The utilization of Wi-Fi-based technology for pervasive indoor user identification has gained prominence due to its cost-effective nature and compatibility with user devices. Previous works proposed capturing the media access control (MAC) address emitted from a user’s device and using information element (IE)-based MAC de-randomization methods to mitigate the impairment caused by random MAC. However, IE types of different Wi-Fi devices are not consistently differentiated, leading to identification errors in IE-based methods. Additionally, typical Wi-Fi fingerprinting approaches require densely predeployed Wi-Fi stations, contradicting the principle of pervasive localization. To address these challenges, we propose the mobile single-station-based user identification (MS.Id) technique, which leverages Wi-Fi mobile single stations for pervasive indoor user identification. MS.Id includes mobile single-station localization (MSL) and MAC de-randomization based on users’ spatiotemporal location and IE information (DR.LIE). MSL can be implemented on a standard mobile Wi-Fi station without extensive predeployment. DR.LIE performs MAC de-randomization using the LIC algorithm to identify users with random MAC addresses. Experimental results demonstrate that MS.Id outperforms previous IE-based user identification methods and multistation localization techniques. MSL achieves a localization error of 1.15 m which is better than multistation with 12 APs of 1.40 m. DR.LIE demonstrates an identification accuracy of 95.24% which is better than AIMAC of 85.48%.

Leveraging the widespread deployment of existing WiFi sites, WiFi-based techniques offer substantial potential for achieving pervasive indoor localization among various indoor localization techniques. Conventional WiFi-based indoor localization techniques primarily focus on providing fine-grained accuracy. However, previous techniques are not pervasive due to the following constraints: 1) they can hardly be implemented in environments with limited resources of WiFi sites; and 2) they are constrained by high hardware requirements, such as the need for multiple antennas. In this paper, we propose a novel technique called Mobile Single-site Localization (MS-Loc), which leverages a mobile single-site to perform indoor localization. Specifically, MS-Loc utilizes existing hardware at off-the-shelf mobile WiFi sites to achieve pervasive localization rather than relying on multiple sites or multiple antennas. Moreover, in MS-Loc, a tailor-designed path planning algorithm guides the movement of the mobile single-site to locate targets quickly and accurately. We conducted extensive experiments using a real-world testbed. The experimental results demonstrate that MS-Loc presents a competitive localization accuracy compared to previous techniques but is pervasive.

Neural network positioning technology, as one of the mainstream in indoor Wi-Fi positioning systems, is playing an increasingly important role in location-based services. The main challenge is that the samples are prone to be outdated as the indoor environment changes or the wireless signal varies over time, i.e., the samples’ Age of Information (AoI) is large, which leads to the trained model not being available. However, recollecting data to retrain the model is both time-consuming and labor-intensive. To address the above problem, this article proposes a fast adaptation approach based on Bayesian meta-learning that makes the pretrained model acquire a learned learning capability so that it can quickly learn new tasks based on the acquisition of existing knowledge. Specifically, first, a model-agnostic learning scheme is introduced to guide the learning process, which could automatically learn the optimal model parameters and hyperparameter settings. Second, to mitigate the effects of model uncertainty, especially to prevent the overfitting situation based on a limited number of samples, we combine the Stein variational gradient descent (SVGD) with the model-agnostic learning scheme, i.e., Bayesian meta-learning. Compared with traditional meta-learning algorithms, the proposed method makes the training more robust by inferring the Bayesian posterior from a probabilistic perspective. Extensive experimental results show that the proposed approach effectively overcomes the impact of large AoI on localization performance while decreasing labor consumption significantly.

The sparsity of the localization problem makes the compression sensing (CS) theory suitable for indoor localization in wireless local area networks (WLANs). However, in practice, we find that the location errors and computing complexity increase significantly as the dimensionality of the sparse vector and measurement matrix are high in a large environment, so most CS-based localization techniques are accompanied by coarse localization and access point (AP) selection stages. Therefore, in this article, we first deduced the relationship between the number of APs and the dimensionality of the sparse vector theoretically to give the guideline that the number of subdatabases and APs should be obtained. Then an adaptive intuitionistic fuzzy C-ordered mean (AIFCOM) clustering is designed for the data with outliers in the environment with multipath effects. Finally, in the fine localization stage, we propose a semi-tensor product CS (STP-CS) model to construct the measurement matrix, compared with the traditional CS model, our model not only remains more number of APs, but also decreases the dimensionality of measurement matrix, which can reduce the storage space and improve localization accuracy simultaneously.

IEEE 802.11ax is the standard for the new generation WiFi networks. In this paper, we formulate the problem of joint access point (AP) placement and power-channel-resource unit assignment for 802.11ax-based dense WiFi. The objective is to minimize the number of APs. Two quality-of-service (QoS) requirements are to be fulfilled: (1) a two-tier throughput requirement which ensures that the throughput of each station is good enough, and (2) a fault tolerance requirement which ensures that the stations could still use WiFi even when some APs fail. We prove that this problem is NP-hard. To tackle this problem, we first develop an analytic model to derive the throughput of each station under the OFDMA mechanism and a widely used interference model. We then design a heuristic algorithm to find high-quality solutions with polynomial time complexity. Simulation results under both fixed-user and mobile-user cases show that: (1) when the area is small (50 × 50$\rm m^2$), our algorithm gives the optimal solutions; when the area is larger (80 × 60$\rm m^2$), our algorithm can reduce the number of APs by 34.9-87.7% as compared to the Random and Greedy algorithms. (2) Our algorithm can always get feasible solutions that fulfill the QoS requirements.

Most of the representative lower positioning error bound (LPEB) derivation works of Wireless Local Area Network(WLAN) fingerprint-based positioning system are on the basis of Cramer-Rao Lower Bound (CRLB). However, there are some limitations, i) to the best of our knowledge, all existed works have not investigated the impact of grid size, which is one of the factors affecting the location accuracy; ii) traditional CRLB-based derivation takes the user's location coordinate as the basic estimated parameter vector, which is not exact because we actually estimate the nearest reference point (RP) to the user rather than estimating the user's location directly; iii) CRLB-based derivation has a fundamental premise that the signal obeys a specific signal distribution so as to formulate Probability Density Function (PDF) clearly, but for an irregular scenario, the signal may not obey one specific signal distribution and the PDF is unknown, which means CRLB is not available. Motivated by these limitations, this paper firstly constructs a new derivation model which takes grid size information into account, and revises the basic estimated parameter vector as the nearest RP's location. Then we deduce the LPEB in terms of the proposed new derivation model under two situations. Specifically, for a regular scenario with specific signal distribution, we re-deduce LPEB based on CRLB. Moreover, for an irregular scenario with non-specific signal distribution, we transform the observations into a linear pattern expression and apply the Gaussian-Markov theorem to conduct the LPEB derivation. Finally, the simulations and experiments are presented to support our claims.

Fingerprint is one of the representative methods for wireless indoor localization. It uses a fingerprint database (measured in the offline phase) and the current received signal strengths (RSSs) (measured by the user's device in the online phase) to determine the location of this device. However, the RSSs and hence the localization accuracy would be affected by time-varying environmental factors (e.g., number of people in a shopping mall). In this paper, we propose a new method for wireless localization in time-varying indoor environments. In the offline phase, the proposed method measures extra information: it measures E fingerprint databases for E respective environmental conditions, where E is a design parameter (e.g., E=2 for the peak period and the non-peak period in a shopping mall). In the online phase, it leverages the extra information for better localization in time-varying indoor environment, even when the current environmental condition is different from the ones considered in the offline phase. The proposed method is particularly suitable for the indoor venues for which their primary concern is to provide good quality localization services while they could afford a moderate amount of extra resources for one-off measurement in the offline phase (e.g., exhibition centers, airports, shopping malls, etc.). We conduct a simulation experiment and a real-world experiment to demonstrate that the proposed method gives accurate localization.

IEEE 802.11ax is a promising standard for the next-generation WiFi network, which uses orthogonal frequency division multiple access (OFDMA) to segregate the wireless spectrum into time-frequency resource units (RUs). In this paper, we aim at designing an 802.11ax-based dense WiFi network to provide WiFi services to a large number of users within a given area with the following objectives: (1) to minimize the number of access points (APs); (2) to fulfil the users' throughput requirement; and (3) to be resistant to AP failures. We formulate the above into a joint AP placement and power-channel-RU assignment optimization problem, which is NP-hard. To tackle this problem, we first derive an analytical model to estimate each user's throughput under the mechanism of OFDMA and a widely used interference model. We then design a heuristic algorithm to find high-quality solutions with polynomial time complexity. Simulation results show that our algorithm can achieve the optimal performance for a small area of 50×50 m2. For a larger area of 100×80 m2where we cannot find the optimal solution through an exhaustive search, our algorithm can reduce the number of APs by 32 ~ 55% as compared to the random and Greedy solutions.

With the pervasive infrastructures of WLAN, user's privacy has emerged as an important security and privacy problem. Rogue Access Points (AP), as one of the threat, is expected to be detected and located accurately. Therefore, in this paper, we propose a novel rogue AP localization method leveraging compressive sensing (CS) via kernel optimization. Although the CS based technique has been widely used in mobile user localization system, this is the first time to apply it to reversely localize AP. In addition, designing an appropriate kernel is the key to successful application of CS technique, however, traditional Gaussian or Bernoulli random kernels could not be utilized in rogue AP localization system, due that the kernel is related to the number and distribution of monitors, which could not randomly change every time. Hence, for CS kernel optimization, we firstly deduce the minimum number of monitors required in this system through a theoretical analysis which aims at justifying the validity of problem formulation. Then an Equiangular Tight Frame (ETF) based monitors distribution scheme is presented to achieve higher location accuracy. Finally, we perform both simulations and experiments to demonstrate the superiority of our approach as compare to other methods theoretically and practically.

Passive indoor localization for mobile Wi-Fi devices, e.g., smartphones, has attracted increasing attention from research communities recently. Existing passive localization techniques leverage received signal strength (RSS) of packets transmitted by target Wi-Fi devices and do not require a dedicated software installed on the devices. However, RSS-based passive localization techniques: 1) are device dependent, which results in poor localization accuracy for a wide variety of mobile devices and 2) cannot perform real-time passive localization. In this article, we present a novel passive localization technique, namely, packet delivery ratio (PDR) fingerprinting, to address these problems. In PDR fingerprinting, the lowest-power and highest-modulation scheme (LPHMS) is proposed to generate device-invariant PDR, which replaces RSS to construct fingerprints, to achieve device-invariant localization accuracy. Moreover, instead of passively monitoring packets rarely sent by mobile devices, in PDR fingerprinting, access points (APs) actively transmit request-to-send (RTS) frames to trigger target devices to reply clear-to-send (CTS) frames to calculate PDR. The RTS/CTS mechanism enables PDR fingerprinting to perform real-time localization. We have conducted extensive experiments in a real-world testbed. The experimental results demonstrate that PDR fingerprinting presents a competitive localization accuracy compared to RSS-based passive fingerprinting methods but is device invariant.

Although indoor localization has been studied over a decade, it is still challenging to enable many IoT applications, such as activity tracking and monitoring in smart home and customer navigation and trajectory mining in smart shopping mall, which typically require meter-level localization accuracy in a highly dynamic and large-scale indoor environment. Therefore, this article aims at designing and implementing an adaptive and scalable indoor tracking system in a cost-effective way. First, we propose a zero site-survey overhead (ZSSO) algorithm to enhance the system scalability. It integrates the step information and map constraints to infer user's positions based on the particle filter and supports the auto labeling of scanned Wi-Fi signal for constructing the fingerprint database without the extra site-survey overhead. Further, we propose an iterative-weight-update (IWU) strategy for ZSSO to enhance system robustness and make it more adaptive to the dynamic changing of environments. Specifically, a two-step clustering mechanism is proposed to delete outliers in the fingerprint database and alleviate the mismatch between the auto-tagged coordinates and the corresponding signal features. Then, an iterative fingerprint update mechanism is designed to continuously evaluate the Wi-Fi fingerprint localization results during online tracking, which will further refine the fingerprint database. Finally, we implement the indoor tracking system in real-world environments and conduct a comprehensive performance evaluation. The field testing results conclusively demonstrate the scalability and effectiveness of the proposed algorithms.

Given a directed graph G(V,E), a k-(Shortest) Path Cover is a subset C of the nodes V such that every simple (or shortest) path in G consisting of k nodes contains at least one node from C. In this paper, we extend the notion of k-Path Covers such that the objects to be covered don't have to be single paths but can be concatenations of up to p simple (or shortest) paths. For the generalized problem of computing concatenated k-(Shortest) Path Covers, we present theoretical results regarding the VC-dimension of the concatenated path set in dependency of p as well as (approximation) algorithms. Subsequently, we study interesting special cases of concatenated k-Path Covers, in particular, covers for piecewise shortest paths, round tours and trees. For those, we show how the pruning algorithm for k-Path Cover computation can be abstracted and modified in order to also solve concatenated k-Path Cover problems. An extensive experimental study on different graph types proves the applicability and efficiency of our approaches.

Calibrators and sniffers have been used in the literature to proactively update the functional relationship between the received signal strength and the distance for wireless localization in time-varying indoor venue, where calibrators and sniffers are Wi-Fi transmitters and Wi-Fi receivers respectively. To be effective, these devices should be suitably placed in the indoor venue. Let there be N calibrators and M sniffers, di,jbe the distance between calibrator i and sniffer j, and RSSi,jbe the received signal strength measured by sniffer j from calibrator i. The points (d1,1, RSS1,1), (d1,2, RSS1,2), ..., (dN,M, RSSN,M) are used to estimate the functional relationship between the received signal strength and the distance. It is desirable that d1,1, d1,2, ..., dN,Mare uniformly scattered so that the estimated functional relationship is more accurate for better localization. In this paper, we propose to minimize the discrepancy of d1,1, d1,2, ..., dN,Min order to determine the optimal locations of the N calibrators and the M sniffers. We formulate this new problem (named minimal discrepancy placement problem) and design an efficient optimization method to solve it. We conduct simulation and real-world experiments to demonstrate that minimal discrepancy placement can effectively improve localization accuracy.

Many indoor localization systems rely on a set of reference anchors with known positions. A target's location is estimated from a set of distances between the target and its surrounding anchors, and hence the selection of anchors affects the localization accuracy. However, it remains a challenge to select the best set of anchors. In this paper, we study how to appropriately make use of the surrounding anchors for localizing a target. We first construct different candidate anchor clusters by selecting different number of anchors with the strongest received signals. Then for each candidate cluster, we propose a weighted min-max algorithm to provide a location estimation. Finally, we introduce a weighted geometric dilution of precision (w-GDOP) algorithm that combines the estimations from multiple clusters by quantifying their estimation accuracy. We evaluate the performance of our solution through simulations and real-world experiments. Our results show that the proposed anchor selection scheme and localization algorithm significantly improve the localization accuracy in large indoor environments.

This work aims at proposing a transfer learning (TL)-based framework to enhance system scalability of fingerprint-based indoor localization by reducing offline training overhead without jeopardizing the localization accuracy. The basic principle is to reshape data distributions in the target domain based on the transferred knowledge from the source domains, so that those data belonging to the same cluster will be logically closer to each other, whereas others will be further apart from each other. Specifically, the TL-based framework consists of two parts, metric learning and metric transfer, which are used to learn the distance metrics from source domains and identify the most suitable metric for the target domain, respectively. Furthermore, this work implements a prototype of the fingerprint-based indoor localization system with the proposed TL-based framework embedded. Finally, extensive real-world experiments are conducted to demonstrate the effectiveness and the generality of the TL-based framework.

The elderly living in smart homes can have their daily movement recorded and analyzed. As different elders can have their own living habits, a methodology that can automatically identify their daily activities and discover their daily routines will be useful for better elderly care and support. In this article, we focus on automatic detection of behavioral patterns from the trajectory data of an individual for activity identification as well as daily routine discovery. The underlying challenges lie in the need to consider longer-range dependency of the sensor triggering events and spatiotemporal variations of the behavioral patterns exhibited by humans. We propose to represent the trajectory data using a behavior-aware flow graph that is a probabilistic finite state automaton with its nodes and edges attributed with some local behavior-aware features. We identify the underlying subflows as the behavioral patterns using the kernel k -means algorithm. Given the identified activities, we propose a novel nominal matrix factorization method under a Bayesian framework with Lasso to extract highly interpretable daily routines. For empirical evaluation, the proposed methodology has been compared with a number of existing methods based on both synthetic and publicly available real smart home datasets with promising results obtained. We also discuss how the proposed unsupervised methodology can be used to support exploratory behavior analysis for elderly care.

In order to maintain a healthy living both physiologically and psychologically, it is important for patients with mild cognitive impairment (MCI) to maintain active in daily life. In this paper, we demonstrate how to use simple motion sensors, e.g., accelerometers, gyroscopes and magnetometers, to design and develop a system, called ActiveLife, for effective tracking of the daily living activities of MCI patients within their living rooms. In order to simplify the activity detection process, in ActiveLife, we adopt the context-based approach to model the common activities performed by the user within a day. Since the accelerometer and gyroscope are tri-axial sensors, the sensor data for different axes can be used to predict the current posture of the user while he is performing an activity. Combining with the heading direction of the posture obtained from the magnetometer and distance travelled during the transition of activities, we can estimate the current activity of the user. To further improve the estimation accuracy, we have designed an algorithm using the machine-learning technique, i.e., support vector machines (SVM), for activity classification.

With the advent of the Internet of things (IoT) and smart sensor technologies, the data-driven paradigm has been found promising to support human behavioral analysis in a smart home for better healthcare and well-being of senior adults. This work focuses on discovering daily activity routines from sensor data collected in a smart home. By representing the sensor data as a matrix, daily activity routines can be identified using matrix factorization methods. The key challenge rests on the fact that the matrix contains discrete labels as its elements, and decomposing the nominal data matrix into basis vectors of the labels is nontrivial. We propose a novel principled methodology to tackle the nominal matrix factorization problem. Assuming that the similarity matrix of the labels is known, the discrete labels are first projected onto a continuous space with the interlabel distance preserving the given similarity matrix of the labels as far as possible. Then, we extend a hierarchical probabilistic model for ordinal matrix factorization with Bayesian Lasso that the factorization can be more robust to noise and more sparse to ease human interpretation. Our experimental results based on a synthetic data set shows that the factorization results obtained using the proposed methodology outperform those obtained using a number of the state-of-the-art factorization methods in terms of the basis vector reconstruction accuracy. We also applied our model to a publicly available smart home data set to illustrate how the proposed methodology can be used to support daily activity routine analysis.

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.

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.

In this paper, we introduce SmartMind, an activity tracking and monitoring system to help Alzheimer's 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 patient's current health status can be estimated on a daily basis.

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.

In this paper, we study how mobile computing and wireless technologies can be explored to provide effective ubiquitous healthcare services. Instead of reinventing the wheels, we make use of smartphones, off-the-shelf components, and existing technologies in ubiquitous computing (i.e. wireless and mobile positioning technologies, and data acquisition techniques and processing via sensors) to develop a middleware, and tools for the development of systems and applications to provide effective ubiquitous healthcare services. Two main tasks to be studied are: 1) Developing a framework, called SmartHealth, to provide the infrastructure and architectural support for realizing ubiquitous healthcare services, and 2) Designing and developing ubiquitous healthcare applications by utilizing the SmartHealth framework to let users experience and benefit from the provided services. We use scenarios to illustrate how mobile/wireless and sensor technologies can enable ubiquitous healthcare services in Smart Health. Some of the examples included in Smart Health are: location tracking, vital signs and well-being data acquisition and analysis, fall detection and behavior monitoring, and sleep analysis. As a start, based on the Smart Health framework, we introduce a smartphone app, called Smart Mood, for tracking the mood of patients who are suffering mood disorder (i.e., manic and depression) to demonstrate how Smart Health can effectively enable ubiquitous healthcare services.

Recent advances in infrastructure-to-vehicle (I2V) and vehicle-to-vehicle (V2V) communications are envisioned to enable a variety of emerging applications in vehicular networks, where it is imperative to provide efficient data services via cooperative vehicular communications. In this work, we present the data dissemination system via cooperative I2V and V2V communications. We formulate the problem by investigating both the communication constraint and the application requirement on data dissemination. The goal is to maximize the system performance by exploiting the joint effects of I2V and V2V communications. On this basis, we propose an on-line scheduling algorithm to enable scalable, fair and robust data dissemination. The algorithm makes scheduling decisions by transforming the data dissemination problem to the maximum weighted independent set (MWIS) problem and approximately solving MWIS using a greedy method. We build the simulation model based on realistic traffic and communication characteristics. A comprehensive simulation study demonstrates that the proposed solution is able to effectively strike a balance between I2V and V2V data services and maximize system performance in terms of scalability, fairness and robustness.

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.

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.

Recent advances in wireless communication technologies have spawned many new applications in vehicular networks. Data dissemination via roadside-to-vehicle communication is a vital approach to enabling most of these applications. In this work, we investigate in the scenario where data items are broadcasted from the road-side unit (RSU) in response to requests submitted by passing vehicles. Data items are associated with temporal constraints and updated periodically to reflect dynamic states of traffic information. Each request may ask for multiple temporal data items, and it is associated with a deadline, which may either be specified by the driver or imposed by the time when the vehicle drives through the service region. In particular, we develop a real-time data dissemination model based on roadside-to-vehicle communication by formulating the time-constraint of requests and the consistency requirement of retrieving temporal data items. On this basis, we propose an online scheduling algorithm to enhance the system performance in terms of maximizing request service and improving bandwidth utilization. Lastly, we build a simulation model to evaluate the algorithm performance in a variety of situations. Experimental results demonstrate that the proposed algorithm outperforms existing algorithms significantly in both request serving and bandwidth utilization.

With the prevalence of mobile Wi-Fi devices and infrastructures, there are growing interests in mobile surveillance and device tracking for providing better location-aware services in metropolitan areas. With a good location estimation integrated into a wireless infrastructure, system administrators can closely monitor the network traffic as well as the behavior of the mobile users. The Received Signal Strength(RSS), easily available information from Access Point(AP) Sensors, has become the most popular research approach. However, in reality received signal strength is affected by factors such as occlusion, signal deflections and reflections. There had been proposed estimation systems that use the Fingerprinting approach to provide good and accurate location recommendation. But such systems have been drawn back by their time-intensive training and retraining process. The solution to Signal Strength-based estimation, therefore, is to devise a system that minimizes the training Andre adaptation process while attaining good accuracy in location estimation. This paper proposes a location estimation system whose estimation method is based on the Center of Gravity(CG)method. This method also serves as an algorithm selector such that the system can switch to another estimation algorithm if need be. The aim of this system is to reduce the high cost of training and re-calibration but attain an accuracy comparable to the Fingerprinting location estimation approach.

With the prevalence of Wi-Fi activity within a wireless network, there are growing interests in mobile surveillance and device tracking for better network services. With a good location estimation algorithm integrated into a wireless network, system administrators can closely monitor the network traffic as well as the behavior of the mobile users. By modifying the embedded software in off-the-shelf WLAN APs, our system can sniff out data packets transmitted by WLAN devices without the need to install client programs on mobile user devices. In this paper we propose the Aggregated Signal Layout location estimation algorithm. Experiment results show that once wireless activities had been detected, our signal strength based localization algorithm can estimate positions of the wireless mobile devices involved with great success rate and good accuracy. With an accuracy comparable to the fingerprinting method but without paying the high costs of training and maintenance of the fingerprint databases inside our WLAN system, the proposed Aggregated Signal Layout method is well-justified for its efficiency and effectiveness in locating and tracking mobile users' activities within a wireless network.

With a good location estimation integrated into a Wi-Fi surveillance system, system administrator can closely monitor the network traffic as well as the behavior of the mobile users. Hence, there is a growing demand to have a quick and efficient way to indentify a specific group of people, or devices or asset within a controlled wireless network. In our proposed system, all the Wi-Fi traffic and information especially the MAC addresses and RSSI from the mobile clients (i.e. Wi-Fi devices) can be sniffed by an open-source Wi-Fi access point (AP) with custom-made embedded software program and without pre-loading any client program on the mobile user devices. These sniffed information is then analyzed and stored in a database which will help network administrator to monitor the wireless network for surveillance purpose and security concerns. In summary, this paper proposes a wireless LAN system that can detect, locate and track down wireless communication within the system by modifying the embedded software in off-the-shelf WLAN APs. Experiment results have shown that abnormal wireless activities can be detected and by our signal strength based localization algorithm, positions of these wireless mobile devices can be identified and be tracked within meters inside our WLAN system.

This article examines a new problem of k -anonymity with respect to a reference dataset in privacy-aware location data publishing: given a user dataset and a sensitive event dataset, we want to generalize the user dataset such that by joining it with the event dataset through location, each event is covered by at least k users. Existing k -anonymity algorithms generalize every k user locations to the same vague value, regardless of the events. Therefore, they tend to overprotect against the privacy compromise and make the published data less useful. In this article, we propose a new generalization paradigm called local enlargement , as opposed to conventional hierarchy- or partition-based generalization. Local enlargement guarantees that user locations are enlarged just enough to cover all events k times, and thus maximize the usefulness of the published data. We develop an O ( H n )-approximate algorithm under the local enlargement paradigm, where n is the maximum number of events a user could possibly cover and H n is the Harmonic number of n . With strong pruning techniques and mathematical analysis, we show that it runs efficiently and that the generalized user locations are up to several orders of magnitude smaller than those by the existing algorithms. In addition, it is robust enough to protect against various privacy attacks.

Mobile location estimation is becoming an important value added service for a mobile phone network. It is well-known that GPS can provide an accurate location estimation. But it is also a known fact that GPS does not perform well in urban areas like downtown New York and cities like Hong Kong. Many mobile location estimation approaches based on cellular networks have been proposed to compensate the problem of the lost of GPS signals in providing location services to mobile users in metropolitan areas. Among different kinds of mobile location estimation technologies, only the class of signal strength based algorithm which estimates the location of the mobile station (MS) by signal strength received from the nearly base stations (BSs) can be applied to different kinds of cellular networks, and therefore, it is a more general solution. In this paper, we design a directional propagation model, the Modified Directional Propagation Model (MDPM), which makes use of a common signal propagation model to perform location estimation. We test MDPM with real data taken in Hong Kong and experimental results show that MDPM outperforms other existing location estimation algorithms among different kinds of terrains and environmental factors.

Indoor positioning technology and its accuracy are crucial research topics for ubiquitous computing. While the GSM-based approach has always been used to provide outdoor positioning to compensate the lost of GPS in urban area, we seldom see systems that utilize the GSM-based approach for indoor positioning. On the other hand, the WLAN-based approach is widely used to provide indoor positioning service. However, with its Ad hoc layout and signal fluctuation, it is hard to provide a good performance based on the WLAN-based approach. In this paper, we develop an indoor positioning system that makes use of both GSM and WLAN signals to do location estimation such that the resultant system is more accurate and more stable and thus enhancing the performance of the whole system. Experimental results show that our system is stable and can reach centimeter-level accuracy, which outperforms other existing indoor positioning systems that utilizes a single network only.

On-demand broadcast is an effective wireless data dissemination technique to enhance system scalability and capability to handle dynamic user access patterns. Previous studies on time-critical on-demand data broadcast were under the assumption that each client requests only one data item at a time. With rapid growth of time-critical information dissemination services in emerging applications, there is an increasing need for systems to support efficient processing of real-time multi-item requests. Little work, however, has considered on-demand broadcast environment with time-critical multi-item requests. In this paper, we investigate the scheduling problem arising in this new environment and observe that existing single item request based algorithms are unable to manage multi-item requests efficiently. Thus, an innovative algorithm that combines the strengths of data item scheduling and request scheduling is proposed. The performance results of our simulation show that the proposed algorithm is superior to other classical algorithms under a variety of circumstances. Our algorithm not only reduces deadline miss ratio of requests, but also saves broadcast channel bandwidth.

Mobile location estimation is drawing considerable attention in the field of wireless communications. In this paper, we present a new estimator which considers all the information to reduce the effect of signal fluctuation and fading-the statistical estimation. The Statistical Estimation is derived from the information of the received signal strengths (RSSs) and the locations of their corresponding base stations (BSs) and then estimates the location of the mobile station (MS). The statistical estimation uses all the information to provide the estimation of the location of the MS, which can provide an accurate estimation and reduce the effect of signal fluctuation and fading. It is a data fusion method to handle the signal fluctuation and fading problem. We test our approach with real data collected from Hong Kong. Experimental results show that our approach outperforms other existing location estimation algorithms among different kinds of terrains. The improvements based on the geometric algorithm with EPM and the iterative algorithm with EPM are 18.87% and 4.46%, respectively.

Location estimation using the Global System for Mobile communication (GSM) is an emerging application that infers the location of the mobile receiver from multiple signals measurements. While geometrical and signal propagation models have been deployed to tackle this estimation problem, the terrain factors and power fluctuations have confined the accuracy of such estimation. Using support vector regression, we investigate the missing value location estimation problem by providing theoretical and empirical analysis on existing and novel kernels. A novel synthetic experiment is designed to compare the performances of different location estimation approaches. The proposed support vector regression approach shows promising performances, especially in terrains with local variations in environmental factors.

Mobile location estimation or mobile positioning is becoming an important service for a mobile phone network. It is well-known that GPS can provide accurate location estimation, but it is also a known fact that GPS does not perform well in urban areas like downtown New York and cities like Hong Kong. Then many mobile location estimation approaches based on radio cellular networks have been proposed to compensate the problem of the lost of GPS signals in providing location services to mobile users in metropolitan areas. In this paper, we present a selector method with the linear discriminant analysis (LDA) among different kinds of mobile location estimation technologies we had proposed in previous work in order to combine their merits, then provide a more accurate estimation for location services. We build up a three-level binary tree to classify these four algorithms. These three levels are named as Stat-Geo level, CG-nonCG level and CT-EPM level. And these success ratios of these three levels are 85.22%, 88.45% and 88.89% respectively. We have tested our selector method with real data taken in Hong Kong and it is proven that it outperforms other existing location estimation algorithms among different kinds of terrains.

Recently, mobile location estimation is drawing considerable attention in the field of wireless communications. Among different mobile location estimation methods, the one which estimates the location of mobile stations with reference to the wave propagation model is drawing much attention on the grounds that it is applicable to different kinds of cellular network. However the estimation accuracy of the signal propagation model deteriorates as the distance between the mobile station and the receiving base station increases. In view of this, we have designed a New Location Estimation Approach which has divided the location estimation into two phases, the Data Collection Phase and the Location Estimation Phase. In this paper, we report our study of the New Location Estimation Approach. We tested the approach with real life data collected from a major mobile operator of Hong Kong. Results show that the New Location Estimation Approach has improved the existing statistical signal propagation model by 30.94%. This improvement can be further enhanced if more data are collected.

This paper presents a performance study on various broadcast algorithms in a Real-Time Information Dispatch System. The objective of the study is to design an efficient broadcast algorithm for providing data in a timely manner for realtime transaction processing applications in broadcast environments. We construct and conduct a series of simulation experiments to look at the performance of our proposed algorithm. Simulation results show that our proposed broadcast algorithm can further reduce the probability of mobile read only transactions missing deadlines in broadcast environments.

Mobile location estimation is becoming an important value-added service for mobile phone operators. Many mobile location estimation algorithms based on the cellular radio networks have been proposed but there exists no general solution since each algorithm has its own advantage depending on specific terrain and environmental factors. In this paper, we propose and investigate three algorithm selectors, one with a LDA classifier and the other two with Bayes classifiers using either a Naive Bayes probabilistic model or a Bayes probabilistic model, to select the best mobile location estimation algorithms from our previous work in order to combine their merits, and provide a more accurate estimation for location services. We have tested these three algorithm selectors with real data taken in Hong Kong. Experiment results have shown that they are all useful in particular, and the one with a Bayes probabilistic model outperforms all other existing location algorithms among different kinds of terrains in terms of average errors

Abstract This paper describes the issues that are involved in designing and implementing a large‐scale cooperative object database server for a collaborative virtual environment. The focus of the paper is about the way to handle the distributing factors and the communication model among nodes within a collaborative virtual environment. Within the system, all objects are decentralized and scattered among three tiers of object databases. By limiting the knowledge and refining the tasks for each server, the network and processor workload can be reduced. The objective of the system is to provide a virtual environment for distributed computing that is k‐fault tolerant and subject to expansion without the interruption of services. Copyright © 2006 John Wiley & Sons, Ltd.

Mobile positioning is becoming an important service on radio cellular network. Among different kind of location estimation technologies, the one, which estimates the location of mobile stations using signal strength is able to be applied to different kinds of cellular network, and therefore, is more general. We have designed a directional propagation model - the ellipse propagation model (EPM), which makes use of a wave propagation model to perform location estimation. The EPM enhanced the traditional propagation model by resembling the contour line of signal strength as an ellipse rather than a circle and hence becoming more realistic. We have tested the EPM with real data taken in Hong Kong and it is proven that the EPM out performing other existing location estimation algorithms in different kinds of terrains.

This paper presents a performance study on various broadcast algorithms and caching strategies for on-time delivery of data in a real time information dispatch system. The objective of the study is not just aiming at on time delivery, but to improve the response time on the data requests. We propose and perform a series of simulation experiments, using real traffic data from the access log of the official web site for FIFA 2002 World Cup. Simulation results show that our proposed broadcast algorithm not only succeeds in providing good on-time delivery of data but at the same time provides 2 to 3 times of improvement in response time over traditional scheduling algorithms like first-in-first-out (FIFO) and earliest-deadline-first (EDF). The simulation results also show that our proposed caching strategy provides further improvement in percentage of requests finished in time over traditional caching strategy like least recently used (LRU).

On-demand broadcast is an attractive data dissemination method for mobile and wireless computing. In this paper, we propose a new online preemptive scheduling algorithm, called PRDS that incorporates the urgency, the data size and the number of pending requests for real-time on-demand broadcast system. Furthermore, we use pyramid preemption to optimize performance and reduce overhead. We have done a series of simulation experiments to evaluate the performance of our algorithm as compared with other previously proposed methods under a range of scenarios. The experimental results show that our algorithm can substantially outperform other algorithms without jeopardizing other performance metrics, such as response time and stretch.

Positioning is a crucial technology for ubiquitous computing. A directional propagation model - the ellipse propagation model (EPM) is proposed by our research group for locating a mobile station (MS) within a radio cellular network with an accuracy that can enable a number of location based services to realize ubiquitous computing. By using a geometric algorithm, the location of the mobile station can be estimated. However, since one parameter in our geometric algorithm is fixed, errors may be induced as the surrounding environment changes. In view of this, we would like to propose a new algorithm - the iterative algorithm to provide the positioning based on EPM. With the technical support of two local mobile phone operators, we have conducted a series of experiments using real data and experiment results showed that the proposed iterative algorithm outperforms the geometric algorithm by a good margin of 18% in terms of average error.

Recently, mobile location estimation is drawing considerable attention in the field of wireless communications. Among different mobile location estimation methods, the one which estimates the location of mobile stations with reference to the wave propagation model is drawing much attention. This approach, in principle, makes use of the most primitive property of wave propagation - signal strength, to perform location estimation. Hence this approach should be able to apply to different kinds of cellular network. We found out that in estimating mobile location with reference to signal strength, the azimuth gain of directional antenna and environmental factors can help to improve the accuracy. In this paper, we report our study of a directional propagation model (DPM) which enhanced the traditional propagation model with these factors. We experimented our model with 3,703 sets of real life data collected from a major mobile phone operator of Hong Kong. Results show that the DPM models have significant improvement over other existing location methods in terms of accuracy and stability.

Comparing actual output with the expected output of some controlled input is a fundamental principle of program correctness testing. However, in some situations, the input is uncontrollable or even undetectable during testing and, hence, it is impossible to decide the expected output or the test oracle. We encountered this problem when we developed programs to extract network data from various mobile stations in the mobile location estimation system project. We propose testing the trustworthiness of the programs instead. Since the input is uncontrollable and undetectable, program output is analyzed and challenged against with the intrinsic properties, environment, the program output itself and their application results, to find evidence that the output is suitable to be used for the planned purposes. Furthermore, in the case of mobile software development, it is common that different programs of the same specification have to be developed for mobile stations of different models. These different implementations provide another source of reference for trustworthiness tests. Our experience of applying trustworthiness test in extracting network data from mobile stations is reported in this paper.

In this paper, we study the performance and impact of maintaining temporal consistency on a recently proposed concurrency control protocol for processing transactions in broadcast environments. This protocol offers autonomy between mobile clients and the server such that mobile clients can read consistent data off the air without contacting the server. However, most of the existing mobile computing applications, such as information dispersal systems for stock prices and weather information, are comprised of real-time read only transactions. In order to deliver timely and useful results, real-time transactions must also read temporal consistent data. A number of approaches to maintaining temporal consistency are studied through a series of simulation experiments. Results show that taking advantage of data semantics and temporal consistency requirement can improve the performance of mobile read only transactions in broadcast environments.

Due to the strong experimental evidence that packet network traffic is self-similar in nature, it is important to study the problems to see whether the superposition of self-similar processes retains the property of self-similarity, and whether the service of a server changes the self-similarity property of the input traffic. In this letter, we first discuss some definitions and superposition properties of self-similar processes. We obtain some good results about the property of merging self-similar data streams. Then we present a model of a single server with infinite buffer and prove that when the queue length has finite second-order moment, the input process, being strong asymptotically second-order self-similar (sas-s), is equivalent to the output process which also bears the sas-s property.

A dual channel system, which is based on the GPS and the GSM network, is being developed to compensate the problem of the loss of GPS signals in providing location services to mobile users in urban areas. In this design, when GPS signals are being blocked in blind spot areas, GSM positioning algorithms would be used as an alterative method to provide location estimations. This research is an investigation in search of a set of location estimation algorithms based on signal attenuation to work with GPS, so as to develop a dual channel positioning system. With the technical support from a local mobile operator we have constructed and conducted several real world experiments for our investigation and results are promising.

This paper describes the issues involved in designing and implementing a large-scale cooperative object database server for collaborative virtual environment. Participants presented in the environment may include humans and computer artifacts. The focus of this paper is the distributing factor and communication model between nodes. In DIVINE, all objects will be decentralized and scattered among three tiers of object databases. By limiting knowledge and refining tasks for each server the network and processor workload can be reduced. The objective of the system is to provide a virtual environment for distributed computing with N+1 fault tolerant and subject to expansion without interruption of services.

Consider a real time communication application running on top of an ATM network; we require the connection to provide a transmission guarantee for the real time service. Such transmission guarantee can only be possible if there exists an absolute and deterministic worst case delay bound on all the ATM cells within the real time connection. We present our approach in finding this worst case cell delay within an ATM switch. In terms of computation complexity, we find out that our proposed method is no harder than any other existing methods in finding such worse case delay. Furthermore, while other researchers tackle different schedulers with different approaches, our method is general enough and is applicable to schedulers that adopt the FIFO, Static Priority (SP), Earliest Deadline First (EDF) and Generalized Processor Sharing (GPS) scheduling policies. In addition, in our proposed "Fixed Points" method, we trade off accuracy with computation complexity for performance. As a result, our improved method is shown to be superior to all existing methods in terms of computation complexity. Through our simulation experiments based on real time MPEG video streams, the loss in accuracy for estimating the worst case cell delay is shown to be negligible for the connection admission control for an ATM network.

We present an efficient and effective method to derive the worst case delay in an ATM switch. In an ATM switch, admitting a hard real-time connection requires the delays of cells belonging to the connection meeting their deadline without violating the guarantees already provided to connections that are currently active. Previous studies have shown that the real-time connection traffic and the available service can both be described by piecewise linear functions in terms of time. By utilizing the inverse of the arrival and service functions, we obtain an efficient and effective method to complete the worst case delay of a connection to an ATM switch. We analyze and compare the performance of an ATM switch with priority driven and FIFO scheduling policies under different utilization. We also compare the performance using our proposed integrated method with the traditional independent method. From simulation experiments, we found that our method always obtains a higher admission probability and a better estimation of cell delay within an ATM switch.

This paper presents a performance study of ATM networks in the support of real-time MPEG-I video transmission. Multiple classes of MPEG-I are used in the study and what makes this simulation study different from the others is the video data used. These data are captured from real video programmes and we categorized these video clips according to their workload characteristics. The performance of the ATM switch is examined in terms of the cell miss ratio due to deadline missing and the cell loss ratio due to buffer overflow in both the ATM switch as well as the gateways. Moreover, a higher level of abstraction in terms of burst loss ratio is also collected. The results indicate that the first-come-first-serve scheduling algorithm is insufficient to handle real-time traffic. This paper presents a better method to improve the performance significantly especially when the virtual path bandwidth negotiated is conservative.

The real-time performance of several existing local area network medium-access protocols under high-speed data transfer rates is studied. the goal is to determine the limitations in the ability of the protocols to guarantee on-time delivery of messages for different types of multimedia applications.

The results of a simulation study in which the real-time performances of a high-speed token ring network and a multiple-ring network were compared are described. The medium access protocols and the interconnection topologies of the multiple-ring network evaluated are discussed. The characteristic of the workload used in this simulation experiment is described. The simulation results are given.>

Simulation experiments show that the token ring protocol gave a lower average message delay at low transfer rates, but the token bus protocol gave a better overall performance for applications where only average delay is of interest. On the other hand, in hard real-time systems, the criterion of importance is not the average message delay, but the maximum message delay and the ability to meet deadlines. Slotted ring in this case is a much better protocol than the others because of its low maximum message delay and more predictable message delay. Because of this, and because the average performance of the slotted ring remains good as the size or the transfer rate of the network increases, the slotted ring protocol is preferred over the token ring and token bus protocols for hard real-time systems.>