Radio Frequency Identification (RFID) technologies are used in many applications for data collection. However, raw RFID readings are usually of low quality and may contain many anomalies. An ideal solution for RFID data cleansing should address the following issues. First, in many applications, duplicate readings (by multiple readers simultaneously or by a single reader over a period of time) of the same object are very common. The solution should take advantage of the resulting data redundancy for data cleaning. Second, prior knowledge about the readers and the environment (e.g., prior data distribution, false negative rates of readers) may help improve data quality and remove data anomalies, and a desired solution must be able to quantify the degree of uncertainty based on such knowledge. Third, the solution should take advantage of given constraints in target applications (e.g., the number of objects in a same location cannot exceed a given value) to elevate the accuracy of data cleansing. There are a number of existing RFID data cleansing techniques. However, none of them support all the aforementioned features. In this paper we propose a Bayesian inference based approach for cleaning RFID raw data. Our approach takes full advantage of data redundancy. To capture the likelihood, we design an n-state detection model and formally prove that the 3-state model can maximize the system performance. Moreover, in order to sample from the posterior, we devise a Metropolis-Hastings sampler with Constraints (MH-C), which incorporates constraint management to clean RFID raw data with high efficiency and accuracy. We validate our solution with a common RFID application and demonstrate the advantages of our approach through extensive simulations.

Trip planning search (TPS) represents an important class of queries in Geographic Information Systems (GIS). In many real-world applications, TPS requests are issued with a number of constraints. Unfortunately, most of these constrained TPS cannot be directly answered by any of the existing algorithms. By formulating each restriction into rules, we propose a novel form of route query, namely the multi-rule partial sequenced route (MRPSR) query. Our work provides a unified framework that also subsumes the well-known trip planning query (TPQ) and the optimal sequenced route (OSR) query. In this paper, we first prove that MRPSR is NP-hard and then present three heuristic algorithms to search for near-optimal solutions for the MRPSR query. Our extensive simulations show that all of the proposed algorithms can answer the MRPSR query effectively and efficiently. Using both real and synthetic datasets, we investigate the performance of our algorithms with the metrics of the route distance and the response time in terms of the percentage of the constrained points of interest (POI) categories. Compared to the LORD-based brute-force solution, the response times of our algorithms are remarkably reduced while the resulting route length is only slightly longer than the shortest route.

People spend a significant amount of time in indoor spaces (e.g., office buildings, subway systems, etc.) in their daily lives. Therefore, it is important to develop efficient indoor spatial query algorithms for supporting various locationbased applications. However, indoor spaces differ from outdoor spaces because users have to follow the indoor floor plan for their movements. In addition, positioning in indoor environments is mainly based on sensing devices (e.g., RFID readers) rather than GPS devices. Consequently, we cannot apply existing spatial query evaluation techniques devised for outdoor environments for this new challenge. Because particle filters can be employed to estimate the state of a system that changes over time using a sequence of noisy measurements made on the system, in this research, we propose the particle filter-based location inference method as the basis for evaluating indoor spatial queries with noisy RFID raw data. Furthermore, two novel models, indoor walking graph model and anchor point indexing model, are created for tracking object locations in indoor environments. Based on the inference method and tracking models, we develop innovative indoor range and k nearest neighbor (kNN) query algorithms. We validate our solution through extensive simulations with real-world parameters. Our experimental results show that the proposed algorithms can evaluate indoor spatial queries effectively and efficiently.

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Abstract-The connected dominating set (CDS) has been extensively used for routing and broadcast in wireless ad hoc networks. While existing CDS protocols are successful in constructing CDS of small size, they either require localized information beyond immediate neighbors, lack the mechanism to properly handle nodal mobility, or involve lengthy recovery procedure when CDS becomes corrupted. In this paper, we introduce the timer-based CDS protocols, which first elect a number of initiators distributively and then utilize timers to construct a CDS from initiators with the minimum localized information. We demonstrate that our CDS protocols are capable of maintaining CDS in the presence of changes of network topology. Depending on the number of initiators, there are two versions of our timer-based CDS protocols. The Single-Initiator (SI) generates the smallest CDS among protocols with mobility handling capability. Built on top of SI, the Multi-Initiator (MI) version removes the single point of failure at single-initiator and possesses most advantages of SI. We evaluate our protocols by both the ns-2 simulation and an analytical model. Compared with the other known CDS protocols, the simulation results demonstrate that both SI and MI produce and maintain CDS of very competitive size. The analytical model shows the expected convergence time and the number of messages required by SI and MI in the construction of CDS, which match closely to our simulation results. This helps to establish the validity of our simulation.