Illinois Institute of Technology

About: Illinois Institute of Technology is a education organization based out in Chicago, Illinois, United States. It is known for research contribution in the topics: Electric power system & Wireless network. The organization has 10188 authors who have published 21062 publications receiving 554178 citations. The organization is also known as: IIT & Illinois Tech.

Data gathering in wireless sensor networks through intelligent compressive sensing

Abstract: The recently emerged compressive sensing (CS) theory provides a whole new avenue for data gathering in wireless sensor networks with benefits of universal sampling and decentralized encoding. However, existing compressive sensing based data gathering approaches assume the sensed data has a known constant sparsity, ignoring that the sparsity of natural signals vary in temporal and spatial domain. In this paper, we present an adaptive data gathering scheme by compressive sensing for wireless sensor networks. By introducing autoregressive (AR) model into the reconstruction of the sensed data, the local correlation in sensed data is exploited and thus local adaptive sparsity is achieved. The recovered data at the sink is evaluated by utilizing successive reconstructions, the relation between error and measurements. Then the number of measurements is adjusted according to the variation of the sensed data. Furthermore, a novel abnormal readings detection and identification mechanism based on combinational sparsity reconstruction is proposed. Internal error and external event are distinguished by their specific features. We perform extensive testing of our scheme on the real data sets and experimental results validate the efficiency and efficacy of the proposed scheme. Up to about 8dB SNR gain can be achieved over conventional CS based method with moderate increase of complexity.

Modeling of Local False Data Injection Attacks With Reduced Network Information

Abstract: Modern power grids are becoming more prone to cyberattacks. Even worse, an attacker without the full topology and parameter information of a power grid can still execute a false data injection attack without being detected by the state estimator. This paper proposes an efficient strategy for determining the optimal attacking region that requires reduced network information. The effectiveness of the proposed algorithm is verified through extensive simulations. This paper introduces a new front in the study of smart grid cyber security: determination of a feasible attacking region by obtaining less network information. This paper is also essential and significant for finding effective protection strategies against false data injection attacks based on the deep understanding of the mechanisms and strategies of the attacks.

Berries: anti-inflammatory effects in humans.

Abstract: A sustained pro-inflammatory state is a major contributing factor in chronic disease development, progression, and complication, including the most commonly known diseases: cardiovascular disease, Alzheimer's, and type 2 diabetes. Fruits, such as berries, contain polyphenol compounds purported to have anti-inflammatory activity in humans. Among the most notable polyphenols in berries are anthocyanins, responsible for their distinctive colors of red, blue, and purple. Berries have been studied widely for their antioxidant properties; however, preclinical data suggest important effects on inflammatory pathways. Correspondingly, the effects of berries, including extracts and purified anthocyanins, have been the subject of a number of human trials. This review aims to evaluate the current state of the human science on berry (products) as a source of dietary polyphenols, particularly anthocyanins, to modulate inflammatory status. Identifying dietary strategies that manage the modern-day inflammatory burden has important implications for chronic disease risk reduction and informing dietary guidelines aimed at achieving and maintaining health.

Stylistic text classification using functional lexical features

Abstract: Most text analysis and retrieval work to date has focused on the topic of a text; that is, what it is about. However, a text also contains much useful information in its style, or how it is written. This includes information about its author, its purpose, feelings it is meant to evoke, and more. This article develops a new type of lexical feature for use in stylistic text classification, based on taxonomies of various semantic functions of certain choice words or phrases. We demonstrate the usefulness of such features for the stylistic text classification tasks of determining author identity and nationality, the gender of literary characters, a text’s sentiment (positive/ negative evaluation), and the rhetorical character of scientific journal articles. We further show how the use of functional features aids in gaining insight about stylistic differences among different kinds of texts.

On Broadcast Authentication in Wireless Sensor Networks

Abstract: Broadcast authentication is a critical security service in wireless sensor networks (WSNs), since it enables users to broadcast the WSN in an authenticated way. Symmetric key based schemes such as muTESLA and multilevel muTESLA have been proposed to provide such services for WSNs; however, these schemes all suffer from serious DoS attacks due to the delay in message authentication. This paper presents several effective public key based schemes to achieve immediate broadcast authentication and thus overcome the vulnerability presented in the muTESLA-like schemes. Several cryptographic techniques, including Merkle hash tree and identity-based signature scheme, are adopted to minimize the scheme overhead regarding the costs on both computation and communication. A quantitative energy consumption analysis of the proposed schemes is given in detail. We believe that this paper can serve as the start point towards fully solving the important multisender broadcast authentication problem in WSNs.