electronics Article A Task Execution Scheme for Dew Computing with State-of-the-Art Smartphones † Matías Hirsch 1, * , Cristian Mateos 1 , Alejandro Zunino 1 , Tim A. Majchrzak 2 , Tor-Morten Grønli 3 and Hermann Kaindl 4   Citation: Hirsch, M.; Mateos, C.; Zunino, A.; Majchrzak, T.A.; Grønli, T.-M.; Kaindl, H. A Task Execution Scheme for Dew Computing with State-of-the-Art Smartphones. Electronics 2021, 10, 2006. https:// doi.org/10.3390/electronics10162006 Academic Editors: Ka Lok Man and Kevin Lee Received: 29 June 2021 Accepted: 16 August 2021 Published: 19 August 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). 1 ISISTAN–UNCPBA–CONICET, Tandil, 7000 Buenos Aires, Argentina; cristian.mateos@isistan.unicen.edu.ar (C.M.); alejandro.zunino@isistan.unicen.edu.ar (A.Z.) 2 Department of Information Systems, University of Agder, 4630 Kristiansand, Norway; timam@uia.no 3 Mobile Technology Lab, Department of Technology, Kristiania University College, 0176 Oslo, Norway; tor-morten.gronli@kristiania.no 4 Institute of Computer Technology, TU Wien, 1040 Vienna, Austria; hermann.kaindl@tuwien.ac.at * Correspondence: matias.hirsch@isistan.unicen.edu.ar † This paper is an extended version of our paper published in HICSS-54. Abstract: The computing resources of today’s smartphones are underutilized most of the time. Using these resources could be highly beneficial in edge computing and fog computing contexts, for example, to support urban services for citizens. However, new challenges, especially regarding job scheduling, arise. Smartphones may form ad hoc networks, but individual devices highly differ in computational capabilities and (tolerable) energy usage. We take into account these particularities to validate a task execution scheme that relies on the computing power that clusters of mobile devices could provide. In this paper, we expand the study of several practical heuristics for job scheduling including execution scenarios with state-of-the-art smartphones. With the results of new simulated scenarios, we confirm previous findings and better comprehend the baseline approaches already proposed for the problem. This study also sheds some light on the capabilities of small-sized clusters comprising mid-range and low-end smartphones when the objective is to achieve real-time stream processing using Tensorflow object recognition models as edge jobs. Ultimately, we strive for industry applications to improve task scheduling for dew computing contexts. Heuristics such as ours plus supporting dew middleware could improve citizen participation by allowing a much wider use of dew computing resources, especially in urban contexts in order to help build smart cities. Keywords: dew computing; edge computing; smartphone; job scheduling; scheduling heuristics 1. Introduction Smartphones have increasing capabilities of processing information, which typically are underutilized [1,2]. Cities (and citizens) could benefit from such a plethora of under- utilized resources if these were properly orchestrated. Any person carrying a smartphone could contribute with valuable resources to help cities grow and to manage them in a more sustainable way. For instance, anyone may help to improve urban road maintenance by collecting pavement data [3]. Participatory platforms have been proposed to enable people to voluntarily contribute data sensed with their personal mobile devices [4,5]. Cities generate vast amounts of data for different smart city applications through Inter- net of Things (IoT) sensors and surveillance cameras [6,7]. Processing locally sensed data can be done in different but not necessarily mutually exclusive ways, for instance, using distant cloud resources, offloaded to proximate fog servers, or with the help of devices with computing capabilities within the data collection context, e.g., with smartphones. This latter architectural option has been considered as an attractive self-supported sensing and computing scheme [8]. In addition, hybrid and volunteer-supported processing architec- tures were proposed to avoid overloading resource-constrained devices [9]. Depending Electronics 2021, 10, 2006. https://doi.org/10.3390/electronics10162006 https://www.mdpi.com/journal/electronics