605 research outputs found
Distributed Wireless Algorithms for RFID Systems: Grouping Proofs and Cardinality Estimation
The breadth and depth of the use of Radio Frequency Identification (RFID) are becoming more substantial. RFID is a technology useful for identifying unique items through radio waves. We design algorithms on RFID-based systems for the Grouping Proof and Cardinality Estimation problems.
A grouping-proof protocol is evidence that a reader simultaneously scanned the RFID tags in a group. In many practical scenarios, grouping-proofs greatly expand the potential of RFID-based systems such as supply chain applications, simultaneous scanning of multiple forms of IDs in banks or airports, and government paperwork. The design of RFID grouping-proofs that provide optimal security, privacy, and efficiency is largely an open area, with challenging problems including robust privacy mechanisms, addressing completeness and incompleteness (missing tags), and allowing dynamic groups definitions. In this work we present three variations of grouping-proof protocols that implement our mechanisms to overcome these challenges.
Cardinality estimation is for the reader to determine the number of tags in its communication range. Speed and accuracy are important goals. Many practical applications need an accurate and anonymous estimation of the number of tagged objects. Examples include intelligent transportation and stadium management. We provide an optimal estimation algorithm template for cardinality estimation that works for a {0,1,e} channel, which extends to most estimators and ,possibly, a high resolution {0,1,...,k-1,e} channel
Visible light communications-based indoor positioning via compressed sensing
This paper presents an approach for visible light communication-based indoor
positioning using compressed sensing. We consider a large number of light
emitting diodes (LEDs) simultaneously transmitting their positional information
and a user device equipped with a photo-diode. By casting the LED signal
separation problem into an equivalent compressed sensing framework, the user
device is able to detect the set of nearby LEDs using sparse signal recovery
algorithms. From this set, and using proximity method, position estimation is
proposed based on the concept that if signal separation is possible, then
overlapping light beam regions lead to decrease in positioning error due to
increase in the number of reference points. The proposed method is evaluated in
a LED-illuminated large-scale indoor open-plan office space scenario. The
positioning accuracy is compared against the positioning error lower bound of
the proximity method, for various system parameters.Comment: to appear in IEEE Communication Letter
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
Reliable Communication in Wireless Networks
Wireless communication systems are increasingly being used in industries and infrastructures since they offer significant advantages such as cost effectiveness and scalability with respect to wired communication system. However, the broadcast feature and the unreliable links in the wireless communication system may cause more communication collisions and redundant transmissions. Consequently, guaranteeing reliable and efficient transmission in wireless communication systems has become a big challenging issue. In particular, analysis and evaluation of reliable transmission protocols in wireless sensor networks (WSNs) and radio frequency identification system (RFID) are strongly required.
This thesis proposes to model, analyze and evaluate self-configuration algorithms in wireless communication systems. The objective is to propose innovative solutions for communication protocols in WSNs and RFID systems, aiming at optimizing the performance of the algorithms in terms of throughput, reliability and power consumption. The first activity focuses on communication protocols in WSNs, which have been investigated, evaluated and optimized, in order to ensure fast and reliable data transmission between sensor nodes. The second research topic addresses the interference problem in RFID systems. The target is to evaluate and develop precise models for accurately describing the interference among readers. Based on these models, new solutions for reducing collision in RFID systems have been investigated
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
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