Fog networks offer computing resources of varying capacities at different distances from end users. A Fog Node (FN) closer to the network edge may have less powerful computing resources compared to the cloud, but the processing of computational tasks in FN limits long-distance transmission. How should the tasks be distributed between fog and cloud nodes? We formulate a universal non-convex Mixed-Integer Nonlinear Programming (MINLP) problem minimizing task transmission-and processing-related energy with delay constraints to answer this question. It is transformed with Successive Convex Approximation (SCA) and decomposed using the primal and dual decomposition techniques. Two practical algorithms called Energy-EFFicient Resource Allocation (EEFFRA) and Low-Complexity (LC)-EEFFRA are proposed and their effectiveness is tested for various network and traffic scenarios. Using EEFFRA/LC-EEFFRA can significantly decrease the number of computational requests with unmet delay requirements when compared with baseline solutions (from 48% to 24% for 10 MB requests). Utilizing Dynamic Voltage and Frequency Scaling (DVFS) minimizes energy consumption (by one-third) while satisfying delay requirements.

In this paper, we review radio resource optimization methods for energy-efficient wireless communication in links and networks using the Orthogonal Frequency Division Multiplexing (OFDM) and Orthogonal Frequency Division Multiple Access (OFDMA) techniques. We first consider the energy-efficiency metrics and optimization goals. We discuss the increasingly complex systems, starting from (i) a single OFDM link, (ii) an OFDMA single-hop network to (iii) multi-hop relay OFDMA interference networks. In each case, we elaborate on the transmission rate estimation, power consumption modelling, existing optimization constraints and the optimization solutions. Specifically, in the power-consumption modelling, we include the signal-processing (and related computing) power. We discuss the practicality of the considered solutions. We also touch upon the problem of nonlinear power amplifier characteristics (causing distortions typical for OFDM signals) to be taken into account for energy-efficient resource allocation. We discuss trade-offs and provide recommendations for future energy-efficient OFDM networks design. We also discuss the future works and challenges in the context of energy efficiency resource allocation for OFDM/OFDMA and their derivative techniques. We conclude that the presented design practices should include computational awareness in the networks to trade-off between information communication, information processing and the required network management energy-efficiency.

The well known cloud computing is being extended by the idea of fog with the computing nodes placed closer to end users to allow for task processing with tighter latency requirements. However, offloading of tasks (from end devices to either the cloud or to the fog nodes) should be designed taking energy consumption for both transmission and computation into account. The task allocation procedure can be challenging considering the high number of arriving tasks with various computational, communication and delay requirements, and the high number of computing nodes with various communication and computing capabilities. In this paper, we propose an optimal task allocation procedure, minimizing consumed energy for a set of users connected wirelessly to a network composed of FN located at AP and CN. We optimize the assignment of AP and computing nodes to offloaded tasks as well as the operating frequencies of FN. The considered problem is formulated as a Mixed-Integer Nonlinear Programming problem. The utilized energy consumption and delay models as well as their parameters, related to both the computation and communication costs, reflect the characteristics of real devices. The obtained results show that it is profitable to split the processing of tasks between multiple FNs and the cloud, often choosing different nodes for transmission and computation. The proposed algorithm manages to find the optimal allocations and outperforms all the considered alternative allocation strategies resulting in the lowest energy consumption and task rejection rate. Moreover, a heuristic algorithm that decouples the optimization of wireless transmission from implemented computations and wired transmission is proposed. It finds the optimal or close-to-optimal solutions for all of the studied scenarios.

Energy efficiency is a key aspect when designing and optimizing contemporary wireless networks and transceivers. Assessment of energy efficiency requires proper energy consumption models. The most common solutions are to measure a single device and propose a device-specific model or to propose a simplified model for many transceivers but not reflecting all phenomena visible in a given transceiver energy consumption. Therefore, it has to be selected to accurately model a single transceiver or coarsely model a wide group of transceivers. This paper proposes a new approach, where a fixed energy consumption model is used but with parameters being random variables. This reflects variability between various transceivers from various vendors. First the model parameters are adjusted separately for each of 14 measured WiFi modems. These devices are treated as samples of a wider population of devices and their parameters are used for stochastic parameters modeling, i.e., choosing the random variables’ distributions, their parameters, and the correlation among parameters. The proposed model can be used, e.g., for system-level network design where variability among transceivers power consumption can be used as a new degree of freedom. The paper presents simulation results for a simple multi-hop link whose energy consumption is characterized in much more detail thanks to the proposed stochastic power consumption model.

In this paper, a new perspective of using flexible, brain-inspired, analog and digital wireless transmission in massive future networks, is presented. Inspired by the nervous impulses transmission mechanisms in the human brain which is highly energy efficient, we consider flexible, wireless analog and digital transmission on very short distances approached from the energy efficiency point of view. The energy efficiency metric is compared for the available transmission modes, taking the circuit power consumption model into account. In order to compare the considered systems, we assume that the transmitted data comes from analog sensors. In the case of the digital transmission scheme, the decoded data are converted back to analog form at the receiving side. Moreover, different power consumption models from the literature and the digital transmission schemes with different performance are analyzed in order to examine if, for some applications and for some channel conditions, the analog transmission can be the energy-efficient alternative of digital communication. The simulation results show that there exist some cases when the analog or simplified digital communication is more energy efficient than digital transmission with $\mathrm{QAM}$ modulation.