Movement-efficient Sensor Deployment in Wireless Sensor Networks

We study a mobile wireless sensor network (MWSN) consisting of multiple mobile sensors or robots. Two key issues in MWSNs - energy consumption, which is dominated by sensor movement, and sensing coverage - have attracted plenty of attention, but the interaction of these issues is not well studied. To take both sensing coverage and movement energy consumption into consideration, we model the sensor deployment problem as a constrained source coding problem. %, which can be applied to different coverage tasks, such as area coverage, target coverage, and barrier coverage. Our goal is to find an optimal sensor deployment to maximize the sensing coverage with specific energy constraints. We derive necessary conditions to the optimal sensor deployment with (i) total energy constraint and (ii) network lifetime constraint. Using these necessary conditions, we design Lloyd-like algorithms to provide a trade-off between sensing coverage and energy consumption. Simulation results show that our algorithms outperform the existing relocation algorithms.Comment: 18 pages, 10 figure

Port-based modeling and optimal control for a new very versatile energy efficient actuator

In this paper, we analyze in depth the innovative very versatile and energy efficient (V2E2) actuator proposed in Stramigioli et al. (2008). The V2E2 actuator is intended to be used in all kind of robotics and powered prosthetic applications in which energy consumption is a critical issue. In particular, this work focuses on the development of a port-based Hamiltonian model of the V2E2 and presents an optimal control architecture which exploits the intrinsic hybrid characteristics of the actuator design. The optimal control guarantees the minimization of dissipative power losses during torque tracking transients

Movement-Efficient Sensor Deployment in Wireless Sensor Networks With Limited Communication Range.

We study a mobile wireless sensor network (MWSN) consisting of multiple mobile sensors or robots. Three key factors in MWSNs, sensing quality, energy consumption, and connectivity, have attracted plenty of attention, but the interaction of these factors is not well studied. To take all the three factors into consideration, we model the sensor deployment problem as a constrained source coding problem. %, which can be applied to different coverage tasks, such as area coverage, target coverage, and barrier coverage. Our goal is to find an optimal sensor deployment (or relocation) to optimize the sensing quality with a limited communication range and a specific network lifetime constraint. We derive necessary conditions for the optimal sensor deployment in both homogeneous and heterogeneous MWSNs. According to our derivation, some sensors are idle in the optimal deployment of heterogeneous MWSNs. Using these necessary conditions, we design both centralized and distributed algorithms to provide a flexible and explicit trade-off between sensing uncertainty and network lifetime. The proposed algorithms are successfully extended to more applications, such as area coverage and target coverage, via properly selected density functions. Simulation results show that our algorithms outperform the existing relocation algorithms

Continuous vocational training in response to the challenge of industry 4.0: Required skills and business results

Purpose: To identify the technical skills which, linked to digitalization processes, are required to achieve different types of business results. Design/methodology/approach: The Delphi method was applied, through the opinions of a group of Spanish experts, to identify the importance of certain skills for the advancement of digitalization and the implementation of Industry 4.0. Findings: The results show that: 1) skills in Robotics, the Internet of Things, Networks and Artificial Intelligence are necessary to achieve results in the management of company technical areas; 2) commercial management needs skills in Intelligent Systems, Big Data, Cybersecurity, Distributed Technology and Contents; and 3) for the business challenges of sustainable development, the environment and energy efficiency, the most needed skills are in Big Data, Intelligent Systems and Artificial Intelligence. Originality/value: The results are useful, firstly, in providing firms with a training and selection tool for the development of I4.0 and, secondly, in offering training centers criteria for drawing up their training programsPeer Reviewe

Electric Vehicle Efficient Power and Propulsion Systems

Vehicle electrification has been identified as one of the main technology trends in this second decade of the 21st century. Nearly 10% of global car sales in 2021 were electric, and this figure would be 50% by 2030 to reduce the oil import dependency and transport emissions in line with countries’ climate goals. This book addresses the efficient power and propulsion systems which cover essential topics for research and development on EVs, HEVs and fuel cell electric vehicles (FCEV), including: Energy storage systems (battery, fuel cell, supercapacitors, and their hybrid systems); Power electronics devices and converters; Electric machine drive control, optimization, and design; Energy system advanced management methods Primarily intended for professionals and advanced students who are working on EV/HEV/FCEV power and propulsion systems, this edited book surveys state of the art novel control/optimization techniques for different components, as well as for vehicle as a whole system. New readers may also find valuable information on the structure and methodologies in such an interdisciplinary field. Contributed by experienced authors from different research laboratory around the world, these 11 chapters provide balanced materials from theorical background to methodologies and practical implementation to deal with various issues of this challenging technology. This reprint encourages researchers working in this field to stay actualized on the latest developments on electric vehicle efficient power and propulsion systems, for road and rail, both manned and unmanned vehicles

Novel infinitely Variable Transmission allowing efficient transmission ratio variations at rest

Recent studies showed that Continuously Variable Transmissions (CVT) and Infinitely Variable Transmissions (IVT) can considerably improve the locomotion efficiency in legged robot. A CVT is a transmission whose ratio can be continuously varied and an IVT is a transmission whose ratio can be continuously varied from positive to negative values. However, efficient use of such transmissions in walking applications requires changing the transmission ratio at a minimal energy cost, even at rest, i.e. when the input shaft is not rotating. This contribution proposes a novel CVT and IVT principle which can achieve such ratio variations at rest. The presented CVT is a modified planetary gear, whose planets are conical and mounted on inclined shafts, and whose ring is made of contiguous diabolo-shaped rollers. This configuration enables the control of the transmission ratio by adjusting the point of contact between the cones and rollers that comprise the ring. A traditional planetary gear system can be added to the CVT to form an IVT

Distributed Control for Robotic Swarms Using Centroidal Voronoi Tessellations

This thesis introduces a design combining an emerging area in robotics with a well established mathematical research topic: swarm intelligence and Voronoi tessellations, respectively. The main objective for this research is to design an economical and robust swarm system to achieve distributed control. This research combines swarm intelligence with Voronoi tessellations to localize a source and create formations. Extensive software coding must be implemented for this design, such as the development of a discrete centroidal Voronoi tessellation (CVT) algorithm. The ultimate purpose of this research is to advance the existing Mobile Actuator and Sensor Network (MASnet) platform to eventually develop a cooperative robot team that can sense, predict, and nally neutralize a diusion process. Previous work on the MASnet platform has served as a foundation for this research. While growing closer to the MASnet goal, results also provide stimulating discoveries for mathematical and swarm research areas

Quality-diversity optimization: a novel branch of stochastic optimization

Traditional optimization algorithms search for a single global optimum that maximizes (or minimizes) the objective function. Multimodal optimization algorithms search for the highest peaks in the search space that can be more than one. Quality-Diversity algorithms are a recent addition to the evolutionary computation toolbox that do not only search for a single set of local optima, but instead try to illuminate the search space. In effect, they provide a holistic view of how high-performing solutions are distributed throughout a search space. The main differences with multimodal optimization algorithms are that (1) Quality-Diversity typically works in the behavioral space (or feature space), and not in the genotypic (or parameter) space, and (2) Quality-Diversity attempts to fill the whole behavior space, even if the niche is not a peak in the fitness landscape. In this chapter, we provide a gentle introduction to Quality-Diversity optimization, discuss the main representative algorithms, and the main current topics under consideration in the community. Throughout the chapter, we also discuss several successful applications of Quality-Diversity algorithms, including deep learning, robotics, and reinforcement learning