UAV-aided Wireless Sensor Networks Research Articles

Detection and Blind Channel Estimation for UAV-Aided Wireless Sensor Networks in Smart Cities Under Mobile Jamming Attack

Unmanned aerial vehicles (UAVs) can be integrated into wireless sensor networks (WSNs) for smart city applications in several ways. Among them, a UAV can be employed as a relay in a "store-carry and forward" fashion by uploading data from ground sensors and metering devices and, then, downloading it to a central unit. However, both the uploading and downloading phases can be prone to potential threats and attacks. As a legacy from traditional wireless networks, the jamming attack is still one of the major and serious threats to UAV-aided communications, especially when also the jammer is mobile, e.g., it is mounted on a UAV or inside a terrestrial vehicle. In this paper, we investigate anti-jamming communications for UAV-aided WSNs operating over doubly-selective channels in the downloading phase. In such a scenario, the signals transmitted by the UAV and the malicious mobile jammer undergo both time dispersion due to multipath propagation effects and frequency dispersion caused by their mobility. To suppress high-power jamming signals, we propose a blind physical-layer technique that jointly detects the UAV and jammer symbols through serial disturbance cancellation based on symbol-level post-sorting of the detector output. Amplitudes, phases, time delays, and Doppler shifts - required to implement the proposed detection strategy - are blindly estimated from data through the use of algorithms that exploit the almost-cyclostationarity properties of the received signal and the detailed structure of multicarrier modulation format. Simulation results corroborate the anti-jamming capabilities of the proposed method, for different mobility scenarios of the jammer.

Residual energy-based clustering in UAV-aided wireless sensor networks for surveillance and monitoring applications

Aim: Unmanned aerial vehicle (UAV)-aided wireless sensor networks (WSNs) are effectively used for surveillance, monitoring, and rescue applications in military and commercial domains. In UAV-aided WSNs (UWSNs), efficient data gathered from sensor nodes are desired to enhance network performance. However, communication between UAV and sensor nodes is challenging due to the high mobility of the UAV and a large number of sensor nodes. Clustering in UWSNs limits the number of sensor nodes communicating with the UAV, i.e., only the cluster head in a cluster can transmit the sensed data to the UAV, which reduces collision probability. Methods: In this paper, we propose a residual energy-based clustering algorithm for sensor-to-UAV communication in UWSNs. The cluster size and the number of sensor nodes in a cluster are determined on the basis of the residual energy of the sensor nodes. The performance of the proposed algorithm is evaluated by using the MATLAB simulator and then compared with that of the conventional clustering algorithm. Results: According to our extensive simulation results, the proposed clustering scheme significantly outperforms the conventional one in terms of network lifetime and data delivery ratio. Conclusion: Hence, through our studies and simulations, it can be assured that the network lifetime of UWSNs can be prolonged and the throughput of the network can also be elevated by controlling the early death of sensor nodes due to the uneven energy consumptions. We will come up with further analysis and validation of our work in the future.

A Collaborative Learning-Based Algorithm for Task Offloading in UAV-Aided Wireless Sensor Networks

Abstract Recently, unmanned aerial vehicles (UAVs) have emerged to enhance data processing, network monitoring, disaster management and other useful applications in many different networks. Due to their flexibility, cost efficiency and powerful capabilities, combining these UAVs with the existing wireless sensor networks (WSNs) could improve network performance and enhance the network lifetime in such networks. In this research, we propose a task offloading mechanism in UAV-aided WSN by implementing a utility-based learning collaborative algorithm that will enhance the service satisfaction rate, taking into account the delay requirements of the submitted tasks. The proposed learning algorithm predicts the queuing delays of all UAVs instead of having a global overview of the system, which reduces the communication overhead in the network. The simulation results showed the effectiveness of our proposed work in terms of service satisfaction ratio compared with the non-collaborative algorithm that only processes the task locally in the WSN cluster.

BEE-DRONES: Ultra low-power monitoring systems based on unmanned aerial vehicles and wake-up radio ground sensors

Nowadays, Unmanned Aerial Vehicles (UAVs) represent a significant aid on scenarios where fixed, ground infrastructures are temporarily or permanently not available; this is the case of large-scale applications of the Internet of Things (IoTs), e.g. smart city and agriculture 3.0, where the UAVs can be employed as mobile data mules and gather the data from Wireless Ground Sensors (WGSs). UAV-aided wireless sensor networks (WSNs) introduce considerable advantages both in terms of performance and costs since they avoid the need of error-prone multi-hop communications, and also the installation of static gateways; at the same time, they pose formidable research challenges for their implementation, like the synchronization issue between the UAV and the WGS and the path planning, which should take into account the extremely limited flight autonomy of the UAVs. In this paper, we address both the issues above by proposing BEE-DRONES, a novel framework for large-scale, ultra low-power UAV-aided WSNs. In order to mitigate the synchronization problem, we investigate the utilization of passive Wake-up Radio (WR) technology on the WGSs, and of wireless power transfer from the UAVs: by harvesting the energy from the UAV hovering over it, the WGS is activated only for the short time required to transfer the data toward the mobile sink, while it experiences zero-consumption in sleep mode. We investigate the performance of passive WR-based WGS through real measurements, under different WGS-UAV distances and antenna orientations. Then, based on such results, we formulate the joint WGS scheduling and UAV path planning problem, where the goal is to determine the optimal trajectory of the UAVs activating the WR-based WGSs while taking into account the Value of the Sensing (VoS) as well as the total lifetime of the WSN. The original problem is transformed into a multi-commodity flow problem, and both centralized and distributed heuristics over the multi-graph are proposed. Finally, we evaluate the proposed algorithms through extensive OMNeT++ simulations; the results demonstrate the gain of BEE-DRONES in terms of extended lifetime compared to traditional, non WR-based solutions (e.g. duty-cycle), and in terms of reduced data-correlation compared to non VoS-aware path planning solutions.

Routing Protocols for UAV-Aided Wireless Sensor Networks

Recently, unmanned aerial vehicles (UAVs) attracted significant popularity in both military and civilian domains for various applications and services. Moreover, UAV-aided wireless sensor networks (UAWSNs) became one of the interesting hot research topics. This is mainly because UAWSNs can significantly increase the network coverage and energy utilization compared to traditional wireless sensor networks (WSNs). However, the high mobility, dynamic path, and variable altitude of UAVs can cause not only unforeseen changes in the network topology but also connectivity and coverage problems, which can affect the routing performance of the network. Therefore, the design of a routing protocol for UAWSNs is a critical task. In this paper, the routing protocols for UAWSNs are extensively investigated and discussed. Firstly, we classify the existing routing protocols based on different network criteria. They are extensively reviewed and compared with each other in terms of advantages and limitation, routing metrics and policies, characteristics, difference performance factors, and different performance optimization factors. Furthermore, open research issues and challenges are summarized and discussed.

Energy-Efficient and Fast MAC Protocol in UAV-Aided Wireless Sensor Networks for Time-Critical Applications.

Unmanned aerial vehicle (UAV)-aided wireless sensor networks (UWSNs) can be effectively used for time-critical sensing applications. UAVs can be used to collect the sensed data from sensors and transfer them to a base station. The real-time transfer of data is highly desired in the time-critical applications. However, the medium access control (MAC) protocols designed for UWSNs so far are primarily focused on the efficient use of UAVs to collect data in the sensing areas. In this paper, we propose an energy-efficient and fast MAC (EF-MAC) protocol in UWSNs for time-critical sensing applications. EF-MAC adopts carrier sense multiple access (CSMA) for the registration of sensor nodes with a UAV and time division multiple access (TDMA) with variable slot time for the transmission of collected data. The UAV is equipped with two transceivers to minimize both energy consumption and delay in air-to-ground communication. The energy consumption and delay are formally analyzed and the performance of EF-MAC is evaluated via extensive simulation. The simulation results show that the proposed EF-MAC outperforms the conventional MAC protocols in terms of energy efficiency and communication delay.

An unmanned aerial vehicle-aided node localization using an efficient multilayer perceptron neural network in wireless sensor networks

Localization of sensor node is decisive for many localization-based scenarios of wireless sensor networks (WSNs). Node localization using fixed terrestrial anchor nodes (ANs) equipped with global positioning system (GPS) modules suffers from high deployment cost and poor localization accuracy, because the terrestrial AN propagates signals to the unknown nodes (UNs) through unreliable ground-to-ground channel. However, the ANs deployed in unmanned aerial vehicles (UAVs) with a single GPS module communicate over reliable air-to-ground channel, where almost clear line-of-sight path exists. Thus, the localization accuracy and deployment cost are better with aerial anchors than terrestrial anchors. However, still the nonlinear distortions imposed in propagation channel limit the performance of classical RSSI and least square localization schemes. So, the neural network (NN) models can become good alternative for node localization under such nonlinear conditions as they can do complex nonlinear mapping between input and output. Since the multilayer perceptron (MLP) is a robust tool in the assembly of NNs, MLP-based localization scheme is proposed for UN localization in UAV-aided WSNs. The detailed simulation analysis provided in this paper prefers the MLP localization scheme as they exhibit improved localization accuracy and deployment cost.

Energy-Effective Data Gathering for UAV-Aided Wireless Sensor Networks.

Unmanned aerial vehicles (UAVs) are capable of serving as a data collector for wireless sensor networks (WSNs). In this paper, we investigate an energy-effective data gathering approach in UAV-aided WSNs, where each sensor node (SN) dynamically chooses the transmission modes, i.e., (1) waiting, (2) conventional sink node transmission, (3) uploading to UAV, to transmit sensory data within a given time. By jointly considering the SN’s transmission policy and UAV trajectory optimization, we aim to minimize the transmission energy consumption of the SNs and ensure all sensory data completed collected within the given time. We take a two-step iterative approach and decouple the SN’s transmission design and UAV trajectory optimization process. First, we design the optimal SNs transmission mode policy with preplanned UAV trajectory. A dynamic programming (DP) algorithm is proposed to obtain the optimal transmission policy. Then, with the fixed transmission policy, we optimize the UAV’s trajectory from the preplanned trace with recursive random search (RRS) algorithm. Numerical results show that the proposed scheme achieves significant energy savings gain over the benchmark schemes.

Medium Access Control Protocols for Unmanned Aerial Vehicle-Aided Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) have been broadly used in various areas of commercial applications as well as public services. Furthermore, emerging unmanned aerial vehicles (UAVs) have been combined into many existing technologies. By employing UAVs in WSNs, sensor data can be effectively collected and the network lifetime is prolonged with reduced energy consumption. In the UAV-aided WSNs (UWSNs), medium access control (MAC) is very important because it affects not only the system performance but also the energy efficiency in battery-powered sensor nodes. It should be noted that the tiny sensor nodes are typically disposable and their batteries are not replaceable in most applications. Thus, a number of MAC protocols with different objectives have been designed for UWSNs. To the best of the authors’ knowledge, there is no survey on MAC protocols for UWSNs in the literature. In this paper, the various MAC protocols for UWSNs are extensively investigated and qualitatively compared with each other in terms of major features, characteristics, and pros and cons. Moreover, open issues and challenges in the design of the MAC protocols for the UWSNs are discussed.

Cooperative Path Selection Framework for Effective Data Gathering in UAV-Aided Wireless Sensor Networks

Cooperative Path Selection Framework for Effective Data Gathering in UAV-Aided Wireless Sensor Networks