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Considering the demand for the optimization of the technical factors of spraying for a greater area coverage and minimal drift, field tests were carried out to determine the interaction between the area coverage, number of droplets per cm2, droplet diameter, and drift. The studies were conducted with two different types of sprayers (axial and radial fan) in an apple orchard and a vineyard. The technical factors of the spraying interactions were nozzle type (ISO code 015, code 02, and code 03), working speed (6 and 8 km h−1), and spraying norm (250–400 L h−1). The airflow of both sprayers was adjusted to the plantation leaf mass and the working pressure was set for each repetition separately. A method using water-sensitive paper and a digital image analysis was used to collect data on coverage factors. The data from the field research were processed using four machine learning models: quantile random forest (QRF), support vector regression with radial basis function kernel (SVR), Bayesian Regularization for Feed-Forward Neural Networks (BRNN), and Ensemble Machine Learning (ENS). Nozzle type had the highest predictive value for the properties of number of droplets per cm2 (axial = 69.1%; radial = 66.0%), droplet diameter (axial = 30.6%; radial = 38.2%), and area coverage (axial = 24.6%; radial = 34.8%). Spraying norm had the greatest predictive value for area coverage (axial = 43.3%; radial = 26.9%) and drift (axial = 72.4%; radial = 62.3%). Greater coverage of the treated area and a greater number of droplets were achieved with the radial sprayer, as well as less drift. The accuracy of the machine learning model for the prediction of the treated surface showed a satisfactory accuracy for most properties (R2 = 0.694–0.984), except for the estimation of the droplet diameter for an axial sprayer (R2 = 0.437–0.503). [ABSTRACT FROM AUTHOR]

Reconfigurable robots are deployed for applications demanding area coverage, such as cleaning and inspections. Reconfiguration per context, considering beyond a small set of predefined shapes, is crucial for area coverage performance. However, the existing area coverage methods of reconfigurable robots are not always effective and require improvements for ascertaining the intended goal. Therefore, this article proposes a novel coverage strategy based on internal rehearsals to improve the area coverage performance of a reconfigurable robot. In this regard, a reconfigurable robot is embodied with the cognitive ability to predict the outcomes of its actions before executing them. A genetic algorithm uses the results of the internal rehearsals to determine a set of the robot's coverage parameters, including positioning, heading, and reconfiguration, to maximize coverage in an obstacle cluster encountered by the robot. The experimental results confirm that the proposed method can significantly improve the area coverage performance of a reconfigurable robot. [ABSTRACT FROM AUTHOR]

One of the fundamental problems in randomly deployed sensor networks is enhancing the network lifetime while providing full area coverage. The problem is more challenging when location information is not available. Scheduling the activities of sensor nodes in a way that each point of the area of interest is covered by at least one sensor node is a promising way when a smaller set of sensor nodes is scheduled autonomously. The autonomous sleep scheduling of sensor nodes can be efficiently achieved based on the topological properties of the sensor network in a distributed fashion. In this paper, we address the problem of autonomously scheduling of sensor nodes to provide full area coverage in wireless sensor networks, even when location information is unavailable. The goal is to prolong the network lifetime. The proposed method is based on homology. The idea is autonomous selection of the minimum number of active sensors with the highest level of energy based on the properties of the simplicial complex of the network. We formulate this problem as an integer programming problem. Then, we propose a distributed algorithm, which does not require the knowledge of the location of nodes or distance between them. Finally, we provide simulation results demonstrating the performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The area coverage problem solution is one of the vital research areas which can benefit from swarm robotics. The greatest challenge to the swarm robotics system is to complete the task of covering an area effectively. Many domains where area coverage is essential include exploration, surveillance, mapping, foraging, and several other applications. This paper introduces a survey of swarm robotics in area coverage research papers from 2015 to 2022 regarding the algorithms and methods used, hardware, and applications in this domain. Different types of algorithms and hardware were dealt with and analysed; according to the analysis, the characteristics and advantages of each of them were identified, and we determined their suitability for different applications in covering the area for many goals. This study demonstrates that naturally inspired algorithms have the most significant role in swarm robotics for area coverage compared to other techniques. In addition, modern hardware has more capabilities suitable for supporting swarm robotics to cover an area, even if the environment is complex and contains static or dynamic obstacles. [ABSTRACT FROM AUTHOR]

In the context of area coverage tasks in three-dimensional space, unmanned aerial vehicle (UAV) clusters face challenges such as uneven task assignment, low task efficiency, and high energy consumption. This paper proposes an efficient mission planning strategy for UAV clusters in area coverage tasks. First, the area coverage search task is analyzed, and the coverage scheme of the task area is determined. Based on this, the cluster task area is divided into subareas. Then, for the UAV cluster task allocation problem, a step-by-step solution is proposed. Afterward, an improved fuzzy C-clustering algorithm is used to determine the UAV task area. Furthermore, an optimized particle swarm hybrid ant colony (PSOHAC) algorithm is proposed to plan the UAV cluster task path. Finally, the feasibility and superiority of the proposed scheme and improved algorithm are verified by simulation experiments. The simulation results show that the proposed method achieves full coverage of the task area and efficiently completes the task allocation of the UAV cluster. Compared with related comparison algorithms, the method proposed in this paper can achieve a maximum improvement of 21.9% in balanced energy consumption efficiency for UAV cluster task search planning, and the energy efficiency of the UAV cluster can be improved by up to 7.9%. [ABSTRACT FROM AUTHOR]

Reconfigurable robots have the potential to perform complex tasks by adapting their morphology to different environments. However, designing optimal morphologies for these robots is challenging due to the large design space and the complex interactions between the robot and the environment. An in-house robot named S m o r p h i , having four holonomic mobile units connected with three hinge joints, is designed to maximize area coverage with its shape-changing features using transformation design principles (TDP). The reinforcement learning (RL) approach is used to identify the optimal morphologies out of a vast combination of hinge angles for a given task by maximizing a reward signal that reflects the robot's performance. The proposed approach involves three steps: (i) Modeling the Smorphi design space with a Markov decision process (MDP) for sequential decision-making; (ii) a footprint-based complete coverage path planner to compute coverage and path length metrics for various Smorphi morphologies; and (iii) pptimizing policies through proximal policy optimization (PPO) and asynchronous advantage actor–critic (A3C) reinforcement learning techniques, resulting in the generation of energy-efficient, optimal Smorphi robot configurations by maximizing rewards. The proposed approach is applied and validated using two different environment maps, and the results are also compared with the suboptimal random shapes along with the Pareto front solutions using NSGA-II. The study contributes to the field of reconfigurable robots by providing a systematic approach for generating optimal morphologies that can improve the performance of reconfigurable robots in a variety of tasks. [ABSTRACT FROM AUTHOR]

Directional sensor nodes deployment is indispensable to a large number of applications including Internet of Things applications. Nowadays, with the recent advances in robotic technology, directional sensor nodes mounted on mobile robots can move toward the appropriate locations. Considering the probabilistic sensing model along with the mobility and motility of directional sensor nodes, area coverage in such a network is more complicated than in a static sensor network. In this paper, we investigate the problem of self-deployment and working direction adjustment in directional sensor networks in order to maximize the covered area. Considering the tradeoff between energy consumption and coverage quality, we formulate this problem as a finite strategic game. Then, we present a distributed payoff-based learning algorithm to achieve Nash equilibrium. The simulation results demonstrate the performance of the proposed algorithm and its superiority over previous approaches in terms of increasing the area coverage. [ABSTRACT FROM AUTHOR]

The literature approaches, devoted to sensor improve network coverage, are deterministic in terms of deployment environment and node configuration parameters. Nevertheless, this type of approaches has not proven to be very successful in uncertain deployment environments. This paper aims to deal with this issue using theories of uncertainty. We consider deployment environment's imperfections and the characteristics of the sensor nodes. The selection of a minimum number of nodes for a minimum number of clusters to guarantee coverage in wireless sensor networks (WSNs) is uncertain. As a consequence, this paper proposes a hybrid Fuzzy-Possibilistic model to Schedule the Active/Passive State of Sensor nodes Strategy (FP-3SNS). This model helps to plan the scheduling of node states (Active/Passive) based on possibilistic information fusion to make a possibilistic decision for the node activation at each period. We evaluated our model (FP-3SNS) with (a) a running example (that shows the best choice of the active node with a probability of 0.81215); (b) a statistical evaluation (calculation of the confidence interface), where the average coverage reliability at 95% of FP-3SNS use is between (92.94, 96.27); and (c) a comparison with maximum sensing coverage region problem (MSCR), coverage maximization with sleep scheduling (CMSS), Spider Canvas Strategy, Semi-Random Deployment Strategy (SRDP), Probing Environment and Adaptive Sleeping with Location Information Protocol (PEAS-LI), and Variable Length Particle Swarm Optimization Algorithm with a Weighted Sum Fitness Function (WS-VLPSO). The simulation results highlight the benefits of using the fuzzy and possibility theories for treating the area coverage problem and the proposed model maintained a coverage between 99.99 and 90.00% for a significant period of time. [ABSTRACT FROM AUTHOR]

In this work, the design and implementation of an autonomous mobile robot that can cover given workspaces, is presented. The motion commands are produced by a bit sequence, which is created from the combination, through an XOR function, of two separate chaotic random bitstreams. The implementation of the proposed method is performed by a robotic platform which is controlled through a microcontroller (Arduino Uno). Also, an ultrasonic sensor is inserted into the robotic platform with basic aim to locate and avoid the boundaries of the workspace. The robot's behavior is studied experimentally in different scenarios with various starting positions. The experimental results confirm that this approach, with an appropriate sensor for avoiding collision with the boundaries, can obtain very satisfactory results in regard to the fast scanning of the robot's workspace with unpredictable way. • Construction of a Random Bit Generator with the use of two Logistic Maps. • The realization of the chaotic random bit generator is based on a microcontroller. • An ultrasonic distance sensor for the detection of the boundaries has been used. • Experimental study of the workspace's coverage. [ABSTRACT FROM AUTHOR]

Reconfigurable robots design based on polyominos or n-Omino is increasingly being explored in cleaning and maintenance (CnM) tasks due to their ability to change shape using intra- and inter-reconfiguration, resulting in various footprints of the robot. On one hand, reconfiguration during a CnM task in a given environment or map results in enhanced area coverage over fixed-form robots. However, it also consumes more energy due to the additional effort required to continuously change shape while covering a given map, leading to a deterioration in overall performance. This paper proposes a new strategy for n-Omino-based robots to select a range of optimal morphologies that maximizes area coverage and minimizes energy consumption. The optimal "morphology" is based on two factors: the shape or footprint obtained by varying the angles between the n-Omino blocks and the number of n-Omino blocks, i.e., "n". The proposed approach combines a Footprint-Based Complete coverage Path planner (FBCP) with a metaheuristic optimization algorithm to identify an n-Omino-based reconfigurable robot's optimal configuration, assuming energy consumption is proportional to the path length taken by the robot. The proposed approach is demonstrated using an n-Omino-based robot named Smorphi, which has square-shaped omino blocks with holonomic locomotion and the ability to change from monomino to tetromino. Three different simulated environments are used to find the optimal morphologies of S m o r p h i using three metaheuristic optimization techniques, namely, MOEA/D, OMOPSO, and HypE. The results of the study show that the morphology produced by this approach is energy efficient, minimizing energy consumption and maximizing area coverage. Furthermore, the HypE algorithm is identified as more efficient for generating optimal morphology as it took less time to converge than the other two algorithms. [ABSTRACT FROM AUTHOR]

Recognizing the patterns of road junctions in a road network plays a crucial role in various applications. Owing to the diversity and complexity of morphologies of road junctions, traditional methods that rely heavily on manual settings of features and rules are often problematic. In recent years, several studies have employed convolutional neural networks (CNNs) to classify complex junctions. These methods usually convert vector-based junctions into raster representations with a predefined sampling area coverage. However, a fixed sampling area coverage cannot ensure the integrity and clarity of each junction, which inevitably leads to misclassification. To overcome this drawback, this study proposes a stacking ensemble learning method for classifying the patterns of complex road junctions. In this method, each junction is first converted into raster images with multiple area coverages. Subsequently, several CNN-based base-classifiers are trained using raster images, and they output the probabilities of the junction belonging to different patterns. Finally, a meta-classifier based on random forest is used to combine the outputs of the base-classifiers and learn to arrive at the final classification. Experimental results show that the proposed method can improve the classification accuracy for complex road junctions compared to existing CNN-based classifiers that are trained using raster representations of junctions with a fixed sampling area coverage. [ABSTRACT FROM AUTHOR]

In recent times, Unmanned Air Vehicles (UAVs) are being deployed for several tasks of terrain coverage such as surveillance, photogrammetry, smart irrigation, civil security, and disaster management. In these applications, one of the most vital issues to be addressed is, covering the area under observation with minimum traversal for the UAV. So, the problem addressed in this paper is as follows: For a given geographic area and the given parameters describing the UAV's coverage capacity, the problem is to find an optimal route that covers the given geographic area. In this paper, an optimal path planning algorithm for the area under observation, given as a closed curve, is proposed. The algorithm partitions the given area of interest into multiple non-uniform rectangles while considering other parameters such as the flying altitude of the UAV and obstacles that could be encountered during its flight. The problem is transformed into Traveling Salesman Problem by constructing a graph from the rectangular partitioning. Effective approximate solutions are provided to this problem, using the Minimum Spanning Tree (MST) approximation algorithm and Ant Colony Optimization (ACO). The experimental results show that ACO outperforms the MST based algorithm as it does not get stuck in local minima. [ABSTRACT FROM AUTHOR]

In this article, an artificially weighted spanning tree coverage (AWSTC) algorithm is proposed for the distributed path planning of multiple flying robots. To balance well the efficiency, redundancy, and robustness in the cooperative coverage problem, each robot simultaneously constructs its spanning tree, which grows toward the center of inertia of the uncovered area and keeps away from the trees of its partners. Based on this, each robot covers a concerned area with almost equal trajectory length and computational burden. To guarantee dynamical consistency, a trajectory-smoothing method is developed utilizing Bézier curve transition. As transition in the spanning tree path planning is always conducted around the corner with right angle, geometric and velocity profiles of this kind of transition can be preevaluated as a basic component and then connected to establish more complex real-time trajectories according to the constructed spanning trees. Numerical evaluations and real-time flight experiments are carried out at last. Results demonstrate that the proposed strategy can generate a smooth trajectory for an area coverage problem while ensuring efficiency and robustness. In particular, the efficiency of the proposed algorithm is quite close to the typical centralized spanning tree coverage (STC) algorithm, while ensuring the fulfillment of the coverage task regardless of any breakdown in the individual robot. Note to Practitioners—In applications such as infrastructure inspection and plant protection, the concerned area or terrain is required to be fully covered with the smallest possible time consumption. In view of these real-world requirements, multiple unmanned systems with proper path planning provide a promising solution that can significantly enhance the efficiency as well as the robustness, compared with a single-robot system. Especially, centralized STC algorithms, concerning the computation efficiency and trajectory redundancy of multiple unmanned systems, demonstrate satisfactory effectiveness, and thereby receive a lot of attention. Unfortunately, most of the STC algorithms are proposed in a centralized control framework. Therefore, it is worth to study its decentralized version that could exploit the advantages of the distributed multiple robots. With this consideration, a decentralized AWSTC algorithm is proposed in this article. This spanning tree-based strategy artificially assigns priority weight for each cell, with respect to each individual robot, in the concerned area. With specially designed weight assignment, each robot tends to construct a spanning tree with cells that are uncovered but keep away from the trees of its partners. Based on this, the task burden as well as computational cost of each robot are almost equal. To improve the real-time performance, a Bézier transition trajectory-generation method is also used. With proper parameter selection, robots can generate smooth trajectory with constant velocity in real-time missions. Numerical evaluations and experiments with real-time flight demonstrate the effectiveness of this methodology. [ABSTRACT FROM AUTHOR]

This paper proposed a novel algorithm based on region segmentation and Voronoi diagram ( RSV) for area coverage problem with certain area in WSN. The algorithm analyzed points of interest ( POI) and geographic information in certain area and designed the grid to divide the area. With the number of POI in divided area, the algorithm designed the initial deployment. According to the initial deployment location and weight, the algorithm constructed Voronoi diagram to fill the coverage holes for different weight positions until all the holes were filled and designed a suitable node dormancy strategy to prolong WSN lifetime. The results of simulation experiment show that the RSV algorithm prolongs WSN lifetime and balances the energy consumption among all sensors with less increased number of sensors. The RSV algorithm also has better coverage of the effective area with limited number of sensors. [ABSTRACT FROM AUTHOR]

Participant selection method is one of the important contents of crowd sensing research. Existing research still has some shortcomings. Only the attributes such as task time or task area coverage are considered, which makes the selected participants perform tasks less efficiently. Therefore, in order to comprehensively consider the task time and task area coverage constraints, this paper proposed a selection method of crowd sensing participant based on the greedy ant colony algorithm ( PSGACO) to achieve the highest task execution efficiency and the minimum incentive cost of the crowd sensing platform. The method mainly selected the participants who were suitable for performing the publishing task by the concentration of the ant pheromone concentration of the candidate participants, and greatly improved the task execution efficiency. Finally, it compared the proposed PS-GA CO method with the common participant selection method through simulation experiments. The experimental results show that PS-GA CO is superior to the other two methods in terms of algorithm running time, task execution efficiency and incentive cost, and has a good application prospect for the crowd sensing participant selection. [ABSTRACT FROM AUTHOR]

In order to solve the problems of slow convergence speed and insufficient coverage of specified target points in coverage distribution of sensor networks based on particle swarm optimization, an improved particle swarm optimization (PSO) algorithm based on virtual potential field (VPF) is proposed to optimize coverage distribution of sensor networks. The virtual potential field between sensor nodes is established and the continuous virtual force of interaction is derived. The continuous force field reduces the oscillation of gravitational and repulsive boundary and accelerates the convergence speed of the algorithm. Enlarging the potential field of the specified target point which is required to be covered and increasing the particle adsorption force ensure that the target point is always within the sensing range of the sensor. The results show that the proposed improved PSO algorithm has faster convergence speed, and has higher coverage rate while target points are fully covered. The research conclusions help to lay out sensor networks in key areas. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSN) are the backbone in various modern Internet of Things (IoT) smart applications ranging from automated control, surveillance, forest fire detection, etc. One of the most important applications is the smart agriculture. The deployment of WSN in agricultural processes can predict crop yield, soil temperature, air quality, water level, crop price, and the appropriate time for market delivery which will help to increase productivity. In this paper, an enhanced metaheuristic algorithm called multi-verse optimizer with overlapping detection phase (DMVO) is introduced for optimizing the area coverage percentage of WSN. The proposed algorithm is tested on many datasets with different criterions and is compared with other algorithms including the original MVO, particle swarm optimization, and flower pollination algorithm. The experimental results are analyzed with one-way ANOVA test. In addition, DMVO is applied to IoT smart agriculture in East Oweinat area in Egypt and compared with Krill Herd algorithm. In addition, the experimental results are analyzed with Wilcoxon signed-rank test. The experimental results and the statistical analysis prove the prosperity and consistency of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Tiling robots with fixed morphology face major challenges in terms of covering the cleaning area and generating the optimal trajectory during navigation. Developing a self-reconfigurable autonomous robot is a probable solution to these issues, as it adapts various forms and accesses narrow spaces during navigation. The total navigation energy includes the energy expenditure during locomotion and the shape-shifting of the platform. Thus, during motion planning, the optimal navigation sequence of a self-reconfigurable robot must include the components of the navigation energy and the area coverage. This paper addresses the framework to generate an optimal navigation path for reconfigurable cleaning robots made of tetriamonds. During formulation, the cleaning environment is filled with various tiling patterns of the tetriamond-based robot, and each tiling pattern is addressed by a waypoint. The objective is to minimize the amount of shape-shifting needed to fill the workspace. The energy cost function is formulated based on the travel distance between waypoints, which considers the platform locomotion inside the workspace. The objective function is optimized based on evolutionary algorithms such as the genetic algorithm (GA) and ant colony optimization (ACO) of the traveling salesman problem (TSP) and estimates the shortest path that connects all waypoints. The proposed path planning technique can be extended to other polyamond-based reconfigurable robots. [ABSTRACT FROM AUTHOR]

Wireless communication technology is increasingly gaining popularity, with aerial wireless sensor networks being highly regarded for their high mobility, ability to handle special environments, and wide coverage. To enhance the performance of aerial wireless sensor networks, the study of area coverage algorithms is crucial. However, most existing coverage algorithms primarily focus on the coverage area without considering coverage quality. To address this limitation, this paper proposes a coverage algorithm based on coverage quality for regional coverage by unmanned aerial vehicles (UAVs). The algorithm incorporates the importance and priority of the coverage area, utilizes an adaptive spatial distribution function for weight distribution, and employs horizontal and vertical control strategies to strike a balance between maximum coverage and optimization of coverage quality. Simulation experiments are conducted to validate the effectiveness of the proposed algorithm. This research contributes to the field by emphasizing the significance and impact of considering coverage quality in aerial wireless sensor networks, thereby enhancing their overall performance. [ABSTRACT FROM AUTHOR]

As the Internet of Things (IoT) evolves, more and more Wireless Sensor Networks (WSNs) are being deployed in the real world. Connected vehicles, smart grids, smart cities, smart healthcare, networks of robots, and disaster recovery networks are some examples. In WSNs, the area coverage is one of the most important quality of service metrics. A WSN without enough area coverage yields incorrect results. So calculating the covered area of a WSN is mandatory. Previous studies have used a simple approach: all nodes send their location to the sink, and it calculates the covered area centrally which makes huge unnecessary communication overhead. In our previous work titled Distributed Exact Coverage Rate Calculation, we calculated the covered area of a homogenous WSN in a distributed manner. In this paper, we provide a Heterogeneous Distributed Precise Coverage Rate (HDPCR) mechanism that calculates the covered area of a Heterogeneous Wireless Sensor Network by using a localized mechanism. With the use of boundary detection mechanisms, the HDPCR detects the boundary of the network and calculates its area. HDPCR also detects holes and calculates their area precisely. By subtracting these two calculated values, the covered area of the network can be computed. Many related studies have evaluated the coverage rate approximately with error and require more calculations to reduce the error rate. HDPCR calculates the coverage rate precisely without an error rate using simple arithmetic calculations. The exhaustive simulation also shows the superiority of HDPCR as compared to the previous approaches. [ABSTRACT FROM AUTHOR]

SUMMARY: The field of robotics has received significant attention in our society due to the extensive use of robotic manipulators; however, recent advances in the research on autonomous vehicles have demonstrated a broader range of applications, such as exploration, surveillance, and environmental monitoring. In this sense, the problem of efficiently building a model of the environment using cooperative mobile robots is critical. Finding routes that are either length or time-optimized is essential for real-world applications of small autonomous robots. This paper addresses the problem of multi-robot area coverage path planning for geophysical surveys. Such surveys have many applications in mineral exploration, geology, archeology, and oceanography, among other fields. We propose a methodology that segments the environment into hexagonal cells and allocates groups of robots to different clusters of non-obstructed cells to acquire data. Cells can be covered by lawnmower, square or centroid patterns with specific configurations to address the constraints of magneto-metric surveys. Several trials were executed in a simulated environment, and a statistical investigation of the results is provided. We also report the results of experiments that were performed with real Unmanned Aerial Vehicles in an outdoor setting. [ABSTRACT FROM AUTHOR]

This paper advances the previous theoretical work of authors by conducting experiments on a generalized coverage optimization algorithm using a team of heterogeneous mobile robots. A scalar measure (herein called the density) of the environment defines a nonuniform coverage metric of the area. Mobile robots are spatially configured such that their asymptotic placements in an area maximize the nonuniform coverage metric. Over the last fifteen years, a large body of research work has been conducted in solving the area coverage optimization problem where the focus was mainly on theoretical results followed by mostly numerical simulations. In some cases, the coverage optimization algorithms were validated with experimental results, but the implementation platforms were suitable to specific homogeneous robot platforms. Here, the emphasis is on the real-time implementation of the authors’ previously published theoretical results on area coverage optimization problems using a team of heterogeneous mobile robots. The robots are heterogeneous in the sense that they have different actuator limits, physical dimensions, and processing capabilities. The modularity of the algorithm stems from the fact that the additional hardware/software architecture is open-source and can be applied to different robots regardless of their internal electromechanical system architectures. The algorithm is implemented using the emerging robot operating system in a multithreaded manner. A commercial robot simulator was used to validate the coverage performance followed by a set of experiments conducted in an indoor environment. [ABSTRACT FROM PUBLISHER]

Unmanned Aerial Vehicles (UAVs) equipped with visual sensors are widely used in area coverage missions. Guaranteeing full coverage coupled with camera footprint is one of the most challenging tasks, thus, in the presented novel approach a coverage path planner for the inspection of 2D areas is established, a 3 Degree of Freedom (DoF) camera movement is considered and the shortest path from the taking off to the landing station is generated, while covering the target area. The proposed scheme requires a priori information about the boundaries of the target area and generates the paths in an offline process. The efficacy and the overall performance of the proposed method has been experimentally evaluated in multiple indoor inspection experiments with convex and non convex areas. Furthermore, the image streams collected during the coverage tasks were post-processed using image stitching for obtaining a single overview of the covered scene. [ABSTRACT FROM AUTHOR]

Coverage and lifetime are two paramount problems in wireless sensor networks (WSNs). In this paper, a method called Multiround Distributed Lifetime Coverage Optimization protocol (MuDiLCO) is proposed to maintain the coverage and to improve the lifetime in wireless sensor networks. The area of interest is first divided into subregions, and then the MuDiLCO protocol is distributed to the sensor nodes in each subregion. The proposed MuDiLCO protocol works in periods during which sets of sensor nodes are scheduled, with one set for each round of a period, to remain active during the sensing phase and thus ensure coverage so as to maximize the WSN lifetime. The decision process is carried out by a leader node, which solves an optimization problem to produce the best representative sets to be used during the rounds of the sensing phase. The optimization problem formulated as an integer program is solved to optimality through a Branch-and-Bound method for small instances. For larger instances, the best feasible solution found by the solver after a given time limit threshold is considered. Compared with some existing protocols, simulation results based on multiple criteria (energy consumption, coverage ratio, and so on) show that the proposed protocol can prolong efficiently the network lifetime and improve the coverage performance. [ABSTRACT FROM AUTHOR]

There are many applications that require mobile robots to autonomously cover an entire area with a sensor or end effector. The vast majority of the literature on this subject is focused on addressing path planning for area coverage under the assumption that the robot’s pose is known or that error is bounded. In this work, we remove this assumption and develop a completely probabilistic representation of coverage. We show that coverage is guaranteed as long as the robot pose estimates are consistent, a much milder assumption than zero or bounded error. After formally connecting robot sensor uncertainty with area coverage, we propose an adaptive sliding window filter pose estimator that provides a close approximation to the full maximum a posteriori estimate with a computation cost that is bounded over time. Subsequently, an adaptive planning strategy is presented that automatically exploits conditions of low vehicle uncertainty to more efficiently cover an area. We further extend this approach to the multi-robot case where robots can communicate through a (possibly faulty and low-bandwidth) channel and make relative measurements of one another. In this case, area coverage is achieved more quickly since the uncertainty over the robots’ trajectories is reduced. We apply the framework to the scenario of mapping an area of seabed with an autonomous underwater vehicle. Experimental results support the claim that our method achieves guaranteed complete coverage notwithstanding poor navigational sensors and that resulting path lengths required to cover the entire area are shortest using the proposed cooperative and adaptive approach. [ABSTRACT FROM AUTHOR]

The positioning techniques and adjusting sensing radius of nodes have an important role in improving the network coverage of hybrid wireless sensor networks. In this paper, we propose a novel optimized bidding-based algorithm that able to identify the coverage holes after deploying the stationary sensor nodes, also adjust sensing radius of nodes effectively based on the Delaunay triangulation. In addition, if necessary, an endeavor is made to relocate a portion of mobile nodes at the appropriate positions to cover the holes and reduce the overlapping area. Furthermore, the structure of created triangles i.e. acute and obtuse, layout vertices, region coverage status in before and after the relocation are considered in moving strategy of the proposed algorithm. Extensive simulations have been performed to validate the effectiveness of the proposed algorithm in terms of the coverage ratio, number of moved nodes, number of alive nodes and average of nodes movement. [ABSTRACT FROM AUTHOR]

Area coverage using imaging reconnaissance .satellites belongs to mission planning problems constrained by spatio-temporal information. The current area coverage algorithms can be divided into two categories: based on single-satellite and multi-satellite. The former cannot fully utilize satellite resources, and is replaced by the latter gradually. However, the latter usually applies the existing intelligent optimization approaches simply. In addition, the drawback of both is heavily depending on user's intervene. Therefore, in this paper, a self-adaptive approach is proposed for the low-effective and time-consuming issue of current covering algorithms. Firstly, a self-adaptive way for grid division is presented to automatically generate grid; secondly, in order to balance imaging accuracy and efficiency, the largest visual coverage computing is proposed to determine the angle of each satellite; thirdly, a semantic-based sliding window optimizing strategy is designed to calculate planning sequence for area coverage. Compared with the classic algorithms, this method reduces human-computer interaction, and is more effective and robust. It has already been applied in area coverage of real conditions. [ABSTRACT FROM AUTHOR]

Area coverage control with multi-agent systems is formulated here in the framework of Bellman's optimality. In the literature, optimal configurations are obtained by Lloyd's algorithm, which iteratively converges the agents to centroidal Voronoi configurations that correspond to local maxima of a coverage metric, where metric sub-domains are assigned to each agent according to Voronoi tessellations. In this work, optimal tracking is achieved by an adaptive control policy using an actor–critic neural network-based reinforcement learning technique, which rewards actions that decrease a Lyapunov function based on the area coverage metric to drive the error dynamics to zero, and uses a feed-forward neural network to approximate the tracking control signal. The implementation of the area-coverage control is model-free in the sense that it relies on neural networks that interpolate local data gathered and shared by each agent, without relying on a global model of the system. We prove that optimality is achieved when the agents converge to Voronoi centroids, therefore maximizing the coverage metric, with the important implication that the obtained class of solutions is consistent with the ones obtained with Lloyd's algorithm and its extension to non-autonomous systems. [ABSTRACT FROM AUTHOR]

This article examines the problem of visual area coverage by a network of Mobile Aerial Agents (MAAs). Each MAA is assumed to be equipped with a downwards facing camera with a conical field of view which covers all points within a circle on the ground. The diameter of that circle is proportional to the altitude of the MAA, whereas the quality of the covered area decreases with the altitude. A distributed control law that maximizes a joint coverage-quality criterion by adjusting the MAAs’ spatial coordinates is developed. The effectiveness of the proposed control scheme is evaluated through simulation studies. [ABSTRACT FROM AUTHOR]

Providing field coverage is a key task in many sensor network applications. In certain scenarios, the sensor field may have coverage holes due to random initial deployment of sensors; thus, the desired level of coverage cannot be achieved. A hybrid wireless sensor network is a cost-effective solution to this problem, which is achieved by repositioning a portion of the mobile sensors in the network to meet the network coverage requirement. This paper investigates how to redeploy mobile sensor nodes to improve network coverage in hybrid wireless sensor networks. We propose a two-phase coverage-enhancing algorithm for hybrid wireless sensor networks. In phase one, we use a differential evolution algorithm to compute the candidate's target positions in the mobile sensor nodes that could potentially improve coverage. In the second phase, we use an optimization scheme on the candidate's target positions calculated from phase one to reduce the accumulated potential moving distance of mobile sensors, such that the exact mobile sensor nodes that need to be moved as well as their final target positions can be determined. Experimental results show that the proposed algorithm provided significant improvement in terms of area coverage rate, average moving distance, area coverage-distance rate and the number of moved mobile sensors, when compare with other approaches. [ABSTRACT FROM AUTHOR]

In this paper, we study the area coverage of directional sensor networks (DSNs) with random node distribution. The coverage of DSNs depends on the sensor's locations, the sensing radiuses, and the working directions, as well as the angle of view (AoV), which is challenging to analyze. We transform the network area coverage problem into cell coverage problems by exploiting the Voronoi diagram, which only needs to optimize local coverage for each cell in a decentralized way. To address the cell coverage problem, we propose three local coverage optimization algorithms to improve the cell coverage, namely Move Inside Cell Algorithm (MIC), RotateWorking Direction Algorithm (RWD) and Rotation based on boundary (RB), respectively. Extensive simulations are performed to prove the effectiveness of our proposed algorithms in terms of the coverage ratio. [ABSTRACT FROM AUTHOR]

Coverage is an important issue that has been widely discussed in wireless sensor networks (WSNs). However, it is still a big challenge to achieve both purposes of full coverage and energy balance. This paper considers the area coverage problem for a WSN in which each sensor has a variable sensing radius. To prolong the network lifetime, a weighted Voronoi diagram (WVD) is proposed as a tool for determining the responsible sensing region of each sensor according to the remaining energy in a distributed manner. The proposed mechanism, which is called sensing radius adaptation (SRA), mainly consists of three phases. In the first phase, each sensor and its neighboring nodes cooperatively construct the WVD to identify the responsible monitoring area. In the second phase, each sensor adjusts its sensing radius to reduce the overlapping sensing region such that the purpose of energy conservation can be achieved. In the last phase, the sensor with the least remaining energy further adjusts its sensing radius with its neighbor for to maximize the network lifetime. Performance evaluation and analysis reveal that the proposed SRA mechanism outperforms the existing studies in terms of the network lifetime and the degree of energy balance. [ABSTRACT FROM PUBLISHER]

The most important problem in a Wireless Sensor Network (WSN) is to optimize the use of its limited energy provision, so that it can fulfil its monitoring task as long as possible. Among known available approaches that can be used to improve power management, lifetime coverage optimization provides activity scheduling which ensures sensing coverage while minimizing the energy cost. In this article an approach called Perimeter-based Coverage Optimization protocol (PeCO) is proposed. It is a hybrid of centralized and distributed methods: the region of interest is first subdivided into subregions and the protocol is then distributed among sensor nodes in each subregion. The novelty of the approach lies essentially in the formulation of a new mathematical optimization model based on the perimeter-coverage level to schedule sensors' activities. Extensive simulation experiments demonstrate that PeCO can offer longer lifetime coverage for WSNs compared to other protocols. [ABSTRACT FROM AUTHOR]

Aim Given its catastrophic consequences, the extinction of apex predators has long been of interest to modern ecology. Despite major declines, no present-day species of marine apex predator has yet become extinct. Because of their vulnerability, understanding the mechanisms leading to their extinction in the past could provide insight into the natural factors that interact with human threats to drive their loss. We studied the geographical distribution patterns of the extinct macro-predatory shark Carcharocles megalodon in order to elucidate its pathway to extinction. Location World-wide from the Miocene to the Pliocene ( c. 23-2.6 Ma). Methods A meta-analysis of C. megalodon occurrence records was performed using the Paleobiology Database as a platform. The data were binned into geological time slices, and the circular home range around each data point was mapped in reconstructions made in GPlates. We then quantitatively assessed the species' geographical range and global abundance over time, and the relationship between distribution and climate. Results The pathway to extinction of C. megalodon probably started in the late Miocene with a decrease in its global abundance. This decrease was then followed by a decline in its geographical range during the Pliocene. Although the extinction of C. megalodon has been attributed to climate change, we found no evidence of direct effects of global temperature. Instead, we found that the collapse in geographical distribution coincided mainly with a drop in the diversity of filter-feeding whales and the appearance of new competitors (large predatory whales and the great white shark). Main conclusions This research represents the first study of the distributional trends of an extinct, cosmopolitan apex predator in deep-time. Our results suggest that biotic factors, and not direct temperature limitations, were probably the primary drivers of the extinction of the largest marine apex predators that ever lived. [ABSTRACT FROM AUTHOR]

Area coverage is an important research issue in the field of visual sensor networks (VSNs) because of the inherent constraints of VSNs, such as non-rechargeable energy resources and directionality of the sensing range of camera nodes. The dense deployment of camera nodes makes it possible to provide a satisfactory area coverage for a longer duration. At the same time the rest of camera nodes can be turned off and be scheduled to alternate the active nodes when it is necessary. In this paper, we define area coverage problem in VSNs aiming to minimize blind and redundantly covered grid cells of a desired area and energy distortion of camera nodes. Then we propose two scheduling algorithms for camera nodes which are randomly deployed to k-cover the desired area. In the first algorithm named evolutionary camera node scheduling (ECNS), we aim to achieve maximal area coverage by putting the smallest number of camera nodes into active mode and to minimize blind and redundantly grid cells. Since the objectives considered in ECNS conflict each other, we employ adaptive weighted sum method to formulate our objectives into a linear equation and then we propose a genetic algorithm to find the minimum value of the integrated linear equation. In the second algorithm named energy aware evolutionary camera node scheduling (EAECNS), we propose a method to strike a balance between the energy consumption of all camera nodes while it is providing satisfactory coverage of the target area and keeping the number of redundantly covered grid cells down. We evaluate the performance of both algorithms in terms of coverage, number of live nodes and redundancy by subsequent simulations. Also, we show that EAECNS has superior performance in comparison with ECNS and other state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Given N points in the plane P 1 , P 2 , … , P N and a location Ω, the union of discs with diameters [ Ω P i ] , i = 1 , 2 , … , N covers the convex hull of the points. The location Ω s minimizing the area covered by the union of discs, is shown to be the Steiner center of the convex hull of the points. Similar results for d -dimensional Euclidean space are conjectured. [ABSTRACT FROM AUTHOR]

One of the main research challenges faced in Wireless Sensor Networks (WSNs) is to preserve continuously and effectively the coverage of an area (or region) of interest to be monitored, while simultaneously preventing as much as possible a network failure due to battery-depleted nodes. In this paper, we propose a protocol, called distributed lifetime coverage optimization protocol (DiLCO), which maintains the coverage and improves the lifetime of a wireless sensor network. First, we partition the area of interest into subregions using a classical divide-and-conquer method. Our DiLCO protocol is then distributed on the sensor nodes in each subregion in a second step. To fulfill our objective, the proposed protocol combines two effective techniques: a leader election in each subregion, followed by an optimization-based node activity scheduling performed by each elected leader. This two-step process takes place periodically, to choose a small set of nodes remaining active for sensing during a time slot. Each set is built to ensure coverage at a low energy cost, allowing to optimize the network lifetime. Simulations are conducted using the discrete event simulator OMNET++. We refer to the characteristics of a Medusa II sensor for the energy consumption and the computation time. In comparison with two other existing methods, our approach is able to increase the WSN lifetime and provides improved coverage performances. [ABSTRACT FROM AUTHOR]

This paper proposed a geometric algorithm based on Delaunay triangulation to deal with the problem of area coverage in video sensor networks. The center of gravity of triangle with maximum area would be selected as the decision direction from all the adjacent triangulations around the sensor. Based on this, it combined the geometric method of Delaunay triangulation with the distributed greedy algorithm, and introduced the concept of “contribution rate” that reflects the size of the area coverage in the node’s candidate direction to solve the problem of redundant coverage. Finally, the simulation results show the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Emerging technologies and robotics solutions for cleaning applications are rapidly progressing nowadays. Reconfigurable robots that can change shapes are introduced to overcome the limitations of fixed-shape robots. Reconfigurable robots should be capable of moving through narrow spaces with the help of continuous shape-changing to improve the coverage performance. In this regard, a reconfigurable robot requires perceiving the narrow spaces in between obstacles and generating the path to navigate. Most importantly, the robot should decide and change the shape configurations appropriately to move on that path without collisions. Furthermore, such a reconfiguration ability could be used for covering around obstacles. Therefore, this paper proposes a method to navigate a reconfigurable robot through narrow spaces or cover obstacles with the aid of continuous reconfiguration. In the first step, the method analyzes the obstacle clusters in a metric map to determine the appropriate paths to navigate. In the second step, a sequence of shapes for reconfiguration that allows the robot's movement on the path without collisions is determined. Metaheuristics, Grey Wolf Optimizer (GWO), Genetic Algorithm (GA), and Simulated Annealing (SA), are proposed to determine the shape sequence. Simulations have been conducted to validate the performance and behavior of the proposed method. The results confirm that the developed method is effective for navigating a reconfigurable robot through narrow spaces or covering obstacles with continuous shape-changing. According to the statistical outcomes, GWO requires less computational time compared to GA and SA. • Area coverage is a crucial requirement for a floor cleaning robot. • A novel navigation strategy that considers continuous reconfigurations is proposed. • Metaheuristic optimization techniques are proposed for the navigation strategy. • The method enables a reconfigurable robot to navigate through narrow spaces. • The method facilitates the navigation around obstacles for coverage. [ABSTRACT FROM AUTHOR]

This paper proposed a network coverage enhancing algorithm for area coverage problem in static homogeneous directional sensor networks, which was so-called virtual force-directed particle swarm coverage-enhancing algorithm for directional sensor networks (VFPSC-DSN). Aiming at maximize the efficient coverage of networks, VFPSC-DSN combined virtual force with particle swarm optimization algorithm. In the algorithm, velocities of the particles was updated according to not only historical solutions and global optimal solutions but also the virtual force between the centroid of the nodes. The key point of this scheme was to use virtual force to direct the updating of particle swarm algorithm for enhancing the convergence speed, and used particle swarm optimization algorithm to improve the global searching ability. By adjusting the main sense direction nodes, it achieved the network coverage optimization. Simulation result shows that VFPSC-DSN solving the coverage problem of directional sensor networks efficiently and rapidly. [ABSTRACT FROM AUTHOR]

Given n sensors on an interval, each of which is equipped with an adjustable sensing radius and a unit battery charge that drains in inverse linear proportion to its radius, what schedule will maximize the lifetime of a network that covers the entire interval? Trivially, any reasonable algorithm is at least a 2-approximation for this Sensor Strip Cover problem, so we focus on developing an efficient algorithm that maximizes the expected network lifetime under a random uniform model of sensor distribution. We demonstrate one such algorithm that achieves an expected network lifetime within 12 % of the theoretical maximum. Most of the algorithms that we consider come from a particular family of RoundRobin coverage, in which sensors take turns covering predefined areas until their battery runs out. [ABSTRACT FROM AUTHOR]

The problem of moving target tracking in directional sensor networks (DSNs) introduces new research challenges, including optimal selection of sensing and communication sectors of the directional sensor nodes, determination of the precise location of the target and an energy-efficient data collection mechanism. Existing solutions allow individual sensor nodes to detect the target's location through collaboration among neighboring nodes, where most of the sensors are activated and communicate with the sink. Therefore, they incur much overhead, loss of energy and reduced target tracking accuracy. In this paper, we have proposed a clustering algorithm, where distributed cluster heads coordinate their member nodes in optimizing the active sensing and communication directions of the nodes, precisely determining the target location by aggregating reported sensing data from multiple nodes and transferring the resultant location information to the sink. Thus, the proposed target tracking mechanism minimizes the sensing redundancy and maximizes the number of sleeping nodes in the network. We have also investigated the dynamic approach of activating sleeping nodes on-demand so that the moving target tracking accuracy can be enhanced while maximizing the network lifetime. We have carried out our extensive simulations in ns-3, and the results show that the proposed mechanism achieves higher performance compared to the state-of-the-art works. [ABSTRACT FROM AUTHOR]

Precision agriculture (PA) is a collage of strategies and technologies to optimize operations and decisions in farms by using spatial and temporal variabilities in yield, crops, and soil within an agricultural plot. It is a data-driven technique, therefore, selective treatment of crops and soil, and managing variabilities using robots and smart sensors is the next improvement in PA. In this paper, it is modeled as a multi-agent patrolling problem, where robots visit subregions that required immediate attention in the agricultural field. Furthermore, for area coverage / patrolling task in the agricultural plot, a centralized Convolutional Neural Network (CNN) based Dual Deep Q-learning (DDQN) is proposed. A customized reward function is designed, which rewards worth-visiting idle regions, and punishes undesirable actions. A proposed algorithm has been compared with various algorithms including individual Q-learning (IRL), uniform coverage (UC), and Behavior-Based Robotics coverage (BBR) for different scenarios in the agricultural plots. [ABSTRACT FROM AUTHOR]

• A novel reconfigurable ascend descend staircase cleaning robot is introduced. • A 3D-point cloud-based perception framework is introduced for autonomous area-coverage. • A sweeping 2D lidar has been implemented to generate 3D point cloud data of the robot's environment. • The developed robot and the perception system is evaluated in real-world scenarios. • Using the perception framework the robot demonstrates a successful area coverage. Cleaning the staircases is the next big leap for every commercial cleaning robot in order to accomplish a full-fledged cleaning of a constructed buildings. Such an effort could be witnessed in the academic literature where a robotic system can autonomously clean the staircase by ascending. However, none of the existing staircase traversing platforms demonstrated the ability to perform both ascending and descending motion while cleaning, which can significantly improvise the overall robot's performance. In this paper, we propose a novel reconfigurable cleaning robotic platform called sTetro_plotter, which can perform both ascend and descend motion in the staircases. Pointedly, in this work, we presented a perception framework for the developed robot to traverse on the staircase and perform area coverage autonomously. The framework was constructed with a pointNet++ based feature extractor and classification and regression network to generate a bounding box on the targeted feature. Also, we discussed the process of a staircase descending through tracking the generated bounding box. We implemented a sweeping-based lidar device that can generate a 3D point cloud by sensing its environment. We evaluated the performance of the proposed robot and its perception system through conducting experiments in real-world scenarios. The experimental trials successfully demonstrate the ability of the sTetro_plotter robot to perform autonomous area coverage while traversing on the staircase using the developed perception framework. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) have several important applications, both in research and domestic use. Generally, their main role is to collect and transmit data from an ROI (region of interest) to a base station for processing and analysis. Therefore, it is vital to ensure maximum coverage of the chosen area and communication between the nodes forming the network. A major problem in network design is the deployment of sensors with the aim to ensure both maximum coverage and connectivity between sensor node. The maximum coverage problem addressed here focuses on calculating the area covered by the deployed sensor nodes. Thus, we seek to cover any type of area (regular or irregular shape) with a predefined number of homogeneous sensors using a genetic algorithm to find the best placement to ensure maximum network coverage under the constraint of connectivity between the sensors. Therefore, this paper tackles the dual problem of maximum coverage and connectivity between sensor nodes. We define the maximum coverage and connectivity problems and then propose a mathematical model and a complex objective function. The results show that the algorithm, called GAFACM (Genetic Algorithm For Area Coverage Maximization), covers all forms of the area for a given number of sensors and finds the best positions to maximize coverage within the area of interest while guaranteeing the connectivity between the sensors. [ABSTRACT FROM AUTHOR]

Mixed Wireless Sensor Network (WSN) is a network that consists of static and mobile sensor nodes. This article presents a collaborative framework where a team of autonomous mobile sensor nodes navigate through a sparse network with static sensors to improve the overall area coverage and search for events that may have occurred in areas not monitored by the static network. The mobile sensor nodes have limited communication and sensing ranges and collaborate to autonomously and dynamically decide their trajectories to enhance the area coverage, avoiding obstacles and collisions and adapting to new information such as failures of static nodes. In the context of the proposed framework, one can address various trade-offs. Examples include the trade off between the area coverage and the energy cost in terms of traveled distance and the one between the area coverage and information exchange among the mobile nodes. Furthermore, the proposed framework can be used to address spatially adaptive sampling. Finally, the proposed framework has been evaluated under different scenarios and has been shown to perform very well. [ABSTRACT FROM AUTHOR]

We present a continuous-space multiscale adaptive search (MAS) algorithm for single or multiple searchers that finds a stationary target in the presence of uncertainty in sensor diameter. The considered uncertainty simulates the influence of the changing environment and terrain as well as adversarial actions that can occur in practical applications. When available, information about the foliage areas and a priori distribution of the target position is included in the MAS algorithm. By adapting to various uncertainties, MAS algorithm reduces the median search time to find the target with a probability of detection of at least PD and a probability of false alarm of at most PFA. We prove that MAS algorithm discovers the target with the desired performance bounds PD and PFA. The unique features of the MAS algorithm are realistic second-order dynamics of the mobile sensors that guarantees uniform coverage of the surveyed area and a two-step Neyman–Pearson-based decision-making process. Computer simulations show that MAS algorithm performs significantly better than lawnmower-type search and billiard-type random search. Our tests suggest that the median search time in the MAS algorithm may be inversely proportional to the number of participating searchers. As opposed to lawnmower search, the median search time in the MAS algorithm depends only logarithmically on the magnitude of uncertainty. [ABSTRACT FROM PUBLISHER]

A qualitative understanding of the particles in dust and their sources helps to identify ways to reduce the accumulation of dust within historic houses. Knowing the possible sources of dust helps to suggest preventive steps. Simple low-technology methods enable rapid identification of dust deposits and require little specialist equipment. Dust samples can be collected on sticky surfaces for examination with a hand lens or microscope. The size of particles and fibres, and amount of surface coverage, can be estimated roughly using a graticule or coverage chart. An atlas of common dust components aids identification of soil dust, soot, insects, plant fragments, hair, skin, paint/plaster, clothing fibres, paper, food and mould. A mixture of fine dust is resolvable into a range of particle and fibre types. Long exposures of sticky samplers, with an estimate of the area covered by dust, indicate the rate of deposition and suggest the cleaning frequency necessary to prevent negative visitor reactions. The atlas lists suppliers and sources of information. [ABSTRACT FROM AUTHOR]

Topology control-TC is a strategy used to save energy in wireless sensor networks. Topology construction-TCn is the area of TC that studies the reduction of the network topology, while maintaining characteristics like connectivity and coverage. There are two main strategies to reduce the topology of a network: reducing the transmission power, like the KNEIGH-Tree, and decreasing the number of active nodes in the network, like A3 and A3Cov. Both strategies reduce the energy consumption and, therefore, increase the network lifetime; however, these strategies have not been compared against one another. This paper compares the three protocols mentioned before in terms of the network lifetime and coverage. The results show that in dense networks, the A3 and A3Cov protocols extend the lifetime of the network more than twice than that of the offered by the KNEIGH-Tree protocol. [ABSTRACT FROM AUTHOR]

Abstract: We report an effective approach to fabricate nanopatterns of alkylsilane self-assembly monolayers (SAMs) with desirable coverage and feature size by gradient photocatalysis in TiO2 aqueous suspension. Growth and photocatalytic degradation of octadecyltrichlorosilane (OTS) were combined to fabricate adjustable monolayered nanopatterns on mica sheet in this work. Systematic atomic force microscopy (AFM) analysis showed that OTS-SAMs that have similar area coverage with different feature sizes and similar feature size with different area coverages can be fabricated by this approach. Contact angle measurement was applied to confirm the gradually varied nanopatterns contributed to the gradient of UV light illumination. Since this approach is feasible for various organic SAMs and substrates, a versatile method was presented to prepare tunable nanopatterns with desirable area coverage and feature size in many applications, such as molecular and biomolecular recognition, sensor and electrode modification. [Copyright &y& Elsevier]