Sheng-Po Kuo

A Scrambling Method for Fingerprint Positioning Based on Temporal Diversity and Spatial Dependency

Signal strength fluctuation is one of the major problems in a fingerprint-based localization system. To alleviate this problem, we propose a scrambling method to exploit temporal diversity and spatial dependency of collected signal samples. We present methods on how these properties can be applied to enhance the positioning accuracy of several existing schemes. Simulation studies and experimental results show that the scrambling method can greatly improve positioning accuracy, especially when the tracked object has some degree of mobility.

Location tracking in a wireless sensor network by mobile agents and its data fusion strategies

The wireless sensor network is an emerging technology that may greatly facilitate human life by providing ubiquitous sensing, computing, and communication capability, through which people can more closely interact with the environment wherever he/she goes. To be context-aware, one of the central issues in sensor networks is location tracking, whose goal is to monitor the roaming path of a moving object. While similar to the location-update problem in PCS networks, this problem is more challenging in two senses: (1) there are no central control mechanism and backbone network in such environment, and (2) the wireless communication bandwidth is very limited. In this paper, we propose a novel protocol based on the mobile agent paradigm. Once a new object is detected, a mobile agent will be initiated to track the roaming path of the object. The agent is mobile since it will choose the sensor closest to the object to stay. The agent may invite some nearby slave sensors to cooperatively position the object and inhibit other irrelevant (i.e., farther) sensors from tracking the object.As a result, the communication and sensing overheads are greatly reduced. Our prototyping of the location-tracking mobile agent based on IEEE 802.11b NICs and our experimental experiences are also reported.

Discriminant Minimization Search for Large-Scale RF-Based Localization Systems

In large-scale fingerprinting localization systems, fine-grained location estimation and quick location determination are conflicting concerns. To achieve finer grained localization, we have to collect signal patterns at a larger number of training locations. However, this will incur higher computation cost during the pattern-matching process. In this paper, we propose a novel discriminant minimization search (DMS)-based localization methodology. Continuous and differentiable discriminant functions are designed to extract the spatial correlation of signal patterns at training locations. The advantages of the DMS-based methodology are threefold. First, with through slope of discriminant functions, the exhaustive pattern-matching process can be replaced by an optimization search process, which could be done by a few quick jumps. Second, the continuity of the discriminant functions helps predict signal patterns at untrained locations so as to achieve finer grained localization. Third, the large amount of training data can be compressed into some functions that can be represented by a few parameters. Therefore, the storage space required for localization can be significantly reduced. To realize this methodology, two algorithms, namely, Newton-PL and Newton-INT, are designed based on the concept of gradient descent search. Simulation and experiment studies show that our algorithms do provide finer grained localization and incur less computation cost.

Cluster-Enhanced Techniques for Pattern-Matching Localization Systems

In location-based services, the response time of location determination is critical, especially for realtime applications. This is especially true for pattern-matching localization methods, which rely on comparing an objects current signal strength pattern against a pre-established location database of signal strength patterns collected in the training phase. In this work, we propose some cluster-enhanced techniques to speed up the positioning process while avoiding the possible positioning errors caused by this accelerated mechanism. Through grouping training locations with similar signal strength patterns together and characterizing them by a single feature vector, we show how to reduce the associated comparison cost so as to accelerate the pattern-matching process. To deal with signal fluctuations, several clustering strategies allowing overlaps are proposed. Extensive simulation studies are conducted. Experimental results show that compared to the pattern-matching systems without clustering techniques, a reduction of more than 90% in computation cost can be obtained in average without degrading the positioning accuracy.

A probabilistic signal-strength-based evaluation methodology for sensor network deployment

The deployment of sensor networks have attracted a lot of attention recently. In essence this issue is concerned with how well a sensing field is monitored by sensors to achieve a particular coverage. In this paper, we propose a signal-strength-based approach to evaluate how well a sensing field is covered/monitored. We first formulate object tracking by a single sensor as a Gaussian-error model. Then we establish an error model on location estimation given that the location of an object is known. This leads to a model to evaluate a sensor network with given locations of sensors. We then apply the result to several applications, such as adding more sensor nodes for error reduction and scheduling power modes (awake or sleep) of sensors, and demonstrate our simulation results.

The Beacon Movement Detection Problem in Wireless Sensor Networks for Localization Applications

Localization is a critical issue in wireless sensor networks. In most localization systems, beacons are being placed as references to determine the positions of objects or events appearing in the sensing field. The underlying assumption is that beacons are always reliable. In this work, we define a new beacon movement detection (BMD) problem. Assuming that there are unnoticed changes of locations of some beacons in the system, this problem concerns how to automatically monitor such situations and identify such unreliable beacons based on the mutual observations among beacons only. Existence of such unreliable beacons may affect the localization accuracy. After identifying such beacons, we can remove them from the localization engine. Four BMD schemes are proposed to solve the BMD problem. Then, we evaluate how these solutions can improve the accuracy of localization systems in case there are unnoticed movements of some beacons. Simulation results show that our solutions can capture most of the unnoticed beacon movement events and thus can significantly alleviate the degradation of such events.

Detecting Movement of Beacons in Location-Tracking Wireless Sensor Networks

Localization is a critical issue in wireless sensor networks. In most localization schemes, there are beacons being placed as references to determine the positions of objects or events appearing in the sensing field. The underlying assumption is that beacons are always static. In this work, we define a new Beacon Movement Detection (BMD) problem. Assuming that there are unnoticed changes of locations of some beacons in the system, this problem is concerned about how to automatically monitor such situations and identify these beacons. Removal of such beacons in the positioning engine may improve the localization accuracy. Two schemes are proposed to solve the BMD problem. Finally, we evaluate how these solutions can improve the accuracy of localization schemes in case that there are unnoticed movement of some beacons. Simulation results show that our solutions alleviate 53% the decrease of positioning accuracy caused by the exceptional beacon movement.

People help people: A pattern-matching localization with inputs from user community

Location-based services are regarded as a killer application of mobile networks. Among all RF-based localization techniques, the pattern-matching scheme is probably the most widely accepted approach. A key factor to its success is the accuracy concern and the calibration efforts to collect its training data. In this paper, we propose a community-based approach to reduce the calibration effort. We show how to get some volunteers (called co-trainers) to help add more training data to our location database. We also show how to rate the credit level of a co-trainer and the trust level of a piece of training data contributed by a co-trainer. We believe that our framework can greatly reduce the calibration effort of the pattern-matching localization scheme.

A Quorum-based Mechanism as an Enhancement to Clock Synchronization Protocols for IEEE 802.11 MANETs

In wireless mobile ad hoc networks (MANETs), it is essential that all mobile hosts (MHs) are synchronized to a common clock to support the power-saving (PS) mechanism. Many protocols have been proposed for clock synchronization in IEEE 802.11 MANETs. However, it is practically impossible for any protocol to completely solve the asynchronism problem especially when connectivity is achieved by multi-hop communication or when a network could be temporarily disconnected. In this work, we propose a quorum-based mechanism, which includes a new structure of beacon intervals for MHs to detect potential asynchronous neighbors and an enhanced beacon transmission rule to assist clock synchronization protocols to discover asynchronous neighbors within bounded time. The proposed mechanism should be regarded as an enhancement to existing clock synchronization protocols. Our simulation results show that the mechanism can effectively relieve the clock asynchronism problem for IEEE 802.11 MANETs

Supporting Multimedia Communication within an 802.11g Ad-hoc Network

By investigating the experimental results of the average throughput with the 802.11g ad-hoc mode, we have collected the different performance characteristics among the indoor, the wall-penetration and the outdoor environment. Given the solid evidences from the baseline experiments, we have embedded the heuristic algorithms into the routing decisions. We have thus simulated a large area of 300 meters by 300 meters with hundreds of routing nodes. After investigating over 100 topology scenarios, the performance results indicate that our proposed scheme produces the higher-bandwidth paths for most of the cases. Even in the less-dense cases, our proposed schemes still can find the better paths with bandwidth about 30% higher than the conventional methods.