Huiguang Liang

Implementation and evaluation of multihop ARQ for reliable communications in underwater acoustic networks

Underwater acoustic networking is an emerging technology platform for oceanographic data collection, pollution monitoring, offshore exploration and tactical surveillance applications. Design of reliable and efficient communications protocols is challenging due to the unique characteristics of underwater acoustic channels. In this paper, we present a modular and lightweight implementation of an opportunistic multihop automatic repeat request (ARQ) scheme in a real system. We evaluate the performance of the opportunistic ARQ using inexpensive underwater acoustic modems in a shallow underwater environment.

Femto-Cells: Problem or Solution? A Network Cost Analysis

Next-generation wireless cellular networks are likely to be multi-tiered, consisting of a macro-cell tier based on the traditional operator-deployed macro-cell structure, and a lower tier of femto-cells that are arbitrarily deployed by end-users. The need for such architectures is driven by increasing demands for higher data rates as well as to provide capacity to serve increasing numbers of subscribers. This paradigm shift in cellular architecture may therefore imply changes in the way cellular networks are managed today. In this paper, we examine operation costs of a two-tier macro-femto-cellular network in a capacity-limited scenario. We identify the key parameters, including nomadic-to-mobile user ratio, non-linear femto-cell capacity growth, femto-cell subscription rate, and QoS provisioning costs, which will impact recurring annual management costs of these networks. We then identify a model which captures these cost components and key parameters, allowing an operator to characterize the management cost of their access networks. We also identify through evaluations key situations where adopting these femto-cells may in fact add to an operators overall costs, and provide key insights into the financial feasibility of femto-cells.

Where am I? Characterizing and improving the localization performance of off-the-shelf mobile devices through cooperation

We are increasingly reliant on cellular data services for many types of day-to-day activities, from hailing a cab, to searching for nearby restaurants. Geo-location has become a ubiquitous feature that underpins the functionality of such applications. Network operators can also benefit from accurate mobile terminal localization in order to quickly detect and identify location-related network performance issues, such as coverage holes and congestion, based on mobile measurements. Current implementations of mobile localization on the wildly-popular Android platform depend on either the Global Positioning System (GPS), Androids Network Location Provider (NLP), or a combination of both. In this paper, we extensively study the performance of such systems, in terms of its localization accuracy. We show through real-world measurements that the performance of GPS+NLP is heavily dependent on the mobility of the user, and its gains on localization performance is minimal, and often even detrimental, especially for network round-trip delays up to 1s. Building upon these findings, we evaluate the efficacy of using Tattle, a cooperative local measurement-exchange system, and propose Delay-Adjusted U-CURE, a clustering algorithm that greatly improves the localization performance of both GPS-only, and GPS+NLP techniques, without keeping expensive system states, nor requiring any location anchors nor additional instrumentation, nor any external knowledge that is not available programmatically to application designers. Our results are promising, demonstrating that median location accuracy improvements of over 30% is achievable with just 3 co-located devices, and close to 60% with just 6 co-located devices. These findings can be used by operators to better manage their networks, or by application designers to improve their location-based services.

Elderly medication adherence monitoring with the Internet of Things

With the growth in elderly population in Singapore, healthcare expenditure and prevalence of age-related illnesses are expected to increase. Non-adherence among the elderly is a common issue that leads to adverse health complications, particularly among those with chronic conditions. However, existing studies typically focus on identifying predictors of medication adherence, and provide neither user-friendly nor actionable solutions that can be easily adopted by the elderly. In this paper, we use the Internet of Things to monitor medication adherence and detect changes in medication consumption patterns among the elderly, thus enabling timely interventions by caregivers to take place. Sensor-enabled medication boxes are deployed in the residences of ten elderly participants for more than four months, since Jul 2015. Preliminary results indicate that our solution can effectively monitor medication intake patterns, and identify elderly who are non medication-adhering.

Efficient data retrieval for large-scale smart city applications through applied Bayesian inference

Recent years have witnessed the proliferation of worldwide efforts towards developing technologies for enabling smart cities, to improve the quality of life for citizens. These smart city solutions are typically deployed across large spatial regions over long time scales, generating massive volumes of data. An efficient way of data retrieval is thus required, for post-processing of the data - such as for analytical and visualization purposes. In this paper, we propose a data prefetching and caching algorithm based on Bayesian inference, for the retrieval of data in large-scale smart city applications. A brute-force approach is used to determine the optimal weight correction factor in the proposed prefetching algorithm. We evaluate the optimized Bayesian prefetching algorithm against the Naïve and Random prefetch baselines, using both simulated and actual data usage patterns. Results show that the Bayesian approach can achieve up to 48.4% reductions in actual user-perceived application delays during data retrieval.

I've heard you have problems: Cellular signal monitoring through UE participatory sensing

The operating environment of cellular networks can be in a constant state of change. One Singaporean operator expressed difficulty with the coverage assertion (CA) problem of whether regulated minimum coverage is met, especially in urban areas. Currently, the operator manually appraises coverage through laborious and expensive walk/drive-tests. In this paper, we propose Tattle, a distributed, low-cost and comprehensive cellular network measurement collection and processing framework. We exemplify Tattle by leveraging on participating UEs to report on network coverage in real-time. Tattle exploits wireless local-area interfaces to exchange RSCP measurements amongst devices to preserve the co-locality of readings and conserve power. We propose U-CURE, a clustering algorithm which considers sample location uncertainty and the knowledge of device co-location to remove erroneously localized readings. We develop a prototype app on the Android™ platform as a proof-of-concept of the Tattle framework. We then use the Tattle framework to perform extensive RSCP measurement collection and processing in various areas in Singapore, collecting over 3.78 million readings. We present visualizations of mean signal coverage and RSCP CDFs for various areas of interest. The latter is a key output of Tattle, which helps operators to appraise coverage and solve the CA problem by relying on subscriber measurements, instead of expensive, laborious and limited-scale walk-/drive-tests.

Sensorem - an efficient mobile platform for wireless sensor network visualisation

We design and implement an Android application, Sensorem, for efficient retrieval and visualization of wireless sensor network (WSN) data. In light of data distribution and visualization being important developmental keystones of smart cities, we seek to enhance sensor data accessibility by developing a user-friendly mobile application (Sensorem) for meaningful visualization of sensor data, targeted at maintenance personnel. Sensor selection can be made with reference to geographical location through an embedded Google Map fragment, as well as a sensor list with sensors in order of WSN node ID. Sensor data is presented through interactive graphs, and graph overlaying functions are offered for easy comparison of data trends. We also employ a Bayesian prefetch algorithm and caching mechanisms to minimize sensor data access latency, such that the app system is able to cope with network and back-end bottlenecks.

So near, and yet so far: Managing ‘far-away’ interferers in dense femto-cell networks

We expect femto-cells to be massively and densely deployed in the future. Numerous existing works on femto-cell interference management assume that the local topology of interfering femto-cells can be sufficiently approximated through sensing, if not already known in advance. We show that this assumption results in poor throughput performance in dense femto-cell networks. For some cell-edge users, using conventional sensing in dense deployments can result in almost 50 times less instantaneous throughput, as compared to having oracular knowledge of interference topology. This sub-optimality is caused by “far-away” interferers. These are femto-cells that are deployed just far enough such that their presence will not be detected by conventional sensing. We then introduce a mobile sensing scheme to detect these “far-away” interferers by exploiting the inherent mobility of femto-cell users. We show through packet-level simulation that this sensing scheme is able to better approximate the interference topology. This results in significantly improved performance over conventional sensing, in dense deployment scenarios.

Identifying elderlies at risk of becoming more depressed with internet-of-things

Depression in the elderly is common and dangerous. Current methods to monitor elderly depression, however, are costly, time-consuming and inefficient. In this paper, we present a novel depression-monitoring system that infers an elderly’s changes in depression level based on his/her activity patterns, extracted from wireless sensor data. To do so, we build predictive models to learn the relationship between depression level changes and behaviors using historical data. We also deploy the system for a group of elderly, in their homes, and run the experiments for more than one year. Our experimental study gives encouraging results, suggesting that our IoT system is able to correctly identify >80% of the elderly at risk of becoming more depressed, with a very low false positive rate.

Technology-Enabled Medication Adherence for Seniors Living in the Community: Experiences, Lessons, and the Road Ahead.

Medication non-adherence in seniors can lead to severe health complications, including morbidity, mortality and decreased quality of life. In view of ageing populations worldwide, there is significant interest among the healthcare sector and researchers to improve medication adherence rates for seniors. However, existing studies in the literature focus primarily on identifying the predictors of medication non-adherence. In this paper, we present our work on technology-enabled medication adherence for 24 community-dwelling seniors over a period of more than 2 years. We leverage Internet of Things (IoT) devices to track inferred medication consumption in the seniors’ homes, and provide quasi real-time alerts to community caregivers, who can then intervene in a timely manner. Our study suggests that seniors generally do not consume medication on a regular basis (in both the frequency and time domains). However, technology-based approaches that allow for real-time tracking and appropriate interventions by caregivers can be effective in improving the medication adherence of these seniors.