## 1 Introduction  
Sensor networks are a well-known concept that a huge amount of sensors are embedded in buildings, machines and other items, and sensing data are collected to form \"big data\". In the future, a variety of applications using a lot of sensors in a system will appear. In such a system, each sensor is wire-connected to a host or main computer, which is often connected to internet wirelessly. In most of such applications, the sensors should be as compact as possible including wirings, and the sensor system should be as simple as possible. This is often the case with a robot because it needs a lot of sensors but has a limited room for the sensor system inside the body. For example, there have been intensive efforts to install many tactile sensors in robots so far, because future robots with human-like tactile sensation will have more natural and seamless interaction with us, which enables safer, more reliable and more accurate human-robot communication. To install a large-scale array of tactile sensors or other sensors in a robot, two readout schemes have been mainly investigated. One scheme is to use a scanning readout circuitry [1]. The digitization of sensor output is performed off the sensor array in a central processing unit. To date, some scanning-readout type sensor arrays, which can include many sensing elements based on capacitive and resistive transduction, were reported [2; 3; 4; 5; 6; 7]. However, the number and length of connecting wires increase with the number of sensing elements and the covering area. The interference effects along a long signal propagation pathway [8], the crosstalk from element circuits [9; 10] and the long scanning time make this scheme difficult to scale efficiently for large-scale robotic sensor networks [1]. Another scheme is to digitize the sensor output at the site of sensation, and build a sensor network system with serial bus communication. This solution has advantages in reducing the amount of wiring [11; 12], the interference effects and the crosstalk. However, the long sensor data conversion time of an analog-to-digital converter chip limits the system performance [13]. For example, Refs. [11; 12] reported data conversion times of 8.73 ms and 1.6 ms, respectively, for tactile sensors based on the conventional serial bus communication. In our previous research [14; 15], we have designed a dedicated Complementary Metal-Oxide-Semiconductor (CMOS) Large-Scale Integration (LSI) (referred as \"sensor platform LSI\") for bus-networked Micro-Electro-Mechanical-Systems (MEMS)-LSI integrated tactile, proximity or shock sensors, for which event-driven operation is convenient. Simple designs in bus communication scheme, protocol and sensor functions were adopted to make the die size smaller than 8 mm\({}^{2}\) for a small pitch of sensor installation. The sensor platform LSI can be also connected with varieties of commercially-available capacitive (range from 1 pF to 10 pF) and resistive (range from 100 \(\Omega\) to 1 M\(\Omega\)) sensors [16] as shown in Figure 1. The detail of the sensor platform LSI is described in Section 2.2. The serial-bus-communication-based sensing system using the sensor platform LSI is beneficial, especially when the number of sensors is large, for example, several tens or more. In such a situation, data collision and congestion in serial bus communication is a problem to address. To relieve this problem, the sensor platform LSI is equipped with collision avoidance, adaptation and event-driven functions. However, we have not connected more than 20 LSIs on a serial bus line in the previous study [15]. In this study, therefore, we designed and prototyped a  
Figure 1: Serial-bus-communication-based sensor network system with multiple sensors using the sensor platform Large-Scale Integration (LSI). with 48 sensor platform LSIs, and investigated the serial communication performance with the adaptation function. This paper is organized as follows.