Interaction‐Feature Enhanced Multiuser Model Learning for a Home Environment Using Ambient Sensors

Activity recognition (AR) is a key enabler for a context‐aware smart home since knowing what the residents’ current activities helps a smart home provide more desirable services. This is why AR is often used in assistive technologies for cognitively impaired people to evaluate their abilities to undertake activities of daily living. In a real‐life scenario, multiple‐resident AR has been considered as a very challenging problem, primarily due to the complexity of data association. In addition, most prior research has not considered the potential interpersonal interactions among residents to simplify complexity, especially in an environment monitored by ambient sensors. In this study, we propose two types of multiuser activity models, both of which are derived from an interaction‐feature enhanced multiuser model learning framework. These two models consider interpersonal interactions and data association for multiuser AR using ambient sensors. We then compare their performance with the other two baseline models with or without consideration of data association and interpersonal interactions. The experimental results show that the derived models outperform other baseline classifiers. Therefore, the proposed approach can increase the opportunities for providing context‐aware services for a multiresident smart home.     

Optimizing the configuration of an heterogeneous architecture of sensors for activity recognition,using the extended belief rule-based inference methodology

Smart environments are heterogeneous architectures with a broad range of heterogeneous electronic devices that are with high in processing capabilities for computing, considering low power consumption. They have the ability to record information about the behavior of the people by means of their interactions with the objects within an environment. This kind of environments are providing solutions to address some of the problems associated with the growing size and ageing of the population by means of the recognition of activities that can offer monitoring activities of daily living and adapting the environment. In order to deploy low-cost smart environments and reduce the computational complexity for activity recognition, it is a key issue to know the subset of sensors which are relevant for activity recognition. By using feature selection methods to optimize the subset of initial sensors in a smart environment, this paper proposes the adaption of the extended belief rule-based inference methodology (RIMER+) to handle data binary sensors and its use as the suitable classifier for activity recognition that keeps the accuracy of results even in situations where an essential sensor fails. A case study is presented in which a smart environment dataset for activity recognition with 14 sensors is set. Two optimizations with 7 and 10 sensors are obtained with two feature selection methods in which the adaptation of RIMER+ for smart environment provides an encouraged performance against the most popular classifiers in terms of robustness.     

Data fusion and multiple classifier systems for human activity detection and health monitoring: Review and open research directions

Activity detection and classification using different sensor modalities have emerged as revolutionary technology for real-time and autonomous monitoring in behaviour analysis, ambient assisted living, activity of daily living (ADL), elderly care, rehabilitations, entertainments and surveillance in smart home environments. Wearable devices, smart-phones and ambient environments devices are equipped with variety of sensors such as accelerometers, gyroscopes, magnetometer, heart rate, pressure and wearable camera for activity detection and monitoring. These sensors are pre-processed and different feature sets such as time domain, frequency domain, wavelet transform are extracted and transform using machine learning algorithm for human activity classification and monitoring. Recently, deep learning algorithms for automatic feature representation have also been proposed to lessen the burden of reliance on handcrafted features and to increase performance accuracy. Initially, one set of sensor data, features or classifiers were used for activity recognition applications. However, there are new trends on the implementation of fusion strategies to combine sensors data, features and classifiers to provide diversity, offer higher generalization, and tackle challenging issues. For instances, combination of inertial sensors provide mechanism to differentiate activity of similar patterns and accurate posture identification while other multimodal sensor data are used for energy expenditure estimations, object localizations in smart homes and health status monitoring. Hence, the focus of this review is to provide in-depth and comprehensive analysis of data fusion and multiple classifier systems techniques for human activity recognition with emphasis on mobile and wearable devices. First, data fusion methods and modalities were presented and also feature fusion, including deep learning fusion for human activity recognition were critically analysed, and their applications, strengths and issues were identified. Furthermore, the review presents different multiple classifier system design and fusion methods that were recently proposed in literature. Finally, open research problems that require further research and improvements are identified and discussed.     

Ubiquitous robotics: Recent challenges and future trends

Ambient intelligence, ubiquitous and networked robots, and cloud robotics are new research hot topics that have started to gain popularity among the robotics community. They enable robots to acquire richer functionalities and open the way for the composition of a variety of robotic services with three functions: semantic perception, reasoning and actuation. Ubiquitous robots (ubirobots) overcome the limitations of stand-alone robots by integrating them with web services and ambient intelligence technologies. The overlap that exists now between ubirobots and ambient intelligence makes their integration worthwhile. It targets to create a hybrid physical–digital space rich with a myriad of proactive intelligent services that enhance the quality and the way of our living and working. Furthermore, the emergence of cloud computing initiates the massive use of a new generation of ubirobots that enrich their cognitive capabilities and share their knowledge by connecting themselves to cloud infrastructures. The future of ubirobots will certainly be open to an unlimited space of applications such as physical and virtual companions assisting people in their daily living, ubirobots that are able to co-work alongside people and cooperate with them in the same environment, and physical and virtual autonomic guards that are able to protect people, monitor their security and safety, and rescue them in indoor and outdoor spaces. This paper introduces the recent challenges and future trends on these topics.     

An algorithm for safe navigation of mobile robots by a sensor network in dynamic cluttered industrial environments

Mobile robots have been widely implemented in industrial automation and smart factories. Different types of mobile robots work cooperatively in the workspace to complete some complicated tasks. Therefore, the main requirement for multi-robot systems is collision-free navigation in dynamic environments. In this paper, we propose a sensor network based navigation system for ground mobile robots in dynamic industrial cluttered environments. A range finder sensor network is deployed on factory floor to detect any obstacles in the field of view and perform a global navigation for any robots simultaneously travelling in the factory. The obstacle detection and robot navigation are integrated into the sensor network and the robot is only required for a low-level path tracker. The novelty of this paper is to propose a sensor network based navigation system with a novel artificial potential field (APF) based navigation algorithm. Computer simulations and experiments confirm the performance of the proposed method.     

Multi-level simulation of Internet of Things on smart territories

In this paper, a methodology is presented and employed for simulating the Internet of Things (IoT). The requirement for scalability, due to the possibly huge amount of involved sensors and devices, and the heterogeneous scenarios that might occur, impose resorting to sophisticated modeling and simulation techniques. In particular, multi-level simulation is regarded as a main framework that allows simulating large-scale IoT environments while keeping high levels of detail, when it is needed. We consider a use case based on the deployment of smart services in decentralized territories. A two level simulator is employed, which is based on a coarse agent-based, adaptive parallel and distributed simulation approach to model the general life of simulated entities. However, when needed a finer grained simulator (based on OMNeT++) is triggered on a restricted portion of the simulated area, which allows considering all issues concerned with wireless communications. Based on this use case, it is confirmed that the ad-hoc wireless networking technologies do represent a principle tool to deploy smart services over decentralized countrysides. Moreover, the performance evaluation confirms the viability of utilizing multi-level simulation for simulating large scale IoT environments.     

Elastic Smart Contracts in Blockchains

In this paper, we deal with questions related to blockchains in complex Internet of Things (IoT)-based ecosystems. Such ecosystems are typically composed of IoT devices, edge devices, cloud computing software services, as well as people, who are decision makers in scenarios such as smart cities. Many decisions related to analytics can be based on data coming from IoT sensors, software services, and people. However, they are typically based on different levels of abstraction and granularity. This poses a number of challenges when multiple blockchains are used together with smart contracts. This work proposes to apply our concept of elasticity to smart contracts and thereby enabling analytics in and between multiple blockchains in the context of IoT. We propose a reference architecture for Elastic Smart Contracts and evaluate the approach in a smart city scenario, discussing the benefits in terms of performance and self-adaptability of our solution.      

基于树莓派的智能家居设计与实现

随着生活水平的提高和物联网的发展,社会对家居的智能化需求越来越迫切,本文阐述了基于树莓派的智能家居系统的设计与实现,通过采用树莓派为主要模块,搭建一款满足大众需要的智能家居系统.本系统以树莓派为主要开发平台,并基于Ubuntu操作系统进行开发的一种智能家居解决方案,其包含了语音合成、语音识别、图像识别、数据采集、AI对话、视频监控、语音控制、语音日志等功能.可通过语音、手机微信、APP与机器人和传感器进行交互,并能登录Web界面查看相应底层数据并对传感器进行相应控制.系统传感器部分采用ZigBee通信协议,与服务器通信采用MQTT通信协议,两种通讯协议低成本,低功耗,节约网络资源.     

Google home: Experience, support and re-experience of social home activities

Ambient intelligence research is about ubiquitous computing and about social and intelligent properties of computer-supported environments. These properties aim at providing inhabitants or visitors of ambient intelligence environments with support in their activities. Activities include interactions between inhabitants and between inhabitants and (semi-) autonomous agents, including mobile robots, virtual humans and other smart objects in the environment. Providing real-time support requires understanding of behavior and activities. Clearly, being able to provide real-time support also allows us to provide off-line support, that is, intelligent off-line retrieval, summarizing, browsing and even replay, possibly in a transformed way, of stored information. Real-time remote access to these computer-supported environments also allows participation in activities and such participation as well can profit from the real-time capturing and interpretation of behavior and activities performed and supported by ambient intelligence technology. In this paper, we illustrate and support these observations by looking at results obtained in several European and US projects, in particular projects on smart environments, whether they are smart meetings or lecture rooms, smart offices or intelligently monitored events in public spaces. In particular, we look at the augmented multi-party interaction (AMI) project in which we are involved and we try to sketch a framework in which we can transform research results from the meeting context to the home environment context.     

Mixed reality participants in smart meeting rooms and smart home environments

Human–computer interaction requires modeling of the user. A user profile typically contains preferences, interests, characteristics, and interaction behavior. However, in its multimodal interaction with a smart environment the user displays characteristics that show how the user, not necessarily consciously, verbally and nonverbally provides the smart environment with useful input and feedback. Especially in ambient intelligence environments we encounter situations where the environment supports interaction between the environment, smart objects (e.g., mobile robots, smart furniture) and human participants in the environment. Therefore it is useful for the profile to contain a physical representation of the user obtained by multi-modal capturing techniques. We discuss the modeling and simulation of interacting participants in a virtual meeting room, we discuss how remote meeting participants can take part in meeting activities and they have some observations on translating research results to smart home environments.     

基于ROS的服务机器人云端协同计算框架

随着机器人应用技术的发展,服务机器人走进人们的生活日渐成为可能. 但机器人本身计算能力有限,同时仅靠自身的传感器接收的信息也有一定的局限性. 现有的机器人还不足以胜任面对复杂场景的应对能力,也不能够满足人们对服务机器人的期待. 本文设计的云机器人计算框架（cloud robot computing framework, CRCF）通过云端,将智能家居以及其他智能硬件与机器人相结合,为机器人提供更多更广的信息. 同时,CRCF通过互联网结合了其它第三方的云端应用API,为机器人提供更多的服务功能. CRCF框架旨在利用云端的大数据处理能力提升机器人的计算和存储能力,并结合第三方云端应用服务和智能硬件设备来拓展机器人的信息来源和服务功能. 最后,本文通过远程语音控制机器人的实验,验证了CRCF系统平台在结合硬件设备以及第三方云端应用的功能和性能.     

Entity Notation: enabling knowledge representations for resource-constrained sensors

The forthcoming ambient systems will contain a large amount of sensors. Representing the data produced by these sensors in a format suitable for ambient intelligence applications would enable a large number of useful services. However, such formats tend to require processing power and communication bandwidth not available in many sensors utilizing ultra low-power microcontrollers and radio chip solutions. This paper presents a lightweight data representation, Entity Notation, to tackle this problem. Sensors with limited computation and communication capabilities can use Entity Notation to describe the data they produce. Entity Notation can be transformed into knowledge representations in a straightforward manner, and hence, the data produced by sensor nodes can be utilized with ease by any ambient intelligence system compatible with the common knowledge representations. This paper presents the design of Entity Notation, its implementations on embedded sensors and the evaluation of its performance.     

A method for ego-motion estimation in micro-hovering platforms flying in very cluttered environments

We aim at developing autonomous miniature hovering flying robots capable of navigating in unstructured GPS-denied environments. A major challenge is the miniaturization of the embedded sensors and processors that allow such platforms to fly by themselves. In this paper, we propose a novel ego-motion estimation algorithm for hovering robots equipped with inertial and optic-flow sensors that runs in real-time on a microcontroller and enables autonomous flight. Unlike many vision-based methods, this algorithm does not rely on feature tracking, structure estimation, additional distance sensors or assumptions about the environment. In this method, we introduce the translational optic-flow direction constraint, which uses the optic-flow direction but not its scale to correct for inertial sensor drift during changes of direction. This solution requires comparatively much simpler electronics and sensors and works in environments of any geometry. Here we describe the implementation and performance of the method on a hovering robot equipped with eight 0.65 g optic-flow sensors, and show that it can be used for closed-loop control of various motions.     

Using smart phones to access site-specific services

We investigate how smart phones can augment site-specific services - that is, electronic services or applications that reside in a specific location. Site-specific services already exist in the form of ticket machines, electronic information kiosks, interactive product catalogues, and so on. However, integrating users' smart phones into these interactions can enhance service functionality while reducing deployment costs. Our approach offers several benefits. By using personal information stored on smart phones, site-specific services can automatically tailor their actions to suit a particular user. Our research led us to develop the Mobile Service Toolkit: a client-server software framework supporting the development of site-specific services that exploit interaction with smart phones. We discuss the Mobile Service Explorer (MST) and present case studies of two site-specific services implemented using it.     

Dynamic knowledge management from multiple sources in crowdsourcing environments

Due to the spread of smart devices and the development of network technology, a large number of people can now easily utilize the web for acquiring information and various services. Further, collective intelligence has emerged as a core player in the evolution of technology in web 2.0 generation. It means that people who are interested in a specific domain of knowledge can not only make use of the information, but they can also participate in the knowledge production processes. Since a large volume of knowledge is produced by multiple contributors, it is important to integrate and manage knowledge efficiently. In this paper, we propose a social tagging-based dynamic knowledge management system in crowdsourcing environments. The approach here is to categorize and package knowledge from multiple sources, in such a way that it easily links to target knowledge.     

Mobile Agent Connection Establishment and Management (CEMA)—Message Exchange for Pervasive Computing Environments

Pervasive computing is an emerging technology that offers new possibilities to distributed computing and computer networking; it employs a wide variety of smart, ubiquitous devices throughout an individual's working and living environment. Mobile agents are software entities that can migrate between servers (mobile agent environments) of the network accomplishing various tasks on the behalf of their owners. The objective of this paper is to describe a test and prototyping environment for experimenting with mobile agents in pervasive environments. A prototype environment for a novel, proactive infrastructure is described for mobile agent assisted pervasive computing. In addition, a new message passing algorithm is provided for mobile agent connection establishment and management (CEMA). Simulation results show the performance of the proposed approach.     

Ant-based service selection framework for a smart home monitoring environment

Selecting ambient media services in a smart home monitoring environment is challenging. Services in such an environment should be ubiquitous, adaptive, and robust with respect to access and delivery. Many different techniques exist for selecting services in smart environments, for example, dynamic programming, genetic algorithms, and fuzzy logic. However, existing approaches to service selection fail to address the dynamic nature of the services and the requirement of considering the user context and user satisfaction. We address this issue by proposing an ant-inspired service selection framework based on dynamic user preferences and satisfaction. This ant-inspired approach is robust to failures and adaptive to dynamic context. The proposed framework enables different categories of residents (e.g., elderly people, fathers with children, mothers, and so on) to access various media services in such a way that their experiences are optimized with regard to their surrounding environment. Experimental results demonstrate the viability of the proposed framework.     

Integration of service robots in the smart home by means of UPnP: A surveillance robot case study

Despite the large number of electronic devices that coexist in homes, only a few of them can be easily integrated in the same network. Given that smart home applications are based on the integration of many heterogeneous devices in the same network, the lack of interoperability can become a major issue in the development of advanced services. In this paper we tackle this issue developing an adapter to integrate the service robot Rovio into a smart home by means of Universal Plug and Play (UPnP). Different advanced services are designed to explore the possibilities derived from the integration of service robots in the smart home.     

Improving location awareness in indoor spaces using RFID technology

Location awareness is a key issue to improve the development of autonomous entities that are embedded into ubiquitous computing environments. GPS seems to be the best solution to develop outdoor location systems, but the performance of these systems is not good enough to locate objects or humans within indoor environments, mainly if accuracy and precision are required. In this article we propose the use of a cheap and reliable technology as RFID to develop a passive RFID-based indoor location system that is able to accurately locate autonomous entities, such as robots and people, within a defined surface. This system is applied to solve the robot tracking problem. We include the evaluation of the proposal by comparing our system technology performance with other alternatives built on different technologies (Wi-Fi, Bluetooth, IrDA, ultrasound, etc.). We have also performed a location awareness proof concept test to analyze the viability of the approach.     

一种基于Java的安全MANET应用框架设计

无线自组织网络(MANET)中不同的终端设备和物理环境对网络行为有着非常大的影响。在对MANET的研究中，软件模拟并不能完全反映新协议在实际应用中的表现。开发现实中的测试平台和应用程序是促进MANET研究和发展必不可少的步骤。该文提出了一种基于Java平台的安全MANET应用框架——J-SMAF，在不影响网络应用的灵活性和扩展性的前提下，加入了对入侵侦测系统的支持，可以在不同的操作系统和应用环境下方便地构筑健壮、安全的MANET测试平台和应用系统。