As the growing trends in technology continue to drive massive transformation throughout the automotive sector, connected and autonomous transportation has become the future vision. Many researchers and practitioners wonder how connected, and autonomous vehicles will affect future transportation. This Special Issue explores some issues in the transition towards autonomous vehicles and their future trends and developments with artificial intelligence (AI) and robotics.

This special issue comprises 12 articles, showcasing the latest advances in computational social systems research.

The papers presented in this special issue focus on technological innovations in wearable, implantable, mobile, and remote healthcare that include Internet of Things (IoT), sensor informatics, and patient monitoring applications. These new technologies are used to track the key signs of people’s health to improve their lifestyle and health disorders. Innovations in IoT devices play a vital role in assisting patients in managing their health conditions. Thanks to the advent of modern communication technologies and Internet of Things (IoT) paradigms that have made the implementation of biomedical devices nearly universal. Now with the evolving industrial revolution, patients and healthcare providers are expecting something more. Practically speaking, healthcare wearables have experienced tremendous growth in the past few years, and it is expected to grow even more shortly, making it an ideal space for the biomedical informatics research community to solve complex healthcare problems and more informed decision making to improve human health.

This paper evaluates a LoRa mesh networking system for large-area monitoring of sensor applications. A prior study [1] proposed a LoRa-based wireless mesh network that supported periodic data collection. In this study, we further evaluated how data recovery mechanisms and time-critical data transmission affect periodic data communication, and a data transmission recovery mechanism was developed to recover lost data. Additionally, in emergency situations (e.g., detecting fire or alarm events), the sensor's data must be immediately sent to the control center. In a LoRa mesh network, these time-critical data packets, called non-periodic packets (NPPs), are designed to minimize transmission delays. We deployed 20 nodes on our university campus to form a LoRa mesh network, and the system achieved an average 99.99% packet delivery ratio with the data recovery mechanism for periodic reporting data. For NPPs, the average transmission delays achieved were 2.284 seconds, 4.597 seconds, and 6.75 seconds to deliver data to the gateway in one-hop, two-hop, and three-hop scenarios, respectively.

Extravasation is a common hazard in intravenous (IV) therapy; especially infants aged 0--2 years old are at high risk. However, in clinical practice, nursing staff are responsible for checking the injection status regularly, which leads to the risk of delayed treatment of extravasation of IV therapy in children. In this study, we propose a sensor-fusion sensing system to detect the early signs of extravasation. Sensor fusion compensates for the limitation of using a single sensor, extends the measurement range, and is more resistant to environmental interference. We design an experimental platform to simulate the occurrence of extravasation and, through this demonstration, show the advantages and feasibility of the proposed concept of using hybrid sensors.

Low-power wireless technology, i.e., IEEE 802.15.4, consumes less energy and is suitable for battery-powered applications; fixed-interval data reporting is a common characteristic of these applications, e.g., a wireless sensor needs to report its temperature or air quality every minute. However, data-receiving failure occurs frequently because the output power of such wireless technology is low, typically 1 mW in 2.4 GHz IEEE 802.15.4; also, crowd wireless traffic on ISM 2.4 GHz usually has serious radio interference. Meanwhile, data sent from a wireless transmitter but is not received by a receiver is not only wasted energy but also causes potential radio interference with other wireless devices. Based on the observation of radio interference, we proposed an adaptive-delay transmission mechanism for increasing the energy efficiency and the packet delivery ratio of the fixed-interval reporting application. The timing of data reporting is adaptively postponed if the recent transmissions fail. By integrating this method, the number of data packet been sent is reduced by up to 42.7% in a high-interference period; therefore, the power consumption of the wireless transmitter is reduced.

LoRa, an emerging wireless technology, is examined for long range communication performance in a large area. Although LoRa shows good performance for long-distance transmission in the countryside, its radio signals can be attenuated and interfered with by buildings, trees and other signal sources. In urban areas, our observation shows that LoRa requires a dense deployment of LoRa gateways to ensure that indoor LoRa devices can transfer data back to remote servers. Mesh networking is a solution for increasing the communication range and packet delivery ratio without the need to install additional LoRa gateways. This study presents our design of a LoRa mesh-networking module for IoT applications (e.g.,collecting data from multiple, widely distributed sensors on a university campus). We deployed 19 LoRa mesh-networking devices distributed in an 800m by 600m area on campus to serve as a gateway and to collect data at one-minute intervals. The results show our LoRa mesh networking module achieved a 88.49% packet delivery ratio (PDR), while the LoRaWAN in star-network topology was only 58.7% under the same conditions.

This paper presents a smart badge system for managing museum visitors and improving their viewing experience. The badge uses a LoRa wireless interface for two-way long-distance communication with tour guides and the museum's control center. The control center can collect information such as the visitors' viewing p, location, and status of activity level, enabling it to wirelessly send suggestions of exhibits and visiting paths to visitors, avoiding the overcrowding of visitors in one location. Meanwhile, visitors can communicate with the control center to update their locations and ask simple questions via the badge. In this paper, we introduce the system architecture and the prototype smart badge that we used to evaluate the performance of this system.

This study presents a room-level people tracking system capable of determining people's movements while carrying a non-sensor-equipped BLE (Bluetooth Low Energy) beacon. The BLE beacon broadcasts BLE advertisement while people are in motion. A beacon receiver (BR) was installed in a room near the doorframe to determine people's movements (i.e., entering, departing, or passing by the room), based on the variations in received signal strength (RSS) over time. This system is aimed at providing tracking information for people in school buildings, hospitals, and nursing homes. Knowing in which room or area people are has many useful applications, and the system is low-cost and easy to install and maintain.

The one of the major difficulties in deploying a wireless sensor to monitor debris flow in the real world is power consumption. Frequently replacing the battery of a sensor that is located in a remote mountainous area is not practical. In this study we examine the newly-introduced wireless debris flow sensor, SMARTCONE, which is designed to minimize the standby power consumption, but still keep alert to detecting debris flow. During standby periods, SMARTCONE turns off all major components and uses an accelerometer to sense vibration generated by moving debris flow, and it wakes up SMARTCONE for collecting the parameters of a moving debris flow. Multiple SMARTCONEs need to change operation modes between standby and wake up, collect physical parameters, and transmit data simultaneously, and these issues are kept in mind while designing the system. The performance of vibration detection, data transmission and records of GPS trajectories are evaluated to ensure the accuracy of the design.

Taking advantages of off-the-shelf cloud platform services, rather than maintaining a private database for logging the data from sensors, can significantly reduce labor and equipment costs for using a reliable database system. In this study, we present an inexpensive (less than 30 USD) embedded gateway with mobile network connectivity that can talk directly to cloud storage service providers, such as Google Apps Engine (hereafter called GAE). This embedded gateway can be used to collect data from networked wireless sensors in the field. The proposed design is characterized by: (1) using mobile networks to upload collected data without the need to pre-deploy conventional network equipment in the field; and (2) using cloud platform services for the logging of data. We designed and implemented this framework and installed it in a test site to evaluate its performance. This gateway framework can facilitate the design of a sensor system for collecting on-site signals, leveraging the benefits of cost-effective, always-ready, and reliable data storage service.

Indoor positioning is one of the key technologies for enabling location-aware applications. To the best of our knowledge, existing indoor positioning technologies are aimed at providing very high-accuracy location information, but are hindered by the need of a infrastructure with high installation and maintenance costs. In addition, their accuracy is usually highly variable and vulnerable to environmental changes or external interference. Thus, for applications of indoor human tracking, the practical considerations of choosing a suitable indoor positioning technique should be to provide adequate accuracy while also being economical. Based on this observation, our study presents an indoor, room-level tracking method characterized by an infrastructure with low costs to build and maintain. The user carries a mobile device that can determine his/her location with the help of Bluetooth beacons installed at the intersections between rooms and hallways. In contrast to traditional radio-fingerprint map and proximity approaches, in this study the mobile device tracks the user's moving direction and the temporal variation of the radio signal strength from nearby Bluetooth beacons, to determine when the device enters or leaves a given room or hallway. Therefore, by determining the sequence of a user's movements, his/her moving path can be recorded for indoor tracking or navigation.

Road lighting management, i.e., finding a faulty street lamp or street lamps blocked by overgrown trees for example, can improve road illumination and reduce traffic accidents. Based on previous work [1], which explores the use of illumination maps to detect faulty lamps, we present a road lighting management system for detecting significant changes in road lighting automatically. This system is characterized by eliminating any need to modify conventional street lamps. Therefore, the cost is extremely low compared to other approaches, e.g., Wireless sensor network for street lamp monitoring. We design a special embedded system, the Hitchhiker, for installation on fixed-route vehicles, such as shuttle buses, to collect illumination readings along the vehicle's route. All data from the Hitchhiker is uploaded to the Google App Engine (GAE) server for data storage, analysis, and user query; this allows identification of possible locations of faulty street lamps on the web-based management interface. We designed and implemented a prototype of the Hitchhiker and GAE server to demonstrate the practicability of this system; we are working toward enabling this design for automatic faulty street lamp detection in the near future.

Precision ball screw assembly (hereafter called "ball screw"), as shown in Fig. 1, is a mechanical wear out part that widely used in CNC (computer numerical control) machine tools to control the movement of processing targets and spindles. Up until now, there has been no simple way to directly measure ball screw for knowing the state of wear quantitatively. An indirect approach is logging all the signals (vibration, temperature, and preload change) during the operation of ball screw, and to use them to construct the wear model for estimating its remaining lifetime. To achieve this goal, we proposed a cloud-based logging system in this study that emphasizes (1) logging all the signals during operation in a ball screw's whole lifetime, and transferring to the data server without data loss; and (2) saving all the data into the cloud data storage of the ball screw's manufacturer. The data collected from many ball screws can be used to analyze and construct the wear model of ball screw, allowing the manufacturer to understand the state of wear and send a warning to the tool machine's owner before excessive wear.

Badly lit roads usually lead to vehicle accidents and encourage crime in the area. Therefore, it is important to detect faulty street lamps rapidly and report them to related authorities to keep roads safe. Currently, communities still mostly depend on electrical inspectors to check street lamps regularly, which may result in long time delays for repair. Recent studies focus on add networking capability into street lamp poles to enable real-time reports on the healthy status of lamps. However, such a smart system increases costs to add sensors and network modules in every street lamp; therefore, it is nearly impossible to realize this kind of system in a short term. We propose a new method to detect faulty lamps. We designed equipment that could be installed on fixed bus routes and collect the lighting intensity along the routes. We created illumination maps in meter-level resolution. The differences between illumination maps created at different times can help identify the changes of lighting intensity in specific locations. We executed a proof-of-concept experiment that shows our method is feasible. This method can be extended to a city-wide scale at low cost. As a result, this would detect faulty street lamps along main roads and prevent accidents and crime by shortening the duration of badly lit streets.

The debris flows caused by typhoon Morakot in August 2009 killed more than 600 people and caused US$ 500 million in damages to agriculture and forestry of Taiwan. Many lives can be saved if we can estimate the occurrence of debris flows and issue timely warnings to inhabitants. Previous literatures [1] have shown that rainfall is highly correlated with debris flows. Thus, the first step to debris flow monitoring and warning is to collect high-resolution, real-time precipitation in the debris-flow-prone areas. In this paper we discuss the considerations of designing a low-cost WSN-based rain gauge grid, which provides highresolution mapping of precipitation. Preliminary experimental results are presented.

In this demo, we present SenPro, a profiling tool that profiles the run-time behaviors of a deployed wireless sensor network (WSN). SenPro runs concurrently with the WSN application and provides information of system execution in a minimally intrusive manner, without requiring any modification to the application being observed. We demonstrate, for a broad class of data gathering applications, how SenPro helps the designer to understand the application behavior by collecting and analyzing the metrics at the centralized sink.

This demo presents a mobile wireless sensor system for debris flow monitoring. The objective of this system is to realize long-term and effective debris flow surveillance using low cost wireless sensors. In the system, a set of robust wireless sensors are designed to deploy on riverbed and cooperatively observe the moving debris flows. Our mobile sensors are intended to be carried along by the debris flow. As the sensors move along, they are able to measure the internal parameters, such as vibration frequency, amplitude, moving direction and velocity, of the debris flow. By utilizing the proposed energy-saving mechanism on the WSN platform, the mobile sensors can continuously operate up to six months with merely two alkaline D cell batteries. The proposed system provides the abilities to collect high-fidelity data for civil engineering applications to analyze and determine the occurrence of debris flows, as well as estimate the damage.

In an event-monitoring wireless sensor network (WSN), the sensors must be waked up fast to engage in active and high-rate sensing when events of interest occur. However, to prolong the lifetime of the network, the sensors should be put into the standby mode as long as possible. There is thus a tradeoff between the standby duration and the wakeup latency. Though the tradeoff is important, what is more important is a flexible transmission schedule that allows the tradeoff to be exploited. In this research, we use debris flow monitoring as a case study to illustrate how the tradeoff between long standby and fast wakeup can be exploited systematically with a flexible transmission schedule.

We present a general framework for building mobility models for floating objects. Such models are useful for studying the behavior of wireless sensors that are deployed to drift along rivers, lakes, oceans, or debris flows. These sensors may be used to track toxic wastes in the water, to study hydrology, or to warn natural hazards. While existing mobility models concentrate on modeling objects moving in autonomy on firm grounds, they cannot be used to describe floating objects that move along with the water flows passively. To build mobility models for such floating objects, our framework entails the use of GPS-equipped wireless sensors that drift along with the water and record the path information. The collected information is converted into a two-dimensional map for modeling the behavior of the floating objects, e.g. generating the simulated drifting paths of the objects. As far as we know, this is the first work on building the mobility models for floating objects.

Most debris flow monitoring systems deployed today use indirect means to track information regarding debris flows. In this work, we introduce a novel debris flow monitoring system for in-situ and direct tracking of debris flows in real-time. The core idea is to throw wireless sensors into the debris flows and collect flow information as they move along. We will describe the design of the wireless sensors and present some preliminary performance results.

The scale of weather monitoring is limited by the cost of the automatic weather stations (AWS), which is mainly the cost of high precision instruments and long-distance wireless telecommunication equipments. We propose a wireless sensor network (WSN) based AWS, which takes advantage of the low-cost, real-time and infrastructure-free characteristics of WSN [1]. We can therefore extend the scale of weather monitoring without increasing the number of telecommunication equipments. This WSN-based AWS is able to cover a plane and gather multiple sets of weather measurements in real-time at a better data resolution.