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The extremely vibrant, scattered, and non–transparent nature of cloud computing formulate trust management a significant challenge. According to scholars the trust and security are the two issues that are in the topmost obstacles for adopting cloud computing. Also, SLA (Service Level Agreement) alone is not necessary to build trust between cloud because of vague and unpredictable clauses. Getting feedback from the consumers is the best way to know the trustworthiness of the cloud services, which will help them improve in the future. Several researchers have stated the necessity of building a robust management system and suggested many ideas to manage trust based on consumers' feedback. This paper has reviewed various reputation-based trust management systems, including trust management in cloud computing, peer-to-peer system, and Adhoc system.

Remarkable resource management and energy efficiency improvements can be achieved in greenhouses using innovative technological advancements and modern agricultural methods. Deployment of Internet of Things (IoT) and optimization algorithms in greenhouse farming is highly desirable for real-time monitoring and controlling various parameters with optimal solutions. However, IoT based greenhouses require more energy as compared to traditional farming. This paper proposes an optimal greenhouse water supplement mechanism with efficient energy consumption based on IoT and optimization techniques. The first contribution of this study is to gather the actual water and soil moisture levels from the greenhouse and tank using IoT devices. Secondly, the formulation and deployment of an objective function to compute the optimal water and soil moisture levels for greenhouse and tank based on user-desired settings, the system constraints and actual sensing values. We applied a rule-based expert system to activate water pumps with the required flow rate and operational duration to achieve efficient energy consumption. To prove the effectiveness of the proposed concept, embedded IoT devices and objective function for optimization are deployed as well as, a number of experiments are conducted to provide the optimal water and soil moisture levels in a real greenhouse and water tank environment.

In recent few years, the widespread applications of indoor navigation have compelled the research community to propose novel solutions for detecting objects position in the Indoor environment. Various approaches have been proposed and implemented concerning the indoor positioning systems. This study propose an fuzzy inference based Kalman filter to improve the position estimation in indoor navigation. The presented system is based on FIS based Kalman filter aiming at predicting the actual sensor readings from the available noisy sensor measurements. The proposed approach has two main components, i.e., multi sensor fusion algorithm for positioning estimation and FIS based Kalman filter algorithm. The position estimation module is used to determine the object location in an indoor environment in an accurate way. Similarly, the FIS based Kalman filter is used to control and tune the Kalman filter by considering the previous output as a feedback. The Kalman filter predicts the actual sensor readings from the available noisy readings. To evaluate the proposed approach, the next-generation inertial measurement unit is used to acquire a three-axis gyroscope and accelerometer sensory data. Lastly, the proposed approach’s performance has been investigated considering the MAD, RMSE, and MSE metrics. The obtained results illustrate that the FIS based Kalman filter improve the prediction accuracy against the traditional Kalman filter approach.

Mountains are attraction spots for tourists, and tourism contributes to the country’s gross domestic product. Mountains have many benefits such as biodiversity, tourism, and the supplication of food, to name a few. However, there are challenges to protect mountain lives from hazards such as fire caused by tourist activities in mountains. The in-time fire detection and notification to the authorities have always been the central point in literature studies, and different studies have been carried out to optimize the notification time. In this paper, we model the fire detection and notification as a real-time internet of things application and uses task orchestration and task scheduling mechanism to provide scalability along with optimal latency. The proposed fire detection and prediction mechanism detect mountain fire at the earliest stage and provide predictive analysis to prevent damage to mountain life and tourists. The architecture is based on microservice-based IoT task orchestration mechanism and device virtualization, which is not only lightweight but also handles a single problem in parallel chunks, thus optimizes the latency. The in-time information about the fire is used for predictive analysis and notified to safety authorities which helps them to make a more informed decisions to minimize the damage caused by mountain fire. The performance of the proposed mechanism is evaluated in terms of different measures such as RMSE, MAPE, MSE, and MAPE. The proposed work approaches the fire detection and notification as a collection of tasks, and thus those tasks are selected for deployment which are guaranteed to be executed and have minimum latency. This idea of pre-planing the latency and task execution is the first attempt to the best of the authors’ knowledge.

It is expected that peer to peer energy trading will constitute a significant share of research in upcoming generation power systems due to the rising demand of energy in smart microgrids. However, the on-demand use of energy is considered a big challenge to achieve the optimal cost for households. This paper proposes a blockchain-based predictive energy trading platform to provide real-time support, day-ahead controlling, and generation scheduling of distributed energy resources. The proposed blockchain-based platform consists of two modules; blockchain-based energy trading and smart contract enabled predictive analytics modules. The blockchain module allows peers with real-time energy consumption monitoring, easy energy trading control, reward model, and unchangeable energy trading transaction logs. The smart contract enabled predictive analytics module aims to build a prediction model based on historical energy consumption data to predict short-term energy consumption. This paper uses real energy consumption data acquired from the Jeju province energy department, the Republic of Korea. This study aims to achieve optimal power flow and energy crowdsourcing, supporting energy trading among the consumer and prosumer. Energy trading is based on day-ahead, real-time control, and scheduling of distributed energy resources to meet the smart grid’s load demand. Moreover, we use data mining techniques to perform time-series analysis to extract and analyze underlying patterns from the historical energy consumption data. The time-series analysis supports energy management to devise better future decisions to plan and manage energy resources effectively. To evaluate the proposed predictive model’s performance, we have used several statistical measures, such as mean square error and root mean square error on various machine learning models, namely recurrent neural networks and alike. Moreover, we also evaluate the blockchain platform’s effectiveness through hyperledger calliper in terms of latency, throughput, and resource utilization. Based on the experimental results, the proposed model is effectively used for energy crowdsourcing between the prosumer and consumer to attain service quality.

Over the last decades, Internet of Things-based applications have become increasingly popular among many researchers and companies in developing a comfortable and safe lifestyle for people. Currently, many Internet of Things-based systems are providing extensive benefits to our daily lifestyle, although low power, failure in connectivity, and lack of computing knowledge are major challenges within heterogeneous devices. As the number of Internet-connected devices exponentially increases, the effectiveness of cloud computing is reaching its limits in terms of scalability and accessibility. The concept of edge computing allows the overload of the cloud to be reduced and provides remarkable advantages for Internet of Things systems. This paper proposes the design and implementation of a knowledge-based edge computing framework for enabling heterogeneous connectivity in Internet of Things networks. EdgeX is used as the edge computing platform, while Hypertext Transfer and Constrained Application Protocols are utilized as communication protocols for the proposed framework. Several experiments have been carried out in order to evaluate the effectiveness of the proposed design and implementation.

In the current era of IoT, home appliances like dishwashers, washing machines, and coffee makers, etc. are being equipped with internet access and sensing resources to provide autonomous or semi-autonomous functionality to its users. It is foreseeable that these appliances will soon be capable to collaborate by sharing their capabilities enabling dynamic operations as per the context and user preferences. This scenario, however, will also add to the complexity of programming/customizing the operational behavior of the appliances. Hence, traditional control interfaces will not suffice. This paper presents a generic architecture through which such collaborative operations of home appliances can be achieved. To minimize complexity for end-users, a centralized drag-n-drop visual interface for operational customization of the home appliances has been presented. In this proof-of-concept implementation, Intel Edison Platforms with associated sensing and actuating components have been utilized to mimic the sensing, actuation and computational resources of future home appliances by utilizing local WIFI network for communication purposes. Simple collaboration scenarios have been executed to find the execution delay of the customized collaborative operations. Based on the same scenarios, a preliminary usability study, based on system usability scale (SUS), has been conducted for the centralized visual interface via two different groups of end-users (programming skills-based). The average SUS score of 79.2 by both groups backs the claim of reduced complexity and ease of use for the end-users.

Enterprise Internet of Things (EIoT) is the next advancement in technology. EIoT allows the involvement of embedded devices to participate in business processes to automate enterprise operations. EIoT demands the modelling of services based on tasks to automate business processes and to allow easy adaptation to new business models. It is anticipated to enhance efficiency, align physical operations on a real-time basis and provide analytics for fostering more customer-centric business. The architecture is the cornerstone for any application, and currently, there exist a variety of architectures for enterprise systems and IoT addressing the requirements of the respective domains. The reference architectures of conventional IoT platforms perform orchestration at the service level, which, despite its success, does not align well with the demands of enterprises processes. This paper proposes a reference architecture to consider both the requirements of EIoT as well as the conventional Internet of Things (IoT). This paper is a novel attempt to propose orchestration at the task level, considering the business process modelling of enterprise systems. This level of orchestration is scalable and flexible due to the automation at a more granular level. In contrast, the service-level orchestration works well for general IoT application but is not suited for enterprises whose sole aim is the modelling of enterprise operation at the very task level. Therefore, a reference architecture named a multi-device multi-tasks management and orchestration (MDMT-MOA) based on a top-down methodology is proposed addressing the needs of IoT and enterprise applications. A case study is implemented on top of the architecture, and the performance is evaluated using various metrics related to EIoT. Results indicate that the proposed architecture is efficient due to lightweight payload, scalable, fault-tolerant and flexible to adapt with new business models.

In recent years, the development of Cloud and Internet of Things (IoT) integrated applications are becoming more and more popular among developers, and this combined system is described as Cloud of Things (CoT). Cloud of Things can be utilized for finding optimal solutions for various problems. CoT is one of the essential methods for the construction and development of smart cities. With the installation of CoT based technologies, people can be able to check, control environmental parameters through intelligent embedded devices. In this paper, we present a CoTarchitecture based on proxy using Peer to Peer (P2P) connectivity for interworking IoT devices and Cloud. Proposed Cloud of Things architecture supports IoT devices’ contextual data transformation to the Cloud-based on P2P connectivity using Open Connectivity Foundation (OCF) IoTivity and Message Queuing Telemetry Transport (MQTT) protocol. The proposed system provides a collection of huge sensing information via Internet of Things devices and upload gathered data to Cloud for further analyzation. The proxy can acquire sensor data from the Internet of Things networks and then publish the data as messages to the subject in Internet of Things global Cloud. Moreover, this Cloud of Things architecture supports the peer to peer internetworking for communication of client devices and embedded devices. Finally, we also compared the IoT service of global Cloud platforms in CoT experiments.

Edge computing is an emerging computing paradigm that distributes the computational capability to the edge of networks for enabling the computation near to the environment where the sensors and actuators are deployed. Therefore, from the network edge, heterogeneous solutions can be provided to the Internet based on sufficient computing ability. Nevertheless, computing and networking resources are constrained for devices in the network edge. Providing secure services from edge computing is a challenge based on constrained resources. In this paper, we propose a secure edge computing to provide management of device, data, user and additional services based on deploying independent microservices providers with a security gateway on an edge gateway. The edge gateway is the hub of a local network where multiple IoT devices are deployed to interact with the physical environment for sensing and actuating. The gateway provides the management functionalities through microservices based on multiple independent server modules. Each gateway-centric local network has a standalone management service based on the gateway. For providing secure edge computing services through the edge gateway, a security gateway is deployed on the proposed edge gateway to provide Representational State Transfer Application Programming Interfaces to expose the security services to the Internet instead of microservices from management modules. Moreover, a client support gateway is deployed in the edge gateway to provide services of User Interface and access forwarding based on web sessions to support user authentication and authorization with the security gateway. Based on the proposed edge gateway including client support and security gateway, IoT clients and IoT devices are enabled to communicate for providing secure edge services of access and visualization to users.

Safe Routes to School is very important for students to have good physical and psychologically healthy in school life. For providing safe routes based on risk analysis, finding out dangerous points and areas can be a target to avoid dangerous locations by pedestrians and drivers. However, analyzing the risk assessment to derive the safe routes requires a large amount of data with a certain time of observation by experts. Deep learning is a solution to provide information regarding safe routes based on expert knowledge. In this paper, we propose a risk assessment inference approach using a Recurrent Neural Network (RNN) model with Long-Short Term Memory (LSTM) cells based on geographical information for safe routes to school. However, geographical information including coordinates is difficult used in learning-based inference models because of the series of float values. For training the RNN model with the geographical data, coordinates of routes and danger points are translated to be geohash through the geohash converter. The geohash data with other data of features are fused and inputted to the one-hot encoder. The one-hot encoded data is used in the inputs of the RNN model to train the LSTMs. The input data of the training model is derived by the risk index model that is proposed to calculate the risk index based on distances of route coordinates and danger points. Therefore, the risk index is correlated with the training dataset. Through the proposed inference approach, the geographical information including multiple coordinates is enabled to be trained by RNN as a geohash-based input string. Moreover, the input string with other features is fused to support the one-hot encoding to get a better result in RNN models.

Waste has a direct impact on human health and the surrounding environment. Apart from the health aspect, many industries' growth is effected by waste material such as the food industry. Waste management authorities are interested in reducing the cost of waste management operations and searching for sustainable waste management solutions. For effective planning of waste management, reliable data analysis is required to produce results that can facilitate the planning process. Data mining and machine learning-based data analysis over the waste data can produce a more detailed, and in-time waste information generation, which can lead to effectively manage the waste amount of specific area. In this paper, a descriptive data analysis approach, along with predictive analysis, is used to produce in-time waste information. The performance of the proposed approach is evaluated using a real waste dataset of Jeju Island, South Korea. Waste bins are virtualized on its actual location on the Jeju map in Quantum Geographic Information Systems(QGIS) software. The performance results of the predictive analysis models are evaluated in terms of Mean Absolute Error(MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error(MAPE). Performance results indicate that predictive analysis models are reliable for the effective planning and optimization of waste management operations.

Smart home environments account to a major portion of the total energy consumption in today's world. The residents of smart home environments wish to find solutions that reduce the energy costs along with providing an optimal indoor environment for the residents. Another significant aspect in smart home systems is efficiency of tasks management and control commands' execution for smart home actuators. In this paper, we propose an optimal control solution for smart home environment based on smart home energy optimization and control tasks' load dispatching and scheduling. Optimal control is achieved by first defining an objective function for minimizing energy cost which is implemented using VB-PSO (velocity boost particle swarm optimization) algorithm. Next, the control tasks are generated using rule set implemented in fuzzy logic; defined based on optimal values achieved from VB-PSO. A Markov model based mechanism dispatches control tasks at scheduler, for efficient scheduling and optimal control. The results show that the proposed optimization scheme saves up to 29.73% energy costs on average, in comparison to baseline scheme. The proposed tasks' load dispatching scheme of admission control, makes the job of load balancing among the processors efficient while giving priority to the urgent tasks. The results for scheduler evidently show the low dropping probabilities for urgent tasks along with showing 34.9% reduction in tasks' starvation rate and 36.82% reduction in average tasks' instances missing rates.

The tourism industry can significantly benefit from the blockchain since its implementation can build trust among stakeholders and improve customer satisfaction. However, most of the existing tourism-specified blockchain platforms are single-chains that provide business support for enterprises without guaranteeing transaction information privacy. Besides, these platforms are specified to a single use case and lack interoperability with other platforms to support heterogenous tourism services. This paper aims to address this issue by introducing a multi-chain architecture that utilizes multiple blockchains to enhance processing capability and provide various business services for the tourism industry. The proposed multi-chain architecture improves the interoperability between the activities in different chains by providing functional requirements in practical applications and supports the inter-ledger application. In addition, the private blockchain will be made available to allow users to access the network through central authorization. It also increases the transaction processing capability by distributing multiple tasks across the chains for large-scale applications. To demonstrate the usability and efficiency of the developed approach, a case study on hotel booking is conducted using the blockchain frameworks Winding Tree and Hyperledger Fabric. A comprehensive evaluation experiment is conducted, and the results show the significance of the proposed system.

The connectivity of our surrounding objects to the internet plays a tremendous role in our daily lives. Many network applications have been developed in every domain of life, including business, healthcare, smart homes, and smart cities, to name a few. As these network applications provide a wide range of services for large user groups, the network intruders are prone to developing intrusion skills for attack and malicious compliance. Therefore, safeguarding network applications and things connected to the internet has always been a point of interest for researchers. Many studies propose solutions for intrusion detection systems and intrusion prevention systems. Network communities have produced benchmark datasets available for researchers to improve the accuracy of intrusion detection systems. The scientific community has presented data mining and machine learning-based mechanisms to detect intrusion with high classification accuracy. This paper presents an intrusion detection system based on the ensemble of prediction and learning mechanisms to improve anomaly detection accuracy in a network intrusion environment. The learning mechanism is based on automated machine learning, and the prediction model is based on the Kalman filter. Performance analysis of the proposed intrusion detection system is evaluated using publicly available intrusion datasets UNSW-NB15 and CICIDS2017. The proposed model-based intrusion detection accuracy for the UNSW-NB15 dataset is 98.801 percent, and the CICIDS2017 dataset is 97.02 percent. The performance comparison results show that the proposed ensemble model-based intrusion detection significantly improves the intrusion detection accuracy.

With the fast development of infrastructure and communication technology, the Internet of Things (IoT) has become a promising field. Ongoing research is looking at the smart home environment as the most promising sector that adopts IoT and cloud computing to improve resident live experiences. The IoT and cloud-dependent smart home services related to recent researches have security, bandwidth issues, and a lack of concerning thermal comfort of residents. In this paper, we propose an environment optimization scheme based on edge computing using Particle Swarm Optimization (PSO) for efficient thermal comfort control in resident space to overcome the aforementioned limitations of researches on smart homes. The comfort level of a resident in a smart home is evaluated by Predicted Mean Vote (PMV) that represents the thermal response of occupants. The PSO algorithm combined with PMV to improve the accuracy of the optimization results for efficient thermal comfort control in a smart home environment. We integrate IoT with edge computing to upgrade the capabilities of IoT nodes in computing power, storage space, and reliable connectivity. We use EdgeX as an edge computing platform to develop a thermal comfort considering PMV-based optimization engine with a PSO algorithm to generate the resident’s friendly environment parameters and rules engine to detects the environmental change of the smart home in real-time to maintain the indoor environment thermal comfortable. For evaluating our proposed system that maintenance resident environment with thermal comfort index based on PSO optimization scheme in smart homes, we conduct the comparison between the real data with optimized data, and measure the execution times of optimization function. From the experimental results, when our proposed system is applied, it satisfies thermal comfort and consumes energy more stably.

Ubiquitous Internet of medical things (U-IoMT) technology is designed to predict the efficiency and quality of health care facilities by using various connected objects to support patient monitoring systems. U- IoMT is connected to devices such as RFID tags and several physical sensor devices (WSN) that collect real-time information. This technology is used to comprehend complex security-related tasks. The ubiquitous platform can be used to access smart health care information through mobile and electronic devices, allowing for faster diagnosis and higher service quality. In this study, we use ontology to describe the semantic representation of medical objects and their data in U-IoMT backup in this analysis. The healthcare system aims to examine patient prescriptions and drug supply chain management records. Emergency healthcare services are connected to smartphone-wearable devices of patients for monitoring purposes to reduce emergency cases and to maintain e-patient records. The mobile health application aims to maintain the health status of a patient wherever the patient is located. The semantic sensor networks (SSN) architecture uses peer-to-peer communication to achieve semantic interoperability, which is described as interoperable IoMT platforms. A framework of SSN and the context-awareness layer was additionally created for visualization of patient remote health monitoring, drug management, patient moment analysis, and patient tracking were monitored using various devices, e-health was used to demonstrate the chronic diseases.

With the rapid development of Internet of Things (IoT) technologies in various domains including smart homes, smart cities, smart factories and smart buildings where Internet-connected devices are deployed to provide IoT services based on heterogeneous frameworks and platforms. Many standard protocols, frameworks, libraries and specifications have been proposed for developing IoT applications. Therefore, providing a consistent scheme is important for supporting the interoperability in heterogeneous IoT devices to interact in the same domain and cross-domain. Moreover, supporting the device transparent access for clients to consume IoT service from the different environment that can provide user-friendly service scenarios although the user consumes services in different IoT networks. In this paper, we propose an improved Resource Directory (RD) based on a Domain Name System (DNS) Name Self-Registration (DNSNSR) for the device transparent access in heterogeneous IoT networks. For supporting proposed DNSNSR, an IoT RD is presented based on the Open Connectivity Foundation (OCF) standard specification to provide device registration and discovery service. Through the registration interface, the IoT RD configures the DNS names using Bind 9 to provide the DNS service in an IoT network based on the published device information. Using the discovery interface of RD and name resolution of DNS, the IoT Client gets devices information including (Identifier) ID and Internet Protocol (IP) to access IoT Devices without considering underlying protocols through the interworking proxy of proposed RD in heterogeneous IoT networks. Therefore, the proposed RD based on DNSNSR enables the IoT devices to be discovered by IoT clients in the various environment through the RD and DNS functions. Furthermore, using the OCF-direct and proxy-based access mechanism, the proposed RD based on DNSNSR supports IoT devices to be accessed by clients in various IoT environment.

Over the past years, numerous Internet of Things (IoT)-based healthcare systems have been developed to monitor patient health conditions, but these traditional systems do not adapt to constraints imposed by revolutionized IoT technology. IoT-based healthcare systems are considered mission-critical applications whose missing deadlines cause critical situations. For example, in patients with chronic diseases or other fatal diseases, a missed task could lead to fatalities. This study presents a smart patient health monitoring system (PHMS) based on an optimized scheduling mechanism using IoT-tasks orchestration architecture to monitor vital signs data of remote patients. The proposed smart PHMS consists of two core modules: a healthcare task scheduling based on optimization and optimization of healthcare services using a real-time IoT-based task orchestration architecture. First, an optimized time-constraint-aware scheduling mechanism using a real-time IoT-based task orchestration architecture is developed to generate autonomous healthcare tasks and effectively handle the deployment of emergent healthcare tasks. Second, an optimization module is developed to optimize the services of the e-Health industry based on objective functions. Furthermore, our study uses Libelium e-Health toolkit to monitors the physiological data of remote patients continuously. The experimental results reveal that an optimized scheduling mechanism reduces the tasks starvation by 14% and tasks failure by 17% compared to a conventional fair emergency first (FEF) scheduling mechanism. The performance analysis results demonstrate the effectiveness of the proposed system, and it suggests that the proposed solution can be an effective and sustainable solution towards monitoring patient’s vital signs data in the IoT-based e-Health domain.

In today’s world, smart buildings are considered an overarching system that automates a building’s complex operations and increases security while reducing environmental impact. One of the primary goals of building management systems is to promote sustainable and efficient use of energy, requiring coherent task management and execution of control commands for actuators. This paper proposes a predictive-learning framework based on contextual feature selection and optimal actuator control mechanism for minimizing energy consumption in smart buildings. We aim to assess multiple parameters and select the most relevant contextual features that would optimize energy consumption. We have implemented an artificial neural network-based particle swarm optimization (ANN-PSO) algorithm for predictive learning to train the framework on feature importance. Based on the relevance of attributes, our model was also capable of re-adding features. The extracted features are then applied as input parameters for the training of long short-term memory (LSTM) and optimal control module. We have proposed an objective function using a velocity boost-particle swarm optimization (VB-PSO) algorithm that reduces energy cost for optimal control. We then generated and defined the control tasks based on the fuzzy rule set and optimal values obtained from VB-PSO. We compared our model’s performance with and without feature selection using the root mean square error (RMSE) metric in the evaluation section. This paper also presents how optimal control can reduce energy cost and improve performance resulting from lesser learning cycles and decreased error rates.

The rapid demand for Cloud services resulted in the establishment of large-scale Cloud Data Centers (CDCs), which ultimately consume a large amount of energy. An enormous amount of energy consumption eventually leads to high operating costs and carbon emissions. To reduce energy consumption with efficient resource utilization, various dynamic Virtual Machine (VM) consolidation approaches (i.e., Predictive Anti-Correlated Placement Algorithm (PACPA), Resource-Utilization-Aware Energy Efficient (RUAEE), Memory-bound Pre-copy Live Migration (MPLM), m Mixed migration strategy, Memory/disk operation aware Live VM Migration (MLLM), etc.) have been considered. Most of these techniques do aggressive VM consolidation that eventually results in performance degradation of CDCs in terms of resource utilization and energy consumption. In this paper, an Efficient Adaptive Migration Algorithm (EAMA) is proposed for effective migration and placement of VMs on the Physical Machines (PMs) dynamically. The proposed approach has two distinct features: first, selection of PM locations with optimum access delay where the VMs are required to be migrated, and second, reduces the number of VM migrations. Extensive simulation experiments have been conducted using the CloudSim toolkit. The results of the proposed approach are compared with the PACPA and RUAEE algorithms in terms of Service-Level Agreement (SLA) violation, resource utilization, number of hosts shut down, and energy consumption. Results show that proposed EAMA approach significantly reduces the number of migrations by 16% and 24%, SLA violation by 20% and 34%, and increases the resource utilization by 8% to 17% with increased number of hosts shut down from 10% to 13% as compared to the PACPA and RUAEE, respectively. Moreover, a 13% improvement in energy consumption has also been observed.

With the development of modern power systems (smart grid), energy consumption prediction becomes an essential aspect of resource planning and operations. In the last few decades, industrial and commercial buildings have thoroughly been investigated for consumption patterns. However, due to the unavailability of data, the residential buildings could not get much attention. During the last few years, many solutions have been devised for predicting electric consumption, however, it remains a challenging task due to the dynamic nature of residential consumption patterns. Therefore, a more robust solution is required to improve the model performance and achieve a better prediction accuracy. This paper presents an ensemble approach based on learning to a statistical model to predict the short-term energy consumption of a multifamily residential building. Our proposed approach utilizes Long Short-Term Memory (LSTM) and Kalman Filter (KF) to build an ensemble prediction model to predict short term energy demands of multifamily residential buildings. The proposed approach uses real energy data acquired from the multifamily residential building, South Korea. Different statistical measures are used, such as mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), and R2 score, to evaluate the performance of the proposed approach and compare it with existing models. The experimental results reveal that the proposed approach predicts accurately and outperforms the existing models. Furthermore, a comparative analysis is performed to evaluate and compare the proposed model with conventional machine learning models. The experimental results show the effectiveness and significance of the proposed approach compared to existing energy prediction models. The proposed approach will support energy management to effectively plan and manage the energy supply and demands of multifamily residential buildings.

The prediction mechanism is very crucial in a smart factory as they widely help in improving the product quality and customer&rsquo, s experience based on learnings from past trends. The implementation of analytics tools to predict the production and consumer patterns plays a vital rule. In this paper, we put our efforts to find integrated solutions for smart factory concerns by proposing an efficient task management mechanism based on learning to scheduling in a smart factory. The learning to prediction mechanism aims to predict the machine utilization for machines involved in the smart factory, in order to efficiently use the machine resources. The prediction algorithm used is artificial neural network (ANN) and the learning to prediction algorithm used is particle swarm optimization (PSO). The proposed task management mechanism is evaluated based on multiple scenario simulations and performance analysis. The comparisons analysis shows that proposed task management system significantly improves the machine utilization rate and drastically drops the tasks instances missing rate and tasks starvation rate.

Computation offloading enables intensive computational tasks in edge computing to be separated into multiple computing resources of the server to overcome hardware limitations. Deep learning derives the inference approach based on the learning approach with a volume of data using a sufficient computing resource. However, deploying the domain-specific inference approaches to edge computing provides intelligent services close to the edge of the networks. In this paper, we propose intelligent edge computing by providing a dynamic inference approach for building environment control. The dynamic inference approach is provided based on the rules engine that is deployed on the edge gateway to select an inference function by the triggered rule. The edge gateway is deployed in the entry of a network edge and provides comprehensive functions, including device management, device proxy, client service, intelligent service and rules engine. The functions are provided by microservices provider modules that enable flexibility, extensibility and light weight for offloading domain-specific solutions to the edge gateway. Additionally, the intelligent services can be updated through offloading the microservices provider module with the inference models. Then, using the rules engine, the edge gateway operates an intelligent scenario based on the deployed rule profile by requesting the inference model of the intelligent service provider. The inference models are derived by training the building user data with the deep learning model using the edge server, which provides a high-performance computing resource. The intelligent service provider includes inference models and provides intelligent functions in the edge gateway using a constrained hardware resource based on microservices. Moreover, for bridging the Internet of Things (IoT) device network to the Internet, the gateway provides device management and proxy to enable device access to web clients.

For providing intelligent services in the network edge, in this paper, we propose a real-time inference approach base on gateway-centric edge computing. The edge computing is deployed on the edge gateway that operates intelligent services in the network entry. The intelligent services are provided based on the inference approaches that are included in the edge gateway. The key component is an intelligent control algorithm based on deep learning. The training of the model is offloaded to the high-performance computing machine, and the resulting inference model is deployed on the edge gateway, which closes the control loop with the IoT device. The inference model is derived by user data and learning model through training the user data on the learning model. Based on deploying the inference model to the edge gateway, the intelligent approach is enabled close to the environment where the data is generated. Therefore, the edge computing architecture reduces the latency to get the control factor for updating the environment based on the real-time sensing data.

The Internet of Things (IoT) enables the number of connected devices to be increased rapidly based on heterogeneous technologies such as platforms, frameworks, libraries, protocols, and standard specifications. Based on the connected devices, various applications can be developed by integrating domain-specific contents using the service composition for providing improved services. The management of the information including devices, contents, and composite objects is necessary to represent the physical objects on the Internet for accessing the IoT services transparently. In this paper, we propose an integrated service composition approach based on multiple service providers to provide improved IoT services by combining various service objects in heterogeneous IoT networks. In the proposed IoT architecture, each service provider provides web services based on Representational State Transfer (REST) Application Programming Interface (API) that delivers information to the clients as well as other providers for integrating the information to provide new services. Through the REST APIs, the integration management provider combines the service result of the IoT service provider to other contents to provide improved services. Moreover, the interworking proxy is proposed to bridge heterogeneous IoT networks for enabling transparent access in the integrated services through proving protocol translating on the entry of the device networks. Therefore, the interworking proxy is deployed between the IoT service provider and device networks to enable clients to access heterogeneous IoT devices through the composited services transparently.

The clinical research faces numerous challenges, from patient enrollment to data privacy concerns and regulatory requirements to spiraling costs. Blockchain technology has the potential to overcome these challenges, thus making clinical trials transparent and enhancing public trust in a fair and open process with all stakeholders because of its distinct features such as data immutability and transparency. This paper proposes a permissioned blockchain platform to ensure clinical data transparency and provides secure clinical trial-related solutions. We explore the core functionalities of blockchain applied to clinical trials and illustrate its general principle concretely. These clinical trial operations are automated using the smart contract, which ensures traceability, prevents a posteriori reconstruction, and securely automates the clinical trial. A web-based user interface is also implemented to visualize the data from the blockchain and ease the interaction with the blockchain network. A proof of concept is implemented on Hyperledger Fabric in the case study of clinical management for multiple clinical trials to demonstrate the designed approach’s feasibility. Lastly, the experiment results demonstrate the efficiency and usability of the proposed platform.

Renewable energy sources are environmentally friendly and cost-efficient. However, the problem with these renewable resources is their heavy reliance on weather conditions. Thus, at times, these solutions are not guaranteed to meet the required demand all the time. For this, hybrid microgrids are introduced, which have a combination of both renewable energy sources and non-renewable energy resources. In this paper, a cost-efficient optimization algorithm is proposed that minimizes the use of non-renewable energy sources. It maximizes the use of renewable energy resources by meeting the demand for utility grids. Real data based on the load and demand of the utility grids in Italy is used, and a system that determines the optimal sizing of the microgrid and a daily plan is introduced to optimize the renewable resources operations. As part of the proposal, the objective function for the operation and planning of the microgrid in such a way to minimize cost is formulated. Moreover, a variant of the PSO algorithm named recurrent PSO is implemented. The recurrent PSO algorithm solves the proposed optimization objective function by minimizing the cost for the installation and working of the microgrid. Afterwards, the energy management system algorithm lays out a plan for the daily operation of the microgrid. The performance of the system is evaluated using different state-of-the-art optimization methods. The proposed work can help minimize the use of diesel generators, which not only saves financial resources but also contributes toward a green environment.

The Internet of Things refers (IoT) to the billions of physical devices around the globe that are connected to the Internet, collecting and sharing data. The overall Internet of Things market is projected to be worth more than 50.6 billion U.S. dollars in 2020. IoT devices possess low processing capabilities, limited memory, limited storage, and minimal network protocol support. With the help of cloud computing technology, we can overcome the limited resources of IoT devices. A lot of research has been conducted on IoT device virtualization to facilitate remote access and control. The concept of virtualization in IoT is to provide a virtual representation of physical devices in the form of virtual objects. IoT devices are more likely to be accessed and communicate through virtual objects in the near future. In this paper, we present the design and implementation of building a virtual IoT network for a smart home. The virtual network is based on virtual objects and IoT controller. We derived the concept from Software Defined Network (SDN) and separated the control plane and data plane in the virtual IoT network. This enhanced the rapid development of diverse applications on top of the virtualization layer by establishing a dynamic end-to-end connection between IoT devices. This article briefly explains the design and development of the virtual network. Results achieved during experiments and performance analysis show that IoT controller enhances the capabilities of a virtual network by dynamically controlling the traffic congestion, handling mapping requests, and routing mechanisms.

Over the past several years, many healthcare applications have been developed to enhancethe healthcare industry. Recent advancements in information technology and blockchain technologyhave revolutionized electronic healthcare research and industry. The innovation of miniaturizedhealthcare sensors for monitoring patient vital signs has improved and secured the human healthcaresystem. The increase in portable health devices has enhanced the quality of health-monitoringstatus both at an activity/fitness level for self-health tracking and at a medical level, providing moredata to clinicians with potential for earlier diagnosis and guidance of treatment. When sharingpersonal medical information, data security and comfort are essential requirements for interactionwith and collection of electronic medical records. However, it is hard for current systems to meetthese requirements because they have inconsistent security policies and access control structures.The new solutions should be directed towards improving data access, and should be managed bythe government in terms of privacy and security requirements to ensure the reliability of data formedical purposes. Blockchain paves the way for a revolution in the traditional pharmaceuticalindustry and benefits from unique features such as privacy and transparency of data. In this paper,we propose a novel platform for monitoring patient vital signs using smart contracts based onblockchain. The proposed system is designed and developed using hyperledger fabric, which isan enterprise-distributed ledger framework for developing blockchain-based applications. Thisapproach provides several benefits to the patients, such as an extensive, immutable history log, andglobal access to medical information from anywhere at any time. The Libelium e-Health toolkitis used to acquire physiological data. The performance of the designed and developed system isevaluated in terms of transaction per second, transaction latency, and resource utilization usinga standard benchmark tool known as Hyperledger Caliper. It is found that the proposed systemoutperforms the traditional health care system for monitoring patient data.

With the gradual popularization of Internet-of-Things (IoT) applications and the development of wireless networking technologies, the use of heterogeneous devices and runtime verification of task fulfillment with different constraints are required in real-world IoT scenarios. As far as IoT systems are concerned, most of them are built on centralized architectures, which reveal various assailable points in data security and privacy threats. Hence, this paper aims to investigate these issues by delegating the responsibility of a verification monitor from a centralized architecture to a decentralized manner using blockchain technology. We present a smart contract-based task management scheme to provide runtime verification of device behaviors and allows trustworthy access control to these devices. The business logic of the proposed system is specified by the smart contract, which automates all time-consuming processes cryptographically and correctly. The usability of the proposed solution is further demonstrated by implementing a prototype application in which the Hyperledger Fabric is utilized to implement the business logic for runtime verification and access control with one desktop and one Raspberry Pi. A comprehensive evaluation experiment is conducted, and the results indicate the effectiveness and efficiency of the proposed system.

As the world population is increasing rapidly, food and water demands are the most crucial problem for humanity. In some areas of the world, water or environment is unsuitable for plant growth, hydroponic systems can provide a suitable environment for crop production with effective management of natural resources. Internet of Things paradigm based automated systems has been creating an excellent opportunity for monitoring and controlling agriculture by minimizing the cost and maximizing the profit significantly over the past decade. The reduction of the cost can be achieved by sufficient usage of resources and setting up optimum operational parameters for agricultural devices. This paper presents an optimization scheme with novel objective function for hydroponics environment parameters management with efficient energy consumption. The proposed approach provides optimal energy and resource utilization in the hydroponics system with setting up a working level and operational duration to the actuators. We have developed an optimization scheme with objective function for optimal humidity and water level control based on fuzzy logic, which can support the optimal measurement for crop growth with energy efficiency. Fuzzy logic control is applied for the compromise between actuators working level and operational duration. A real hydroponics environment has been implemented and presented to evaluate the effectiveness of the proposed approach. It can be assessed through the simulation results that the optimization module achieves a signification reduction (18%) in energy consumption as compared to the other scheme.

In recent past, Internet of Things- (IoT-) based applications have experienced tremendous growth in various domains, and billions of devices are expected to be connected to the Internet in near future. The first step for development of IoT-based applications is to virtualize the physical devices by abstracting device properties in virtual objects. Later, these virtual objects can be combined to compose different services for diverse applications. Many existing systems provide virtualization service for physical devices and service composition. But, with the growth of the network, when too many devices and services are added in the IoT network, its management will become a cumbersome task. This paper presents an architecture of IoT services and virtual objects management in hyperconnected things network to facilitate the management tasks. We also have implemented a Service and Virtual Objects Management (SVOM) system prototype to effectively organize and monitor the physical devices through corresponding virtual objects and services composed in the IoT environment. The proposed system also provides interface for user interaction to perform supported control operations on selected device and check device operational and fault status. For scalability analysis of the proposed system, we have performed simulation in the OMNeT++ simulator to study impact of the IoT network size on key performance measures like response time, throughput, and packet delivery ratio. Simulation results reveal that with the growing network size, the gateway nodes become the performance bottleneck. We have also performed resources requirement analysis for virtual objects and control overhead analysis of the proposed management system. Simulation results reveal that control overhead is insignificant in normal scenarios; however, in extreme network conditions, we may have to sacrifice fewer bits which is, in fact, worth nothing when compared to the flexibility and control offered by the proposed management system.