Mobile radio frequency identification (RFID) systems typically experience the continual movement of many tags rapidly going in and out of the interrogating range of readers. Readers that are deployed to maintain a current, real-time list of tags, which are present in the interrogating zone at any moment, must repeatedly execute a series of reading cycles. Each of these reading cycles provides the readers very limited time to identify unknown tags (those newly entering into the reader’s range), and, at the same time, to detect missing tags (those just leaving the reader’s range). In this paper, we study the continuous tag recognition problem, which is critical for mobile RFID systems. First, we obtain a lower bound on communication time for solving this problem. We then design a near-OPTimal protocoL, called OPT-L, and prove that its communication time is approximately equal to the lower bound. Finally, we present extensive simulation and experimental results that demonstrate OPT-L’s superior performance over other existing protocols.

Radio frequency identification (RFID) has been widely used in many fields such as object tracking and inventory management. For RFID systems, grouping is a fundamental issue which can support efficient multicast transmissions, dynamic tag management, and accurate aggregate queries. Existing grouping protocols have drawbacks of unknown theoretical communication time, high computational cost on the server end and inability to deal with unexpected tags which are those tags whose IDs have not been collected by readers. In this paper, we would like to address the above limitations and consider a more general grouping problem that allows an arbitrary number of unexpected tags to present. Our objective is to design a protocol that guarantees the reader to efficiently and correctly notify each known tag of its group-ID, while the probability that an unexpected tag is mistakenly notified of any group-ID is smaller than a pre-determined value. In this paper, we first obtain a lower bound on the communication time for solving this generalized grouping problem. Then, we propose a near-optimal protocol, called OPT-G, and prove that its communication time approximately equals the lower bound. Finally, we report extensive simulation results that demonstrate OPT-G's near-optimal performance and its superiority over existing baseline schemes.

In many real-time RFID-enabled applications (e.g., logistic tracking and warehouse controlling), a subset of wanted tags is often selected from a tag population for monitoring and querying purposes. How this subset of tags is rapidly selected, which is referred to as the subset selection problem, becomes pivotal for boosting the efficiency in RFID systems. Current state-of-the-art schemes result in high communication latencies, which are far from the optimum, and this degrades the system performance. This problem is addressed in this paper by using a simple Bit-Counting Function BCF(), which has also been employed widely by other protocols in RFID systems. In particular, we first propose a near-OPTimal SeLection protocol, denoted by OPTSL, to rapidly solve this problem based on the simple function BCF(). Second, we prove that the communication time of OPTSL is near-optimal with rigorous theoretical analysis. Finally, we conduct extensive simulations to verify that the communication time of the proposed OPT-SL is not only near-optimal but also significantly less than that of benchmark protocols.

We explore a novel model of computation based on nodes that have no public addresses (ids). We define nodes as concurrent, message-passing computational entities in an abstract communication medium, similar to the Actor model, but with all public node ids elided. Instead, drawing inspiration from biological systems, we postulate a send-to-behavior language construct to enable anonymous one-way communication. A behavior, defined as a function of input to actions, is also an intensional definition of the subset of nodes that express it. Sending to a behavior is defined to deliver the message to one or more nodes that implement that behavior.

Abstract For model-based black-box testing, test cases are often selected from the syntactic appearance of the specification of the system under test, according to a pre-given test data adequacy criterion. We introduce a novel approach that is semantics-based, independent of the syntactic appearance of the system specification. Basically, we model the system under test as a random variable, whose sample space consists of all possible behavior sets (with respect to the specification) over the known interface of the black-box. The entropy of the system is measured as the (Shannon) entropy of the random variable. In our criterion, the coverage of a test set is measured as the expected amount of entropy decrease (i.e. the expected amount of information gained) once the test set is run. Since our criterion is syntactic independent, we study the notion of information-optimal software testing where, within a given constraint, a test set is selected to gain the most information.

We introduce (finite and infinite) typical paths of a graph and prove that the typical paths carry all information with probability 1, asymptotically. An automata-theoretic characterization of the typical paths is shown: finite typical paths can be accepted by reversal-bounded multicounter automata and infinite typical paths can be accepted by counting Büchi automata (a generalization of reversal-bounded multicounter automata running on ω-words). We take a statechart example to show how to generate typical paths from a graph using SPIN model checker. The results are useful in automata theory since one can identify an information-concentrated-core of a regular language such that only words in the information-concentrated-core carry nontrivial information. When the graph is used to specify the system under test, the results are also useful in software testing by providing an information-theoretic approach to select test cases that carry nontrivial information of the system specification.

ASTRAL is a high-level formal specification language for real-time systems. It has structuring mechanisms that allow one to build modularized specifications of complex real-time systems with layering. Based upon the ASTRAL symbolic model checler reported in [13], three approximation techniques to speed-up the model checking process for use in debugging a specification are presented. The techniques are random walk, partial image and dynamic environment generation. Ten mutation tests on a railroad crossing benchmark are used to compare the performance of the techniques applied separately and in combination. The test results are presented and analyzed.

Article Using the ASTRAL model checker to analyze mobile IP Share on Authors: Zhe Dang Reliable Software Group, Computer Science Department, University of California, Santa Barbara, CA Reliable Software Group, Computer Science Department, University of California, Santa Barbara, CAView Profile , Richard A. Kemmerer Reliable Software Group, Computer Science Department, University of California, Santa Barbara, CA Reliable Software Group, Computer Science Department, University of California, Santa Barbara, CAView Profile Authors Info & Claims ICSE '99: Proceedings of the 21st international conference on Software engineeringMay 1999 Pages 132–141https://doi.org/10.1145/302405.302459Online:16 May 1999Publication History 19citation272DownloadsMetricsTotal Citations19Total Downloads272Last 12 Months2Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access