Toward Self-Sustainable Airborne Communication Networks: Comprehensive Modeling and Analysis of Energy Consumption and Harvesting

Energy is a major concern in wireless networks, be it for the target mobile user, as is the case of IEEE 802.11 infrastructure mode, or for all the users as in IEEE 802.11 ad hoc mode where it dictates the lifetime of the whole network. We focus in this paper on the modeling of energy consumption at the MAC layer in the context of QoS-capable WLAN, namely IEEE 802.11e, and propose an analytical model for the lifetime of a terminal for different access categories. Our results quantify the impact of priority on energy consumption and show how the latter varies as a function of the network size, the number of stations in each priority group as well as the propagation environment: indoor versus outdoor. These results can be used at higher layers to optimize energy consumption through differentiation

This paper studies the information-energy ( $\mathrm{I}$ - $\mathrm{E}$ ) region for simultaneous wireless information and power transfer (SWIPT) networks in mobility scenarios, where a moving transmitter simultaneously transmits information and power to a fixed receiver, and the receiver adopts power splitting (PS) receiving architecture. In order to characterize the tradeoff between the received information and harvested energy, the $\mathrm{I}$ - $\mathrm{E}$ region is defined, and corresponding optimization problems are formulated to explore the system $\mathrm{I}$ - $\mathrm{E}$ regions by jointly optimizing the transmit power at the transmitter and the PS ratio at the receiver with three popular energy harvesting (EH) models, i.e., traditional linear EH model, the logistic nonlinear EH model and the piecewise nonlinear EH model. Particularly, to efficiently solve the nonconvex optimization problem with the logistic nonlinear EH model, a successive convex approximate (SCA)-based algorithm is proposed, which is able to characterize the lower bound of the $\mathrm{I}$ - $\mathrm{E}$ with low complexity. To solve the optimization problems with the linear and piecewise EH models, as they are convex, some closed and semi-closed solutions are derived by using Lagrange dual method and KKT conditions. Numerical results show that compared with the $\mathrm{I}$ - $\mathrm{E}$ regions under the linear and piecewise EH models, that under the logistic nonlinear EH model is smaller due to the limitations of practical EH circuit features. Moreover, with a fixed moving speed, when the transmit power is relatively large, the logistic nonlinear EH model can be replaced with the piecewise one due to the relatively small gap between their achieved $\mathrm{I}$ - $\mathrm{E}$ regions. Additionally, for a fixed moving track length, the higher moving speed yields the smaller $\mathrm{I}$ - $\mathrm{E}$ region with all three EH models.

This study explores the analysis and modeling of energy consumption in the context of database workloads, aiming to develop an eco-friendly database management system (DBMS). It leverages vibration energy harvesting systems with self-sustaining wireless vibration sensors (WVSs) in combination with the least square support vector machine algorithm to establish an energy consumption model (ECM) for relational database workloads. Through experiments, the performance of self-sustaining WVS in providing power is validated, and the accuracy of the proposed ECM during the execution of Structured Query Language (SQL) statements is evaluated. The findings demonstrate that this approach can reliably predict the energy consumption of database workloads, with a maximum prediction error rate of 10% during SQL statement execution. Furthermore, the ECM developed for relational databases closely approximates actual energy consumption for query operations, with errors ranging from 1 to 4%. In most cases, the predictions are conservative, falling below the actual values. This finding underscores the high predictive accuracy of the ECM in anticipating relational database workloads and their associated energy consumption. Additionally, this paper delves into prediction accuracy under different types of operations and reveals that ECM excels in single-block read operations, outperforming multi-block read operations. ECM exhibits substantial accuracy in predicting energy consumption for SQL statements in sequential and random read modes, especially in specialized database management system environments, where the error rate for the sequential read model is lower. In comparison to alternative models, the proposed ECM offers superior precision. Furthermore, a noticeable correlation between model error and the volume of data processed by SQL statements is observed. In summary, the relational database ECM introduced in this paper provides accurate predictions of workload and database energy consumption, offering a theoretical foundation and practical guidance for the development of eco-friendly DBMS.

Wireless powered communication networks (WPCNs) are commonly analyzed by using the linear energy harvesting (EH) model. However, since practical EH circuits are non-linear, the use of the linear EH model gives rise to distortions and mismatches. To overcome these issues, we propose a more realistic, nonlinear EH model. The model is based upon the error function and has three parameters. Their values are determined to best fit with measured data. We also develop the asymptotic version of this model. For comparative evaluations, we consider the linear and rational EH models. With these four EH models, we investigate the performance of a WPCN. It contains a multiple-antenna power station (PS), a signal-antenna wireless device (WD), and a multiple-antenna information receiving station (IRS). The WD harvests the energy broadcast by the PS in the PS-WD link, and then it uses the energy in the WD-IRS link to transfer information. We analyze the average throughput of delay-limited and delay-tolerant transmission modes as well as the average bit error rate (BER) of binary phase-shift keying (BPSK) and binary differential phase-shift keying (BDPSK) over the four EH modes. As well, we derive the asymptotic expressions for the large PS antenna case and the effects of transmit power control. Furthermore, for the case of multiple WDs, we optimize energy beamforming and time allocation to maximize the minimum rate of the WDs. Finally, the performances of four EH models are validated by Monte-Carlo simulations.

Optimal resource management in wireless communication networks is usually modeled in discrete state space, for example, using Markov decision process. This requires the energy harvesting (EH) model to be discrete. Since the existing discrete EH models in the literature use simple discrete distributions, for example, Bernoulli and Poisson, with no regard to the properties of the EH circuits, the proposed work handles this dilemma by deriving discrete EH models based on the practical continuous radio frequency to direct current (RF‐to‐DC) power conversion models. General closed‐form expressions are derived for the probability density functions of the harvested energy in the case of linear, piecewise linear, and non‐linear EH models. These general closed‐form expressions are applied to some special fading models, for example, the Nakagami‐ fading model. Simulation results verify the derived models. Moreover, statistical analysis is performed to compare the derived models to other discrete models in the literature. Finally, the derived models are applied to a case study where the effect of the number of discrete EH levels and the importance of the derived models compared to other models in the literature are presented. These comparisons show the deviation in the performance of the discrete models commonly used in the literature from the actual performance when the proposed piecewise linear EH model is used. Based on these comparisons, the proposed models, especially the piecewise linear EH model with an adequate number of segments, are recommended to be used for accurate discrete modeling of the EH process.

In the transportation sector, the fuel consumption model is a fundamental tool for vehicles’ energy consumption and emission analysis. Over the past decades, vehicle-specific power (VSP) has been enormously adopted in a number of studies to estimate vehicles’ instantaneous driving power. Then, the relationship between the driving power and fuel consumption is established as a fuel consumption model based on statistical approaches. This study proposes a new methodology to improve the conventional energy consumption modeling methods for hybrid vehicles. The content is organized into a two-paper series. Part I captures the driving power equation development and the coefficient calibration for a specific vehicle model or fleet. Part II focuses on hybrid vehicles’ energy consumption modeling, and utilizes the equation obtained in Part I to estimate the driving power. Also, this paper has discovered that driving power is not the only primary factor that influences hybrid vehicles’ energy consumption. This study introduces a new approach by applying the fundamental of hybrid powertrain operation to reduce the errors and drawbacks of the conventional modeling methods. This study employs a new driving power estimation equation calibrated for the third generation Toyota Prius from Part I. Then, the Traction Force-Speed Based Fuel Consumption Model (TFS model) is proposed. The combination of these two processes provides a significant improvement in fuel consumption prediction error compared to the conventional VSP prediction method. The absolute maximum error was reduced from 57% to 23%, and more than 90% of the predictions fell inside the 95% confidential interval. These validation results were conducted based on real-world driving data. Furthermore, the results show that the proposed model captures the efficiency variation of the hybrid powertrain well due to the multi-operation mode transition throughout the variation of the driving conditions. This study also provides a supporting analysis indicating that the driving mode transition in hybrid vehicles significantly affects the energy consumption. Thus, it is necessary to consider these unique characteristics to the modeling process.

Cloud computing delivers computing as a utility to users worldwide. A consequence of this model is that cloud data centres have high deployment and operational costs, as well as significant carbon footprints for the environment. We need to develop Green Cloud Computing (GCC) solutions that reduce these deployment and operational costs and thus save energy and reduce adverse environmental impacts. In order to achieve this objective, a thorough understanding of the energy consumption patterns in complex Cloud environments is needed. We present a new energy consumption model and associated analysis tool for Cloud computing environments. We measure energy consumption in Cloud environments based on different runtime tasks. Empirical analysis of the correlation of energy consumption and Cloud data and computational tasks, as well as system performance, will be investigated based on our energy consumption model and analysis tool. Our research results can be integrated into Cloud systems to monitor energy consumption and support static or dynamic system-level optimisation.

Cloud computing delivers computing as a utility to users worldwide. A consequence of this model is that cloud data centres have high deployment and operational costs, as well as significant carbon footprints for the environment. We need to develop Green Cloud Computing (GCC) solutions that reduce these deployment and operational costs and thus save energy and reduce adverse environmental impacts. In order to achieve this objective, a thorough understanding of the energy consumption patterns in complex Cloud environments is needed. We present a new energy consumption model and associated analysis tool for Cloud computing environments. We measure energy consumption in Cloud environments based on different runtime tasks. Empirical analysis of the correlation of energy consumption and Cloud data and computational tasks, as well as system performance, will be investigated based on our energy consumption model and analysis tool. Our research results can be integrated into Cloud systems to monitor energy consumption and support static or dynamic system-level optimisation.

This paper investigates the information-energy (I-E) region for simultaneous wireless information and power transfer (SWIPT) system in mobility scenarios, where a moving transmitter transmits information and energy to a power splitting (PS)-based receiver. An optimization problem is formulated to explore the system I-E region under the nonlinear energy harvesting (EH) model by jointly optimizing the transmit power at the transmitter and the PS ratio at the receiver. Since the problem is nonconvex, a successive convex approximate-based (SCA-based) algorithm is proposed, which is able to find the sub-optimal solution with low complexity. For comparison, the I-E region of the system under the linear model is also studied, where some closed and semi-closed solutions are derived by using Lagrange dual method and KKT conditions. Numerical results show that compared with the linear EH model, the nonlinear EH model yields a smaller I-E region due to the limitations of EH circuit features. Nevertheless, using the nonlinear EH model avoids the false achievable I-E region for practical mobile SWIPT systems. Besides, it shows that the higher moving speed yields the smaller I-E region. Moreover, with the increment of the required information amount, the harvested energy bias caused by the linear EH model decreases, but the bias ratio increases.

Hot mix asphalt (HMA) mixture is a major building material in paving engineering. To decrease energy consumption in HMA, a prediction model of energy consumption was investigated systematically in this paper, employing kernel principal component analysis (KPCA) and a support vector machine (SVM). The purpose of the work is to optimize production and structure parameters of an HMA plant. A prediction model of energy consumption cannot only be used for understanding but can also be used to develop new asphalt mixing plants and to achieve optimization. The main relationships between energy consumption and production parameters are studied in some field tests. To build a multi-parameter model of energy consumption, eigenvectors of many factors are optimized employing KPCA. The three kernel principal components of higher contribution rates are chosen. A prediction model of energy consumption is built using KPCA and SVM. Energy consumption of aggregate drying is predicted using the constructed model. The prediction model is optimized by particle swarm optimization (PSO). Influence coefficients of energy consumption are obtained by SVM and piecewise least-squares regression (PLSR) and are found to be consistent with test results. There is about a 5% error in energy consumption between the model prediction and the test. The prediction error can meet engineering requirements in the hot mix asphalt mixing plant.

Analysis of energy consumption which could explore energy saving potential is of important significance for energy saving in manufacturing enterprises. An analysis model including physical and monetary input-output models for energy consumption in manufacturing enterprises based on input-output theory is constructed. Physical and monetary energy consumption coefficient matrices are obtained from the analysis model. Then, some application ideas are proposed, including vertical and horizontal comparison of coefficient matrices elements of a manufacturing enterprise in per unit time, comparison of coefficient matrices elements of a manufacturing enterprise in different unit times, comparison of coefficient matrices elements of manufacturing enterprises within the same industry in per unit time, effect analysis of energy price change to energy consumption of a manufacturing enterprise. Finally, a case study of a heavy-machinery manufacturing enterprises validates its practicability.

Simultaneous wireless information and power transfer (SWIPT) is a promising technique to prolong the lifetime of energy constrained relay-based systems. Most of the existing literature on relay-based SWIPT systems incorporate linear energy harvesting (EH) model. This article incorporates a non-linear EH model into the full-duplex (FD) amplify-and-forward (AF) relay-based system for the first time in the literature. We consider a practical non-linear energy harvester model namely constant-linear-constant (CLC) EH model, which takes into account the sensitivity and saturation characteristics of the EH circuit. First, the end-to-end outage probability of the system is derived for the time-switching (TS) based relay protocol. To prevent the outage performance degradation, the outage throughput and energy efficiency (EE) of the system is maximized by optimizing the TS parameter. Since the formulated problems are convex in nature, the golden-section method is used to find the optimal TS solution. Numerical results reveal the significance of employing a non-linear EH model by demonstrating the difference of the proposed model from the traditional linear EH model system and importance of using full-duplex relay by showing large performance gain over half-duplex relay-based (HDR) system, in terms of outage probability, throughput, and EE.

In this paper, we study simultaneous lightwave information and power transfer (SLIPT) systems employing photovoltaic optical receivers (RXs). We consider the case, where the optical RX is illuminated by ambient light and an intensity-modulated information-carrying free space optical (FSO) signal. To overcome the possible absence of ambient light, e.g., indoors or at night, we additionally assume that the optical RX receives a dedicated energy-bearing broadband optical signal. Additionally, to efficiently harvest energy from broadband light, we propose a novel optical RX based on multi-junction photovoltaic cells. Exploiting the analysis of the equivalent two-diode electrical circuit for the multi-junction photovoltaic RX, we carefully model the current flow through the photovoltaic cell and derive an accurate energy harvesting (EH) model. Furthermore, we also derive novel approximate EH models for the two cases, where the optical RX is equipped with a single and multiple p-n junctions, respectively. Next, we derive the distribution of the transmit information signal that maximizes the achievable information rate and, for a practical pulse amplitude modulated information signal, we determine the symbol error rate at the RX. We validate the proposed EH models by circuit simulations and show that the photovoltaic RXs saturate for high received signal powers. For single-junction RXs, we compare the proposed EH model with two well-known baseline EH models, which are based on maximum point tracking and a single-diode electrical circuit, respectively. We demonstrate that, in contrast to the proposed EH model, both baseline EH models are not able to fully capture the non-linear behavior of photovoltaic RXs. Moreover, our results reveal that, since multi-junction RXs allow a more efficient allocation of the optical power, they are more robust against saturation, and thus, are able to harvest significantly more power and achieve higher data rates than RXs employing a single p-n junction. Finally, we highlight a tradeoff between the information rate and harvested power in SLIPT systems and demonstrate that the proposed transmit signal distribution yields significantly higher achievable information rates compared to uniformly distributed transmit signals, which are optimal for linear optical information RXs.

Simultaneous wireless information and power transfer (SWIPT) is a promising technique to prolong the lifetime of energy‐constrained relay systems. Most previous works optimize power‐splitting (PS) scheme based on a linear or a simple two‐piecewise linear energy harvesting (EH) model, while the employed EH model may not characterize the properties of practical EH harvesters well. This leads to a mismatch between the existing PS scheme and the practical EH harvester available for relay systems. Motivated by this, this paper is devoted to the design of PS scheme in a nonlinear EH amplify‐and‐forward energy‐constrained relay system in the presence of a direct link between the source and the destination. In particular, we formulate an optimization problem to maximize the system capacity according to the instantaneous channel state information, subject to a nonlinear EH model based on the logistic function. The objective function of the formulated problem is proven to be unimodal and there is no closed‐form expression for the optimal PS ratio due to the complexity of logistic function. In order to reduce overhead cost of optimizing PS ratio, a simpler nonlinear EH model based on the inverse proportional function is employed to replace the nonlinear EH model based on the logistic function and we further derive the closed‐form expression for the optimal PS ratio. Simulation results reveal that a higher system capacity can be achieved when the PS scheme is optimized based on nonlinear EH models instead of the linear EH model, and that there is only a marginal difference between the capacity under the two optimal PS schemes optimized for two different nonlinear EH models.

Cloud computing has made it possible for users to spend only for the volume of a service that they consume rather than purchasing a predetermined amount. Cloud computing and virtualization, when coupled, make it possible to access and share information technology resources whenever and wherever they are needed. When it comes to cloud computing, a dependable data centre infrastructure is unrivalled by anything else. In the context of this discussion, the term "infrastructure" refers to the collection of computers, wiring, and power sources that together serve as a host for an organization's information and store it. When it comes to cloud computing, great overall performance has always been the most important factor, but this has resulted in an increase in the amount of energy that is required. The major objective is to achieve as low a level of power consumption as possible while maintaining as high a level of performance and quality of service as possible inside the carrier network. Our strategy calls for an in-depth study of the patterns of energy consumption exhibited by cloud-based systems. Authors perform an analysis of energy use and show how suitable optimization methods based on our models of energy consumption can assist cloud data centres in saving even more electricity. These methods are informed by our analysis of energy consumption. Because of capsule optimization, this system is able to generate more accurate projections of the costs that will be incurred in the future. The accuracy rate for the forecasting component is 97%.