Accurate localization from Cyber-Physical Systems (CPS) is a critical enabling technology for context-aware applications and control. As localization plays an increasingly safety-critical role, location systems must be able to identify and eliminate faulty measurements to prevent dangerously inaccurate localization. In this article, we consider the range-based localization problem and propose a method to detect coordinated adversarial corruption on anchor positions and distance measurements. Our algorithm, G ordian , rapidly finds attacks by identifying geometric inconsistencies at the graph level without requiring assumptions about hardware, ranging mechanisms, or cryptographic protocols. We give necessary conditions for which attack detection is guaranteed to be successful in the noiseless case, and we use that intuition to extend G ordian to the noisy case where fewer guarantees are possible. In simulations generated from real-world sensor noise, we empirically show that G ordian ’s trilateration counterexample generation procedure enables rapid attack detection even for combinatorially difficult problems.

We present a novel unsupervised deep learning approach that utilizes an encoder-decoder architecture for detecting anomalies in sequential sensor data collected during industrial manufacturing. Our approach is designed to not only detect whether there exists an anomaly at a given time step, but also to predict what will happen next in the (sequential) process. We demonstrate our approach on a dataset collected from a real-world Additive Manufacturing (AM) testbed. The dataset contains infrared (IR) images collected under both normal conditions and synthetic anomalies. We show that our encoder-decoder model is able to identify the injected anomalies in a modern AM manufacturing process in an unsupervised fashion. In addition, our approach also gives hints about the temperature non-uniformity of the testbed during manufacturing, which was not previously known prior to the experiment.

Smart buildings today are aimed at providing safe, healthy, comfortable, affordable, and beautiful spaces in a carbon and energy-efficient way. They are emerging as complex cyber-physical systems with humans in the loop. Cost, the need to cope with increasing functional complexity, flexibility, fragmentation of the supply chain, and time-to-market pressure are rendering the traditional heuristic and ad hoc design paradigms inefficient and insufficient for the future. In this paper, we present a platform-based methodology for smart building design. Platform-based design (PBD) promotes the reuse of hardware and software on shared infrastructures, enables rapid prototyping of applications, and involves extensive exploration of the design space to optimize design performance. In this paper, we identify, abstract, and formalize components of smart buildings, and present a design flow that maps high-level specifications of desired building applications to their physical implementations under the PBD framework. A case study on the design of on-demand heating, ventilation, and air conditioning (HVAC) systems is presented to demonstrate the use of PBD.

The dual-mode delay model, while being effective for characterizing on-chip timing variations, also yields timing analysis results that are overly pessimistic due to the Common Path Pessimism (CPP). In this paper, we develop a fast and accurate block-based algorithm for removing this pessimism in timing analysis, when the dual-mode delay model is used. We illustrate the effectiveness of our algorithm on a set of benchmarks from the TAU 2014 Contest [1].

The "early/late split" in delay modeling is an effective approach to handle the variability in deep submicron integrated circuits. However, static timing analysis with early/late split is often too conservative due to the common path pessimism, where the common path pessimism removal (CPPR) technique is helpful to eliminate the unnecessary pessimism. In this paper, we propose a fast and simple CPPR algorithm with sub-quadratic time complexity.

In a data-driven Optimal Control (OP) scheme, one or more involved components, such as objective function, system dynamics, or operation constraints, are described with statistical models and learned from data. In this work, we focus on the machine learning of operation constraints which is rarely addressed in previous research. Although a rich collection of supervised learning methods exist in literature, most of them are not suitable for modeling operation constraints, because their decision rules usually induce undesirable non-linear couplings in system variables. In order to surpass simple linear models while at the same time maintaining compatibility with downstream control applications, we propose to describe system operation requirement by convex piecewise linear machine (CPLM), which does not incur any difficulties in optimization and is directly pluggable. The generalization performance of the proposed classifier is analyzed through bounding its VC-dimension, and a large margin cost sensitive learning objective is formulated with Bayes consistent hinge loss. We solve the training problem by online stochastic gradient descent and propose a mixed integer based initialization method. A case study on Heating, Ventilation and Air Conditioning (HVAC) systems control with comfort requirement is conducted and the results show that CPLM is not only a promising candidate for cost sensitive learning in general, but also enables much better description and exploitation of the system operation region for optimal control purpose.