Stevens Institute of Technology

About: Stevens Institute of Technology is a education organization based out in Hoboken, New Jersey, United States. It is known for research contribution in the topics: Computer science & Cognitive radio. The organization has 5440 authors who have published 12684 publications receiving 296875 citations. The organization is also known as: Stevens & Stevens Tech.

Cellular-Base-Station-Assisted Device-to-Device Communications in TV White Space

Abstract: This paper presents a systematic approach to exploiting TV white space (TVWS) for device-to-device (D2D) communications with the aid of the existing cellular infrastructure. The goal is to build a location-specific TVWS database, which provides a lookup table service for any D2D link to determine its maximum permitted emission power (MPEP) in an unlicensed digital TV (DTV) band. To achieve this goal, the idea of mobile crowd sensing is first introduced to collect active spectrum measurements from massive personal mobile devices. Considering the incompleteness of crowd measurements, we formulate the problem of unknown measurements recovery as a matrix completion problem and apply a powerful fixed point continuation algorithm to reconstruct the unknown elements from the known elements. By joint exploitation of the big spectrum data in its vicinity, each cellular base station further implements a nonlinear support vector machine algorithm to perform irregular coverage boundary detection of a licensed DTV transmitter. With the knowledge of the detected coverage boundary, an opportunistic spatial reuse algorithm is developed for each D2D link to determine its MPEP. Simulation results show that the proposed approach can successfully enable D2D communications in TVWS while satisfying the interference constraint from the licensed DTV services. In addition, to our best knowledge, this is the first try to explore and exploit TVWS inside the DTV protection region resulted from the shadowing effect. Potential application scenarios include communications between internet of vehicles in the underground parking and D2D communications in hotspots such as subway, game stadiums, and airports.

A comprehensive review of krill herd algorithm: variants, hybrids and applications

Abstract: Krill herd (KH) is a novel swarm-based metaheuristic optimization algorithm inspired by the krill herding behavior. The objective function in the KH optimization process is based on the least distance between the food location and position of a krill. The KH method has been proven to outperform several state-of-the-art metaheuristic algorithms on many benchmarks and engineering cases. This paper presents a comprehensive review of different versions of the KH algorithm and their engineering applications. The study is divided into the following general parts: KH variants, engineering optimization/application, and theoretical analysis. In addition, specific features of KH and future directions are discussed.

Privacy-preserving queries on encrypted data

Abstract: Data confidentiality is a major concern in database systems. Encryption is a useful tool for protecting the confidentiality of sensitive data. However, when data is encrypted, performing queries becomes more challenging. In this paper, we study efficient and provably secure methods for queries on encrypted data stored in an outsourced database that may be susceptible to compromise. Specifically, we show that, in our system, even if an intruder breaks into the database and observes some interactions between the database and its users, he only learns very little about the data stored in the database and the queries performed on the data. Our work consists of several components. First, we consider databases in which each attribute has a finite domain and give a basic solution for certain kinds of queries on such databases. Then, we present two enhanced solutions, one with a stronger security guarantee and the other with accelerated queries. In addition to providing proofs of our security guarantees, we provide empirical performance evaluations. Our experiments demonstrate that our solutions are fast on large-sized real data.

Intrinsic viscosity and the electrical polarizability of arbitrarily shaped objects

Abstract: The problem of calculating the electric polarizability tensor ${\ensuremath{\alpha}}_{e}$ of objects of arbitrary shape has been reformulated in terms of path integration and implemented computationally. The method simultaneously yields the electrostatic capacity C and the equilibrium charge density. These functionals of particle shape are important in many materials science applications, including the conductivity and viscosity of filled materials and suspensions. The method has been validated through comparison with exact results (for the sphere, the circular disk, touching spheres, and tori), it has been found that ${10}^{6}$ trajectories yield an accuracy of about four and three significant figures for C and ${\mathbf{\ensuremath{\alpha}}}_{\mathbf{e}},$ respectively. The method is fast: For simple objects, ${10}^{6}$ trajectories require about 1 min on a PC. It is also versatile: Switching from one object to another is easy. Predictions have also been made for regular polygons, polyhedra, and right circular cylinders, since these shapes are important in applications and since numerical calculations of high stated accuracy are available. Finally, the path-integration method has been applied to estimate transport properties of both linear flexible polymers (random walk chains of spheres) and lattice model dendrimer molecules. This requires probing of an ensemble of objects. For linear chains, the distribution function of C and of the trace $({\mathbf{\ensuremath{\alpha}}}_{\mathbf{e}}),$ are found to be universal in a size coordinate reduced by the chain radius of gyration. For dendrimers, these distribution functions become increasingly sharp with generation number. It has been found that C and ${\ensuremath{\alpha}}_{e}$ provide important information about the distribution of molecular size and shape and that they are important for estimating the Stokes friction and intrinsic viscosity of macromolecules.