Joseph Kee-Yin Ng

Cooperative data scheduling in hybrid vehicular ad hoc networks: VANET as a software defined network

This paper presents the first study on scheduling for cooperative data dissemination in a hybrid infrastructure-to-vehicle (I2V) and vehicle-to-vehicle (V2V) communication environment. We formulate the novel problem of cooperative data scheduling (CDS). Each vehicle informs the road-side unit (RSU) the list of its current neighboring vehicles and the identifiers of the retrieved and newly requested data. The RSU then selects sender and receiver vehicles and corresponding data for V2V communication, while it simultaneously broadcasts a data item to vehicles that are instructed to tune into the I2V channel. The goal is to maximize the number of vehicles that retrieve their requested data. We prove that CDS is NP-hard by constructing a polynomial-time reduction from the Maximum Weighted Independent Set (MWIS) problem. Scheduling decisions are made by transforming CDS to MWIS and using a greedy method to approximately solve MWIS. We build a simulation model based on realistic traffic and communication characteristics and demonstrate the superiority and scalability of the proposed solution. The proposed model and solution, which are based on the centralized scheduler at the RSU, represent the first known vehicular ad hoc network (VANET) implementation of software defined network (SDN) concept.

Temporal Data Dissemination in Vehicular Cyber–Physical Systems

Efficient data dissemination is one of the fundamental requirements to enable emerging applications in vehicular cyber-physical systems. In this paper, we present the first study on real-time data services via roadside-to-vehicle communication by considering both the time constraint of data dissemination and the freshness of data items. Passing vehicles can submit their requests to the server, and the server disseminates data items accordingly to serve the vehicles within its coverage. Data items maintained in the database are periodically updated to keep the information up-to-date. We present the system model and analyze challenges on data dissemination by considering both application requirements and communication characteristics. On this basis, we formulate the temporal data dissemination (TDD) problem by introducing the snapshot consistency requirement on serving real-time requests for temporal data items. We prove that TDD is NP-hard by constructing a polynomial-time reduction from the Clique problem. Based on the analysis of the time bound on serving requests, we propose a heuristic scheduling algorithm, which considers the request characteristics of productivity, status, and urgency in scheduling. An extensive performance evaluation demonstrates that the proposed algorithm is able to effectively exploit the broadcast effect, improve the bandwidth efficiency, and enhance the request service chance.

An effective signal strength-based wireless location estimation system for tracking indoor mobile users

Abstract Localization is an essential function for location-dependent services. Although various efficient localization methods have been proposed, many of them have not tested with practical applications. Different location-dependent applications may have very different performance and operation requirements such as the accuracy in localization and frequency of location tracking. In this paper, based on our previous works on the design of efficient localization methods, we study the important issues in the design and implementation of a location tracking system for monitoring people in an indoor environment within a large building such as in rehabit centres and schools. In estimating the current location of a mobile user, we adopt the received signal strength indicator ( RSSI ) approach. Enhancements are proposed to improve the accuracy in localization using RSSI. To ensure the reliability of the location results and the efficiency in location tracking, it is important to minimize the traffic of location data on the network, as well as the chance of overloading the estimation system. Various issues in the design of the system are discussed and the performance results of the system are provided to illustrate the efficiency and the limitations of the proposed methods.

Network-Coding-Assisted Data Dissemination via Cooperative Vehicle-to-Vehicle/-Infrastructure Communications

Vehicle-to-vehicle/vehicle-to-infrastructure (referred to as V2X) communications have potential to revolutionize current road transportation systems with respect to vehicle safety, transportation efficiency, and travel experience. This paper puts the first effort on applying network coding in cooperative V2X communication environments to improve bandwidth efficiency and enhance data service performance. Specifically, we investigate new arising challenges on network-coding-assisted data dissemination by considering both communication constraints and application requirements in vehicular networks. We present the system model and give an insight into the characteristics of cooperative data dissemination with network coding. On this basis, we formulate the problem and propose a network-coding-assisted scheduling algorithm to enable the hybrid of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications and exploit their joint effects on providing efficient data services. We design a cache strategy that allows vehicles to retrieve their unrequested data items. This strategy not only increases the opportunity of data sharing among vehicles but also gives higher probability of packet decoding, which in turn enhances the data service performance. We give an intensive analysis on the scheduling overhead, which shows the scalability of the algorithm. Finally, we build the simulation model and conduct a comprehensive performance evaluation to demonstrate the superiority of the proposed solution.

Activity tracking and monitoring of patients with alzheimer's disease

In this paper, by applying motion detection and machine learning technologies, we have designed and developed an activity tracking and monitoring system, called SmartMind, to help Alzheimer’s Disease (AD) patients to live independently within their living rooms while providing emergency assistances and supports when necessary. Allowing AD patients to handle their daily activities not only can release the burdens on their families and caregivers, it is also highly important to help them regain confidence towards a healthy life. The daily activities of a patient captured from SmartMind can also serve as an important indicator to describe his/her normal living habit (NLH). By checking NLH, the patient’s current health status can be estimated on a daily basis. In the testing experiments of SmartMind, we have demonstrated the accuracy of SmartMind in activity detection and investigated its performance when different machine learning algorithms were adopted for posture detection. The performance results indicate that both support vector machine (SVM) and naive bayes (NB) can achieve an accuracy of higher than 97 % while the random forrests (RF) only gives an accuracy of around 73 %.

SmartMood : Toward Pervasive Mood Tracking and Analysis for Manic Episode Detection

This paper describes SmartMood, a mood tracking and analysis system designed for patients with mania. By analyzing the voice data captured from a smartphone while the user is having a conversation, statistics are generated for each behavioral factor to quantitatively describe his/her mood status. By comparing the newly generated statistics with those under normal mood, SmartMood tries to identify any new manic episodes so that appropriate consultation and medication actions can be taken. The daily behavioral statistics may serve as important references for psychiatrists to show the effectiveness of treatments. To reduce the probability of false alarms, we propose an adaptive running range method to estimate the normal mood range for each behavioral factor, and study methods to minimize the effects of background noise on the generated statistics. The preliminary experimental results on SmartMood show that a method using the pitch of a voice data sample to identify silent periods can better differentiate the voice of a normal or manic user in a call session than other methods. The results from the limited proof of concept testing indicate that moving to clinical testing is warranted.

Scheduling Temporal Data with Dynamic Snapshot Consistency Requirement in Vehicular Cyber-Physical Systems

Timely and efficient data dissemination is one of the fundamental requirements to enable innovative applications in vehicular cyber-physical systems (VCPS). In this work, we intensively analyze the characteristics of temporal data dissemination in VCPS. On this basis, we formulate the static and dynamic snapshot consistency requirements on serving real-time requests for temporal data items. Two online algorithms are proposed to enhance the system performance with different requirements. In particular, a reschedule mechanism is developed to make the scheduling adaptable to the dynamic snapshot consistency requirement. A comprehensive performance evaluation demonstrates the superiority of the proposed algorithms.

SmartMind: Activity Tracking and Monitoring for Patients with Alzheimer's Disease

In this paper, we introduce SmartMind, an activity tracking and monitoring system to help Alzheimers diseases (AD) patients to live independently within their living rooms while providing emergent help and support when necessary. Allowing AD patients to handle their daily activities not only can release some of the burdens on their families and caregivers, but also is highly important to help them regain confidence towards a healthy life and reduce the degeneration rates of their memories. The daily activities of a patient captured from SmartMind can also serve as important indicators to describe his/her normal living habit (NLH). By checking with NLH, the patients current health status can be estimated on a daily basis.

A Pervasive Promotion Model for Personalized Promotion Systems on Using WLAN Localization and NFC Techniques

In this paper, we propose a novel pervasive business model for sales promotion in retail chain stores utilizing WLAN localization and near field communication (NFC) technologies. The objectives of the model are to increase the customers’ flow of the stores and their incentives in purchasing. In the proposed model, the NFC technology is used as the first mean to motivate customers to come to the stores. Then, with the use of WLAN, the movements of the customers, who are carrying smartphones, within the stores are captured and maintained in the movement database. By interpreting the movements of customers as indicators of their interests to the displayed items, personalized promotion strategies can be formulated to increase their incentives for purchasing future items. Various issues in the application of the adopted localization scheme for locating customers in a store are discussed. To facilitate the item management and space utilization in displaying the items, we propose an enhanced R-tree for indexing the data items maintained in the movement database. Experimental results have demonstrated the effectiveness of the adopted localization scheme in supporting the proposed model.

Linked Block-based Multiversion B-Tree index for PCM-based embedded databases

In this paper, by exploring the application characteristics of cyber-physical systems (CPS) and the performance characteristics of PCM, we propose a new B-tree index structure, called Linked Block-based Multi-Version B-Tree (LBMVBT), for indexing multi-version data in an embedded multi-version database for CPS. In LBMVBT, to reduce the number of writes to PCM in maintaining the index and improve the efficiency in serving version-range queries, we introduce the block-based scheme for indexing in which multiple versions of a data item are grouped into a version block to be indexed by a single entry in the multi-version index. To reduce the index re-organization cost (i.e., number of writes to PCM) due to node overflow and underflow, we add external entries in each index leaf node so that a node re-organization can be done by only updating pointers without copying the index entries of the re-organized leaf nodes to the new node. Analytic studies have been performed on LBMVBT and a series of experiments has been conducted to evaluate the efficacy of LBMVBT. The experimental results show that LBMVBT can effectively reduce the number of writes to the index and achieve a good overall performance on serving update transactions while version-range queries can be served with smaller number of reads to the index compared with MVBT.