UAV-Assisted Low-Consumption Time Synchronization Utilizing Cross-Technology Communication.

Wireless sensor networks (WSNs) are an important means of collecting data in a variety of situations, such as the monitoring of large or hazardous areas. The retrieval of WSN data can yield better results through the use of unmanned aerial vehicles (UAVs), for example, concerning the increase in the amount of data collected and the decrease in the time between the collection and use of the data. In particular, disaster areas may be left without communication resources and with high residual risk to humans, at which point a WSN can be quickly launched by air to collect relevant data until other measures can be established. The set of rules of each problem’s component (e.g., number of UAVs, UAVs dislocation control, sensors, communication) is considered the approaches to solve the problem. In this meaning, some studies present approaches for the use of UAVs for the collection of WSN data, focusing primarily on optimizing the path to be covered by a single UAV and relying on long-range communication that is always available; these studies do not explore the possibility of using several UAVs or the limitations on the range of communication. This work describes DADCA, a distributed scalable approach capable of coordinating groups of UAVs in WSN data collection with restricted communication range and without the use of optimization techniques. The results reveal that the amount of data collected by DADCA is similar or superior to path optimization approaches by up to 1%. In our proposed approach, the delay in receiving sensor messages is up to 46% shorter than in other approaches, and the required processing onboard UAVs can reach less than 75% of those using optimization-based algorithms. The results indicate that the DADCA can match and even surpass other presented approaches, since the path optimization is not a focus, while also incorporating the advantages of a distributed approach.

This paper analyzes the synchronization of a bistatic synthetic aperture radar (SAR) system based upon a geostationary transmitter and small receive-only SAR systems mounted on airplanes or unmanned aerial vehicles (UAVs), namely GEO Spaceborne-Airborne Bistatic SAR. This paper will analyze and present our solution of the space synchronization, time synchronization, frequency synchronization and phase synchronization for the GEO Spaceborne-Airborne bistatic SAR respectively. It will first introduce the GEO Spaceborne-Airborne bistatic SAR system and geometry model briefly. Then possible solutions of the system synchronization will be discussed. It will introduce the synchronization scheme based on the direct path between the transmitter and receiver. It uses synchronization modules to realize the time and frequency synchronization. A mathematical model considering the time and frequency synchronization error is given and a phase error analysis is performed. Range and azimuth compressions of a single target are implemented by simulations.

Time Synchronization is one of the problems and still ignored problem in area of wireless sensor network (WSN). After reviewing the existing literatures, it is found that there are few studies that combinely address the problem of energy conservation, clustering, routing along with minimizing the errors due to time synchronization in sensor network. Therefore, this manuscript presents a delay-based routing which considers the propagation delay to formulate a mechanism for delay compensation in large scale wireless sensor network. The prime goal of this technique is to jointly address energy problems, time synchronization, and routing in wireless sensor network. The outcome of the proposed study was found to posse’s minimized communication overhead, minimized synchronization errors, lower energy consumption, and reduced processing time when compared with the existing standards of time synchronization techniques.

Wireless sensor networks (WSNs) are quickly becoming an essential part of today's modern infrastructure, and it is becoming crucial to consider time synchronisation problems in WSNs. Furthermore, one of the main design issues in WSN time synchronisation is the efficient use of energy at each sensor node to prolong the lifespan of the network. This paper describes a new energy-efficient time protocol based on the spanning tree of the network model graph in a WSN. Three major cases are discussed: 1) time synchronisation using the energy-efficient spanning tree (EST) protocol in a WSN with approximately 300 sensors; 2) an improved EST protocol in large-scale WSNs; 3) an improved EST protocol under weighted graph. Additionally, a series of practical algorithms are proposed in this paper, where both energy and time efficient can be clearly shown through a number of simulated examples along with their comparative results.

OFDM based systems are extremely sensitive to time and frequency synchronization errors. This paper addresses the issue of time synchronization. In this paper we are presenting a simple and novel technique for time synchronization. The time synchronization error causes Inter Symbol Interference and hence must be corrected at receiver. In many wireless communication systems, the data packets or frame start with the preamble called training symbol followed by data symbols. The training symbol consists of some specific pattern. The similar frame structure is used for this new algorithm which consists of training symbol followed by OFDM data symbols. This novel technique uses new pattern for training symbol which consists of all identical samples. After applying our new algorithm, a peak is obtained at the end of training symbol. This is used to mark beginning of frame and estimation of timing error. Estimation error graph and BER graph are presented for 4QAM and 16 QAM in this paper. The results are shown for AWGN channel. The technique used is simple and results are encouraging and can be used in any of the OFDM based system.

The rapid development of the agricultural Internet of Things (IoT) is inseparable from the support of wireless sensor networks (WSNs) in recent years. To further facilitate the adaptation of WSNs to agricultural applications, reducing the energy consumption of sensor nodes in agricultural environments has become a crucial problem. Sleep scheduling in randomly deployed WSNs is an effective method to reduce the energy consumption of sensor nodes, which can extend the network lifetime while ensuring network coverage. However, most of the existing sleep scheduling algorithms require frequent information exchange (such as broadcasting to find neighboring nodes and collecting nodes’ residual energy), which will inevitably lead to massive energy consumption. To address this problem, in this article, an unmanned aerial vehicle (UAV)-assisted sleep scheduling algorithm (UAVSS) is proposed, which avoids excessive information exchange among nodes and ensures sufficient network coverage with the least number of sensor nodes. The UAV traverses all the sensor nodes along the shortest path to help information exchange and gather sensing data. Furthermore, the UAV path is planned by using the latest monarch butterfly optimization (MBO) algorithm. Simulation results indicate that the proposed UAVSS can prolong the network lifetime while ensuring the required area coverage.

Structural damage detection based on wireless sensor networks can be affected significantlyby time synchronization errors among sensors. Precise time synchronization of sensor nodeshas been viewed as crucial for addressing this issue. However, precise time synchronizationover a long period of time is often impractical in large wireless sensor networks due to twoinherent challenges. First, time synchronization needs to be performed periodically,requiring frequent wireless communication among sensors at significant energy cost.Second, significant time synchronization errors may result from node failures which arelikely to occur during long-term deployment over civil infrastructures. In this paper, adamage detection approach is proposed that is robust against time synchronizationerrors in wireless sensor networks. The paper first examines the ways in whichtime synchronization errors distort identified mode shapes, and then proposes astrategy for reducing distortion in the identified mode shapes. Modified values forthese identified mode shapes are then used in conjunction with flexibility-baseddamage detection methods to localize damage. This alternative approach relaxesthe need for frequent sensor synchronization and can tolerate significant timesynchronization errors caused by node failures. The proposed approach is successfullydemonstrated through numerical simulations and experimental tests in a lab.

To satisfy the increasing demand of wireless sensor network (WSN) distribution in the different monitoring area and enlarge the coverage of the WSNs in various scenes of application, in this paper, we constructed a new type of mobile wireless data sinking platform for collecting the data of WSN based on the technology of unmanned aerial vehicle (UAV). First, the authors studied a wireless environmental monitoring system containing the UAV platform and WSN is constructed and both test on wireless communication capacity and data collecting are carried out. The communication capacity test show that when the RF modules are operated with transmission power greater than 1 dBm and communication distance less than 100 m, the UAV wireless sink node can maintain a high quality communication data link which has a lower PLR (less than the 5 %) and better signal level (RSSI greater than −90 dBm) with transmission data rate of 250 kbps, using this RF transceiver a simple communication & data collection protocol between UAV and ground WSN sink node was proposed. The whole work of this paper should provide some experience for building a UAV cooperative WSN system in the future.

Global navigation satellite systems (GNSS) are often integrated with inertial measurement units (IMU) for navigation in urban environments. The ability of these integrated systems to achieve precise positioning and navigation is highly dependent on accurate time synchronization. While the current time synchronization algorithms are capable of achieving millisecond-level synchronization in open environments, their performance deteriorates significantly in complex urban environments due to the impact on GNSS measurements of non-line-of-sight (NLOS) signals and multipath effects. Here we propose a loosely coupled GNSS/IMU integration time synchronization error modeling and compensation algorithm to tackle this problem in the context of urban vehicle navigation. In the algorithm, the time synchronization error is augmented as a state variable in the Robust Extended Kalman Filter (REKF), with the robustness factor threshold being dynamically adjusted based on the time synchronization error. Furthermore, we have designed a system time synchronization detection scheme based on two detection factors, with the aim of achieving high-precision GNSS/IMU time synchronization in complex urban environments. Experimental results show that the proposed algorithm synchronizes data time to the millisecond level in urban positioning environments. This represents an improvement of more than 90% in time synchronization precision compared to the traditional EKF-based time synchronization algorithm, and an improvement of more than 60% compared to the REKF-based time synchronization algorithm. Furthermore, compared to the EKF-based GNSS/IMU fusion algorithm, the proposed algorithm enhances the accuracy of the determination of both position and velocity in the horizontal and 3D directions by more than 50% and heading accuracy by 85%.

With the development of Internet of Things (IoT) technology, wireless sensor networks (WSNs) composed of multiple sensor nodes (SNs) have been able to provide a large number of high-quality services, such as continuous environmental monitoring, automatic control, and transmission of data for intelligent decision-making. SNs in WSNs are typically energy limited and often exist in large numbers, which are widely distributed in hard-to-reach areas. Hence, it is difficult to collect data from SNs without infrastructure support such as base stations (BSs). The low cost and high maneuverability of unmanned aerial vehicles (UAVs) provide an efficient solution for data acquisition in WSNs but with the challenge of control system of the UAV. In this paper, we proposed a deep reinforcement learning based UAV control system to intelligently solve the data harvesting problem in WSNs. Our optimization target is to jointly optimize the trajectory of the UAV and the bandwidth resources of the airborne base station to maximize the data acquisition success rate when the UAV is limited in energy. The objective function and constraints of this problem are highly non-convex. To solve this problem, we first modeled the movement of the UAV and the bandwidth allocation of the airborne base station in each time slot as a Markov decision process (MDP). Then we designed a deep reinforcement learning architecture to solve the above MDP. Compared with the existing data harvesting algorithms, the proposed method has a great improvement in data acquisition success rate.

Wireless sensor networks (WSNs) are used to collect large amounts of data over a wide area. However, the sensor nodes used in WSNs have a short lifespan because they are generally battery-operated. Research is actively underway to increase the lifespan of WSNs via techniques such as energy harvesting and wireless power transfer (WPT). This study proposes a scheme for clustering and adjusting the WSN data collection rate to alleviate the hotspot problem and improve network connectivity. The proposed scheme is targeted at WSNs in which the sensor nodes harvest solar energy. In addition, an unmanned aerial vehicle (UAV) traverses along a given path and delivers energy to sensor nodes using WPT, while collecting data as a sink node. In this scheme, the number of cluster heads is determined by considering the number of nodes at each hop distance and the maximum amount of data that can be transferred with the available energy. Further, sensor nodes limit the amount of data they collect to reduce the relay load on intermediate nodes. To achieve this, nodes consider the amount of data transferred by parent nodes, especially nodes in the hotspot, and the number of child nodes. The sink node traverses along a given path, and collects the data accumulated in the cluster head while supplying its remaining energy to the cluster head. The amount of energy it transfers is determined by considering the number of cluster heads. Consequently, nodes in the hotspot are prevented from becoming blacked out from a lack of energy, and the energy harvesting efficiency is increased. Simulation results indicate that the proposed scheme reduces the number of blackout nodes in the hotspot areas near cluster heads. Furthermore, the data collection and monitoring performance increases with the increase in network connectivity.

Unmanned Aerial Vehicles (UAVs) can collaborate to collect and relay data for ground sensors in remote and hostile areas. In multi-UAV-assisted wireless sensor networks (MA-WSN), the UAVs' movements impact on channel condition and can fail data transmission, this situation along with newly arrived data give rise to buffer overflows at the ground sensors. Thus, scheduling data transmission is of utmost importance in MA-WSN to reduce data packet losses resulting from buffer overflows and channel fading. In this paper, we investigate the optimal ground sensor selection at the UAVs to minimize data packet losses. The optimization problem is formulated as a multi-agent Markov decision process, where network states consist of battery levels and data buffer lengths of the ground sensor, channel conditions, and waypoints of the UAV along the trajectory. In practice, an MA-WSN contains a large number of network states, while the up-to-date knowledge of the network states and other UAVs' sensor selection decisions is not available at each agent. We propose a Multi-UAV Deep Reinforcement Learning based Scheduling Algorithm (MUAIS) to minimize the data packet loss, where the UAVs learn the underlying patterns of the data and energy arrivals at all the ground sensors. Numerical results show that the proposed MUAIS achieves at least 46 % and 35% lower packet loss than an optimal solution with single-UAV and an existing non-learning greedy algorithm, respectively.

Time synchronization and node localization over a wireless sensor network (WSN) is a vital problem in applications such as Internet of Things (IoT) and context-aware pervasive systems. Addressing this problem leads to an efficient data fusion, power scheduling, bandwidth utilization, geographic routing, time-based channel sharing, and clustering among sensor nodes. In a WSN, sensor nodes utilize multi-hop data transmission model towards the data transfer. Utilization of large number of hops for the data transfer leads to poor time synchronization resulting in erroneous node location estimates. In this letter, we make use of a recent development in social networks termed as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">small world characteristics</i> and propose a novel method of time synchronized node localization over a small world WSN (SW-WSN). The results are obtained via simulations over a medium scale WSN. The performance of the proposed method is evaluated by conducting detailed analysis of time synchronization and node localization errors over the SW-WSN. The results obtained illustrate that the proposed small world model yields improved localization results when compared to results obtained over a regular WSN.

The selection of wireless sensor networks (WSNs) communication topology and the application of unmanned aerial vehicles (UAV) in data collection are studied in the scenario of being far away from the base station. The topology consists of a set of cluster head nodes that communicate with the UAV. After considering the network energy consumption factors, the optimal number of cluster heads of the network is derived, the network is clustered and the cluster head node is selected. Finally, the cruising path of the drone to collect data from the cluster head is optimized. Simulation results show that the proposed algorithm can effectively save the energy of nodes, prolong network life, and reduce the total distance of the UAV collects data.

Human-centric autonomous systems, based at the convergence of wireless sensor networks (WSN) and unmanned aerial vehicles (UAV), mark a transformative epoch in the amalgamation of technology. The widespread adoption of UAVs is attributable to their multifaceted applications and services. Within this realm, the utilization of UAVs to augment WSNs, defined as UAV-aided wireless sensor networks (UAWSN), has emerged as a captivating focus of research. The paramount rationale lies in UAWSN's potential to significantly enhance coverage and mitigate energy consumption when compared to traditional WSNs. However, the substantial mobility, variable altitude, and dynamic trajectory of UAVs may induce unforeseen changes in network topology, giving rise to connectivity and coverage challenges that can adversely impact the routing performance of networks. In the context of WSNs, the imperative to minimize energy consumption during data transmission by sensors is a central concern. A promising solution involves leveraging UAVs to collect data from sensors distributed across the network. This paper formulates an optimization problem aimed at maximizing the residual energy of sensors post data transmission, while accounting for the limitations imposed by the UAV's travel distance. The proposed solution involves the derivation of an optimized solution through the adaptation of a Voronoi diagram to explore a set of UAV hovering locations, with a preference for Voronoi vertices for data acquisition from adjacent sensors. Capitalizing on the benefits afforded by Voronoi diagrams, the authors delineate a UAV route by iteratively adjusting each UAV's hovering location based on the energy status of the sensors.