13019699
Ehrhart polynomials of cyclic polytopes
The Ehrhart polynomial of an integral convex polytope counts the number of lattice points in dilates of the polytope. In (Coefficients and roots of Ehrhart polynomials, preprint), the authors conjectured that for any cyclic polytope with integral parameters, the Ehrhart polynomial of it is equal to its volume plus the Ehrhart polynomial of its lower envelope and proved the case when the dimension d=2. In our article, we prove the conjecture for any dimension.
598798
(k, m)-Catalan numbers and hook length polynomials for plane trees
Motivated by a formula of A. Postnikov relating binary trees, we define the hook length polynomials for m-ary trees and plane forests, and show that these polynomials have a simple binomial expression. An integer value of this expression is C"k","m(n)=1mn+1(mn+1)kn, which we call the (k,m)-Catalan number. For proving the hook length formulas, we also introduce a combinatorial family, (k,m)-ary trees, which are counted by the (k,m)-Catalan numbers.
285048
Location-centric storage for safety warning based on roadway sensor networks
We propose a novel vision for roadway safety warning based on sensor networks, aiming at providing user-friendly zero-delay safety warnings to motorists. Our idea leverages the advanced sensing, networking and storage technologies. Roadway sensors detect events and store event records at multiple designated locations along the against traffic direction, such that the passing-by drivers can be alerted to potential dangers or traffic delays through the wireless communication between roadway sensors and the vehicle. We design a location-centric storage (LCS) protocol, which manages the propagation and storage of event records based on the time needed to clear the road. In LCS, the density of the sensors storing an event record decreases logarithmically with respect to the distance to the event location. Thus, the closer to the event position, the more number of warnings a driver may obtain. LCS is further tailored for the case of ''highway'' sensor networks when all sensors are deployed along a straight line mimicking a highway, and the more complex case when two roads intersect at some place. We conduct both theoretic analysis and simulation study to verify the performance of LCS when applied to roadway sensor networks for safety warning. The results indicate that LCS is fair to all sensors. We conclude that roadway safety warning based on sensor networks is a promising idea for realizing ITS's ''Zero Fatality, Zero Delay'' roadway safety philosophy.
17825087
Feature Matrix Models for Information Extraction
To extract useful information which really contribute to the target issue from huge amounts of data or text description is an important task towards a number of research fields (e.g., genomics study and text mining). In this paper, a general feature matrix model (FMM) is proposed aiming to provide partial answer to this task. Specifically, one instantiation of FMM is used for identifying feature genes based on microarray data, and the other for extracting information according to case description.
12323265
SeGrid: A Secure Grid Infrastructure for Sensor Networks
In this paper, we propose SeGrid, a secure grid infrastructure for large scale sensor networks. The basic idea relies on the availability of a low-cost public cryptsystem (e.g. Blom’s key management scheme [4]) that can be used for shared key computation between the source and the destination, as long as the public shares are known to each other. In SeGrid, each sensor resides in a grid computed from its physical location. Within a grid, one or a few number of sensors, with one of them the grid head, are active at any instant of time and all other sensors fall asleep for energy conservation. We intend to compute a shared key for two grids instead of two nodes, such that the grid heads can securely communicate with each other. The public shares of a grid are stored at designated locations based on our public share management protocol such that the closer two grids, the shorter distance to obtain each other’s public shares. We instantiate SeGrid based on Blom’s key management scheme [4] to illustrate the computation of a grid key. To our best knowledge, this is the first work that simultaneously considers both key management and network lifetime extension, which explores along the dimension of network density.
11279692
TPSS: A Time-Based Positioning Scheme for Sensor Networks with Short Range Beacons
Location discovery is a challenging problem in sensor networks. However, many sensor network applications require the availability of the physical sensor positions. In this paper, we present TPSS, a time-based positioning scheme for sensor networks when a number of short-range beacons are randomly and uniformly deployed. By measuring the Time Difference of Arrivals (TDoAs) of signals from nearby beacons, nodes can use TPSS to effectively estimate their locations based on the range differences through trilateration. TPSS requires no long-range beacons to cover the entire network, an essential difference compared to TPS [2] and iTPS [15]. Features of TPSS include high scalability, low communication and computation overheads, no requirement for time synchronization, etc. Simulation results indicate that TPSS is an effective and efficient self-positioning scheme for sensor networks with short range beacons.
130150
Insider Attacker Detection in Wireless Sensor Networks
Though destructive to network functions, insider attackers are not detectable with only the classic cryptography-based techniques. Many mission-critic sensor network applications demand an effective, light, flexible algorithm for internal adversary identification with only localized information available. The insider attacker detection scheme proposed in this paper meets all the requirements by exploring the spatial correlation existent among the networking behaviors of sensors in close proximity. Our work is exploratory in that the proposed algorithm considers multiple attributes simultaneously in node behavior evaluation, with no requirement on a prior knowledge about normal/malicious sensor activities. Moreover, it is application-friendly, which employs original measurements from sensors and can be employed to monitor many aspects of sensor networking behaviors. Our algorithm is purely localized, fitting well to the large-scale sensor networks. Simulation results indicate that internal adversaries can be identified with a high accuracy and a low false alarm rate when as many as 25% sensors are misbehaving.