Learning and Management for Internet-of-Things: Accounting for Adaptivity and Scalability

Internet-of-Things (IoT) envisions an intelligent infrastructure of networked smart devices offering task-specific monitoring and control services. The unique features of IoT include extreme heterogeneity, massive number of devices, and unpredictable dynamics partially due to human interaction. These call for foundational innovations in network design and management. Ideally, it should allow efficient adaptation to changing environments, and low-cost implementation scalable to massive number of devices, subject to stringent latency constraints. To this end, the overarching goal of this paper is to outline a unified framework for online learning and management policies in IoT through joint advances in communication, networking, learning, and optimization. From the network architecture vantage point, the unified framework leverages a promising fog architecture that enables smart devices to have proximity access to cloud functionalities at the network edge, along the cloud-to-things continuum. From the algorithmic perspective, key innovations target online approaches adaptive to different degrees of nonstationarity in IoT dynamics, and their scalable model-free implementation under limited feedback that motivates blind or bandit approaches. The proposed framework aspires to offer a stepping stone that leads to systematic designs and analysis of task-specific learning and management schemes for IoT, along with a host of new research directions to build on.Comment: Submitted on June 15 to Proceeding of IEEE Special Issue on Adaptive and Scalable Communication Network

Byzantine Attack and Defense in Cognitive Radio Networks: A Survey

The Byzantine attack in cooperative spectrum sensing (CSS), also known as the spectrum sensing data falsification (SSDF) attack in the literature, is one of the key adversaries to the success of cognitive radio networks (CRNs). In the past couple of years, the research on the Byzantine attack and defense strategies has gained worldwide increasing attention. In this paper, we provide a comprehensive survey and tutorial on the recent advances in the Byzantine attack and defense for CSS in CRNs. Specifically, we first briefly present the preliminaries of CSS for general readers, including signal detection techniques, hypothesis testing, and data fusion. Second, we analyze the spear and shield relation between Byzantine attack and defense from three aspects: the vulnerability of CSS to attack, the obstacles in CSS to defense, and the games between attack and defense. Then, we propose a taxonomy of the existing Byzantine attack behaviors and elaborate on the corresponding attack parameters, which determine where, who, how, and when to launch attacks. Next, from the perspectives of homogeneous or heterogeneous scenarios, we classify the existing defense algorithms, and provide an in-depth tutorial on the state-of-the-art Byzantine defense schemes, commonly known as robust or secure CSS in the literature. Furthermore, we highlight the unsolved research challenges and depict the future research directions.Comment: Accepted by IEEE Communications Surveys and Tutoiral

Bridges Structural Health Monitoring and Deterioration Detection Synthesis of Knowledge and Technology

INE/AUTC 10.0

Differentiating ethernet devices through SNR maximization

The Detecting Intrusions at Layer ONe (DILON) project investigates the use of the digital and analog characteristics of digital devices for security purposes. To this end, we present a method capable of identifying Ethernet cards based upon minute variations in their network signaling resulting from hardware and manufacturing inconsistencies, using an optimal detector, the matched filter. Our results indicate that a matched filter can easily discriminate between Ethernet cards of different models, and with sufficient preprocessing of data, cards of the same model, to an acceptable degree of accuracy. Several non-traditional applications of the filter are presented in order to improve its ability to discriminate between signals from seemingly identical devices of the same manufacturing lot. The experimental results of applying these filters to three different models of Ethernet cards, totaling 16 devices, are presented and discussed. Important applications of this technology include intrusion detection (discovering node impersonation and network tampering), authentication (preventing unauthorized access to the physical network), forensic data collection (tying a physical device to a specific network incident), and assurance monitoring (determining whether a device will or is in the process of failing). Open questions include identifying, tracking, and adapting to long-term changes in system behavior caused by device aging, and how to cope with the intermittent connectivity of devices on the network