Chi-Han Lin

A Distributed Flow-Based Guiding Protocol in Wireless Sensor Networks

Guiding navigation is an important application in wireless sensor networks to make moving objects leave dangerous areas safely and quickly. However, guiding policy without considering congestion problem will postpone the escape time. In this paper, we propose a distributed flow-based guiding protocol for indoor environments to evacuate mobile objects from dangerous area to exit. Our goal is to construct less congested paths to minimize the escape time and the congestion time. Each sensor in the network holds an artificial potential value determined by the moving speed of object on a path monitored by it, the distances to exits, and the capacity of exits that have the maximum potential values, and objects are directed to paths with higher potential values. Nevertheless, our approach also solves the local maximum problem that objects are trapped at a non-exit location holding a local maximum potential value. In addition, we adopt a traffic flow model for transforming object density of a road into objects velocity to reduce the computational cost and the communication overhead. Simulation results show our proposed protocol can efficiently decrease the escape time and congestion time.

Load-Balanced Sensor Grouping for IEEE 802.11ah Networks

The traditional IEEE 802.11 network is designed for the use of small scale local wireless networks such as home or campus WLANs, where one access point serves a reasonably few number of devices like smartphones, laptops, tablets, and so on. However, the emergence of the Internet of Things (IoT) have changed the scene of wireless communications, while the number of devices rapidly increases and becomes far larger than before. Thus, recently, the IEEE task group ah (TGah) is dedicated to the standardization of a new protocol, called IEEE 802.11ah, which is customized for this type of large-scale networks. IEEE 802.11ah adopts the grouping-based MAC protocol to reduce the contention overhead of each group of devices. However, most existing designs simply randomly assign devices to groups, and less attention has been paid to the problem of forming efficient groups. Therefore, in this paper, we argue that the performance of grouping is closely related to the heterogeneous traffic demands of devices, and propose a load-balanced grouping algorithm to improve channel utilization of each group. Our evaluation shows that the proposed load-balanced grouping outperforms simple random grouping, especially when the network is almost saturated.

An Adaptive Guiding Protocol for Crowd Evacuation Based on Wireless Sensor Networks

Wireless sensor networks have been widely used in many applications. One of them is indoor guiding service for emergency evacuation whose goal is to assist moving objects in escaping a hazardous region safely and quickly when an emergency occurs. In this paper, we propose a distributed and adaptive guiding protocol that takes several factors such as hazardous regions, distance to exits, width of exits, and congestion degree of each location into consideration. This protocol guides moving objects with load balancing among multiple navigation paths to multiple exits and avoids congestion to reduce the evacuation time. Simulation results show that the proposed protocol can guide moving objects (e.g., people) to exits in shorter time and have higher survival rate in comparison to those without balancing the traffic load on navigation paths and exits.

acPad: Enhancing channel utilization for 802.11ac using packet padding

Multi-User Multiple Input Multiple Output (MU-MIMO) enables a multi-antenna access point (AP) to serve multiple users simultaneously, and has been adopted as the IEEE 802.11ac standard. While several PHY-MAC designs have recently been proposed to improve the throughput performance of a MU-MIMO WLAN, they, however, usually assume that all the concurrent streams are of roughly equal length. In reality, users usually have frames with heterogeneous lengths even after aggregation, leading to different lengths of transmission time. Hence, the concurrent transmission opportunities might not always be fully utilized when some streams finish earlier than the others in a transmission opportunity (TXOP). To resolve this inefficiency, this paper presents acPad, a PHY-MAC design that adds additional frames to fill up the idle channel time and better utilize the spatial multiplexing gain. Our acPad identifies proper users as the padding so as to improve the padding gain, while preventing this padding from harming all the ongoing streams. Our evaluation via large-scale trace-driven simulations demonstrates that acPad improves the throughput by up to 2.83×, or by 1.36× on average, as compared to the conventional 802.11ac.

Channel-Aware Polling-Based MAC Protocol for Body Area Networks: Design and Analysis

Body area networks (BANs) enable wearable/implanted devices to exchange information or collect monitored data. The channel quality of a link in a BAN is typically highly dynamic, since sensors equipped on a human body usually move with gesture, posture, or mobility. Therefore, existing sleep-wake-up scheduling mechanisms used in traditional static sensor networks could be very inefficient in a BAN, because they do not consider channel fluctuation of body sensors. Sensors might be waked up to transmit during bad channel conditions, leading to transmission failures and energy waste. To remedy this inefficiency, this paper proposes a Channel-aware Polling-based MAC protocol CPMAC. Our design only wakes sensors up and triggers them to transmit when the channel is strong enough to ensure fast and reliable transmissions. We further analyze the energy consumption and derive a queueing model to estimate the probability of completing all data transmissions of all sensors in our CPMAC. Benefiting from these analyses, we are able to optimize energy efficiency of our CPMAC by adapting the number of polling periods in a superframe to dynamic traffic demands and channel fluctuation. Our simulation results show that, as compared with TDMA-based scheduling and the IEEE 802.15.6 CSMA/CA protocol, CPMAC significantly improves energy efficiency and, meanwhile, keeps the latency short.

pSafety: A Collision Prevention System for Pedestrians Using Smartphone

We implement a collision prevention system, called pSafety, which instantaneously informs pedestrians and drivers of the potential threatening accidents. Unlike other systems, pSafety alerts pedestrians of threatening vehicles coming from not only the line-of-sight, but also non-line-of-sight due to obstructions of the wall corner or other vehicles. pSafety collects GPS information from smartphones of pedestrians and vehicle drivers through mobile networks. The main challenge of pSafety is that current smartphones demonstrate larger distance errors on the order of a few meters due to its intrinsic low-cost GPS receivers. To address the impact of large-error positioning to pSafety, we regard each participant on the map as a sector that indicates a predicted location. We subsequently design the Sector Overlap Detection Algorithm, called SODA, to detect whether two sectors are overlapping in time O(1). To avoid warning fatigue, we additionally provide a threat ranking method to evaluate the degree of risk for each potential collision event. Through our designed App, pedestrians and drivers both could receive a clear view of potential risks and then take proper actions to avoid accidents. In our implementation, we show that pSafety rapidly informs participants (i.e., pedestrians and drivers) and provides each participant a sufficient response time to avoid collision.

Breaking Bandwidth Limitation for Mission-Critical IoTs using Semi-Sequential Multiple Relays

Most existing or currently developing Internet of Things (IoT) communication standards are based on the assumption that the IoT services only require low data rate transmission and therefore can be supported by limited resources such as narrow-band channels. This assumption rules out those IoT services with burst traffic, critical missions, and low latency requirements. In this paper, we propose to utilize the idle devices in mission-critical IoT networks to boost the transmission data rate for critical tasks through multiple concurrent transmissions. This approach virtually expands the existing narrow-band IoT protocols to break the bandwidth limitation in order to provide low latency services for critical tasks. In this approach, we propose the task-balance method and the first-link descending order to determine the relay order and data partition in a given relay set. We theoretically prove that the optimal relay configuration that minimizes the uploading latency in single source scenario can be derived by the proposed algorithms in polynomial time when we have sufficient number of available channels. We also propose a greedy algorithm to approximate the optimal solution within a 1/2 performance lower bound in general scenarios. The simulation results shows that the proposed approach can reduce the latency of critical tasks up to 76% comparing with traditional approaches.

Rate Adaptation for Highly Dynamic Body Area Networks

Body Area Networks (BANs) is a type of wireless sensor networks that enables wearable/implanted sensors to exchange information or collect monitored data. Unlike traditional wireless networks, such as WiFi or bluetooth, where equipments are usually quite stable, sensors are equipped on human body and hence are moved with gesture, posture or human mobility. Such dynamics also make coordination between sensors and the hub more difficult, because the channel quality between nodes could fluctuate significantly within a short period of time. Bit-rate adaptation (i.e., Selection of the modulation and coding scheme) hence becomes one of the most challenging issues needed to be addressed in such a highly-dynamic network. This paper proposes a bit-rate adaptation scheme upon a polling-based MAC protocol. Our design enables real-time channel quality estimation before every data transmission. Body sensors can thus adapt their bit-rates to dynamic channel conditions. Our simulation results show that our rate adaptation scheme maintains the delivery success ratio near 100%. It hence also spends less energy for transmitting data, which further prolongs the lifetime of body sensors.