Workshop

Vikrant Kaulgud

Accenture Labs, India

India

Vibhu Saujanya Sharma

Accenture Labs, Bangalore, India

Workshop

Sanjay Podder

Accenture

India

https://labs.accenture.com/sanjay.podder

Workshop

Nowadays, robots are widely used in many areas of our lifes, such as autonomous storage, self-driving vehicles, drones, industrial automation, etc. Energy is a critical factor for robotic systems, especially for mobile robots where energy is a finite resource (e.g., surveillance autonomous rovers). Since software is becoming the central focus of modern robotic systems, it is important to understand how it influences the energy consumption of the entire system. However, there is no systematic study of the state of the art in energy efficiency of robotics software that could guide research or practitioners in finding solutions and tools to develop robotic systems with energy efficiency in mind.

The goal of this paper is to present a review of existing research on energy efficiency in robotics software. Specifically, we investigate on (i) the used metrics for energy efficiency, (ii) the application domains within the robotics area covered by research on energy efficiency, (iii) the identified major energy consumers within a robotic system, (iv) how existing approaches are evaluated, (v) the used energy models, (vi) the techniques supporting the development of energy-efficient robotics software, and (vii) which quality attributes tend to be traded off when dealing with energy efficiency in robotics. We also provide a replication package to assess, extend, and/or replicate the study.

The results of this work can guide researchers and practitioners in robotics and software engineering in better reasoning and contributing to energy efficient robotics software.

Stan Swanborn

Vrije Universiteit Amsterdam, The Netherlands

Ivano Malavolta

Workshop

Conversion of ordinary houses into smart homes has been a rising trend for past years. Smart house development is based on the enhancement of the quality of the daily activities of normal people. But many smart homes have not been designed in a way that is user friendly for differently abled people such as immobile, bedridden (disabled people with at least one hand movable). Due to negligence and forgetfulness, there are cases where the electrical devices are left switched on, regardless of any necessity. It is one of the most occurred examples of domestic energy wastage. To overcome those challenges, this research represents the improved smart home design: MobiGO that uses cameras to capture gestures, smart sockets to deliver gesture-driven outputs to home appliances, etc. The camera captures the gestures done by the user and the system processes those images through advanced gesture recognition and image processing technologies. The commands relevant to the gesture are sent to the specific appliance through a specific IoT device attached to them. The basic literature survey content, which contains technical words, is analyzed using Deep Learning, Convolutional Neural Network (CNN), Image Processing, Gesture recognition, smart homes, IoT. Finally, the authors conclude that the MobiGO solution proposes a smart home system that is safer and easier for people with disabilities. Keywords—Deep Learning; Computer Vision; Gesture; Smart Appliances; Internet of Things

Nimna Jayaweera

Sri Lanka Institute of Information Technology

Sri Lanka

Binura Hetti Gamage

Mihiri Samaraweera

Sachintha Liyanage

Workshop

This paper presents a preliminary study on the energy consumption of two popular web browsers. In order to properly measure the energy consumption of both environments, we simulate the usage of various applications, which the goal to mimic typical user interactions and usage.

Our preliminary results show interesting findings based on observation, such as what type of interactions generate high peaks of energy consumption, and which browser is overall the most efficient. We aim that this preliminary study may show users how very different the efficiency of browsers are, and may serve as a stepping stone to further broaden this field of study.

João Aloísio

Universidade do Minho

Nelson Gonçalves

Universidade do Minho

Portugal

João de Macedo

Universidade do Minho

Portugal

Rui Pereira

HASLab/INESC TEC & Universidade do Minho & Universidade da Beira Interior

Portugal

João Saraiva

Workshop

In recent times, there has been a considerable increase in Cloud- Based applications and infrastructure. This has led to quicker innovations, agile businesses, availability of new services over the internet, improved collaboration, and better security. With the growth of new technologies like blockchain, quantum computing, mobility-focused applications, and edge computing, there has been an increased interest in adopting cloud services. In this paper, we highlight the different sustainability metrics and benefits while migrating workloads from the on-prem data center to the public clouds. Also, the clouds are elastic, scalable, cost-efficient, robust, and overall a better alternative to host the client applications and services. We present how the major Cloud Service Providers (CSPs) are continuously working on improving their infrastructure for a more energy efficient cloud. But with so many factors like the cost of cloud services, the location of the data center to name a few, it becomes quite a tedious task for the clients to select a cloud service provider when moving from their on-premise data center(s). Hence, we also briefly propose our solution that we are currently working on. The final goal is to have a cross-platform advisory that based on a wide-range of client-based inputs and a rich repository of current energy efficient clouds and their sustainability metrics, aims to provide them a detailed recommendation about their preferred cloud service provider. In case the client does not provide any such preference, the advisory should also recommend an ideal cloud service provider for their particular workload. This suggested action will be able to fulfill the client’s constraints as well as provide them an energy efficient cloud along with a sustainability score. This score is indicative of how much improvement in the energy consumed and carbon footprint can be achieved through this migration to the suggested cloud.

Priyavanshi Pathania

Accenture Labs

Rajan Dilavar Mithani

Accenture

India

Workshop

This paper extends previous work on the concept of a new software energy metric: Energy Debt. This metric is a reflection on the implied cost, in terms of energy consumption over time, of choosing an energy flawed software implementation over a more robust and efficient, yet time consuming, approach.

This paper presents the implementation a SonarQube tool called E-Debitum which calculates the energy debt of Android applications throughout their versions. This plugin uses a robust, well defined, and extendable smell catalogue based on current green software literature, with each smell defining the potential energy savings. To conclude, an experimental validation of E-Debitum was executed on 3 popular Android applications with various releases, showing how their energy debt fluctuated throughout releases.

Daniel Maia

HASLab/INESC TEC & Universidade do Minho

Portugal

Marco Couto

HASLab/INESC TEC & Universidade do Minho

Portugal

Rui Pereira

HASLab/INESC TEC & Universidade do Minho & Universidade da Beira Interior

Portugal

João Saraiva

Workshop