Energy Efficient Content Research Articles

An ultra-high-density and energy-efficient content addressable memory design based on 3D-NAND flash

An ultra-high-density and energy-efficient content addressable memory design based on 3D-NAND flash

5G-EECC: Energy-Efficient Collaboration-Based Content Sharing Strategy in Device-to-Device Communication

In the past few years, mobile data traffic has seen exponential growth due to the emergence of smart applications. Although throughput enhancement techniques such as macro- and femtocells reduce cell size, they are relatively expensive to implement. Mobile device-to-device (D2D) communication has emerged as a solution to support the growing popularity of multimedia content for local service in next-generation 5G cellular networks. Content sharing is the prominent feature, which helps D2D communication in reducing offload traffic on the network, improving the energy efficiency of the device, and reducing backhaul connectivity costs. In traditional mapping approaches such as one to one or one to many, a massive amount of traffic is distributed among the devices resulting in high-energy consumption. In this paper, we propose a novel energy-efficient content sharing scheme called Energy-Efficient Collaboration-based Content (EECC) sharing strategy in D2D communication that shares content equally across devices based on their capacities and battery life under mobility. The proposed work includes cluster formation, cluster head selection, and helper node selection. In addition, we relied on a cooperative caching policy to ensure that content is distributed efficiently. The simulation results indicate a 12.05% reduction in energy compared to the state-of-the-art technique with a 2-gigabyte video file. To evaluate scalability, we increased the file size from 3 to 4 gigabytes, yet the performance in terms of energy consumption remained the same.

Fresh, Fair and Energy-Efficient Content Provision in a Private and Cache-Enabled UAV Network

In this paper, we investigate a private and cache-enabled unmanned aerial vehicle (UAV) network for content provision. Aiming at delivering fresh, fair, and energy-efficient content files to terrestrial users, we formulate a joint UAV caching, UAV trajectory, and UAV transmit power optimization problem. This problem is confirmed to be a sequential decision problem with mixed-integer non-convex constraints, which is intractable directly. To this end, we propose a novel algorithm based on the techniques of subproblem decomposition and convex approximation. Particularly, we first propose to decompose the sequential decision problem into multiple repeated optimization subproblems via a Lyapunov technique. Next, an iterative optimization scheme incorporating a successive convex approximation (SCA) technique is explored to tackle the challenging mixed-integer non-convex subproblems. Besides, we analyze the convergence and computational complexity of the proposed algorithm and derive the theoretical value of the expected peak age of information (PAoI) to estimate the content freshness. Simulation results demonstrate that the proposed algorithm can achieve the expected PAoI close to the theoretical value and is more 22.11% and 70.51% energy-efficient and fairer than benchmark algorithms.

Power and Rate Adaptive Pushing Over Fading Channels

Proactive caching is capable of reducing access latency and improving network throughput, thereby attracting attention from both industry and academia. However, the energy efficiency (EE) of proactive caching over fading channels has not been well studied yet. In this paper, we aim at presenting an energy-efficient content pushing policy by carefully adapting the transmit rate and power in pushing and on-demand delivery phases, while assuring delivery delay constraints. To this end, the average delay, delay-outage probability, and EE are analyzed for a general adaptive pushing policy based on the saddle point approximation. According to these results, we formulate EE maximization problems under average delay and delay-outage constraints, respectively, for two special types of adaptive pushing policies, namely, opportunistic pushing and water-filling-based pushing. Due to the high complexity of the grid search for solving the formulated problems, suboptimal algorithms are presented based on gradient descent and golden section search methods. Moreover, we mathematically derive the scaling property and numerically obtain request probability and delivery delay thresholds for content pushing. Simulations show that the presented pushing policies achieve higher EE than on-demand transmissions, especially when the content item is popular and the tolerable delivery delay is small.

Multi-UAV Trajectory Planning for Energy-Efficient Content Coverage: A Decentralized Learning-Based Approach

In next-generation wireless networks, high-mobility unmanned aerial vehicles (UAVs) are promising to provide content coverage, where users can receive sufficient requested content within a given time. However, trajectory planning for multiple UAVs to provide content coverage is challenging since 1) UAVs cannot provide content coverage for all users due to the limited energy and caching storage, and 2) the trajectory planning of UAV is coupled with each other. Moreover, the complete information based trajectory planning methods are unusable since UAVs cannot obtain prior information on the rapidly changing environment. In this paper, we investigate the multi-UAV trajectory planning for energy-efficient content coverage. We first formulate an energy efficiency maximization problem considering recharging scheduling, which aims to reduce the total length of trajectories of UAVs under the quality of service (QoS) constraints. To settle environment uncertainty, the trajectory planning problem is modeled as two coupled multi-agent stochastic games, whose equilibrium constitute the optimal trajectory. To obtain the equilibrium, we propose a decentralized reinforcement learning algorithm, which can decouple the two games. We prove that the proposed algorithm can converge to the optimal solution of the Bellman equation with a higher rate compared to the centralized one. Moreover, simulation results show that the energy efficiency of the proposed algorithm is smaller than 5% compared the optimal, which is obtained with the prior information of environments.

Content-Centric Heterogeneous Fog Networks Relying on Energy Efficiency Optimization

Next-generation wireless communications are expected to provide reliable high-rate connectivity. The recent concept of fog-enabled architecture comes to rescue by invoking a substantial amount of data storage close to end-user devices for meeting these challenges. Thus, caching popular contents at heterogeneous devices, e.g., fog nodes (FNs) or fog access points (FAPs), constitutes a promising technique of reducing both the traffic and the energy consumption of the backhaul links. Therefore, in this paper, we propose an energy-efficient caching and node association algorithm for cache-aided fog networks. First, we solve the problem of energy-efficient content caching and delivery in the FNs/FAPs in conjunction with a fixed node association strategy, where the FNs communicate either with other FNs by node-to-node (N2N) communications or with the FAPs in their proximity. In both caching scenarios, we investigate the relationship between the caching probability of the file and the energy-efficient content delivery by formulating the associated energy efficiency (EE) optimization problem under caching memory constraints. Then, we derive a joint modulation mode allocation strategy and caching policy for each content caching node and conceive a joint node association and caching algorithm for maximizing the EE. Finally, we quantify both the overall EE and throughput for demonstrating that the proposed caching and transmission strategy achieves significant performance improvements.

Energy-Efficient Content Placement With Coded Transmission in Cache-Enabled Hierarchical Industrial Internet of Things Networks

Industrial Internet of things (IIoT) is expected to improve efficiency and productivity by connecting massive devices, but it will cause potential congestions in backhual link and high energy consumptions. Caching with coded transmission is an effective method to reduce backhual load for content delivery. However, due to the hierarchy and heterogeneity in IIoT, it is very challenging to perform content placement with lower energy consumption. In this article, we propose an energy-efficient content placement strategy in cache-enabled hierarchical IIoT network with coded transmission. We derive a closed-form expression including the energy consumption for content placement and transmission by the macro base station and the small base stations. In addition, we establish an optimization problem to minimize the total energy consumption, whereby we find the optimal content placement matrix and optimal cache size allocation, respectively. Simulation results show that, the proposed content placement strategy can greatly improve energy efficiency in IIoT.

Energy Efficiency Education and Training: Australian Lessons on What Employers Want—Or Need

This paper explores current approaches and future directions for energy efficiency education and training in the tertiary sector. Energy efficiency is a significant element in many jobs across the economy, with potential for substantial growth. It crosses disciplinary boundaries, as the range of skills and knowledge required by practitioners is broad, reflecting the diversity and expanding range of work roles that require energy efficiency skills and knowledge. Limitations of education and training contribute to a situation where business and consumer decision-making often involves little or no consideration of energy, so outcomes are often sub-optimal. This increases costs, environmental and social impacts and undermines productivity improvement. As the significance of energy efficiency skills and knowledge in workplaces increases, more flexible and varied education and training models are needed to allow workers to upskill, gain new skills and integrate energy efficiency into business models. The paper discusses the barriers to adoption of more comprehensive energy efficiency content in programs and presents options for inclusion. The paper concludes that, in order to capture the potential of energy efficiency to contribute to a productive, sustainable economy, appropriate teaching resources and certification must be developed and introduced across most disciplines, while employers and recruitment consultants must be informed of the benefits, so they value energy efficiency skills and qualifications.

Implementation of Network-Coded Cooperation for Energy Efficient Content Distribution in 5G Mobile Small Cells

The continuous increase of mobile data traffic calls for the design of energy-efficient content distribution mechanisms, to be incorporated in the fifth generation of mobile networks, 5G. One of the biggest concerns of the companies and the research community is to reduce the energy consumption in both the user equipments (UEs) and the network equipment. In this article, we present a novel content distribution framework called Network-Coded Cooperative (NCC) Networks, which benefits from the interplay between mobile clouds (MC) and Random Linear Network Coding (RLNC) to reduce the overall energy consumption in the devices that take part in the communication. This novel framework leads to reduced energy consumption by offloading the cellular interface to a link with greater energy efficiency, for instance, WiFi, within the mobile small cell. We evaluate the performance of our framework analytically and in practical implementation (i.e., testbed) in terms of throughput, energy savings, packet decoding ratio, latency, and synchronicity. In comparison to the conventional content distribution system, for the case of four users, the analytical model and the testbed implementation show energy savings of more than 12% and 8%, respectively. Furthermore, network usage is reduced, losses are neutralized, and the content is synchronously distributed to all users.

Energy efficiency and net energy prediction of feed in laying hens

Using accurate nutrient values for ingredients is of vital importance for efficient diet formulation. The net energy (NE) system accounts for the real available amount of feedstuff energy for body maintenance and production as it considers energy dissipated as heat increment. The NE content of diets for pigs and broilers has been estimated from their nutrient contents. However, such estimates have not been made specifically for laying hens. This study reports the development of equations to predict NE for laying hens based on the chemical composition of 16 different diets meeting minimum nutrient specifications but varying in nutrient composition. Heat production and energy metabolism were measured in layers ranging from 32 to 62 weeks of age in closed-circuit calorimetry chambers with 8 replicates per diet in a randomized design. Each replicate consisted of a chamber with 3 layers that were adapted for 4 D to diets and chambers prior to measurement. The measurements included feed intake, metabolizable energy (ME) content, nitrogen balance, egg production, gas exchange, heat production, energy efficiency, and energy partition for a 3-D period. The average AME/GE and NE/AME ratios of the 16 diets were 77 and 74%, respectively. The latter ratio increased with energy efficiency (EE) content and decreased with CP content of diets. The results indicate that diet NE content can be predicted from AME, CP, and EE contents and the NE/AME ratio varied positively with EE and negatively with CP. A validation experiment with 2 diets fed to layers in calorimetry chambers confirmed the estimation from NE prediction equations. In conclusion, NE of diets can be predicted in laying hens from equations based on AME and CP and EE contents in laying hens being similar to those reported in broilers.

A Lyapunov optimization approach to energy-efficient content delivery networks

A content delivery network (CDN) supports fast growing services (such as streaming media) for end users and uses request a routing algorithm to enhance the users’ perceived quality of experience (QoE). This working process can increase the power consumption and eventually the operational cost of the CDN system substantially. In addition, a smart grid facilitates real-time electricity pricing for the consumers as well as the integration of green energy resources into the electrical grid for improving the system efficiency. In this paper, we first take into account these key functionalities of a smart grid to propose a real-time energy management algorithm for reducing the operational cost within a CDN cluster. We then define the proposed stochastic optimization algorithm for jointly optimizing the energy cost and perceived quality within a CDN system based on the popular Lyapunov-drift methodology. Numerical results are presented to evaluate and validate the aforementioned solution. The obtained results indicate large operational and energy cost savings for a single CDN or more than one CDN providers over a conventional method that only optimizes the users’ perceived QoE.

ADMC-MAC: Energy efficient adaptive MAC protocol for mission critical applications in WSN

Energy efficient wireless sensor networks play a very important role in mission critical applications, as in these applications it is difficult to deploy and change the nodes frequently or to change their battery. Some of the mission critical applications are healthcare applications, environmental monitoring and control applications (Tsunami alert, earthquake alert, volcanic eruption prone areas) and surveillance applications. These applications require quick response time along with power management, so that the energy efficiency can be achieved without having latency beyond a critical threshold limit. Several MAC protocols have been proposed by researchers with a preview to enhance energy efficiency but hey failed to fulfill the need of mission critical applications. An in-depth survey analysis has helped in exploring several MAC protocols for their compatibility towards mission critical scenarios. In this paper we propose a novel energy efficient contention based ADMC-MAC protocol. This protocol is more adaptable to mission critical applications by improving the data delivery performance based on traffic conditions considering the queue size of the node and also have improved energy efficient performance. This protocol overcomes the lacunas of standard MAC protocols. Two algorithms have been proposed in this protocol. The first algorithm, which is priority based, select the node as cluster head with high number of packets in queue and having maximum energy among the neighboring nodes forming virtual cluster. The second algorithm uses regression technique for deciding the duty cycle of selected node based on traffic conditions and residual energy of the node. Only the selected nodes take part in transmission of packets. So the packets overloading at intermediate nodes is reduced. This helps in increasing the overall efficiency of the system by enhancing energy saving up to 71.452% as compare to S-MAC protocol in mission critical scenarios. The packet delivery ratio is enhanced by 15.198 percent in mission critical scenarios along with considerable improvement in throughput. S-MAC is found to be the most appropriate existing MAC protocol which can be enhanced for mission critical scenarios. So the proposed protocol is enhanced S-MAC protocol implemented in NS-2.35 and the results are compared with standard S-MAC protocol and latest MC-MAC mission critical MAC protocol. It is found that the proposed protocol ADMC-MAC outperforms the latest MC-MAC protocol in terms of energy saving and also provides efficient data delivery performance.

Energy-Efficient Video-On-Demand Content Caching and Distribution in Metro Area Networks

The success of novel multimedia services, such as video-on-demand (VoD) is leading to a tremendous growth of Internet traffic. Content caching can help mitigate such uncontrolled growth by storing video content closer to the users in core, metro, and access network nodes. So far, fixed, and, especially, mobile access networks have evolved independently, leveraging logically (and often also physically) separate infrastructures. This means that mobile users cannot access caches placed in the fixed access network (and vice-versa) even if they are geographically close to them, and energy consumption implications of such undesired effect must be investigated. In this paper we perform an evaluation of energy-efficient VoD content caching and distribution under static and dynamic traffic in converged networks as well as in non-converged networks. We define an integer linear programming optimization problem modeling an energy-efficient placement of caches in core, metro, and fixed/mobile access nodes where energy is minimized by powering-on and -off caches located in different segments of the network and by performing an energy-efficient VoD-request routing. To deal with problem complexity, we propose an energy-efficient content caching and VoD-request routing heuristic algorithm, which is also adopted under dynamic traffic scenarios. Our results show how deploying caches in the access and metro network segments can reduce the overall energy consumption of the network. Moreover, results show how the evolution toward a fixed-mobile converged metro/access network, where fixed and mobile users can share caches, can reduce the energy consumed for VoD content delivery.

RELIABLE DETECTION OF SIGNAL TONES IN PRESENCE OF COLLISIONS

A reliable and energy-efficient contention resolution mechanism (CRM) is a crucial component of MAC protocols for wireless sensor networks (WSNs). Among a number of CRM proposals, CRMs based on exchanging short signal tones between competing nodes have recently drawn attention due to their promise of collision-freedom and energy-efficiency. However, the basic assumption of these protocols, i.e. the possibility to reliably detect the presence/absence of signal tone in case of simultaneous transmissions from multiple nodes, has not yet been confirmed. In this paper we present a technique for signal tone generation/detection, which uses only standard features of off-the-shelf RF transmitters. Furthermore, we present results of an experimental validation carried out in a real testbed which demonstrate a high accuracy of signal tone detection in presence of multiple simultaneous transmissions.

Energy-Efficient Predictive HTTP Adaptive Streaming in Mobile Cellular Networks

The rapid growth of mobile video traffic puts significant pressure on energy drain at the network as well as on the end users. Exploiting predicted channel information and designing energy-efficient content delivery protocols have recently drawn attention, which is referred to as predictive, anticipatory, or context-aware resource allocation. In this paper, we investigate how predicted user rates can be exploited for mobile video streaming with the popular HyperText Transfer Protocol (HTTP) based Adaptive Streaming (HAS) (e.g., dynamic adaptive streaming over HTTP). To this end, we develop a robust mobile edge-cloud assisted stochastic Predictive HTTP Adaptive Streaming (PHAS) optimization framework that utilizes unreliable predictions of wireless data rates in a finite look-ahead window to achieve the following objectives: first, to obtain an edge-cloud assisted framework for prediction-based HAS and identify its key functional entities and their interactions; second, to model uncertainty in predicted user rates and propose a robust two-stage QoE optimization approach that dynamically allocates the risks and optimizes system efficiency over a time horizon; and third, to propose a heuristic algorithm allocating time slot ratio for multi-users, which improves network efficiency, fairness, and overall QoE under different prediction error variances, wireless link conditions, and buffer length constraints. Simulation studies and analytical results show that the proposed solution outperforms traditional methods in terms of average QoE, fairness, and energy efficiency.

Green content sharing mode: D2D Coordination Multiple Points Transmission

In next-generation 5G cellular networks, device-to-device (D2D) communication has emerged as an effective solution to support the growing popularity of multimedia contents for local service. Conventionally, the D2D content sharing mode is “one-to-one” matching, i.e, one demander will select one provider to request files from it. Under this mode, it is hard to cope with the growing demand for multimedia services for mobile users due to limited battery capacity for mobile devices. In this work, we propose an energy-efficient content sharing system via a novel D2D Coordination Multiple Points Transmission (D2D-CoMP), which shares content among multiple users to reduce the power consumption per user device. The highlights of this work lie in three parts. Firstly, the strategy for matching providers to demanders subject to self-interference constraints is formulated as a classical maximum weighted matching problem, in which the optimal solution can be derived when network-wide information is known, and also an effective distributed algorithm. Secondly, we design an optimal packet split algorithm for D2D-CoMP under comprehensive consideration of two aspects of communication efficiency and energy consumption to solve the problem how many data packets each provider transfers. Thirdly, we model the file reconstruction problem of collaboration demanders as the shortest Hamiltonian path problem and illustrate the file reconstruction process. Moreover, we develop a best-effort distributed greedy algorithm framework to find the shortest file reconstruction path. Importantly, numerical results demonstrate that the proposed mechanism can greatly reduce the energy consumption of each device with little or no increase in transmission delay.

An energy efficient teaching learning based optimization approach for common content distribution in mobile ad hoc networks

An energy efficient content distribution technique in mobile ad hoc networks is proposed in this paper. Based on bit rate, remaining energy and maximum allowable delay, a fitness value is evaluated for each node in the network. A set of nodes having maximum fitness values are elected as cluster heads. It is determined by applying teaching learning based optimization technique, only after the nodes satisfy an energy constraint. The clusters of nodes are formed by assigning the remaining nodes to their corresponding cluster heads. The cluster heads can receive the required packets from the base station via long range communication and forward the same to the cluster members. Efficient energy expenditure by this approach leads to an increased network lifetime. The experimental results show effectiveness of the proposed approach over K-means Clustering and U+M techniques in terms of reduced energy consumption and improved network lifetime.

Ethylene cracking furnace TOPSIS energy efficiency evaluation method based on dynamic energy efficiency baselines

It is significant to evaluate accurately energy efficiency of ethylene cracking furnace as the highest energy consumption device in ethylene production. However, previous energy efficiency evaluation methods mainly concentrate on system-level evaluation of ethylene production and fail to consider effects of production load on operation conditions, which are unsuitable for detailed evaluation online. Considering single energy efficiency content and impractical improvement scheme of DEA and unreasonable static baselines in TOPSIS, a modified TOPSIS energy efficiency evaluation method is proposed. A set of new energy efficiency indicators is designed through matter conversion and energy transformation together with matter and energy interaction. To acquire real-time energy efficiency a simulation model of cracking furnace is established by employing radial basis function neural network. To improve evaluation accuracy, the relations among energy efficiency and operation conditions and production load are quantified by calculation formulas of energy efficiency indicators and functions extracted from simulation model. The sequential quadratic programming algorithm is suggested to solve dynamic baselines according to real-time production load by adjusting operation conditions within constraints. Furthermore, optimal operation conditions are provided by searching for maximum comprehensive energy efficiency. Finally, validity of proposed evaluation method is illustrated by applying in a practical cracking furnace.

Greening the Smart Cities: Energy-Efficient Massive Content Delivery via D2D Communications

Massive multimedia services have been considered as one the most prominent characteristics for smart cities. In this paper, we propose an energy-efficient content delivery system via the device-to-device communications, which realizes the large-scale content delivery among mobile devices with constrained energy, unpredictable demand, limited storage, random mobility, and opportunistic transmission. The highlights of this paper lie in two parts. On the theoretical end, through exploring the relationship among the coding, storage, and transmission, a systematic energy-saving content delivery fashion is investigated. On the technical end, a totally distributed content delivery system is designed in a simple and efficient manner, in which each device only utilizes local information to make decisions and implements its own scheme individually. Importantly, the proposed scheme is realized in a practical smart city system, and numerical results demonstrate that it is flexible to various users’ needs and communication environments.

GreenTouch GreenMeter Core Network Energy-Efficiency Improvement Measures and Optimization

In this paper, we discuss energy-efficiency improvements in core networks obtained as a result of work carried out by the GreenTouch consortium over a five-year period. A number of techniques that yield substantial energy savings in core networks were introduced, including (i) the use of improved network components with lower power consumption, (ii) putting idle components into sleep mode, (iii) optically bypassing intermediate routers, (iv) the use of mixed line rates, (v) placing resources for protection into a low power state when idle, (vi) optimization of the network physical topology, and (vii) the optimization of distributed clouds for content distribution and network equipment virtualization. These techniques are recommended as the main energy-efficiency improvement measures for 2020 core networks. A mixed integer linear programming optimization model combining all the aforementioned techniques was built to minimize energy consumption in the core network. We consider group 1 nations' traffic and place this traffic on a US continental network represented by the AT&T network topology. The projections of the 2020 equipment power consumption are based on two scenarios: a business as usual (BAU) scenario and a GreenTouch (GT) (i.e., BAU + GT) scenario. The results show that the 2020 BAU scenario improves the network energy efficiency by a factor of 4.23 x compared with the 2010 network as a result of the reduction in the network equipment power consumption. Considering the 2020 BAU + GT network, the network equipment improvements alone reduce network power by a factor of 20 x compared with the 2010 network. Including of all the BAU + GT energy-efficiency techniques yields a total energy efficiency improvement of 315×. We have also implemented an experimental demonstration that illustrates the feasibility of energy-efficient content distribution in IP/WDM networks.