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, the power consumption model of a wireless transceiver is proposed. It is based on measurements of a number of off-the-shelf WiFi cards. The power consumption was divided into an idle, associated, and transmission states. Then, the eight-coefficients power consumption model depending on the rate and path-loss has been proposed and the values of coefficients have been determined. While there are significant differences in the power consumption among considered devices, the proposed model is well aligned with the measurements for each of them. Moreover, this paper shows that models based on a single device characterization can result in significant errors if employed to assess network power consumption.

In this paper, we investigate joint subcarrier (SC) allocation, pairing and power loading for optimized energy efficiency (EE) in multiuser, multicell, multicarrier downlink decode-and-forward (DF) relay interference networks with computational awareness, i.e., taking computations-related energy into account. In order to maximize EE of the network, the transmission mode is adapted to instantaneous channel conditions. For the benefit of spectral-efficiency, both direct- and relayed transmission is allowed to use the same SCs simultaneously. Linearly rate-dependent power consumption of signal processing is considered. The formulated optimization problem is the nonconvex fractional mixed binary-integer programming problem, which has NP-hard complexity. Hence, we approximate the problem by the series of equivalent convex problems applying convex relaxation techniques such as a Successive Convex Approximation (SCA). Based on these transformations, we develop an iterative algorithm exploiting the Dinkelbach method to tackle the nonlinear fractional programming problem which maximizes EE of the system. Moreover, in our considerations, the total transmission power constraint and the minimum required rate constraints have been included. Simulation results demonstrate the effectiveness of our solution for future relay networks.

Fog computing can be used to offload computationally intensive tasks from battery powered Internet of Things (IoT) devices. Although it reduces energy required for computations in an IoT device, it uses energy for communications with the fog. This paper analyzes when usage of fog computing is more energy efficient than local computing. Detailed energy consumption models are built in both scenarios with the focus set on the relation between energy consumption and distortion introduced by a Power Amplifier (PA). Numerical results show that task offloading to a fog is the most energy efficient for short, wideband links.

In this paper, we investigate the energy-efficient resource allocation problem in the downlink of an Orthogonal Frequency Division Multiple Access (OFDMA) system with multiple users. A iterative algorithm is developed to maximize the overall energy efficiency of the downlink transmission by allocating the resource blocks (RBs) and the transmit power, which is constant for all RBs assigned to a given user. Moreover, we assume that all RBs assigned to the same user must use the same modulation and coding scheme (MCS), as in the LTE specification. In this form our optimization problem belongs to a broad class of problems called Mixed-Integer Nonlinear Fractional Problem (MINLFP), which is very difficult to solve analytically in the original form. Hence, in order to solve it, we propose the iterative algorithm with fast convergence based on the Dinkelbach method. In each iteration, the solution for the power and resource blocks allocation is derived by the numerical method using the Karush-Kuhn-Tucker (KKT) multipliers and KKT conditions, while modulation and coding scheme, for each user, is obtained by the iterative algorithm. Simulation results show that despite the computational simplicity of the proposed solution, it achieves better results than the solutions known from the literature.

In this paper, the energy efficiency (EE) of an adaptive OFDM (Orthogonal Frequency-Division Multiplexing) system is analyzed taking not just the transmit power, but also the transceiver computation-power consumption into account. Such an energy-efficiency optimization problem belongs to a broader class of problems called convex-concave fractional programs, which in the case of the adaptive modulation and coding techniques is also a mixed-integer nonlinear problem (MINLP). In order to solve it, we propose the joint adaptive power and adaptive modulation and coding (AMC) algorithm with fast convergence based on Dinkelbach method, where the analytical solutions are derived by the numerical method according to the Lagrange multipliers and Karush-Kuhn-Tucker (KKT) conditions. Simulation results show that the adaptive modulation and coding techniques allow for: increased energy efficiency, increased average throughput, reduced required transmit power and reduced total power consumption. Moreover, there exists the optimal point for the transmit power maximizing the energy efficiency.