WSN-UAV Monitoring System with Collaborative Beamforming and ADS-B Based Multilateration

Abstract

Abstract-This paper presents wireless sensor networkunmanned aerial vehicle (WSN-UAV) system for military remote monitoring and surveillance. Large scale WSN is deployed in a battlefield or wide hostile region to collect information of interest and send it to a UAV. Collaborative beamforming (CB) is used to achieve the ground-to-air transmissions. An automatic dependent surveillance-broadcast (ADS-B) based multilateration is used to obtain the UAV location and tracking information. It is found that a minimum distance between the UAV and the WSN is required for proper operation of the CB due to the precision of the multilateration and the movement of the UAV.Index Terms-Monitoring and surveillance, Geolocation, ADS-B based multilateration, Large scale wireless sensor networks, Collaborative beamforming. I. INTRODUCTIONWireless sensor networks (WSNs) is an attractive tool for monitoring and surveillance applications due to its flexible deployment and ability of unattended operation [1], [2]. Sensor nodes are deployed in an ad-hoc fashion over large and remote area to collect information from the surrounding neighbourhood and send it to a fusion center (FC) for processing and decision making. In order to have reasonable cost, sensor nodes are fabricated with off-the-shelf microcontrollers and, consequently, have limited processing capability. Moreover, energy is typically supplied in sensor nodes from batteries or energy harvesting devices [3].Sensor nodes are deployed close to the event or phenomena under surveillance which is typically far from the FC. Data gathering can be implemented using unmanned aerial vehicle (UAV) as a FC. The UAV flies over the sensing field while sensor nodes, which are deployed on the ground, transmit the collected data directly to the UAV. UAVs have the advantage to explore vast geographical region.In this paper, we focus on military tactical monitoring and surveillance to remotely monitor the battlefield or wide hostile regions [4]. In such scenarios, a UAV is utilized as the FC which is expected to fly at high altitudes. The distances between the sensor nodes and the UAV can be larger than the transmission range of individual sensor node. In this case, collaborative beamforming (CB) [5], [6] is utilized for ground-to-air transmission in the proposed system to increase the transmission range and achieve directional gain. In order to implement CB, the location and tracking information of the UAV should be available at the sensor nodes. Typically, global positioning system (GPS) can be used to obtain the UAV location information and broadcast it to the sensor nodes. However, we are interested in military and tactical applications where there is a threat of GPS jamming, spoofing and message infringement type attacks. In this case, other navigation and guidance subsystems can be used to obtain this information [4]. Recently, automatic dependent surveillancebroadcast (ADS-B) has been introduced to support avionics navigation. The ADS-B signal can carry location information relative to loca...

A Game of Drones: Cyber-Physical Security of Time-Critical UAV Applications with Cumulative Prospect Theory Perceptions and Valuations

“…1 composed of N = 10 nodes and E = 18 edges. We label the 18 paths, from 1 to 18, as follows: [1, 2, ..., 18] [ (2,5,7), (2,5,8), (2,5,9), (2,6,7), (2,6,8), (2,6,9), (3,5,7), (3,5,8), (3,5,9), (3,6,7), (3,6,8), (3,6,9), (4,5,7), (4,5,8), (4,5,9), (4,6,7), (4,6,8), (4,…”

“…3 shows that path 8, i.e. (3,5,8), is the shortest path followed by paths 11, i.e. (3,6,8), and path 9, (3,5,9); while node 8 is the most risky node followed by nodes 5 and 3, respectively.…”

“…In case the UAV is successfully 4 Here, our model can also accommodate future scenarios in which the UAV's flight may be regulated and, as a result, restricted to a prescribed set of paths. 5 Cycles are naturally dismissed by a UAV operator aiming at minimizing delivery time. 6 An O-to-D path h ∈ H is represented by its sequence of nodes connecting O to D. Hence, we use the notation n ∈ h to represent a node n that is in h.…”

Sidelobe Reductions of Antenna Arrays via an Improved Chicken Swarm Optimization Approach

“…Moreover, the collaborative beamforming (CB) technology, which is constructed with the virtual node random antenna array (RAA), is successfully introduced in distributed wireless networks [10]- [12], for extending the communication distance of a node with limited hardware resources [13]- [15]. Furthermore, CB can be also regarded as a promising approach for improving the performance of the device to device (D2D) communication [16], Internet of Things (IoT) [17]- [19], and unmanned aerial vehicle (UAV) networks [20].…”

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“…Fortunately, the explorations of unmanned aerial vehicles (UAVs) as flying wireless communication networks at various altitudes have recently made significant progress to achieve dynamic and adaptive coverage [ 4 , 5 ]. Because UAVs are flexible, portable, inexpensive, and convenient [ 6 ], they have been widely applied for various purposes [ 7 ], i.e., traffic monitoring, forest fire detection emergency event supervision [ 6 , 8 ], data ferrying [ 9 ], communication enhancement [ 10 ], data collection [ 11 ], 3D city map construction [ 12 , 13 ], and wireless connectivity provisioning (e.g., projects Loon by Google and Internet.org by Facebook) [ 14 ]. According to the Federal Aviation Administration, sales of UAVs for commercial purposes are expected to grow from 600,000 in 2016 to 2.7 million by 2020 [ 15 ].…”