We analyze Age of Information (AoI) in wireless networks where nodes use a spatially adaptive random access scheme to send status updates to a central base station. We show that the set of achievable AoI in this setting is convex, and design policies to minimize weighted sum, min-max, and proportionally fair AoI by setting transmission probabilities as a function of node locations. We show that under the capture model, when the spatial topology of the network is considered, AoI can be significantly improved, and we obtain tight performance bounds on weighted sum and min-max AoI. Finally, we design a policy where each node sets its transmission probability based only on its own distance from the base station, when it does not know the positions of other nodes. We show that this policy achieves an AoI for all nodes within a factor of 2 from the optimal, and that it converges to the optimal proportionally fair policy as the size of the network goes to infinity.

We analyze the problem of scheduling in wireless networks to meet end-to-end service guarantees. Using network slicing to decouple the queueing dynamics between flows, we show that the network's ability to meet hard throughput and deadline requirements is largely influenced by the scheduling policy. We characterize the feasible throughput/deadline region for a flow under a fixed route and set of slices, and find throughput- and deadline-optimal policies for a solitary flow. We formulate the feasibility problem for multiple flows in a general topology, and show its equivalence to finding a bounded-cost cycle on an exponentially large graph, which is un-solvable in polynomial time by the best-known algorithm. Using a novel concept called delay deficit, we develop a sufficient condition for meeting deadlines as a function of inter-scheduling times, and show that regular schedules are optimal for satisfying this condition. Motivated by this, we design a polynomial-time algorithm that returns an (almost) regular schedule, optimized to meet service guarantees for all flows.

We consider the design of distributed scheduling algorithms that minimize age of information in single-hop wireless networks. The centralized max-weight policy is known to be nearly optimal in this setting; hence, our goal is to design a distributed CSMA scheme that can mimic its performance. To that end, we propose a distributed protocol called Fresh-CSMA and show that in an idealized setting, Fresh-CSMA can match the scheduling decisions of the max-weight policy with high probability in each frame, and also match the theoretical performance guarantees of the max-weight policy over the entire time horizon. We then consider a more realistic setting and study the impact of protocol parameters on the probability of collisions and the overhead caused by the distributed nature of the protocol. Finally, we provide simulations that support our theoretical results and show that the performance gap between the ideal and realistic versions of Fresh-CSMA is small.

There is currently significant demand for training in how to use metals additive manufacturing (AM) machines. Such training is important not only for the technicians who run and maintain the machines, but also for engineers and strategic decision makers who need to support AM part fabrication. Furthermore, there are a variety of AM machines, each with different details to be learned and potential hazards to overcome, and it is difficult to train more than a handful of users at one time. To address these challenges, a prototype training system has been developed, the AM Training Tutor, which uses interactive virtual reality (VR) to train users on a specific AM machine - the EOS M290. To make the training technology more widely available and expand its use across a variety of different AM machines, efforts are underway to develop a modularized and generic version of the AM Training Tutor that can be customized with relatively little effort to train users to operate other AM machines. This work-in-progress paper details the progress to-date, challenges and proposed solutions with the aim to demonstrate how standalone VR-based training systems can be redesigned for relatively easy repurposing and generalization.

We study collaborative adaptive cruise control as a representative application for safety services provided by autonomous cars. We provide a detailed analysis of attacks that can be conducted by a motivated attacker targeting the collaborative adaptive cruise control algorithm, by influencing the acceleration reported by another car, or the local LIDAR and RADAR sensors. The attacks have a strong impact on passenger comfort, efficiency and safety, with two of such attacks being able to cause crashes. We also present two detection methods rooted in physical-based constraints and machine learning algorithms. We show the effectiveness of these solutions through simulations and discuss their limitations.