Pervasive Communicating Distributed Embedded Systems

Dear Colleagues,

The constant miniaturization of electronic circuits has made possible the rise 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 a cloud in every aspect of the society (from consumers –smartphones– to industries –industry 4.0–).

Yet, the computing capacity of these communicating embedded systems—so-called smart objects—is scarcely dedicated to an in-situ calculation and more often used only to manage display and send data through the network to a remote computation entity (a physical dedicated computing system or a virtual computing entity such as a cloud). Thus, whereas today’s embedded systems have never been so pervasive, the paradigm of centralized computation remains paradoxically predominant.

In a world of context-aware electronic embedded systems where the energy storage capacity progress is a bottleneck compared to the evolution of communication and computation capacity of embedded systems, the energy cost of every operation realized in embedded systems is becoming prevalent or even predominant with the introduction of energy harvesting autonomous embedded systems. Hence the ratio between computation energy cost and communication energy cost is becoming a key parameter in pervasive communicating embedded systems and leads the community to switch to a distributed computation paradigm.

This Special Issue aims at focusing on these practices of effective distributed and parallel computing on pervasive communicating embedded systems at three levels: In-situ (computation inside the embedded system), local (computation shared between neighbors communicating systems) and global (computation distributed on the whole network of communicating embedded systems). This issue will address equally the four main stakes of pervasive communicating embedded systems networks: energy efficiency, connectivity, context-awareness and distributed computation. This Special Issue will deal mainly with wireless communicating embedded systems but is not excluding wired communication embedded systems and will span from hardware architecture to adaptive network distributed strategies.

Requested contributions will concern but are not limited to the following topics:

• distributed and/or parallel computing algorithms for communicating embedded systems networks (artificial neural network, parallel computing, etc.)
• global strategies of parallel/distributed computation over a communicating network of embedded systems
• hardware design of embedded communicating systems dedicated to distributed and parallel computation
• hardware design of RF architecture dedicated to local communication at a high rate of data
• wireless access control layers and protocols, routing strategies for distributed/parallel computing
• scalability and context-awareness of typologies for distributed computation
• pervasive distributed computing wireless sensor networks
• adaptive power management of distributed computing embedded systems
• security for distributed computing in pervasive communicating embedded systems
• modeling approaches, formal methods, simulation and development framework for pervasive distributed computing embedded systems
• operating systems and middleware for distributed computing in embedded systems

Prof. Fabien Mieyeville
Assoc. Prof. David Navarro
Guest Editors

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