EDeepRFID-IPS: Enhanced RFID Indoor Positioning with Deep Learning for Internet of Things

Our goal is to develop 'smart indoor environments' that are monitored unobtrusively by biometric capture devices, such as video cameras, microphones, etc. Such environments will keep track of their occupants and be capable of answering queries about the occupants' whereabouts. In order to develop a unified model that is applicable across diverse biometric modalities, we propose an abstract state transition framework in which different recognition steps are abstracted by events, and the reasoning necessary to effect state transitions is abstracted by a transition function. We define the metrics of 'precision' and 'recall' of a smart environment to evaluate how well it tracks its occupants. We show how the overall performance of our smart environment can be improved through the use of spatio-temporal knowledge of the environment. A prototype based upon our proposed abstract framework indicates that integrating recognition and reasoning capabilities substantially improves the overall performance of the environment.

Internet of Things (IoT) become one of the buzzwords in the Information Technology. It an ecosystem of technologies monitoring the status of physical objects, capturing significant data, and communicating that information to software applications through IP networks. Today, Indoor localization systems based on underlying technologies like RFID have been designed and used to locate the movement of objects and subjects in the indoor environment. This technology finds its use in various indoor localization applications ranging from healthcare, tracking people and objects in real time to helped living in the smart environment and many more. The technology of RFID is known very convenient due to many benefits of flexibility, automatic identification, its cost effective and its energy efficiency. This paper presents some IoT Smart Homes applications based on RFID technologies. its presents also a number of comparable studies and discusses the techniques, applications, algorithms, and challenges presented during the integration of RFID technologies in IoT smart home systems.

The home automation controls the electronic devices remotely and automatically with the help of context aware behaviour. Context awareness is the process where data from various sensors and input from user or user action must be interpreted and corresponding commands to actuators must be generated. This context aware automation and decision support process is done with the help of adaptive rules which is defined for the automatic behaviour of devices in the smart home environment. The consistent behaviour of the devices in the smart environment will capture the customer satisfaction. This work describes the process of checking the consistent behaviour of the context aware system in smart home environment using formal modelling and verification method. We have discussed a mechanism to check the consistent behaviour of the adaptive mechanism of smart home environment. The result ensures the consistent behaviour of the context aware system in the smart environment.

The increasing availability of low-cost smart devices is bringing them to be used more and more in the smart home. However, the development of a smart home environment requires to take into account several aspects. First of all, designers must consider the end user (namely the person that actually uses the smart home), and not only the technology, at the center of any intervention. Another important aspect is the interaction between the smart home and the appliances that are already deployed in the same smart environment. Moreover, most of smart home solutions are static and do not allow end users to customize them according to their real needs and preferences. Finally, not all end users may possess the necessary knowledge and skills to customize a smart home, but someone else, such as an adult child or a caregiver, may be called on to carry out this task for them. In this paper, we analyze all these aspects that can influence the development and evolution of a smart home. We then propose a model supporting developers and software engineers to deploy and evaluate a smart home solution that adopts end-user development techniques. It is based on the International Classification of Functioning scale, which is used to characterize the person that is mainly going to live in the smart home and define a solution suitable to his/her needs.

The Internet of Things (IoT) carries the meaning of a developing prototype that focuses on the connection between devices, objects, sensors and users. Using IoT, the method of communications and interactions changes in form of ‘Human to Thing’ to ‘Thing to Thing’. IoT technology is rapidly evolving and spreading its technology influence across various domains including healthcare (personal and enterprise), smart campuses, smart homes, smart mobility, and Industry 4.0. As a result, a lot of IoT devices are being used as a crucial component as one of human made high-tech butlers of everyday lifestyle. This paper focuses on the IoT security threats and vulnerabilities that shall be considered in the implementation of IoT ecosystem by conducting a research in the area of smart home environment. The current security threats and vulnerabilities are being discussed in the contexts of the IoT architecture layers, which is the application layer, network layer and physical layer are also presented. This paper also provides an analysis of the possible threats and proposes possible solutions for improving the security of the IoT smart home, that may influence as beneficial factors that are able to promote a secured eco-system of IoT devices in smart home operational environment.

Monitoring and detecting changes in the environment and can be used for several purposes. To develop new advanced services for smart environments, data gathered during the monitoring need to be stored, processed and correlated to different parts of the information that characterizes or influences the environment itself. With the quick advancement in enabling technologies for ubiquitous computing, more and more active or passive devices/sensors are augmented in the smart indoor environment. Service innovations, enabled by the confluence of big data, mobile solutions, cloud, social, and cognitive computing, and the Internet of Things (IoT), have gained a lot of attention among many enterprises in the past few years because they represent promising ways for companies to effectively and rapidly deliver new services. But one of today's most pervasive and bedeviling challenges is how to start this journey and stay on course. In this paper we present our state-of-the-art technology and IoT devices, how they are playing a vital role in making the world advanced. The biggest conclusion is that all the information and communications technology (ICT)-enabled service innovations need to be human-centered and focused on co-creating value. We took into consideration smart home/office IoT systems, the related security and privacy challenges and an outlook on the possible solutions towards a holistic security framework for these IoT systems.

Optical Camera Communication (OCC) is an emerging technology that has attracted research interest in recent decades. Unlike previous communication technologies, OCC uses visible light as the medium to transmit data from receivers and cameras to receive the data. OCC has several advantages that can be capitalized in several implementations. However, the Internet of Things (IoT) has emerged as a technology with immense potential. Numerous research endeavors support the IoT’s prospective technology that can be implemented in various sectors, including the healthcare system. This study introduces a novel implementation of the Internet of Medical Things (IoMT) system, using OCC for real-time health monitoring and indoor location tracking. The innovative system uses standard closed-circuit television CCTV setups, integrating deep learning-based OCC to monitor multiple patients simultaneously, each represented by an LED matrix. The effectiveness of the system was demonstrated through two scenarios: the first involves dual transmitters and a single camera, highlighting real-time monitoring of vital health data; the second features a transmitter with dual cameras, focusing patient movement tracking across different camera fields of view. To accurately locate and track the position of LED arrays in the camera, the system used YOLO (You Only Look Once). Data are securely transmitted to an edge server and stored using the REST API, with a web interface providing real-time patient updates. This study highlights the potential of OCC in IoMT for advanced patient care and proposes future exploration in larger healthcare systems and other IoT domains.

Unobtrusive and multiple-sensor interfaces enable observation of natural human behavior in smart environments. Being able to detect, analyze and interpret this activity allows for the implementation of various applications, including real-time surveillance and real-time support in smart home and office environments. We focus on smart meeting environments, where nonverbal behavioral cues sometimes tell more about issues such as discussion participation, involvement and contribution, than information obtained from verbal contributions. An important aspect of this behavior is the interaction between meeting participants. We regard the special case where some of the participants are at physically different locations, which hinders natural interaction. We discuss how we can exploit the ability of a sensor-equipped environment to detect nonverbal interaction cues and use these to allow and improve natural interaction between collaborating participants at distributed locations. We focus on research efforts to detect nonverbal interaction cues by looking at various modalities. Also, we discuss how information obtained from the fusion of these modalities allows us to generate and display behavioral cues that allow remote participants to take part in a distributed meeting in a natural way.

Smart environments are becoming ubiquitous despite many potential security and privacy issues. But, do people understand what consequences could arise from using smart environments? To answer this research question, we conducted a survey with 575 participants from three different countries (Germany, Spain, Romania) considering smart home and health environments. Less than half of all participants mentioned at least one security and privacy issue, with significantly more German participants mentioning issues than the Spanish ones and the Spanish participants in turn mentioning significantly more security and privacy issues than the Romanian participants. Using open coding, we find that among the 275 participants mentioning security and privacy issues, 111 only expressed abstract concerns such as “security issues” and only 34 mentioned concrete harms such as “Burglaries (physical and privacy)”, caused by security and privacy violations. The remaining 130 participants who mentioned security and privacy issues named only threats (i.e. their responses were more concrete than just abstract concerns but they did not mention concrete harming scenarios).

Internet of Things (IoT) is characterized by various of heterogeneous devices that facing numerous threats, which makes modeling security situation of IoT still a certain challenge. This paper defines a Stochastic Colored Petri Net (SCPN) for IoT-based smart environment and then proposes a Game model for security situational awareness. All possible attack paths are computed by the SCPN, and antagonistic behavior of both attackers and defenders are taken into consideration dynamically according to Game Theory (GT). Experiments on two typical attack scenarios in smart home environment demonstrate the effectiveness of the proposed model. The proposed model can form a macroscopic trend curve of the security situation. Analysis of the results shows the capabilities of the proposed model in finding vulnerable devices and potential attack paths, and even facilitating the choice of defense strategy. To the best of our knowledge, this is the first attempt to use Game Theory in the IoT-based SCPN to establish a security situational awareness model for a complex smart environment.

The Internet of Things (IoT) is envisioned as a ubiquitous computing infrastructure in which everything becomes connected, enabling gigantic information exchange among Things and people. These connected smart Things generate an enormous amount of data which need to be efficiently managed to form a unified global IoT. Unfortunately, due to the lack of acceptable open standards, communication protocols, and support for device/service discovery, the recent IoT deployments in smart environments (e.g., smart home, smart building, smart city) are posing imperative challenges related to interoperability, discovery, and the configuration of deployed objects, since the number of objects is expected to grow over time. Therefore, it is of utmost importance to provide open and scalable solutions for the discovery of devices (i.e., Things), their configuration, and data management. This paper introduces an open and scalable IoT platform by adopting the modular characteristics of edge computing for smart environments. This paper: (i) performs a systematic literature review of IoT-based infrastructures and analyzes the scalability requirements; (ii) proposes a layered IoT platform for smart environments that fosters heterogeneity, interoperability, discovery, and scalability; and (iii) demonstrates the applicability of the proposed solution by relying on a comprehensive study of a Väre smart building use case at Aalto University.

In recent times, a massive amount of smart devices or objects are connected that enhances the scale of the digital world. These smart objects are referred as “things” or physical devices that have the potential to sense the real-world physical objects, collect the data, and network with others. The objects are connected through the internet, which crafts the terminology of Internet of Things (IoT). IoT has been developed and become the center of consideration due to the novelty of embedded device and a rapid enhancement in its number. This increase is resulting in the creative applications of smart environments. Smart transportation is a central stake for the quality of life of citizens in smart environment. Smart transportation involves the use of devices and sensors in the control system of vehicle; for example navigation system of cars, traffic signal management system, number recognition system and speed monitoring system. In this research article, we propose architecture for smart transportation system using Big Data analytics, in order to achieve real time processing and facilitate a friendly communication in the environment of IoT based smart transportation. The proposed architecture is a 3-phase scheme which is responsible for the organization and management of Big Data, real-time processing of Big Data and service management. The proposed architecture is a generic solution for the smart transportation planning using real time Big Data processing. The proposed scheme is realized using Spark over single node Hadoop setup with various input libraries. A huge amount of data from different authentic and reliable sources is measured to validate the proposed architecture. In addition, the effectiveness of proposed scheme also highlighted with regard to throughput.

Nowadays, the increasing in the use of Internet of Things (IoT) devices is growing the realization of pervasive Smart Environments (SEs) and Smart Urban Ecosystems, where all the data gathered by the “Things” can be elaborated and used to improve the livability, the safety and the security of the environment, and to make inhabitants lives easier. Many efforts have been already done in the direction of SEs development and in the implementation of platforms specifically designed for SE realization. Anyway, such efforts miss of solutions regarding the interoperability among the realized SEs and other third-part “Things”. This chapter gives an overview of iSapiens, which is a Java-based platform specifically designed for the development and implementation of SEs. iSapiens tries to overcome the interoperability issue by leveraging the Social Internet of Things (SIoT) paradigm that allows to dynamically include in an SE the new “Things” that can appear in an environment without requiring interventions from humans. iSapiens provides tools for the realization of pervasive SEs and relies on the edge computing paradigm. Such paradigm is extremely important in a distributed system since it allows to use distributed storage and computation at the edge of a network, so reducing latencies with respect to move all the executions and storages in the cloud. Moreover, the chapter will review some SE applications realized by exploiting iSapiens concepts.

Radio frequency signals are commonly used in the development of indoor localization systems. The infrastructure of these systems includes some beacons placed at known positions that exchange radio packets with users to be located. When the system is implemented using wireless sensor networks, the wireless transceivers integrated in the network motes are usually based on the IEEE 802.15.4 standard. But, the CSMA-CA, which is the basis for the medium access protocols in this category of communication systems, is not suitable when several users want to exchange bursts of radio packets with the same beacon to acquire the radio signal strength indicator (RSSI) values needed in the location process. Therefore, new protocols are necessary to avoid the packet collisions that appear when multiple users try to communicate with the same beacons. On the other hand, the RSSI sampling process should be carried out very quickly because some systems cannot tolerate a large delay in the location process. This is even more important when the RSSI sampling process includes measures with different signal power levels or frequency channels. The principal objective of this work is to speed up the RSSI sampling process in indoor localization systems. To achieve this objective, the main contribution is the proposal of a new MAC protocol that eliminates the medium access contention periods and decreases the number of packet collisions to accelerate the RSSI collection process. Moreover, the protocol increases the overall network throughput taking advantage of the frequency channel diversity. The presented results show the suitability of this protocol for reducing the RSSI gathering delay and increasing the network throughput in simulated and real environments.

We are in a time when a large number of electronic devices influence human interaction with the environment. The physical place where these various devices interact with each other to provide useful services for end-users gives birth to the concept of a Smart Environment. The interaction of the multiple devices that make up a smart environment produces a large amount of information or Big Data problems, so the insertion of the Internet of Things (IoT) technology in the environment is essential. IoT integration and cloud computing technology are needed as the impact of evolution on the next generation of smart environments. Trends in research related to IoT and Smart Environments are increasing rapidly beyond the trend of Smart City that first emerged. This paper reviewed current trends about smart environments, architectural concepts, frameworks, and applications of IoT-based Smart Environments.