The state of the art of macroprogramming in IoT: An update

Abstract

Macroprogramming's primary goal is to increase developers' productivity by providing high-level specifications of applications' behaviour at the system level. Macroprogramming may be a viable solution for developing complex IoT applications, such as those manipulating high data volume and heterogeneity. This paper updates a recent work identifying and analysing primary research on macroprogramming in IoT through a systematic literature mapping (SLM). We extended the search strategy scope by conducting an automatic search over five new databases and also performed the snowballing technique. As a result, besides the 38 studies group found in previous SLM, nine new papers were classified as relevant and rigorously analysed, totalising forty-seven studies. In comparison to previous work, results still point out the recurrence of abstractions in the network infrastructure, highlighting the use of frameworks in one-third of the applications and contributing with an overview of macroprogramming by researchers in different knowledge areas.

Editorial: Understanding and engineering cyber-physical collectives

“…CPCs can be approached from several theoretical and engineering perspectives. Informative, but non-exhaustive, exemplars include: • cyber-physical and hybrid systems ( Cassandras and Lafortune, 2008 ; Kim and Kumar 2012 ); • coordination models and languages ( Malone and Crowston, 1994 ; Ciatto et al, 2020 ); • autonomic computing and self-* systems ( Kephart and Chess, 2003 ; de Lemos et al, 2010 ; Bellman et al, 2021 ; Harvey et al, 2021 ); • artificial life ( Aguilar et al, 2014 ; Gershenson, 2023 ); • multi-agent systems (MASs) ( Wooldridge, 2009 ; Mascardi et al, 2019 ; Boissier et al, 2020 ); • grammar systems ( Klavins et al, 2006 ; Csuhaj-Varjú et al, 2018 ); • swarm robotics ( Brambilla et al, 2013 ; Dorigo et al, 2021 ); • collective intelligence ( Malone and Bernstein, 2022 ; Casadei, 2023a ); • complex adaptive systems ( Bucchiarone and Mongiello, 2019 ; Abeywickrama et al, 2020 ; Wirsing et al, 2023 ); • multi-agent reinforcement learning ( Zhang et al, 2019 ); and • macro-programming ( Júnior et al, 2022 ; Casadei, 2023b ). …”

“…Such algorithmic and language-based approaches also promote the identification of reusable patterns of collective behaviour and organisation ( Horling and Lesser, 2004 ; Pianini et al, 2021 ). A crucial aspect of macro-systems is also their efficient deployment on heterogeneous infrastructures across the edge-cloud continuum ( Casadei et al, 2022 ; Júnior et al, 2022 ). Another key theme is the integration of artificial collective systems with humans, leading to notions like human-in-the-loop cyber-physical systems ( Annaswamy et al, 2023 ), social machines ( Hendler and Berners-Lee, 2010 ), and complex socio-cognitive systems ( Galesic et al, 2023 ).…”

“…• multi-agent systems (MASs) (Wooldridge, 2009;Mascardi et al, 2019;Boissier et al, 2020); • grammar systems (Klavins et al, 2006;Csuhaj-Varjú et al, 2018); • swarm robotics (Brambilla et al, 2013;Dorigo et al, 2021); • collective intelligence (Malone and Bernstein, 2022;Casadei, 2023a); • complex adaptive systems (Bucchiarone and Mongiello, 2019;Abeywickrama et al, 2020;Wirsing et al, 2023); • multi-agent reinforcement learning (Zhang et al, 2019); and • macro-programming (Júnior et al, 2022;Casadei, 2023b).…”

The Exchange Calculus (Xc): A Functional Programming Language Design for Distributed Collective Systems

“…Building applications that fully exploit the potential of such distributed systems is a matter of supporting intelligent behaviour not just at the individual level, but also at the system or collective level (Tumer and Wolpert, 2004;Nicola et al, 2020). The goal may be addressed by automated approaches like multi-agent reinforcement learning (Zhang et al, 2019), or by language-based, programming approaches sometimes referred to as macro-programming (Casadei, 2023;Newton and Welsh, 2004;Sene Júnior et al, 2022), that notably include spatial computing (De-Hon et al, 2007), field-based computing (Viroli et al, 2019;Lluch-Lafuente et al, 2017;Mamei and Zambonelli, 2004), ensemble-based programming (De Nicola et al, 2014;Abd Alrahman et al, 2020).…”