On realistic target coverage by autonomous drones

Low-cost mini-drones with advanced sensing and maneuverability enable a new class of intelligent sensing systems. To achieve the full potential of such drones, it is necessary to develop new enhanced formulations of both common and emerging sensing scenarios. Namely, several fundamental challenges in visual sensing are yet to be solved including (1) fitting sizable targets in camera frames; (2) positioning cameras at effective viewpoints matching target poses; and (3) accounting for occlusion by elements in the environment, including other targets. In this article, we introduce Argus, an autonomous system that utilizes drones to collect target information incrementally through a two-tier architecture. To tackle the stated challenges, Argus employs a novel geometric model that captures both target shapes and coverage constraints. Recognizing drones as the scarcest resource, Argus aims to minimize the number of drones required to cover a set of targets. We prove this problem is NP-hard, and even hard to approximate, before deriving a best-possible approximation algorithm along with a competitive sampling heuristic which runs up to 100× faster according to large-scale simulations. To test Argus in action, we demonstrate and analyze its performance on a prototype implementation. Finally, we present a number of extensions to accommodate more application requirements and highlight some open problems

Low complexity target coverage heuristics using mobile cameras

Wireless sensor and actuator networks have been extensively deployed for enhancing industrial control processes and supply-chains, and many forms of surveillance and environmental monitoring. The availability of low-cost mobile robots equipped with a variety of sensors in addition to communication and computational capabilities makes them particularly promising in target coverage tasks for ad hoc surveillance, where quick, low-cost or non-lasting visual sensing solutions are required, e.g. in border protection and disaster recovery. In this paper, we consider the problem of low complexity placement and orientation of mobile cameras to cover arbitrary targets. We tackle this problem by clustering proximal targets, while calculating/estimating the camera location/direction for each cluster separately through our cover-set coverage method. Our proposed solutions provide extremely computationally efficient heuristics with only a small increase in number of cameras used, and a small decrease in number of covered targets. 2014 IEEE.Qatar National Research FundScopu

Up and away: A visually-controlled easy-to-deploy wireless UAV Cyber-Physical testbed

Cyber-Physical Systems (CPS) have the promise of presenting the next evolution in computing with potential applications that include aerospace, transportation, and various automation systems. These applications motivate advances in the different sub-fields of CPS such as mobile computing, context awareness, and computer vision. However, deploying and testing complete CPSs is known to be a complex and expensive task. In this paper, we present the design, implementation, and evaluation of Up and Away (UnA): a testbed for Cyber-Physical Systems that use Unmanned Aerial Vehicles (UAVs) as their main physical component. UnA aims to abstract the control of physical system components to reduce the complexity of UAV oriented CPS experiments. UnA provides APIs to allow for converting CPS algorithm implementations, developed typically for simulations, into physical experiments using a few simple steps. We present two scenarios of using UnA's API to bring mobile-camera-based surveillance algorithms to life, thus exhibiting the ease of use and flexibility of UnA. 2014 IEEE.Qatar National Research FundScopu

EdgeHealth: An Energy-Efficient Edge-based Remote mHealth Monitoring System

Promoting smart and scalable remote health monitoring systems is challenging due to the enormous amount of collected data that needs to be processed and transferred given the limited network resources and battery-operated devices. Thus, the conventional cloud computing paradigm alone, is not always the most suitable solution for enabling such systems. In this context, we propose and implement a smart edge-based health system that aims at decreasing the system latency and energy consumption, while optimizing the delivery of the medical data. In particular, we formulate a multi-objective optimization framework that enables an edge node to dynamically adjust compression parameters and select the optimal radio access technology (RAT) while maintaining a trade-off between energy consumption, latency, and distortion. Furthermore, to evaluate and verify our framework, we develop an experimental testbed, where a data emulator is implemented to send EEG data to an edge node that classifies, compresses, and transfers the gathered data through the optimal RAT to the health cloud. Our experimental results show that the proposed system can offer about 30% energy savings while decreasing the delivery time to half of its value compared to a system that lacks edge processing capabilities.This work was made possible by NPRP grant # 7-684-1-127 from the Qatar National Research Fund (a member of Qatar Foundation). The work of Amr Mohamed and Alaa Abdellatif is partially supported by NPRP grant #8-408-2-172.Scopu

On target coverage in mobile visual sensor networks

Recent advancements in manufacturing low-cost wireless battery operated cameras has made their application in Wireless Video Sensor Networks (WVSN) increasingly more feasible and affordable. The application of robotic sensing agents equipped with cameras in WVSNs, seems particularly promising in performing coverage tasks for ad hoc surveillance. Their application in this context can be specifically useful for surveying areas with little to no available or affordable infrastructure, or where quick deployment is necessary. In this paper, we address the target coverage problem for finding the minimum number of cameras, their placement, and orientation to cover arbitrarily located targets in an area of interest. We propose a computationally light-weight heuristic, where the number of used mobile cameras is close to those found by near-optimal algorithms. Specifically, we address this problem for non-uniform target distributions that naturally form clusters. Having light-weight heuristics will be particularly useful when the application is required to adapt to target mobility and/or is implemented in embedded systems. Our simulation study shows that when clusters are sufficiently separated, the required number of cameras found by our proposed method is very close to those acquired by the near-optimal algorithm, whereas the computational complexity of our algorithm is about ten times less. We also deploy our algorithm on a drone testbed using off-the-shelf components to verify its feasibility.Qatar National Research FundScopu

Design Challenges of Multi-UAV Systems in Cyber-Physical Applications: A Comprehensive Survey and Future Directions

Unmanned aerial vehicles (UAVs) have recently rapidly grown to facilitate a wide range of innovative applications that can fundamentally change the way cyber-physical systems (CPSs) are designed. CPSs are a modern generation of systems with synergic cooperation between computational and physical potentials that can interact with humans through several new mechanisms. The main advantages of using UAVs in CPS application is their exceptional features, including their mobility, dynamism, effortless deployment, adaptive altitude, agility, adjustability, and effective appraisal of real-world functions anytime and anywhere. Furthermore, from the technology perspective, UAVs are predicted to be a vital element of the development of advanced CPSs. Therefore, in this survey, we aim to pinpoint the most fundamental and important design challenges of multi-UAV systems for CPS applications. We highlight key and versatile aspects that span the coverage and tracking of targets and infrastructure objects, energy-efficient navigation, and image analysis using machine learning for fine-grained CPS applications. Key prototypes and testbeds are also investigated to show how these practical technologies can facilitate CPS applications. We present and propose state-of-the-art algorithms to address design challenges with both quantitative and qualitative methods and map these challenges with important CPS applications to draw insightful conclusions on the challenges of each application. Finally, we summarize potential new directions and ideas that could shape future research in these areas. 1998-2012 IEEE.Qatar UniversityScopu