Internet of Things (IoT) technologies have so deeply penetrated our daily lives that IoT devices have been everywhere. Not only do IoT devices provide basic means to convenient living but they also offer intelligent services tailored to diverse user needs. For instance, beyond remotely turning ON and OFF small gadgets, such as lamps and switches, users can now control appliances via smartphones and also interact with them verbally. In the backdrop of multifarious, complex IoT applications, their reliability becomes of paramount importance. This article focuses on achieving user-level reliability in an IoT operating system (OS) that executes both real-time (RT) and non-real-time (NRT) tasks concurrently. For example, smart appliances typically run RT tasks such as a motor controller, as well as NRT tasks such as Wi-Fi-based network functions, concurrently. In general, RT tasks are critical and should be protected from error-prone tasks. Otherwise, a motor controller failure may cause appliances to malfunction. To this end, we propose a novel, reliable IoT OS, called TizenRT that achieves user-level reliability through two key features, namely, fault isolation and fast recovery, while maintaining RT constraints. Our evaluation shows that TizenRT can isolate faulty tasks in memory space, while guaranteeing normal RT tasks to complete their missions within a required time, e.g., 50 $\mu \text{s}$ . Moreover, corrupted tasks can be recovered within a few milliseconds, e.g., 10 ms, without the need for a system reboot. This makes TizenRT a preferred choice for mission-critical IoT applications.

Recognizing the location of an individual in a home environment is crucial in order to enable various context-aware home applications such as elderly health monitoring and in home appliance automation. However, due to the limited number of dedicated Wi-Fi access points (APs), it is challenging to guarantee the reliable localization performance in a home environment by using the traditional Wi-Fi fingerprinting (WF) technique. In this paper, we propose a room-level localization system for the typical residential home environments which comprise of a living room, a kitchen, a bathroom, and a bedroom. Specifically, we make use of appearance frequency (AF) information of APs at each location in order to narrow down the number of candidate locations before performing the Wi-Fi Fingerprinting scheme. Our system improves the localization performance by up to 17.5 % (11.29 % on average) over that of the traditional WF-based approach which does not exploit AF information. We achieved the room-level positioning accuracy of 84.76% on the dataset of 6 home environments.

Research of daily activity recognition has been extensively conducted in the field of ubiquitous computing. However, previous daily activity recognition schemes are either obtrusive or inaccurate since they use just special-purpose devices. In this paper, we propose the collaborative classification for recognizing daily activities with a smartwatch. We exploit a single off-the-shelf smartwatch to distinguish 5 different daily activities such as eating, vacuuming, sleeping, showering, and TV watching. More precisely, we conduct experiments for collecting sensor data from accelerometer and acoustic sensor which are embedded in a smartwatch. However, the simple combination of the raw acceleration and acoustic data does not deliver accurate recognition accuracy. In order to achieve high accuracy, we propose a collaborative classification algorithm which integrates sensor data and ground-truth label for improving recognition accuracy by constructing a mapping table. We evaluate accuracies using single-sensor based approach, multi-sensor based approach, and our collaborative classification approach. The results from activity recognition for about 20 hours data collected by subjects show reliable accuracies for all 5 activities, and the overall accuracy of our collaborative approach is about 91.5%. Experimental results reveal that our approach improves the recall rate of each activity by up to 21.5% as compared to that of the simply combined multi-sensor based approach.

Multimedia streaming between smart devices has come into general use in Wi-Fi networks, for example, streaming a video content in mobile phone to smart TV. However, drastic and frequent changes of link quality caused by wireless channel characteristics such as multipath fading and Doppler effect leads the network throughput unstable. In order to support seamless play even in unstable wireless link, the buffer control for media playback should be smarter and more robust against radical network variations. To this end, we propose a novel dynamic buffering mechanism for HTTP streaming. This is evaluated by experiments: streaming a multimedia content from a smartphone to a display device when they are connected to the same Wi-Fi Access Point. We show that the proposed mechanism outperforms an existing solution in terms of the number of buffering and the total buffering time. We also verify how fast the proposed method adapts to sudden changes of wireless link condition.

In this paper, we consider how to exploit multiuser diversity in the uplink of IEEE 802.11 WLAN when its uplink and downlink are asymmetric. In the uplink, there is no central node that arranges transmission schedules of all stations. To overcome this limitation, we devise a novel MAC protocol, called WLAN Opportunistic Waiver (WOW), where an access point (AP) indirectly controls transmission instants of stations by not sending a CTS frame if the received signal strength (RSS) of an RTS frame is below a certain threshold that is station- dependent. We develop an analytic model for WOW with a three- dimensional Markov chain. We can find the optimal threshold to maximize the system throughput. To avoid optimization burdens, we propose a simple algorithm which can determine a near- optimal threshold. Both analysis and ns-2 simulation results show that the throughput of WOW increases with the number of stations and the improvement compared with Receiver Based Auto Rate (RBAR) is up to 43 % while maintaining access fairness as achieved by IEEE 802.11.