Static Coverage Research Articles

A unified Lloyd-based framework for multi-agent collective behaviours

Different authors have addressed a number of problems in the area of distributed control proposing convincing solutions to specific problems such as static coverage, dynamic coverage/exploration, rendezvous, flocking, formation control. However, a major limitation of problem-specific approaches is a fundamental lack of flexibility when the group meets unexpected conditions and has to change its goal on the fly. In this paper, we show that a large class of distributed control problems can be cast into a general framework based on the adoption of the Lloyd methodology. The adoption of a unified framework enables efficient solutions for the specific problems guaranteeing at the same time important safety and functional properties and a large degree of flexibility in the execution of group tasks. The paper sets the theoretical basis for this development and proves the efficacy of the proposed solutions through extensive simulations and experimental results.

Research on Continuous Static Coverage of Multiple Heterogeneous Warning Vehicles Considering No-fly Areas

Through the analysis of the types of tasks and flight routes faced by the warning vehicle, this paper proposes the problem of continuous static coverage of the multi-heterogeneous warning vehicle (referred to as WV) considering No-fly areas to be solved. By analysing things like detection area, no-fly area, WV patrol strategy form, patrol strategy planning mechanism and detection capability of WV, based on the philosophy of model stratification and time segmentation, a multi-layer multi-stage patrol strategy planning method (referred to as MLMPSPM) for determining the patrol strategies of multiple heterogeneous warning vehicles (WVs) during multi-patrol cycles considering no-fly areas is proposed, including the process of determining the planning stage index. In detail, MLMPSPM mainly introduces the patrol strategy planning method of EF and ER . Whether EF or ER includes input section, planning section, stage warning effect of planning stage, and patrol strategy objective planning, etc. EU planning processes is the extension of EF and ER . In the experimental results section, the relevant experimental parameters are designed in detail, the experimental results show that MLMPSPM can not only determine the patrol strategies of all WVs throughout the alert process considering time-varying no-fly areas, but also achieve a large total warning effect.

A Static Area Coverage Algorithm for Heterogeneous AUV Group Based on Biological Competition Mechanism.

Static area coverage of the heterogeneous autonomous underwater vehicle (AUV) group is widely used in many fields. With the use of the centroidal Voronoi tessellation (CVT) algorithm, the coverage problem can be resolved. However, the CVT method, which is optimized with the location evaluation function, cannot consider the heterogeneity of AUVs when the group performs the static area coverage task and will cause a waste of resources. In this paper, considering different AUVs’ task requirements and detection capabilities comprehensively, we propose a coverage control optimization algorithm based on a biological competition mechanism (BCM). By using BCM, the task load of each AUV can be distributed consistently. In addition, we provide strict proof of the consistency of the algorithm based on the Lyapunov method. Simulation results demonstrate that with the proposed algorithm, the location distribution of the heterogeneous AUV group for area coverage is close to the balanced value, and the performance is better than the CVT algorithm for static area coverage.

A Common Optimization Framework for Multi-Robot Exploration and Coverage in 3D Environments

This paper studies the problems of static coverage and autonomous exploration of unknown three-dimensional environments with a team of cooperating aerial vehicles. Although these tasks are usually considered separately in the literature, we propose a common framework where both problems are formulated as the maximization of online acquired information via the definition of single-robot optimization functions, which differs only slightly in the two cases to take into account the static and dynamic nature of coverage and exploration respectively. A common derivative-free approach based on a stochastic approximation of these functions and their successive optimization is proposed, resulting in a fast and decentralized solution. The locality of this methodology limits however this solution to have local optimality guarantees and specific additional layers are proposed for the two problems to improve the final performance. Specifically, a Voronoi-based initialization step is added for the coverage problem and a combination with a frontier-based approach is proposed for the exploration case. The resulting algorithms are finally tested in simulations and compared with possible alternatives.

Target coverage computation protocols in wireless sensor networks: a comprehensive review

Wireless sensor network comprises tiny sensor nodes having the ability of sensing to surveil an area of interest. The amount of area sensed by a sensor node is known as coverage. Coverage is a multi-dimensional quality of service (QoS) metric in terms of diversity in its computation algorithms according to the real-time applications. Different applications require a distinctive level of coverage. But every coverage computation algorithm has one common objective, i.e. to maximize the network’s lifespan and coverage quality with minimum number of sensor nodes. Several algorithms have derived optimal or close-to-optimal solutions based on similar or distinct parameters. This paper presents a concise review of various target coverage protocols for both static and mobile targets. Total target coverage provided by the deployed sensor nodes is a measure of sensing quality. Target coverage is bifurcated as static coverage and dynamic coverage. This paper highlights and describes various application specific target coverage protocols and their QoS metrics and parameters. Each protocol has solved multi-dimensional coverage issues and challenges and proposed optimum solutions. The comprehensive review of the target coverage protocol helps in understanding about various research challenges and its existed solutions in target coverage, detection and tracking in a better way.

Human-swarm collaboration with coverage control under nonidentical and limited sensory ranges

Human intelligence plays a significant role in the operation of a multi-agent system. This study proposes a control framework that allows a human operator to collaboratively interact with a swarm robot to accomplish environmental exploration, detection, and coverage. A ri-limited Voronoi partition is proposed herein for improving the all-territory sensing range for coverage control. Subsequently, an interactive control framework and control algorithms are presented for an abstract task function that allows a human operator to control the movement of a swarm robot in a working environment. Environmental information is fed back to the master devices so that the human operator can realize the swarm robots coverage control situation. Stability and position tracking with static coverage control and input-to-state stability with dynamic coverage control of the human-swarm system are investigated. The efficiency and efficacy of the proposed system are validated via numerical examples and experiments.

A Hybrid-Fuzzy Logic Guided Genetic Algorithm (H-FLGA) Approach for Resource Optimization in 5G VANETs

To support diversified quality of service demands and dynamic resource requirements of users in 5G driven VANETs, network resources need flexible and scalable resource allocation strategies. Current heterogeneous vehicular networks are designed and deployed with a connection-centric mindset with fixed resource allocation to a cell regardless of traffic conditions, static coverage, and capacity. In this paper, we propose a hybrid-fuzzy logic guided genetic algorithm (H-FLGA) approach for the software defined networking controller, to solve a multi-objective resource optimization problem for 5G driven VANETs. Realizing the service oriented view, the proposed approach formulates five different scenarios of network resource optimization in 5G VANETs. Furthermore, the proposed fuzzy inference system is used to optimize weights of multi-objectives, depending on the type of service requirements of customers. The proposed approach shows the minimized value of multi-objective cost function when compared with the GA. The simulation results show the minimized value of end-to-end delay as compared to other schemes. The proposed approach will help the network service providers to implement a customer-centric network infrastructure, depending on dynamic customer needs of users.

Fault Injection and Test Approach for Behavioural Verilog Designs using the Proposed RASP-FIT Tool

Soft-core processors and complex Field Pro-grammable Gate Array (FPGA) designs are described as an algorithmic manner, i.e. behavioural abstraction level in Hard-ware Description Languages (HDL). Lower abstraction levels add complexity and delays in the design cycle as well as in the fault injection approach. Therefore, fault simulation/emulation techniques are demanded to develop an approach for testing of design and to evaluate dependability analysis of FPGA designs at this abstraction level. Broadly, the fault injection techniques for FPGA-based designs at the HDL code level are categorised into emulation and simulation-based techniques. This work is an extension of our previous methodologies developed for FPGA designs written at data-flow and gate abstraction levels under the proposed RASP-FIT tool. These methodologies include fault injection by code parsing of the SUT, test approach for finding the test vectors using dynamic and static compaction techniques, fault coverage, and compaction ratio directly at the code level of the design. In this paper, we described the proposed approaches briefly, and the enhancement of a Verilog code modifier for the behavioural designs is presented in detail.

Coverage and timeliness of vaccination and the validity of routine estimates: Insights from a vaccine registry in Kenya

BackgroundThe benefits of childhood vaccines are critically dependent on vaccination coverage. We used a vaccine registry (as gold standard) in Kenya to quantify errors in routine coverage methods (surveys and administrative reports), to estimate the magnitude of survivor bias, contrast coverage with timeliness and use both measures to estimate population immunity. MethodsVaccination records of children in the Kilifi Health and Demographic Surveillance System (KHDSS), Kenya were combined with births, deaths, migration and residence data from 2010 to 17. Using inverse survival curves, we estimated up-to-date and age-appropriate vaccination coverage, calculated mean vaccination coverage in infancy as the area under the inverse survival curves, and estimated the proportion of fully immunised children (FIC). Results were compared with published coverage estimates. Risk factors for vaccination were assessed using Cox regression models. ResultsWe analysed data for 49,090 infants and 48,025 children aged 12–23 months in 6 birth cohorts and 6 cross-sectional surveys respectively, and found 2nd year of life surveys overestimated coverage by 2% compared to birth cohorts. Compared to mean coverage in infants, static coverage at 12 months was exaggerated by 7–8% for third doses of oral polio, pentavalent (Penta3) and pneumococcal conjugate vaccines, and by 24% for the measles vaccine. Surveys and administrative coverage also underestimated the proportion of the fully immunised child by 10–14%. For BCG, Penta3 and measles, timeliness was 23–44% higher in children born in a health facility but 20–37% lower in those who first attended during vaccine stock outs. ConclusionsStandard coverage surveys in 12–23 month old children overestimate protection by ignoring timeliness, and survivor and recall biases. Where delayed vaccination is common, up-to-date coverage will give biased estimates of population immunity. Surveys and administrative methods also underestimate FIC prevalence. Better measurement of coverage and more sophisticated analyses are required to control vaccine preventable diseases.

A Sequential Sampling Algorithm for Multistage Static Coverage Problems

In many system-engineering problems, such as surveillance, environmental monitoring, and cooperative task performance, it is critical to allocate limited resources within a restricted area optimally. Static coverage problem (SCP) is an important class of the resource allocation problem. SCP focuses on covering an area of interest so that the activities in that area can be detected with high probabilities. In many practical settings, primarily due to financial constraints, a system designer has to allocate resources in multiple stages. In each stage, the system designer can assign a fixed number of resources, i.e., agents. In the multistage formulation, agent locations for the next stage are dependent on previous-stage agent locations. Such multistage static coverage problems are nontrivial to solve. In this paper, we propose an efficient sequential sampling algorithm to solve the multistage static coverage problem (MSCP) in the presence of resource intensity allocation maps (RIAMs) distribution functions that abstract the event that we want to detect/monitor in a given area. The agent's location in the successive stage is determined by formulating it as an optimization problem. Three different objective functions have been developed and proposed in this paper: (1) L2 difference, (2) sequential minimum energy design (SMED), and (3) the weighted L2 and SMED. Pattern search (PS), an efficient heuristic algorithm has been used as optimization algorithm to arrive at the solutions for the formulated optimization problems. The developed approach has been tested on two- and higher dimensional functions. The results analyzing real-life applications of windmill placement inside a wind farm in multiple stages are also presented.

Optimal Configurations in Coverage Control with Polynomial Costs

We revisit the static coverage control problem for placement of vehicles with simple motion on the real line, under the assumption that the cost is a polynomial function of the locations of the vehicles. The main contribution of this paper is to demonstrate the use of tools from numerical algebraic geometry, in particular, a numerical polynomial homotopy continuation method that guarantees to find all solutions of polynomial equations, in order to characterize the global minima for the coverage control problem. The results are then compared against a classic distributed approach involving the use of Lloyd descent, which is known to converge only to a local minimum under certain technical conditions.

The CUSCUS simulator for distributed networked control systems: Architecture and use-cases

The current merging of networking and control research fields within the scope of robotic applications is creating fascinating research and development opportunities.However, the tools for a proper and easy management of experiments still lag behind.Although different solutions have been proposed to simulate and emulate control systems and, more specifically, fleets of Unmanned Aerial Vehicles (UAVs), still they do not include an efficient and detailed network-side simulation, which is usually available only on dedicated software. On the other hand, current advancements in network simulations suites often do not include the possibility to include an accurate description of controlled systems. In the middle 2010s, integrated solutions of networking and control for fleets of UAVs are still lacking. In this paper, we fill such gap by presenting a simulation architecture for networked control systems which is based on two well-known solutions in both the fields of networking simulation (the NS-3 tool) and UAV control simulation (the FL-AIR tool). Three main research contributions are provided: (i) first, we show how the existing tools can be integrated on a closed-loop architecture, so that the network propagation model (NS-3 side) is influenced by the drone mobility and by the 3D scenario map (FL-AIR side); (ii) second, we implement a novel module, which allows modeling realistic 3D environments by importing city-wide characteristics by the popular OpenStreetMap service; (iii) third, we demonstrate the modeling capabilities of the CUSCUS framework on two realistic use-cases, corresponding to well-known application scenarios of UAVs, i.e. dynamic formation control and static coverage of a target area.

A coverage algorithm for multiple autonomous surface vehicles in flowing environments

This paper is concerned with the coverage problem with multiple autonomous surface vehicles (ASVs) in time-varying flowing environment, where the interest information distribution is unknown to the coverage networks. While taking the model parameter uncertainty into consideration, a decentralized, adaptive control law is proposed such that the coverage network will converge to the optimal assigned region from arbitrary positions. For ease of exploration, we first investigate the static coverage problem of two-agent systems in flowing environment and present an example by extending the two-agent systems into the general case. In addition, Gaussian Estimation is introduced to predict the value of the sensory function through the sampled measurements. By using the static coverage partition as theoretical foundation, we transform the optimal coverage control into the moving target tracking problems, where the target is the centroid of the assigned region for each ASV. Based on these techniques, a decentralized kinematic control algorithm is developed to navigate the multi-ASV systems. Furthermore, the adaptive back-stepping techniques are employed to extend the kinematic controller into dynamic case with uncertain model parameters. Finally, simulation studies are provided to demonstrate the feasibility and effectiveness of the proposed approaches.

A Novel Sampling Technique for Probabilistic Static Coverage Problems

Resource allocation in the presence of constraints is an important activity in many systems engineering problems such as surveillance, infrastructure planning, environmental monitoring, and cooperative task performance. The resources in many important problems are agents such as a person, machine, unmanned aerial vehicles (UAVs), infrastructures, and software. Effective execution of a given task is highly correlated with effective allocation of resources to execute the task. An important class of resource allocation problem in the presence of limited resources is static coverage problem. In static coverage problems, it is necessary to allocate resources (stationary configuration of agents) to cover an area of interest so that an event or spatial property of the area can be detected or monitored with high probability. In this paper, we outline a novel sampling algorithm for the static coverage problem in presence of probabilistic resource intensity allocation maps (RIAMs). The key intuition behind our sampling approach is to use the finite number of samples to generate an accurate representation of RIAM. The outlined sampling technique is based on an optimization framework that approximates the RIAM with piecewise linear surfaces on the Delaunay triangles and optimizes the sample placement locations to decrease the difference between the probability distribution and Delaunay triangle surface. Numerical results demonstrate that the algorithm is robust to the initial sample point locations and has superior performance in a wide range of theoretical problems and real-life applications. In a real-life application setting, we demonstrate the efficacy of the proposed algorithm to predict the position of wind stations for monitoring wind speeds across the U.S. The algorithm is also used to give recommendations on the placement of police cars in San Francisco and weather buoys in Pacific Ocean.

Energy-efficient opportunistic coverage for people-centric urban sensing

Human-carried or vehicle-mounted sensors can be exploited to collect data ubiquitously for urban sensing. In this work, we study a new coverage problem, opportunistic coverage, to characterize the sensing quality of such people-centric sensing systems. Compared with the traditional static coverage and dynamic coverage in sensor networks, opportunistic coverage has some unique characteristics caused by the requirements of urban sensing applications and human mobility features such as spatio-temporal correlation, hotspots effects and randomness. In order to achieve good trade-off between energy consumption and coverage quality, we propose an offline node selection mechanism and an online adaptive sampling mechanism. The former can select the minimum number of nodes to achieve coverage requirements, based on the history trajectories of the given set of nodes, and the latter can help each selected node to decide whether to perform the sampling task at some time adaptively. Based on a real human mobility dataset and a taxi mobility dataset, extensive simulation results evaluate that our proposed models and mechanisms are effective and efficient in terms of energy consumption and coverage quality.

A Sweep Coverage Scheme Based on Vehicle Routing Problem

As an emerging coverage problem in wireless sensor networks, sweep coverage which introducing mobile sensors to cover points of interest within certain time interval can satisfy monitoring request in some particular application scenarios with less number of nodes than the conventional static coverage approach. In this work, aiming to support dynamical POI coverage and data delivery simultaneously, a novel sweep coverage scheme, named VRPSC(Vehicle Routing Problem based Sweep Coverage), is proposed by modeling the minimum number of required sensors problem in sweep coverage as a Vehicle Routing Problem (VRP). In VRPSC, an insertion algorithm is first introduced to create the initial scanning routes for POIs, and then the Simulated Annealing is employed to optimize these routes. The simulation results show that the VRPSC scheme achieves better performance than existing schemes. DOI: http://dx.doi.org/10.11591/telkomnika.v11i4.2380

Sweep Coverage with Mobile Sensors

Many efforts have been made for addressing coverage problems in sensor networks. They fall into two categories, full coverage and barrier coverage, featured as static coverage. In this work, we study a new coverage scenario, sweep coverage, which differs with the previous static coverage. In sweep coverage, we only need to monitor certain points of interest (POIs) periodically so the coverage at each POI is time-variant, and thus we are able to utilize a small number of mobile sensors to achieve sweep coverage among a much larger number of POIs. We investigate the definitions and model for sweep coverage. Given a set of POIs and their sweep period requirements, we prove that determining the minimum number of required sensors (min-sensor sweep-coverage problem) is NP-hard, and it cannot be approximated within a factor of 2. We propose a centralized algorithm with constant approximation ratio 3 for the min-sensor sweep-coverage problem. We further characterize the nonlocality of the problem and design a distributed sweep algorithm, DSWEEP, cooperating sensors to provide efficiency with the best effort. We conduct extensive simulations to study the performance of the proposed algorithms. Our simulations show that DSWEEP outperforms the randomized scheme in both effectiveness and efficiency.

Coverage Optimization and Spatial Load Balancing by Robotic Sensor Networks

This technical note studies robotic sensor networks performing static coverage optimization with area constraints. Given a density function describing the probability of events happening and a performance function measuring the cost to service a location, the objective is to position sensors in the environment so as to minimize the expected servicing cost. Moreover, because of load balancing considerations, the area of the region assigned to each robot is constrained to be a pre-specified amount. We characterize the optimal configurations as center generalized Voronoi configurations. The generalized Voronoi partition depends on a set of weights, one per robot, assigned to the network. We design a Jacobi iterative algorithm to find the weight assignment whose corresponding generalized Voronoi partition satisfies the area constraints. This algorithm is distributed over the generalized Delaunay graph. We also design the ?move-to-center-and-compute-weight? strategy to steer the robotic network towards the set of center generalized Voronoi configurations while monotonically optimizing coverage.

An Evaluation of the Sample Registration Scheme in India

For meaningful planning for social and economic development and for policy making, basic demographic information of a current and continuous nature is a must. Though census data are basic and are of great importance, but due to their static nature and inherent restricted subject coverage, they are only of limited value. Dynamic dependable data through continuous registration system coupled with census and other adhoc data are the ones really needed. Unfortunately, in most of the developing countries, where the resources are limited, and the need for proper planning most, such dynamic data are either not available or are not dependable due to several reasons. One-time surveys can fill in some of the gaps in knowledge, but they have their own limitations and problems and are static as well.