Unmanned Aerial Vehicles (UAVs) are increasingly used for surveillance, mapping, delivery, and search and rescue. These applications require continuous data streams processed through low-latency pipelines to remain situation-aware. Despite advances in hardware and software, UAV uptime is constrained by limited battery or fuel. Adding onboard compute further shortens flight time; where extra payload increases propulsion demand, and the compute itself draws power, so either UAVs carry limited compute or are treated as mobile sensors. Emerging 5G/edge deployments hold promise for supporting such compute limited drones by enabling drone-to-edge task offloading and collaboration for latency-critical processing. However, such infrastructure support is not yet widespread, complicating design and evaluation. Practical and privacy constraints also limit real-world testing, pushing much current research toward theoretical models with uncertain practicality. A key gap is the absence of realistic emulator that couples mobile UAVs and edge nodes within a dynamic virtual world (energy, location, mobility). This work envisions addressing that gap by: 1)adding emulation support on top of existing simulators to emulate necessary infrastructure (mobile drones, edge servers, charging stations) that support data streaming, processing, and control while managing time-varying state; 2)integrating user-defined plug-and-play models (e.g. energy and FPS) to realize realistic emulation behaviors of heterogeneous infrastructure with varying capabilities; and 3)exposing high-level APIs for integrations with application processing pipelines. Using a city-scale vehicle-tracking scenario, we demonstrate the emulator's usability and ease of application integration. We present this as an ongoing step toward an end-to-end platform for developing and assessing drone-edge applications, motivating and easing further research in the domain.

We investigate the issue of deploying applications on a set of loosely coupled compute devices, connected through a wireless mesh, typical in community networks. Wireless mesh networks experience significant temporal and spatial variations in link bandwidth. When application components, modeled as a directed acyclic graph, need to be scheduled on such a mesh with bandwidth constraints (and variations), the problem of mapping components to specific compute nodes becomes an instance of bin packing with constraints of CPU, memory, and bandwidth limits within the mesh. To make the scheduling tractable, we propose BASS (Bandwidth Aware Scheduling System), and develop heuristics for scheduling, based on the directed graph topology of the application components. We evaluate BASS on an emulated mesh using bandwidth traces collected from an actual wireless testbed - CityLab. Detailed evaluations show that contemporary orchestration frameworks can plug in BASS to provide better end-to-end performance for the applications deployed on the mesh while reducing resource utilization.

On-demand video contributes a large fraction of the data traffic on mobile networks. This share is expected to increase even more drastically in the coming years. While the cellular infrastructure is continuously evolving to keep pace with this increasing demand, it is necessary to ensure that sufficient bandwidth is reserved for other latency-sensitive realtime applications like video conferencing and multiplayer video games. A tangible approach involves reducing on-demand video load on cellular networks, especially from users on the move. We see an opportunity for cellular load reduction using edge nodes based on two observations: (1) video streaming is mostly a download-only operation with sequential data access; and (2) short-range mmWave links can deliver an extremely high throughput for nearby recipients of data. The knowledge of the user's planned travel route creates opportunities for prescient prefetching and delivering the content as the vehicle passes through just in time, using mmWave devices on en route edge nodes. ClairvoyantEdge is a novel networked system infrastructure that leverages inter-edge node communication and the knowledge of users' trajectories to plan and deliver buffered video segments to the vehicles passing by. To evaluate ClairvoyantEdge, we built a comprehensive end-to-end emulation-based workflow that incorporates in situ field measurements of mmWave links into our own homegrown emulation framework. With a minuscule 0.12% coverage of a 46km2geographical area employing 20 edge nodes distributed in that area providing short-range mmWave access to passing vehicles, we achieve an average reduction of up to 21% in cellular bandwidth usage for video downloads, using a real-world workload comprising 758 vehicles. Our results validate the promise of ClairvoyantEdge for incorporation in future edge infrastructure evolution.

Spatiotemporal variation in cellular bandwidth availability is well-known and could affect a mobile user's quality of experience (QoE), especially while using bandwidth intensive streaming applications such as movies, podcasts, and music videos during commute. If such variations are made available to a streaming service in advance it could perhaps plan better to avoid sub-optimal performance while the user travels through regions of low bandwidth availability. The intuition is that such future knowledge could be used to buffer additional content in regions of higher bandwidth availability to tide over the deficits in regions of low bandwidth availability. Foresight is a service designed to provide this future knowledge for client apps running on a mobile device. It comprises three components: (a) a crowd-sourced bandwidth estimate reporting facility, (b) an on-cloud bandwidth service that records the spatiotemporal variations in bandwidth and serves queries for bandwidth availability from mobile users, and (c) an on-device bandwidth manager that caters to the bandwidth requirements from client apps by providing them with bandwidth allocation schedules. Foresight is implemented in the Android framework. As a proof of concept for using this service, we have modified an open-source video player---Exoplayer---to use the results of Foresight in its video buffer management. Our performance evaluation shows Foresight's scalability. We also showcase the opportunity that Foresight offers to ExoPlayer to enhance video quality of experience (QoE) despite spatiotemporal bandwidth variations for metrics such as overall higher bitrate of playback, reduction in number of bitrate switches, and reduction in the number of stalls during video playback.