Pico-satellite (picosat) constellations aim to become the de facto connectivity solution for Internet of Things (IoT) devices. These constellations rely on a large number of small picosats and offer global plug-and-play connectivity at low data rates, without the need for Earth-based gateways. As picosat constellations scale, they run into new bottlenecks due to their traditional medium access designs optimized for single (or few) satellite operations. We present CosMAC - a new constellation-scale medium access and scheduling system for picosat networks. CosMAC includes a new overlap-aware medium access approach for uplink from IoT to picosats and a new network layer that schedules downlink traffic from satellites. We empirically evaluate CosMAC using measurements from three picosats and large-scale trace-driven simulations for a 173 picosat network supporting 100k devices. Our results demonstrate that CosMAC can improve the overall network throughput by up to 6.5X over prior state-of-the-art satellite medium access schemes.

This paper introduces Wireless IoT-based Noise Cancellation (WINC) which defines a framework for leveraging a wireless network of IoT microphones to enhance active noise cancellation in noise-canceling headphones. The IoT microphones forward ambient noise to the headphone over the wireless link which travels a million times faster than sound and gives the headphone a future lookahead into the incoming noise. While leveraging wireless lookahead has been explored in past work, prior systems are limited to a single noise source. WINC, however, can simultaneously cancel multiple noise sources by using a network of IoT nodes. Scaling wireless lookahead aware noise cancellation is non-trivial because the computational and protocol delays can defeat the purpose of leveraging wireless lookahead. WINC introduces a novel algorithm that operates in the frequency domain to efficiently cancel multiple noise sources. We implement and evaluate WINC to show that it can cancel three noise sources and outperforms past work and state-of-the-art headphones without requiring completely blocking the users’ ears.

This paper demonstrates high-resolution imaging using millimeter Wave (mmWave) radars that can function even in dense fog. We leverage the fact that mmWave signals have favorable propagation characteristics in low visibility conditions, unlike optical sensors like cameras and LiDARs which cannot penetrate through dense fog. Millimeter-wave radars, however, suffer from very low resolution, specularity, and noise artifacts. We introduce HawkEye, a system that leverages a cGAN architecture to recover high-frequency shapes from raw low-resolution mmWave heat-maps. We propose a novel design that addresses challenges specific to the structure and nature of the radar signals involved. We also develop a data synthesizer to aid with large-scale dataset generation for training. We implement our system on a custom-built mmWave radar platform and demonstrate performance improvement over both standard mmWave radars and other competitive baselines.

This dataset contains the graphs used in “An Experimental Study of the Treewidth of Real-World Graph Data” by Silviu Maniu, Pierre Senellart, and Suraj Jog, published at ICDT 2019.

Fragmentation of expensive resources, e.g., the spectrum for wireless services, between providers can introduce inefficiencies in resource utilization and worsen overall system performance. In such cases, resource pooling between independent service providers can be used to improve performance. However, for providers to agree to pool their resources, the arrangement has to be mutually beneficial. The traditional notion of resource pooling, which implies complete sharing, need not have this property. For example, under full pooling, one of the providers may be worse off and hence has no incentive to participate. In this paper, we propose partial resource sharing models as a generalization of full pooling, which can be configured to be beneficial to all participants. We formally define and analyze two partial sharing models between two service providers, each of which is an Erlang-B loss system with the blocking probabilities as the performance measure. We show that there always exist partial sharing configurations that are beneficial to both providers, irrespective of the load and the number of circuits of each of the providers. A key result is that the Pareto frontier has at least one of the providers sharing all its resources with the other. Furthermore, full pooling may not lie inside this Pareto set. The choice of the sharing configurations within the Pareto set is formalized based on the bargaining theory. Finally, large system approximations of the blocking probabilities in the quality-efficiency-driven regime are presented.