ADDSEN: Adaptive Data Processing and Dissemination for Drone Swarms in Urban Sensing

Wu, Di; Arkhipov, Dmitri I.; Kim, Minyoung; Talcott, Carolyn; Regan, Amelia; McCann, Julie A.; Venkatasubramanian, Nalini

doi:10.1109/tc.2016.2584061

Cited by 72 publications

(46 citation statements)

References 31 publications

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Section: B Contributionsmentioning

confidence: 99%

“…Besides, UAVs can be used to perform remote surveillance and deliver real-time video data to ground terminals (GTs) [3]. UAVs are also useful for data collection and dissemination in wireless sensor networks [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

“…This scenario is well motivated in practice. For example, one use case is that the UAVs collect data from a field and deliver the data to their respective serving GTs [4,5]. Note that the UAV-IC considered in this paper is generic and the techniques developed here can be generalized to other similar scenarios with multiple UAVs [22][23][24].In contrast to traditional interference channels with static terminals [25,26], the UAV-IC allows one to exploit the mobility of UAVs as a new degree of freedom to dynamically control the interference among K communication links via joint trajectory optimization and power control.…”

mentioning

confidence: 99%

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Multi-UAV Interference Coordination via Joint Trajectory and Power Control

Shen

Chang

Gong

et al. 2020

IEEE Trans. Signal Process.

132

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In this paper, we consider an unmanned aerial vehicle-enabled interference channel (UAV-IC), where each of the K UAVs communicates with its associated ground terminals (GTs) at the same time and over the same spectrum. To exploit the new degree of freedom of UAV mobility for interference coordination between the UAV-GT links, we formulate a joint trajectory and power control (TPC) problem for maximizing the aggregate sum rate of the UAV-IC for a given flight interval, under the practical constraints on the UAV flying speed, altitude, as well as collision avoidance. These constraints couple the TPC variables across different time slots and UAVs, leading to a challenging large-scale and non-convex optimization problem. By exploiting the problem structure, we show that the optimal TPC solution follows the fly-hover-fly strategy, based on which the problem can be handled by firstly finding an optimal hovering locations followed by solving a dimension-reduced TPC problem with given initial and hovering locations of UAVs. For the reduced TPC problem, we propose a successive convex approximation algorithm. To improve the computational efficiency, we further develop a parallel TPC algorithm that is effciently implementable over multi-core CPUs. We also propose a segment-bysegment method which decomposes the TPC problem into sequential TPC subproblems each with a smaller problem dimension. Simulation results demonstrate the superior computation time efficiency of the proposed algorithms, and also show that the UAV-IC can yield higher network sum rate than the benchmark orthogonal schemes.UAV trajectory design for data collection in wireless sensor networks, and the authors in [17] studied an interesting throughput-delay trade-off for UAV-enabled multi-user system. Reference [18] considered the UAV heading optimization for an uplink scenario with multiple antennas at the UAV. A general UAV-enabled radio access network (RAN) supporting multi-mode communications of the ground users was considered in [19], where new designs for the UAV initial trajectory were proposed. The work [20] utilized the UAV to offer dynamic computation offloading for GTs. Besides, a UAV-enabled wireless power transfer (WPT) system is studied in [21], where the harvested energy profile of GTs is characterized by optimizing the UAV trajectory.Besides the works considering single UAV only, references [22-24] studied more complicated scenarios with multiple UAVs. Specifically, [22] considered a scenario with multiple UAVs communicating with one GT and derived the optimal TPC for minimizing both transmission and propulsion energy. [23] considered the joint TPC and user association/scheduling problem for multiple UAVs serving multiple GTs.[24] considered the use of multiple UAVs for coordinated multipoint (CoMP) transmission to serve a set of GTs, and derived the optimal TPC and UAV deployment solutions for maximizing the ergodic sum rate of users under random channel phases. B. ContributionsIn this paper, we study a UAV-enabled interference channel (UAV-I...

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Section: B Contributionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Multi-UAV Interference Coordination via Joint Trajectory and Power Control

Shen

Chang

Gong

et al. 2020

IEEE Trans. Signal Process.

132

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“…It supports both periodic and stimulated data transmission with minimal broadcasting. Wu et al 40 proposed an adaptive data processing and dissemination for drone swarms in the urban SENsing (ADDSEN) framework that works on the sharing of partially ordered knowledge. ADDSEN employs online learning and optimal balancing for energy and storage balancing.…”

Section: Related Workmentioning

confidence: 99%

Efficient data management and control over WSNs using SDN‐enabled aerial networks

Sayeed

Kumar

Sharma

2019

Int J Communication

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The recent developments in collaborative search, acquisition, and tracking have hoisted the geographical barrier. The network between unmanned aerial vehicles (UAVs) and wireless sensor networks (WSNs) is one such collaboration, which comprises battery-powered static sensor nodes that act as sources and sinks and UAVs that act as relays. This collaborative network presents with opportunities and advantages, but at the same time, configuration of such networks is an arduous task. The WSN nodes are characterized by constant depleting power. Their network itself requires constant management and reconfiguration. These requisites can be slaked through the formation of an efficient data dissemination algorithm, which acclimates according to the network state. Considering this, a data dissemination approach is presented in this paper, which constructs a virtual topology predicated on the charge of WSN nodes utilizing software-defined networks (SDNs) through UAVs. The topology is constantly monitored and reconfigured when required. The aerial nodes are equipped with multiple-input multiple-output (MIMO) antennas in order to facilitate simultaneous communication with the ground nodes, the base station, and the SDN controller. An efficient sleep timer and backoff counter strategies are also utilized by the proposed approach. The SDN controller facilitates the topology formation and maintenance of a sleep timer and a backoff counter. The proposed model is compared with clustered hierarchical layouts and hexagonal cell layouts through the network simulations. The results suggest significant improvements in the proposed model for various metrics, such as lifetime, delay, latency, delivery ratio, and throughput in comparison with the existing solutions.High radio frequency (RF) coverage, high altitude, high throughput, heavy payload, less operating time, ease of use, and low cost are the basic requirements of UAV-CONOPS. Chemical, Biological, Radiological, and Nuclear Reconnaissance (CBRN)-CONOPS whose major purpose is the containment of hazards are also turning towards UAV networks. Cellular network-based air-to-ground links and unmanned aircraft system (UAS)-backbone systems, communication aware sensor distribution, etc are some of the promising aspects of the UAV systems. 2,3 The crucial step towards UAV networks is the development of sustainable multi-UAV environments. National regulations, routing, path planning, quality of service (QoS), integration with Global Information Grid (GIG), mobility control, coordination, and standardization are the steps required for efficient migration towards flying networks (multi-UAV). 4,5 The complexity of the multi-UAV ad hoc networks makes data dissemination also a challenging prospect. Latency, delay, antenna type, timing, etc come into play when we are talking about a network that is so fast moving and dynamic. 6 Multi-UAVs collaborate to achieve common objectives. It is evident that the collaborative UAV and the ground networks together can perform complex tasks. Some of these tasks...

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“…[59]. A cyberphysical sensing framework, using partially ordered knowledge for distributed knowledge management in drone swarms, was proposed to efficiently process sensed data in the middleware.…”

Section: Drone and Uavmentioning

confidence: 99%

Survey of wireless big data

Qian

Zhu

Zhang

2017

J. Commun. Inf. Netw.

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Wireless big data describes a wide range of massive data that is generated, collected and stored in wireless networks by wireless devices and users. While these data share some common properties with traditional big data, they have their own unique characteristics and provide numerous advantages for academic research and practical applications. This article reviews the recent advances and trends in the field of wireless big data. Due to space constraints, this survey is not intended to cover all aspects in this field, but to focus on the data aided transmission, data driven network optimization and novel applications. It is expected that the survey will help the readers to understand this exciting and emerging research field better. Moreover, open issues and promising future directions are also identified.

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ADDSEN: Adaptive Data Processing and Dissemination for Drone Swarms in Urban Sensing

Cited by 72 publications

References 31 publications

Multi-UAV Interference Coordination via Joint Trajectory and Power Control

Multi-UAV Interference Coordination via Joint Trajectory and Power Control

Efficient data management and control over WSNs using SDN‐enabled aerial networks

Survey of wireless big data

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