Explore the vast range of titles available.

At present, in pharmacology one of the most serious problems is counterfeit drugs. The Health Research Funding organization reported that in developing countries, nearly 10–30% of the drugs are fake. Counterfeiting is not the main issue itself, but, rather, the fact that, as compared to traditional drugs, these counterfeit drugs produce different side effects to human health. According to WHO, around 30% of the total medicine sold in Africa, Asia, and Latin America is counterfeit. This is the major worldwide problem, and the situation is worse in developing countries, where one out of every 10 medicines are either fake or do not follow drug regulations. The rise of Internet pharmacies has made it more difficult to standardize drug safety. It is difficult to detect counterfeits because these drugs pass through different complex distributed networks, thus forming opportunities for counterfeits to enter the authentic supply chain. The safety of the pharmaceutical supply chain has become a major concern for public health, which is a collective process. In this paper, we propose a novel drug supply chain management using Hyperledger Fabric based on blockchain technology to handle secure drug supply chain records. The proposed system solves this problem by conducting drug record transactions on a blockchain to create a smart healthcare ecosystem with a drug supply chain. A smart contract is launched to give time-limited access to electronic drug records and also patient electronic health records. We also carried out a number of experiments in order to demonstrate the usability and efficiency of the designed platform. Finally, we used Hyperledger Caliper as a benchmarking tool to conduct the performance of the designed system in terms of transactions per second, transaction latency, and resource utilization.

Rule-enabled Internet of Things (IoT) systems operate autonomous and dynamic service scenarios through real-time events and actions based on deployed rules. For handling the increasing events and actions in the IoT networks, the computational ability can be distributed and deployed to the edge of networks. However, operating a consistent rule to provide the same service scenario in heterogeneous IoT networks is difficult because of the difference in the protocols and rule models. In this paper, we propose a transparent rule deployment approach based on the rule translator by integrating the interworking proxy to IoT platforms for operating consistent service scenarios in heterogeneous IoT networks. The rule-enabled IoT architecture is proposed to provide functional blocks in the layers of the client, rule service, IoT service, and device. Additionally, the interworking proxy is used for translating and transferring rules between IoT platforms in different IoT networks. Based on the interactions between the IoT platforms, the same service scenarios are operated in the IoT environment. Moreover, the integrated interworking proxy enables the heterogeneity of IoT frameworks in the IoT platform. Therefore, rules are deployed on IoT platforms transparently, and consistent rules are operated in heterogeneous IoT networks without considering the underlying IoT frameworks.

Internet of Things (IoT) devices are embedded with software, electronics, and sensors, and feature connectivity with constrained resources. They require the edge computing paradigm, with modular characteristics relying on microservices, to provide an extensible and lightweight computing framework at the edge of the network. Edge computing can relieve the burden of centralized cloud computing by performing certain operations, such as data storage and task computation, at the edge of the network. Despite the benefits of edge computing, it can lead to many challenges in terms of security and privacy issues. Thus, services that protect privacy and secure data are essential functions in edge computing. For example, the end user’s ownership and privacy information and control are separated, which can easily lead to data leakage, unauthorized data manipulation, and other data security concerns. Thus, the confidentiality and integrity of the data cannot be guaranteed and, so, more secure authentication and access mechanisms are required to ensure that the microservices are exposed only to authorized users. In this paper, we propose a microservice security agent to integrate the edge computing platform with the API gateway technology for presenting a secure authentication mechanism. The aim of this platform is to afford edge computing clients a practical application which provides user authentication and allows JSON Web Token (JWT)-based secure access to the services of edge computing. To integrate the edge computing platform with the API gateway, we implement a microservice security agent based on the open-source Kong in the EdgeX Foundry framework. Also to provide an easy-to-use approach with Kong, we implement REST APIs for generating new consumers, registering services, configuring access controls. Finally, the usability of the proposed approach is demonstrated by evaluating the round trip time (RTT). The results demonstrate the efficiency of the system and its suitability for real-world applications.

In the smart home environment, efficient energy management is a challenging task. Solutions are needed to achieve a high occupant comfort level with minimum energy consumption. User comfort is measured in terms of three fundamental parameters: (a) thermal comfort, (b) visual comfort and (c) air quality. Temperature, illumination and CO 2 sensors are used to collect indoor contextual information. In this paper, we have proposed an improved optimization function to achieve maximum user comfort in the building environment with minimum energy consumption. A comprehensive formulation is done for energy optimization with detailed analysis. The Kalman filter algorithm is used to remove noise in sensor readings by predicting actual parameter values. For optimization, we have used genetic algorithm (GA) and particle swarm optimization (PSO) algorithms and performed comparative analysis with a baseline scheme on real data collected for a one-month duration in our lab’s indoor environment. Experimental results show that the proposed optimization function has achieved a 27 . 32 % and a 31 . 42 % reduction in energy consumption with PSO and GA, respectively. The user comfort index was also improved by 10 % i.e., from 0 . 86 to 0 . 96 . GA-based optimization results were better than PSO, as it has achieved almost the same user comfort with 4 . 19 % reduced energy consumption. Results show that the proposed optimization function gives better results than the baseline scheme in terms of user comfort and the amount of consumed energy. The proposed system can help with collecting the data about user preferences and energy consumption for long-term analysis and better decision making in the future for efficient resource utilization and overall profit maximization.

The Internet of Things is comprised of heterogeneous devices, applications, and platforms using multiple communication technologies to connect the Internet for providing seamless services ubiquitously. With the requirement of developing Internet of Things products, many protocols, program libraries, frameworks, and standard specifications have been proposed. Therefore, providing a consistent interface to access services from those environments is difficult. Moreover, bridging the existing web services to sensor and actuator networks is also important for providing Internet of Things services in various industry domains. In this paper, an Internet of Things proxy is proposed that is based on virtual resources to bridge heterogeneous web services from the Internet to the Internet of Things network. The proxy enables clients to have transparent access to Internet of Things devices and web services in the network. The proxy is comprised of server and client to forward messages for different communication environments using the virtual resources which include the server for the message sender and the client for the message receiver. We design the proxy for the Open Connectivity Foundation network where the virtual resources are discovered by the clients as Open Connectivity Foundation resources. The virtual resources represent the resources which expose services in the Internet by web service providers. Although the services are provided by web service providers from the Internet, the client can access services using the consistent communication protocol in the Open Connectivity Foundation network. For discovering the resources to access services, the client also uses the consistent discovery interface to discover the Open Connectivity Foundation devices and virtual resources.

Internet of Things (IoT) has attracted tremendous research attention in the recent past fromindustry and academia. IoT is quite helpful in uplifting living standards by transforming conventional technology into smart systems. Greenhouse production is considered as an ultimate solution for rising global food demands with the growing population. Greenhouse provides a year-round production facility for fresh vegetables with around 50% increased production rate in comparison to open-air cultivation. However, energy consumption and labor cost in greenhouses account for more than 50% of the cost of greenhouse production. In this paper, we have proposed a novel optimization scheme that aims to achieve a trade-off between energy consumption and desired climate setting in greenhouse i.e. temperature, CO2 level, and humidity. For performance evaluation of the proposed system, we have developed an ad-hoc emulator of the greenhouse environment. For the proposed model validation and experimental analysis, we have used 15 days of external environmental data collected in Jeju, South Korea. Proposed optimization scheme results are compared with a baseline scheme. Comparative analysis of experimental results shows that our proposed model maintains desired indoor environment for maximizing crop production with 26.56% reduced energy consumption than the baseline scheme. Furthermore proposed model achieve a 27.76% cost reduction when compared to the baseline scheme. Better optimization results of the proposed scheme give us the confidence to further investigate its effectiveness in a real environment for achieving improved energy efficiency.

Energy management in residential buildings has grabbed the attention of many scientists for the last few years due to the fact that the residential sector consumes the highest amount of total energy produced by different energy resources. To manage the energy in residential buildings effectively, an efficient energy control system is required, capable of decreasing the total energy consumption without compromising the user-preferred environment inside the building. In the literature, many approaches have been proposed to achieve the goals of minimizing the energy consumption and maximizing the user preferred comfort by keeping different parameters under consideration, but all these methods face some problems in resolving the issue properly. The bat algorithm is one of the most recently introduced optimization approaches that has drawn the attention of researchers to apply it for solving different types of optimization problems. In this paper, the bat algorithm is applied for energy optimization in residential buildings, which is one of the most focused optimization problems in recent years. Three environmental parameters, namely temperature, illumination and air quality are bat algorithm inputs and optimized values of these parameters are the outputs. The error difference between the environmental parameters and optimized parameters are inputs of the fuzzy controllers which give energy as output which in turn change the status of the concerned actuators. It is proven from the experimental results that the proposed approach has been effectively successful in managing the whole energy consumption management system.

Internet of Things (IoT) is considered as one of the future disruptive technologies, which has the potential to bring positive change in human lifestyle and uplift living standards. Many IoT-based applications have been designed in various fields, e.g., security, health, education, manufacturing, transportation, etc. IoT has transformed conventional homes into Smart homes. By attaching small IoT devices to various appliances, we cannot only monitor but also control indoor environment as per user demand. Intelligent IoT devices can also be used for optimal energy utilization by operating the associated equipment only when it is needed. In this paper, we have proposed a Hidden Markov Model based algorithm to predict energy consumption in Korean residential buildings using data collected through smart meters. We have used energy consumption data collected from four multi-storied buildings located in Seoul, South Korea for model validation and results analysis. Proposed model prediction results are compared with three well-known prediction algorithms i.e., Support Vector Machine (SVM), Artificial Neural Network (ANN) and Classification and Regression Trees (CART). Comparative analysis shows that our proposed model achieves 2.96 % better than ANN results in terms of root mean square error metric, 6.09 % better than SVM and 9.03 % better than CART results. To further establish and validate prediction results of our proposed model, we have performed temporal granularity analysis. For this purpose, we have evaluated our proposed model for hourly, daily and weekly data aggregation. Prediction accuracy in terms of root mean square error metric for hourly, daily and weekly data is 2.62, 1.54 and 0.46, respectively. This shows that our model prediction accuracy improves for coarse grain data. Higher prediction accuracy gives us confidence to further explore its application in building control systems for achieving better energy efficiency.

Citation analysis-based systems are premised on assuming that all citations are equally important. The scientific community argues that a citation may hold divergent reasons and thus, should not be treated at par. In this regard, a plethora of existing studies classifies citations for varying reasons. Presently, the community has a propensity toward binary citation classification with the notion of contemplating only important reasons while employing quantitative analysis-based measures. We argue that outcomes yielded by the contemporary state-of-the-art models cannot be deemed ideal as the plethora of them has been evaluated on a data set with minimal number of instances due to which the outcomes cannot be generalized. The scope of results from such approaches is restricted to a single domain only which may exhibit entirely different behavior for the different data sets. Most of the studies are ruled by the content based features evaluated by harnessing traditional classification models like Support Vector Machine (SVM), and random forest (RF), while an inconsiderable number of studies employ metadata which holds the potential to serve as a quintessential indicator to tackle meaningful citations. In this study, we introduce Multilayer perceptron artificial neural network (MLP-ANN) binary citation classifier, which exploits the best combinations of features formed using both sources. We also introduce a new benchmark data set from the electrical engineering domain which is consolidated with two existing benchmark data sets for model evaluation. The outcomes reveal that the results produced by the proposed MLP model outperform the contemporary models achieving a precision of 0.92.

Without a well-defined energy management plan, achieving meaningful improvements in human lifestyle becomes challenging. Adequate energy resources are essential for development, but they are both limited and costly. In the literature, several solutions have been proposed for energy management but they either minimize energy consumption or improve the occupant’s comfort index. The energy management problem is a multi-objective problem where the user wants to reduce energy consumption while keeping the occupant’s comfort index intact. To address the multi-objective problem this paper proposed an energy control system for a green environment called PMC (Power Management and Control). The system is based on hybrid energy optimization, energy prediction, and multi-preprocessing. The combination of GA (Genetic Algorithm) and PSO (Particle Swarm Optimization) is performed to make a fusion methodology to improve the occupant comfort index (OCI) and decrease energy utilization. The proposed framework gives a better OCI when compared with its counterparts, the Ant Bee Colony Knowledge Base framework (ABCKB), GA-based prediction framework (GAP), Hybrid Prediction with Single Optimization framework (SOHP), and PSO-based power consumption framework. Compared with the existing AEO framework, the PMC gives practically the same OCI but consumes less energy. The PMC framework additionally accomplished the ideal OCI (i-e 1) when compared with the existing model, FA–GA (i-e 0.98). The PMC model consumed less energy as compared to existing models such as the ABCKB, GAP, PSO, and AEO. The PMC model consumed a little bit more energy than the SOHP but provided a better OCI. The comparative outcomes show the capability of the PMC framework to reduce energy utilization and improve the OCI. Unlike other existing methodologies except for the AEO framework, the PMC technique is additionally confirmed through a simulation by controlling the indoor environment using actuators, such as fan, light, AC, and boiler.