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Distributed Low-Energy Computation on Cyber-Physical/IoT Systems

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: closed (1 March 2022) | Viewed by 569

Special Issue Editors

Laboratoire Ampère, University Claude Bernard Lyon 1, 69134 Villeurbanne, France
Interests: hierarchical design methodology; heterogeneous embedded systems; WSN; IoT; distributed computing; energy harvesting; pervasive embedded systems
Special Issues, Collections and Topics in MDPI journals
Telecommunications Department, University of Lyon, F-69621 Villeurbanne, France
Interests: fog and edge computing; distributed systems; operating systems; middleware; wireless sensor networks; internet of things

Special Issue Information

Dear Colleagues,

The constant miniaturization of electronic circuits has made possible the rising of pervasive computing, as stated by Mark Weiser in the late 1990s. From the beginning of wireless sensor networks in the early 2000s to the current Internet of Things, the physical world is migrating towards a cyber-physical world with embedded systems interacting through the cloud in every aspect of society (from consumers to smartphones to industries and Industry 4.0, and beyond).

Two scientific communities are now working towards pervasive computing. The electrical engineering community, whose approach takes its roots in the physical world, has a material behavior emphasis which relies on simulation and automation, and aims at designing “closed ecosystems” of networks of cyber-physical systems (CPSs): those systems tend to focus on timed communication and information exchange, with controlled interaction between participants. In such systems, numerous means tend to hundreds of entities. The computer science community, heirs of the digital world, works from an internet architecture and service-oriented approach, and relies on semantics and agents or things and aims at designing “open ecosystems” called the Internet of Things (IoT), relying on embedded systems whose communication pattern can be delayed, where interaction can integrate unknown or unidentified participants. In such systems, numerous means thousands or more. In the domain of pervasive computing, energy consumption is becoming a major concern driving pervasive computing from high-end devices to middle- and low-end devices.

From the energy point of view, both artificial intelligence (AI) and ambient intelligence (AmI) are currently experiencing an evolution: the current computational energy needs with machine learning are not very compatible with societal and planetary issues. Both communities have followed different pathways: the development of ultra-low-power design methodologies and hardware for frugal embedded systems for cpss and the migration of computation capacity from the cloud to the edge of the network for iot, with low-end WSN devices in a total energy autonomy by recovering ambient energy.

If historically in both approaches data has never been processed in the networks (i.e., a centralized paradigm), the transition to a distributed paradigm is possible:

  • Edge computing and cloud computing for connected everyday objects;
  • Low-energy hardware platforms with computing capabilities as close as possible to WSN devices.

Both communities are now interacting around systems of distributed intelligence and are facing the same issues: designing efficient and adaptive systems consuming as little energy as possible and/or embedding energy-harvesting capacities by distributing and load-balancing the computation between tiny devices and the network to allow low-life lasting systems.

Potential topics of interest include but are not limited to the following:

- Distributed and/or parallel computing algorithms for communicating embedded systems networks with minimal energetic footprint.

- Global strategies of parallel/distributed computation over a communication network of embedded systems.

- Hardware design of ultra-low-power embedded communication systems dedicated to distributed and parallel computation.

- Wireless access control layers and protocols, routing strategies for low-power distributed/parallel computing.

- Scalable and context-aware network topologies for distributed computation.

- Pervasive distributed computing wireless sensor networks.

- Adaptive power management of distributed computing embedded systems.

- Modeling approaches, formal methods, simulation and development frameworks for pervasive distributed computing embedded systems.

- Operating systems and middleware for distributed computing in embedded systems.

- Distributed applications over communicating embedded systems networks.

- Distributed artificial intelligence/machine learning over communicating embedded systems networks.

Prof. Dr. Fabien Mieyeville
Prof. Dr. Frédéric Le Mouël
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Internet of Things
  • ambient intelligence
  • artificial intelligence
  • wireless sensor networks
  • distributed computing

Published Papers

There is no accepted submissions to this special issue at this moment.
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