Search

The integration of Cloud and IoT known as Cloud IoT offers a novel approach for designing coherent and structured healthcare monitoring systems. With Cloud IoT, diverse IoT-based healthcare applications interconnect and exchange information with each other for dispensing efficient clinical healthcare solutions. As sensor-based communication has seen an exponential growth in recent years, humongous amount of data gets generated, which becomes difficult to handle with limited processing and storage available in these sensor nodes. To overcome this, Cloud and IoT amalgamation also known as Cloud IoT provides an efficient solution for bridging communication between heterogeneous devices and handling ever-increasing data demands in healthcare applications. Cloud IoT framework permits seamless application deployment and service rendering using Cloud service–based models. In this chapter, we review the available Cloud IoT literature and present a holistic vision on the Cloud IoT–based healthcare integration components. The chapter also presents seamless applications dispensed by Cloud IoT platform and contemplates discussion on factors driving Cloud IoT health integration. The chapter presents a conceptual architectural framework for healthcare monitoring system that considers a range of aspects including data collection, transmission, and processing including cloud storage. The chapter also discusses a use case scenario that identifies actors and dataflows responsible for transforming sensor data into real-time transmission to cloud. Also, a brief discussion on design considerations for healthcare architecture will be provided. The work in this chapter also highlights security issues affecting IoT layered architecture including vulnerabilities inherent in the Cloud. These vulnerabilities could render healthcare services nonfunctional and critical patient information can be abused by malevolent users. Also a brief discussion on some potential mitigation measures will be provided. The chapter also elaborates discussion on various Cloud IoT platforms that aim at solving heterogeneity issue between the Cloud and Things. Finally, the chapter concludes by identifying some open research issues and challenges hampering Cloud IoT–based healthcare adoption.

In these days, basic tumor ailment is a big health issue. A primary tumor is a cyst developing at the anatomy site where tumor growth starts and progresses to produce carcinogenic stuff. Internet of things (IoT) devices has the ability to sense and disseminate patient data. The huge amount of data created by intelligent IoT equipments is processed by fog devices. Fog computing in healthcare is becoming very popular as it brings processing capabilities to the edge of the network. In this paper, we have proposed three layered architecture based on fog computing to detect primary tumors which leads to reduction in propagation latency time, network use and energy consumption. As a result, real-time response to primary tumor problems is now possible. The data produced in IoT devices is preserved in cloud for long-term processing to produce statistical results. It helps in strong backups, recovery and high availability. For detecting primary tumors, hybrid approach of data mining is used to uncover hidden patterns, correlations and make decision related to person’s health. In hybrid approach of data mining, we combine three data mining techniques, namely simple logistics, J48 and random forest to obtain high-accuracy, kappa statistics result, TP rate, recall value, F-measure, area under ROC curve and low-root mean square error, root relative squared error. WEKA tool is used for the implementing hybrid approach of data mining.

In the future, every physical thing or object will be connected in scenarios that are divergent than merely connected human and make everything intelligent. As everything becomes connected and intelligent, it is driving digital transformation of all industries. However, the IoT brings with it, new security threats because the working IoT applications mechanisms are differ due to the heterogeneity nature of IoT environments. The comprehensive review of the security concerns with regards to the IoT layered architecture, encryption mechanisms, threats, and vulnerabilities is the major contribution of this paper. Furthermore, the IoT security challenges and solutions are emphasized by the layered architecture in order to get a greater understanding and knowledge on the addressing and implementing proper approaches to prevent the recent security threats in IoT on every layer.

This chapter deals with the layered architecture and describes design flow of IoT device for an application in a step-by-step manner. Also discussed in this chapter are the different types of architectures including edge computing- and cloud computing-based IoT solutions. IoT design is explained with the help of body temperature monitoring IoT as an example design.

Wireless Sensor Network has been increasingly used for remote monitoring system and its adoption in increasing exponentially for larger application too. However, there are various challenges associated with both resource management and security that roots up when the deployment scale goes massive and distributed in order. The proposed system considers a case study of smart city management where the problems associated with data transmission and security has been addressed. This is carried out using the provisioning of urban environment monitoring system that is an essential system for smart city projects to assure the citizens' better-quality well-being. The scalable and effective urban environment monitoring system requires a seamless transmission of the data from the sensor nodes to the analytics engine. The existing architectures are more designed to suit very specifically the use-cases. As a contribution, the proposed system introduces a cost-effective architecture for environmental monitoring in urban zones of smart city named as a Smart Sensor Surveillance System (4S-UEM). The core idea of the proposed system is to offer a balance between resource efficiency and resilience secure communication in large scale deployment of WSN considering smart city as deployment and assessment area. The proposed system makes use of urban geographical clustering process in order to develop an organized structure of sensor nodes. Different from any existing studies, the proposed system introduces data analytical engine followed by secure routing using gateway. The design of the proposed system is carried out using layered architecture of the communication model targeting towards a cost-effective, energy optimal, and secure data transmission to the analytics engine.

A diverse term in itself that is Internet of Things (IoT) verb-initiates to the interconnection of rich number of physical items which are fit for social events and sharing electronic chunks of information. Directing of information from source to objective assumes an essential part in any enormous scope organization. Guaranteeing quality for transmission of defer delicate data is of foremost significance in organization of shrewd gadgets officially known as Internet of Things (IoTs). With the expansion in number of IoT devices on the network it has gotten essential to focus on security of gadgets and simultaneously security of information that is being moved over the networks. For these qualitative service provisioning methodologies, the challenges are investigated at all the domains of layered architecture. At last, the future assessment for QoS techniques are examined for investigation and application before closing this paper.

Cloud computing has gained an excellent response across several industries due to the agility and elasticity it offers. With the increase in the usage of cloud computing in both businesses and research industries, there was a significant ascent in threats and vulnerabilities. The cloud since, its inception, has a layered architecture with security deployed at each layer. Risks like access ambiguity, lack of central governance, data loss, data leakage, and data privacy are rising day by day. A multi-cloud strategy is to mitigate these risks and vulnerabilities. Even then, issues of data loss, privacy persist. This paper presents a methodology that reduces these limitations with the design and implementation of multi-cloud architecture and shuffling algorithm. The application developed provides security to the data deployed on the public cloud as well as maintains data reliability. This paper addresses the issues of data loss and theft. This design-pattern focuses on micro-services that also require reliability and availability of data.

An Epidemic is a big threat to humanity. To reduce its catastrophic effect, many clinical practices and AI-based models are introduced to detect the onset of future Epidemic. An Analytical System can be useful for the prediction of an epidemic by collecting Quality data, modelling them and visualizing in different dimensions. This study deals with designing a Local Analytical System for early Epidemic detection in which the data related to human regular needs and responses are stored in the in-cube format. Analytical rules are used to produced faster pre-computed and pre-summarized inputs of the warehouse. Some desired inputs are selected from many local Warehouses which are then consolidated to form an incremental next higher-level data using the Layered Architecture style. This system can find the most commonly deviated data from the most frequently occurred patterns in the data submitted from the participating warehouses. The above-summarized patterns are mined using an FP-Growth algorithm to predict a new pattern. The patterns are ranked and inspected with their correlations for a possible unknown Epidemic.

Decentralized management and coordination of energy systems are emerging trends facilitated by the uptake of the Internet of Things and Blockchain offering new opportunities for more secure, resilient, and efficient energy distribution. Even though the use of distributed ledger technology in the energy domain is promising, the development of decentralized smart grid management solutions is in the early stages. In this paper, we define a layered architecture of a blockchain-based smart grid management platform featuring energy data metering and tamper-proof registration, business enforcement via smart contracts, and Oracle-based integration of high computational services supporting the implementation of future grid management scenarios. Three such scenarios are discussed from the perspective of their implementation using the proposed blockchain platform and associated challenges: peer to peer energy trading, decentralized management, and aggregation of energy flexibility and operation of community oriented Virtual Power Plants., Accepted and presented at: 19th RoEduNet Conference: Networking in Education and Research, December 11-12, 2020

Dataset plays a major role in evaluating Machine Learning (ML), Artificial Intelligence (AI), Business Intelligence (BI) and enterprise software. To prove the efficacy of any algorithm / software and also its limitation with respect to the size of the input data, it has to be exercised on a large dataset. Creating such a large dataset is a challenging task especially with near real time data. In this paper, a layered architecture to construct Virtual DataStack (VDS) is proposed. The VDS contains near real time synthetic virtual datasets (specific to a particular domain) along with other components that comprises the generation of datasets. Virtual DataStack for CampusStack (academic campus management) domain has been generated and results prove that Query Inclusive Computational Logic reduces lines of code as well as the time taken significantly. This paper focuses mainly on efficient generation of near real time data for enterprise domains with a specific attention to Education Domain and CampusStack.

Agriculture is the backbone of India, and its produce must be optimized. The technology trio of IoT, cloud, and machine learning can have a trending impact on the agriculture domain. Each one of these techniques can contribute to a great extent in increasing agriculture productivity. Most of the research carried out in this direction has implemented a layered architecture. The lowermost layer is the sensor layer, followed by the data collection layer, and finally the data analytics layer. The sensor layer consists of various kinds of sensors like moisture, light, temperature, etc. and along with the IoT provides an ambiance to understand the feature of nomenclature of the plant and generates the data. The second layer is the data collection layer which uses cloud or fog computing basically to structure the enormous amount of data generated from the sensors and finally analysis layer where machine learning or any other rule framework is used to analyze the data to provide the best possible solution for better crop yield. The various types of sensors used, the type of IoT support that is developed, and the kind of data analytics used in research in recent times have been discussed in this review paper.

Existing works on Automated Machine Learning (AutoML) are mainly based on predefined search space. This paper seeks synergetic automation of two ingredients, i.e., search space and search strategies. Specifically, we formulate the automation of search space and search strategies as a combinatorial optimization problem. Our empirical study on many architecture benchmarks shows that identifying the suitable search space exerts more effect than choosing a sophisticated search strategy. Motivated by this, we attempt to leverage a machine learning method to solve the discrete optimization problem, and thus develop a Layered Architecture Search Tree (LArST) approach to synergize these two components. In addition, we use a probe model-based method to extract dataset-wise features, i.e., meta-features, which is able to facilitate the estimation of proper search space and search strategy for a given task. Experimental results show the efficacy of our approach under different search mechanisms and various datasets and hardware platforms.

This paper addresses remarkable changes that the traditional grid is currently undergoing to withstand the modern- day requirements. It also presents the important characteristics that a modern smart grid should possess. A smart grid is an integration of technological advancements in communication, control, and information in the power grid to make it intelligent and autonomous. The aim of this paper is to address the complexity of optimality and control functions implemented at different layers of the smart grid. This paper has suggestions that a distributed, decentralized control and optimality functions based on a layered architecture will be an appropriate management strategy to realize the smart grid.

The Internet of Things (IoT) refers to a heterogeneous network of physical and virtual objects embedded. Most of the data collection solution today relies on specialized electronic devices located far apart, then perform data collection and analysis based on a fixed schedule. This paper proposes an improved IoT architecture takes into consideration the environment surrounding the devices, including the objects and fires to be detected, and other factors affecting the remote management and monitoring. We present the results of research and build an Integrated Monitoring System (IMS) based on IoT technology. The collected data in IMS can be used to cater to the different requirements from the various environment. The analysis results indicate that the designed platform is suitable for the resource-constrained IoT architecture and is scalable to be extended in various IoT scenarios.

With the rapid use of internet technology, security and performance are always at risk. The data on the internet can be compromised and the performance can be bottlenecked. The solution to this problem is the blockchain technology. Reason for choosing this field is because blockchain is a new technology and now it is one of the most demanding field in the IT sector. Blockchain is based on peer-to-peer topology containing connected nodes. Each node has cryptographic hash of the previous node (no malicious or unauthorized node can enter the linked nodes), timestamp and relevant data. Blockchain provides resistance against the unwanted interference and modification of the data and each node has separate processing power thus enhancing security and performance of the host network. Permission-ed and Permission-less blockchain have their own properties and limitations and they can be used in different scenarios. In permission-less blockchain the nodes can be public, anyone can add nodes to increase the performance (Bitcoin), while permission-ed blockchain is used within enterprise or between enterprises where the nodes are private and new nodes need authorization to be added in link nodes. The aim of this paper is to study the permission-ed and permission less blockchain, their similarities and differences, some of the consensus algorithms they use. Outcome of the paper will be understanding of some of the consensus algorithms and proposing a two-tier architecture which will contain voting based algorithm for enhanced performance.

The recent development in mobile computing resulted in widespread application of Internet of Things (IoT). IoT promises a world where smart and intelligent communication from most of the devices is possible through Internet anywhere, anytime with least possible human assistance. However, security and privacy are major concerns of IoT which could affect its sustainable development. In this work, we have dealt with IoT security from two main perspectives that are IoT architecture and protocols. We discuss different layers in IoT architecture and investigated the security concerns associated with different IoT layers along with their possible solutions. We have reviewed various protocols in IoT layered architecture and the security mechanism developed for each protocol. Also, we provide certain future directions of possible research for IoT security.

Nowadays, many countries around the world are exploring how to build smart university. With the advent of NB-IoT and 5G technologies, it has become possible to create a campus data network based on multi-resource data collected from diverse sensors and online platforms. However, the current approach of data storage of smart universities is mostly cen-tralized which can’t enable data intercommunication between departments, meanwhile, the owner of data such as students and teachers can’t really own their data physically. In this paper, we propose a novel layered architecture based on blockchain network for smart university data system where departments, students, teachers, and external organization could get access to real-time data while all the data’s authenticity and privacy is well guaranteed.

This paper presents the FPGA ASIC-like implementation of LDPC decoder for 5G NR BG2 and single lifting factor. The proposed implementation has two main features: architectural and algorithmic. The first is the parallel layered architecture with special offsets and inter-layer network which provides great scalability for variable lifting factors support. The second one is an adaptive normalization approach to increase decoder BER performance. The proposed implementation requires relatively small number resources of Xilinx Kintex Ultrascale FPGA and provides 1 information bit/s/cycle throughput for 10 iterations.

The advancement of technology has seen the growth of IoT based devices all around the globe. With the introduction of wearable devices, smart appliances, the amount of accumulated data has increased exponentially. For soft real time systems, it is a major issue when it comes to analytics and providing the accurate results for future strategies leading to profitability aspects of an organization to the estimation of life expectancy of an individual. Soft real-time systems, where huge amount of data processing is equally important to context awareness, pervasive computing systems can use another layer for its data flow and this paper looks at an idea which benefits such systems. The proposed paper introduces an intermediate layer between User interfaces and the databases along with the traditional application layer and context or networking layer that already exists. The proposed paper also explains at how this architecture will be implemented and can be used as a generic architecture model.

Recent developments in software, hardware, and communication technologies resulted in a sudden increase of things connected to the Internet. Not only did the number of devices connected to the Internet increase exponentially, so did their kind - devices previously unthought-of are now participants of the Internet of Things/Everything ecosystem. Subsequently, the reusability of existing IoT solutions or solution components is crucial to face the high demand for new types of things to be connected to the network. A well-defined, architectural approach that implements reusability is crucial. In this paper, IoT Systems are viewed as a whole and abstracted to an immense spread-out device that is sensing everything and acting to everything in the environment. Consequently, a concept which views the Internet of Things as an Internet of Sensor-Actuator is proposed. This concept assumes that interaction with the environment is provided by well-defined Sensor-Actuator pattern components that provide services to the applications. By combining practices from the automotive layered architecture approaches, as well as the Sensor-Actuator Pattern defined for the system scaling and reuse purposes, the similarity of the signal conditioning for acquiring and acting is pointed out. Furthermore, the diagnosis and protection procedures are discussed for both sensors and actuators, and their auxiliary functions are considered. The proposed architecture is analyzed on an industrial robotic arm application.

This paper addresses energy management and security having as basis sensing and monitoring of cyber-physical infrastructure of consumers and prosumers, and their participation in the Local Energy Market (LEM). The vision is to create a layered multi-agent framework that brings a complete view of the cyber-physical system of LEM participants, and provides optimization and control of energy for said participants. The proposed system is separated into a Market layer and a Cyber-Physical layer, each of them providing different services. The cyber-physical layer, represented by SMARTERCtrol system, provides Data Monitoring and Optimized Energy Control of individual building resources. The Market layer, represented by LEM Multi-Agent System, provides Negotiation, Forecasting, and Trust Evaluation. Both systems work together to provide and integrate a tool for simulation and control of LEM., This work has received funding from National Funds through FCT (Fundaçao da Ciencia e Tecnologia) under the project SPET – 29165, call SAICT 2017.

Multi-factor authentication (MFA) systems are being deployed for user authentication in online and personal device systems, whereas physical spaces mostly rely on single-factor authentication; examples are entering offices and homes, airport security, and classroom attendance. The Internet of Things (IoT) growth and market interest has created a diverse set of low-cost and flexible sensors and actuators that can be used for MFA. However, combining multiple authentication factors in a physical space adds several challenges, such as complex deployment, reduced usability, and increased energy consumption. We introduce MAFIA (Multi-layered Architecture For IoT-based Authentication), a novel architecture for co-located user authentication composed of multiple IoT devices. In MAFIA, we improve the security of physical spaces while considering usability, privacy, energy consumption, and deployment complexity. MAFIA is composed of three layers that define specific purposes for devices, guiding developers in the authentication design while providing a clear understanding of the trade-offs for different configurations. We describe a case study for an Automated Classroom Attendance System, where we evaluated three distinct types of authentication setups and showed that the most secure setup had a greater usability penalty, while the other two setups had similar attributes in terms of security, privacy, complexity, and usability but varied highly in their energy consumption.

Multi-access edge computing (MEC) has become an essential technology for collecting, analyzing, and processing data generated by widely distributed user equipment (UE), wireless end-hosts, Internet of things (IoT) sensors, etc., providing real-time and high-quality networking services with ultralow end-to-end latency guaranteed between various user devices and edge cloud computing nodes. However, the cloud resources at the MEC on-site (access point) and edge site are restricted and insufficient mainly because of the operation and management constraints (e.g., limited space and capacity), particularly in the case of on-demand and dynamic service resource deployment. In this regard, we propose a selective MEC resource allocation scheme adopting a multitier architecture over a wide-area software-defined network (SDN) on the basis of our recent research work on virtual network slicing and resource orchestration. The proposed scheme provides an optimized MEC selection model considering end-to-end latency and efficient service resource utilization on the basis of the hierarchical MEC service architecture.

The Internet of Things (IoT) is rapidly becoming a part of our regular activities and environment. According to Cisco, by 2030, it is expected that 500 billion devices will be connected to the IoT. Every passing day, new devices and sensors coming in the market, which use different protocols to connect to the network. With this expected growth of the Internet of Things and the emergence of Fog and Cloud Computing, there is a need for a standard protocol suite to handle different aspects of standardization and cloud protocols in the IoT environment that is not covered by currently existing Three, four, and five-layered IoT models. A T2C architecture is proposed that provides the most prominent standard protocols into nine layers and shows why this extension is essential. The proposed T2C architecture fulfills the prescribed activities for smart sensors and fog networking in the backbone, which brings significant benefits, scalability, and low-power consumption in the IoT architecture. It emphasizes on how IoT device classification can be mapped to our proposed nine-layered T2C Architecture.

A main objective of eGovernment is the provision of user-centric public services (PS) in a personalized and user-friendly way. Life events (LE) have been proposed as an intuitive method for personalised and integrated PS provision. A technology that could be employed for providing personalised PS and LE information is chatbots, which however is currently unexploited. The aim of this paper is twofold: (a) to present an architecture for the integration of chatbot technology and LE for better PS information provision and (b) to implement a proof-of-concept chatbot prototype that exploits LE information available at the Greek eGovernment portal ERMIS. As a result, we propose a layered architecture consisting of four layers, namely graphical user interface, chatbot engine, application programming interface and linked data repositories. A chatbot pilot is implemented based on the proposed architecture. Two usage scenarios of this pilot are demonstrated and evaluated using a TAM based questionnaire. Benefits and challenges are also discussed.

The adaptation of sensor-cloud in healthcare infrastructure has enabled the use of machine learning techniques for efficient healthcare provisioning. In the context of the smart healthcare system, biomedical wireless sensor networks (BWSNs) are one of the key infrastructure enabling the development of healthcare applications and services. With the increasing number of healthcare information collected through BWSN, different types of medical data can be exploited to design a predictive analytics system, thereby transforming the traditional healthcare system. In this paper, we propose and highlight smart healthcare monitoring framework using state-of-the-art technologies. In particular, we focus on sensor-cloud computing and machine learning as emerging technologies, which are suitable for a proactive healthcare system by the advancement in various aspects, including computational capability, data storage, and learning techniques. Besides, we describe the components of our proposed framework with data analysis techniques and sensor-cloud layered architecture.

This Work has been Retracted by ACM because it was found to have been submitted to two conferences at the same time, which violates the ACM Prior Publication and Simultaneous Submission Policy. Raimundas Savukyans submitted his Work "Integration of Safety Means with Functions of Blockchain in Multi-layered Architecture of IoT for Safer Data Transmission Procedures" to the International Conference on Blockchain and Internet of Things (BIoT 2020) conference and ACM ICEMIS 2020 Proceedings of the 6th International Conference on Engineering and MIS. The paper was accepted and published by both conferences. https://dl.acm.org/doi/10.1145/3410352.3410782

Scientific research in the area of human genomics is no longer restricted to scientists. With the introduction of companies such as 23andme and Ancestry, the interested people of the public have been getting involved in genetic studies. However, the nature of the data being collected and its link to confidential, personal information will need to be properly secured and complete to prevent the same issues faced by electronic medical records in healthcare: fragmentation and risk of disclosure. By integrating blockchains with genomics and healthcare, patients and research participants will be able to participate in their own healthcare and research, increasing patient and user centricity. In this paper, a layered architecture of how blockchains can be implemented in genomics and healthcare is proposed. The proposed architecture shows how communication between users, researchers, and medical professionals can be improved while encouraging genetic research while improving security and privacy.

Feedback control theory has been extensively implemented to theoretically model human sensorimotor control. However, experimental platforms capable of manipulating important components of multiple feedback loops lack development. This paper describes WheelCon, an open-source platform aimed at resolving such insufficiencies. Using only a computer, a standard display, and inexpensive gaming steering wheel equipped with a force feedback motor, WheelCon safely simulates the canonical sensorimotor task of riding a mountain bike down a steep, twisting, bumpy trail. The platform provides flexibility, as will be demonstrated in the demos provided, so that researchers may manipulate the disturbances, delay, and quantization (data rate) in the layered feedback loops, including a high-level advanced plan layer and a low-level delayed reflex layer. In this paper, we illustrate WheelCon's graphical user interface (GUI), the input and output of existing demos, and how to design new games. In addition, we present the basic feedback model and the experimental results from the demo games, which align well with the model's prediction. The WheelCon platform can be downloaded at https://github.com/Doyle-Lab/WheelCon. In short, the platform is featured to be cheap, simple to use, and flexible to program for effective sensorimotor neuroscience research and control engineering education.

Agriculture education in India is unique with a flexible course credit system.The workflow behind this unique education system needs customizededucation management information software for educational planningand management with a reduced process time in education workflow. Thispaper put forth a prototype of the Education Management InformationSystem (EMIS) developed for Forest College and Research Institute (FCRI),Tamil Nadu Agricultural University, Mettupalayam. Three-tier architectureis used for FCRI-EMIS to be an effective distributed client/server system.The development is carried out in an open-source development platform.FCRI-EMIS is developed with four modules namely: College ManagementInformation System, Degree Management Information System, SyllabusManagement Information System and Student Management InformationSystem to access, generate a report and analyze the identified entities. Thedeveloped prototype is executed and found an effective tool for decisionmaking towards education planning and management

There is a demand from the next generation of technology in the modern age of communication, which can help reform and include the world in seamless connectivity through trigonous smart devices. Modern wireless standards such as 5G have been popularized for their faster transmission speed or high-speed access, low latency, and omnipresent coverage. The exponential growth of 5G networks will boost with recent innovations like IoT that will improve cellular operations, network problems, and drive the internet's future to the brink. So in the future 5G in IoT will prove to be a game-changer and open a door to new IoT infrastructure and smart services. IoT requires new requirements for success such as broad bandwidth, security, high-speed, high-speed, and high-level coverage. We first consider the 5G-IoT architecture in this paper, which gives a basic idea about 5G activated IoT. Then, along with major technologies like Software Defined Networking (SDN), Cloud Computing, and Network Function Virtualization (NFV). We introduced a new layered architecture for 5G powered IoT. Finally, we analyzed the proposed 5G enabled IoT system architecture.

International audience; In recent years, technological advances and the ever-increasing power of embedded systems have seen the emergence of so-called smart cities. In these cities, application needs are increasingly calling for Large-Scale Wireless Sensor Networks (LS-WSN). However, the design and implementation of such networks pose several important and interesting challenges. These low-cost, low-power devices are characterized by limited computing, memory storage, communication, and battery power capabilities. Moreover, sensors are often required to cooperate in order to route the collected data to a single central node (or sink). The many-to-one communication model that governs dense and widely deployed Wireless Sensor Networks (WSNs) most often leads to problems of network overload and congestion. Indeed, it is easy to show that the closer a node is geographical to the sink, the more data sources it has to relay. This leads to several problems including overloading of nodes close to the sink, high loss rate in the area close to the sink, and poor distribution of power consumption that directly affects the lives of these networks. In this context, we propose a contribution to the problem of LS-WSN energy consumption. We designed a hierarchical 3-tier architecture of LS-WSNs coupled with a modeling of the activities of the different sensors in the network. This architecture that is based on clustering also includes a redeployment function to maintain the topology in case of coverage gaps. The results of the performed simulations show that our architecture maximizes the lifetime than compared solutions.

Providing useful information to the users by recommending highly demanded products and services is a fundamental part of the business of many top tier companies. Recommender Systems make use of many sources of information to provide users with accurate predictions and novel recommendations of items. Here we propose, DeepMF, a novel collaborative filtering method that combines the Deep Learning paradigm with Matrix Factorization (MF) to improve the quality of both predictions and recommendations made to the user. Specifically, DeepMF performs successive refinements of a MF model with a layered architecture that uses the acquired knowledge in a layer as input for subsequent layers. Experimental results showed that the quality of both the predictions and recommendations of DeepMF overcome the baselines., This work has been supported by Spanish Ministry of Science and Education and Competitivity (MINECO) and European Regional Development Fund (FEDER) under grants TIN2017-85727-C4-3-P (DeepBio).

The triboelectric nanogenerator (TENG) has been proved to be a green and efficient energy harnessing technology for electricity generation from ambient mechanical motions based on its ability to leverage the triboelectrification process. Enhancing TENG output performance through rational structural design still triggers increasing research interest. Here, we report a ternary electrification layered architecture beyond the current binary TENG systems, with improved performance for mechanical energy harvesting. Introducing a ternary Kapton layer into the traditional binary electrification layered architecture of TENGs consisting of copper and fluorinated ethylene propylene, yields a 2.5 times enhancement of peak power output, representing a 6.29-fold increase compared to the TENG composed of copper and Kapton. A wide-range of material configurations were systematically tested using this ternary electrification layered architecture to prove its practical effectiveness. The ternary electrification layered architecture invented in this work provides an alternative strategy to enhance TENG output performance, which represents a solid step for TENGs application in high-performance mechanical energy harvesting.

Precision agriculture employs IoT devices to smartly monitoring plant vegetation and support food production. Precision agriculture is highly required to improve product quality and better suit the requirements of the market. Among the IoT devices, Unmanned Aerial Vehicles (UAVs), can be equipped with many sensors that allow precise assessments of plant stress by flying over the plots. Notwithstanding the great benefits introduced, IoT devices may suffer from some issues. Many devices provide data in different formats on the same task, therefore they need solutions to integrate data and support a more thorough crop monitoring. This paper introduces a multitier architecture to deal with IoT-based intelligent monitoring, as well as an implementation of the architecture through multiagent modeling of the IoT devices for precision agriculture. The introduced model allows data acquisition from various sources (i.e., IoT devices), an ontology-based integration of data provided by the devices and a knowledge integration process to deal with domain-specific applications.

The distributed fault-tolerant computer technology of deterministic communication takes the needs of a new generation of IVMS as background in this paper, the system architecture design and software architecture of distributed fault-tolerant computer are studied, and one kind of communication software based on VxWorks653 partition operating system is designed and implemented, moreover, it has been practically applied in engineering, and it can meet the communication needs of big data and high-performance in avionics applications after function and performance verification. This communication protocol software is implemented with layered architecture, which is conducive to cross-platform migration, software reuse, and subsequent hardware upgrades.

As the infrastructure to provide support for distributed database management systems, the distributed database platform is very important to unify the management of data distributed in intricate environments. However, a traditional distributed database platform with centralized entities faces diverse and serious threats when the central entity is compromised. Consensus mechanisms in distributed ledger technology (DLT) can enhance the capability of defending threats by decentralizing the platform, but the efficiency and cost of consensus mechanisms in classic blockchain techniques are notable issues. In this paper, we propose a novel decentralized database platform, named Dagbase, with the support of an efficient and cost-effective consensus mechanism that uses the directed acyclic graph (DAG) as the structure. Dagbase decentralizes the management and distributes data to prevent threats in untrustworthy environments, which gains benefits from recent DLT. The performance of near-native data reading and high-efficiency data writing is ensured by a layered architecture and DAG-based consensus. Furthermore, we ensure flexibility by decoupling the consensus mechanism from the architecture. Dagbase is also easy-to-use and can be integrated with mainstream database products seamlessly on account of great interoperability. The implementation demonstrates our work and the security and performance analysis are enforced for evaluation.

Background Data coordination across multiple health care facilities has become increasingly important for many emerging health care applications. Distrust has been recognized as a key barrier to the success of such applications. Leveraging blockchain technology could provide potential solutions tobuild trust between data providers and receivers by taking advantage of blockchain properties such as security, immutability, anonymity, decentralization, and smart contracts. Many health technologies have empirically proven that blockchain designs fit well with the needs of health care applications with certain degrees of success. However, there is a lack of robust architecture to provide a practical framework for developers to implement applications and test the performance of stability, efficiency, and scalability using standard blockchain designs. A generalized blockchain model is needed for the health care community to adopt blockchain technology and develop applications in a timely fashion. Objective This study aimed at building a generalized blockchain architecture that provides data coordination functions, including data requests, permission granting, data exchange, and usage tracking, for a wide spectrum of health care application developments. Methods An augmented, 3-layered blockchain architecture was built on a private blockchain network. The 3 layers, from bottom to top, are as follows: (1) incorporation of fundamental blockchain settings and smart contract design for data collection; (2) interactions between the blockchain and health care application development environment using Node.js and web3.js; and (3) a flexible development platform that supports web technologies such as HTML, https, and various programing languages. Two example applications, health information exchange (HIE) and clinical trial recruitment, were developed in our design to demonstrate the feasibility of the layered architecture. Case studies were conducted to test the performance in terms of stability, efficiency, and scalability of the blockchain system. Results A total of 331,142 simulated HIE requests from accounts of 40,000 patients were successfully validated through this layered blockchain architecture with an average exchange time of 11.271 (SD 2.208) seconds. We also simulated a clinical trial recruitment scenario with the same set of patients and various recruitment criteria to match potential subjects using the same architecture. Potential subjects successfully received the clinical trial recruitment information and granted permission to the trial sponsors to access their health records with an average time of 3.07 seconds. Conclusions This study proposes a generalized layered blockchain architecture that offers health technology community blockchain features for application development without requiring developers to have extensive experience with blockchain technology. The case studies tested the performance of our design and empirically proved the feasibility of the architecture in 2 relevant health application domains.

According to the business characteristics and functional requirements of the smart grid ICT platform, a layered architecture of the Internet of things (IoT) for the transmission, transformation, distribution, and use of electricity in the smart grid was proposed, and it was developed with the traditional power communication network. Comparedto one of the most important components of the end of the Internet of things, wireless sensor networks play an important role in the application of IoT for the smart grid, and a sensor network application solution for the production of smart grids is proposed. At the same time, on the basis of the analysis of the interactive requirements of the smart grid, an IoT solution for smart electricity is proposed.

Due mainly to digital transformation, enterprises are immersed in dynamic environments. They are dealing with internal and external changes (gradual or abrupt, frequent or occasional, in the short or long term) that impact various parts of their Enterprise Architecture. To cope with the requirements of this new digital era, agility and adaptation are compulsory. Though Enterprise Architecture (EA) was designed as a catalyst for change management, traditional Enterprise Architecture approaches do not rely on well-defined concepts, lack agility and are constrained by layered architecture (business layer, functional layer, deployment layer, etc.). Thus, a new generation of EA, allowing continuous self-improvement and proactive adaptation to better match the new conditions, is required. In this paper, we explore and investigate some approaches of EA that deal with change management. Then, we, particularly, note the various definitions of the notion of adaptive EA, according to literature. Finally, we suggest some criteria, regarding the adaptation of EA, to highlight the limitations of the approaches analyzed.

As data collected and provided by Internet of Things (IoT) devices power an ever-growing number of applications and services, it is crucial that this data can be trusted. Data provenance solutions combined with blockchain technology are one way to make data more trustworthy by providing tamper-proof information about the origin and history of data records. However, current blockchain-based solutions for data provenance fail to take the heterogeneous nature of IoT applications and their data into account. In this work, we identify functional and non-functional requirements for a secure and extensible IoT data provenance framework, and conceptualise the framework as a layered architecture. Evaluating the framework using a proof-of-concept implementation based on Ethereum smart contracts, we conclude that our framework can be used to realise data provenance concepts for a wide range of IoT use cases. While blockchain technology generally poses constraints on scalability and privacy, we discuss multiple solutions aiming to overcome these issues.

Vehicular Ad hoc Networks (VANETs) face several technical challenges in deployment and management due to poor scalability, flexibility, connectivity and lack of intelligence. The integration of Cloud, Fog and Edge Computing in VANETs together with the use of Software Defined Networking (SDN) are seen as a way to cope with these communication challenges. In this work, we propose a fuzzy-based system for coordination and management of the cloud-fog-edge resources in SDN-VANETs. The proposed system called Fuzzy-based System for Resource Management (FSRM) decides the appropriate resources to be used by a vehicle in a cloud-fog-edge layered architecture. If a task requires computing resources that are beyond of those of the vehicle, based on the decision of FSRM, the vehicle can use the resources of its neighbors, fog or cloud servers. The decision is made by considering the task requirements in terms of latency constrains and complexity, and the available connections of the vehicle. We demonstrate by simulations the feasibility of FSRM to improve the management of the network resources.

Nowadays, enterprises are facing many changes due to digital transformation. So, they have to deal with new requirements that impact various parts of their Enterprise Architecture. To address the needs created by this new digital era, agility and adaptation are compulsory. Though Enterprise Architecture (EA) was designed as a catalyst for change management, traditional Enterprise Architecture approaches do not rely on well-defined concepts, lack agility and are constrained by layered architecture (business layer, functional layer, deployment layer, etc.). Thus, a new generation of EA, allowing continuous self-improvement and proactive adaptation to better match the new conditions, is required. In this paper, we highlighted some criteria, regarding the adaptation of EA and exploited them to show the limitations of the EA approaches analyzed. Then, we presented agile methods principles and discussed the mapping between them and the criteria that we proposed before. Finally, we proposed a simplified model based on Scrum for adaptive enterprise architecture.

There are a wide variety kinds of cryptographic devices involved in the space-ground integrated information network systems, which will generate a large amount of data. How to implement efficient storage and processing of cryptographic data in a big data environment is particularly important. According to the overall technical requirements of integration of heaven and earth cross-domain hierarchical password on-demand service, this paper preliminary designed the cryptographic data management platform architecture based on Hadoop. And proposed a solution for monitoring data storage and processing based on Hadoop. Aiming at massive heterogeneous cryptographic data, designed a layered architecture. Improved system data processing speed by using distributed computing processing. In addition, designed the security of the data management platform to solve the bottleneck problem of massive cryptographic data management in the space-ground integrated information network.

With the ever-rising pollution of water bodies caused by human-made actions as well as natural calamities, water monitoring systems are the focal point for society. Water-related application has much potential for automation. With the advent of big data analytics, there is a multitude of verticals in water quality, smart irrigation, and smart water distribution network. All these work well with accurate and real-time data. The requirements for multiple sensor parameters and robust communication framework that can be extended to various domains of water monitoring applications are the most critical hindrance for the development of an automation application in this domain. This work proposes a system that overcomes these problems. With the use of an industrial multi-parameter sensor node, EXO Sonde the desired sensor values can be extracted from the physical world. The sensed data is processed and communicated forward to the server forward through a layered architecture to a server. The sensor node utilizes a file system to save the sensor data in case of network loss. The communication technology between nodes and sensors is GSM. In the server, the data gets stored and gets visualized in a web UI.

The Dementias Platform UK (DPUK) Data Portal is a data repository facilitating access to data for 3 370 929 individuals in 42 cohorts. The Data Portal is an end-to-end data management solution providing a secure, fully auditable, remote access environment for the analysis of cohort data. All projects utilising the data are by default collaborations with the cohort research teams generating the data.The Data Portal uses UK Secure eResearch Platform (UKSeRP) infrastructure to provide three core utilities: data discovery, access, and analysis. These are delivered using a 7 layered architecture comprising: data ingestion, data curation, platform interoperability, data discovery, access brokerage, data analysis and knowledge preservation. Automated, streamlined, and standardised procedures reduce the administrative burden for all stakeholders, particularly for requests involving multiple independent datasets, where a single request may be forwarded to multiple data controllers. Researchers are provided with their own secure ‘lab’ using VMware which is accessed using two factor authentication.Over the last 2 years, 160 project proposals involving 579 individual cohort data access requests were received. These were received from 268 applicants spanning 72 institutions (56 academic, 13 commercial, 3 government) in 16 countries with 84 requests involving multiple cohorts. Project are varied including multi-modal, machine learning, and Mendelian randomisation analyses. Data access is usually free at point of use although a small number of cohorts require a data access fee.

As smartphones carry more and more private information, it has become the main target of malware attacks. Threats on mobile devices have become increasingly sophisticated, making it imperative to develop effective tools that are able to detect and counter such threats. Unfortunately, existing malware detection tools based on machine learning techniques struggle to keep up due to the difficulty in performing online incremental update on the detection models. In this paper, a Two-tier Architecture Malware Detection (TAMD) method is proposed, which can learn from the statistical features of network traffic to detect malware. The first layer of TAMD identifies uncertain samples in the training set through a preliminary classification, whereas the second layer builds an improved classifier by filtering out such samples. We enhance TAMD with an incremental leaning based technique (TAMD-IL), which allows to incrementally update the detection models without retraining it from scratch by removing and adding sub-models in TAMD. We experimentally demonstrate that TAMD outperforms the existing methods with up to 98.72% on precision and 96.57% on recall. We also evaluate TAMD-IL on four concept drift datasets and compare it with classical machine learning algorithms, two state-of-the-art malware detection technologies, and three incremental learning technologies. Experimental results show that TAMD-IL is efficient in terms of both update time and memory usage.

The train-ground communication system in this paper realized the information interaction between the equipment on the train and the ground through continuous, bi-directional, and large-capacity communication. While ensuring the accurate closed-loop control of the train, it also provided entertainment business services for high-speed rail passengers. Due to the limited resources of the railway private network, this paper used public network resources to achieve the integration of emergency communications services and ordinary communications services. Aiming at the communication problems caused by the rapidly changing communication environment and the coexistence of heterogeneous networks in public network resources, a layered architecture of intelligent collaborative identification networks was proposed. At the same time, this paper proposed a dynamic adaptive algorithm for network resources based on fuzzy logic Q-learning, which was used for emergency communication services to issue resource scheduling policies under a new network architecture. Finally, simulation experiments verify the superiority of the adaptive resource scheduling algorithm in system communication performance.

For many years, different architectures for controlling robotic systems have aroused with the goal of connecting the real world with the deliberation capabilities of the artificial system. In most cases, different modules are defined and connected to make data to flow from sensors to deliberation, and commands from deliberation to actuators. In such path, the different modules implement hierarchical levels, abstractions or layers that are traversed. However, in most cases, such modules are defined specifically for the architectures, and reusing them is typically very difficult or even unfeasible. In this work, we propose a layered architecture based on a specific set of modules, which could be replicated and reused in many different circumstances. The architecture is based on the use of automated planning, although any other deliberation mechanism could be used. We demonstrate its usability in the Robocup Logistics competition, and report a first implementation ready to compete in a future edition.