Data Buffer Research Articles

Joint Optimization of Data Offloading and Resource Allocation With Renewable Energy Aware for IoT Devices: A Deep Reinforcement Learning Approach

A large number of connected sensors and devices in Internet of Things (IoT) can generate large amounts of computing data and increase massive energy consumption. Real-time states monitoring and data processing of IoT nodes are of great significance, but the processing power of IoT devices is limited. Using the emerging mobile edge computing (MEC), IoT devices can offload computing tasks to an MEC server associated with small or macro base stations. Moreover, the use of renewable energy harvesting capabilities in base stations or IoT nodes may reduce energy consumption. As wireless channel conditions vary with time and the arrival rates of renewable energy, computing tasks are stochastic, and data offloading and renewable energy aware for IoT devices under a dynamic and unknown environment are major challenges. In this work, we design a data offloading and renewable energy aware model considering an MEC server performing multiple stochastic computing tasks and involving time-varied wireless channels. To optimize data transmission delay, energy consumption, and bandwidth allocation jointly, and to avoid the curse of dimensionality caused by the complexity of the action space, we propose a joint optimization method for data offloading, renewable energy aware, and bandwidth allocation for IoT devices based on deep reinforcement learning (JODRBRL), which can handle the continuous action space. JODRBRL can minimize the total system cost(including data buffer delay cost, energy consumption cost, and bandwidth cost) and obtain an efficient solution by adaptively learning from the dynamic IoT environment. The numerical results demonstrate that JODRBRL can effectively learn the optimal policy, which outperforms Dueling DQN, Double DQN (DDQN), and greedy policy in stochastic environments.

Routing Selection With Reinforcement Learning for Energy Harvesting Multi-Hop CRN

This paper considers the routing problem in the communication process of an energy harvesting (EH) multi-hop cognitive radio network (CRN). The transmitter and the relay harvest energy from the environment use it exclusively for transmitting data. In a relay on the path, a limited data buffer is used to store the received data and forward it. We consider a real-world scenario where the EH node has only local causal knowledge, i.e., at any time, each EH node only has knowledge of its own EH process, channel state, and currently received data. An EH routing algorithm based on Q learning in reinforcement learning (RL) for multi-hop CRNs (EHR-QL) is proposed. Our goal is to find an optimal routing policy that can maximize throughput and minimize energy consumption. Through continuous intelligent selection under the partially observable Markov decision process (POMDP), we use the Q learning algorithm in RL with linear function approximation to obtain the optimal path. Compared with the basic Q learning routes, the EHR-QL is superior for longer distances and higher hop counts. The algorithm produces more EH, less energy consumption, and predictable residual energy. In particular, the time complexity of the EHR-QL is analyzed and its convergence is proved. In the simulation experiments, first, we verify the EHR-QL using six EH secondary users (EH-SUs) nodes. Second, the performance (i.e., network lifetime, residual energy, and average throughput) of the EHR-QL is evaluated under the influences of different the learning rates $\alpha $ and discount factors $\gamma $ . Finally, the experimental results show that the EHR-QL obtains a higher throughput, a longer network lifetime, less latency, and lower energy consumption than the basic Q learning routing algorithms.

An agricultural data storage mechanism based on HBase

With the development of agricultural space-time localisation, sensor network and cloud computing, the amount of agricultural data is increasing rapidly and the data structure becomes more complicated and changeable. Currently, the widely used agricultural database is the relational database. This database handles a large amount of data with very limited throughput and is not suitable for organisation and management of distributed data. HBase is a non-relational database of distributed file storage built on Hadoop platform. HBase is suitable for unstructured data storage database and it can handle large volume of data with high scalability. To better store agricultural big data in HBase, we propose a special agricultural data buffer structure which stores data based on the data category and a two-level indexing memory organisation strategy on HBase. The method proposed saves more than a quarter of the time compared to traditional buffering methods. Experimental results show the higher efficiency of the agricultural data buffer structure and memory-organisation strategy.

A resource and timing optimized PCIe DMA architecture using FPGA internal data buffer

A resource and timing optimized PCIe DMA architecture using FPGA internal data buffer

Decentralized event-triggered H∞ control for switched systems with network communication delay

This paper is concerned with the decentralized event-triggered H∞ control for switched systems subject to network communication delay and exogenous disturbance. Depending on different physical properties, the system state is divided into multiple communication channels and decentralized sensors are employed to collect signals on these channels. Furthermore, decentralized event-triggering mechanisms (DETMs) with a switching structure are proposed to determine whether the sampled data needs to be transmitted. In particular, an improved data buffer is presented which can guarantee more timely utilization of the sampled data. Then, with the proposed DETMs and data buffer, a time-delay closed-loop switched system is developed. After that, sufficient conditions are presented to guarantee the H∞ performance of the closed-loop switched system by utilizing the average dwell time and piecewise Lyapunov functional method. Since the event-triggered instants and the switching instants may stagger with each other, the influence of their coupling on the H∞ performance analysis is systematically discussed. Subsequently, sufficient conditions for designing the event-triggered state feedback controller gains are provided. Finally, numerical simulations are given to verify the effectiveness of the proposed method.

HiBuffer: Buffer Analysis of 10-Million-Scale Spatial Data in Real Time

Buffer analysis, a fundamental function in a geographic information system (GIS), identifies areas by the surrounding geographic features within a given distance. Real-time buffer analysis for large-scale spatial data remains a challenging problem since the computational scales of conventional data-oriented methods expand rapidly with increasing data volume. In this paper, we introduce HiBuffer, a visualization-oriented model for real-time buffer analysis. An efficient buffer generation method is proposed which introduces spatial indexes and a corresponding query strategy. Buffer results are organized into a tile-pyramid structure to enable stepless zooming. Moreover, a fully optimized hybrid parallel processing architecture is proposed for the real-time buffer analysis of large-scale spatial data. Experiments using real-world datasets show that our approach can reduce computation time by up to several orders of magnitude while preserving superior visualization effects. Additional experiments were conducted to analyze the influence of spatial data density, buffer radius, and request rate on HiBuffer performance, and the results demonstrate the adaptability and stability of HiBuffer. The parallel scalability of HiBuffer was also tested, showing that HiBuffer achieves high performance of parallel acceleration. Experimental results verify that HiBuffer is capable of handling 10-million-scale data.

Achievable Rate Region of the Buffer-Aided Two-Way Energy Harvesting Relay Network

In this paper, we investigate the buffer-aided two-way wireless energy harvesting (EH) relay network comprising two users and one relay, where two users exchange information with the help of the relay via three phases of EH, multiple-access, and broadcast . By transforming the opportunistic scheduling design problem into an equivalent convex problem, we present the adaptive design for the buffer-aided two-way EH relay network to maximize the long-term achievable rate region. To fulfill the delay sensitive transmission requirements, a delay-aware adaptive transmission (DAAT) scheduling scheme is proposed to guarantee the average end-to-end delivery delay by employing the Lyapunov optimization framework. Our analysis discloses that the average achievable rate region of the two-way wireless EH relay network can be improved when fully considering the potentials by deploying data buffer and energy storage at the relay. There exists an inherent tradeoff among the achievable sum rate, the delivery delay, and the power consumption. It is shown that, when a certain time delay is tolerable, the DAAT scheduling scheme is able to realize a rate region arbitrarily close to the achievable rate region of the buffer-aided two-way wireless EH relay network.

A novel distributed extended Kalman filter for aircraft engine gas-path health estimation with sensor fusion uncertainty

This paper is concerned with state estimation approach to track aircraft engine gas-path health condition in an advanced distributed architecture. The sensor measurements are divided into several subsets by installation position along gas path, and they are integrated to estimate engine health state changes with sensor fusion uncertainty. The uncertain sensor fusion is characterized by time delay and packet dropout in the fusion behavior of sensor measurements, and the delay steps occur randomly. A novel distributed extended Kalman filter with the data buffer bank (DEKF) is developed, and self-tuning buffer strategy of recursive fusion estimation is combined to the DEKF to form the self-tuning DEKF (SDEKF) algorithm for improving state estimation performance. The lengths of data buffer bank related to the local filters of SDEKF are different, and they are independently adaptive to the information loss level and local estimation accuracy. Local states are calculated using the measurements collected at the latest steps in self-tuning buffer banks, and then sent to master filter to yield global state and covariance by fusion estimation. The contribution of this study is to propose a novel EKF algorithm for state estimation in the distributed framework with sensor fusion uncertainty, and it achieves better trade-off between the estimation accuracy and computational efforts. The systematical comparisons of basic EKF, constant buffer DEKF and SDEKF algorithms are carried out for aircraft engine gas-path health estimation with sensor fusion uncertainty. The simulation results show the superiority of the SDEKF, and it confirms our viewpoints in this paper.

Resource allocation for hybrid energy powered cloud radio access network with battery leakage

This study proposes a resource allocation policy for hybrid energy powered cloud radio access network with battery leakage. To optimise the network utility, the authors first formulate a network utility maximisation problem while jointly considering multiple random processes, which include energy harvesting, data arrival, wireless channel condition, and grid energy prices. To tackle this problem, they exploit the Lyapunov optimisation technique to develop an online dynamic resource allocation framework, which contains four subproblems, i.e. data admission, hybrid energy management, power allocation and route scheduling. Based on the solutions of these subproblems, a network utility optimisation resource allocation (ORA) algorithm is proposed. Specifically, the ORA algorithm only needs to track the current system states without requiring a prior knowledge about channel and energy conditions. Theoretical performance analyses and simulation results verify that the proposed algorithm can achieve close-to-optimal utility with bounded data buffer and battery capacity.

A Case of On-Chip Memory Subsystem Design for Low-Power CNN Accelerators

The rapid development of machine learning is enabling a plenty of novel applications, such as image and speech recognition for embedded and mobile devices. However, state-of-the-art deep learning models like convolutional neural networks (CNNs) are demanding so much on-chip storage and compute resources that they cannot be smoothly handled by low-power mobile or embedded systems. In order to fit large CNN models into mobile or more cutting-edge devices for IoT or cyberphysics applications, we proposed an efficient on-chip memory architecture for CNN inference acceleration, and showed its application to in-house single-instruction multiple-data structure machine learning processor. The redesigned on-chip memory subsystem, Memsqueezer, includes an active weight buffer and data buffer set that embraces specialized compression methods to reduce the footprint of CNN parameters (weights) and activation data, respectively. Memsqueezer buffer can compress the data and weight set according to the dataflow in computation, and it also includes a built-in redundancy detection mechanism that actively scans through the working-set of CNNs to boost their inference performance by eliminating the computation redundancy in CNN models. In our experiments, it is shown that the CNN processors with Memsqueezer buffers achieve more than $2{\times }$ performance improvement and reduces 85% energy consumption on average over the conventional buffer design with the same area budget.

Optimal Use of Harvested Solar, Hybrid Storage and Base Station Resources for Green Cellular Networks

Renewable energy (RE) is a promising solution to reduce grid energy consumption and carbon dioxide emissions of cellular networks. However, the benefit of utilizing RE is limited by its highly intermittent and unreliable nature, resulting in low savings in grid energy. To minimize the grid energy cost, we propose to utilize data buffer of user equipments as well as energy storage at the base station (BS) to better adapt the BS resource allocation and hence its energy consumption to the dynamic nature of RE. We consider the scenario of downlink orthogonal frequency division multiple access networks with non-ideal hybrid energy supply. To jointly optimize the energy consumption and the quality of service (QoS) of users, we adopt the weighted sum of users’ utility of data rates and grid energy consumption as our performance metric. We propose a low-complexity online control scheme based on Lyapunov optimization framework. The proposed technique can provide asymptotically optimal performance bound without requiring the stochastic distribution information of RE arrival and channel state condition. The experimental results demonstrate the ability of the proposed approach to significantly improve the performance in terms of grid power consumption and user QoS compared with the existing schemes.

Relaying Protocols and Delay Analysis for Buffer-aided Wireless Powered Cooperative Communication Networks

In this paper, we investigate a buffer-aided wireless powered cooperative communication network (WPCCN), in which the source and relay harvest the energy from a dedicated power beacon via wireless energy transfer, then the source transmits the data to the destination through the relay. Both the source and relay are equipped with an energy buffer to store the harvested energy in the energy transfer stage. In addition, the relay is equipped with a data buffer and can temporarily store the received information. Considering the buffer-aided WPCCN, we propose two buffer-aided relaying protocols, which named as the buffer-aided harvest-then-transmit (HtT) protocol and the buffer-aided joint mode selection and power allocation (JMSPA) protocol, respectively. For the buffer-aided HtT protocol, the time-averaged achievable rate is obtained in closed form. For the buffer-aided JMSPA protocol, the optimal adaptive mode selection scheme and power allocation scheme, which jointly maximize the time-averaged throughput of system, are obtained by employing the Lyapunov optimization theory. Furthermore, we drive the theoretical bounds on the time-averaged achievable rate and time-averaged delay, then present the throughput-delay tradeoff achieved by the joint JMSPA protocol. Simulation results validate the throughput performance gain of the proposed buffer-aided relaying protocols and verify the theoretical analysis.

A Step Forward?

Drawing on Rogers’ diffusion of innovation theory and Moore’s concept of chasm, this paper investigates how data journalism is locally adopted and historically embedded in China, as well as what facilitates or impedes its diffusion into the country’s media practices. A combined study of content analysis (n = 290) and semi-structured interviews with 20 Chinese media practitioners shows that despite enthusiasm for implementing this innovative form of journalism, it is only practiced at a cursory level with differences observed among traditional media, internet portals, and new media. While practitioners see clear advantages in data-driven sense-making and storytelling, there remains skepticism among those who doubt its compatibility with China’s existing media system and have complexity concerns, or else are perplexed about its trialability in the context of a compressed innovation cycle. China’s insufficient inheritance of quantitative reporting tradition and its long-held synthetic cognitive mode, together with the predicament of data availability and buffering tactics deployed by Chinese media to tackle uncertainties, all call into question how far data-driven journalistic innovation can extend its reach in the Chinese context.

A Data Buffering and Transmission System: A Study of the Performance of a Disk Subsystem

A method for evaluation of the performance of the disk subsystem of a data buffering and transmission system and results of experimental studies of the performance of the disk subsystem according to the developed method are presented. A conclusion is drawn on the reasonability of the use of hard-disk drives that provide the required read/write rates with a sufficiently high level of reliability in prospective development of the modern high-capacity NearLine SAS (NL-SAS).

Thoroughly Exploring GPU Buffering Options for Stencil Code by Using an Efficiency Measure and a Performance Model

Stencil computations form the basis for computer simulations across almost every field of science, such as computational fluid dynamics, data mining, and image processing. Their mostly regular data access patterns potentially enable them to take advantage of the high computation and data bandwidth of GPUs, but only if data buffering and other issues are handled properly. Finding a good code generation strategy presents a number of challenges, one of which is the best way to make use of memory. GPUs have several types of on-chip storage including registers, shared memory, and a read-only cache. The choice of type of storage and how it’s used, a buffering strategy , for each stencil array ( grid function , [GF]) not only requires a good understanding of its stencil pattern, but also the efficiency of each type of storage for the GF, to avoid squandering storage that would be more beneficial to another GF. For a stencil computation with $N$ GFs, the total number of possible assignments is $\beta ^{N}$ where $\beta$ is the number of buffering strategies. Our code-generation framework supports five buffering strategies ( $\beta =5$ ). Large, complex stencil kernels may consist of dozens of GFs, resulting in significant search overhead. In this work, we present an analytic performance model for stencil computations on GPUs and study the behavior of read-only cache and L2 cache. Next, we propose an efficiency-based assignment algorithm which operates by scoring a change in buffering strategy for a GF using a combination of (a) the predicted execution time and (b) on-chip storage usage. By using this scoring, an assignment for $N$ GFs can be determined in $(\beta -1)N(N+1)/2$ steps. Results show that the performance model has good accuracy and that the assignment strategy is highly efficient.

Data Reuse Buffer Synthesis Using the Polyhedral Model

Current high-level synthesis (HLS) tools for the automatic design of computing hardware perform excellently for the synthesis of computation kernels, but they often do not optimize memory bandwidth. As accessing memory is a bottleneck in many algorithms, the performance of the generated circuit could benefit substantially from memory access optimization. In this paper, we present a method and a tool to automate the optimization of memory accesses to array data in HLS by introducing local memory tailored perfectly to store only the data that are used repeatedly. Our method detects data reuse in the source code of the algorithm to be implemented in hardware, selects and parameterizes data reuse buffers, and generates a register transfer level design of the data buffers and a matching loop controller that coordinates reuse buffers and datapath operations. Throughout this paper, the polyhedral representation is used extensively as it proves to be well suited for calculations on loop nests and data accesses. As a consequence, this paper is limited to affine programs which can be represented in this model. Experiments show that our method outperforms state-of-the-art academic and commercial HLS tools.

Delay Minimal Policies in Energy Harvesting Communication Systems

We characterize delay minimal power scheduling policies in energy harvesting communication systems. We consider a continuous-time system, where the delay experienced by each bit is given by the time spent by the bit in the queue waiting to be transmitted to its receiver. We first consider a single-user channel, where the transmitter has a finite-sized battery to save its harvested energy. Data arrives during the course of communication and are saved in a finite data buffer as well. We find the optimal power policy that minimizes the average delay experienced by the bits subject to energy and data causality constraints. We characterize the optimal solution in terms of Lagrange multipliers, and calculate their values in a recursive manner. We show that, different from the existing literature, the optimum transmission power is not constant between the energy and data arrival events; the transmission power starts high, decreases linearly, and potentially reaches zero between energy and data arrivals. Intuitively, untransmitted bits experience cumulative delay due to the bits to be transmitted ahead of them, and hence the reason for transmission power starting high and decreasing over time. Next, we study a multiuser version of this problem, namely, a two-user broadcast channel, and characterize the optimal transmission policies that minimize the sum delay. For this setting, we consider the case, where the transmitter has an infinite-sized battery, and that all data packets intended for the receivers are available at the beginning of the communication session. We characterize the optimal solution in terms of Lagrange multipliers, and present an iterative solution that calculates their values. Our results show that in the optimal policy, both users may not be served simultaneously all the time; there may be times, where only one of the two users is served alone. We also show that the optimal policy may have gaps in transmission in between energy arrivals, where none of the users is served, echoing the results of the single-user setting.

Remote Estimation of Correlated Sources Under Energy Harvesting Constraints

Remote estimation with an energy harvesting sensor with a limited data and energy buffer is considered. The sensor node observes an unknown temporally correlated field and communicates its observations to a remote fusion center using the energy it harvested. The fusion center employs linear minimum mean-square error estimation to reconstruct the unknown field. We provide performance guarantees for the estimation error under a block transmission scheme, where at each transmission block, data and energy buffers are completely emptied. Our bounds provide insights into how statistical properties of the energy harvesting process and buffer sizes may affect the estimation error. In particular, these bounds suggest insensitivity of the performance to buffer sizes for signals with low degree of freedom and suggest performance improvements with increasing buffer sizes for signals with relatively higher degree of freedom. Depending only on the mean, variance, and finite support of the energy arrival process, these results provide insights for the energy and data buffer sizes for deployment in future energy harvesting wireless sensing systems.

Adaptive Buffer-Aided Wireless Powered Relay Communication With Energy Storage

In this paper, we consider wireless powered relay network, where the energy-constrained decode-and-forward relay is provisioned with both data buffer and energy storage. First, the optimal parameter in time-switching-based relaying scheme is derived to realize the maximal achievable throughput. Second, an adaptive relaying scheme is proposed to dynamically allocate time slots in order to maximize the throughput under the stability constraint of data buffer and the feasibility constraint of energy storage. Third, the relaying scheme is reformulated as a stochastic optimization problem. By employing the Lyapunov optimization framework, an online buffer-energy-aware adaptive transmission scheme is proposed to adjust the transmission according to the dynamic channel state information, the data buffer state information, and the energy state information. Moreover, the tradeoff between the achievable throughput and the transmission delay is presented to show the great potential of the proposed adaptive transmission design to improve the achievable throughput. Finally, simulations are conducted to validate the efficiency of the proposed relaying protocols and confirm the great potentials of buffer-aided relaying in wireless powered relay networks with energy storage.

Real-time Event Recognition and Analysis System for Nanopore Study

To achieve fast and accurate analysis of weak current signal of nanopore-based single molecule detection, an online data process based on adaptive threshold algorithm with data buffering technique and finite impulse response filtering was designed. A software system based on the data process was developed for online recognition and analysis of nanopore events during nanopore experiment. To testify the performance of the algorithm and software system, ideal signals with different noise level (20–100 pA) were generated at bandwidth ranging from 3 kHz to 100 kHz. The result showed that this software system was stable at different bandwidths and sampling rates and could be used in analyzing the signals at high noise. The proposed software system was further applied to aerolysin nanopore experiment for detection of poly(dA)4 molecules. The results showed that the data process system could be applied in real nanopore recording experiment with high accuracy and speed.