Total Energy Consumption Of Network Research Articles

تحسين استهلاك الطاقة لشبكات الهاتف الخلوي ذات الجيل الخامس باستخدام خوارزمية تحسين مدمجة

The 5G network technology is a promising technology that can successfully meet the demand for network capacity growth, due to its high-speed, low-latency, and wide connectivity capabilities. Adapting 5G will increase the energy consumption and CO2 emissions caused by this high consumption. Base stations (BSs) consume the most power in mobile networks accounting for approximately 57% of total energy consumption. This study proposes an integrated meta-heuristic optimization algorithm that combines the Arithmetic Optimization Algorithm (AOA) and the Particle Swarm Optimization (PSO) method. This combination aims to leverage the strengths of both approaches improving the speed and accuracy of the optimization process, to achieve better energy efficiency (EE) in 5G network technology. The proposed algorithm performance is evaluated based on a set of benchmarks functions and compared with other optimization methods. And to demonstrate the applicability of the proposed algorithm in terms of network energy efficiency problem, it was tested against a real-world case study involving power allocation in 5G, the results showed that the proposed algorithm outperforms more recent meta-heuristics state-of-the-art methods in solving a wide range of benchmark functions and obtaining a fair power allocation for multiple users in 5G network. Moreover, it maximizes energy efficiency while maintaining users service quality and availability and reducing total network energy consumption.

Neural energy computations based on Hodgkin-Huxley models bridge abnormal neuronal activities and energy consumption patterns of major depressive disorder

Limited by the current experimental techniques and neurodynamical models, the dysregulation mechanisms of decision-making related neural circuits in major depressive disorder (MDD) are still not clear. In this paper, we proposed a neural coding methodology using energy to further investigate it, which has been proven to strongly complement the neurodynamical methodology. We augmented the previous neural energy calculation method, and applied it to our VTA-NAc-mPFC neurodynamical H–H models. We particularly focused on the peak power and energy consumption of abnormal ion channel (ionic) currents under different concentrations of dopamine input, and investigated the abnormal energy consumption patterns for the MDD group. The results revealed that the energy consumption of medium spiny neurons (MSNs) in the NAc region were lower in the MDD group than that of the normal control group despite having the same firing frequencies, peak action potentials, and average membrane potentials in both groups. Dopamine concentration was also positively correlated with the energy consumption of the pyramidal neurons, but the patterns of different interneuron types were distinct. Additionally, the ratio of mPFC's energy consumption to total energy consumption of the whole network in MDD group was lower than that in normal control group, revealing that the mPFC region in MDD group encoded less neural information, which matched the energy consumption patterns of BOLD-fMRI results. It was also in line with the behavioral characteristics that MDD patients demonstrated in the form of reward insensitivity during decision-making tasks. In conclusion, the model in this paper was the first neural network energy computational model for MDD, which showed success in explaining its dynamical mechanisms with an energy consumption perspective. To build on this, we demonstrated that energy consumption levels can be used as a potential indicator for MDD, which also showed a promising pipeline to use an energy methodology for studying other neuropsychiatric disorders.

Machine Learning Prediction Based Adaptive Duty Cycle MAC Protocol for Solar Energy Harvesting Wireless Sensor Networks

The dynamic nature of energy harvesting rate, arising because of ever changing weather conditions, raises new concerns in energy harvesting based wireless sensor networks (EH-WSNs). Therefore, this drives the development of energy aware EH solutions. Formerly, many Medium Access Control (MAC) protocols have been developed for EH-WSNs. However, optimizing MAC protocol performance by incorporating predicted future energy intake is relatively new in EH-WSNs. Furthermore, existing MAC protocols do not fully harness the high harvested energy to perform aggressively despite the availability of sufficient energy resources. Therefore, a prediction-based adaptive duty cycle (PADC) MAC protocol has been proposed, called PADC-MAC, that incorporates current and future harvested energy information using the mathematical formulation to improve network performance. Furthermore, a machine learning model, namely nonlinear autoregressive (NAR) neural network, is employed that achieves good prediction accuracy under dynamic harvesting scenarios. As a result, it enables the receiver node to perform aggressively better when there is sufficient inflow of incoming harvesting energy. In addition, PADC-MAC uses a self-adaptation technique that reduces energy consumption. The performance of PADC-MAC is evaluated using GreenCastalia in terms of packet delay, network throughput, packet delivery ratio, energy consumption per bit, receiver energy consumption, and total network energy consumption using realistic harvesting data for 96 consecutive hours under dynamic solar harvesting conditions. The simulation results show that PADC-MAC provides lower average packet delay of the highest priority packets and all packets, energy consumption per bit, and total energy consumption by more than 10.7%, 7.8%, 81%, and 76.4%, respectively when compared to three state-of-the-art protocols for EH-WSNs.

A Mine Ventilation System Energy Saving Technique Based on an Improved Equilibrium Optimizer

Over the years, the ventilation systems used in coal mines have become more and more complex. Due to a lack of scientific and effective management, the energy consumed by ventilation systems has rapidly increased, resulting in considerable wasted energy. To solve this problem, the authors established a nonlinear optimization model aimed at minimizing the total energy consumption of a mine ventilation network. Furthermore, the authors propose an improved equilibrium optimizer algorithm to solve the model. First, the population is initialized by a chaotic map. Second, the adjustment strategy of a trigonometric function is introduced to improve the index F, which improves the global development ability of the algorithm, avoids falling into local optimum, and can improve the local search ability in the later stages. Then, Gaussian disturbance is introduced to enhance the diversity of particles and avoid falling into the local optimum. Finally, a learning factor was introduced to improve the generation rate G and improve the algorithm’s integrity; it was then compared with other algorithms. The simulation results show that the performance of the improved algorithm is significantly better than other algorithms. It was tested with the ventilation system of Wangjialing mine of Zhongmei Huajin Energy Co., Ltd. The results showed that the energy consumption of the ventilation system was reduced by 17.83%. This method can save the mine about 2 million yuan RMB every year. The economic effect is remarkable, we achieved the purpose of energy conservation and emission reduction, and the effectiveness of the proposed method was verified.

Clustering the sensor networks based on energy-aware affinity propagation

Due to the limited energy of wireless sensor networks (WSNs), improving energy efficiency and prolonging network lifetime are the key issues of WSNs application. In this paper, we proposed a cluster routing protocol based on energy-aware affinity propagation(CAP). The CAP protocol enhances network performance from three aspects. First, an energy-aware affinity propagation clustering algorithm is proposed, which finds the optimal network clustering topology taking into account the total network energy consumption and load balancing. Second, an inter-cluster relay selection method is proposed for CH relay selection in multi-hop routing. At last, the energy threshold re-cluster scheme is applied to avoid frequent re-clustering that consumes a large amount of energy. The simulation experiments are conducted in two sink node deployment scenarios: sink external(case 1) and central(case 2) deployment. The results indicate that our proposed CAP outperforms LEACH, LEACHC, and KCE in terms of energy efficiency and network lifetime. In case 1, the first node death of the proposed CAP algorithm occurs in 1114 rounds, which is 61.4%, 64.5%, and 33.3% longer compared to LEACH, LEACHC, and KCE, respectively. In case 2, the first node death of the CAP algorithm occurs in 1372 rounds, which is 68.8%, 31.9%, and 49.8% longer compared to LEACH, LEACHC, and KCE, respectively.

Optimization of Heterogeneous Clustering Routing Protocol for Internet of Things in Wireless Sensor Networks

Wireless sensor network technology is widely used in various modern scenarios, and various industries have higher and higher requirements for the performance indicators of wireless sensor networks. A reasonable and effective layout of wireless sensor networks is conducive to the monitoring of environmental quality, various transactions, and status and transmits a large number of sensing data to the data aggregation center for processing and analysis. However, the operation and development of traditional wireless sensor networks are extremely dependent on the energy supply of the network. When the corresponding supply energy is limited, the operation life of the corresponding wireless sensor network will be greatly reduced. Based on the above situation, this paper proposes a nonuniform clustering routing protocol optimization algorithm from the energy loss of cluster head and clustering form algorithm in wireless sensor networks. At the level of cluster head calculation in wireless sensor networks, firstly, based on the adaptive estimation clustering algorithm, the core density is used as the estimation element to calculate the cluster head radius of wireless sensor networks. At the same time, this paper creatively proposes a fuzzy logic algorithm to further solve the uncertainty of cluster head selection, integrate the residual energy of cluster head nodes, and finally complete the reasonable distribution of cluster heads and realize the balance of node energy consumption. In order to further reduce the algorithm overhead of transmission between cluster heads and realize energy optimization, an intercluster routing optimization algorithm based on the ant colony algorithm is proposed. The pheromone is updated and disturbed by introducing chaotic mapping to ensure the optimal solution of the algorithm, and the optimal path is selected from the perspective of energy dispersion coefficient and distance coefficient, so as to optimize the energy consumption between cluster heads. The experimental results show that compared with the traditional algorithm, the proposed nonuniform clustering routing protocol optimization algorithm prolongs the corresponding life cycle by 75% and reduces the total network energy consumption by about 20%. Therefore, the algorithm achieves the purpose of optimizing network energy consumption and prolonging network life to a certain extent and has certain practical value.

Adaptive Energy Efficient Circular Spinning Protocol for Dynamic Cluster Based UWSNs

Under Water Sensor Network (UWSN) is a novel paradigm for exploring marine environments such as offshore and mineral exploration, underwater surveillance, and sea habitat monitoring. However, a good quality underwater communication is difficult to achieve due to different constraints such as limited bandwidth, acoustic propagation issues, delays, battery replacement hitches, etc. In recent works, efficient energy-based designing and overall performance evaluation of the UWSN has become a major consideration. Cluster-based sensor networks have proven to be a successful way to increase the network’s load congruency and scalability while lowering the system’s total energy consumption. Usually, clustering algorithms work in three phases; cluster setup, data collection, and transmission to sink. In these types of dynamic cluster-based networks, energy consumed in cluster setup has been considered insignificant. Since these network energy consumptions are not part of data communication, we consider it extra energy consumption. In this paper, a new Energy Efficient Circular Spinning (EECS) dynamic clustering algorithm has been proposed to provide an improved cluster setup system and to minimize energy usage in re-clustering or cluster setup. Our proposed EECS mechanism suggests that system performance can improve by reducing the Cluster Head (CH) selection phase or cluster setup phase and can ultimately minimize the energy consumption of networks. It is demonstrated that by reducing the transmission of superfluous control messages during the cluster arrangement stage, approximately 21.5% to28.4% of the total network energy expended can be saved. This paper also compares the extra energy consumption, total network energy consumption, and life of the network in our proposed EECS mechanism to two different mechanisms, (1) Adaptive LEACH for UW, (2) UMOD_LEACH. The optimum value of cluster head has been calculated from energy consumption of different protocols and results show that our proposed EECS can prolong network lifetime by 21.5% and 28.4%from the above-mentioned algorithms consequently. In future, we will extend outwork for multi-hop dynamic cluster base mechanism for UW.

New approach for optimizing the consumed energy of the TDEEC protocol based on the firefly algorithm in HWSN

The energy consumption optimization in the routing protocols of wireless sensor networks has always been at the center of scientific research. In this context, an improved version of the TDEEC protocol entitled ITDDEC allowing maximization lifetime of the network by excluding the closest nodes to the base station from the election process of the cluster heads. In fact, this approach has led to a new energy balance that depends on two critical parameters: the number of excluded nodes and the number of clusters. However, the implementation of this proposition by traditional optimization methods, must take into consideration the implementing cost and its speed in each sensor node because it has low memory and modest processing capacities. In this paper, we present a comparative study between different classical optimization methods and one nature-inspired algorithm entitled Firefly. This in the goal to highlight the different indicators, such as the number of iterations, the execution time, the convergence, the precision and the memory capacity. Simulations under Matlab proves that this last optimization method offers an improvement of lifetime by 48% compared to the TDEEC protocol and 13.58% compared to the ITDEEC protocol and an increase in the number of packets received of 178% compared to the TDEEC protocol and 3% compared to the ITDEEC protocol. The proposed solution is suitable for application because it offers satisfactory optimization enhancement of the total network energy consumption by recovering the energy lost in the node's calculation.

Symmetric Connectivity of Underwater Acoustic Sensor Networks Based on Multi-Modal Directional Transducer.

Topology control is one of the most essential technologies in wireless sensor networks (WSNs); it constructs networks with certain characteristics through the usage of some approaches, such as power control and channel assignment, thereby reducing the inter-nodes interference and the energy consumption of the network. It is closely related to the efficiency of upper layer protocols, especially MAC and routing protocols, which are the same as underwater acoustic sensor networks (UASNs). Directional antenna technology (directional transducer in UASNs) has great advantages in minimizing interference and conserving energy by restraining the beamforming range. It enables nodes to communicate with only intended neighbors; nevertheless, additional problems emerge, such as how to guarantee the connectivity of the network. This paper focuses on the connectivity problem of UASNs equipped with tri-modal directional transducers, where the orientation of a transducer is stabilized after the network is set up. To efficiently minimize the total network energy consumption under constraint of connectivity, the problem is formulated to a minimum network cost transducer orientation (MNCTO) problem and is provided a reduction from the Hamiltonian path problem in hexagonal grid graphs (HPHGG), which is proved to be NP-complete. Furthermore, a heuristic greedy algorithm is proposed for MNCTO. The simulation evaluation results in a contrast with its omni-mode peer, showing that the proposed algorithm greatly reduces the network energy consumption by up to nearly half on the premise of satisfying connectivity.

Intelligent Power Control Algorithm in Heterogeneous Wireless Cellular Networks

Appropriate power control algorithm can increase total network rate and reduce energy consumption through optimizing signal-to-interference-plus-noise ratio (SINR) received by users. Thus it is vital for heterogeneous cellular networks, which are composed of macro base stations (BSs) and micro BSs. However, most existing power control schemes neglect the dynamic traffic variation and backhaul bandwidth constraint due to the variability of wireless traffic and difficulty of problem solving. This results in the performance reduction and resource waste under the highly dynamic traffic. In view of this, we propose an intelligent power control algorithm based on accurate traffic prediction. First, hybrid prediction scheme, composed of long short term memory (LSTM) and historical average value (HAV), is used to predict the traffic demand of each BS. Based on the predicted results, we propose a power control scheme. It achieves a weighted tradeoff between the total network rate and energy consumption under the consideration of backhaul constraint. By using the properties of concave function and the function structure of backhaul constraint, the Marks-Wright algorithm and difference of two convex functions (DC) programming are respectively employed to solve the non-convex optimization model approximately and accurately. Simulation results demonstrate that the prediction accuracy of proposed scheme is 3.73% and 6.03% higher than that of the autoregressive integrated moving average (ARIMA) and support vector machine (SVM) algorithm, respectively. Compared with full power transmission, energy consumption of the proposed algorithm is reduced by 58.29%, while total network rate is increased by 3.26%.

Topological Optimization of Lung Wireless Nanosensor Network

The early detection and prevention of lung disease is always a difficult point in the medical field, but it is of great significance. Some biological signals of the lung contain abundant information, especially the disease signals with great value. With the development of sensing technology, these specific biological signals are enough to be detected by sensors. However, if the information is to be transmitted from lung sensors to medical institutions in the macroworld, a suitable sensor network should be proposed. At the same time, the structure of lung is complex, and the channel in human body is special, so the construction of network is subject to many constraints. In order to construct an in vivo wireless nanosensor network (WNSN) that can meet the requirements of delay and energy, the model of lung WNSN is constructed in this article. On this basis, the performance of in vivo WNSNs (iWNSNs) with different equalization methods and different topologies is compared. Finally, a low-delay energy-efficient (LDEE) lung WNSN topology model is proposed. In this model, the total network delay and energy consumption of iWNSNs are taken as optimization objectives, the terahertz channel environment of lung and the properties of nanonodes are taken as constraint variables, and the topology model with the best performance is solved by MATLAB. The model can meet the necessary conditions of lung signal transmission, and has the characteristics of low network delay, high throughput, and long network lifetime.

EMSNDP: Energy Minimized Optimal Zone Selection of Sensor Network to Build the Data Path for IoTN

Wireless sensor network contains a huge volume of sensors that are capable of observing environmental data and conveying that information to the base station. The zone routing is a way to group sensors into multiple clusters in sensing field. In general, each zone has a single co-ordinator that operates as a zone head and accountable to collect the data from all sensors inside the zone and then forward it to the base station. Clustering and energy efficient path construction to base station is a major issue in sensor networks. This paper concentrates on clustering the sensing field into zones and further splitting it into zone quadrants to maximize the energy efficiency of sensor nodes with minimum delay transmission time. An “Energy Minimized optimal zone selection of Sensor Networks to build the Data Path” protocol is proposed in this paper to minimize the load on zone heads by using the zone coordinators to gather the information from different quadrants of zone. Distance-based communication with the minimum hop is opted to minimize the energy cost. A critical evaluation is carried out on the proposed protocol in the context of total energy consumption of network and average energy consumed per node, lifetime of the Network, delay in reception of packet and packet delivery ratio. The results of our research work and analyzed and benchmarked against similar protocols using Network Simulator 2. The outcome of this research is a reduced delay in packet processing with low energy consumption which significantly increases lifetime of sensor network.

Design and Improvement of Routing Protocol for Field Observation Instrument Networking Based on LEACH Protocol

Field observation instruments in cold and arid areas are deployed with many difficulties caused by the harsh natural environment, which leads to the lag of information acquisition ability and severely restricts the geoscience research in these areas. Therefore, it is urgent to study the suitable routing technology of observation instrument networks according to the characteristics of cold and arid areas. In this paper, we have studied and designed an improved routing protocol for the field observation instruments network based on the LEACH protocol (FOI-LEACH). Firstly, the FOI-LEACH was proposed to mainly improve the LEACH protocol in three aspects: (1) the network nodes are heterogeneous and combined with the characteristics of field observation instrument networking. The residual energy and the rechargeable energy of nodes are added in the process of cluster head (CH) election to reduce the risk of premature death of CHs and shortened network life cycle caused by the selection of nodes with less energy as CH. (2) In the process of cluster forming, the distance from CH to the base station (BS) and the residual energy of CH is considered when setting the cluster radius, to reasonably plan the cluster size and alleviate the “hot spot” problem. The nonuniform distribution of clusters in the network is enhanced to balance the total network energy consumption. (3) The autonomous zone-based multihop routing mechanism is adopted to solve the low reliability of data transmission caused by the poor quality of intercluster communication links and premature death of nodes in long-distance transmission. Then, MATLAB was used to compare the network routing protocol model of the observation instrument from four aspects, network life, energy consumption rate, stability, and throughput. The results showed that the improved algorithm FOI-LEACH balances the network energy consumption and alleviates the “hot spot” problem, to extend the lifetime of network nodes.

Layerwise Buffer Voltage Scaling for Energy-Efficient Convolutional Neural Network

In order to effectively reduce buffer energy consumption, which constitutes a significant part of the total energy consumption in a convolutional neural network (CNN), it is useful to apply different amounts of energy conservation effort to the different levels of a CNN as the buffer energy to total energy usage ratios can differ quite substantially across the layers of a CNN. This article proposes layerwise buffer voltage scaling as an effective technique for reducing buffer access energy. Error-resilience analysis, including interlayer effects, conducted during design-time is used to determine the specific buffer supply voltage to be used for each layer of a CNN. Then these layer-specific buffer supply voltages are used in the CNN for image classification inference. Error injection experiments with three different types of CNN architectures show that, with this technique, the buffer access energy and overall system energy can be reduced by up to 68.41% and 33.68%, respectively, without sacrificing image classification accuracy.

Fault Tolerance Mechanism Combining Static Backup and Dynamic Timing Monitoring for Cluster Heads

Internet of things (IoT) monitoring systems have been extensively applied in smart homes, underwater monitoring, volcano monitoring, and health monitoring. In IoT applications, a wireless sensor network (WSN) is deployed to collect data. Hierarchical routing protocols that effectively maintain the energy consumed by sensor nodes (SNs) are usually employed in WSNs. Cluster heads (CHs) are important in this type of protocol. An effective fault tolerance mechanism for CHs in this system can guarantee reliable data acquisition. In this paper, a fault tolerance mechanism that combines CH static backup and dynamic timing monitoring (SBDTM) is proposed, a CH reliability model based on the Markov model is developed, and the minimum number of CHs necessary to satisfy the given reliability requirement is obtained. The data structures and fault-tolerant operations are described, and the energy consumption and the latency of the recovery of the SBDTM mechanism are quantitatively analysed. Simulations were carried out to compare the total network energy consumption, number of dead nodes, throughput, and packet loss rate of the proposed model with those of other methods presented in the literature. The simulation results show that the proposed SBDTM fault tolerance mechanism is superior to current models. This study presents important theoretical and application-based knowledge that can guarantee reliable data acquisition for IoT-based monitoring systems.

Transfer restless multi-armed bandit policy for energy-efficient heterogeneous cellular network

This paper proposes a learning policy to improve the energy efficiency (EE) of heterogeneous cellular networks. The combination of active and inactive base stations (BS) that allows for maximizing EE is identified as a combinatorial learning problem and requires high computational complexity as well as a large signaling overhead. This paper aims at presenting a learning policy that dynamically switches a BS to ON or OFF status in order to follow the traffic load variation during the day. The network traffic load is represented as a Markov decision process, and we propose a modified upper confidence bound algorithm based on restless Markov multi-armed bandit framework for the BS switching operation. Moreover, to cope with initial reward loss and to speed up the convergence of the learning algorithm, the transfer learning concept is adapted to our algorithm in order to benefit from the transferred knowledge observed in historical periods from the same region. Based on our previous work, a convergence theorem is provided for the proposed policy. Extensive simulations demonstrate that the proposed algorithms follow the traffic load variation during the day and contribute to a performance jump-start in EE improvement under various practical traffic load profiles. It also demonstrates that proposed schemes can significantly reduce the total energy consumption of cellular network, e.g., up to 70% potential energy savings based on a real traffic profile.

Energy Efficient Designs of Ultra-Dense IoT Networks With Nonideal Optical Front-Hauls

We study the optimum designs of the downlink of user-centric ultra-dense Internet of Things (IoT) networks with fiber-wireless communications (FWCs). A large number of low power radio access points (RAPs) are densely deployed in the network to provide service to spatially distributed IoT physical devices (PDs). The RAPs are connected to a central unit (CU) through optical fiber (OF) front-hauls. Radio-frequency-over-fiber (RFoF) is employed in the optical front-hauls to reduce RAP complexity, cost, and energy consumption. With RFoF front-hauls, wireless signals received by PDs are subject to distortions accumulated through the optical and wireless links, including optical loss, optical chromatic distortion, optical and thermal noises, wireless pathloss, and small scale fading. The optimum designs are performed across the optical and wireless domains with the help of a newly developed model that quantifies the combined effects of the optical and wireless links. One of the main challenges faced by the design of an ultra-dense IoT network is the high energy consumption due to dense RAP deployment. The objective of this paper is to minimize the total energy consumption of the entire IoT network, including both optical and wireless links, by jointly optimize RAP power allocation and RAP-PD association, subject to quality-of-service (QoS) constraints for each PD. We propose a low complexity suboptimum binary forcing gradient search (BFGS) algorithm, which performs a gradient-based search based on the unique structure of the problem. Simulation results show that the optical front-hauls have significant impacts on the performance and design of ultra-dense IoT networks.

Energy harvesting, cooperative spectrum sensing, and data transmission improvement in energy‐efficient multichannel cognitive sensor networks

SummaryIn this paper, we investigate the energy harvesting capability in a multichannel wireless cognitive sensor networks for energy‐efficient cooperative spectrum sensing and data transmission. Spectrum sensors can cooperatively scan the spectrum for available channels, whereas data sensors transmit data to the fusion center (FC) over those channels. We select the sensing, data transmission, and harvesting sensors by setting the sensing time, data transmission time, and also harvesting time to maximize the network data transmission rate and improve the total energy consumption in the multichannel network under global probability of false alarm and global probability of detection constraints. We formulate our optimization problem and employ the convex optimization method to obtain the optimal times and nodes for spectrum sensing, data transmission, and harvesting energy in each subchannel for multiband cognitive sensor networks. Simulation results show that in our proposed algorithm, the network data transmission rate is improved while more energy is saved compared with the baseline methods with equal sensing time in all subchannels.

Using Battery-Saving Modes in Quadcopter Relay Networks

A new scheme for combining energy efficiency, relay location and spectral efficiency in a Quadcopter Relay (QR) network is proposed in this paper. The scheme enables the energy consumption of a QR network to be reduced under the constraints of data rate and limited power source. The QR network uses a quadcopter aircraft (commonly known as a drone) fitted with an amplify-and-forward relay to increase the range and flexibility of a communication system. The proposed scheme considers a quadcopter that has a hovering function. It enables the energy consumption to be evaluated for all hovering positions in the QR’s flight path and to set the position that gives the minimum energy consumption as the quadcopter’s reference location, which in turn minimizes the total energy consumption in the QR network. Numerical examples are provided to validate the analysis.

Assessing the Energy Consumption of Proactive Mobile Edge Caching in Wireless Networks

Multiaccess edge computing and caching (MEC) is regarded as one of the key technologies of fifth-generation (5G) radio access networks. By bringing computing and storage resources closer to the end users, MEC could help to reduce network congestion and improve user experience. However, deploying many distributed MEC servers at the edge of wireless networks is challenging not only in terms of managing resource allocation and distribution but also in regard to reducing network energy consumption. Here, we focus on the latter by assessing the network energy consumption of different cache updating and replacement algorithms. First, we introduce our proposed proactive caching (PC) algorithm for mobile edge caching with Zipf request patterns, which could potentially improve the cache hit rates compared to other caching algorithms such as least recently used, least frequently used, and popularity-based caching. Then, we present the energy assessment models for mobile edge caching by breaking down the total network energy consumption into transmission and storage energy consumption. Finally, we perform a comprehensive simulation to assess the energy consumption of the PC algorithm under different key factors and compare with that of conventional algorithms. The simulation results show that improving cache hit rates by using the PC algorithm comes at the expense of additional energy consumption for network transmission.