Weiping Zhu

A hybrid method for achieving high accuracy and efficiency in object tracking using passive RFID

Passive RFID tags have been widely utilized for object tracking in indoor environment due to their low cost and convenience for deployment. The RFID readings gathered from real world are often noisy. Existing approaches for tracking objects with noisy RFID readings are mostly based on using Particle Filter (PF). However, continuous execution of particle filter will suffer from high computational cost on resource constrained RFID-enabled devices. In this paper, we propose a hybrid method for tracking mobile objects with high accuracy and low computational cost. This is achieved by an adaptively switching between using WCL (Weighted Centroid Localization) and PF according to the estimated velocity of the moving object. We have evaluated the performance of our hybrid method through extensive simulations. We have also validated the performance results by implementing the method in two applications, namely, indoor wheelchair navigation and in-station LRV (Light Rail Vehicle) tracking in one of the Hong Kong MTR depots. The result shows that our proposed method outperforms both WCL and PF in either accuracy or computational cost.

Accurate and Efficient Object Tracking Based on Passive RFID

RFID technology has been widely used for object tracking in indoor environment due to their low cost and convenience for deployment. In this paper, we consider RFID reader tracking which refers to continuously locating a mobile object by attaching it with a RFID reader that communicates with passive RFID tags deployed in the environment. One difficulty is that the RFID readings gathered from the environment are often noisy. Existing approaches for tracking with noisy RFID readings are mostly based on using Particle Filter (PF). However, continuous execution of PF has extremely high computational cost, and may be difficult to be done on mostly resource constrained mobile RFID devices. In this paper, we propose a hybrid method which combines PF with Weighted Centroid Localization (WCL) to achieve high accuracy and low computational cost. Our observation is that WCL has the same accuracy with PF with much lower cost if the objects velocity is low. Our method has two critical features. The first feature is adaptive switching between using WCL and PF based on the estimated velocity of the mobile object. The second feature is the further reduction of computational cost by offloading costly PF algorithm onto nearby servers. We evaluate the performance of our method through extensive simulations and experiments in two real world applications, namely, indoor wheelchair navigation and in-station Light Rail Vehicle (LRV) tracking at one of Hong Kong MTR depots. The result shows that our proposed approach has significantly less computational cost than existing PF based methods, while being as accurate as them.

Fault-Tolerant RFID Reader Localization Based on Passive RFID Tags

With the growing use of RFID-based devices, RFID reader localization attracts increasing attentions recently. In this technology, an object carrying an RFID reader is located by communicating with some passive RFID tags deployed in the environment. One important problem of RFID reader localization is that frequent occurred RFID faults affect localization accuracy. Specifically, complex localization environment (may include metal, water, obstacles, etc.) makes some tags fail to communicate with the reader, which makes the localization result deviate from the real location. Existing approaches can tolerate the faults occurred in individual tags and lasting for a short time period, but suffer serious localization error if the faults exist in a large region and last for a long time period. Moreover, existing approaches do not provide quality measurement of a localization result. In this paper, we propose an effective fault-tolerant RFID reader localization approach suitable for the above-mentioned situations, and illustrate how to measure the quality of a localization result. We have taken extensive simulations and implemented an RFID-based localization system. In both cases, our solution outperforms existing approaches in localization accuracy and can provide additional quality information.

Self-orienting the cameras for maximizing the view-coverage ratio in camera sensor networks

In recent years, camera sensor networks are widely studied due to the strength of the camera sensors in retrieving more types of information in terms of videos or images. Different from traditional scalar sensor networks, camera sensors from distinct positions can get distinct images with the same object. The object is more likely to be recognized if its image is captured around the frontier view of the camera. To this end, a new coverage model full-view coverage Wang and Cao (2011) is proposed for the camera sensors, to judge whether an object is recognized no matter which direction it faces to. However, the full-view coverage model fails to evaluate the coverage quality when the object is not full-view covered. In this paper, we introduce a novel view-coverage model which measures the coverage quality with a finer granularity for the purpose of face recognition. Based on this model, we propose a distributed multi-round view-coverage enhancing (VCE) algorithm by the self-orientation of the camera sensors. In this algorithm, sensors are continuously rotated to reduce the overlapping view-coverage with their neighbors until reaching the stable state. Furthermore, we address two important issues in the VCE algorithm, and propose the corresponding refinement procedures. The first one is about the sensors near the boundary of the target region whose view-coverage may include the outside of the target region, which is meaningless for our problem. The second one is about the rotating angle which should be set appropriately to achieve a global optimal solution. Simulation results show that our algorithm brings a significant improvement on the view-coverage ratio compared with random deployment. Also, the refinement procedures make a remarkable improvement over the basic VCE algorithm. Moreover, we evaluate the performance of our algorithm with real deployed camera sensors.

LASEC: A Localized Approach to Service Composition in Pervasive Computing Environments

Pervasive computing environments (PvCE) are embedded with interconnected smart devices which provide users with services desired. To meet requirements of users, smart devices with different kinds of functions may need to be associated together to provide the service described in the user requirement, which is called service composition. As the service composition environment may be dynamic and large scale, centralized service composition algorithm is usually inefficient due to message cost. On the other hand, a decentralized approach, which employs pre-determined coordinators to search and compose service, may have high cost as well. In this paper, we discuss a localized approach for service composition based on the Ubiquitous Interacting Object (UIO) model we have proposed earlier. UIO is an abstraction of physical devices in PvCE with ability to find and collaborate with other devices through exposing their capabilities as services. In our localized service composition algorithm (LASEC), UIOs collaborate with each other in a bottom-up, localized manner to compose required service without requiring global knowledge. To solve the problem of blind compositions in LASEC, we propose a novel mechanism called Alien-information-based Acknowledging (A-Ack), in which a UIO decides on collaborating with another UIO only after obtaining some additional information from the collaboration candidate. Specifically, this information refers to ability of a given UIO to compose another part of the service. Proposed LASEC is message-efficient and quality-guaranteed. Extensive simulations of LASEC as well as existing decentralized and pull-based centralized algorithms have been conducted. The results show the relatively low communication cost and composition time of LASEC. Moreover, we demonstrate feasibility of our approach with a prototype implementation.

Mobile RFID with a High Identification Rate

An important category of mobile RFID systems is the RFID system with mobile RFID tags. The mobility of RFID tags poses new challenges to designing RFID anti-collision protocols. Existing RFID anti-collision protocols cannot support high tag moving speed and high identification rate simultaneously. These protocols do not distinguish the identification deadlines of moving tags. Also, when tags move fast, they cannot determine the number of unidentified tags in the interrogation area of an RFID reader. In this paper, we propose a schedule-based RFID anti-collision protocol which, given a high identification rate, achieves the maximal tag moving speed. The protocol, without the need to estimate the number of unidentified tags, schedules an optimal number of tags to compete for the channel according to their identification deadlines, so as to achieve the optimal identification performance. The simulation and experiment results show that our approach can increase the moving speed of tags significantly compared with existing approaches, while achieving a high identification rate.

Predicate Detection in Asynchronous Distributed Systems: A Probabilistic Approach

In an asynchronous distributed system, a number of processes communicate with each other via message passing that has a finite but arbitrary long delay. There is no global clock in that system. Predicates, denoting the states of processes and their relations, are often used to specify the information of interest in such a system. Due to the lack of a global clock, the temporal relations between the states at different processes cannot be uniquely determined, but have multiple possible circumstances. Existing works of predicate detection are based on the definitely modality or the possibly modality, denoting that a predicate holds in all of the possible circumstances or in one of them, respectively. No information is provided about the probability that a predicate will hold, which hinders the taking of countermeasures for different situations. Moreover, the detection is based on single occurrence of a predicate, so the results are heavily affected by environmental noise and detection errors. In this paper, we propose a new approach to predicate detection to address these two issues. We generalize the definitely and possibly modalities to an occurrence probability to provide more detailed information, and further investigate how to detect multiple occurrences of a predicate. We propose a unified algorithm framework for detecting various types of predicates and demonstrate the use of it for three typical types of predicates, including simple predicates, simple sequences, and interval-constrained sequences. Theoretical proofs and simulation results show that our approach is effective and outperforms existing approaches.

Context Map for Navigating the Physical World

Pervasive computing environments are composed of numerous smart entities (objects and human alike) which are interconnected through contextual links in order to create a Web of physical objects. The contextual links can be based on matching context attribute-values (e.g., co-location) or social connections. We call such a Web of smart physical objects as context map. Context maps can be used for context-aware search and browse of the physical world. However, changes of dynamic context values over time may render a context map inconsistent. So, it is important to update contextual links with changes in specific context values. Given the asynchronous nature of pervasive environments, it is non-trivial to detect events generated by contextual changes in real time. We propose two algorithms for instantaneous and periodic detection of events with concurrent timing relations. Our algorithms have low time complexity and they can address the needs of different types of pervasive computing applications. We have evaluated our proposed algorithms through simulations as well as test bed experiments.

Connectivity-based virtual potential field localization in wireless sensor networks

In wireless sensor networks, the connectivity-based localization protocols are widely studied due to low cost and no requirement for special hardware. Many connectivity-based algorithms rely on distance estimation between nodes according to their hop count, which often yields large errors in anisotropic sensor network. In this paper, we propose a virtual potential field algorithm, in which the estimated positions of unknown nodes are iteratively adjusted by eliminating the inconsistency to the connectivity constraint. Unlike current connectivity-based algorithms, VPF effectively exploits the connectivity constraint information, regardless of distance estimation between nodes, thus achieving high localization accuracy in both isotropic and anisotropic sensor networks. Simulation results show that VPF improves the localization accuracy by an average of 47% compared with MDS in isotropic network, and 42% compared with PDM in anisotropic network. As a refinement procedure, the average improvement factor of VPF is 56% and 50%, based on MDS and PDM respectively.

Event Aggregation with Different Latency Constraints and Aggregation Functions in Wireless Sensor Networks

Event aggregation in Wireless Sensor Networks (WSNs) is a process of combining several low-level events into a high-level event to eliminate redundant information to be transmitted and thus save energy. Existing works on event aggregation consider either latency constraint or aggregation function, but not both. A solution jointly considering the two issues will be desirable. Moreover, existing works only consider optimal aggregation for single high-level event type, but many applications are composed of multiple types of high-level events. This paper studies the problem of aggregating multiple high-level events in WSNs with different latency constraints and aggregation functions. We first propose an event aggregation algorithm considering the two issues for single high-level event, and then extend it for multiple high-level events. The simulation results show that our algorithm outperforms existing approaches and saves significant amount of energy (up to 35% in our system).