Smart Home Scenario Research Articles

ANGUISH: Security attack in narrowband‐Internet of Things (NB‐IoT) using game theory and hardware analysis

AbstractIn recent years, the Internet of Things (IoT) plays a vital role in our daily life. IoT is dominating in many areas like industries, health care, smart home, social network, defense sector, and the education sector. In the last couple of years, users are facing different challenges like security issues related to social network and defense sector. In this article, our prime focus is to secure the social and defense network associated with narrowband‐Internet of Things. This article proposes the artificial intelligence (AI)‐based defense security attacks using unmanned aerial vehicle (UAV). In the first part of the article, we have proposed attack modeling on smart home scenario and finalize the critical parameter for performance analysis. In the next section, the performance analysis based on game theory (GT) (AI based) approach is attained, using different techniques like nash equilibrium and Prisoner's dilemma. In the last section, detailed hardware analysis has been carried out for analyzing attack in real‐time scenario and is illustrated with the help of step‐by‐step process. Our proposed model proves that UAV‐based router can spoof by the intruder with the probability of 0.4 using GT and hardware analysis.

Edge Computing-Based Self-Organized Device Network for Awareness Activities of Daily Living in the Home

Activities of daily living (ADL) are important indicators for awareness of brain health in the elderly, and hospitals use ADL as a standard test for diagnosing chronic brain diseases such as dementia. However, since it is difficult to judge real-life ADL in hospitals, doctors typically predict ADL ability through interviews with patients or accompanying caregivers. Recently, many studies have attempted to diagnose accurate brain health by collecting and analyzing the real-life ADL of patients in their living environments. However, most of these were conducted by constructing and implementing expensive smart homes with the concept of centralized computing, and ADL data were collected from simple data about patients’ home appliance usage and the surrounding environment. Despite the high cost of building a smart home, the collected ADL data are inadequate for predicting accurate brain health. In this study, we developed and used three types of portable devices (wearable, tag, and stationary) that can be easily installed and operated in typical existing houses. We propose a self-organized device network structure based on edge computing that can perform user perception, location perception, and behavioral perception simultaneously. This approach enables us to collect user activity data, analyze ADL in real-time to determine if the user’s behavior was successful or abnormal, and record the physical ability of the user to move between fixed spaces. The characteristics of this proposed system enable us to distinguish patients from other family members and provide real-time notifications after a forgetful or mistaken action. We implemented devices that constitute the edge network of the smart home scenario and evaluated the performance of this system to verify its usefulness.

An Autonomic IoT Gateway for Smart Home Using Fuzzy Logic Reasoner

With recent advancements in communications and sensor technologies, the Internet of Things (IoT) has been experiencing rapid growth. It is estimated that billions of objects will be connected, which would create a vast amount of data. Cloud computing has been the predominant choice for monitoring connected objects and delivering data-based intelligence, but high response time and network load of cloud-based solutions are limiting factors for IoT deployment. In order to cope with this challenge, this paper proposes a novel approach to provide low-level intelligence for IoT applications through an IoT edge controller that is leveraging the Fuzzy Logic Controller along with edge computing. This low-level intelligence, together with cloud-based intelligence, forms the distributed IoT intelligence. The proposed controller allows distributed IoT gateway to manage input uncertainties; besides, by interacting with its environment, the learning system can enhance its performance over time, which leads to improving the reliability of the IoT gateway. Therefore, such a controller is able to offer different context-aware reasoning to alleviate the distributed IoT. A simulated smart home scenario has been done to prove the plausibility of the low-level intelligence concerning reducing latency and more accurate prediction through learning experiences at the edge.

Real-Time Scheduling of Operational Time for Smart Home Appliances Based on Reinforcement Learning

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.

Context-Aware Service Modeling and Conflicts Discovery Based on Petri Net

In smart environments, context-aware service sense various kinds of context information and reacts automatically to adapt to changing circumstances. These adaptions may lead to context changes which trigger other services elsewhere in the environment and thus produce cascading reactions. Conflict adaptions or unexpected side effects may exist in this process. This article focuses on service conflicts related to frustrations in service execution and violations of the service's objective. A petri net-based approach is proposed to model and simulate context changes as well as services execution, and a conflicts discovery mechanism was built to detect potential conflicts. The case study of smart home scenarios shows how the method helps locate conflicts caused by different services, validating the effectiveness of the proposed approach.

Intracluster cooperative communication in smart home scenario through stable election protocol

SummaryA wireless sensor network (WSN) can give a coordinated and minimal effort checking and control answer for such keen emotionally supportive systems. In this paper, we have discussed about the SEP in comparison with LEACH for smart home applications. Smart home scenario of an industry is taken into consideration in our research. The normal sink node is represented in the name of link node that connects the multiple sectors in the smart home scenario. In addition, it will act as a cluster head for the entire smart home setup. Moreover, the present research work proposes the plan of a low energy and high throughput and various levelled cluster‐based directing convention for smart home sensor organize application. Proposed protocol performs dynamic cluster head choice in light of various node qualities like node enduring energy, remoteness of the node from the link (sink), and average distance of the node from various neighbor nodes. Cluster head appropriation is even and brings about upgraded organization lifetime. Sensor nodes process the sense information utilizing nearby on‐board processors and exclude the repetitive information from transmission. In the final section, the energy level and the through put can be compared with the LEACH for getting a better understanding. The simulation results of the proposed technique clarify the energy consumed and the energy remaining in the stream. The energy will be measured in Joules. Moreover, the dead node will be tracked and intimated through our proposed work. The packet delivery and the delayed process of each and every node will be discussed in the proposed simulation result. The energy level remained will be calculated in Joules measurement. The SEP level will be more when compared with LEACH protocol. The energy level will be improved in case of SEP as 5, 10, 15, and 20 J/ ms.

Identifying IoT devices and events based on packet length from encrypted traffic

Recently, machine learning algorithms have been used to identify Internet of Things (IoT) devices and events. However, existing proposals may inspect the packet payload, what creates risks to IoT users’ privacy, and may use several features, increasing the computational complexity for traffic classification. In addition, existing techniques may also use complex mechanisms for extracting traffic characteristics, including the creation of vectors containing data from the Transmission Control Protocol (TCP) sessions. This paper proposes a solution that uses packet length statistics from encrypted traffic to characterize the behavior of IoT devices and events in a smart home scenario. The solution uses only the statistical mean, the standard deviation and the number of bytes transmitted over a one-second window, which can be extracted from the encrypted traffic, making the use of TCP vectors unnecessary. The solution identifies IoT devices and events, such as voice commands to smart assistants, and also distinguishes between IoT and non-IoT devices. The solution to characterize IoT devices and events is evaluated with traffic from two real-world testbeds and five classifiers. The evaluation included the algorithms k-Nearest Neighbors (k-NN), Decision Tree, Random Forest, Support Vector Machine (SVM) and Majority Voting, some of the most popular algorithms for traffic classification. The results show that the Random Forest algorithm can achieve up to 96% of accuracy in the identification of devices, 99% of precision in distinguishing between IoT and non-IoT devices and 99% of accuracy in the identification of IoT device events. Hypothesis testing is used to validate the obtained results. Also, the results show that the Decision Tree presented the lowest latency among the five classifiers evaluated in the identification of the devices, followed by k-NN, Random Forest, SVM and Majority Voting.

Merging Securely M2M Protocols, Internet of Things and Cloud Computing

The Internet of Things provides new ways for communication through the Web world using object-enabled networks. At the same time, M2M devices intercommunication and their communication through the web if they were connected to the Internet, presents new challenges, especially in security, that traditional communication models have not yet fully solved. Because of their inborn un-watched, minimal effort and mass-sent nature, M2M devices, and remote communication architectures and solutions for these devices, would encapsulate new dangers in security. These threats are not fully faced by use of security technologies and methods implemented in existing wireless devices, cellular networks or WLANs. The use of cloud computing gives a convenient, on demand and scalable network access to a shared pool of configurable computing resources and devices. This paper concentrates on a secure method to integrate the M2M protocols with the Internet of Things (IoT) and Cloud Computing under the name of Secure Machine-to-Internet Clouding (SM2IC) architecture. The secure design for integrating M2M protocols, along with IoT and cloud computing is proposed. To apply this design, an IoT enabled smart home scenario was examined to analyze secure communication between M2M devices and IoT applications. Also, the cloud computing is used to include different cloud applications, such as, IaaS, PaaS, and SaaS for monitoring the quality of service of M2M devices through IoT applications. Then, simulations were performed to test the proposed security technique, followed by conclusions and future work.

Robust Device-Free Intrusion Detection Using Physical Layer Information of WiFi Signals

WiFi infrastructures are widely deployed in both public and private buildings. They make the connection to the internet more convenient. Recently, researchers find that WiFi signals have the ability to sense the changes in the environment that can detect human motion and even identify human activities and his identity in a device-free manner, and has many potential security applications in a smart home. Previous human detection systems can only detect human motion of regular moving patterns. However, they may have a significant detection performance degradation when used in intrusion detection. In this study, we propose Robust Device-Free Intrusion Detection (RDFID) system leveraging fine-grained Channel State Information (CSI). The noises in the signals are removed by a Principle Component Analysis (PCA) and a low pass filter. We extract a robust feature of frequency domain utilizing Continuous Wavelet Transform (CWT) from all subcarriers. RDFID captures the changes from the whole wireless channel, and a threshold is obtained self-adaptively, which is calibration-free in different environments, and can be deployed in smart home scenarios. We implement RDFID using commodity WiFi devices and evaluate it in three typical office rooms with different moving patterns. The results show that our system can accurately detect intrusion of different moving patterns and different environments without re-calibration.

Empowering the selection of demand response methods in smart homes: development of a decision support framework

Demand Response (DR) facilitates the monitoring and management of appliances in energy grids by employing methods that, for example, increase the reliability of energy grids and reduce users’ cost. Within energy grids, Smart Home scenarios can be characterized by a unique combination of appliances and user preferences. To increase their impact, a scenario-specific selection of the best performing DR methods is necessary. As the user faces a multitude of heterogeneous DR methods to choose from, a complex decision problem is present. The primary goal of this study is to develop a decision support framework that can determine the best-performing DR methods. Building on literature analyses, expert workshops and expert interviews, we identify seven requirements, derive solution concepts addressing these requirements, and develop the framework by combining the concepts using a benchmarking process as a template. To demonstrate the framework’s applicability, we conduct a simulation study that uses artificial (simulated) data for seven types of households. Within this study, we employ four DR methods, assume changing appliances over time and cost minimization as primary objective. The study indicates, that by using the framework and thus by identifying and using the best DR method for each scenario, the users can achieve further cost benefits. The application of the framework allows practitioners to increase the efficiency of the DR method selection process and to further enhance DR-related benefits, such as cost minimization, load profile flattening, and peak load reduction. Researchers benefit from guidance for benchmarking and evaluating DR methods.

User-centric Privacy Engineering for the Internet of Things

User privacy concerns are widely regarded as a key obstacle to the success of modern smart cyber-physical systems. In this paper, we analyse, through an example, some of the requirements that future data collection architectures of these systems should implement to provide effective privacy protection for users. Then, we give an example of how these requirements can be implemented in a smart home scenario. Our example architecture allows the user to balance the privacy risks with the potential benefits and take a practical decision determining the extent of the sharing. Based on this example architecture, we identify a number of challenges that must be addressed by future data processing systems in order to achieve effective privacy management for smart cyber-physical systems.

Emotion-Aware Connected Healthcare Big Data Towards 5G

The recent development of big data-oriented wireless technologies in terms of emerging 5G, edge computing, interconnected devices of the Internet of Things (IoT), and data analytics, as well as techniques, have enabled connected healthcare services for a happier and healthier life. Although, the quality of the healthcare services can be enhanced through big data-oriented wireless technologies, however, the challenges remain for not considering emotional care, especially for children, elderly, and mentally ill people. In this paper, we propose an emotion-aware connected healthcare system using a powerful emotion detection module. Different IoT devices are used to capture speech and image signals of a patient in a smart home scenario. These signals are used as the input to the emotion detection module. Speech and image signals are processed separately, and classification scores using these signals are fused to produce a final score to take a decision about the emotion. If the emotion is detected as pain, caregivers can visit the patient. Several experiments were performed to validate the proposed system, and good accuracies, up to 99.87%, were achieved for emotion detection. The proposed framework would greatly contribute personalized and seamless emotion-aware healthcare services toward 5G.

QoE Assessment for IoT-Based Multi Sensorial Media Broadcasting

One of the goals of next generation TV broadcast services is to provide realistic media contents to the users. The user's sense of reality can be reinforced by adding to conventional media multiple sensorial effects, through five-sense stimulus (i.e., taste, sight, touch, smell, and hearing). In a smart TV broadcasting context, especially in a home environment, to deliver the additional effects, customary devices (e.g., air conditioning, lights, etc.), provided of opportune smart features, have to be preferred to ad-hoc devices, often deployed in other applications as for example in gaming systems. In this context, a key issue is the interconnection among the smart TV and the customary devices that deliver the additional sensorial effects to the user. In smart home use cases, the Internet of Things (IoT) paradigm has been widely adopted to connect smart devices and this paper presents an IoT-based architecture for multi sensorial media delivery to TV users in a home entertainment scenario. In such a framework, home customary devices, act as smart objects interconnected via IoT network to the smart TV and play a role to implement additional effects to the conventional broadcast TV service. In this paper, the requirements in terms of synchronization between media and devices is analyzed and the architecture of the system is defined accordingly. Furthermore, a prototype is implemented in a real smart home scenario with real customary devices, which allowed a subjective test measurement campaign to assess the quality of experience of the users and the feasibility of the proposed multi sensorial media TV service.

SIMADL: Simulated Activities of Daily Living Dataset

With the realisation of the Internet of Things (IoT) paradigm, the analysis of the Activities of Daily Living (ADLs), in a smart home environment, is becoming an active research domain. The existence of representative datasets is a key requirement to advance the research in smart home design. Such datasets are an integral part of the visualisation of new smart home concepts as well as the validation and evaluation of emerging machine learning models. Machine learning techniques that can learn ADLs from sensor readings are used to classify, predict and detect anomalous patterns. Such techniques require data that represent relevant smart home scenarios, for training, testing and validation. However, the development of such machine learning techniques is limited by the lack of real smart home datasets, due to the excessive cost of building real smart homes. This paper provides two datasets for classification and anomaly detection. The datasets are generated using OpenSHS, (Open Smart Home Simulator), which is a simulation software for dataset generation. OpenSHS records the daily activities of a participant within a virtual environment. Seven participants simulated their ADLs for different contexts, e.g., weekdays, weekends, mornings and evenings. Eighty-four files in total were generated, representing approximately 63 days worth of activities. Forty-two files of classification of ADLs were simulated in the classification dataset and the other forty-two files are for anomaly detection problems in which anomalous patterns were simulated and injected into the anomaly detection dataset.

Multi-Residential Activity Labelling in Smart Homes with Wearable Tags Using BLE Technology.

Smart home platforms show promising outcomes to provide a better quality of life for residents in their homes. One of the main challenges that exists with these platforms in multi-residential houses is activity labeling. As most of the activity sensors do not provide any information regarding the identity of the person who triggers them, it is difficult to label the sensor events in multi-residential smart homes. To deal with this challenge, individual localization in different areas can be a promising solution. The localization information can be used to automatically label the activity sensor data to individuals. Bluetooth low energy (BLE) is a promising technology for this application due to how easy it is to implement and its low energy footprint. In this approach, individuals wear a tag that broadcasts its unique identity (ID) in certain time intervals, while fixed scanners listen to the broadcasting packet to localize the tag and the individual. However, the localization accuracy of this method depends greatly on different settings of broadcasting signal strength, and the time interval of BLE tags. To achieve the best localization accuracy, this paper studies the impacts of different advertising time intervals and power levels, and proposes an efficient and applicable algorithm to select optimal value settings of BLE sensors. Moreover, it proposes an automatic activity labeling method, through integrating BLE localization information and ambient sensor data. The applicability and effectiveness of the proposed structure is also demonstrated in a real multi-resident smart home scenario.

Finger Angle-Based Hand Gesture Recognition for Smart Infrastructure Using Wearable Wrist-Worn Camera

The arising of domestic robots in smart infrastructure has raised demands for intuitive and natural interaction between humans and robots. To address this problem, a wearable wrist-worn camera (WwwCam) is proposed in this paper. With the capability of recognizing human hand gestures in real-time, it enables services such as controlling mopping robots, mobile manipulators, or appliances in smart-home scenarios. The recognition is based on finger segmentation and template matching. Distance transformation algorithm is adopted and adapted to robustly segment fingers from the hand. Based on fingers’ angles relative to the wrist, a finger angle prediction algorithm and a template matching metric are proposed. All possible gesture types of the captured image are first predicted, and then evaluated and compared to the template image to achieve the classification. Unlike other template matching methods relying highly on large training set, this scheme possesses high flexibility since it requires only one image as the template, and can classify gestures formed by different combinations of fingers. In the experiment, it successfully recognized ten finger gestures from number zero to nine defined by American Sign Language with an accuracy up to 99.38%. Its performance was further demonstrated by manipulating a robot arm using the implemented algorithms and WwwCam to transport and pile up wooden building blocks.

Designing Smart Control Systems Based on Internet of Things and Big Data Analytics

The Internet of Things (IoT) lay down a platform for global communication among millions of electronic devices connected to the internet. These devices and electronic appliances included both wireless and wired sensors, home appliances such as Television, refrigerator, etc., radio frequency identifications (RFID), and so. Similarly, heterogeneous networks provide a platform for media independent communications. However, there exist several issues in using heterogeneous technologies for IoT communications. These challenges include the co-existence of wireless technologies such as ZigBee, Bluetooth and WIFI, cross-layer communications, high packet loss due to interferences with electronic devices, etc. Similarly, IoT is a new paradigm for interconnecting electronic devices, thus, it needs major refinement for standardising it for various services. In order to address the aforementioned challenges, we proposed a scheme for enabling a generic IoT framework and platform for various IoT embedded devices. The working of the proposed scheme is twofold, (1) discovering and collecting information from IoT enabled devices using sensors and (2) scheduling the working of these appliances based on the data collected using sensors attached to these devices. Further, the data is transferred to Hadoop ecosystem for processing and analyzing to disseminate the relevant information to the citizen. Moreover, the proposed system is tested in a smart home scenario by installing sensors attached to various home appliances. The energy consumption and packet loss occur due to available electronic appliances, and heterogeneous devices are computed and analyzed for planning an optimal scheduling scheme and load balancing. Similarly, data from various authentic sources is analyzed using Hadoop ecosystem and disseminate it to the citizen in real-time.

Toward a framework for self-adaptive workflows in cyber-physical systems

With the establishment of Cyber-physical Systems (CPS) and the Internet of Things, the virtual world of software and services and the physical world of objects and humans move closer together. Despite being a useful means for automation, BPM technologies and workflow systems are yet not fully capable of executing processes in CPS. The effects on and possible errors and inconsistencies in the physical world are not considered by “traditional” workflow engines. In this work we propose a framework for self-adaptive workflows in CPS based on the MAPE-K feedback loop. Within this loop monitoring and analysis of additional sensor and context data is used to check for unanticipated errors in the physical world. Planning and execution of compensation actions restores Cyber-physical Consistency, which leads to an increased resilience of the process execution environment. The framework facilitates the separation of CPS aspects from the “regular” workflow views. We show the feasibility of this approach in a smart home scenario and discuss the application of our approach for legacy BPM systems.

Design of a Gait Phase Recognition System That Can Cope With EMG Electrode Location Variation

Electromyogram (EMG) signal-based gait phase recognition for walking-assist devices warrants much attention in human-centered system design as it well exemplifies human-in-the-loop control where the system's prediction directly affects subsequent walking motion. Since walking motion poses considerable variations in electrode placement, performance reliability of such systems is contingent on a combination of electrode montage and a feature extraction method that takes into account underlying physiological factors of peripheral muscles where electrodes are placed. In many practical applications, however, proper consideration of effects of the electrode location variation on performance reliability of the system has received scant empirical attention. Here, based on a user-centered design principle, we establish a gait phase recognition system that is capable of rigidly controlling ill effects due to this covariate by carrying out a large-scale analysis that combines statistical, model-based, and empirical approaches. In doing so, we have developed a special sensing suit for the control of electrode placement and a reliable data acquisition. We then have conducted a nonparametric statistical analysis on class separability values of thirty types of EMG feature sets, followed by a model-based analysis to address the tradeoff between class separability and dimensionality. To further address the issue of how these results generalize to independent systems and data sets, we have carried out an empirical performance assessment over six classification methods. First, the two feature types, Integral of Absolute Value and Histogram, and a combination of the two are shown to be robust against electrode location variations while providing a firm performance guarantee. Second, system organization scenarios are presented on a case-by-case basis, allowing us to trade off system complexity for on-line adaptation capability. Collectively, our integrated analysis lends itself to formulating a guideline for design of highly reliable EMG signal-based walking assistant systems in a variety of smart home scenarios.

Enabling distributed intelligence assisted Future Internet of Things Controller (FITC)

The unprecedented prevalence of ubiquitous sensing will revolutionise the Future Internet where state-of-the-art Internet-of-Things (IoT) is believed to play the pivotal role. In the fast forwarding IoT paradigm, hundreds of billions of things are estimated to be deployed which would give rise to an enormous amount of data. Cloud computing has been the prevailing choice for controlling the connected things and the data, and providing intelligence based on the data. But response time and network load are on the higher side for cloud based solutions. Recently, edge computing is gaining growing attention to overcome this by employing rule-based intelligence. However, requirements of rules do not scale well with the proliferation of things. At the same time, rules fail in uncertain events and only offer pre-assumed intelligence. To counter this, this paper proposes a novel idea of leveraging the belief-network with the edge computing to utilize as an IoT edge-controller the aim of which is to offer low-level intelligence for IoT applications. This low-level intelligence along with cloud-based intelligence form the distributed intelligence in the IoT realm. Furthermore, a learning approach similar to reinforcement learning has been proposed. The approach, i.e. enabling a Future IoT Controller (FITC) has been verified with a simulated SmartHome scenario which proves the feasibility of the low-level intelligence in terms of reducing rules domination, faster response time and prediction through learning experiences at the edge.