Vehicle Cardinality Estimation in VANETs by Using RFID Tag Estimator

Nowadays, many vehicles equipped with RFID-enabled chipsets traverse the Electronic Toll Collection (ETC) systems. Here, we present a scheme to estimate the vehicle cardinality with high accuracy and efficiency. A unique RFID tag is attached to a vehicle, so we can identify vehicles through RFID tags. With RFID signal, the location of vehicles can be detected remotely. Our scheme makes vehicle cardinality estimation based on the location distance between the first vehicle and second vehicle. Specifically, it derives the relationship between the distance and number of vehicles. Then, it deduces the optimal parameter settings used in the estimation model under certain requirement. According to the actual estimated traffic flow, we put forward a mechanism to improve the estimation efficiency. Conducting extensive experiments, the presented scheme is proven to be outstanding in two aspects. One is the deviation rate of our model is 50 % of FNEB algorithm, which is the classical scheme. The other is our efficiency is 1.5 times higher than that of FNEB algorithm.Second International Conference, IOV 2015Chengdu, ChinaDecember 19-21, 201

Estimation of RFID Tag Population Size by Gaussian Estimator

Radio Frequency IDenti cation (RFID) systems are prevalent in all sorts of daily life endeavors. In this thesis we propose a new method to estimate RFID tag population size. We have named our algorithm Gaussian Estimation of RFID Tags, namely, GERT. We present GERT under both {0,1} and {0,1,e} channel models, and in both cases the estimator we use is a well justi ed Gaussian random variable for large enough frame size based on Central Limit Theorem for triangular arrays. The most prominent feature of GERT is the quality with which it estimates a tag population size. We support all the required approximations with detailed analytical work and account for all the approximation errors when we consider the overall quality of the estimation. Our simulation results agree well with analytical ones. GERT, based on standardized frame slotted Aloha protocol, can estimate any tag population size with desired level of accuracy using fewer number of frame slots than previously proposed algorithms

Extending Complex Event Processing for Advanced Applications

Recently numerous emerging applications, ranging from on-line financial transactions, RFID based supply chain management, traffic monitoring to real-time object monitoring, generate high-volume event streams. To meet the needs of processing event data streams in real-time, Complex Event Processing technology (CEP) has been developed with the focus on detecting occurrences of particular composite patterns of events. By analyzing and constructing several real-world CEP applications, we found that CEP needs to be extended with advanced services beyond detecting pattern queries. We summarize these emerging needs in three orthogonal directions. First, for applications which require access to both streaming and stored data, we need to provide a clear semantics and efficient schedulers in the face of concurrent access and failures. Second, when a CEP system is deployed in a sensitive environment such as health care, we wish to mitigate possible privacy leaks. Third, when input events do not carry the identification of the object being monitored, we need to infer the probabilistic identification of events before feed them to a CEP engine. Therefore this dissertation discusses the construction of a framework for extending CEP to support these critical services. First, existing CEP technology is limited in its capability of reacting to opportunities and risks detected by pattern queries. We propose to tackle this unsolved problem by embedding active rule support within the CEP engine. The main challenge is to handle interactions between queries and reactions to queries in the high-volume stream execution. We hence introduce a novel stream-oriented transactional model along with a family of stream transaction scheduling algorithms that ensure the correctness of concurrent stream execution. And then we demonstrate the proposed technology by applying it to a real-world healthcare system and evaluate the stream transaction scheduling algorithms extensively using real-world workload. Second, we are the first to study the privacy implications of CEP systems. Specifically we consider how to suppress events on a stream to reduce the disclosure of sensitive patterns, while ensuring that nonsensitive patterns continue to be reported by the CEP engine. We formally define the problem of utility-maximizing event suppression for privacy preservation. We then design a suite of real-time solutions that eliminate private pattern matches while maximizing the overall utility. Our first solution optimally solves the problem at the event-type level. The second solution, at event-instance level, further optimizes the event-type level solution by exploiting runtime event distributions using advanced pattern match cardinality estimation techniques. Our experimental evaluation over both real-world and synthetic event streams shows that our algorithms are effective in maximizing utility yet still efficient enough to offer near real time system responsiveness. Third, we observe that in many real-world object monitoring applications where the CEP technology is adopted, not all sensed events carry the identification of the object whose action they report on, so called €œnon-ID-ed€� events. Such non-ID-ed events prevent us from performing object-based analytics, such as tracking, alerting and pattern matching. We propose a probabilistic inference framework to tackle this problem by inferring the missing object identification associated with an event. Specifically, as a foundation we design a time-varying graphic model to capture correspondences between sensed events and objects. Upon this model, we elaborate how to adapt the state-of-the-art Forward-backward inference algorithm to continuously infer probabilistic identifications for non-ID-ed events. More important, we propose a suite of strategies for optimizing the performance of inference. Our experimental results, using large-volume streams of a real-world health care application, demonstrate the accuracy, efficiency, and scalability of the proposed technology

Fast RFID counting under unreliable radio channels.

Sze, Wai Kit.Thesis (M.Phil.)--Chinese University of Hong Kong, 2009.Includes bibliographical references (leaves 77-83).Abstracts in English and Chinese.Abstract --- p.iAcknowledgement --- p.viChapter 1 --- Introduction --- p.1Chapter 2 --- Background and Related Work --- p.8Chapter 3 --- RFID Tag-set Cardinality estimation based on a Two-parameter implicit Channel Model --- p.13Chapter 3.1 --- System Model --- p.14Chapter 3.2 --- Number of Empty Slots Observed by the Reader --- p.16Chapter 3.3 --- Estimator Accuracy and Performance Analysis --- p.25Chapter 3.4 --- Results and Discussions --- p.32Chapter 3.5 --- Chapter Summary --- p.41Chapter 4 --- RFID Tag-set Cardinality estimation over Unknown Channel --- p.42Chapter 4.1 --- System Model --- p.43Chapter 4.2 --- Baseline: The Union-based approach --- p.45Chapter 4.2.1 --- Motivation --- p.46Chapter 4.2.2 --- Union Algorithm --- p.46Chapter 4.2.3 --- Analysis of the Union algorithm --- p.47Chapter 4.3 --- "Probabilistic Tag-counting over Lossy, Unknown channels via the Mh model" --- p.52Chapter 4.3.1 --- "Novel Interpretation of Mh for RFID Counting over Lossy, Unknown Channels" --- p.52Chapter 4.3.2 --- The Moment Estimator --- p.55Chapter 4.3.3 --- Sample Coverage Estimator --- p.57Chapter 4.3.4 --- Estimating the overall Tag population t --- p.59Chapter 4.4 --- Performance Validation and Comparison --- p.62Chapter 4.5 --- Chapter Summary --- p.65Chapter 5 --- Conclusions and Future Work --- p.73Chapter A --- Proof of Equation (3.6) in Chapter 3 --- p.75Bibliography --- p.7

Co-Design Strategies for Energy-Efficient UWB and UHF Wireless Systems

This paper reviews the most recent methods, combining nonlinear harmonic-balance-based analysis with electromagnetic (EM) simulation, for optimizing, at the circuit level, modern radiative RF/microwave systems. In order to maximize the system efficiency, each subsystem must be designed layoutwise, accounting for the presence of the others, that is, accounting for its actual terminations, rather than the ideal ones (50 Ω). In this way, the twofold goal of minimizing size and losses of the system is obtained by reducing intersystem matching networks. Indeed, terminations are complex, frequency-dispersive, and variable with the signal level, if active operations are concerned, and are responsible for performance degradation if not properly optimized. This approach is nowadays necessary, given the ever increased spread of pervasively distributed RF microsystems adopting miniaturized antennas, such as radio frequency identification (RFID) or wireless sensor networks, that must be low-cost, low-profile, low-power, and must simultaneously perform localization, identification, and sensing. For the design of a transmitter and a receiver connected with the respective antennas, suitable figures of merit are considered, encompassing radiation and nonlinear performance. Recent representative low-profile realizations, adopting ultra-wideband (UWB) excitations are used to highlight the benefit of the proposed nonlinear/EM approach for next generation energy autonomous microsystem, such as UWB-RFID tags