Region coverage control for multiple stratospheric airships with combined self-/event-triggered mechanism

The region coverage control problem of multiple stratospheric airships system is firstly addressed in this paper.Towards it,we propose a two-layer control framework with the artificial potential field(APF)-based region coverage control law and the adaptive tracking control law.The APF-based region coverage control law ensures the coverage task is achieved until every single stratospheric airship ends up performing station keeping where near the respective global minimum point,in which an innovative solution to the local minimum problem is put forward.The adaptive tracking control law is designed to realize motion control using tracking the desired velocity and angular velocity given by coverage control law,with the consideration of several practical control problems as unknown individual differences and external disturbances.To save resources,the combined self-/event-triggered mechanism designed therein significantly reduces the times of state information transmission and control law calculation.The effectiveness of the proposed control framework is verified through simulations.

A Comparative Evaluation of Test Coverage Techniques Effectiveness

Software systems have become complex and challenging to develop and maintain because of the large size of test cases with increased scalability issues. Test case prioritization methods have been successfully utilized in test case management. However, the prohibitively exorbitant cost of large test cases is now the mainstream in the software industry. The growth of agile test-driven development has increased the expectations for software quality. Yet, our knowledge of when to use various path testing criteria for cost-effectiveness is inadequate due to the inherent complexity in software testing. Existing researches attempted to address the issue without effectively tackling the scalability of large test suites to reduce time in regression testing. In order to provide a more accurate way of fault detection in software projects, we introduced novel coverage criteria, called Incremental Cluster-based test case Prioritization (ICP), and investigated its potentials by making a comparative evaluation with three un-clustered traditional coverage-based criteria: Prime-Path Coverage (PPC), Edge-Pair Coverage (EPC) and Edge Coverage (EC) based on mutation analysis. By clustering test suites, based on their dynamic run-time behavior, the number of pair-wise comparisons is reduced significantly. To compare, we analyzed 20 functions from 25 C programs, instrumented faults into the programs, and used the Mull mutation tool to generate mutants and perform a statistical analysis of the results. The experimental results show that ICP can lead to cost-effective improvements in fault detection.

A Method for Deploying the Minimal Number of UAV Base Stations in Cellular Networks

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

Soil methane emission suppression control using unmanned aircraft vehicle swarm application of biochar mulch-A simulation study

In this paper,we present a soil methane emissions suppression approach using swarms of unmanned aerial vehicles(UAVs),by spreading biochar mulch on top of the detected methane emissions area/source.Soil microorganisms can produce methane and release it into the atmosphere causing climate change such as global warming.However,people lack methods to manage soil methane emissions,especially quantification of methane emissions from the soil.Current measurement and suppression of methane methods are often limited due to the maintenance,installation,and calibration requirements of these sensing systems.To overcome these drawbacks,we present a new method called FADE-MAS2D(Fractional Advection Diffusion Mobile Actuator and Sensor)in which swarming UAVs are applied as optimal coverage control actuators to various methane release scenarios(from single to multi-source disturbances)utilizing an anomalous diffusion model with different time,and space fractional orders subject to wind fields.This strategy is based on the premise that methane diffusion can be modeled as an anomalous diffusion equation,and swarming UAVs can be applied to tackle the optimal coverage control issue.To simulate methane diffusion under the wind,we utilize the fractional calculus to solve the anomalous diffusion equation and define wind force with the drag equation.In addition,we integrated emissions control,UAV control efforts,and UAV location error in our cost function.Finally,we evaluated our approach using simulation experiments with methane diffusion and multiple methane emission sources in the time and space domain,respectively.The results show that when α=0.8 and β=1.8,the shape and emissions of methane perform well.Furthermore,our approach resulted in great control performance with multiple methane emission sources and different wind velocities and directions.

Distributed multi-robot sweep coverage for a region with unknown workload distribution

This paper considers the scenario where multiple robots collaboratively cover a region in which the exact distribution of workload is unknown prior to the operation.The workload distribution is not uniform in the region,meaning that the time required to cover a unit area varies at different locations of the region.In our approach,we divide the target region into multiple horizontal stripes,and the robots sweep the current stripe while partitioning the next stripe concurrently.We propose a distributed workload partition algorithm and prove that the operation time on each stripe converges to the minimum under the discrete-time update law.We conduct comprehensive simulation studies and compare our method with the existing methods to verify the theoretical results and the advantage of the proposed method.Flight experiments on mini drones are also conducted to demonstrate the practicality of the proposed algorithm.

Locomotion trajectory with cooperative metrics in wireless sensor networks

Detection coverage control is one of the most important topics in the intrusion detection problem of wireless sensor networks （WSN）. However, its converse, i.e., to design an object locomotion trajectory in WSN, has not received enough attention. This article proposes a heuristic algorithm, namely, the security ＆ speed （SS） algorithm, to depict such a trajectory that takes into consideration both security and speed. The merit of the SS algorithm is its topology independency. When compared with traditional algorithms, the SS algorithm approaches the optimal trajectory better, and enjoys considerably lower computational load, and a better and adjustable tradeoff between trajectory security and speed.