In this work, a heterogeneous set of wireless devices sharing a common access point (AP) or base station (BS) collaborates to complete a set of computing tasks within a given deadline in the most energy-efficient way. This pool of devices somehow acts like a distributed mobile edge computing (MEC) server to augment the computing capabilities of individual devices while reducing their total energy consumption. Using the Map-Reduce distributed computing framework -which involves both local computing at devices and communications between them -the tasks are optimally distributed amongst the nodes, taking into account their diversity in term of computing and communications capabilities.In addition to optimizing the computing load distribution, local parameters of the nodes such as CPU frequency and RF transmit power are also optimized for energy-efficiency. The corresponding optimization problem can be shown to be convex and optimality conditions offering insights into the structure of the solutions can be obtained through Lagrange duality theory. A waterfilling-like interpretation for the size of the computing task assigned to each node is given. Numerical experiments demonstrate the benefits of the proposed optimal collaborative-computing scheme over various other schemes in several respects.Most notably, the proposed scheme exhibits increased probability of successfully dealing with larger computing loads and/or smaller latency and energy-efficiency gains of up to two orders of magnitude.Both improvements come from the scheme ability to optimally leverage devices diversity.

Information and communication technologies are often considered by policymakers, industrial stakeholders and scientists as a key lever in the run towards sustainability, since they should ease energy efficiency and dematerialization. In this opinion article, nurtured by the inputs of a broad panel of experts, we challenge this widely spread view by highlighting the detrimental social and environmental footprints caused by digital technologies. We further take a critical look on the ways innovation is conducted nowadays, i.e., with an almost exclusive focus on performance and few considerations for externalities. This leads us to call for academic teaching programs advocating for a holistic approach, for new business models, and for ambitious political decisions able to drive a paradigm shift in the mainstream agenda of electronics innovation and digital transition that shall significantly contribute to the well-being of everyone, everywhere, without compromising future generations. We conclude that digital technologies cannot support long-term sustainability if their only purpose remains the optimization of the current system.

With the increasing number of heterogeneous resource-constrained devices populating the current wireless ecosystem, enabling ubiquitous computing at the edge of the network requires moving part of the computing burden back to the edge to reduce user-side latency and relieve the backhaul network. Motivated by this challenge, this work investigates edge-facilitated collaborative fog computing to augment the computing capabilities of individual devices while optimizing for energy-efficiency. Collaborativecomputing is modeled using the Map-Reduce framework, consisting in two computing rounds and a communication round. The computing load is optimally distributed among devices, taking into account their diversity in terms of computing and communication capabilities. Devices local parameters such as CPU frequency and RF transmit power are also optimized for energy-efficiency. The corresponding optimization problem is shown to be convex and optimality conditions are obtained through Lagrange duality theory. A waterfilling-like interpretation for the size of the computing load assigned to each device is given. Numerical experiments demonstrate the benefits of the proposed collaborative-computing scheme over various other schemes in several respects. Most notably, the proposed scheme exhibits increased probability of successfully dealing with more demanding computations in time, along with significant energy-efficiency gains. Both improvements come from the scheme ability to advantageously leverage devices diversity.INDEX TERMS wireless collaborative computing, Map-Reduce, energy-efficiency, joint computation and communications optimization, fog computing.

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