Coverage Control

Metal−organic overlayer structures formed by 1,4phenylene-diisocyanide (PDI) and Au adatoms on Au(111) in UHV, their stability in air, and the tip-induced Au nanoparticle formation on PDI−Au(111) surfaces in air were investigated using scanning tunneling microscopy (STM) and vibrational spectroscopy. This study reveals that the distribution of Au nanoparticles created during tip-induced release of Au atoms from molecule-Au adatom complexes shows strong dependence on the PDI coverage. Ordered Au nanoparticle arrays form in the medium-coverage regime, while more disordered distributions are observed at low and saturation coverages. The different distributions of Au nanoparticles are a direct consequence of the coverage-dependent assembly of (PDI−Au) n chains, their different stability in air, and a templating effect of the Au(111) surface, which is most pronounced for medium coverage, where phases of densely packed (PDI−Au) n chains and disordered PDI−Au assemblies are confined, respectively, to the fcc and hcp regions of the (22 × √3) surface reconstruction of Au(111). The Au nanoparticles nucleate preferentially in the disordered or defective regions of the PDI−Au precursor overlayer, and their formation requires ambient air and high negative tip-bias, suggesting an electrochemical initiation of Au release from the molecule−Au adatom complexes.

In this paper, the efficient 3D placement of UAV as an aerial base station in providing wireless coverage for users in a small and large coverage area is investigated. In the case of providing wireless coverage for outdoor and indoor users in a small area, the Particle Swarm Optimization (PSO) and K-means with Ternary Search (KTS) algorithms are invoked to find an efficient 3D location of a single UAV with the objective of minimizing its required transmit power. It was observed that a single UAV at the 3D location found using the PSO algorithm requires less transmit power, by a factor of 1/5 compared to that when using the KTS algorithm. In the case of providing wireless coverage for users in three different shapes of a large coverage area, namely square, rectangle and circular regions, the problems of finding an efficient placement of multiple UAVs equipped with a directional antenna are formulated with the objective to maximize the coverage area and coverage density using the Circ...

This paper presents a novel sensor-based online coverage path-planning algorithm that guarantees the complete coverage of an unknown rectilinear workspace for the task of a mobile robot. The proposed algorithm divides the workspace of the robot into cells at each scan sample. This division can be classified as an exact cell decomposition method, which incrementally constructs cell decomposition while the robot covers an unknown workspace. To guarantee complete coverage, a closed map representation based on a feature extraction that consists of a set of line segments called critical edges is proposed. In this algorithm, cell boundaries are formed by extended critical edges, which are the sensed partial contours of walls and objects in the workspace. The robot uses a laser scanner to sense the critical edges. Sensor measurement is sampled twice in each cell. Scan matching is performed to merge map information between the reference scan and the current scan. At each scan sample, a two-direction oriented rectilinear decomposition is achieved in the workspace and presented by a closed map representation. The construction order of the cells is very important in this incremental cell decomposition algorithm. To choose the next target cell from candidate cells, the robot checks for redundancy in the planned path and for possible positions of the ending points of the current cell. The key point of the algorithm is memorizing the covered space to define the next target cell from possible cells. The path generation within the defined cell is determined to minimize the number of turns, which is the main factor in saving time during the coverage. Therefore, the cell's long boundary should be chosen as the main path of the robot. This algorithm is verified by an experiment under the LABVIEW environment.

The rising number of technological advanced devices making network coverage planning very challenging tasks for network operators. The transmission quality between the transmitter and the end users has to be optimum for the best performance out of any device. Besides, the presence of coverage hole is also an ongoing issue for operators which cannot be ignored throughout the whole operational stage. Any coverage hole in network operators' coverage region will hamper the communication applications and degrade the reputation of the operator's services. Presently, there are techniques to detect coverage holes such as drive test or minimization of drive test. However, these approaches have many limitations. The extreme costs, outdated information about the radio environment and high time consumption do not allow to meet the requirement competently. To overcome these problems, we take advantage of Unmanned aerial vehicle (UAV) and Q-learning to autonomously detect coverage hole in a given area and then deploy UAV based base station (UAV-BS) by considering wireless backhaul with the core network and users demand. This machine learning mechanism will help the UAV to eliminate human-in-the-loop (HiTL) model. Later, we formulate an optimisation problem for 3D UAV-BS placement at various angular positions to maximise the number of users associated with the UAV-BS. In summary, we have illustrated a cost-effective as well as time saving approach of detecting coverage hole and providing on-demand coverage in this article.

This paper considers the problem of efficient network design for data collection from various sensors in smart environments using flying base stations, which are realized using unmanned aerial vehicles (UAVs), or drones. The system efficiency is enhanced by maximizing the number of served sensors using the minimum number of UAVs while satisfying particular network constraints. Towards this end, a joint optimization problem is formulated for UAVs placement and sensors assignment in smart environments with massive sensor deployment. Due to the complexity of the optimal solution, a probabilistic learning approach is utilized to find a near-optimal solution. Further, a non-death penalty constraint handling approach is used to deal with difficult and conflicting constraints. Monte Carlo simulation is performed to evaluate the performance of the proposed algorithm in various scenarios, and compare it with the optimal solution to validate the efficiency of the proposed solution. The presented numerical results show that the solutions obtained using the proposed algorithm are generally close or equal to the optimal solution for several scenarios, but with significant complexity reduction, which confirms the efficiency of the proposed algorithm. Moreover, the proposed solution shows significant performance improvement when compared with an efficient greedy algorithm. INDEX TERMS Resource management, flying base stations (UAVs), probabilistic learning, constraints handling.

This paper presents a novel sensor-based online coverage path-planning algorithm that guarantees the complete coverage of an unknown rectilinear workspace for the task of a mobile robot. The proposed algorithm divides the workspace of the robot into cells at each scan sample. This division can be classified as an exact cell decomposition method, which incrementally constructs cell decomposition while the robot covers an unknown workspace. To guarantee complete coverage, a closed map representation based on a feature extraction that consists of a set of line segments called critical edges is proposed. In this algorithm, cell boundaries are formed by extended critical edges, which are the sensed partial contours of walls and objects in the workspace. The robot uses a laser scanner to sense the critical edges. Sensor measurement is sampled twice in each cell. Scan matching is performed to merge map information between the reference scan and the current scan. At each scan sample, a two-direction oriented rectilinear decomposition is achieved in the workspace and presented by a closed map representation. The construction order of the cells is very important in this incremental cell decomposition algorithm. To choose the next target cell from candidate cells, the robot checks for redundancy in the planned path and for possible positions of the ending points of the current cell. The key point of the algorithm is memorizing the covered space to define the next target cell from possible cells. The path generation within the defined cell is determined to minimize the number of turns, which is the main factor in saving time during the coverage. Therefore, the cell's long boundary should be chosen as the main path of the robot. This algorithm is verified by an experiment under the LABVIEW environment.

Unmanned aerial vehicles (UAVs)-based communication system is a promising solution to meet coverage and capacity requirements of future wireless networks. However, UAV-enabled communications is constrained with its coverage, energy consumption, and flying regulations, and the number of works focusing on the sustainability aspect of UAV-assisted networking has been limited in the literature so far. In this paper, we propose a solution to this limitation; particularly, we design a $Q$-learning-based UAV positioning scheme for sustainable wireless connectivity considering key constraints, that are, altitude regulations, non-flight zones, and transmit power. The objective is to find the optimal position of the UAV base station (BS) and minimize the energy consumption while maximizing the number of users covered. Moreover, a weighting mechanism is developed, where the energy consumption and number of users covered can be prioritized according to network/battery conditions. The proposed Q...

In the last decade, the attention on unmanned aerial vehicles has rapidly grown, due to their ability to help in many human activities. Among their widespread benefits, one of the most important uses regards the possibility of distributing wireless connectivity to many users in a specific coverage area. In this study, we focus our attention on these new kinds of networks, called flying ad-hoc networks. As stated in the literature, they are suitable for all emergency situations where the traditional networking paradigm may have many issues or difficulties to be implemented. The use of a software simulator can give important help to the scientific community in the choice of the right UAV/drone parameters in many different situations. In particular, in this work, we focus our main attention on the new ways of area covering and human mobility behaviors with the introduction of a UAV/drone behavior model to take into account also drones energetic issues. A deep campaign of simulations was carried out to evaluate the goodness of the proposed simulator illustrating how it works.

The rising number of technological advanced devices making network coverage planning very challenging tasks for network operators. The transmission quality between the transmitter and the end users has to be optimum for the best performance out of any device. Besides, the presence of coverage hole is also an ongoing issue for operators which cannot be ignored throughout the whole operational stage. Any coverage hole in network operators' coverage region will hamper the communication applications and degrade the reputation of the operator's services. Presently, there are techniques to detect coverage holes such as drive test or minimization of drive test. However, these approaches have many limitations. The extreme costs, outdated information about the radio environment and high time consumption do not allow to meet the requirement competently. To overcome these problems, we take advantage of Unmanned aerial vehicle (UAV) and Q-learning to autonomously detect coverage hole in a given area and then deploy UAV based base station (UAV-BS) by considering wireless backhaul with the core network and users demand. This machine learning mechanism will help the UAV to eliminate human-in-the-loop (HiTL) model. Later, we formulate an optimisation problem for 3D UAV-BS placement at various angular positions to maximise the number of users associated with the UAV-BS. In summary, we have illustrated a cost-effective as well as time saving approach of detecting coverage hole and providing on-demand coverage in this article.

The rising number of technological advanced devices making network coverage planning very challenging tasks for network operators. The transmission quality between the transmitter and the end users has to be optimum for the best performance out of any device. Besides, the presence of coverage hole is also an ongoing issue for operators which cannot be ignored throughout the whole operational stage. Any coverage hole in network operators' coverage region will hamper the communication applications and degrade the reputation of the operator's services. Presently, there are techniques to detect coverage holes such as drive test or minimization of drive test. However, these approaches have many limitations. The extreme costs, outdated information about the radio environment and high time consumption do not allow to meet the requirement competently. To overcome these problems, we take advantage of Unmanned aerial vehicle (UAV) and Q-learning to autonomously detect coverage hole in a given area and then deploy UAV based base station (UAV-BS) by considering wireless backhaul with the core network and users demand. This machine learning mechanism will help the UAV to eliminate human-in-the-loop (HiTL) model. Later, we formulate an optimisation problem for 3D UAV-BS placement at various angular positions to maximise the number of users associated with the UAV-BS. In summary, we have illustrated a cost-effective as well as time saving approach of detecting coverage hole and providing on-demand coverage in this article.

Unmanned aerial vehicles (UAVs) can be users that support new applications, or be communication access points that serve terrestrial and/or aerial users. In this paper, we focus on the connectivity problem of aerial users when they are exclusively served by aerial base stations (BS), i.e., UAVBSs. Specifically, the 3D placement problem of a directionalantenna equipped UAV-BS, aiming to maximize the number of covered aerial users under a spectrum sharing policy with terrestrial networks, is investigated. Given a known spectrum sharing policy between the aerial and terrestrial networks, we propose a 3D placement algorithm that achieves optimality. Simulation results show the performance of our approach, in terms of number of covered aerial users for different configurations and parameters, such as the spectrum sharing policy, antenna beamwidth, transmit power, and aerial users density. These results represent novel guidelines for exclusive aerial networks deployment and applications, ...

Multi-rotor drones have witnessed a drastic usage increase in several smart city applications due to their threedimensional (3D) mobility, flexibility, and low cost. Collectively, they can be used to accomplish different short-term and longterm missions that require low altitude motion in urban areas. Therefore, it is important to efficiently manage the operation of the fleet to leverage its use and maximize its application performances. In this paper, we propose to investigate the path routing problem for multiple drones in urban areas where obstacles with different heights exist. The objective is to find the best trajectories in this 3D environment while ensuring collision-free navigation. The collision is prevented by three possible alternatives: forcing the drone to statically hover so its peer can pass first, making it fly at a different altitude, or completely changing its path. Multiple charging stations are made available to allow the drones recharge their batteries when needed. A mixed integer linear program is first developed to model the problem and achieve optimal navigation of the fleet. Afterwards, two heuristic algorithms with different conceptual construction are designed to solve the trajectory planning problem with a faster convergence speed. Selected simulation results illustrate the performance of our framework in realistic 3D maps and show that the designed heuristic approaches provide close performances to the optimal ones.

Research in the field of Wireless Sensor Networks (WSNs) has been plagued by difficulties in performing realistic simulations. For instance, most of the existing coverage optimization techniques presuppose that the region covered by a sensor node is a disc characterized by the radio transmission range. This assumption is rigorously false because of the propagation phenomena including fading and shadowing. This paper investigates the impact of these radio propagation phenomena on the coverage optimization strategy in a WSN. We rely on the log-normal shadowing model to represent the effect of the environmental features on the WSN performance. We propose a coverage model taking into account irregular radio propagation. We carry out a mathematical analysis to compute the average number of targets a sensor would detect under both perfect and irregular propagation conditions. We also conduct simulations to assess our random deployment model with respect to the existing strategies.

Unmanned aerial vehicles (UAVs), also named as drones, have become a modern model to provide a quick wireless communication infrastructure. They have been used when conventional base stations’ capacity is suffering in some extreme cases such as congestion inside the cell or a special event. This paper proposes an efficient three-dimension (3D) placement of a single UAV-assisted wireless network in such cases. Our proposed model assists the ground base station (GBS) using the UAV to serve arbitrary distributed users considering the impact of the obstacle blockage over the well-known air-to-ground (A2G) path model. This work is aimed at optimizing the percentage of available bandwidth that must be provided to the UAV in order to maximize the number of served users. In addition, it finds the 3D placement of the UAV base station (UAVBS) that maximizes the number of served users, each with maximum quality-of-service (QoS). The exhaustive search and particle swarm optimization (PSO) algor...

Unmanned aerial vehicles (UAVs) can be used as aerial wireless base stations when cellular networks go down. Prior studies on UAV-based wireless coverage typically consider an Air-to-Ground path loss model, which assumes that the users are outdoor and they are located on a 2D plane. In this paper, we propose using a single UAV to provide wireless coverage for indoor users inside a high-rise building under disaster situations (such as earthquakes or floods), when cellular networks are down. We assume that the locations of indoor users are uniformly distributed in each floor and we propose a particle swarm optimization algorithm to find an efficient 3D placement of a UAV that minimizes the total transmit power required to cover the indoor users.

Nowadays, Unmanned Aerial Vehicles (UAV) are frequently present in the civilian environment. However, proper implementations of different solutions based on these aircraft still face important challenges. This article deals with multi-UAV systems, forming aerial networks, mainly employed to provide Internet connectivity and different network services to ground users. However, the mission duration (hours) is longer than the limited UAVs’ battery life-time (minutes). This paper introduces the UAV replacement procedure as a way to guarantee ground users’ connectivity over time. This article also formulates the practical UAV replacements problem in moderately large multi-UAV swarms and proves it to be an NP-hard problem in which an optimal solution has exponential complexity. In this regard, the main objective of this article is to evaluate the suitability of heuristic approaches for different scenarios. This paper proposes betweenness centrality heuristic algorithm (BETA), a graph theo...

In this paper, we consider the scenario of using unmanned aerial vehicles base stations (UAV-BSs) to serve cellular users. In particular, we focus on finding the minimum number of UAV-BSs as well as their deployment. We propose an optimization model which minimizes the number of UAV-BSs and optimize their positions such that the user equipment (UE) covered ratio is no less than the expectation of network suppliers, the UEs receive acceptable downlink rates, and the UAV-BSs can work in a sustainable manner. We show the NP-hardness of this problem and then propose a method to address it. The method first estimates the range of the number of UAV-BSs and then converts the original problem to one which maximizes the UE served ratio, given the number of UAV-BSs within that range. We present a maximizing algorithm to solve it with the proof of convergence. Extensive simulations based on a realistic dataset have been conducted to demonstrate the effectiveness of the proposed method.

Due to the battery capability of drone or unmanned aerial vehicle, the ability of services and flying distance provided by drone are limited. Considering the frequent monitoring of the agricultural disease, the power recharging station for unmanned aerial vehicle without manual operation is important issue. Based on the proposed Environment and Location Aware Drone Services Corresponding to Green Energy Charging Station, the drone services can be started at one green energy charging station and ended at another. According to the complete-coverage path planning algorithm, the drone service such as environment monitoring or pesticide spraying can fully cover the farm. In addition, the implementation shows that suitable green energy power source can be dynamically embedded into power recharging station. The feasibility of the proposed system is verified.

Recent years have witnessed the deployments of
wireless sensor networks for mission-critical applications such
as battlefield monitoring and security surveillance. These appli-
cations often impose stringent Quality of Surveillance (QoSv)
requirements including low false alarm rate and short detection
delay. In practice, collaborative data fusion techniques that can
deal with sensing uncertainty and enable sensor collaboration
have been widely employed in sensor systems to achieve stringent
QoSv requirements. However, most previous analytical studies
on the surveillance performance of wireless sensor networks are
based on simplistic models (such as the disc model) that cannot
capture the stochastic and collaborative nature of sensing. In this
paper, we systematically analyze the fundamental relationship
between QoSv, network density, sensing parameters, and target
properties. The results show that data fusion is effective in
achieving stringent QoSv requirements, especially in the senarios
with low signal-to-noise ratios (SNRs). In contrast, the disc model
is only suitable when the SNR is sufficiently high. Our results
help understand the limitations of disc model and provide insights
into improving QoSv of sensor networks using data fusion.

Recent years have witnessed the deployments of
wireless sensor networks for mission-critical applications such
as battlefield monitoring and security surveillance. These appli-
cations often impose stringent Quality of Surveillance (QoSv)
requirements including low false alarm rate and short detection
delay. In practice, collaborative data fusion techniques that can
deal with sensing uncertainty and enable sensor collaboration have
been widely employed in sensor systems to achieve stringent QoSv
requirements. However, most previous analytical studies on the
surveillance performance of wireless sensor networks are based
on simplistic models (such as the disc model) that cannot capture
the stochastic and collaborative nature of sensing. In this paper,
we systematically analyze the fundamental relationship between
QoSv, network density, sensing parameters, and target properties.
The results show that data fusion is effective in achieving stringent
QoSv requirements, especially in the senarios with low signal-to-
noise ratios (SNRs). In contrast, the disc model is only suitable
when the SNR is sufficiently high. Our results help understand
the limitations of disc model and provide insights into improving
QoSv of sensor networks using data fusion.

Wireless Sensor Networks (WSN) are formed by a large number of sensing nodes at the ground level. These devices are monitoring and measuring physical parameters from the environment. Simulation is used to study WSN, since deploying test-beds supposes a huge effort. However simulation results rely on physical layer assumptions, which are not usually accurate enough to capture the real behaviour of WSN. In this work several measurement campaigns are performed in three different scenarios: an open quasi-ideal area, a university yard and a park. The main contribution of this work is that a two slopes lognormal path-loss near ground outdoor channel model at 868 MHz is validated, and compared to the widely used one slope model. This model is useful for simulations because its computational cost is low.

Research in the field of Wireless Sensor Networks (WSNs) has been plagued by difficulties in performing realistic simulations. For instance, most of the existing coverage optimization techniques presuppose that the region covered by a sensor node is a disc characterized by the radio transmission range. This assumption is rigorously false because of the propagation phenomena including fading and shadowing. This paper investigates the impact of these radio propagation phenomena on the coverage optimization strategy in a WSN. We rely on the log-normal shadowing model to represent the effect of the environmental features on the WSN performance. We propose a coverage model taking into account irregular radio propagation. We carry out a mathematical analysis to compute the average number of targets a sensor would detect under both perfect and irregular propagation conditions. We also conduct simulations to assess our random deployment model with respect to the existing strategies.

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