This paper presents ZEL+, a wearable lifelogging system designed to operate with net-zero energy consumption by leveraging multiple energy harvesting technologies for continuous context sensing. Self-powered wearable devices often encounter difficulties in environments with inconsistent or low-intensity ambient energy, particularly in indoor settings. To address this challenge, ZEL+ incorporates three key design features. First, it employs a power-switching mechanism based on dual comparators and a capacitor to manage surplus energy and support operation under varying lighting conditions. Second, the system integrates heterogeneous energy harvesters not only as power sources but also as sensing elements. Specifically, a dye-sensitized solar cell provides stable responses under low-light indoor environments, while an amorphous solar cell exhibits sensitivity to changes in ambient illumination; together with a piezoelectric element capturing motion-induced signals, these components contribute complementary cues for location and activity recognition. Third, a Spatial Consistency-Based Correction (SCC) algorithm is applied as a post-processing step to mitigate transient recognition errors and improve the coherence of inferred lifelogs. The system is implemented as a 192 g nametag-shaped wearable device and evaluated in a real-world office environment with 11 participants. Under a person-dependent setting, ZEL+ achieved an accuracy of 96.62% for 8-location place recognition and 97.09% for static/dynamic activity recognition, while maintaining robust performance on more fine-grained tasks. In terms of energy sustainability, the device sustained autonomous operation using harvested energy alone for approximately 93.97% of a standard 8-hour office workday. These results indicate that ZEL+ provides a practical and energy-sustainable solution for continuous lifelogging in indoor mobile computing environments.

To reduce the cost of Internet of Things (IoT) system deployment, we are focusing on the cost reduction of device location information setup and developing a room-by-room device grouping system. In our previous work, we presented a room-by-room IoT device grouping based on wireless LAN (WLAN) channel state information (CSI) using unsupervised learning. The performance in a practical environment, however, is significantly degraded due to the nonuniform time distributions of where people stay in each room. In this paper, we present CSI sampling, namely, a CSI data selection method, relying on independent component analysis (ICA) to improve the device grouping performance in a practical environment. An experimental evaluation conducted in a practical environment reveals that our CSI sampling greatly improved device grouping performance with an adjusted Rand index (ARI) of up to 44.9%.

Figure 1: Overview of picoRing mouse, enabling 30-500 uW-class ultra-low-power wireless ring mouse for ubiquitous finger input.The ring can potentially operate over a month on a single charge of a 27 mAh battery (https://youtu.be/7RazVNMx0Ms).

IoT malware is rapidly increasing due to variants easily generated from publicly available source codes. Malware image classification capable of fast and accurate malware identification attracts attention. Since the classification by imaging is affected by malware binary changes, a binary modification without behavioral changes can be a potential attacking method to the classification by imaging. There are concerns that by combining the publicly available malware source code with readily available source code obfuscation tools, it is possible to construct an effective attack that bypasses image classifiers relatively simply. In this study, we show the effectiveness of the attack by source code obfuscation and the possibility of defense against the attack. The contribution of this research is twofold. 1) We showed that Obfuscator-LLVM (oLLVM) code obfuscation could be used as an attack method on malware image classification. The obfuscated malware binaries made by oLLVM were misclassified by VGG16-based image classifier for all the attacked malware families including Mirai, Lightaidra, and Bashlite. 2) We showed that classifier training with obfuscated samples could address this attack method. We confirmed that the malware image classifier trained with obfuscated malware binaries made by oLLVM could classify with an accuracy of 100% the malware family with obfuscation as the obfuscated original malware family.

Recently, the studies of wireless device-free human sensing technology have dramatically advanced with enabling a variety of applications, from activity recognition to vital sign monitoring. In this paper, we propose Wi-Nod which leverages the Wi-Fi Channel State Information (CSI) to detect head nodding gestures for each Morse code symbol based on time-frequency features for accurate recognition accuracy in multi-human context environment. The system consists of three basic modules: data collection, data preprocessing, and learning part based on the inception model. The model was trained to perform the head movement detection based on the CSI spectrogram collected by the ESP32 nodes. We evaluated the performance of the system on four different data sets collected in two different sessions. Our system achieves over 95% recognition accuracy that reveals the feasibility of Wi-Nod system for real-life deployment.

In this study, we propose a Put-and-Play (PnP) Internet of Things (IoT) system, an IoT system that requires no initial setup. IoT systems require the initial setup consisting of device location information setup, network configurations, and device coordination. Although configuration automation and assistant methods for network configurations and device coor-dination have been proposed, device location information setup still needs manual operations. This paper therefore proposes a room-by-room device grouping method that groups IoT devices in the same room. We utilize IEEE 802.11ac Channel State Information (CSI) to group IoT devices in the same room with a non-supervised learning algorithm. Experimental evaluations conducted in a smart house environment reveal that our device grouping method successfully groups IoT devices in the same room with an adjusted Rand index (ARI) of up to 1.00.

We present ZEL, the first net-zero-energy lifelogging system that allows office workers to collect semi-permanent records of when, where, and what activities they perform on company premises. ZEL achieves high accuracy lifelogging by using heterogeneous energy harvesters with different characteristics. The system is based on a 192-gram nametag-shaped wearable device worn by each employee that is equipped with two comparators to enable seamless switching between system states, thereby minimizing the battery usage and enabling net-zero-energy, semi-permanent data collection. To demonstrate the effectiveness of our system, we conducted data collection experiments with 11 participants in a practical environment and found that the person-dependent (PD) model achieves an 8-place recognition accuracy level of 87.2% (weighted F-measure) and a static/dynamic activities recognition accuracy level of 93.1% (weighted F-measure). Additional testing confirmed the practical long-term operability of the system and showed it could achieve a zero-energy operation rate of 99.6% i.e., net-zero-energy operation.

Due to the spread of COVID-19, we are desired to avoid crowded places including public transportation. Kyushu University has the largest campus in Japan, called "Ito campus", and the population there is about 20,000 in which 23% of students and 46% of staff use a bus for reaching the campus. The lectures in the first half of 2020 have been conducted online, but we plan to resume face-to-face lectures gradually. At that time, we expect the bus stops and buses to be crowded, especially during rush hour. In this paper, we introduce a system, called Itocon, to visualize the human congestion of bus stops around the campus.

As society becomes increasingly interconnected, the need for sophisticated signal processing and data analysis techniques becomes increasingly apparent, particularly in the field of Intelligent Transportation Systems (ITS) where various sensing applications generate data at an exponential rate. In this paper, we put a forward a compressive sensing-based system to extract information from passing vehicle sounds sampled at sub-Nyquist rates for Acoustic Vehicle Detection and Identification (AVDI) applications. The obtained compressive measurements are used to detect and identify passing vehicles. Initial evaluation performed using data obtained from roads on a university campus presents an accuracy of 86.2 % with a back-end ADC sample rate of 3 kHz.

Bluetooth Low Energy (BLE)-based localization is a promising candidate of indoor localization for low power mobile and Internet of Things (IoT) systems. Localization accuracy of BLE-based localization systems is lower than other localization technologies relying on wideband wireless communication such as WiFi due to limited channel bandwidth. In our previous paper, we proposed an accuracy improvement method named separate channel fingerprinting (SCF), which, however, suffers from a high maximum localization error more than 6 meters. This paper therefore presents 2-step separate channel fingerprinting (2SSCF). 2S-SCF first coarsely estimates location by conventional BLE fingerprinting and utilizes SCF to estimate the fine-grained location. We experimentally demonstrate that 2S-SCF successfully reduced localization errors 95 % of time with a small maximum localization error.

Enabling direct communication between wireless technologies immediately brings significant benefits including, but not limited to, cross-technology interference mitigation and context-aware smart operation. To explore the opportunities, we propose FreeBee-a novel cross-technology communication technique for direct unicast as well as cross-technology/channel broadcast among three popular technologies of WiFi, ZigBee, and Bluetooth. The key concept of FreeBee is to modulate symbol messages by shifting the timings of periodic beacon frames already mandatory for diverse wireless standards. This keeps our design generically applicable across technologies and avoids additional bandwidth consumption (i.e., does not incur extra traffic), allowing continuous broadcast to safely reach mobile and/or duty-cycled devices. A new interval multiplexing technique is proposed to enable concurrent broadcasts from multiple senders or boost the transmission rate of a single sender. Theoretical and experimental exploration reveals that FreeBee offers a reliable symbol delivery under a second and supports mobility of 30 mph and low duty-cycle operations of under 5%.

Sensor localization is one of the big problems when building large scale indoor sensor networks since GPS is unavailable in indoor environments. Many indoor localization systems have been proposed to tackle the sensor localization problem, yet user cooperation or many anchor nodes are required. In this paper, we propose a sensor localization system using WiFi APs as anchors. WiFi APs are largely installed in indoor environments and are managed by a network system manager. Using WiFi APs as anchors, we can localize sensor nodes without newly deployed anchor nodes. As a first step of our sensor localization system, this paper presents a WiFi AP-RSS monitoring system using sensor nodes. Sensor nodes are equipped with IEEE802.15.4 (ZigBee) modules, which cannot demodulate WiFi (IEEE802.11) signals. We therefore developed a cross-technology signal extraction scheme on sensor nodes. We herein describe the design and implementation of our AP-RSS monitoring system. The experimental evaluations show that our AP-RSS monitoring system successfully retrieves AP-RSS with an average error of 1.26dB.

To reduce the complexity of scheduling while exploiting the advantages of TDMA-based data collection, we propose TKN-TWN, a token-scheduled multi-channel data collection protocol. The TKN-TWN uses two tokens to arbitrate data-packet transmissions and associates the ownership of tokens with transmission slot assignment. For the reduction of scheduling burden, the TKN-TWN provides topology adaptability while maintaining high throughput in burst data transfer. In this paper, we present the system design of TKN-TWN with enhancement toward topology adaptability based on a previous work. We evaluate TKN-TWN on a local test bed using 31 TelosB sensor nodes. Evaluation results show that the TKN-TWN achieves throughput of 6.4 KByte/s with more than 99% delivery ratio even with occasional node failures.

This paper presents a preambleless communication system to realize industrial wireless control networks (WCN) which are characterized by short payloads and strict real-time requirements. The reduction of the preamble is greatly beneficial for the real-time operation because the transmission time of preambles is longer than that of the short payloads. Our system is implemented on a software defined radio (SDR) platform using GNU Radio toolkit. Experiments show the packet error rate (PER) performance is close to that of the theoretical value in an AWGN (additive white Gaussian noise) environment. Demonstrations show the real-time remote control of inverted pendulums on this system.