Research on Swarm Mobile Sensing 

We study swarm mobile sensing, in which mobile robots cooperatively move and execute a given task through intelligent behaviors emerging as a result of local information exchange by wireless communications among neighboring robots. We investigate wireless network control achieving reliable information exchange among a large number of robots moving with a complex mobility pattern. We also develop mobility control algorithms for a swarm of robots to search for targets to execute the required tasks in an unknown environment. These technologies are envisioned to be applied to the search for affected people at a disaster site, identification of sources of toxic gas, search for people and objects by exploiting odor emitted by them. We evaluate the proposed algorithms by computer simulations, and also conduct experiments with a platform developed by using irobot Create and raspberry pi. The video on the right shows an experiment, in which a group of robots autonomously discover a target at an unknown position based on the signal emitted by it, and approach while forming a swarm.
 Research on Wake-up Radio 

IoT devices, such as wireless sensors, in general keep their wireless modules to be active even during idle period in order to detect the communication requests from their communication peers. This energy consumed for standby operations causes IoT devices to reduce their battery lifetime. We aim to reduce this wasteful standby energy by introducing wake-up radio: the main components including wireless modules of IoT devices are turned off (i.e., devices are forced to sleep) during standby operation while a wake-up receiver operating with ultra low-power consumption waits for the communication (i.e., wake-up) request from the other nodes. This contributes to a significant reduction of wasteful energy consumed by IoT devices during idle period. In the upcoming data-centric environment, which is realized by the proliferation and diversification of IoT devices as well as the advancement of data processing technologies represented by machine learning, only devices owning necessary data need to be selectively woken up among a massive number of sleeping devices deployed over a given area. We aim to develop such a data-oriented wake-up control, which consumes precious radio resource (frequency and energy) only for the collection of truly valuable data.
 Research on Advanced Wi-Fi Technologies 

While the widely spread Wi-Fi technology enables low-cost and flexible network deployment, it is difficult to ensure reliability due to interference specific to unlicensed operations and limited maximum transmission power. In order to improve the reliability of Wi-Fi supporting real-time video transfer, we develop practical protocols and video transmission mechanisms realizing reliability in hostile environments, e.g., video transmission from highly mobile entities such as drones or at a factory site with many metal objects and interference from industrial machines. We also apply the developed protocols and video transmission schemes to platform monitoring of railway stations and to remote control system for forestry. Furthermore, we aim to improve spectrum efficiency or realize location/activity sensing by integrating Wi-Fi transmissions, sensing data obtained by IoT devices, and image recognition and machine learning techniques. The video on the right shows an experiment to validate the effectiveness and practicality of our proposed schemes realizing reliable video transfer from drones to Wi-Fi devices deployed over the ground.
  Research on Vital Data Collection from a Large Number of Exercisers

We develop a wireless sensor network, which realizes real-time collection of vital data (e.g., heartrate, energy expenditure, body temperature, etc.) from a large number of people playing sports, envisioned to be applied for training management of sports athletes or disease prevention, such as heat stroke, which is common in a class or sports festival at schools in Japan. We develop multi-hop networking technologies, which can dynamically construct routing path between a data collection node and sensor nodes attached to exercisers with high mobility, and its extension to multi-channel operations to increase the number of sensor nodes supported by our system. We aim to accommodate a few hundreds of exercisers in our system with reliable, low-latency, and real-time data collections being ensured. The picture on the right shows our experiment using the developed prototype of vital sensors and a data collection node.
 Research on Wireless Sensor Network considering Data Processing/Freshness 

In order to improve the utilization efficiency of radio resource in wireless sensor networks, it is crucial to consider how the collected data is aggregated/processed, focusing on their importance and freshness. For instance, when aggregating information represented as a function of individual sensing data, e.g., sum or average, we can integrate the operations of data collection and calculation by exploiting the additive nature of wireless medium, i.e., multiple access channel (MAC). One of such techniques is called Over-the-air-computation (AirComp), for which we develop related protocols to enhance its reliability and efficiency. Furthermore, we also study data collection strategies considering Age of Information (AoI), a measure to evaluate the information freshness.

Basic Concept of Over-the-air-computation (AirComp)
Besides the aforementioned topics, we aim to explore new applications of wireless communications. The evaluations in our laboratory are conducted through theoretical analysis, computer simulations, and experiments. We use Matlab to run custom-made simulations or network simulators such as NS-3 and Scenargie (see the link below). The experimental platform is built by using drones, iRobot Create, prototypes of wireless modules, and embedded devices such as Raspberry Pi.