Showing papers by "Telcordia Technologies published in 2013"

Efficient protocols for identifying the missing tags in a large RFID system

Abstract: Compared to the classical barcode system, radio frequency identification (RFID) extends the operational distance from inches to a number of feet (passive RFID tags) or even hundreds of feet (active RFID tags). Their wireless transmission, processing, and storage capabilities enable them to support full automation of many inventory management functions in industry. This paper studies the practically important problem of monitoring a large set of active RFID tags and identifying the missing ones--the objects that the missing tags are associated with are likely to be missing as well. This monitoring function may need to be executed frequently and therefore should be made efficient in terms of execution time in order to avoid disruption of normal inventory operations. Based on probabilistic methods, we design a series of missing-tag identification protocols that employ novel techniques to reduce the execution time. Our best protocol reduces the time for detecting the missing tags by an order of magnitude when compared to existing protocols.

Optimizing Evaluation Patterns and Data Acquisition for Stream Analytics in Resource-Constrained Wireless Environments

Abstract: Mobile wireless devices may receive data streams from multiple remote sensors. The sensors may have limited power supplies and memory capacity. Aspects of the invention use statistical characteristics of the sensor data streams and the cost of acquiring a single element of each stream to determine what sequence the sensors should send their data streams in. The cost of acquiring the data may be modified dynamically, depending on parameters such as block size. Additional factors, such as a sensor's buffer capacity, may limit the amount of stream elements that may be cached and affect the sensors' stream transmit sequence. The evaluation order may be dynamically modified using an event processing engine, to reflect both changing statistics of underlying sensor stream tuples and time-varying acquisition costs associated with individual streams. This helps to increase in the operational lifetime of the sensors and associated monitoring applications.

On Generalizable Low False-Positive Learning Using Asymmetric Support Vector Machines

Abstract: The Support Vector Machines (SVMs) have been widely used for classification due to its ability to give low generalization error. In many practical applications of classification, however, the wrong prediction of a certain class is much severer than that of the other classes, making the original SVM unsatisfactory. In this paper, we propose the notion of Asymmetric Support Vector Machine (ASVM), an asymmetric extension of the SVM, for these applications. Different from the existing SVM extensions such as thresholding and parameter tuning, ASVM employs a new objective that models the imbalance between the costs of false predictions from different classes in a novel way such that user tolerance on false-positive rate can be explicitly specified. Such a new objective formulation allows us of obtaining a lower false-positive rate without much degradation of the prediction accuracy or increase in training time. Furthermore, we show that the generalization ability is preserved with the new objective. We also study the effects of the parameters in ASVM objective and address some implementation issues related to the Sequential Minimal Optimization (SMO) to cope with large-scale data. An extensive simulation is conducted and shows that ASVM is able to yield either noticeable improvement in performance or reduction in training time as compared to the previous arts.

Privacy-preserving aggregated data mining

Abstract: An apparatus, system and method are introduced for preserving privacy of data in a dataset in a database with a number n of entries. In one embodiment, the apparatus includes memory including computer program code configured to, with a processor, cause the apparatus to form a random matrix of dimension m by n, wherein m is less than n, operate on the dataset with the random matrix to produce a compressed dataset, form a pseudoinverse of the random matrix, and operate on the dataset with the pseudoinverse of the random matrix to produce a decompressed dataset.

Traffic model for long term evolution networks

Abstract: Long Term Evolution (LTE) network planning is a very important and challenging task. It requires careful attention from all service providers since it affects the cost and the performance of the network and its services. In order to be able to plan a good network, the traffic provided for network planning has to include as many realistic parameters as possible. Previous network planning approaches tend to collect measurements regardless of how time consuming it is considered, while others find it easier to assume or predict values. The main objective of this paper is to introduce a traffic model that considers different realistic parameters that can be used for the planning of LTE networks. To that end, a set of equations is proposed to generate reasonable traffic profiles while considering various practical aspects such as signaling, bandwidth, busy hour session attempts and number of simultaneous bearers.

System and method for spammer host detection from network flow data profiles

Abstract: A system and method for spammer host detection from network flow data profiles comprises constructing one or more cluster profiles and detecting spammer hosts. Construction cluster profiles comprises observing network flow data from one or more hosts; for each host, representing the network flow data associated with the host as a multidimensional vector; clustering the vectors of the hosts into the plurality of cluster profiles; annotating each cluster profile using at least one of black lists and white lists; and calculating a confidence in each cluster profile annotation. Detecting spammer hosts comprises observing the network flow data from a new host; representing the network flow data associated with the new host as a multidimensional vector, and placing the new multidimensional vector of the new host into one cluster profile of the one or more cluster profiles.

Querying target databases using input-related reference database

Abstract: Input data queries directed at a plurality of target databases and originating from any of a plurality of sources are first converted to validated canonical forms, which are then used to query the target databases. Specifically, upon receiving an input data query, a relatively accurate reference database is selected based on the type of the input data. This reference is then queried for the input data with the intent of finding an exact matching record or a near-matching record that can be considered an exact match and thereby validating the input data. Otherwise, the requesting source is instructed to provide a new query. Once having a validated record, it is converted to a canonical form, which is then used to query the target databases intended to be searched. In a further embodiment, multiple reference databases are queried to determine a canonical form of the data or to determine multiple canonical forms of the data.

Using novel distributed heuristics on hexagonal connected dominating sets to model routing dissemination

Abstract: Network-centric Future Force must support a large and diverse group of communication nodes. Despite the fact that there is ample scope for network design in networks such as Future Combat Systems (FCS) and Warfighter Information Network-Tactical (WIN-T), there are important performance limits when network conditions become more extreme (highly mobile or dense). In previous work, we modeled and analyzed the expected user performance of a number of novel approaches to flooding link state routing information in wireless ad hoc networks for routing update. We compared routing dissemination schemes such as this of Flat Flooding and Multi-Point Relays (MPRs), with our own variations on Connected Dominating Sets (CDSs), assuming other parts of the routing protocol were taken from standard Link State Routing protocol (OLSR) or Open Shortest Path First protocol (OSPF), widely used in the Internet and WIN-T. Although the existing literature provides a variety of models for Flat Flooding and MPRs, it lacks similar analytical work for relays placement under a CDS approach. Towards the latter, we selected one representative from our novel CDS-based models--the CDS Hexagon, as it provides the lowest routing overhead among other properties. Our analysis demonstrates the difference in the impact of conditions on key performance metrics, such as this of network density on routing overhead as well as a tradeoff between routing overhead and routing stretch. We then advance CDS-HEX dissemination from the limited-scope centralized scenarios with strictly symmetrical relay placement to dynamic scenarios with totally random relay placement. We use novel heuristics to adapt the analytical CDS-HEX to dynamic environments. We show that: (a) although the distributed scheme naturally operates sub-optimally compared to its centralized ancestor, it is still superior to MPRs over certain metrics of interest, (b) the set-up overhead of CDS-HEX is not significantly higher than this of MPRs, while at the same time, the steady state overhead of CDS-HEX appears adequately lower than this of MPR, and (c) all the closed analytical formulae and asymptotic results derived in our previous analysis are verified by our simulations which also provide additional insight on metrics that cannot be analytically measured. Our scheme on one hand is not overly expensive to set up despite the more complex generation process, and on the other hand has a superior performance for the majority of network conditions, and close to the optimal one anticipated by the corresponding centralized model.

Adaptive-diversity for compressible return oriented programs

Abstract: A method of transforming return oriented programming executables into functionally equivalent yet different forms with specific structural and/or functional characteristics that can assist in the use of such executables. A method automatically biases the structural and/or functional diversity of the return oriented programming software executables to achieve specific program representation objectives while preserving the programmatic capabilities of the original executable.

Dynamic IMS Reconfiguration using Session Migration for Power Saving

Abstract: Legacy telecommunication services have been tending to shift to All-IP networks from closed and circuit switched networks by the deployment of high bandwidth and stable mobile broadband networks such as Long Term Evolution (LTE). The shift to an All-IP network leads to the integration of telecommunication services and internet services, which are called Over-The-Top (OTT) services. The trend towards increasing and unpredictable traffic makes it important for the IP Multimedia Subsystem (IMS), which is a call control system running over the All-IP network, to be flexible and reliable. With that goal, the authors have proposed an IMS reconfiguration mechanism using call session state migration, which allows the IMS to distribute live call sessions as required by the failure of IMS servers and the quantity of calls in progress. Furthermore, in this paper we propose a model to determine the configuration of the IMS dynamically from the perspective of electrical power consumption and performance. A formula based on the model calculates which IMS servers should be active and which call sessions should move to which IMS servers to reduce the overall power consumption, using linear programming. Moreover, this paper implements the proposal and shows its results. Keywords-IMS; SIP; Session Migration; Power Saving; Linear Programming; Optimization; CPLEX

System and method for creating bgp route-based network traffic profiles to detect spoofed traffic

Abstract: An inventive system and method for creating source profiles to detect spoofed traffic comprises obtaining a routing path for data to traverse nodes using traffic profiles, each routing path comprising at least a target AS, initializing one or more AS sets with last hop ASes, enhancing the AS sets by connecting the AS sets to routers, for each enhanced AS set, filtering observed traffic flows, and using the filtered flows to associate enhanced AS sets with network monitoring points to create the source profiles. In one aspect, filtering flows comprise TCP session filtering and/or destination bogon filtering. In one aspect, the routers are border gateway protocol routers. In one aspect, the last hop ASes are one hop away from the target AS.