other security models. Ultimately, this research strives to contribute to the ongoing efforts to fortify the security of cloud systems, making them resilient against cyber threats more simply and efficiently. [ABSTRACT FROM AUTHOR]

Handling service access in a cloud environment has been identified as a critical challenge in the modern internet world due to the increased rate of intrusion attacks. To address such threats towards cloud services, numerous techniques exist that mitigate the service threats according to different metrics. The rule-based approaches are unsuitable for new threats, whereas trust-based systems estimate trust value based on behavior, flow, and other features. However, themethods suffer frommitigating intrusion attacks at a higher rate. This article presents a novelMulti Fractal Trust Evaluation Model (MFTEM) to overcome these deficiencies. Themethod involves analyzing service growth, network growth, and quality of service growth. The process estimates the user's trust in various ways and the support of the user in achieving higher service performance by calculating Trusted Service Support (TSS). Also, the user's trust in supporting network stream by computing Trusted Network Support (TNS). Similarly, the user's trust in achieving higher throughput is analyzed by computing Trusted QoS Support (TQS). Using all these measures, the method adds the Trust User Score (TUS) value to decide on the clearance of user requests. The proposed MFTEM model improves intrusion detection accuracy with higher performance. [ABSTRACT FROM AUTHOR]

Cloud computing has gained immense popularity in the logistics industry. This innovative technology optimizes computing operations by eliminating the requirement for physical equipment for calculations. Instead, specialized companies provide cloud-based computing services, relying heavily on computers and servers that consume substantial amounts of energy. Hence, ensuring the availability of affordable and dependable electricity is paramount for the efficient design and management of these logistics services. Cloud centers, which are power-intensive, face the challenge of reducing their energy consumption due to escalating power costs. To address this issue, efficient data placement and node management strategies are commonly employed in logistics operations. An AlexNet model has been designed to optimize storage relocation and predict power prices. The outcome of this initiative has resulted in a considerable reduction in energy consumption at data centres. The model uses Dwarf Mongoose Optimization Algorithm (DMOA) to produce an optimal solution for the AlexNet and increase its performance with a real-world dataset from IESO in Ontario, Canada. 75% of the available data was used for training to assure the model’s precision, with the remaining 25% allocated to testing purposes. The model forecasts power prices with an MAE of 2.22% and an MSE of 6.33%, resulting in an average reduction of 22.21% in electricity expenses. Our proposed method has an accuracy of 97% compared to 11 benchmark algorithms, including CNN, DenseNet, and SVM having an accuracy of 89%, 88%, and 82%, respectively.

Significant research has been done on combining intrusion detection and blockchain to increase data privacy and find both current and future threats. This research suggests a machine blockchain framework (MBF) in order to provide distributed intrusion detection with security and blockchain with privacy with the help of smart contracts in IoT networks. An XGBoost algorithm was implemented to work with sequential network data and the intrusion detection approach is explored using the N-BaIoT dataset. In order to protect the network against known malware threats (Mirai, Gafgyt, or Bashlite), the IoT malware attack prediction model created in this study offers a deterrent strategy based on the network traffic statistics. On the other hand, the models need to be taught to recognize new varieties of malware. In this work, we observe how different machine learning models, like Random Forest algorithm and proposed XGBoost algorithm, can accurately predict the infected malware in certain traffic instance. However, we provide a honeypot-based strategy that employs machine learning techniques for the detection of malware in this study. Using data from an IoT Botnet as a dataset helps train a machine learning model in a way that is effective and changes over time. [ABSTRACT FROM AUTHOR]

Cloud systems allow data sharing capabilities for providing several benefits to users and organizations. However, authentication accuracy (AA) was not improved, and time consumption was not reduced. To increase authentication accuracy, Whirlpool Hash Mutual Biometric Serpent Authentication (WPHMBSA) Technique is designed to access data on a server in a secured manner. During the registration process, users’ data are registered and stored on the server. After registering, the cloud server generates an ID and password for every registered user. For authentication, the user needs to login with an ID and password to the cloud server. During authentication, WPHMBSA Technique authenticates the biometric keys of the users. When a user is legitimate, WPHMBSA Technique confirms their authenticity to the server. Experimental evaluation of the WPHMBSA Technique and existing methods are performed by various parameters with the amount of cloud user’s information. The experimental results show that the WPHMBSA Technique obtains high accuracy and confidentiality rate within minimum time.

Developing a simple and efficient attack detection system for ensuring the security of cloud systems against cyberthreats is a crucial and demanding process in the present time. In traditional work, various machine-learning-based detection methodologies have been developed for securing the cloud network. However, those methodologies face the complications of overfitting, complex system design, difficulty understanding, and higher time consumption. Hence, the proposed work contributes to the design and development of an effective security model for detecting cyberthreats from cloud systems. The proposed framework encompasses the modules of preprocessing and normalization, feature extraction, optimization, and prediction. An improved principal component analysis (IPCA) model is used to extract the relevant features from the normalized dataset. Then, a hybrid grasshopper–crow search optimization (GSCSO) is employed to choose the relevant features for training and testing operations. Finally, an isolated heuristic neural network (IHNN) algorithm is used to predict whether the data flow is normal or intrusive. Popular and publicly available datasets such as NSL-KDD, BoT-IoT, KDD Cup’99, and CICIDS 2017 are used for implementing the detection system. For validation, the different performance indicators, such as detection accuracy (AC) and F1-score, are measured and compared with the proposed GSCSO-IHNN system. On average, the GSCO-IHNN system achieved 99.5% ACC and 0.999 F1 scores on these datasets. The results of the performance study show that the GSCSO-IHNN method outperforms the other security models. Ultimately, this research strives to contribute to the ongoing efforts to fortify the security of cloud systems, making them resilient against cyber threats more simply and efficiently.

In recent years, with the rapid growth of Internet of Things (IoT) and cloud services having received special attention from the research community across the world. IoT provides a platform of creating a world connected through internet. The implementation of smart devices collects information from our surroundings and works as per our needs. The implementation of IoT is very challenging as it requires the use of different new technologies like the emergence of fog and edge computing. The growth of fog and edge computing introduces many new requirements that needs to be investigated. The line monitoring system requirements for edge computing scenarios are not yet fully accomplished. The prime focus behind this study is to identify the challenges in the field of line monitoring within the application based on edge computing and to present the requirements of line monitoring for adaptive applications depending on edge computing frameworks. In this article we describes the architecture of fog and edge computing and presented their benefits among each other. The objective behind this study is to study the evolution of edge computing and present their benefits. The main contribution of this article is to present the layered architecture study for fog and edge computing. Moreover this article presents the key differences of fog and edge computing. On comparison with cloud computing, the edge computing performs the processing and storage on network edge closer to the user. The latest advances in the edge computing states that edge technology is an optimal solution for issues like latency, data privacy and bandwidth requirements. [ABSTRACT FROM AUTHOR]

In the era of cloud computing, massive quantities of data are encrypted and uploaded to the cloud to realize a variety of applications and services while protecting user confidentiality. Accordingly, the formulation of methods for efficiently searching encrypted data has become a critical problem. Public-key encryption with keyword search is an efficient solution that allows the data owner to generate encrypted keywords for a given document while also allowing the data user to generate the corresponding trapdoor for searching. Huang and Li proposed a public-key authenticated encryption with keyword search (PAEKS) scheme to resist keyword guessing attacks, where the data owner not only encrypts keywords but also authenticates them. However, existing PAEKS-related schemes carry a tradeoff between efficiency, storage cost, and security. In this article, we introduce a novel framework called identity-certifying authority-aided identity-based searchable encryption, which has the advantage of reducing storage space while remaining the efficiency and security. We formally define the system model and desired security requirements to represent attacks in a real scenario. In addition, we propose a provably secure scheme based on the gap bilinear Diffie–Hellman assumption and experimentally evaluate our scheme in terms of its performance and theoretical features against its state-of-the-art counterparts. [ABSTRACT FROM AUTHOR]

Cloud computing is the delivery of on‐demand computing resources. It shares the resources or provides vir‐utilization that enables single user to access various Cloud services such as CPU, memory, storage devices, network, and so on. However, more commercial cloud services offered by several cloud service providers (CSPs) are available in the market place. Most CSPs must, therefore, deal with the dynamic resource allocation where the mobile services are migrating from one cloud to another cloud environment to provide heterogeneous resources based on user needs. There is still a lack of heuristics that are able to check requested resources and available resources to allocate and deallocate before it begins the secure service migration. We proposed a resource allocation security protocol that allows resources to be allocated and migrated efficiently in a secure service migration between cloud infrastructures. Furthermore, formal methods can be used for protocols to verify the desired properties, detecting attacks and producing accurate outcomes. This article presents formal modeling and verification of this abstract protocol using ProVerif cryptographic tool to validate the security properties such as secrecy of resources, authentication from both parties and key exchange in order to securely migrate resources in commercial cloud environments. [ABSTRACT FROM AUTHOR]

Among multiple fault-tolerant strategies, resubmission, and replication are fundamental and widely recognized in distributed computing systems. In recent years, many algorithms based on replication or resubmission have been proposed. However, few of them consider these two techniques together, especially in Cloud systems. In this article, we propose a Hybrid Fault-Tolerant Scheduling Algorithm (HFTSA) for independent tasks with deadlines by integrating the above techniques in virtualized Cloud systems. During the task scheduling process, HFTSA selects fault-tolerant strategies from resubmission and replication for each accepted task based on the characteristics of both task and Cloud resources and then reserves suitable resources. During the task execution process, HFTSA adopts an online adjustment scheme for fault-tolerant strategies of some tasks if necessary while providing an online scheduling scheme for faults. Moreover, an elastic resource provisioning mechanism is designed and incorporated into HFTSA to dynamically adjust the provided resources to improve resource utilization. Experiments on a real cloud platform and a simulated platform are conducted to verify the effectiveness of the proposed HFTSA. The results demonstrate that HFTSA can provide an efficient fault-tolerant scheduling strategy for deadline-constrained tasks with high resource utilization and performs better than corresponding competitors. [ABSTRACT FROM AUTHOR]

The current digital age, mainly characterized by an economy based upon information technology, demands a data integrity service, even more so because organizations and companies are migrating their services over the cloud. This is not a simple task; it is cumbersome since traditional schemes in databases could be subject to modifications. However, it can be solved using blockchain technology. This paper provides a data integrity verifiability architecture for cloud systems based on blockchain. The architecture provides a mechanism to store events (as logs) within a blockchain platform from any cloud system. Users can then consult data integrity through a microservice, acting as an intermediate server that carries out a set of verification steps within the blockchain, which confirms the integrity of a previously stored log. Our architecture takes advantage of the blockchain strength concerning integrity, providing a traceability track of the stored logs. A prototype system and a case study were implemented based on the proposed architecture. Our experimental results show that the proposed decentralized architecture can be adapted to cloud existing systems that were born without blockchain technology and require a modular and scalable audit characteristic. [ABSTRACT FROM AUTHOR]

This paper reviews the background and related studies in the areas of cloud systems, intrusion detection and blockchain applications against cyber attacks. This work aims to discuss collaborative anomaly detection systems for discovering insider and outsider attacks from cloud centres, including the technologies of virtualisation and containerisation, along with trusting intrusion detection and cloud systems using blockchain. Moreover, the ability to detect such malicious attacks is critical for conducting necessary mitigation, at an early stage, to minimise the impact of disruption and restore cloud operations and their live migration processes. This paper presents an overview of cloud architecture and categorises potential state-of-the-art security events based on their occurrence at different cloud deployment models. Network Intrusion Detection Systems (NIDS) in the cloud, involving types of classification and common detection approaches, are also described. Collaborative NIDSs for cloud-based blockchain applications are also explained to demonstrate how blockchain can address challenges related to data privacy and trust management. A summary of the research challenges and future research directions in these fields is also explained.

Despite the salient feature of cloud computing, the cloud provider still suffers from electricity bill, which in part comes from 1) the power consumption of running physical machines (PMs) to guarantee the resource/time requirements of virtual machines (VMs), and 2) the dynamically varying electricity price offered by smart grids. In the literature, there exist viable solutions adaptive to electricity price variation to reduce the electricity bill. However, they are not applicable to serving time-sensitive VM requests. In serving time-sensitive VM requests, it is potential for the cloud provider to apply proper consolidation strategies to further reduce the electricity bill. Few prior works have provided theoretical solutions of VM consolidation strategies that are adaptive to electricity price variations in serving time-sensitive VM requests. In this work, to address this challenge, we develop electricity-price-aware consolidation algorithms for both the offline and online scenarios. For the offline scenario, we first develop a consolidation algorithm with constant approximation, which always approaches the optimal solution within a constant factor of 5. For the online scenario, we propose an $O(\log (\frac{L_{max}}{L_{min}}))$ O (log (L m a x L m i n )) -competitive algorithm that is able to approach the optimal offline solution within a logarithmic factor, where $\frac{L_{max}}{L_{min}}$ L m a x L m i n is the ratio of the longest length of the processing time requirement of VMs to the shortest one. Our trace-driven simulation results further demonstrate that the average performance of the proposed algorithms produce near-optimal electricity bill. [ABSTRACT FROM AUTHOR]

In recent decades, artificial intelligence techniques have been adopted for many real-time applications. The Internet of Things (IoT) network comprises many sensing devices and physical objects for information gathering and further transmission. In addition to being sent to the receiving nodes, the collected data also needs to be received promptly. Also, many solutions have been proposed for IoT-based embedded systems using edge computing but they are not fully protected against unidentified communication threats. In such circumstances, such systems decrease the trust ratio, and communication performance is compromised. In this research, we describe an optimization model based on IoT-edged technology that incorporates cloud computational intelligence. Furthermore, edge nodes employ artificial intelligence algorithms to provide the optimal outcome for selecting trustworthy forwarded data and lengthen the connected time for smart devices. Firstly, the edge devices extract useful information from the IoT nodes, and accordingly, it provides a decision module based on optimization computing. Secondly, utilizing cryptographic approaches, edge technology secures the multi-layers of the IoT system and ensures data privacy with integrity. Finally, the proposed model is tested and verified for its performance than other related studies in terms of energy consumption, packet delivery ratio, and data delay. [ABSTRACT FROM AUTHOR]

Subject of Research. The existing approaches to the automatic scaling of non-stationary operating cloud systems are analyzed. The drawbacks of the existing approaches to workload prediction are revealed due to the insufficient performance of the algorithms being used. The analysis of the properties of periodic non-stationary processes and automatic length estimation of their period are performed on the basis of measured data. The accuracy of the developed analytical models was confirmed in the course of numerous simulation experiments in the AnyLogic Professional modeling environment. Method. The basis of the developed method of automatic length estimation of the non-stationary processes period is the consistent approximation of the intermediate result to the desired value. The proposed method ranks the expected results in accordance with the probability of their compliance with the determined period of the non-stationary process. Main Results. The possibility is provided toestimate the period length for an adequate time. The testing was carried out on a system with an AMD FX 8120 CPU with a clock frequency of 3.1 GHz in one thread. The original signal was generated with amplitude 1. The waveform, the period value, the amplitude multiplier and the magnitude of the superimposed random noise were varied. According to the data from the largest transport network hub of Russia, Joint-Stock Company Center for Interaction of Computer Networks MSK-IX, the period of total transit traffic has been successfully determined, and also the periods of non-stationary processes for the cloud system model have been successfully determined. Practical Relevance. The developed method can be used as part of the services of cloud systems automatic scaling and provides more efficient management of the infrastructure resources of cloud computing systems.

Bu çalışmada, meteorolojik verilerin takibi, güneş ve rüzgar enerjisi açısından bölge potansiyelinin gerçek veriler ile saptanması, hava veya alternatif enerji sistemleri tahmin algoritmalarında kullanılmak üzere meteorolojik verilerin toplanması amaçlarıyla Kocaeli Üniversitesi Uzunçiftlik Nuh Çimento Meslek Yüksekokulu çatısına kurulan hava istasyonu ele alınmıştır. Kurulan hava istasyonu ile bölgenin global ışıma, rüzgar hızı, rüzgar yönü, sıcaklık ve nem verileri 32 bit Arduino Due mikrodenetleyici ile toplanarak SD hafıza modülüne kayıt edilmiştir. Ayrıca veriler ESP8266 WiFi modülü aracılığıyla ThingSpeak bulut ortamına alınarak sistem bir nesnelerin interneti uygulamasına dönüştürülmüştür. Çalışma içerisinde ek olarak Ağustos 2018-Aralık 2018 döneminde sistemden elde edilen veriler kullanılarak günlük metrekare başına toplam ışıma, günlük ortalama, maksimum, minimum sıcaklık ve nem, günlük ortalama ve maksimum rüzgar hızı verileri grafikler yardımıyla sunulmuştur.

The problem of secure routing in cloud centric IoT systems has been well studied. Number of approaches has been discussed earlier to support the security performance in IoT systems. Still the performance of algorithms in security measures is below the expected level. To solve this issue, an efficient behavior analysis model is described in this article. The method monitors the behaviors of edge nodes in cloud, to fetch the information like number of service access, number of transmissions involved, and number of IoT systems, total bytes supported and so on. First, available ways to reach the service point is discovered through each edge nodes of the network, and estimate the edge support measure, service support measure, secure routing measure. The method evaluates the suitability of route for data transmission at each time session based on different factors. According to these factors, trusted data support measure is estimated to select the transmission route. The behavior based service specific secure routing algorithm improves the performance of security towards the growth of QoS in IoT systems. [ABSTRACT FROM AUTHOR]

Data-intensive cloud computing systems are growing year by year due to the increasing volume of data. In this context, data replication technique is frequently used to ensure a Quality of service, e.g., performance. However, most of the existing data replication strategies just reproduce the same number of replicas on some nodes, which is certainly not enough for more accurate results. To solve these problems, we propose a new data Replication and Placement strategy based on popularity of User Requests Group (RPURG). It aims to reduce the tenant response time and maximize benefit for the cloud provider while satisfying the Service Level Agreement (SLA). We demonstrate the validity of our strategy in a performance evaluation study. The result of experimentation shown robustness of RPURG. [ABSTRACT FROM AUTHOR]

The current digital age, mainly characterized by an economy based upon information technology, demands a data integrity service, even more so because organizations and companies are migrating their services over the cloud. This is not a simple task; it is cumbersome since traditional schemes in databases could be subject to modifications. However, it can be solved using blockchain technology. This paper provides a data integrity verifiability architecture for cloud systems based on blockchain. The architecture provides a mechanism to store events (as logs) within a blockchain platform from any cloud system. Users can then consult data integrity through a microservice, acting as an intermediate server that carries out a set of verification steps within the blockchain, which confirms the integrity of a previously stored log. Our architecture takes advantage of the blockchain strength concerning integrity, providing a traceability track of the stored logs. A prototype system and a case study were implemented based on the proposed architecture. Our experimental results show that the proposed decentralized architecture can be adapted to cloud existing systems that were born without blockchain technology and require a modular and scalable audit characteristic.

Currently, security breaches in public places like airports and official buildings are a major concern by both governmental and corporative organizations. In these situations, X-ray devices must scan a vast amount of baggage in a short time frame. Hence, deploying scanners that automatize the task of detecting suspicious artefacts becomes of vital importance to prevent from threats. In this paper we present FORTIFIER, a formal distributed framework designed to detect suspicious artefacts. This approach consists in the integration of several image detection algorithms executed in a distributed environment, which are aimed to detect a wide spectrum of weapons like guns, knifes and bombs. The main core of our proposed framework for recognizing suspicious artefacts is divided in different phases, where each one is modelled with a specific finite state machine (FSM). Several FSMs are combined to detect different artefacts. We also present a case of study where some performance experiments are carried out for analysing the scalability of FORTIFIER. Initially, FORTIFIER is deployed in a single cloud environment. Once the main features that have a significant impact on the overall system performance are analysed, our proposed framework is deployed in a multi-cloud environment.

Crop sustainability is essential for balancing economic development and environmental care, mainly in strong and very competitive regions in the agri-food sector, such as the Region of Murcia in Spain, considered to be the orchard of Europe, despite being a semi-arid area with an important scarcity of fresh water. In this region, farmers apply efficient techniques to minimize supplies and maximize quality and productivity; however, the effects of climate change and the degradation of significant natural environments, such as, the “Mar Menor”, the most extent saltwater lagoon of Europe, threatened by resources overexploitation, lead to the search of even better irrigation management techniques to avoid certain effects which could damage the quaternary aquifer connected to such lagoon. This paper describes the Irriman Platform, a system based on Cloud Computing techniques, which includes low-cost wireless data loggers, capable of acquiring data from a wide range of agronomic sensors, and a novel software architecture for safely storing and processing such information, making crop monitoring and irrigation management easier. The proposed platform helps agronomists to optimize irrigation procedures through a usable web-based tool which allows them to elaborate irrigation plans and to evaluate their effectiveness over crops. The system has been deployed in a large number of representative crops, located along near 50,000 ha of the surface, during several phenological cycles. Results demonstrate that the system enables crop monitoring and irrigation optimization, and makes interaction between farmers and agronomists easier.

Manufacturing industry profoundly impact economic and societal progress. As being a commonly accepted term for research centers and universities, the Industry 4.0 initiative has received a splendid attention of the business and research community. Although the idea is not new and was on the agenda of academic research in many years with different perceptions, the term "Industry 4.0" is just launched and well accepted to some extend not only in academic life but also in the industrial society as well. While academic research focuses on understanding and defining the concept and trying to develop related systems, business models and respective methodologies, industry, on the other hand, focuses its attention on the change of industrial machine suits and intelligent products as well as potential customers on this progress. It is therefore important for the companies to primarily understand the features and content of the Industry 4.0 for potential transformation from machine dominant manufacturing to digital manufacturing. In order to achieve a successful transformation, they should clearly review their positions and respective potentials against basic requirements set forward for Industry 4.0 standard. This will allow them to generate a well-defined road map. There has been several approaches and discussions going on along this line, a several road maps are already proposed. Some of those are reviewed in this paper. However, the literature clearly indicates the lack of respective assessment methodologies. Since the implementation and applications of related theorems and definitions outlined for the 4th industrial revolution is not mature enough for most of the reel life implementations, a systematic approach for making respective assessments and evaluations seems to be urgently required for those who are intending to speed this transformation up. It is now main responsibility of the research community to developed technological infrastructure with physical systems, management models, business models as well as some well-defined Industry 4.0 scenarios in order to make the life for the practitioners easy. It is estimated by the experts that the Industry 4.0 and related progress along this line will have an enormous effect on social life. As outlined in the introduction, some social transformation is also expected. It is assumed that the robots will be more dominant in manufacturing, implanted technologies, cooperating and coordinating machines, self-decision-making systems, autonom problem solvers, learning machines, 3D printing etc. will dominate the production process. Wearable internet, big data analysis, sensor based life, smart city implementations or similar applications will be the main concern of the community. This social transformation will naturally trigger the manufacturing society to improve their manufacturing suits to cope with the customer requirements and sustain competitive advantage. A summary of the potential progress along this line is reviewed in introduction of the paper. It is so obvious that the future manufacturing systems will have a different vision composed of products, intelligence, communications and information network. This will bring about new business models to be dominant in industrial life. Another important issue to take into account is that the time span of this so-called revolution will be so short triggering a continues transformation process to yield some new industrial areas to emerge. This clearly puts a big pressure on manufacturers to learn, understand, design and implement the transformation process. Since the main motivation for finding the best way to follow this transformation, a comprehensive literature review will generate a remarkable support. This paper presents such a review for highlighting the progress and aims to help improve the awareness on the best experiences. It is intended to provide a clear idea for those wishing to generate a road map for digitizing the respective manufacturing suits. By presenting this review it is also intended to provide a hands-on library of Industry 4.0 to both academics as well as industrial practitioners. The top 100 headings, abstracts and key words (i.e. a total of 619 publications of any kind) for each search term were independently analyzed in order to ensure the reliability of the review process. Note that, this exhaustive literature review provides a concrete definition of Industry 4.0 and defines its six design principles such as interoperability, virtualization, local, real-time talent, service orientation and modularity. It seems that these principles have taken the attention of the scientists to carry out more variety of research on the subject and to develop implementable and appropriate scenarios. A comprehensive taxonomy of Industry 4.0 can also be developed through analyzing the results of this review. [ABSTRACT FROM AUTHOR]

We propose a dynamic replication strategy that satisfies simultaneously availability and performance tenant requirements while taking into account the tenant budget and the provider profit. The proposed strategy is based on a cost model that aims to calculate the minimum number of replicas required to maintain a high data availability. A replica creation is triggered only when this number of replicas is not reached or when the response time objective is not satisfied. Then, the replication must be profitable for the provider when creating a new replica. Furthermore, data replication and query scheduling are coupled in order to place these replicas in a load balancing way while dealing with the tenant budget. The experiment results prove that the proposed strategy can significantly improve availability and performance while the tenant budget is taken into account. [ABSTRACT FROM AUTHOR]

IoT has played an essential role in many industries over the last few decades. Recent advancements in the healthcare industry have made it possible to make healthcare accessible to more people and improve their overall health. The next step in healthcare is to integrate it with IoT-assisted wearable sensor systems seamlessly. This review rigorously discusses the various IoT architectures, different methods of data processing, transfer, and computing paradigms. It compiles various communication technologies and the devices commonly used in IoT-assisted wearable sensor systems and deals with its various applications in healthcare and their advantages to the world. A comparative analysis of all the wearable technology in healthcare is also discussed with tabulation of various research and technology. This review also analyses all the problems commonly faced in IoT-assisted wearable sensor systems and the specific issues that need to be tackled to optimize these systems in healthcare and describes the various future implementations that can be made to the architecture and the technology to improve the healthcare industry.

Recently, the Internet of Things (IoT) has emerged as a significant advancement for Internet and mobile networks with various public safety network applications. An important use of IoT-based solutions is its application in post-disaster management, where the traditional telecommunication systems may be either completely or partially damaged. Since enabling technologies have restricted authentication privileges for mobile users, in this paper, a strategy of mobile-sink is introduced for the extension of user authentication over cloud-based environments. A seamless secure authentication and key agreement (S-SAKA) approach using bilinear pairing and elliptic-curve cryptosystems is presented. It is shown that the proposed S-SAKA approach satisfies the security properties, and as well as being resilient to nodecapture attacks, it also resists significant numbers of other well-known potential attacks related with data confidentiality, mutual authentication, session-key agreement, user anonymity, password guessing, and key impersonation. Moreover, the proposed approach can provide a seamless connectivity through authentication over wireless sensor networks to alleviate the computation and communication cost constraints in the system. In addition, using Burrows-Abadi-Needham logic, it is demonstrated that the proposed S-SAKA framework offers proper mutual authentication and session key agreement between the mobile-sink and the base station.