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Electromagnetic jamming results in a loss of link reliability, increased energy consumption and packet delays. In the context of energy-constrained wireless networks, nodes are scheduled to maximize the common sleep duration and coordinate communication to extend their battery life. This coordination results in statistical and predictable activity patterns that may be easily detected and jammed. To eliminate spatio-temporal patterns of communication in the link and network layers, we present WisperNet, an energy-efficient anti-jamming protocol.WisperNet employs hardware-based time synchronization and lightweight cryptographic hashing for coordinated temporal randomization of slot schedules at the link layer and adapts routes to avoid static jammers in the network layer.We demonstrate thatWisperNet reduces the efficiency of any statistical jammer to that of a random jammer, which has the lowest censorship-to-link utilization ratio. In the presence of a statistical jammer, WisperNet provides sub-2% packet drop ratios for link utilization up to 50%. In addition, the jammer's censorship efficiency is linear with link utilization as it is unable to extract any communication patterns. WisperNet is more efficient than low-power CSMA protocols in terms of energy, effective network throughput, reliability and delay. [ABSTRACT FROM AUTHOR]

Demand response (DR) is becoming increasingly important as the volatility on the grid continues to increase. Current DR approaches are predominantly completely manual and rule-based or involve deriving first principles based models which are extremely cost and time prohibitive to build. We consider the problem of data-driven end-user DR for large buildings which involves predicting the demand response baseline, evaluating fixed rule based DR strategies and synthesizing DR control actions. The challenge is in evaluating and taking control decisions at fast time scales in order to curtail the power consumption of the building, in return for a financial reward. We provide a model based control with regression trees algorithm (mbCRT), which allows us to perform closed-loop control for DR strategy synthesis for large commercial buildings. Our data-driven control synthesis algorithm outperforms rule-based DR by 17% for a large DoE commercial reference building and leads to a curtailment of up to 380 kW and over $45,000 in savings. Our methods have been integrated into an open source tool called DR-Advisor, which acts as a recommender system for the building’s facilities manager and provides suitable control actions to meet the desired load curtailment while maintaining operations and maximizing the economic reward. DR-Advisor achieves 92.8–98.9% prediction accuracy for 8 buildings on Penn’s campus. We compare DR-Advisor with other data driven methods and rank 2nd on ASHRAE’s benchmarking data-set for energy prediction. [ABSTRACT FROM AUTHOR]

Model Predictive Control (MPC) is a model-based technique widely and successfully used over the past years to improve control systems performance. A key factor prohibiting the widespread adoption of MPC for complex systems such as buildings is related to the difficulties (cost, time and effort) associated with the identification of a predictive model of a building. To overcome this problem, we introduce a novel idea for predictive control based on historical building data leveraging machine learning algorithms like regression trees and random forests. We call this approach Data-driven model Predictive Control (DPC), and we apply it to three different case studies to demonstrate its performance , scalability and robustness . In the first case study we consider a benchmark MPC controller using a bilinear building model, then we apply DPC to a data-set simulated from such bilinear model and derive a controller based only on the data. Our results demonstrate that DPC can provide comparable performance with respect to MPC applied to a perfectly known mathematical model. In the second case study we apply DPC to a 6 story 22 zone building model in EnergyPlus, for which model-based control is not economical and practical due to extreme complexity, and address a Demand Response problem. Our results demonstrate scalability and efficiency of DPC showing that DPC provides the desired power curtailment with an average error of 3%. In the third case study we implement and test DPC on real data from an off-grid house located in L’Aquila, Italy. We compare the total amount of energy saved with respect to the classical bang-bang controller, showing that we can perform an energy saving up to 49.2%. Our results demonstrate robustness of our method to uncertainties both in real data acquisition and weather forecast. [ABSTRACT FROM AUTHOR]

Bringing new safety-critical medical devices to market faces several major challenges, but modeling and formal methods can facilitate this process from early system requirements verification to platform-level testing to late-stage clinical trials. [ABSTRACT FROM AUTHOR]

The design and implementation of software for medical devices is challenging due to the closed-loop interaction with the patient, which is a stochastic physical environment. The safety-critical nature and the lack of existing industry standards for verification make this an ideal domain for exploring applications of formal modeling and closed-loop analysis. The biggest challenge is that the environment model(s) have to be both complex enough to express the physiological requirements and general enough to cover all possible inputs to the device. In this effort, we use a dual chamber implantable pacemaker as a case study to demonstrate verification of software specifications of medical devices as timed-automata models in UPPAAL. The pacemaker model is based on the specifications and algorithm descriptions from Boston Scientific. The heart is modeled using timed automata based on the physiology of heart. The model is gradually abstracted with timed simulation to preserve properties. A manual Counter-Example-Guided Abstraction and Refinement (CEGAR) framework has been adapted to refine the heart model when spurious counter-examples are found. To demonstrate the closed-loop nature of the problem and heart model refinement, we investigated two clinical cases of Pacemaker Mediated Tachycardia and verified their corresponding correction algorithms in the pacemaker. Along with our tools for code generation from UPPAAL models, this effort enables model-driven design and certification of software for medical devices. [ABSTRACT FROM AUTHOR]

• Formal representation of multi-drone missions with spatial, temporal and reactive requirements. • Trajectory planning optimization for robustly satisfying such multi-drone missions. • Case studies in urban and rural scenarios over long mission horizons. • Scalability beyond state-of-art, and a graphical interface for easily specifying missions. In this work, we present an integrated Framework for Autonomous Drone Safety (FADS). The demand for safe and efficient mobility of people and goods is growing rapidly, in line with the growth in population in US urban centers. In response, new technologies to meet these urban mobility demands are also rapidly maturing in preparation for future full-scale deployment. As surface congestion increases and the technology surrounding unmanned aerial systems (UAS) matures, more people are looking to the urban airspace and Urban Air Mobility (UAM) as a piece of the puzzle to promote mobility in cities. However, the lack of coordination between UAS stakeholders, federal UAS safety regulations, and researchers developing UAS algorithms continues to be a critical barrier to widespread UAS adoption. FADS takes into account federal UAS safety requirements, UAM challenge scenarios, contingency events, as well as stakeholder-specific operational requirements. FADS formalizes these requirements, through Signal Temporal Logic (STL) representations, and a trajectory planning optimization for multi-rotor UAS fleets guarantees robust and continuous-time satisfaction of the requirements and mission objectives. The intuitive FADS user interface makes it easy to plan missions in a variety of environments; we demonstrate this through several rural and urban environment-based case studies. FADS holistically integrates high-level stakeholder objectives with low-level trajectory planning; combined with a user-friendly interface, FADS reduces the complexity of stakeholder coordination within the UAM context. [ABSTRACT FROM AUTHOR]