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In a smart home environment, multiple users can access a single smart device simultaneously. Moreover, these multiple users may have conflicting demands at a time; that is, one user’s demands differ from another for the same device based on the role of users and environmental factors. Therefore, existing single-user access control systems cannot handle such conflicting and dynamically changing demands, considering both roles and environmental factors in the multi-user smart home environment. Considering this issue, we proposed a Smart Access Control and Authorization framework (SAAC). It is a multi-user access control solution that has four modules, namely, a user interaction module, a backend server module, a policy manager module, and a policy execution module. The user interaction module collects user data and resource policies, which are processed by the backend server and forwarded to the policy manager. The policy manager resolves conflicts and generates final policies, which are stored in the backend server for enforcement by the policy execution module. The finalized policies are shared with the backend server module and saved there till needed for execution by the policy execution module to enforce the access control decision. We have implemented a proof of concept of the proposed framework on VS Code using the Casbin library. The performance evaluation results show our framework’s effectiveness and efficiency with lower computational complexity requirements than existing methods. Finally, we performed a security analysis of the proposed model based on the STRIDE model that confirms its robustness against access control attacks.

The smart home’s functional requirements should include the capability to differentiate between various user categories, such as gender and voice recognition. The data-driven Internet of Things (IoT) can present challenges for the elderly and people with disabilities, but voice recognition technology could offer an effective solution. In addition, developing an accurate gender prediction model for voice recognition is still challenging due to the large time variation and randomness. Therefore, we propose gender classification and detection models based on voice frequency using Convolutional Neural Networks (CNN) with ResNet50 and ResNet101 architectures to enhance smart home functionality. We also introduce an algorithm for converting voice frequencies into images to speed up the recognition and detection processes. The research method involves converting voice frequencies into images to expedite the recognition and detection processes. The CNN models were trained and tested with various learning rates using audio datasets. Performance was evaluated through simulations that measured training accuracy, validation accuracy, recall, precision, and F1 scores. The simulation results show high training accuracy: ResNet50 achieved 99.67% and ResNet101 achieved 99.82%. The validation accuracy of the models also exceeded the accuracy of traditional CNN models in previous studies. The simulation results based on recall, precision, and F1 score for each proposed model are 99.3%, 100%, and 99.65%, respectively. Finally, we successfully used the ResNet50 model to create a low-latency smart home prototype. Thus, this paper significantly contributes to the practical applications of voice-based gender recognition in smart home environments with high accuracy and efficiency in detection.

Network packet sniffing is one of the techniques that is widely used in the network and cyber security fields. However, sniffing can also be used as a malicious technique that allows threat actors to intercept and capture data flow to collect various information within the victim network. Where the wireless network environment can be vulnerable to sniffing vulnerabilities attacks due to the broadcasting function of Wi-Fi network. Wi-Fi access point devices can often be compromised, and critical information is leaked through sniffing attacks. Moreover, since sniffing is usually one of passive attacks, it is very challenging to detect sniffing activity in the network completely. The primary aim of this research is to contribute to enhancing the security of Internet of Things (IoT) home Wi-Fi systems. This is achieved by applying ensemble machine learning technology with sniffing detection methods using a Network Monitoring System (NMS) to effectively identify and mitigate potential sniffing behaviour within the IoT home Wi-Fi environment. Ultimately, this research will prove whether it is possible to precisely detect abnormal sniffing in a smart home Wi-Fi environment using machine learning techniques.

Smart homes are the most adaptable and utilized application of the IoT. Smart homes enable end users to define and control the automation remotely by defining automation rules. The automation rules interaction results in the defined tasks and activities but also delivers unwanted interactions, which show adverse effects. Their interactions may consist of chained and covert rules interaction, which are hard to trace and identify. Attackers’ malicious apps installed in smart homes may leverage rules’ adverse effects to compromise smart home security and harm users, such as opening the window at night or when no one is home. To address such issues, we developed an ontology of smart home automation rules to identify such interactions. Prior works failed to provide the complete classification and could not identify all possible rule conflicts. In this work, we proposed the classification of automation rule interactions into five categories and recognized the potential rule conflicts. In addition, the ontology can examine rules interactions against the defined safety policies, providing an extra layer of security. The ontology will aid the designers in better understanding and developing a robust security mechanism at the design phase to resist rules interaction conflicts and their adverse effects. We examine this work on the thirty-five rules formed in the light of prior work test cases and validation. The results show that the proposed work is promising and can efficiently identify all the rule interaction conflicts in the smart home.

Energy harvesting and Internet of Things (IoT) technology is being integrated with home appliances. IoT devices could harvest energy from alternative energy sources to convert the available energy from home appliances into electrical energy. Smart home energy management systems face isolated scheduling horizons and unreliable inputs. In this research, an IoT Heterogeneous Energy Harvesting (IHEH) technique has been proposed for residential home energy management to operate the smart home appliances which consists of four layers, namely, the Energy Harvesting Layer (EHL), the Control and Sensing Layer (CSL), the Application Layer (AL), and the Information Processing Layer (IPL). The proposed technique keeps track of the distribution of several types of energy, including thermal, piezoelectric, and light energy. The overall efficiency of the proposed method is 90% per day. The proposed IHEH technique yields more power and increases the lifetime of batteries.

The development of smart sensors and appliances can provide a lot of services. Nevertheless, the act of aggregating data containing sensitive information related to privacy in a single location poses significant issues. Such information can be misused by a malicious attacker. Also, some previous studies attempted to apply privacy mechanisms, but they decreased data utility. In this paper, we propose privacy protection mechanisms to preserve privacy-sensitive sensor data generated in a smart home. We leverage Rényi differential privacy (RDP) to preserve privacy. However, the preliminary result showed that using only RDP still significantly decreases the utility of data. Thus, a novel scheme called feature merging anonymization (FMA) is proposed to preserve privacy while maintaining data utility by merging feature dataframes of the same activities from other homes. Also, the expected trade-off is defined so that data utility should be greater than the privacy preserved. To evaluate the proposed techniques, we define privacy preservation and data utility as inverse accuracy of person identification (PI) and accuracy of activity recognition (AR), respectively. We trained the AR and PI models for two cases with and without FMA, using 2 smart-home open datasets i.e. the HIS and Toyota dataset. As a result, we could lower the accuracy of PI in the HIS and Toyota dataset to 73.85% and 41.18% with FMA respectively compared to 100% without FMA, while maintaining the accuracy of AR at 94.62% and 87.3% with FMA compared to 98.58% and 89.28% without FMA in the HIS and Toyota dataset, respectively. Another experiment was conducted to explore the feasibility of implementing FMA in a local server by partially merging frames of the original activity with frames of other activities at different merging ratios. The results show that the local server can still satisfy the expected trade-off at some ratios.

Electricity theft is a pervasive issue with economic implications that necessitate innovative approaches for its detection, given the critical challenge of limited labeled data. However, connecting smart home devices introduces numerous vectors for electricity theft. Therefore, this study introduces an innovative approach to detecting electricity theft in smart homes, leveraging knowledge-based, fine-grained, time-series appliance benign and anomalous consumption patterns. We simulated five attack classes and extended our model’s detection capabilities to unknown anomalies across residential settings by segmenting the anonymized data into three different home categories. We validated our experiment using simulated and real building attack data. Extreme Gradient Boost (XGB), Random Forest, and Multilayer Perceptron (MLP) outperform the legacy unsupervised model (LUM), which included MLP-Autoencoder (AE), 1D-CONV-AE, and Isolation Forest (RF). XGB had the highest average AUC scores of 98.69% and 98.74% for simulated and real attack detection, respectively, followed by RF at 96.76% and 97.07%, respectively, across all homes, indicating the robustness of our model in detecting benign and anomalous appliance consumption patterns. This study contributes to the academic discourse in the field and offers practical solutions to energy providers and stakeholders in the smart home industry.

About 11 million tons of retired batteries are expected to be produced globally by 2030, of which a huge percentage will proceed from individual owners who charge their vehicles at home. To overcome this issue, multiple studies focused on the viability of reusing mobility batteries for different applications. However, this kind of analysis in individual dwellings are still scarce. This paper aims at filling this gap by developing a novel optimization methodology for design of photovoltaic arrays in domestic installations considering second-purpose batteries from mobility. In particular, we aim at analyzing the effect of using batteries from vehicles when they are no longer valid for mobility. Thus, we consider the problem in which these batteries are used for stationary applications at home, observing their effects on the photovoltaic design as well as other aspects like total project cost and self-consumption rates. To this end, a planning strategy is developed including suitable models of photovoltaic units, flexible loads and electrical vehicle. The developed methodology is tested on a benchmark prosumer environment and different scenarios are profusely studied. The results obtained demonstrate that the use of second-purpose batteries seem profitable in smart homes, reducing the total cost by 15% and increasing the self-consumption rate by 20%. Moreover, other aspects like its effect on the final scheduling and the behavior under limited photovoltaic capacity are commented. Lastly, the importance of the developed methodology for future similar studies is highlighted.

The IPv4 address architecture has been declared ended finally due to the fast growth of the Internet of Things (IoT). IPv6 is becoming a next-generation communication protocol and provides all the requirements that the industry needs. A smart home is an emerging technological revolution in which IoT-enabled smart physical objects such as smart TVs, smart refrigerators, smart locks, etc. are linked to the Internet to make human life more comfortable. There are several resource-constrained smart devices interconnecting with 6LoWPAN to control the smart home remotely. The communication channels used by cellular communication are vulnerable and increase security threats especially related to authentication. A reliable and portable remote authentication method is critical for ensuring safe communication in the next-generation smart home environment. Recently, many authentication schemes have been proposed but adopt complex mathematical techniques or protocols that are viewed as heavyweights in the context of computation and communication costs. This research proposes a lightweight and reliable remote authentication mechanism for the next generation of IoT-based smart homes. Informal and formal security assessments through the AVISPA tool determine the robustness of our proposed scheme. Moreover, we implemented our authentication scheme on a Linux-based client-server network model by using Android programming. In addition, we compared our proposed scheme with existing schemes based on computation and communication costs. Results show that our proposed mechanism reduced computation costs by up to 54.03 % and reduced communication costs by up to 25.28 % related to existing schemes. So, our proposed scheme is better, more secure, and most suitable for smart home ecosystems.

Since the 20th century, there has been a wave of digitalization in homes and businesses worldwide, and smart homes have gradually become a high-profile business sector. However, behind fierce competition in the digital home market, the importance of user experience for companies to enhance their competitiveness as a business model for providing services to home users is often overlooked. Combining the industry’s basic theories and our own empirical research, this study expands the assessment perspective and assessment methods on the basis of the industry’s existing assessment methods and constructs a comprehensive assessment system that is both innovative and practical - the Smart Home Product Competitiveness Index (PCI). The index constructs a three-in-one smart home product competitiveness index assessment system for brand power, product power, and marketing power from the perspective of long-term user experience. Twelve representative enterprises were selected from the five categories of camps, covering three demand levels, six typical scenarios, and 17 mainstream products. This study shows that Internet enterprises are characterized as all-round. Traditional home appliance manufacturers, Internet enterprises, and communication carriers must strengthen the construction of product power. The competitiveness assessment method for digital home enterprises from the perspective of long-term user experience proposed in this study is effective and can provide a reference for subsequent related studies.

Internet of Things (IoT) technology is a complementary part of our style of life. IoT technology coexists with existing cellular communication for developing 5G. Both academic researchers and industrial householders pay attention to the benefits of IoT technology. However, the rapid growth of IoT networks suffers from some limitations, such as heavy traffic load, traffic congestion,... etc. In this paper, a parallel distributed smart home architecture is proposed to solve human-being problems and achieve welfare in society. The proposed network has a light traffic load and low traffic congestion to avoid a single-point failure. The architecture of the proposed smart home network involves connectivity, security, registration, protocols, and scenarios to update registration. Also, the paper presents a framework of state diagrams, processes, and algorithms for IoT smart home networks. This framework includes a design and implementation of a pilot model of IoT smart home domains to meet human beneficiaries. The model consists of six practical smart home network domains in both normal and urgent modes to proof of the concept. The pilot model is programmed based on three major aspects: objectives, processes with algorithms, and state diagrams. The novelty of the paper lies in the capability to implement a smart home architecture pilot model using simple development kits. The design and implementation of the practical IoT smart home networks are tested and measured using a CoolTerm sniffer tool. The results of measurement prove that the proposed model achieves light traffic profiles and low average data rates.

Owing to the strong protection of data privacy, federated learning (FL) has become a key method to achieve intelligent decision making in smart homes. However, under realistic conditions, such as differentiated requirements and heterogeneous service environments, FL in smart homes faces the problems of non-independent and identically distributed (non-IID) data and uneven computing power, which lead to the poor adaptability of global models. To address this issue, this study proposes a cluster FL architecture based on edge-cloud collaboration. Firstly, a Gaussian mixture model-based cluster FL is proposed to improve the model accuracy by clustering features on the FL dataset and ensuring an independent identical distribution of the data. Subsequently, a model training strategy based on edge-cloud collaboration is proposed to achieve the sharing of edge-cloud computing power by split learning, which provides sufficient computing power for model training. The simulation results show that the proposed architecture improves the accuracy of global models, meanwhile ensures the provision of normal network service.

Smart cities have seen a growing interest among governments, researchers, and industries. Smart cities use digital technologies to enhance the quality of life for residents while promoting sustainability and efficient resource management. By integrating various technologies such as Information and Communication Technologies (ICT), Artificial Intelligence (AI), and Internet of Things (IoT), smart cities can improve the delivery of public services, optimize transportation systems, reduce energy consumption, and enhance public safety, among other benefits. Smart cities focus on automating different disciplines, including smart environment, smart home, smart economy, smart mobility, and smart governance. As a result, multi-agent driven smart cities have received tremendous attention from the research community for obtaining intelligent solutions to complex problems in different disciplines by subdividing responsibilities into multiple agents and empowering agents through AI. In this regard, it is vital to explore the usage of multi-agent systems in different critical application areas of smart cities. In this paper, a detailed description of the multi-agent process for smart city application areas is provided, along with resources and future research directions. Four different application areas: smart home, smart governance, smart environment, and smart mobility are discussed in detail.

The rapid development of technology has led to the prevalence of smart homes as one of the most prominent intelligent devices. This research aims to identify the factors that significantly influence individuals’ intention to use smart homes and provide direction for efficient product positioning and updates. Drawing from the Technology Acceptance Model (TAM), Value-based Adoption Model (VAM), Innovation Diffusion Theory (IDT), and Maslow’s Hierarchy Needs Theory, a comprehensive research model is introduced. The model incorporates external factors such as perceived value (PV), price perception (PP), safety perception (SP), social influence (SI), self-esteem (SE), and visual aesthetics (VA), while self-innovation (SINN) is considered a moderator with a conditional effect between perceived usefulness (PU) and PV. The research model was validated with 405 samples, and exploratory factor analysis was conducted. Structural equation modeling (SEM) was used to analyze the results, which indicate that: 1). customers’ intention to use smart homes is significantly determined by PV and VA through PU and perceived ease of use (PEOU). 2). Additionally, SINN moderates the relationship between PV and PU negatively within a specific interval. 3). SE and SI have slight effects on PU or PEOU, while PP and SP have no direct effect. These empirical results provide valuable conclusions and implications for understanding suitable smart homes for customers and finding actionable directions for smart home development

The prediction of energy consumption plays a significant role in energy conservation and reducing the cost of power generation, to improve energy sustainability and economic stability. Current studies show an increased interest in the application of Machine Learning algorithms to forecast energy utilisation in smart homes. The performance of these Machine Learning algorithms is evaluated using accuracy algorithms. The process of manually selecting best-performing Machine Learning algorithms is still very challenging for data analysts and decision makers because the algorithms might not work well in a different use case or data-set. To address this, we propose a decision algorithm model using machine learning based data mining and picture fuzzy operators. First, Machine Learning algorithms are trained and tested to predict energy consumption of smart home appliances with respect to the weather information. Second, score values of Lasso Regression are used to understand the patterns and features of weather information for smart house micro-climate. We then propose a decision matrix using fuzzy operators to aggregate Machine Learning algorithms, prior to ranking using a score function. Finally, the electricity consumption of appliances as well as total energy consumed in the smart home is provided in Kilowatts (KW).

Currently, smart homes rely heavily on wireless sensor networks (WSNs), which typically consist of wireless sensor nodes with limited resources and are scattered throughout the network. This topology makes them vulnerable to packet sniffing, spoofing, and other malicious attacks, which can result in the leakage of private data collected by devices. Additionally, managing the device key for the entire system becomes difficult as more devices are added. Moreover, the centralization of current cloud service management in smart home systems poses a serious single-point-of-failure problem, and the private data of cloud outsourcing cannot receive strict privacy supervision, ultimately relying entirely on the trust of enterprises. To address these issues, this paper proposes using a consortium blockchain and InterPlanetary File System (IPFS) instead of the existing centralized structure. An improved pairing-free certificateless aggregated signature(CLAS) scheme ensures the security of message authentication and solves the device key management problem. Our scheme reduces the computational and communication overheads at the device side by 50% and 25%, respectively, compared with existing schemes in WSNs. The overall computational overhead is also reduced by 28.6%, making it more suitable for smart home scenarios. Additionally, we use an auditing method based on Merkle root hash verification to ensure the reliability of data storage in IPFS.

Powered by IoT technologies, smart home systems effectively support aging-in-place. However, they have some limitations in comprehensive event perception and timely appropriate action. Homecare robot systems have been proven to be effective in homecare task executions but still have significant technological challenges. To address this problem, this paper proposes a smart home system architecture integrating a mobile robot with better event perception and task execution performance. To support the proposed system architecture, an ontology of the smart home is built to address the data heterogeneity issue. Then an event perception method is built upon multi-data integration. To perform complex tasks, this work implements an improved genetic algorithm for task planning. Finally, simulations and physical experiments are conducted to validate the feasibility of the proposed system architecture.

Smart homes have been recently important sources for providing Activity of Daily Living (ADL) data about their residents. ADL data can be a great asset while analyzing residents’ behavior to provide residents with better and optimized services. A popular example is to analyze residents’ behavior to predict their future activities and optimize smart homes performance accordingly. This paper proposes a forecasting framework that utilizes ADL data to predict residents’ next activities in a smart home environment. Forecasting is performed via the conjunction of embedding algorithm to encode the data and Bidirectional Long Short-Term Memory (BiLSTM) deep neural networks to process the data. The proposed framework is evaluated over five real ADL datasets where the experiments show the outperformance of the proposed framework with accuracy scores ranging from 98.7% to 93.8%.

The rapid proliferation of embedded devices has led to the growth of the Internet of Things (IoT) with applications in numerous domains such as home automation, healthcare, education and agriculture. However, many of the connected devices particularly in smart homes are the target of attacks that try to exploit security vulnerabilities such as hard-coded passwords and insecure data transfer. Recent studies show that there is a considerable surge in the number of phishing attacks targeting smart homes during the COVID-19 pandemic. Moreover, many of the existing user authentication protocols in the literature incur additional computational overhead and need to be made more resilient to smart home targeted attacks. In this paper, we propose a novel lightweight and privacy-preserving remote user authentication protocol for securing smart home applications. Our approach is based on Photo Response Non-Uniformity (PRNU) to make our protocol resilient to smart home attacks such as smartphone capture attacks and phishing attacks. In addition, the lightweight nature of our solution is suitable for deployment on heterogeneous and resource constrained IoT devices. Besides, we leverage geometric secret sharing for establishing mutual authentication among the participating entities. We validate the security of the proposed protocol using the AVISPA formal verification tool and prototype it on a Raspberry Pi to analyze the power consumption. Finally, a comparison with existing schemes reveals that our scheme incurs a 20% reduction in communication overhead on smart devices. Furthermore, our proposed scheme is usable as it absolves users from memorizing passwords and carrying smart cards.

Smart Energy Applications are particularly impacting, especially due to energy resource scarcity and its high associated costs. Smart management of energy consumption derives both from the user lifestyle, in terms of efficient and responsible behaviors, and from automatic algorithms that control and counteract energy waste and inefficient management. Focusing the attention on the latter, the development of methodologies and well-working techniques to monitor and optimize consumption often requires an important effort in long measurement campaigns to get raw data to work with. Whenever this should be too much expensive or proper instrumentation is unavailable, public datasets could solve the problem. The current literature review on the dataset availability showed a large presence of information, especially related to electrical energy consumption. Nevertheless, several limitations affect them, from the low number of calculated electrical parameters (i.e. 4-5 in most cases) to short analysis periods, passing by the lack of detailed frequency domain information or poor consumption habit transitions analysis. Accordingly, this work aims to overcome current dataset limitations, by proposing a real-measurement based simulated dataset, extracting more than 400 discriminative electrical parameters on 36 different home appliances, discussing preliminary acquisition set-ups, simulation process, extracted electrical parameters and examples of applicability to smart energy applications. To provide a data quality index, a validation procedure has also been carried out, showing how simulated data match real acquisition with a reference measurement instrument. The produced dataset is available for downloading and analysis in public free access and its repository link is provided in the reference section.

With the continuous development of Internet of Things (IoT) technology, research on smart home environments is being conducted by many researchers. In smart home environments, home users can remotely access and control a variety of home devices such as smart curtains, lights, and speakers placed throughout the house. Despite providing convenient services, including home monitoring, temperature management, and daily work assistance, smart homes can be vulnerable to malicious attacks because all messages are transmitted over insecure channels. Moreover, home devices can be a target for device capture attacks since they are placed in physically accessible locations. Therefore, a secure authentication and key agreement scheme is required to prevent such security problems. In 2021, Zou et al. proposed a two-factor-based authentication and key agreement scheme using elliptic curve cryptography (ECC) in smart home environments. They claimed that their scheme provides user anonymity and forward secrecy. However, we prove that their scheme suffers from forgery, ephemeral secret leakage, and session key disclosure attacks. To overcome the security vulnerabilities of Zou et al.’s scheme and provide home users with secure communication in smart home environments, we propose a secure user authentication scheme using physical unclonable functions (PUF). We utilize Real-or-Random (ROR) model and Burrows-Abadi-Needham (BAN) logic to verify the session key security and mutual authentication of the proposed scheme, respectively. Furthermore, we use the Automated Validation of Internet Security Protocols and Applications (AVISPA) tool to simulate the resistance of our scheme to security attacks. After that, we analyze and compare the communication costs, computational consumption, and security functionalities along with related schemes.

Although smart home has received wide attention in recent years, numerous scholars focus more on energy optimization strategy than energy dispatch hardware device (named energy router). Meanwhile, this energy router should have several features, i.e., high renewable energy utilization, energy multi-port and low volume. Thus, this paper designs a nine-port energy router regarding smart home and proposes a multimode hierarchical management strategy for this energy router. First, for the multi-port demand of wind, solar, storage and utilization, this paper presents a nine-port energy router to improve the renewable energy consumption and power supply flexibility. In addition, to reduce the volume of the energy router, a non-isolated AC/DC hybrid topology is constructed through embedding the integrated power electronic converters, which achieves the miniaturization of the energy router. In order to improve the renewable energy utilization rate, the decentralized module control is proposed for the components of energy router to provide the voltage and frequency support for system, and realizes the power sharing of distributed generations (DGs). Furthermore, the power exchange control with three-mode switching is proposed to guarantee the global energy flow balance under complex conditions. Eventually, the feasibility of the energy router is verified by the simulation and experiments. [ABSTRACT FROM AUTHOR]

The advanced metering infrastructure in modern networked smart homes brings various advantages, such as multiple pricing and energy scheduling. However, smart homes are vulnerable to many cyberattacks, and the most striking one is energy theft. Researchers have developed many countermeasures, fostered by advanced machine learning (ML) techniques. Nevertheless, recent advances are not robust enough in practice, partially due to the vulnerabilities of employed ML algorithms. Given a group of smart homes, in this article, we present a covert electricity theft strategy through mimicking normal consumption patterns and compromising neighboring meters concurrently. Such attack is almost impossible to be detected by existing solutions as the manipulated data have little deviation against honest usage records. To address this threat, we initially identify and define two levels of consumption deviations: home-level and interpersonal-level, respectively. Then, we design a feature extraction scheme that can capture the correlation between attacks and honest customers. Finally, we develop a new deep learning-based detection model. Extensive experiments based on real-world datasets show that the presented attack could evade existing mainstream detectors but still gain high profits. In addition, the proposed countermeasure outperforms state-of-the-art detection methods. [ABSTRACT FROM AUTHOR]

Sensor communication in the smart home environment is still in its infancy as the information exchange between sensors is vulnerable to security threats. Many traditional solutions use single-layer or multi-layer (i.e., onion routing protocol) encryption/decryption algorithms. But, in the traditional onion routing protocol, if the directory server is compromised, it may not track the malicious onion nodes within the onion network. It questioned the path anonymity of the onion routing protocol. Motivated by this, we proposed a blockchain and onion routing (OR)-based secure and trusted framework in the paper. The anonymity of the proposed OR network is maintained by storing and tracking the onion nodes threshold values through the blockchain network. A long short-term memory (LSTM) model is also utilized to classify the sensors data requests as malicious and non-malicious. The performance of the proposed system is evaluated with different performance metrics such as F1 score and accuracy. The LSTM model significantly improves the initial detection rate of malicious data requests from smart home sensors. Over these benefits, we considered the entire communication via 6G channel, reducing the overall communication latency. Additionally, the OR network is simulated over the shadow simulator to analyze the OR network’s performance considering parameters such as packet delivery ratio and malicious onion node detection rate. [ABSTRACT FROM AUTHOR]

The unprecedented proliferation of smart home together with wireless sensor networks has enriched human’s daily life. Inevitably, the smart home without the guard of authentication mechanism is bound to bring a series of security issues. Hence, a great number of authentication protocols have been designed to verify the user’s identity and ensure that the data be accessed with authorization in smart home. Recently, (M. Shuai et al., 2019) (Computers & Security, 2019) proposed a two-factor anonymous authentication scheme for smart home and presented that their scheme can be immune to various attacks. However, by careful analysis, we found that (M. Shuai et al.’s, 2019) scheme needed further improvements in session key forward secrecy and against: type-I node capture attack and node impersonation attack. As a going step to this direction, here, we first design a robust two-factor user authentication also for smart home based on the elliptic curve cryptography. Second, we give security proofs to demonstrate that the construction of the proposed scheme can be of user anonymity, session key forward secrecy, and resist all attacks shown in this article. Third, performance comparisons covered in storage/network communication/computation costs are carried out to indicate that our scheme can be comparable to those newly designed schemes, especially, the user and gateway in our scheme need only 352 and 320 bits, respectively. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) networks are obstructed by security vulnerabilities that hackers can leverage to operate intrusions in many environments, such as smart homes, smart factories, and smart healthcare systems. To overcome this obstruction, researchers have come up with different intrusion detection and prevention systems (IDPSs). Out of all the implemented technologies, Machine Learning (ML) has emerged as the most promising approach. Therefore, to improve the detection accuracy, most ML-based intrusion detection solutions focus only on investigating appropriate ML algorithms. Yet, the limitations in terms of detection accuracy in various attacks are often caused by lack of appropriate detection features. Moreover, the majority of the previous works lack intrusion prevention mechanisms and deployment architectures. Thus, in this research, we study the properties of different smart home security attacks and the quality of the features that can be brought out and employed in ML algorithms to detect each of these attacks efficiently. Furthermore, this research proposes effective intrusion prevention mechanisms and a Software-Defined Networking (SDN) based deployment architecture of the IDPSs within home networks. Experimental evaluations of the proposed solution are provided using different feature sets and various ML models. The contributions and advancements discussed in this paper will upgrade future research and engineering works on IDPSs for IoT. [ABSTRACT FROM AUTHOR]

The millimeter-wave (mmWave) radar technology has attracted significant attention because it is susceptible to environmental lighting, wall shielding, and privacy concern. This article proposes a novel noninvasive human activity recognition system using a mmWave radar. The proposed framework first transforms mmWave signals into point clouds. Generally speaking, it consists of four major components: denosing, enhanced voxelization, data augmentation, and dual-view machine learning to lead to accurate and efficient human activity recognition. The proposed new methodology considers the spatial–temporal point clouds in physical environments through a modified voxelization approach, enriches the sparse data based on the symmetry property of radar rotations, and learns the activity using a dual-view convolutional neural network. To evaluate the performance of the proposed learning models, a dataset involving seven different activities has been established using a mmWave radar platform. The experimental results have demonstrated that the proposed system can achieve 97.61% and 98% accuracies during the tests of fall detection and activity classification, respectively. In comparison, the proposed scheme greatly outperforms four other conventional machine learning schemes in terms of the overall accuracy. [ABSTRACT FROM AUTHOR]

A smart home, which is an extension of a traditional home, is equipped with ubiquitous sensors embedded in consumer appliances, connected via sensing technologies such as radio frequency, and communicating through the internet to gather information. They often communicate using appropriate protocols such as MQTT, CoAP, or HTTP to ensure the smooth transmission of data used by a variety of smart home services. Human activity recognition is one of the services provided by this IoT method of data collection from the sensor network when activated by residents. The obtained data can be subjected to extensive preprocessing and feature extraction tasks before being learned using appropriate machine learning or deep learning algorithms to generate a model capable of managing human activities more effectively. This technique is challenged by the nature of IoT technology and perceived data, as well as by human differences, which necessitated additional processing tasks to select significant features for the learning algorithms. In this work, we focus our review on activity recognition implementation strategies by examining various sensors and sensing technologies used to collect useful data from IoT devices, reviewing preprocessing and feature extraction techniques, as well as classification algorithms used to recognize human activities in smart homes. Many relevant works were examined and their achievements compared.The research demonstrates that IoT sensor technology for recognizing human activity in a smart home is practically feasible and efficient, even with individual differences in the smart home. However, it was discovered to be susceptible to issues such as insufficient and imbalanced data, annotation scarcity, and computational complexity. Finally, the study suggests that associating sensor data from the Internet of Things with numerous labels of activities based on time can help decrease computing overhead and improve activity recognition. [ABSTRACT FROM AUTHOR]

Appliance load monitoring in smart homes has been gaining importance due to its significant advantages in achieving an energy efficient smart grid. The methods to manage such processes can be classified into hardware-based methods, including intrusive load monitoring (ILM) and software-based methods referring to non-intrusive load monitoring (NILM). ILM is based on low-end meter devices attached to home appliances in opposition to NILM techniques, where only a single point of sensing is needed. Although ILM solutions can be relatively expensive, they provide higher efficiency and reliability than NILMs. Moreover, future solutions are expected to be hybrid, combining the benefits of NILM along with individual power measurement by smart plugs and smart appliances. This paper proposes a novel ILM approach for load monitoring that aims to develop an activity recognition system based on IoT architecture. The proposed IoT architecture consists of the appliances layer, perception layer, communication network layer, middleware layer, and application layer. The main function of the appliance recognition module is to label sensor data and allow the implementation of different home applications. Three different classifier models are tested using real data from the UK-DALE dataset: feed-forward neural network (FFNN), long short-term memory (LSTM), and support vector machine (SVM). The developed activities of daily living (ADL) algorithm maps each ADL to a set of criteria depending on the appliance used. The features are extracted according to the consumption in Watt-hours and the times where they are switched on. In the FFNN and the LSTM networks, the accuracy is above 0.9 while around 0.8 for the SVM network. Other experiments are performed to evaluate the classifier model using a new test set. A sensitivity analysis is also carried out to study the impact of the group size on the classifier accuracy.

Hand-gesture based control has enormous potential both theoretically and for practical applications due to its convenience and intuitiveness. This study presents a real-time interactive control system for household appliances. The interactive control system allowing wireless control of household appliances using a combination of 11 hand gestures and 2 waving motions is tested on hundreds of samples. It is implemented using a regular personal computer (PC) and existing digital single processing (DSP) platforms. The evaluation results show that the system performs efficiently reaching an accuracy recognition rate of 91% and spending around 30 seconds to complete the control operation for household appliances. The contributions of this work are both academic (1) successful demonstration of the integration of algorithms for solving image detection, processing, and pattern recognition, and practical (2) showing its feasibility and using commonly available hardware and software configurations for practical uses, and finally (3) establishing a mechanism for intuitively interactive control system that facilitates smart living.

Residential energy trading systems (RETS) enable homeowners with distributed energy resources (DERs) to participate in virtualized energy markets that have the potential to reduce the peak demand of residential communities. Blockchains are key enablers of RETS, by virtue of providing a decentralized, self-governed network that mitigates concerns regarding privacy and transparency. However, more real-world case studies are needed to evaluate the techno-economic viability of blockchain-based RETS to improve their positive uptake. Thus, this article develops a permissioned blockchain-based RETS, which enables homeowners to select bidding strategies that consider the individual preferences of their DERs, and further evaluates the impact of the bidding strategies on reducing the peak demand of the community. The proposed system is implemented on the permissioned Hyperledger Fabric platform, where a decentralized ledger is used to store all energy bids, and a smart contract is used to execute a double auction mechanism and dispatch the homeowner DERs. The proposed system is validated by conducting simulations on a 8-home community using real-world data, and also by deploying the system to a Canadian microgrid, where the smart contract execution time is benchmarked. Simulation results demonstrate the efficacy of the proposed system by achieving a peak demand reduction of up to 48 kW (62%), which leads to an average savings of $1.02 M for the distribution system operator by avoiding transformer upgrades. Also, the simulation results show that the execution time of the proposed smart contract is 17.12 seconds across 12 nodes, which is sufficient for RETS.

The advances in the Internet of Things (IoT) provide several chances to develop a variety of innovations supporting smart home users in several industries including healthcare, energy management, etc. Ubiquitous support by intelligent appliances at modern homes, which constantly work to gather information can help us to solve everyday issues. In this article, we present a comparative study of recent advances in smart home development. The study aims to present the main trends in this field. During the analysis of the research reports and patents, we identify the propositions that constitute the main research streams. Through extensive analysis, we provide an outlook on the wide spectrum of the proposed solutions. We also analyze the main market to present which publishers are leading with the innovative science in this field. We also show the leaders of science and technology in the World. Finally, we define the ratio of the developments and outline the next stage of the development in the smart home industry.

Mobile edge computing which provides computing capabilities at the edge of the radio access network can help smart home reduce response time. However, the limited computing capacity of edge servers is the bottlenecks for the development of edge computing. We integrate cloud computing and edge computing in the Internet of Things to expand the capabilities. Nevertheless, the cost of leasing computing resources has been seldom considered. In this paper, we study the joint transmission power and resource allocation to minimize the users’ overhead which is measured by the sum of energy consumption and cost leasing servers. We formulate the problem as a Mixed Integer Linear Programming which is NP-hard and present the Branch-and-Bound to solve it. Due to high complexity, a learning method is proposed to accelerate the algorithm. The branching policy can be learned to reduce the time-cost of the Branch-and-Bound algorithm. Simulation results show our approach can improve the Branch-and-Bound efficiency and performs closely to the traditional branching scheme.

The emergence of the Internet of Things concept has provided a great vision for the technological future, intending to enable the extraction and comprehension of information from the environment around us, making use of the interaction and cooperation between several technological devices. The example of Smart Homes, in particular, aims to integrate these devices into households, enabling the automation of tasks previously performed by humans, to simplify their daily lives and create a more comfortable environment. However, many of these devices fail to keep their promise, since they were not developed taking into account the frequent change of habits and tastes of the user, being necessary reprogramming of the device to follow the new behaviors. Taking this problem into account, this article presents the design and end-to-end implementation of a voice-activated smart home controller for intelligent devices, deployed in a real environment and validated in an experimental setup of motorized blinds. The architecture of the proposed solution integrates evolvable intelligence with the use of an Online Learning framework, enabling it to automatically adapt to the user’s habits and behavioral patterns. The results obtained from the various evaluation tests provide a validation of the operation and usefulness of the developed system. The main contributions of this work are: I) design of a smart home controller’s architecture; II) end-to-end implementation of a smart home controller and respective guidelines; III) open-source dataset of user behavior from the smart blinds scenario; IV) comparison between Online and Offline Learning approaches.

The IoT, or Internet of Things has been a major talking point amongst technology enthusiasts in recent years. The internet of thing (IoT) has been emerged and evolved rapidly, making the world’s fabric around us smarter and more responsive. The smart home uses one such transformation of IoT, which seems to be the wave of the future. However, with the increasing wide adoption of IoT, data security, and privacy concerns about how our data is collected and shared with others, has also risen. To solve these challenges, an approach to data privacy and security in a smart home using blockchain technology is proposed in this paper. We propose authentication scheme that combines attribute-based access control with smart contracts and edge computing to create a secure framework for IoT devices in smart home systems. The edge server adds scalability to the system by offloading heavy processing activities and using a differential privacy method to aggregate data to the cloud securely and privately. We present several aspects of testing and implementing smart contracts, the differential private stochastic gradient descent algorithm, and system architecture and design. We demonstrate the efficacy of our proposed system by fully examining its security and privacy goals in terms of confidentiality, integrity, and availability. Our framework achieves desired security and privacy goals and is resilient against modification, DoS attacks, data mining and linkage attacks. Finally, we undertake a performance evaluation to demonstrate the proposed scheme’s feasibility and efficiency.

Enhancing distribution system resilience is a new challenge for researchers. Supplying distribution loads, especially the residential customers and high-priority loads after disasters, is vital for this purpose. In this paper, the internal combustion engine (ICE) vehicles are firstly introduced as valuable backup energy sources in the aftermath of disasters and the use of this technology is explained. Then, the improvement of distribution system resilience is investigated through supplying smart residential customers and injecting extra power to the main grid. In this method, it is assumed that the infrastructure of distribution system is partially damaged (common cases) and it can be restored in less than one day. The extra power of residential customer can be delivered to other loads. A novel formulation for increasing the injected power of the smart home to the main grid using ICE vehicles is proposed. Moreover, the maximum backup duration in case of extensive damages in the distribution system is calculated for some commercial ICE vehicles. In this case, the smart home cannot deliver extra energy to the main grid because of its survivability. Simulation results demonstrate the effectiveness of the proposed method for increasing backup power during power outages. It is also shown that ICE vehicles can supply residential customers for a reasonable amount of time during a power outage.

A well-designed demand response (DR) program is essential in smart home to optimize energy usage according to user preferences. In this study, we proposed a multiobjective reinforcement learning (MORL) algorithm to design a DR program. The proposed approach improved conventional algorithms by mitigating the effect of the change in user preferences and addressed the uncertainty induced by future price and renewable energy generation. Because two Q-tables were used, the proposed algorithm simultaneously considers electricity cost and user dissatisfaction; when user preference changes, the proposed MORL algorithm uses the previous experience to customize appliances’ scheduling and swiftly achieve the best objective value. The generalizability of the proposed algorithm is high. Therefore, the algorithm can be implemented in a smart home equipped with an energy storage system, renewable energy source, and various types of appliances such as inflexible, time-flexible, and power-flexible ones. Numerical analysis using real-world data revealed that in case of price and renewable uncertainty, the proposed approach can deliver excellent performance after a change of user preference; it achieved 8.44% cost reduction as compared with mixed-integer nonlinear programming based DR while increasing the dissatisfaction level only by 1.37% on average.

Heating, ventilation, and air-conditioning (HVAC) systems use the most electricity of any household appliance in residential communities. HVAC system modeling facilitates the study of demand response (DR) at both the residential and power system levels. In this article, the equivalent thermal model of a reference house is proposed. Parameters for the reference house were determined based on the systematic study of experimental data obtained from fully instrumented field demonstrators. Energy storage capacity of HVAC systems is calculated and an equivalent state-of-charge is defined. The uniformity between HVAC systems and battery energy storage system is demonstrated by DR control. The aggregated HVAC load model is based on the reference house and considers a realistic distribution of HVAC parameters derived from one of the largest smart grid field demonstrators in rural America. A sequential DR scheme as part of a virtual power plant control is proposed to reduce both ramping rate and peak power at the aggregated level, while maintaining human comfort according to ASHRAE standards. [ABSTRACT FROM AUTHOR]

This article proposesa novel federated reinforcement learning (FRL) approach for the energy management of multiple smart homes with home appliances, a solar photovoltaic system, and an energy storage system. The novelty of the proposed FRL approach lies in the development of a distributed deep reinforcement learning (DRL) model that consists of local home energy management systems (LHEMSs) and a global server (GS). Using energy consumption data, DRL agents for LHEMSs construct and upload their local models to the GS. Then, the GS aggregates the local models to update a global model for LHEMSs and broadcasts it to the DRL agents. Finally, the DRL agents replace the previous local models with the global model and iteratively reconstruct their local models. Simulation results obtained under heterogeneous home environments indicate the advantage of the proposed approach in terms of convergence speed, appliance energy consumption, and number of agents. [ABSTRACT FROM AUTHOR]

Internet of energy based smart power grids demonstrate high in-feed from renewable energy resources (RESs) and lofty out-feed to energy consumers. Uncertainties evolved by incorporating RESs and time-varying energy consumption present immense challenges to the optimal control of smart power networks. To deal with these challenges, it is important to make the system deterministic by making time-ahead prediction and scheduling of power supply and demand. The present work confers a model of a co-scheduling framework, organizing cost-efficient activation of energy supply entities (ESEs) and load demands in a home area power network (HAPN). It integrates roof-top photovoltaic (PV) panels, diesel energy generator (DE), energy storage devices (ESDs), and smart load demands (SLDs) along with grid-supplied power. The scheduling model is based on mixed-integer linear programming (MILP) framework, incorporates a “min-max” optimization algorithm that reduces the daily energy bills, maintains high comfort level for the energy consumers, and increases the self-sufficiency of the home. The proposed strategy exploits the flexibility in dynamic energy price signals and SLDs of various classes, providing day-ahead cost-optimal scheduling decisions for incorporated energy entities. A linearized component-based model is developed, considering inefficiencies, taking various power phase modes of the SLDs along with the cost of operation, maintenance, and degradation of the equipment. A case study based on numerical analysis determines the particular features of the proposed HAPN model. Simulation results demonstrate the real prospect of our implemented strategy, utilizing a cost-effective optimal blend of distinct energy entities in a smart home.

I-energy and its applications used in the smart grid are chosen as attractive research issues due to their widespread applications in the smart home. In this vein, the optimization of energy demand in smart home appliances, in a smart grid, is considered the main challenge faced by power companies during peak periods regarding its considerable effect on the global power system stabilities. Therefore, this paper aims to show explicitly the performance of smart grid on electricity and hydrogen applications with reused communication methods. To facilitate the understanding of the communication-reused methods in the smart grid, several components are identified and presented in terms of improvements, benefits and lack of features. In the smart grid, most innovation communications are being used as wireline and wireless bundle, where the main features are aggregated. Indeed, reducing consumers' electricity bills in smart home electrical systems relies on increasing the relationship between the highest energy demand and annual energy use. The most appropriate research has been reviewed on optimization approaches discussing the smart home electric system. The examined methods are categorized into trustworthy and metaheuristic algorithms.

Existing work in energy demand side management focuses on the interaction between the utility grid and consumers. However, the previous technique is not focused on energy trading in local community of a renewable energy generation, distributed demand side management and not suitable for real-time environment. This paper presents a distributed demand side management system among multiple homes in community microgrid, with the integration of the internet of things smart meter and in the presence of renewable energy sources. The proposed energy consumption game is formulated for minimizing the cost of electricity in the individual home and the total cost of energy consumption in the whole community. The smart home users are playing game by optimizing their own daily energy consumption of appliances. The multiple participants include the self renewable generation of users, shared community microgrid and optional utility company. Each participant applies its best strategy to minimize energy consumption cost and users can maintain their own privacy of energy consumption. Moreover, the proposed scheme is distributed on blockchain, which provides a trusted communication medium between the participants. It enforces the autonomous monitoring of smart appliances and the billing of electricity consumption via smart contracts. Solidity smart contract is deployed to facilitate the execution of transactions without the involvement of third party in the smart community. Comparison of the results show that the proposed approach minimizes the total cost of energy consumption as well as each user's energy consumption cost.

Smart home is an emerging form of the Internet of things (IoT), which provides a convenient and comfortable living environment for the smart home users. With the explosive growth of smart home information, people pay more attention to the privacy protection of smart home, including the choice of privacy and effective privacy protection scheme. This paper proposes a privacy protection scheme based on information hiding, the scheme guarantees the sensitive data transmitted securely. First, the smart home Real-time sensor data are classified into sensitive data and non-sensitive data through machine learning, the process can be controlled according to the user's preferences. Second, the sensitive data can be transmitted securely by ordinary channels using method of combination encryption with information hiding. Experiment results show that this scheme can help users to express their preferences to the privacy protection, changing the previous practice that users can only accept the privacy settings provided by the supplier. What's more, the scheme combines encryption with information hiding, providing a double guarantee for the sensitive data during they are transmitted on ordinary channels. Comparing to the previous practice of using encryption merely, this scheme greatly improves the security of data transmission and is more suitable for the real environment.

The scheduling of the operational time of household appliances requires several parameters to be tuned according to the available energy supplied to a smart home. However, scheduling of operational time of multiple appliances in a smart home itself is the NP-hard problem and thus requires an intelligent, heuristic method to be solved in polynomial time. In this research work, we propose Real-time Scheduling of Operational Time of Household Appliances based on the well-known value iterative reinforcement learning called Quality learning (RSOTHA-QL). The proposed RSOTHA-QL scheme operates in two phases. In the first phase, the agents of the Q learning act by interacting with the smart home environment and obtain a reward. The reward value is further utilized to schedule the operational time of household appliances in the next state ensuring minimum energy consumption. In the second phase, the dissatisfaction arises due to scheduling of operational time of the household appliances of the home user is maintained by categorizing the household appliances into three groups: 1) deferrable, 2) non-deferrable, and 3) controllable. Besides, using the shared memory synchronization phenomenon, the agents attached to each appliance of the smart home become coordinated. The simulation and experiments are performed in a smart home scenario comprised of a single user and multiple appliances. As compared with our previous research work using the Least Slack Time (LST) scheduling algorithm and scheduling based on demand-response strategy, it is revealed that the operational time of the household appliances is efficiently scheduled to reduce the energy consumption and dissatisfaction level of the home users significantly.

With the development of Internet of Things(IoT), more and more smart devices and services appeared in people's life. These services make life more convenient, while it is difficult for people, especially the elderly, to find them in time, because they are widely distributed in great numbers with small sizes. In order to solve this problem, the service recommendation scheme, which is becoming increasingly important, needs to be used in IoT. However, the traditional web service recommendation schemes are not suitable for the IoT, because they rely more on the historical information rather than the energy state or the user's habit attributes, which are important for IoT. We research the service recommendation scheme, finding that the service recommendation process in IoT is a process of matter flows, which follows the conservation of matter. Therefore, we propose the service recommendation scheme based on tripartite graph with matter diffusion and use the habit feature as a dynamic tag. Based on the balance of matter, we use the positive and negative matter diffusion results on the tripartite as the recommendation results. The results of our evaluation show that the performance of the service recommendation scheme is improved in the precision and recall.

As energy demand continues to increase, demand response (DR) programs in the electricity distribution grid are gaining momentum and their adoption is set to grow gradually over the years ahead. Demand response schemes seek to incentivise consumers to use green energy and reduce their electricity usage during peak periods which helps support grid balancing of supply-demand and generate revenue by selling surplus of energy back to the grid. This paper proposes an effective energy management system for residential demand response using Reinforcement Learning (RL) and Fuzzy Reasoning (FR). RL is considered as a model-free control strategy which learns from the interaction with its environment by performing actions and evaluating the results. The proposed algorithm considers human preference by directly integrating user feedback into its control logic using fuzzy reasoning as reward functions. Q-learning, a RL strategy based on a reward mechanism, is used to make optimal decisions to schedule the operation of smart home appliances by shifting controllable appliances from peak periods, when electricity prices are high, to off-peak hours, when electricity prices are lower without affecting the customer's preferences. The proposed approach works with a single agent to control 14 household appliances and uses a reduced number of state-action pairs and fuzzy logic for rewards functions to evaluate an action taken for a certain state. The simulation results show that the proposed appliances scheduling approach can smooth the power consumption profile and minimise the electricity cost while considering user's preferences, user's feedbacks on each action taken and his/her preference settings. A user-interface is developed in MATLAB/Simulink for the Home Energy Management System (HEMS) to demonstrate the proposed DR scheme. The simulation tool includes features such as smart appliances, electricity pricing signals, smart meters, solar photovoltaic generation, battery energy storage, electric vehicle and grid supply.

The impressive advantages offered by of demand-side participation have accelerated deployment of demand response (DR) programs. However, the first step to attain the benefits of DR programs is to increase awareness level of the customers. This paper proposes a simple-but-efficient platform to enlighten the costumers on manifested merits of demand-side load control. The proposed platform is a web-based application which acquires the load profile of the customer, associated flexible appliances, and the customer preferences for using the appliances. In turn, presents the optimal operation schedule for flexible appliances and attained benefits from using the optimal schedule. To calculate the optimal operation schedule, a mixed-integer linear optimization model is devised where the decision variables are settings of flexible appliances, charge/discharge status and amount of storage device, charge/discharge status, and amount of electrical vehicle. The devised optimization engine is linked to a database to acquire required data for optimization which encompasses historical data for customer load, forecasts of renewables, ratings of customers' flexible appliances, and subjected energy tariff. The attained optimal scheduling for the customer is then returned to the database. On the other hand, the database is linked to the web-based user interface to get the user preferences (write to the database) and represent the recommendation for optimal operation and attained benefits (read from database). To manage the links between web-based user interface, database, and optimization tool, proper linking application programming interfaces (APIs) are devised. The proposed platform is testified using real-world data and its effectiveness is assured by experimental studies.

Residential power scheduling for demand response in a smart grid is a complex task. The traditional methods aim to minimize consumption costs and maximize consumption payoffs. In this paper, a power scheduling algorithm based on cost efficiency for smart homes is proposed to improve consumers' consumption efficiency and satisfaction. In this proposed method, a definition of cost efficiency for residential power scheduling is introduced. Consumers' consumption costs are modelled based on electricity payments and users' discomfort. A pair of parameters for a trade-off between users' discomfort and their electricity payment is designed to model cost efficiency based on the consumer's preference. A power scheduling algorithm based on cost efficiency is developed by adopting a fractional programming approach. Four consumption models are analysed and discussed. The results show that this proposed method can effectively improve consumers' consumption efficiency and satisfaction while saving costs. It is shown that discomfort and additional payment can impact consumers' consumption behaviour and smooth their consumption profile curves.

Location-based services (LBSs) for elderly care is a trending topic in smart homes. The key issue is the high accurate positioning for the elderly. Ultra-wideband (UWB) is a centimeter-level positioning accuracy in line-of-sight (LOS) environments. However, most of existing UWB positioning methods need non-line-of-sight (NLOS) identification and compensation, and thus leading to severe deterioration in positioning accuracy in presence of complex indoor environments where the elderly lived. This article proposes a phase-difference-of-arrival (PDOA) assisted UWB positioning method (PDOA-UWB) for elderly care. In our positioning framework, we first calculate the phase difference of arrival from PDOA chip integrated in the UWB base stations (BSs) to obtain a coarse location of the elderly, which is further used to distinguish which nearby BSs are in LOS environments and which are in NLOS environments. Then, a PDOA-UWB positioning solution is derived to improve the positioning accuracy of the elderly. Compared with some existing methods, our method achieves higher accuracy with less NLOS compensation, and is easier to be implemented in complex indoor environments for simple practical engineering applications. Experimental results show the efficacy of our proposed method.

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.