In this paper, we investigate different power allocation optimization problems with interferences for distributed antenna systems (DAS) with and without D2D communication, respectively. The first objective problem is maximizing spectral efficiency (SE) of the DAS with D2D communication under the constraints of the minimum SE requirements of user equipment (UE) and D2D pair, maximum transmit power of each remote access unit (RAU) and maximum transmit power of D2D transmitter. We transform this non-convex objective function into a difference of convex functions (D.C.) then using the concave-convex procedure (CCCP) algorithm to solve the optimization problem. The second objective is maximizing energy efficiency (EE) of the DAS with D2D communication under the same constraints. We first exploit fractional programming theory to obtain the equivalent objective function of the second problem with subtract form, and then transform it into a D.C. problem and use CCCP algorithm to obtain the optimal power allocation. In each part, we summarize the corresponding optimal power allocation algorithms and also use similar method to obtain optimal solutions of the same optimization problems in DAS. Simulation results are provided to demonstrate the effectiveness of the designed power allocation algorithms and illustrate the SE and EE of the DAS by using D2D communication are much better than DAS without D2D communication.

Cooperative communication can reduce energy consumption effectively due to its superior diversity gain. To further prolong network lifetime and improve the energy efficiency, this paper studies energy-efficient packet transmission in wireless ad-hoc networks and proposes a novel cluster-based cooperative packet transmission (CCPT) protocol to mitigate the packet loss and balance the energy consumption of networks. The proposed CCPT protocol first constructs a highly energy-efficient initial routing path based on the required energy cost of non-cooperative transmission. Then an iterative cluster recruitment algorithm is proposed that selects cooperative nodes and organizing them into clusters, which can create transmit diversity in each hop of communication. Finally, a novel two-step cluster-to-cluster cooperative transmission scheme is designed, where all cluster members cooperatively forward the packet to the next-hop cluster. Simulation results show that the CCPT protocol effectively reduces the energy cost and prolongs the network lifetime compared with the previous CwR and noC schemes. The results also have shown that the proposed CCPT protocol outperforms the traditional CwR protocol in terms of transmit efficiency per energy, which indicates that CCPT protocol has achieved a better trade-off between energy and packet arrival ratio.

This paper focuses on on-demand wireless sensor networks (WSNs) where a wake-up receiver is installed into each node. In on-demand WSNs, each node sends a wake-up signal including a wake-up ID assigned to a specific destination node in order to remotely activate its main radio interface. This wake-up control helps each node to reduce energy consumed during idle periods, however, the wake-up signal transmitted before every data transmission results in overhead, which degrades communication quality and increases energy consumption at each sender node. In order to reduce the overhead for wake-up control, in this paper, we propose three schemes. First, we propose a scheme called Double Modulation (DM), where each node embeds the sensing data to be transmitted into the payload field of a wake-up signal. The destination interprets the wake-up message differently depending on its wake-up state: if it is in a sleep state, it treats the message as a wake-up signal, otherwise it extracts the sensing data from the detected message. Second, we propose a scheme called Overhearing (OH), where each node observes the frames transmitted by a destination node and suppresses the transmission of wake-up signal when detecting the active state of their destination. Finally, we propose a hybrid scheme that combines OH and DM schemes. Our simulation results show that the proposed schemes can effectively reduce the negative impact of wake-up overhead, and significantly improve data collection rate and energy-efficiency in comparison to on-demand WSN without the proposed schemes.

One of essential requirements for the next generation communications is to support higher spectral efficiency (SE) and energy efficiency (EE) than the existing communication system. For increasing the SE, carrier aggregation (CA) has received great attention. In this paper, we propose an energy efficient smart crest factor reduction (E2S-CFR) method for increasing the EE while satisfying the required SE when the CA is applied. The proposed E2S-CFR exploits different weights on each carrier according to the required error vector magnitude (EVM), and efficiently reduces the peak to average power ratio (PAR). Consequently, we can reduce the bias voltage of a power amplifier, and it leads to save total consumed energy. Through performance evaluation, we demonstrate that the proposed E2S-CFR improves the EE by 11.76% compared to the existing schemes.

This paper proposes an enhanced BLE scanner with user-level channel awareness and simultaneous channel scanning to increase theoretical scanning capability by up to three times. With better scanning capability, channel analysis quality also has been improved by considering channel-specific signal characteristics, without the need of beacon-side changes.

This paper investigates energy-efficient resource allocation problem for the wireless power transfer (WPT) enabled multi-user massive multiple-input multiple-output (MIMO) systems. In the considered systems, the sensor nodes (SNs) are firstly powered by WPT from the power beacon (PB) with a large scale of antennas. Then, the SNs use the harvested energy to transmit the data to the base station (BS) with multiple antennas. The problem of optimizing the energy efficiency objective is formulated with the consideration of maximum transmission power of the PB and the quality of service (QoS) of the SNs. By adopting fractional programming, the energy-efficient optimization problem is firstly converted into a subtractive form. Then, a joint power and time allocation algorithm based on the block coordinate descent and Dinkelbach method is proposed to maximize energy efficiency. Finally, simulation results show the proposed algorithm achieves a good compromise between the spectrum efficiency and total power consumption.

We describe the preparation of an α-phenyl-4'-(diphenylamino)stilbene (TPA) single crystal and the evaluation of its hole transport property. Based on the characterization using optical microscopy, polarizing microscopy, and X-ray diffraction, a large-scale TPA single crystal of dimensions 7.0×0.9×0.8mm is successfully synthesized using a solution method based on the solubility and supersolubility curves of the TPA. Notably, the current in the long-axis direction is larger than those in the short-axis and thickness directions (i(long) > i(short) > i(thickness)), which reveals the anisotropic charge transfer of the TPA single crystal. The observed anisotropic conductivity is well explained by the orientation of the triphenylamine unit in the TPA single crystal. Furthermore, the activation energy of the long-axis direction in the TPA single crystal is lower than that of the short-axis in TPA and all the axes in the α-phenyl-4'-[bis(4-methylphenyl)amino]stilbene single crystal reported in our previous study.

On-line object tracking (OLOT) has been a core technology in computer vision, and its importance has been increasing rapidly. Because this technology is utilized for battery-operated products, energy consumption must be minimized. This paper describes a method of adaptive frame-rate optimization to satisfy that requirement. An energy trade-off occurs between image capturing and object tracking. Therefore, the method optimizes the frame-rate based on always changed object speed for minimizing the total energy while taking into account the trade-off. Simulation results show a maximum energy reduction of 50.0%, and an average reduction of 35.9% without serious tracking accuracy degradation.

As energy efficiency has become a major design constraint or objective, heterogeneous manycore architectures have emerged as mainstream target platforms not only in server systems but also in embedded systems. Manycore accelerators such as GPUs are getting also popular in embedded domains, as well as the heterogeneous CPU cores. However, as the number of cores in an embedded GPU is far less than that of a server GPU, it is important to utilize both heterogeneous multi-core CPUs and GPUs to achieve the desired throughput with the minimal energy consumption. In this paper, we present a case study of mapping LBP-based face detection onto a recent CPU-GPU heterogeneous embedded platform, which exploits both task parallelism and data parallelism to achieve maximal energy efficiency with a real-time constraint. We first present the parallelization technique of each task for the GPU execution, then we propose performance and energy models for both task-parallel and data-parallel executions on heterogeneous processors, which are used in design space exploration for the optimal mapping. The design space is huge since not only processor heterogeneity such as CPU-GPU and big.LITTLE, but also various data partitioning ratios for the data-parallel execution on these heterogeneous processors are considered. In our case study of LBP face detection on Exynos 5422, the estimation error of the proposed performance and energy models were on average -2.19% and -3.67% respectively. By systematically finding the optimal mappings with the proposed models, we could achieve 28.6% less energy consumption compared to the manual mapping, while still meeting the real-time constraint.

The energy efficiency of Internet of Things (IoT) could be improved by RF energy transfer technologies.Aiming at IoT applications with a mobility-constrained mobile sink, a double-sourced energy transfer (D-ET) scheme is proposed. Based on the hierarchical routing information of network nodes, the Simultaneous Wireless Information and Power Transfer (SWIPT) method helps to improve the global data gathering performance. A genetic algorithm and graph theory are combined to analyze the node energy consumption distribution. Then dedicated charger nodes are deployed on the basis of the genetic algorithm's output. Experiments are conducted using Network Simulator-3 (NS-3) to evaluate the performance of the D-ET scheme. The simulation results show D-ET outperforms other schemes in terms of network lifetime and data gathering performance.

Large-scale virtualized data centers are increasingly becoming the norm in our data-intensive society. One pressing challenge is to reduce the energy consumption of servers while maintaining a high level of service agreement fulfillment. Due to the convenience of virtualization, virtual machine migration is an effective way to optimize the trade-off between energy and performance. However, there are obvious drawbacks in the current static threshold strategy for migration. This paper proposes a new decision strategy based on decision-theoretic rough sets. In the new strategy, the status of a server is determined by the Bayesian rough set model. The space is divided into positive, negative and boundary regions. According to this information, a migration decision with minimum risk will be made. This three-way decision framework in our strategy can reduce over-migration and delayed migration. The experiments in this paper show that this new strategy outperforms the benchmark examined. It is an efficient and flexible approach to the energy and performance trade-off in the cloud.

High energy cost is a big challenge faced by the current data centers, wherein computing energy and cooling energy are main contributors to such cost. Consolidating workload onto fewer servers decreases the computing energy. However, it may result in thermal hotspots which typically consume greater cooling energy. Thus the tradeoff between computing energy decreasing and cooling energy decreasing is necessary for energy saving. In this paper, we propose a minimized-total-energy virtual machine (VM for short) migration model called C2vmMap based on efficient tradeoff between computing and cooling energies, with respect to two relationships: one for between the resource utilization and computing power and the other for among the resource utilization, the inlet and outlet temperatures of servers, and the cooling power. Regarding online resolution of the above model for better scalability, we propose a VM migration algorithm called C2vmMap_heur to decrease the total energy of a data center at run-time. We evaluate C2vmMap_heur under various workload scenarios. The real server experimental results show that C2vmMap_heur reduces up to 40.43% energy compared with the non-migration load balance algorithm. This algorithm saves up to 3x energy compared with the existing VM migration algorithm.

The power consumption of server platforms has been increasing as the amount of hardware resources equipped on them is increased. Especially, the capacity of DRAM continues to grow, and it is not rare that DRAM consumes higher power than processors on modern servers. Therefore, a reduction in the DRAM energy consumption is a critical challenge to reduce the system-level energy consumption. Although it is well known that improving row buffer locality(RBL) and bank-level parallelism (BLP) is effective to reduce the DRAM energy consumption, our preliminary evaluation on a real server demonstrates that RBL is generally low across 15 multithreaded benchmarks. In this paper, we investigate the memory access patterns of these benchmarks using a simulator and observe that cache line-grained channel interleaving schemes, which are widely applied to modern servers including multiple memory channels, hurt the RBL each of the benchmarks potentially possesses. In order to address this problem, we focus on a row-grained channel interleaving scheme and compare it with three cache line-grained schemes. Our evaluation shows that it reduces the DRAM energy consumption by 16.7%, 12.3%, and 5.5% on average (up to 34.7%, 28.2%, and 12.0%) compared to the other schemes, respectively.

In this letter, we study the resource allocation for the downlink cooperative non-orthogonal multiple access (NOMA) systems based on the amplifying-and-forward protocol relay transmission. A joint power allocation and amplification gain selection scheme are proposed. Fractional programming and the iterative algorithm based on the Lagrangian multiplier are used to allocate the transmit power to maximize the energy efficiency (EE) of the systems. Simulation results show that the proposed scheme can achieve higher energy efficiency compared with the minimum power transmission (MPT-NOMA) scheme and the conventional OMA scheme.

The energy of a threshold circuit C is defined to be the maximum number of gates outputting ones for an input assignment, where the maximum is taken over all the input assignments. In this paper, we study computational power of threshold circuits of energy at most two. We present several results showing that the computational power of threshold circuits of energy one and the counterpart of energy two are remarkably different. In particular, we give an explicit function which requires an exponential size for threshold circuits of energy one, but is computable by a threshold circuit of size just two and energy two. We also consider MOD functions and Generalized Inner Product functions, and show that these functions also require exponential size for threshold circuits of energy one, but are computable by threshold circuits of substantially less size and energy two.

We address a BEMS (Building Energy Management System) to guarantee reliability of electric-power supply in dynamic uncertain environments. The building microgrid as the target of BEMS has multiple distributed power sources including a photo-voltaic power system and Electric-Vehicle (EV). EV is regarded as an autonomously-moving battery due to the original means of transportation and is hence a cause of dynamic uncertainty of the building microgrid. The main objective of synthesis of BEMS in this paper is to guarantee the continuous supply of power to the most critical load in a building microgrid and to realize the power supply to the other loads according to a ranking of load importance. We synthesize the BEMS as a reactive control system that monitors changes of dynamic uncertain environment of the microgrid including departure and arrival of an EV, and determines a route of power supply to the most critical load. Also, we conduct numerical experiments of the reactive BEMS using models of power flows in the building and of charging states of the batteries. The experiments are incorporated with data measured in a practical office building and demonstration project of EMS at Osaka, Japan. We show that the BEMS works for extending the time duration of continuous power supply to the most critical load.

A penta-band antenna based on the mu-negative transmission line is presented for radio frequency (RF) energy harvesting application. The antenna utilizes five radiation modes; two quarter wavelength resonances, three quarter wavelength resonance, zeroth order resonance, and first order resonance. The parasitic radiating strip antenna generates quarter wavelength resonance radiation. The dual band antenna based on two unit cell mu-negative (MNG) transmission line gives birth to the zeroth order resonance (ZOR) mode and the first order resonance (FOR) mode. The parasitic radiating strip and dual band antenna based on two unit mu-negative (MNG) transmission line are magnetically coupled by a feed monopole with gap. This feed monopole, simultaneously, radiates at quarter and three quarter wavelength resonance frequency to cover the other bands. The multi-mode coupling mechanism of this penta-band antenna is well modeled by our derived equivalent circuit. The measured radiation efficiencies are more than 87% over the entire penta-band.

In this letter, we consider a multiple-input multiple-output (MIMO) simultaneous wireless information and power transfer (SWIPT) system, in which the confidential message intended for the information receiver (IR) should be kept secret from the energy receiver (ER). Our goal is to design the optimal transmit covariance matrix so as to maximize the secrecy energy efficiency (SEE) of the system while guaranteeing the secrecy rate, energy harvesting and transmit power constraints. To deal with the original non-convex optimization problem, we propose an alternating optimization (AO)- based algorithm and also prove its convergence. Simulation results show that the proposed algorithm outperforms conventional design methods in terms of SEE.

The energy consumption of network virtualization environments (NVEs) has become a critical issue. In this paper, we focus on reducing the data switching energy consumption of NVE. We first analyze the data switching energy of NVE. Then, we propose a dynamic energy efficient virtual link resource reallocation (eEVLRR) approach for NVE. eEVLRR dynamically reallocates the energy efficient substrate resources (s-resources) for virtual links with dynamic changes of embeddable s-resources to save the data switching energy. In order to avoid traffic interruptions while reallocating, we design a cross layer application-session-based forwarding model for eEVLRR that can identify and forward each data transmission flow along the initial specified substrate data transport path until end without traffic interruptions. The results of performance evaluations show that eEVLRR not only guarantees the allocated s-resources of virtual links are continuously energy efficient to save data switching energy but also has positive impacts on virtual network acceptance rate, revenues and s-resources utilization.

Virtual Machine Placement (VMP) plays an important role in ensuring efficient resource provisioning of physical machines (PMs) and energy efficiency in Infrastructure as a Service (IaaS) data centers. Efficient server consolidation assisted by virtual machine (VM) migration can promote the utilization level of the servers and switch the idle PMs to sleep mode to save energy. The trade-off between energy and performance is difficult, because consolidation may cause performance degradation, even service level agreement (SLA) violations. A novel residual available capacity (RAC) resource model is proposed to resolve the VM selection and allocation problem from the cloud service provider (CSP) perspective. Furthermore, a novel heuristic VM selection policy for server consolidation, named Minimized Square Root available Resource (MISR) is proposed. Meanwhile, an efficient VM allocation policy, named Balanced Selection (BS) based on RAC is proposed. The effectiveness validation of the BS-MISR combination is conducted on CloudSim with real workloads from the CoMon project. Evaluation results of experiments show that the proposed combinationBS-MISR can significantly reduce the energy consumption, with an average of 36.35% compared to the Local Regression and Minimum Migration Time (LR-MMT) combination policy. Moreover, the BS-MISR ensures a reasonable level of SLAs compared to the benchmarks.