With the surging of smartphone sensing, wireless networking, and mobile social networking techniques, Mobile Crowd Sensing and Computing (MCSC) has become a promising paradigm for cross-space and large-scale sensing. MCSC extends the vision of participatory sensing by leveraging both participatory sensory data from mobile devices (offline) and user-contributed data from mobile social networking services (online). Further, it explores the complementary roles and presents the fusion/collaboration of machine and human intelligence in the crowd sensing and computing processes. This article characterizes the unique features and novel application areas of MCSC and proposes a reference framework for building human-in-the-loop MCSC systems. We further clarify the complementary nature of human and machine intelligence and envision the potential of deep-fused human--machine systems. We conclude by discussing the limitations, open issues, and research opportunities of MCSC.

Mobile Crowd Sensing and Computing: The Review of an Emerging Human-Powered Sensing Paradigm

In-home healthcare services based on the Internet-of-Things (IoT) have great business potential; however, a comprehensive platform is still missing. In this paper, an intelligent home-based platform, the iHome Health-IoT, is proposed and implemented. In particular, the platform involves an open-platform-based intelligent medicine box (iMedBox) with enhanced connectivity and interchangeability for the integration of devices and services; intelligent pharmaceutical packaging (iMedPack) with communication capability enabled by passive radio-frequency identification (RFID) and actuation capability enabled by functional materials; and a flexible and wearable bio-medical sensor device (Bio-Patch) enabled by the state-of-the-art inkjet printing technology and system-on-chip. The proposed platform seamlessly fuses IoT devices (e.g., wearable sensors and intelligent medicine packages) with in-home healthcare services (e.g., telemedicine) for an improved user experience and service efficiency. The feasibility of the implemented iHome Health-IoT platform has been proven in field trials.

/pdf/a-health-iot-platform-based-on-the-integration-of-17ypefb4pq.pdf

A Health-IoT Platform Based on the Integration of Intelligent Packaging, Unobtrusive Bio-Sensor, and Intelligent Medicine Box

Social networking concepts have been applied to several communication network settings, which span from delay-tolerant to peer-to-peer networks. More recently, one can observe a flourish of proposals aimed at giving social-like capabilities to the objects in the Internet of Things. Such proposals address the design of conceptual (and software) platforms, which can be exploited to easily develop and implement complex applications that require direct interactions among objects. The major goal is to build techniques that allow the network to enhance the level of trust between objects that are "friends" with each other. Furthermore, a social paradigm could definitely guarantee network navigability even if the number of nodes becomes orders of magnitude higher than in the traditional Internet. Objectives of this article are to analyze the major opportunities arising from the integration of social networking concepts into the Internet of Things, present the major ongoing research activities, and point out the most critical technical challenges.

From "smart objects" to "social objects": The next evolutionary step of the internet of things

Physical activity recognition using embedded sensors has enabled many context-aware applications in different areas, such as healthcare Initially, one or more dedicated wearable sensors were used for such applications However, recently, many researchers started using mobile phones for this purpose, since these ubiquitous devices are equipped with various sensors, ranging from accelerometers to magnetic field sensors In most of the current studies, sensor data collected for activity recognition are analyzed offline using machine learning tools However, there is now a trend towards implementing activity recognition systems on these devices in an online manner, since modern mobile phones have become more powerful in terms of available resources, such as CPU, memory and battery The research on offline activity recognition has been reviewed in several earlier studies in detail However, work done on online activity recognition is still in its infancy and is yet to be reviewed In this paper, we review the studies done so far that implement activity recognition systems on mobile phones and use only their on-board sensors We discuss various aspects of these studies Moreover, we discuss their limitations and present various recommendations for future research

https://ris.utwente.nl/ws/files/6518323/sensors-15-02059.pdf

A survey of online activity recognition using mobile phones

The research on the efforts of combining human and machine intelligence has a long history. With the development of mobile sensing and mobile Internet techniques, a new sensing paradigm called Mobile Crowd Sensing (MCS), which leverages the power of citizens for large-scale sensing has become popular in recent years. As an evolution of participatory sensing, MCS has two unique features: (1) it involves both implicit and explicit participation; (2) MCS collects data from two user-participant data sources: mobile social networks and mobile sensing. This paper presents the literary history of MCS and its unique issues. A reference framework for MCS systems is also proposed. We further clarify the potential fusion of human and machine intelligence in MCS. Finally, we discuss the future research trends as well as our efforts to MCS.

From Participatory Sensing to Mobile Crowd Sensing

The Internet of Things (IoT) represents the future technology trend of sensing, computing, and communication. Under the Wisdom Web of Things (W2T) vision, the next-generation Internet will promote harmonious interaction among humans, computers, and things. Current research on IoT is primarily conducted from the perspective of identifying, connecting, and managing objects. In this paper, however, we attempt to enhance the IoT with intelligence and awareness under the W2T vision. By exploring the various interactions between humans and the IoT, we extract the "embedded" intelligence about individual, environment, and society, which can augment existing IoT systems with user, ambient, and social awareness. The characteristics, major applications, research issues, the reference architecture, as well as our ongoing efforts to embedded intelligence are also presented and discussed.

From the internet of things to embedded intelligence

Activity recognition plays an important role for pervasive healthcare such as health monitoring, assisted living and pro-active services. Despite of the continuous and transparent sensing with various built-in sensors in mobile devices, activity recognition on mobile devices for pervasive healthcare is still a challenge due to the constraint of resources, such as battery limitation, computation workload, etc. Keeping in view the demand of energy-efficient activity recognition, we propose a hierarchical method to recognize user activities based on a single tri-axial accelerometer in smart phones for health monitoring. Specifically, the contribution of this paper is two-fold. First, it is demonstrated that the activity recognition based on the low sampling frequency is feasible for the long-term activity monitoring. Second, this paper presents a hierarchical recognition scheme. The proposed algorithm reduces the opportunity of usage of time-consuming frequency-domain features and adjusts the size of sliding window to improve recognition accuracy. Experimental results demonstrate the effectiveness of the proposed algorithm, with more than 85 % recognition accuracy rate for 11 activities and 3.2 h extended battery life for mobile phones. Our energy efficient recognition algorithm extends the battery time for activity recognition on mobile devices and contributes to the health monitoring for pervasive healthcare.

Energy-Efficient Motion Related Activity Recognition on Mobile Devices for Pervasive Healthcare

A survey on big data-driven digital phenotyping of mental health

The massive spread of false information on social media has become a global risk, implicitly influencing public opinion and threatening social/political development. False information detection (FID) has thus become a surging research topic in recent years. As a promising and rapidly developing research field, we find that much effort has been paid to new research problems and approaches of FID. Therefore, it is necessary to give a comprehensive review of the new research trends of FID. We first give a brief review of the literature history of FID, based on which we present several new research challenges and techniques of it, including early detection, detection by multimodal data fusion, and explanatory detection. We further investigate the extraction and usage of various crowd intelligence in FID, which paves a promising way to tackle FID challenges. Finally, we give our views on the open issues and future research directions of FID, such as model adaptivity/generality to new events, embracing of novel machine learning models, aggregation of crowd wisdom, adversarial attack and defense in detection models, and so on.

The Future of False Information Detection on Social Media: New Perspectives and Trends

In the Internet-of-Things (IoT) era, user authentication is essential to ensure the security of connected devices and the customization of passive services However, conventional knowledge-based and physiological biometric-based authentication systems (eg, password, face recognition, and fingerprints) are susceptible to shoulder surfing attacks, smudge attacks, and heat attacks The powerful sensing capabilities of IoT devices, including smartphones, wearables, robots, and autonomous vehicles enable continuous authentication (CA) based on behavioral biometrics The artificial intelligence (AI) approaches hold significant promise in sifting through large volumes of heterogeneous biometrics data to offer unprecedented user authentication and user identification capabilities In this survey article, we outline the nature of CA in IoT applications, highlight the key behavioral signals, and summarize the extant solutions from an AI perspective Based on our systematic and comprehensive analysis, we discuss the challenges and promising future directions to guide the next generation of AI-based CA research

Yunji Liang

Papers

From the internet of things to embedded intelligence

Energy-Efficient Motion Related Activity Recognition on Mobile Devices for Pervasive Healthcare

A survey on big data-driven digital phenotyping of mental health

The Future of False Information Detection on Social Media: New Perspectives and Trends

Behavioral Biometrics for Continuous Authentication in the Internet-of-Things Era: An Artificial Intelligence Perspective