Continuous monitoring of human's breathing and heart rates is useful in maintaining better health and early detection of many health issues. Designing a technique that can enable contactless and ubiquitous vital sign monitoring is a challenging research problem. This paper presents mmVital, a system that uses 60 GHz millimeter wave (mmWave) signals for vital sign monitoring. We show that the mmWave signals can be directed to human's body and the RSS of the reflections can be analyzed for accurate estimation of breathing and heart rates. We show how the directional beams of mmWave can be used to monitor multiple humans in an indoor space concurrently. mmVital relies on a novel human finding procedure where a human can be located within a room by reflection loss based object/human classification. We evaluate mmVital using a 60 GHz testbed in home and office environment and show that it provides the mean estimation error of 0.43 Bpm (breathing rate) and 2.15 bpm (heart rate). Also, it can locate the human subject with 98.4% accuracy within 100 ms of dwell time on reflection. We also demonstrate that mmVital is effective in monitoring multiple people in parallel and even behind the wall.

Mobile devices supporting the “Internet of Things” often have limited capabilities in computation, battery energy, and storage space, especially to support resource-intensive applications involving virtual reality, augmented reality, multimedia delivery, and artificial intelligence, which could require broad bandwidth, low response latency, and large computational power. Edge cloud or edge computing is an emerging topic and a technology that can tackle the deficiencies of the currently centralized-only cloud computing model and move the computation and storage resources closer to the devices in support of the above-mentioned applications. To make this happen, efficient coordination mechanisms and “offloading” algorithms are needed to allow mobile devices and the edge cloud to work together smoothly. In this survey article, we investigate the key issues, methods, and various state-of-the-art efforts related to the offloading problem. We adopt a new characterizing model to study the whole process of offloading from mobile devices to the edge cloud. Through comprehensive discussions, we aim to draw an overall “big picture” on the existing efforts and research directions. Our study also indicates that the offloading algorithms in the edge cloud have demonstrated profound potentials for future technology and application development.

Continuous monitoring of human’s breathing and heart rates is useful in maintaining better health and early detection of many health issues. Designing a technique that can enable contactless and ubiquitous vital sign monitoring is a challenging research problem. This article presents mmVital, a system that uses 60GHz millimeter wave (mmWave) signals for vital sign monitoring. We show that the mmWave signals can be directed to human’s body and the Received Signal Strength (RSS) of the reflections can be analyzed for accurate estimation of breathing and heart rates. We show how the directional beams of mmWave can be used to monitor multiple humans in an indoor space concurrently. mmVital also provides sleep monitoring with sleeping posture identification and detection of central apnea and hypopnea events. It relies on a novel human finding procedure where a human can be located within a room by reflection loss-based object/human classification. We evaluate mmVital using a 60GHz testbed in home and office environment and show that it provides the mean estimation error of 0.43 breaths per minute (Bpm; breathing rate) and 2.15 beats per minute (bpm; heart rate). Also, it can locate the human subject with 98.4% accuracy within 100ms of dwell time on reflection. We also demonstrate that mmVital is effective in monitoring multiple people in parallel and even behind a wall.

Wireless sensor-actuator networks (WSANs) technology is appealing for use in the industrial Internet of Things (IoT) applications because it does not require wired infrastructure. Battery-powered wireless modules easily and inexpensively retrofit existing sensors and actuators in the industrial facilities without running cabling for communication and power. The IEEE 802.15.4-based WSANs operate at low-power and can be manufactured inexpensively, which makes them ideal where battery lifetime and costs are important. Almost, a decade of real-world deployments of WirelessHART standard has demonstrated the feasibility of using its core techniques including reliable graph routing and time slotted channel hopping (TSCH) to achieve reliable low-power wireless communication in the industrial facilities. Today, we are facing the fourth Industrial Revolution as proclaimed by political statements related to the Industry 4.0 Initiative of the German Government. There exists an emerging demand for deploying a large number of field devices in an industrial facility and connecting them through the WSAN. However, a major limitation of current WSAN standards is their limited scalability due to their centralized routing and scheduling that enhance the predictability and visibility of network operations at the cost of scalability. This paper decentralizes the network management in WirelessHART and presents the first Di stributed ${G}$ raph routing and autonomous ${S}$ cheduling (DiGS) solution that allows the field devices to compute their own graph routes and transmission schedules. The experimental results from two physical testbeds and a simulation study shows our approaches can significantly improve the network reliability, latency, and energy efficiency under dynamics.