Digital Twins (DTs) are rapidly emerging as a transformative paradigm for real-time monitoring, simulation, and decision-making across domains such as smart mobility, industrial automation, and intelligent infrastructure. However, current DT implementations often rely on computationally intensive deep learning pipelines and vision-based sensing, which hinder their deployment in resource-constrained or privacy-sensitive environments. In this work, we introduce RadarVision-Twin, a lightweight, interpretable, and edge-deployable Digital Twin system that fuses mmWave radar and RGB camera inputs for real-time anomaly detection and feedback-driven visualization. Unlike traditional camera-centric systems, RadarVision-Twin leverages radar spectrograms to extract motion energy and camera frames to estimate edge density—two physically meaningful features that capture dynamic behavior and scene structure, respectively. These features are fused and fed into an XGBoost classifier, chosen for its efficiency and explainability, to detect motion anomalies in real-time. Our system supports synchronized multimodal visualization, live anomaly flagging, and operator feedback, forming a closed-loop DT that evolves with its environment. We validate our approach on a large-scale multimodal dataset comprising over 14,000 radar-camera frames, demonstrating that our fusion strategy achieves macro F1-scores of 98%, surpassing unimodal baselines. Furthermore, the system runs at low latency on commodity hardware and requires no GPU acceleration, making it suitable for embedded or edge deployments. By eliminating the need for deep models while maintaining interpretability and responsiveness, RadarVisionTwin paves the way for transparent, low-power, and privacy-aware Digital Twins in future 6G-enabled environments.

Millimeter-Wave radar has emerged as a promising sensing modality for autonomous vehicles due to its robustness in adverse environmental conditions such as fog, rain, and low light. However, traditional radar processing pipelines relying on range-Doppler and angle-of-arrival estimation often produce sparse and noisy outputs, making object detection challenging. In this paper, we propose FouriDAR, a lightweight radar-only object detection network that, for the first time in this domain, applies the Fourier Network architecture for global frequency-domain modeling. By replacing the attention mechanism in Transformer-Based designs with Fourier transforms, our approach captures long-range spatial dependencies with significantly lower computational complexity. In addition, a self-learned MIMO preprocessing pipeline restructures radar input into learnable feature tensors, enhancing angular and Doppler cues for robust detection. Evaluated on the RADIal benchmark, FouriDAR achieves 72.3% average precision and 75.8% recall, outperforming existing radar-only models, while requiring only 1.2M parameters and 3.5 GFLOPs. These results demonstrate that introducing the Fourier Network to radar-based perception, coupled with adaptive MIMO-aware preprocessing, provides a new and efficient foundation for autonomous driving.

Accurate measurement of propeller rotation speed is essential for stable drone control. While various sensors have been employed for this purpose, their cost, size, and additional weight pose challenges, particularly for small indoor drones. This paper presents a method for estimating propeller rotational speed using Wi-Fi Channel State Information (CSI), which captures fine-grained wireless signal fluctuations and is obtainable on many commercial Wi-Fi devices. The proposed approach applies resampling and short-time Fourier transform (STFT) to the amplitude component of CSI to estimate propeller rotational speed. In this study, experiments were conducted using both fixed propellers and a hovering drone in indoor environments. The results revealed that antenna placement and polarization alignment significantly affect the accuracy of rotation speed estimation. With a tilted antenna configuration, the estimation error rate remained below 3.16% in all STFT windows (each 0.5 -second estimation interval). Furthermore, even with a horizontally placed antenna, selecting dominant subcarriers through signal processing reduced the estimation error rate to below 4% in over 95% of STFT windows. In addition, by evaluating three distances ($2 \mathrm{m}, 4 \mathrm{m}$, and 8 m) and varying the antenna elevation from 0° to 90°, we identified optimal orientations that reduced RMSE to as low as 23.60 RPM, achieving a mean error of only 1.24% at 8 m. These findings demonstrate the feasibility of noncontact propeller rotational speed estimation using Wi-Fi CSI and suggest its potential for real-time control applications.

Due to the rapid development of the Internet of Things (IoT) applications, which generate vast volumes of data at high speeds, security and privacy issues have become challenging. IoT devices use the advanced encryption standard algorithm before transmission. However, since the system communicates through amplify-and-forward relays, the data may be leaked through the untrusted relays. Depending on a mathematical tool may affect the data security vulnerability. Thus, to enhance the system security, physical layer security is used to transmit a jamming signal to confuse the untrusted relay nodes. Hence, the proposed framework ensures security by combining the physical and data layer security which comes with a cost regarding system complexity, system latency, and energy consumption. The proposed framework addresses the joint problem of physical and data layer security, multicell association, task offloading, users’ power allocation, and service caching in multitier communication and edge computing networks. The objective is to minimize the system latency and energy consumption under the secrecy capacity constraint. Due to the NP-hard nature of the joint problem, we use a low-complexity Lyapunov drift-plus-penalty optimization technique based on the Gibbs sampling algorithm. The simulation results demonstrate the proposed framework’s superiority over the state-of-the-art in terms of high secrecy capacity and low computational complexity. When the secrecy capacity threshold increases, the secrecy capacity is enhanced by approximately 5.72%, while the system latency and energy consumption increase by 38.18% and 69.99%, respectively, compared to the literature.

Indoor localization has become a critical area of research with increasing relevance in applications. While numerous technologies have been explored, WiFi-based fingerprinting solutions using Received Signal Strength Indicators from multiple access points have garnered substantial attention due to the ubiquity of WiFi networks. However, these methods often encounter challenges, including fluctuation of access points and their noise signal measurements. Furthermore, they struggle to adapt effectively to cluttered or dynamically changing environments. In this paper, we introduce GraphLy: a Graph Neural Network-based model explicitly designed to tackle these challenges. GraphLy captures complex spatial relationships between different locations and adapts to environmental complexities and clutter, offering a robust solution for indoor localization. Our experiments demonstrate that GraphLy outperforms state-of-the-art WiFi-based localization techniques in two cluttered and propagation complex testbeds. In particular, we achieved a performance improvement of at least 37% and 65% in the two environments, respectively. These findings underscore the potential of GraphLy to enhance indoor localization accuracy and reliability for various real-world applications.

Recently, there is growing interest in Wi-Fi CSI-based activity recognition due to its low setup costs. However, accurate CSI-based activity recognition depends on the number of Wi-Fi devices, which is suboptimal cost-wise. Our proposed solution is to use low-power backscatter tags within a Wi-Fi CSI sensing system, collecting multiple CSI data streams from various Wi-Fi channels. This enhances the number of observations without the need to install a large number of Wi-Fi devices. We evaluated classification of five daily activities using traditional Wi-Fi CSI and backscattered CSI, finding an accuracy improvement by combining them.

WiFi-based human sensing has exhibited remarkable potential to analyze user behaviors in a non-intrusive and device-free manner. However, most previous works focus on single-user sensing, which has limited practicability in scenarios involving multiple users. In this paper, we introduce YOSO a novel system employing a multi-label multi-view Transformer-based architecture to address these issues, enabling simultaneous localization and activity recognition. By applying advanced preprocessing techniques and utilizing the Transformer's self-attention mechanism, our system effectively learns high-dimensional representations of human activities and locations from CSI data. This approach overcomes traditional sequential data processing limitations, offering precise activity recognition and localization in multiperson environments. Our experimental results showcase superior performance in both localization and activity recognition tasks, surpassing existing methods. The real-time processing capability of our system paves the way for applications in smart environments, security, and healthcare monitoring, providing an efficient tool for situational awareness and advancing wireless sensing technology.

Due to the wide range of intensive computational applications and ubiquitous connectivity of the Internet of Things (IoT) paradigms, it has become crucial to develop a new platform that can achieve low delay, high network throughput, and enhanced quality of service (QoS). This paper proposes a joint user association, service caching, and task offloading strategy to reduce delay and enhance users’ QoS in multi-tier communication and multi-tier edge computing heterogeneous networks (HetNets). The considered system model consists of multi-users with different tasks and service data sizes communicating in a heterogeneous network of one massive multiple-input multiple-output (M-MIMO) macro base station and some small base stations. The proposed work investigates user association, power allocation , optimum service data caching, and task offloading strategies at the computing network edges. Thereby, the objectives of this work are to propose an efficient framework to reduce the system delay, increase the network throughput, and meet the user requirements in multi-tier communication and multi-tier edge computing heterogeneous networks . The simulation results show that the proposed algorithm outperforms the state-of-the-art with a 49.48% decrease in system delay, 80% reduction in cost and hardware complexity in terms of the reduced number of installed antennas, and 48.58% enhancement in the network throughput.

The increasing use of trajectory data and machine learning has advanced our understanding of human behavior. Specifically, in situations such as team sports, in which multiple players form a group and interact with each other, predicting performance using their trajectory data as input has been attracting attention. However, understanding the role and contributions of individuals in the group by a deep understanding of the whole trajectory has not yet been well-investigated. In this study, we propose a method to quantitatively evaluate the single player’s contribution based on a deep learning model that predicts shooting success from the trajectory data of all players and a ball, leveraging official data of a professional basketball league. We use the difference of two output values by the prediction model when the trajectory data of the target player is given or not given as the model inputs. Our evaluation using the professional basketball dataset for one season confirmed that the predictive model had an accuracy of AUC=0.92. We also confirmed that the scoring contribution of each player calculated from this predictive model was significantly correlated with an existing player’s overall performance metric (R = 0.37, p < 0.001). The results suggest that our proposed method could be a new method to quantify a player's contribution to the team performance from trajectory data only instead of conventional experience-based player performance metrics.

Understanding humans and its environment is a key enabler of smart, intelligent applications and services for a future smart society. To deploy such services in our ambient environment, it is expected to fully utilize battery-less and maintenance-free IoT devices and technologies for more ambient, distributed computing. In recent years, Wi-Fi-based communications are becoming more energy-efficient, and channel state information (CSI) has the potential to sense more detailed information about the things in the real world. Besides, ambient backscatter has appeared as a promising technology for zero-energy sensing and communications. Leveraging those state-of-the-art technologies, energy harvested IoT devices for context recognition of humans and objects will be in reality. A significant challenge is how to make use of inferior, less-powerful zero-energy IoT devices to achieve processing of interest, i.e., accurate recognition of humans and objects, while a single device does not work. Therefore, we consider orchestrating distributed tiny IoT devices for both sensing and communications. Particularly, distributed machine learning in the local environment will achieve highly promising sensing in our ambient environment. In this paper, we survey the state-of-the-art technologies for zero-energy sensing and communications in the context of humans and objects sensing and recognition. Then, we address the challenges to be tackled in terms of such distributed, intelligent sensing using zero-energy devices. Finally, we introduce the concept of utilizing distributed IoT devices, followed by the statement about our ongoing work toward future zero-energy sensing and processing.

Recently, several problems concerning smart and connected communities (S&CC) have been studied. In many S&CC applications such as autonomous driving, mobile crowdsourcing and crowd sensing, the mobility of vehicles and pedestrians has large impact on their performance and reliability. Many research works have used mobility generators to simulate realistic mobility, and evaluated the performance and reliability of the proposed applications and protocols. Although such mobility generators might be useful for producing typical mobility patterns, those mobility patterns are just snapshots and they cover only some part in the possible mobility patterns. Since many S&CC applications are used as social systems, their reliability and efficiency are very important. In order to improve the reliability of such mobility aware applications and accurately evaluate their performance, we need to collect many mobility patterns via simulation and/or observation from the real world, analyze their mobility influence statistically and provide adequate design platforms. In this paper, we first show the fact that many research works adopt snapshot-based mobility analyses. Then we propose a technique to reproduce a large part of possible mobility patterns, and provide a design platform to analyze their features and develop high-reliable mobility aware applications for S&CC. Some experimental results are also given.

We present CrowdMeter: a participatory system that leverages the sensed data collected from users' phones during their daily train commutes to gauge the real-time congestion level in railway stations. CrowdMeter tracks the passenger's position in the station as well as identifies her context (e.g., waiting for a train, buying a ticket) along her trajectory from the station's entrance to the train. Therefrom, CrowdMeter extracts novel features, based on the user's location and context, from the phone sensors. These features capture the passenger's behavior (e.g., the walking pattern) and the ambient environment characteristics (e.g., the ambient sound) that can indicate the surrounding congestion level along the passenger's route in a railway station. Finally, the system highlights each area of the station with a specific color (green, amber, red) that corresponds to one of a three congestion levels (low, medium, high).Evaluation of CrowdMeter through a field experiment in 10 different train stations in Japan shows that it can infer the congestion levels accurately, highlighting its promise as a ubiquitous travel-support service.

Recently, several researches concerning with smart and connected communities have been studied. Soon the 4G / 5G technology becomes popular, and cellular base stations will be located densely in the urban space. They may offer intelligent services for autonomous driving, urban environment improvement, disaster mitigation, elderly/disabled people support and so on. Such infrastructure might function as edge servers for disaster support base. In this paper, we enumerate several research issues to be developed in the ICDCS community in the next decade in order for building safe, smart cities resistant to disasters. In particular, we focus on (A) up-to-date urban crowd mobility prediction and (B) resilient disaster information gathering mechanisms based on the edge computing paradigm. We investigate recent related works and projects, and introduce our on-going research work and insight for disaster mitigation.

Water accounts for about 60% of the human body, and when the body loses it (e.g., through urine, sweat, etc.) in higher rate than its intake rate (through drinking), dehydration symptoms occur. The dehydration causes many severe health problems like organ and cognitive impairment. Therefore, it is critical for the human to drink water in a sustained manner to avoid dehydration. To prevent humans from dehydration, continuous day-scale tracking of the water intake is needed. In this paper, we propose an unobtrusive method to recognize the drinking activity as well as estimate the water intake amount in milliliter scale by leveraging smartwatches. Our basic idea is to track the arm motion and discriminate the drinking activities from the similar hand-based motions like food intake, phone calls, etc. Thereafter, we estimate the water intake amount from the drinking duration.

We present TransitLabel, a crowd-sensing system for automatic enrichment of transit stations indoor floorplans with different semantics like ticket vending machines, entrance gates, drink vending machines, platforms, cars' waiting lines, restrooms, lockers, waiting (sitting) areas, among others. Our key observations show that certain passengers' activities (e.g., purchasing tickets, crossing entrance gates, etc) present identifiable signatures on one or more cell-phone sensors. TransitLabel leverages this fact to automatically and unobtrusively recognize different passengers' activities, which in turn are mined to infer their uniquely associated stations semantics. Furthermore, the locations of the discovered semantics are automatically estimated from the inaccurate passengers' positions when these semantics are identified. We evaluate TransitLabel through a field experiment in eight different train stations in Japan. Our results show that TransitLabel can detect the fine-grained stations semantics accurately with 7.7% false positive rate and 7.5% false negative rate on average. In addition, it can consistently detect the location of discovered semantics accurately, achieving an error within 2.5m on average for all semantics. Finally, we show that TransitLabel has a small energy footprint on cell-phones, could be generalized to other stations, and is robust to different phone placements; highlighting its promise as a ubiquitous indoor maps enriching service.

We present PassActiv, a mobile sensing system for the automatic activity recognition of railway passengers. Our key observations show that certain passengers' activities (e.g., purchasing tickets, etc) present identifiable signatures on one or more cell-phone sensors which can be leveraged to automatically recognize those activities. Evaluation of PassActiv through a field experiment in major train and subway stations in Japan shows that PassActiv can detect different activities accurately with at most 3% false positive rate and 4% false negative rate for all types of passengers' activities.

This paper proposes a method of aggregating tempo-spatial data generated by sensors deployed in buildings or houses. The size of each sensor data such as temperature is usually small, but it often involves many additional data to represent its attribute values like time, location, data type and data precision. This would often increase the traffic volume between sensor gateway at building/home side and service providers at server side. In our method, such sensor data are packed into multidimensional matrices indexed by those attribute values for more compact representation, and the compressed sensing technique is adaptively applied to further reduce the data size. The method was applied to a field trial with KDDI corporation to collect data from 29 community facilities, and the traffic volume was reduced to 50% with reasonable precision of data restoration.

We propose a method to estimate car-level train congestion using Bluetooth RSSI observed by passengers' mobile phones. Our approach employs a two-stage algorithm where car-level location of passengers is estimated to infer car-level train congestion. We have learned Bluetooth signals attenuate due to passengers' bodies, distance and doors between cars through the analysis of over 50,000 Bluetooth real samples. Based on this prior knowledge, our algorithm is designed as a Bayesian-based likelihood estimator, and is robust to the change of both passengers and congestion at stations. The car-level positions are useful for passengers' personal navigation inside stations and car-level train congestion information helps determine better strategies of taking trains. Through a field experiment, we have confirmed the algorithm can estimate the location of 16 passengers with 83% accuracy and also estimate train congestion with 0.82 F-measure value in average.

We propose an opportunistic ad hoc localization algorithm called Urban Pedestrians Localization (UPL), for estimating locations of mobile nodes in urban districts. The design principles of UPL are twofold. First, we assume that location landmarks are deployed sparsely due to deployment-cost constraints. Thus, most mobile nodes cannot expect to meet these location landmarks frequently. Each mobile node in UPL relies on location information received from its neighboring mobile nodes instead in order to estimate its area of presence in which the node is expected to exist. Although the area of presence of each mobile node becomes inexact as it moves, it can be used to reduce the areas of presence of the others. Second, we employ information about obstacles such as walls, and present an algorithm to calculate the movable areas of mobile nodes considering obstacles for predicting the area of presence of mobile nodes accurately under mobility. This also helps to reduce each node's area of presence. The experimental results have shown that UPL could be limited to 0.7r positioning error in average, where r denotes the radio range by the above two ideas.

The electronic triage system developed by our research group is a wireless sensor network, constructed by electronic triage tags composed of vital sign sensors and ZigBee modules. The system assumes a network of hundreds of patients, simultaneously transmitting data to a remote sink. We have investigated the performance of a simple data collection mechanism in real scenarios, based on IEEE 802.15.4 in a ZigBee device like SunSPOT, and have examined some challenges in such scenarios. The data delivery ratio of 98% has been recorded while sending data equivalent to that of 100 devices in a triage tent scenario, where every device is one hop away from the sink. Adjustment of hello packet interval according to the scenarios has been observed to be an important factor. The results are found to be useful in adjusting parameters in our future work.

WiFi localization and tracking of indoor moving objects is an important problem in many contexts of ubiquitous buildings, first responder environments, and others. Previous approaches in WiFi-based indoor localization and tracking either assume prior knowledge of indoor environment or assume many data samples from location-fixed WiFi sources (i.e. anchor points). However, such assumptions are not always true, especially in emergency scenarios. This paper explores the possibility of real-time indoor localization and tracking without any knowledge of indoor environment and with real-time data samples from only few anchor points outside the building. By using a small set of synchronized directional antennas as outdoor anchor points to actively scan in various directions, a moving device inside the building can be localized and tracked from the received signal in real-time manner. The paper proposes an angle-of-arrival estimator for accurate localization and adaptive per-antenna angular scheduling for real-time indoor tracking. The validation results from real experiment and simulation yield effectiveness and accuracy of the proposed schemes.

In this paper, we propose an off-line algorithm called TRACKIE to estimate trajectories of mobile nodes based on encounter information. This method only assumes reasonable number of landmarks and ad-hoc wireless communication facility of mobile nodes, and does not rely on multi-hop ad-hoc networks nor global positioning system. The method achieves low-cost estimation of trajectories and provides accurate solution (the average estimation error was less than 40% of the wireless range in simulations). We have evaluated TRACKIE with Micaz Mote and shown that estimation error is about 2 m in real environments where wireless range is about 3 m.

In this paper, we propose a method for realistic mobility aware information gathering in disaster areas. In the proposed method, a disaster area is divided into a grid, and each node's safety information is held in its pre-defined home grid cells, and it can be referred by sending a query to mobile nodes in its home cells. In this paper, we propose an autonomous adaptable protocol so that it can adapt to a variety of realistic network environments. In order to achieve speedy and high propagation, we combine the notions of store/forward in opportunistic networks and geographical routing in MANETs. In the proposed protocol, if intermediate nodes cannot relay safety information to its home cells by multi-hop communication, they hold it until they meet preceding nodes and re-transmit it as proxies. If a shortest path is not available, a detour is autonomously found. We have evaluated the proposed method under realistic pedestrian flows using our network simulator MobiREAL, and shown that it can work well.

In this paper, we present a range-free ad-hoc localization algorithm called UPL (Urban Pedestrians Localization), for positioning mobile nodes in urban district. The design principle of UPL is two-fold. (1) We assume that location seeds are deployed sparsely due to deployment-cost constraints. Thus most mobile nodes cannot expect to meet these location seeds frequently. Therefore, each mobile node in UPL relies on location information received from its neighboring mobile nodes in order to estimate its area of presence. The area of presence of each mobile node becomes inexact as it moves, but it is helpful to reduce the areas of presence of the other mobile nodes. (2) To predict the area of presence of mobile nodes accurately under mobility, we employ information about obstacles such as walls, and present an algorithm to calculate the movable areas of mobile nodes considering obstacles. This also helps to reduce each node's area of presence. The experimental results have shown that by the above two ideas UPL could achieve 8mpositioning error in average with 10mof radio range.

In this paper, we propose middleware for efficient and robust distributed network monitoring. In our technique, we specify network monitoring items, detection methods of viruses and DDoS attacks and the corresponding reactive actions as a management scenario. At the same time, we specify logical neighboring relations between separate network segments so that we can efficiently detect problems occurring in multiple segments. When a problem occurs in multiple network segments, the corresponding monitoring nodes form a group dynamically using logical relations. Based on the pre-defined management scenario, the nodes in the group carry out the reactive actions autonomously. We have designed and implemented our middleware consisting of several useful APIs. We have confirmed effectiveness of our technique through ns-2 simulation

In order for precise evaluation of MANET applications, more realistic mobility models are needed in wireless network simulations. In this paper, we focus on the behavior of pedestrians in urban areas and propose a new method to generate a mobility scenario called Urban Pedestrian Flows (UPF). In the proposed method, we classify pedestrians in a simulation field into multiple groups by their similar behavior patterns (simply called flows hereafter, which indicate how they move around geographic points). Given the observed road density in the target field, we derive using linear programming techniques how many pedestrians per minute follow each flow. Using the derived flows, we generate a UPF scenario which can be used in network simulators. In particular, we have enhanced a network simulator called MobiREAL, which has been developed in our research group, so that we can generate and use the UPF scenario. MobiREAL simulator has three main facilities: the behavior simulator, network simulator and animator. The behavior simulator can generate/delete mobile nodes according to the UPF scenario. The network simulator can simulate MANET protocols and applications. The animator offers elegant visualization of simulation traces as well as graphical user interfaces for facilitating derivation of UPF scenarios. Through several case studies, we show similarity of the derived flows to the observed ones, as well as the metrics that characterize the mobility of the scenario.