This paper investigates open-loop Stackelberg games for a class of stochastic systems with multiple players. First, the necessary conditions for the existence of an open-loop Stackelberg strategy set are established using the stochastic maximum principle. Such conditions can be represented as solvability conditions for cross-coupled forward-backward stochastic differential equations (CFBSDEs). Second, in order to obtain the open-loop strategy set, a computational algorithm based on a four-step scheme is developed. A numerical example is then demonstrated to show the validity of the proposed method.

In this letter, we focus on the subcarrier allocation problem for device-to-device (D2D) communication in cellular networks to improve the cellular energy efficiency (EE). Our goal is to maximize the weighted cellular EE and its solution is obtained by using a game-theoretic learning approach. Specifically, we propose a lower bound instead of the original optimization objective on the basis of the proven property that the gap goes to zero as the number of transmitting antennas increases. Moreover, we prove that an exact potential game applies to the subcarrier allocation problem and it exists the best Nash equilibrium (NE) which is the optimal solution to optimize the lower bound. To find the best NE point, a distributed learning algorithm is proposed and then is proved that it can converge to the best NE. Finally, numerical results verify the effectiveness of the proposed scheme.

Spectrum sharing effectively improves the spectrum usage by allowing secondary users (SUs) to dynamically and opportunistically share the licensed bands with primary users (PUs). The concept of cooperative spectrum sharing allows SUs to use portions of the PUs' radio resource for their own data transmission, under the condition that SUs help the PUs' transmission. The key issue with designing such a scheme is how to deal with the resource splitting of the network. In this paper we propose a relay-based cooperative spectrum sharing scheme in which the network consists of one PU and multiple SUs. The PU asks the SUs to relay its data in order to improve its energy efficiency, in return it rewards the SUs with a portion of its authorized spectrum. However each SU is only allowed to transmit its data via the rewarded channel at a power level proportional to the contribution it makes to the PU. Since energy cost is considered, the SUs must carefully determine their power level. This scheme forms a non-cooperative Stackelberg resource allocation game where the strategy of PU is the bandwidth it rewards and the strategy of each SU is power level of relay transmission. We first investigate the second stage of the sub-game which is addressed as power allocation game. We prove there exists an equilibrium in the power allocation game and provide a sufficient condition for the uniqueness of the equilibrium. We further prove a unique Stackelberg equilibrium exists in the resource allocation game. Distributed algorithms are proposed to help the users with incomplete information achieve the equilibrium point. Simulation results validate our analysis and show that our proposed scheme introduces significant utility improvement for both PU and SUs.

Mobile traffic is experiencing tremendous growth, and this growing wave is no doubt increasing the use of radio component of mobile devices, resulting in shorter battery lifetime. In this paper, we present an Energy-Aware Download Method (EDM) based on the Markov Decision Process (MDP) to optimize the data download energy for mobile applications. Unlike the previous download schemes in literature that focus on the energy efficiency by simply delaying the download requests, which often leads to a poor user experience, our MDP model learns off-line from a set of training download workloads for different user patterns. The model is then integrated into the mobile application to deal the download request at runtime, taking into account the current battery level, LTE reference signal receiving power (RSRP), reference signal signal to noise radio (RSSNR) and task size as input of the decision process, and maximizes the reward which refers to the expected battery life and user experience. We evaluate how the EDM can be used in the context of a real file downloading application over the LTE network. We obtain, on average, 20.3%, 15% and 45% improvement respectively for energy consumption, latency, and performance of energy-delay trade off, when compared to the Android default download policy (Minimum Delay).

High Efficiency Video Coding (HEVC/H.265) obtains 50% bit rate reduction than H.264/AVC standard with comparable quality at the cost of high computational complexity. Merge mode is one of the most important new features introduced in HEVC's inter prediction. Merge mode and traditional inter mode consume about 90% of the total encoding time. To address this high complexity, this paper utilizes the merge mode to accelerate inter prediction by four strategies. 1) A merge candidate decision is proposed by the sum of absolute transformed difference (SATD) cost. 2) An early merge termination is presented with more than 90% accuracy. 3) Due to the compensation effect of merge candidates, symmetric motion partition (SMP) mode is disabled for non-8×8 coding units (CUs). 4) A fast coding unit filtering strategy is proposed to reduce the number of CUs which need to be fine-processed. Experimental results demonstrate that our fast strategies can achieve 35.4%-58.7% time reduction with 0.68%-1.96% BD-rate increment in RA case. Compared with similar works, the proposed strategies are not only among the best performing in average-case complexity reduction, but also notably outperforming in the worst cases.

Protection of the licensed user (LU) and utilization of the spectrum are the most important goals in cognitive radio networks. To achieve the first goal, a cognitive user (CU) is required to sense for a longer time period, but this adversely affects the second goal, i.e., throughput or utilization of the network, because of the reduced time left for transmission in a time slot. This tradeoff can be controlled by simultaneous sensing and data transmission for the whole frame duration. However, increasing the sensing time to the frame duration consumes more energy. We propose a new frame structure in this paper, in which transmission is done for the whole frame duration whereas sensing is performed only until the required detection probability is satisfied. This means the CU is not required to perform sensing for the whole frame duration, and thus, conserves some energy by sensing for a smaller duration. With the proposed frame structure, throughput of all the CUs is estimated for the frame and, based on the estimated throughput and consumed energy in sensing and transmission, the energy efficient pair of CUs (transmitter and receiver) that maximizes system throughput by consuming less energy, is selected for a time slot. The selected CUs transmits data for the whole time slot, whereas sensing is performed only for certain duration. The performance improvement of the proposed scheme is demonstrated through simulations by comparing it with existing schemes.

The write operations on emerging Non-Volatile Memory (NVM), such as NAND Flash and Phase Change Memory (PCM), usually incur high access latency, and are required to be optimized. In this paper, we propose Asymmetric Read-Write (ARW) policies to minimize the write traffic sent to NVM. ARW policies exploit the asymmetry costs of read and write operations, and make adjustments on the insertion policy and hit-promotion policy of the replacement algorithm. ARW can reduce the write traffic to NVM by preventing dirty data blocks from frequent evictions. We evaluate ARW policies on systems with PCM as main memory and NAND Flash as disk. Simulation results on an 8-core multicore show that ARW adopted on the last-level cache (LLC) can reduce write traffic by more than 15% on average compared to LRU baseline. When used on both LLC and DRAM cache, ARW policies achieve an impressive reduction of 40% in write traffic without system performance degradation. When employed on the on-disk buffer of the Solid State Drive (SSD), ARW demonstrates significant reductions in both write traffic and overall access latency. Moreover, ARW policies are lightweight, easy to implement, and incur negligible storage and runtime overhead.

Dynamic voltage and frequency scaling (DVFS) is an essential mechanism for power saving in smartphones and mobile devices. Central processing unit (CPU) load based DVFS algorithms are widely used due to their simplicity of implementation. However, such algorithms often lead to a poor response time, which is one of the most important factors of user experience, especially for interactive applications. In this paper, the response time is mathematically modeled by considering the CPU frequency and characteristics of the running applications based on the Linux kernel's completely fair scheduler (CFS), and a Response time constrained Frequency & Priority (RFP) control scheme for improved power efficiency of smartphones is proposed. In the RFP algorithm, the CPU frequency and priority of the interactive applications are adaptively adjusted by estimating the response time in real time. The experimental results show that RFP can save energy up to 24.23% compared to the ondemand governor and up to 7.74% compared to HAPPE while satisfying the predefined threshold of the response time in Android-based smartphones.

This paper investigates the data persistence problem with compressive sensing (CS) in wireless sensor networks (WSNs) where the sensed readings should be temporarily stored among the entire network in a distributed manner until gathered by a mobile sink. Since there is an energy-performance tradeoff, conventional CS-based schemes only focus on reducing the energy consumption or improving the CS construction performance. In this paper, we propose an efficient Compressive Sensing based Data Persistence (CSDP) scheme to achieve the optimum balance between energy consumption and reconstruction performance. Unlike most existing CS-based schemes which require packets visiting the entire network to reach the equilibrium distribution, in our proposed scheme information exchange is only performed among neighboring nodes. Therefore, such an approach will result in a non-uniform distribution of measurements, and the CS measurement matrix depends heavily on the node degree. The CS reconstruction performance and energy consumption are analyzed. Simulation results confirm that the proposed CSDP scheme consumes the least energy and computational overheads compared with other representative schemes, while almost without sacrificing the CS reconstruction performance.

This paper discusses a pulse generator implemented by CMOS flipped on a glass substrate aiming at low power applications with low duty cycle. The pulse generator is theoretically possible to generate a pulse at a frequency near and beyond Fmax. It also features a quick starting time and zero stand-by power. By using a simplified circuit model, analytical expressions for Q factor, energy conversion efficiency, output energy, and oscillation frequency of the pulse generator are derived. Pulse generator prototypes are designed on a 0.18 μm CMOS chip flipped over a transmission line resonator on a glass substrate. Measurement results of two different prototypes confirm the feasibility of the proposed circuit and the analytical model.

This paper deals with the diffraction of a monochromatic plane wave by a periodic grating. We discuss a problem how to obtain a numerical diffraction efficiency (NDE) satisfying the reciprocity theorem for diffraction efficiencies, because diffraction efficiencies are the subject of the diffraction theories. First, this paper introduces a new formula that decomposes an NDE into two components: the even component and the odd one. The former satisfies the reciprocity theorem for diffraction efficiencies, but the latter does not. Therefore, the even component of an NDE becomes an answer to our problem. On the other hand, the odd component of an NDE represents an unwanted error. Using such the decomposition formula, we then obtain another new formula that decomposes the conventional energy error into two components. One is the energy error made by even components of NDE's. The other is the energy error constructed by unwanted odd ones and it may be used as a reciprocity criterion of a numerical solution. This decomposition formula shows a drawback of the conventional energy balance. The total energy error is newly introduced as a more strict condition for a desirable solution. We point out theoretically that the reciprocal wave solution, an approximate solution satisfying the reciprocity for wave fields, gives another solution to our problem. Numerical examples are given for the diffraction of a TM plane wave by a very rough periodic surface with perfect conductivity. In the case of a numerical solution by the image integral equation of the second kind, we found that the energy error is much reduced by use of the even component of an NDE as an approximate diffraction efficiency or by use of a reciprocal wave solution.

Energy efficiency is an important requirement to forth-coming NDN (Named Data Networking) networks and caching inherent to NDN is a main driver of energy reduction in such networks. This paper addresses the research question “Does caching really reduce the energy consumption of the entire network?”. To answer the question, we precisely estimate how caching reduces energy consumption of forth-coming commercial NDN networks by carefully considering configurations of NDN routers. This estimation reveals that energy reduction due to caching depends on energy-proportionality of NDN routers.

In this paper, an analysis of the basic process of a class of interactive-graph-cut-based image segmentation algorithms indicates that it is unnecessary to construct n-links for all adjacent pixel nodes of an image before calculating the maximum flow and the minimal cuts. There are many pixel nodes for which it is not necessary to construct n-links at all. Based on this, we propose a new algorithm for the dynamic construction of all necessary n-links that connect the pixel nodes explored by the maximum flow algorithm. These n-links are constructed dynamically and without redundancy during the process of calculating the maximum flow. The Berkeley segmentation dataset benchmark is used to prove that this method can reduce the average running time of segmentation algorithms on the premise of correct segmentation results. This improvement can also be applied to any segmentation algorithm based on graph cuts.

This paper presents a fully integrated voltage boost converter consisting of a charge pump (CP) and maximum power point tracking (MPPT) controller for ultra-low power energy harvesting. The converter is based on a conventional CP circuit and can deliver a wide range of load current by using nMOS and pMOS driver circuits for highly efficient charge transfer operation. The MPPT controller we propose dissipates nano-watt power to extract maximum power regardless of the harvester's power generation conditions and load current. The measurement results demonstrated that the circuit converted a 0.49-V input to a 1.46-V output with 73% power conversion efficiency when the output power was 348µW. The circuit can operate at an extremely low input voltage of 0.21V.

Networks-on-Chip (or NoCs, for short) play important roles in modern and future multi-core processors as they are highly related to both performance and power consumption of the entire chip. Up to date, many optimization techniques have been developed to improve NoC's bandwidth, latency and power consumption. But a clear answer to how energy efficiency is affected with these optimization techniques is yet to be found since each of these optimization techniques comes with its own benefits and overheads while there are also too many of them. Thus, here comes the problem of when and how such optimization techniques should be applied. In order to solve this problem, we build a runtime framework to throttle these optimization techniques based on concise performance and energy models. With the help of this framework, we can successfully establish adaptive selections over multiple optimization techniques to further improve performance or energy efficiency of the network at runtime.

File system workloads are increasing write-heavy. The growing capacity of RAM in modern nodes allows many reads to be satisfied from memory while writes must be persisted to disk. Today's sophisticated local file systems like Ext4, XFS and Btrfs optimize for reads but suffer from workloads dominated by microdata (including metadata and tiny files). In this paper we present an LSM-tree-based file system, RFS, which aims to take advantages of the write optimization of LSM-tree to provide enhanced microdata performance, while offering matching performance for large files. RFS incrementally partitions the namespace into several metadata columns on a per-directory basis, preserving disk locality for directories and reducing the write amplification of LSM-trees. A write-ordered log-structured layout is used to store small files efficiently, rather than embedding the contents of small files into inodes. We also propose an optimization of global bloom filters for efficient point lookups. Experiments show our library version of RFS can handle microwrite-intensive workloads 2-10 times faster than existing solutions such as Ext4, Btrfs and XFS.

This paper proposes an NDN-based message delivery protocol over a cellular network in disasters. Collaborative communication among cellular devices is integrated into the protocol so that power consumed by battery-operated base stations (BSs) is reduced when a blackout occurs. A key idea is to reduce consumed radio resources by making cellular devices of which radio propagation quality are better forward messages of neighboring devices. The radio resource reduction contributes to reducing power consumed by a battery-operated BS. We empirically and analytically evaluate how the proposed message delivery protocol reduces the power consumption of a BS assuming a densely populated shelter.

We propose an exponent-based partitioning broadcast protocol (EPBP) to promise the prompt dissemination of emergency message (EM) in vehicular networks. EPBP divides the communication range into segments with different widths iteratively. The width varies corresponding to the exponential curve. The design makes the farther no-empty segment thinner, as a result of which the collision rate of candidates' contention for the relay node decreases and the one-hop message progress increases efficiently. In addition, we adjust the interval of back-off timers to avoid the spurious forwarding problem, and develop more accurate analytical models for the performance. Our simulation verifies these models and show a significant increase of EPBP compared with the state-of-the-art protocols. EM dissemination speed can be improved as 55.94% faster in dense vehicle networks, and packet delivery ratio has risen to higher than 99.99%.

In this paper, an infinite-horizon team-optimal incentive Stackelberg strategy is investigated for a class of stochastic linear systems with many non-cooperative leaders and one follower. An incentive structure is adopted which allows for the leader's team-optimal Nash solution. It is shown that the incentive strategy set can be obtained by solving the cross-coupled stochastic algebraic Riccati equations (CCSAREs). In order to demonstrate the effectiveness of the proposed strategy, a numerical example is solved.

Energy harvesting wireless sensor networks (EH-WSNs) are being actively studied in order to solve the problems faced by battery-operated WSNs, namely the cost for battery replacement and the negative impact on the environment. In EH-WSNs, each node harvests ambient energy, such as light, heat, vibration, and uses it for sensing, computations, and wireless communications, where the amount of harvested energy of each node varies depending on their environments. MAC protocols for EH-WSNs need to be designed to achieve high throughput and fairness, however, the conventional MAC protocols proposed for EH-WSNs do not adapt to the harvesting rate of each node, resulting in poor fairness. In this paper, we propose a fair MAC protocol based on polling scheme for EH-WSNs. The proposed scheme adjusts contention probability of each node according to its harvesting rate, thereby increasing the throughput of nodes with low harvesting rate. We evaluate throughput and fairness of the proposed fair polling scheme by theoretical analysis and computer simulations, and show that the proposed scheme can improve fairness with little degradation of the overall network throughput.