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Erlang as an enabling technology for resilient general-purpose applications on edge IoT networks

Published:18 August 2019Publication History

ABSTRACT

Edge computing is one of the key success factors for future Internet solutions that intend to support the ongoing IoT evolution. By offloading central areas using resources that are closer to clients, providers can offer reliable services with higher quality. But even industry standards are still lacking a valid solution for edge systems with actual sense-making capabilities when no preexisting infrastructure whatsoever is available. The current edge model involves a tight coupling with gateway devices and Internet access, even when autonomous ad hoc IoT networks could perform partial or even complete tasks correctly.

In our previous research efforts, we have introduced Achlys, an Erlang programming framework that takes advantage of the GRiSP embedded system capabilities in order to bring edge computing one step further. GRiSP is an embedded board that can easily be programmed directly in Erlang without requiring deep low level knowledge, which offers the extensive toolset of the Erlang ecosystem directly on bare metal hardware. We have been able to demonstrate that our framework allows building reliable applications on unreliable networks of unreliable GRiSP nodes with a very simple programming API. In this paper, we present how Erlang can successfully be used to address edge computing challenges directly on IoT sensor nodes, taking advantage of our existing framework. We display results of deployed distributed programs at the edge and examples of the unique advantage that is offered by Erlang higher-order and concurrent programming in order to achieve reliable general-purpose computing through Achlys.

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            • Published in

              cover image ACM Conferences
              Erlang 2019: Proceedings of the 18th ACM SIGPLAN International Workshop on Erlang
              August 2019
              73 pages
              ISBN:9781450368100
              DOI:10.1145/3331542

              Copyright © 2019 ACM

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              • Published: 18 August 2019

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