This paper describes a non-contact and noise-tolerant heart rate monitoring system using a 24-GHz microwave Doppler sensor. The microwave Doppler sensor placed at some distance from the user's chest detects the small vibrations of the body surface due to the heartbeats. The objective of this work is to detect the instantaneous heart rate (IHR) using this non-contact system in a car, because the possible application of the proposed system is a driver health monitoring based on heart rate variability analysis. IHR can contribute to preventing heart-triggered disasters and to detect mental stress state. However, the Doppler sensor system is very sensitive and it can be easily contaminated by motion artifacts and road noise especially while driving. To address this problem, time-frequency analysis using the parametric method and template matching method are employed. Measurement results show that the Doppler sensor, which is pasted on the clothing surface, can successfully extract the heart rate through clothes. The proposed method achieves 13.1-ms RMS error in IHR measurements conducted on 11 subjects in a car on an ordinary road.

In this paper, we propose a method to construct a scalable sensor data stream delivery system that guarantees the specified delivery quality of service (i.e., total reachability to destinations), even when delivery server resources (nodes) are in a heterogeneous churn situation. A number of P2P-based methods have been proposed for constructing a scalable and efficient sensor data stream system that accommodates different delivery cycles by distributing communication loads of the nodes. However, no existing method can guarantee delivery quality of service when the nodes on the system have a heterogeneous churn rate. As an extension of existing methods, which assign relay nodes based on the distributed hashing of the time-to-deliver, our method specifies the number of replication nodes, based on the churn rate of each node and on the relevant delivery paths. Through simulations, we confirmed that our proposed method can guarantee the required reachability, while avoiding any increase in unnecessary resource assignment costs.

Wireless sensor and actuator networks (WSANs) are expected to become key technologies supporting machine-to-machine (M2M) communication in the Internet of things (IoT) era. However, sensors must be able to provide high demand response (DR) levels despite severely limited battery power. Therefore, as part of efforts to achieve a high DR, we are working on research and development related to radio-on-demand sensor and actuator networks (ROD-SANs). ROD-SAN nodes are equipped with wake-up receivers that allow all nodes to stay in sleep mode for a long period of time, and transmit only after the receiver receives a wake-up signal. In addition, sender nodes can direct the receiver nodes to switch communication channels because the wake-up signal also includes information on the channel to use for communication between each other. However, as the number of nodes utilizing the same channel increases, frequent packet collisions occur, thereby degrading response performance. To reduce packet collisions, we propose an own-channel-utilization based channel switching (OCS) scheme, which is a modification of the average-channel-utilization based switching (ACS) as our previous works. The OCS scheme decides whether or not to switch channels based on a probability value that considers not only average-channel utilization of nearby nodes but also own-channel utilization. This approach permits node switching to other channels by considering the overall utilization states of all channels. In this paper, based on simulations, we show that our scheme can improve the delivery ratio by approximately 15% rather than ACS scheme.

A Brillouin optical correlation domain reflectometry (BOCDR) system, which can set measuring point to arbitrary distance that is aligned in a random order along an optical fiber (i.e., random accessibility), is proposed to measure dynamic strain and experimentally evaluated. This random-access system can allocate measurement bandwidth to measuring point by assigning the measurement times at each measuring point of the total number of strain measurements. This assigned number is not always equally but as necessary for plural objects with different natural frequencies. To verify the system, strain of two vibrating objects with different natural frequencies was measured by one optical fiber which is attached to those objects. The system allocated appropriate measurement bandwidth to each object and simultaneously measured dynamic strain corresponding to the vibrating objects.

Real-time monitoring of heart rate (HR) and body temperature (BT) is crucial for the prognosis and the diagnosis of cardiovascular disease and healthcare. Since current monitoring systems are too rigid and bulky, it is not easy to attach them to the human body. Also, their large current consumption limits the working time. In this paper, we develop a wireless sensor patch for HR and BT by integrating sensor chip, wireless communication chip, and electrodes on the flexible boards that is covered with non-toxic, but skin-friendly adhesive patch. Our experimental results reveal that the flexible wireless sensor patch can efficiently detect early diseases by monitoring the HR and BT in real time.

This paper proposes a distributed delay-compensated observer for a wireless sensor network with delay. Each node of the sensor network aggregates data from the other nodes and sends the aggregated data to the neighbor nodes. In this communication, each node also compensates communication delays among the neighbor nodes. Therefore, all of the nodes can synchronize their sensor measurements using scalable and local communication in real-time. All of the nodes estimate the state variables of a system simultaneously. The observer in each node is similar to the delay-compensated observer with multi-sensor delays proposed by Watanabe et al. Convergence rates for the proposed observer can be arbitrarily designed regardless of the communication delays. The effectiveness of the proposed method is verified by a numerical simulation.

The methods proposed in this paper enable resynchronization when a synchronization deviation occurs in a sensor node without a beacon or an ack in a wireless sensor network under ultra-limited but stable resources such as the energy generated from tiny solar cell batteries. The method for a single-hop network is straightforward; when a receiver does not receive data, it is simply placed in recovery mode, in which the receiver sets its cycle length TB to (b±γ)T, where b is non-negative integer, 0 < γ < 1, and T is its cycle length in normal mode, and in which the receiver sets its active interval WB to a value that satisfies WB ≥ W + γT, where W is its active interval in normal mode. In contrast, a sender stays in normal mode. Resynchronization methods for linear multi-hop and tree-based multi-hop sensor networks are constructed using the method for a single-hop network. All the methods proposed here are complete because they are always able to resynchronize networks. The results of simulations based on the resynchronization methods are given and those of an experiment using actual sensor nodes with wireless modules are also presented, which show that the methods are feasible.

Recent works on enhancing the comfort and convenience of vehicles have revealed the advantage of various wireless sensor nodes for information collection. For the highly required reliability in vehicles, Reliable Wireless Communication based on Substitute Forwarding (RWCSF) has been proposed. It ensures that 99.999% of communications can finish within 20ms, which is the quality of communication equivalent to wired links. However, the performance was evaluated in very limited situations, where two or more communications never occurred simultaneously and no noise was present. In this paper, we enhance RWCSF to achieve the target communication quality even if two or more communications occur simultaneously. In addition, we evaluate the enhanced method in various types of vehicles that create internal noise.

This study proposes a design and calibration method for a small-footprint, low-frequency, and low-power gate leakage timer using a differential leakage technique for IoT applications. The proposed gate leakage timer is different from conventional ones because it is composed of two leakage sources and exploits differential leakage current for the charging capacitor. This solution alleviates the inherent trade-off between small-footprint and low-frequency in the conventional gate leakage timer. Furthermore, a calibration method to suppress variations of the output frequency is proposed in this paper. To verify the effectiveness of the proposed gate leakage timer, a test chip was fabricated using 55-nm-DDC-CMOS technology. The test chip successfully demonstrates the highest figure of merit (FoM) of the product of the capacitor area (0.072µm2) and output frequency (0.11Hz), which corresponds to an improvement by a factor of 2,121 compared to the conventional one. It also demonstrates the operation with 4.5pW power consumption. The total footprint can be reduced to be 28µm2, which enables low-cost and low-power IoT edges. The scaling scenario shows that the proposed technique is conducive to technology scaling.

Very few existing works about inertial sensor based air-writing focused on writing constraints' effects on recognition performance. We proposed a LSTM-based system and made several quantitative analyses under different constraints settings against CHMM, DTW-AP and CNN. The proposed system shows its advantages in accuracy, real-time performance and flexibility.

This paper focuses on on-demand wireless sensor networks (WSNs) where a wake-up receiver is installed into each node. In on-demand WSNs, each node sends a wake-up signal including a wake-up ID assigned to a specific destination node in order to remotely activate its main radio interface. This wake-up control helps each node to reduce energy consumed during idle periods, however, the wake-up signal transmitted before every data transmission results in overhead, which degrades communication quality and increases energy consumption at each sender node. In order to reduce the overhead for wake-up control, in this paper, we propose three schemes. First, we propose a scheme called Double Modulation (DM), where each node embeds the sensing data to be transmitted into the payload field of a wake-up signal. The destination interprets the wake-up message differently depending on its wake-up state: if it is in a sleep state, it treats the message as a wake-up signal, otherwise it extracts the sensing data from the detected message. Second, we propose a scheme called Overhearing (OH), where each node observes the frames transmitted by a destination node and suppresses the transmission of wake-up signal when detecting the active state of their destination. Finally, we propose a hybrid scheme that combines OH and DM schemes. Our simulation results show that the proposed schemes can effectively reduce the negative impact of wake-up overhead, and significantly improve data collection rate and energy-efficiency in comparison to on-demand WSN without the proposed schemes.

As an emerging and promising technique, device-free localization (DFL) has drawn considerable attention in recent years. By exploiting the inherent spatial sparsity of target localization, the compressive sensing (CS) theory has been applied in DFL to reduce the number of measurements. In practical scenarios, a prior knowledge about target locations is usually available, which can be obtained by coarse localization or tracking techniques. Among existing CS-based DFL approaches, however, few works consider the utilization of prior knowledge. To make use of the prior knowledge that is partly or erroneous, this paper proposes a novel faulty prior knowledge aided multi-target device-free localization (FPK-DFL) method. It first incorporates the faulty prior knowledge into a three-layer hierarchical prior model. Then, it estimates location vector and learns model parameters under a variational Bayesian inference (VBI) framework. Simulation results show that the proposed method can improve the localization accuracy by taking advantage of the faulty prior knowledge.

We have developed the flexible dual-gate OFET based pressure sensor using a thin polyethylene naphthalate (PEN, 25 µm) film as a substrate. The performance was equivalent to that fabricated on the glass substrate, and it could also be used on the curved surface. Drain current in the flexible pressure sensor was increased according to the pressure load without application of gate voltage. The magnitude of the change in drain current with respect to pressure application was about 2.5 times larger than that for the device on the glass substrate.

In this work, we report the in situ growth of gold nanoparticles (AuNPs) for the improvement of a transmission surface plasmon resonance (T-SPR) sensor to detect human immunoglobulin G (IgG). Human IgG was immobilized on an activated self-assembled monolayer of 11-mercaptoundecanoic on a gold-coated grating substrate. The T-SPR system was also used to monitor the construction of sensor chips as well as the binding of IgG and anti-IgG conjugated with AuNPs. After specific adsorption with IgG, the T-SPR signal was further enhanced by the in situ growth of AuNPs bound with anti-IgG. Using AuNP conjugation and in situ growth of bound AuNPs, the sensitivity of the IgG immunosensor was improved by two orders of magnitude compared with that without conjugated AuNPs.

Topological inference is the foundation of network performance analysis and optimization. Due to the difficulty of obtaining prior topology information of wireless sensor networks, we propose routing topology inference, RTI, which reconstructs the routing topology from source nodes to sink based on marking packets and probing locally. RTI is not limited to any specific routing protocol and can adapt to a dynamic and lossy networks. We select topological distance and reconstruction time to evaluate the correctness and effectiveness of RTI and then compare it with PathZip and iPath. Simulation results indicate that RTI maintains adequate reconstruction performance in dynamic and packet loss environments and provides a global routing topology view for wireless sensor networks at a lower reconstruction cost.

The changes of temperature, salinity and ocean current in underwater environment, have adverse effects on the communication range of sensors, and make them become temporary failure. These temporarily misbehaving sensors are called dumb nodes. In this paper, an energy-efficient connectivity re-establishment (EECR) scheme is proposed. It can reconstruct the topology of underwater acoustic sensor networks (UASNs) with the existing of dumb nodes. Due to the dynamic of underwater environment, the generation and recovery of dumb nodes also change dynamically, resulting in intermittent interruption of network topology. Therefore, a multi-band transmission mode for dumb nodes is designed firstly. It ensures that the current stored data of dumb nodes can be sent out in time. Subsequently, a connectivity re-establishment scheme of sub-nodes is designed. The topology reconstruction is adaptively implemented by changing the current transmission path. This scheme does't need to arrange the sleep nodes in advance. So it can reduce the message expenses and energy consumption greatly. Simulation results show that the proposed method has better network performance under the same conditions than the classical algorithms named LETC and A1. What's more, our method has a higher network throughput rate when the nodes' dumb behavior has a shorter duration.

Automatic monitoring of food intake in free living conditions is still an open problem to solve. This paper presents a novel necklace-type wearable system embedded with a piezoelectric sensor to monitor ingestive behavior by detecting skin motion from the lower trachea. Detected events are incorporated for food classification. Unlike the previous state-of-the-art piezoelectric sensor based system that employs spectrogram features, we have tried to fully exploit time-domain based signals for optimal features. Through numerous evaluations on the length of a frame, we have found the best performance with a frame length of 70 samples (3.5 seconds). This demonstrates that the chewing sequence carries important information for food classification. Experimental results show the validity of the proposed algorithm for food intake detection and food classification in real-life scenarios. Our system yields an accuracy of 89.2% for food intake detection and 80.3% for food classification over 17 food categories. Additionally, our system is based on a smartphone app, which helps users live healthy by providing them with real-time feedback about their ingested food episodes and types.

The research on inertial sensor based human action detection and recognition (HADR) is a new area in machine learning. We propose a novel time sequence based interval convolutional neutral networks framework for HADR by combining interesting interval proposals generator and interval-based classifier. Experiments demonstrate the good performance of our method.

Data aggregation, which is the process of summarizing a large amount of data, is an effective method for saving limited communication resources, such as radio frequency and sensor-node energy. Packet aggregation in wireless LAN and sensed-data aggregation in wireless sensor networks are typical examples. We propose and analyze two queueing models of fundamental statistical data aggregation schemes: constant interval and constant aggregation number. We represent each aggregation scheme by a tandem queueing network model with a gate at the aggregation process and a single server queue at a transmission process. We analytically derive the stationary distribution and Laplace-Stieltjes transform of the system time for each aggregation and transmission process and of the total system time. We then numerically evaluate the stationary mean system time characteristics and clarify that each model has an optimal aggregation parameter (i.e., an optimal aggregation interval or optimal aggregation number), that minimizes the mean total system time. In addition, we derive the explicit optimal aggregation parameter for a D/M/1 transmission model with each aggregation scheme and clarify that it provides accurate approximation of that of each aggregation model. The optimal aggregation interval was determined by the transmission rate alone, while the optimal aggregation number was determined by the arrival and transmission rates alone with explicitly derived proportional constants. These results can provide a theoretical basis and a guideline for designing aggregation devices, such as IoT gateways.

This paper proposes a novel environmental monitoring strategy, incremental environmental monitoring, that enables scientists to reveal the ecology of wild animals in the field. We applied this strategy to the habitat of endangered freshwater fish. Specifically, we designed and implemented a network-based system using distributed sensors to continuously monitor and record the habitat of endangered fish. Moreover, we developed a set of analytical tools to exploit a variety of sensor data, including environmental time-series data such as amount of dissolved oxygen, as well as underwater video capturing the interaction of fish and their environment. We also describe the current state of monitoring the behavior and habitat of endangered fish and discuss solutions for making such environmental monitoring more efficient in the field.