Data Rate Change Research Articles

Optimizing Energy Harvesting in Wireless Body Area Networks: A Deep Reinforcement Learning Approach to Dynamic Sampling

In energy-harvesting wireless body area networks (EH-WBAN), the self-sustainability of body sensor nodes without compromising the service quality criteria is very important. In remote vital signs monitoring applications, the sampling rate of body nodes in each period should be determined to achieve this goal. There are two fundamental challenges in determining the sampling rate: 1) In EH-WBAN, the rate of the harvestable energy is time-dependent and unpredictable. 2) The vital signs of the patient have different change rates relevant to the time. Therefore, a technique is required that can learn the changes of the harvestable energy and the change rates of vital signs simultaneously and specify the proper sampling rate for BNs. The reinforcement learning algorithms are among the efficient solutions for this problem because they are capable of learning environmental uncertainties. Previous RL-based methods for determining the BN's sampling rate have three fundamental problems: 1) focus on the optimization of the transmission energy and ignore the sensing energy, 2) only affect one of the two aspects of energy or sensed data in determining the sampling rate and 3) discretization of the problem space does not ensure the determination of the optimal sampling rate. Therefore, this paper proposes a method named “deep reinforcement learning-based dynamic sampling” (DRDS) which first formulates the sampling rate determination problem as a Markov decision process (MDP) and proposes a deep deterministic policy gradient algorithm (DDPG) to solve it. The proposed method considers both energy and data variability aspects in determining the sampling rate. Two parameters, the super-capacitor’s voltage level, and ambient light intensity, are considered for the energy aspect; for the data variability aspect, the long short-term memory (LSTM) algorithm is developed to predict the change rate of data in the next round. Simulations indicate that by preserving the data integrity, the proposed method can decrease the sampling rate and unnecessary data transmission by about 49.95% and 89.7%, respectively, compared with state-of-the-art methods, and allow the sensor to achieve self-sustainability.

A Spherical Volume-Rendering Method of Ocean Scalar Data Based on Adaptive Ray Casting

There are some limitations in traditional ocean scalar field visualization methods, such as inaccurate expression and low efficiency in the three-dimensional digital Earth environment. This paper presents a spherical volume-rendering method based on adaptive ray casting to express ocean scalar field. Specifically, the minimum bounding volume based on spherical mosaic is constructed as the proxy geometry, and the depth texture of the seabed terrain is applied to determine the position of sampling points in the spatial interpolation process, which realizes the fusion of ocean scalar field and seabed terrain. Then, we propose an adaptive sampling step algorithm according to the heterogeneous depth distribution and data change rate of the ocean scalar field dataset to improve the efficiency of the ray-casting algorithm. In addition, this paper proposes a nonlinear color-mapping enhancement scheme based on the skewness characteristics of the datasets to optimize the expression effect of volume rendering, and the transparency transfer function is designed to realize volume rendering and local feature structure extraction of ocean scalar field data in the study area.

Distributed reinforcement learning-based memory allocation for edge-PLCs in industrial IoT

The exponential device growth in industrial Internet of things (IIoT) has a noticeable impact on the volume of data generated. Edge-cloud computing cooperation has been introduced to the IIoT to lessen the computational load on cloud servers and shorten the processing time for data. General programmable logic controllers (PLCs), which have been playing important roles in industrial control systems, start to gain the ability to process a large amount of industrial data and share the workload of cloud servers. This transforms them into edge-PLCs. However, the continuous influx of multiple types of concurrent production data streams against the limited capacity of built-in memory in PLCs brings a huge challenge. Therefore, the ability to reasonably allocate memory resources in edge-PLCs to ensure data utilization and real-time processing has become one of the core means of improving the efficiency of industrial processes. In this paper, to tackle dynamic changes in arrival data rate over time at each edge-PLC, we propose to optimize memory allocation with Q-learning distributedly. The simulation experiments verify that the method can effectively reduce the data loss probability while improving the system performance.

RLBEEP: Reinforcement-Learning-Based Energy Efficient Control and Routing Protocol for Wireless Sensor Networks

One of the most important topics in the field of wireless sensor networks is the development of approaches to improve network lifetime. In this paper, an energy-efficient control and routing protocol for wireless sensor networks is presented. This algorithm is based on reinforcement learning for energy management in the network. This protocol seeks to optimize routing policies to maximize the long-term reward received by each node, using reinforcement learning, which is a machine learning approach. In order to improve the lifetime of wireless sensor network, three energy management approaches have been proposed. The first approach is to navigate correctly using reinforcement learning to reduce the length of the routes and to improve energy consumption. The second approach is to exploit a sleep scheduling technique to improve node energy consumption. The last approach is used to restrict data transmission of each node based on the received data change rate. Simulation results show that in terms of network lifespan, the proposed method significantly outperforms previous reported methods.

A Bayesian Inference Model for Dynamic Neighbor Discovery in Tactical Networks

This paper introduces a Bayesian Inference model for dynamically changing the neighbor discovery parameters in order to minimize the time to recover from long radio link disconnections in tactical networks. We start with the hypothesis that given a multi-layer tactical system, we can use the in/out chains to learn the distribution of changes in the radio data link with an increasing confidence as time goes by. Moreover, we propose that a static neighbor discovery configuration (e.g., hello time, aggregation time, and so on) in dynamic networks can never be optimal and that it is possible to calculate the close to optimal neighbor discovery configuration dynamically. Thus, we introduce a Bayesian Inference model composed of a Markov-Chain-Monte-Carlo (MCMC) and a Long Short-Term Memory (LSTM) to compute a dynamic configuration by learning the probability distribution of the link conditions (computed using the in/out model) and adapting the configuration parameters dynamically according to this distribution. Our hypothesis was verified with numerical calculations using different patterns of link data rate change. The quantitative analysis suggests that at the tactical edge dynamic control signaling can improve connectivity in ever-changing network scenarios.

Maximizing the Probability of Message Delivery Over Ever-Changing Communication Scenarios in Tactical Networks

This letter introduces a stochastic model to maximize the probability of message delivery over ever-changing communication scenarios in tactical networks. Our model improves modern tactical systems implementing store-and-forward mechanisms organized in a hierarchy of layers for messages, IP packets and radios. The goal is to estimate the optimum redundancy for the user data-flow to overcome packet loss during changes in the link data rate, including disconnections. Experiments in a VHF network illustrate the numerical results from our model using messages with different sizes over two patterns of data rate change.

An edge streaming data processing framework for autonomous driving

In recent years, with the rapid development of sensing technology and the Internet of Things (IoT), sensors play increasingly important roles in traffic control, medical monitoring, industrial production and etc. They generated high volume of data in a streaming way that often need to be processed in real time. Therefore, streaming data computing technology plays an indispensable role in the real-time processing of sensor data in high throughput but low latency. However, there are two problems in deploying streaming data process ability in cloud computing data centre. Firstly, massive sensor nodes simultaneously upload data to the remote cloud computing data centre, which requires a large number of bandwidth resources supports. The existing network infrastructure cannot provide enough bandwidth at a reasonable price. Secondly, due to the geographical distribution characteristics of the cloud computing data centre, there will inevitably be large transmission delay during the process of data transmission. Such end-to-end delay is intolerable to mobile applications especially for those latency sensitive tasks. In view of the above problems, this paper proposes an autonomous driving oriented edge streaming data processing framework, which migrates the computing and storage capability from the remote cloud data centre to the edge data centre. It focuses on the change of vehicle flow in a specific geographical area, and uses the computing power sunk to edge node to process the massive streaming data generated by autonomous vehicles nearby. The proposed framework is implemented on top of Spark Streaming, which builds up a gray model based traffic flow monitor, a traffic prediction orientated prediction layer and a fuzzy control based Batch Interval dynamic adjustment layer for Spark Streaming. It could forecast the variation of sensors data arrive rate, make streaming Batch Interval adjustment in advance and implement real-time streaming process by edge. Therefore, it can realise the monitor and prediction of the data flow changes of the autonomous driving vehicle sensor data in geographical coverage of edge computing node area, meanwhile minimise the end-to-end latency but satisfy the application throughput requirements. The experiments show that it can predict short-term traffic with no more than 4% relative error in a whole day. By making batch consuming rate close to data generating rate, it can maintain system stability well even when arrival data rate changes rapidly. The Batch Interval can be converged to a suitable value in two minutes when data arrival rate is doubled. Compared with vanilla version Spark Streaming, where there has serious task accumulation and introduces large delay, it can reduce 35% latency by squeezing Batch Interval when data arrival rate is low; it also can significantly improve system throughput by only at most 25% Batch Interval increase when data arrival rate is high.

Quantizing Radio Link Data Rates to Create Ever-Changing Network Conditions in Tactical Networks

Several sources of randomness can change the radio link data rate at the edge of tactical networks. Simulations and field experiments define these sources of randomness indirectly by choosing the mobility pattern, communication technology, number of nodes, terrain, obstacles and so on. Therefore, the distribution of change in the network conditions is unknown until the experiment is executed. We start with the hypothesis that a model can quantize the network conditions, using a set of states updated within a time window, to define and control the distribution of change in the link data rate before the experiment is executed. The goal is to quantify how much variation in the link data rate a tactical system can handle and how long it takes to resume IP data-flows after link disconnections. Our model includes functions to combine patterns of change together, transforming one pattern into another, jumping between patterns, and creating loops among different patterns of change. We use exemplary patterns to show how the change in the data rate impacts other link metrics, such as latency and jitter. Our hypothesis is verified with experiments using VHF radios over different patterns of change created by our model. We compute the inter-packet latency of three types of IP data-flows (broadcast, unicast and overlay) to highlight the time to resume data-flows after long link disconnections. The experimental results also support the discussion on the advantages and limitations of our model, which was designed to test tactical systems using military radios.

MATHEMATICAL MODEL OF SPEED DATA TRANSMISSION FROM SEGMENT LENGTH OF SPECIALIZED G3-PLC DIGITAL NETWORK

Power Line Communication (PLC) systems are actively evolving and becoming more and more widespread worldwide. They are used in the automation of technological processes, the organization of video surveillance systems and to control the "smart" home. The G3-PLC provides high-speed and high-reliability long-distance communication over the existing power grid. Due to the fact that G3-PLC provides the ability to transmit data including through transformers, infrastructure costs are reduced. In addition, the G3-PLC network can support IPv6, which will allow the G3-PLC to easily integrate into common IPv6-based communication lines in the future. G3-PLC-based bilateral communications networks can provide grid operators with intelligent monitoring and control capabilities. Operators will be able to monitor electricity consumption across the network in real time, apply variable tariff schedules and set limits on electricity consumption. In turn, consumers will be able to control electricity consumption in real time. By using variable tariffs, users can reduce their electricity consumption during peak use. The G3-PLC dedicated digital network can be used in process automation systems where traditional or traditional data transmission is difficult or impossible. As a result of the research, a polynomial mathematical model was found that best reflects the change in data rate depending on the length of the G3-PLC network segment. It is also found that for the simplified calculation, a linear model determined during the studies can be used. The established mathematical models of data transmission rate dependence on the segment length of the G3-PLC dedicated digital network will contribute to better design of G3-PLC-based networks.

An Improved Data Rate Change Algorithm based on Adaptive Frame Length

In order to solve the high bit error rate (BER) caused by rate oscillation in traditional Data Rate Change (DRC) algorithm, an improved DRC algorithm based on Adaptive Frame Length (AFL) was proposed. Firstly, the frame length and transmission rate of the initial transmission were determined by the parameters of the current channel and the information of previous empirical values. Then, if two frames with the same length were successively sent in the transmission process, the frame length would be accordingly increased. If the retransmission failed twice in a row, the frame length would be halved in the next transmission. Finally, the frame error rate was calculated based on the current frame length. The data rate would be updated If the value was less than the present threshold. Compared with RapidM DRC, the average bit error rate of the link decreased by 4.8 percentage points, and the link availability increased by 10 percentage points. The experimental results show that the proposed algorithm can eliminate the rate oscillation and improve the communication capability of the HF communication system.

GPS Self-adaption Data Correction Based on the Data Change Rate

GPS Self-adaption Data Correction Based on the Data Change Rate

Energy-Efficient Clustering Using Correlation and Random Update Based on Data Change Rate for Wireless Sensor Networks

This paper presents an energy-efficient clustering method using random update (EECRU) for wireless sensor networks. We divide the network into preliminary clusters depending on the temporal and spatial correlation of sensor data. These clusters are then updated by applying a dynamic update policy and a cluster head rotation scheme, where the change of sensor data and the energy residue of sensors are taken into consideration to achieve high energy efficiency and balance. The larger the change rate of the sensor data in a cluster, the higher its update frequency. A sensor node that has a high data frequency is more likely to be selected as the head of its cluster. In the data transmission phase, a sampling rate control method is adopted to improve the efficiency of data sampling. The experimental results indicate that the proposed EECRU method achieves high energy efficiency and energy balance.

DRC Protocol for the Response Time Reduction in CAN based Distributed Embedded System

Controller Area Network operational characteristic supports periodic, sporadic and event based task behavior of distributed embedded system for industrial applications. CAN connect distributed Electronics Control Units (ECU) serially in the network and share measured data of different parameters and control information from the different places. System reliability and redundancy can be affected by changing the number of connected ECUs, Bus length, data rate, etc. In this paper proposed algorithm tries to improve reliability of CAN by changing data rates, according to the length of transmission line for message transmission in the network. Also, Data Rate Change DRC protocol improves temporal behavior of the system with small CPU overheads.

A 7 Gb/s Embedded Clock Transceiver for Energy Proportional Links

A rapid-on/off transceiver for embedded clock architecture that enables energy proportional communication over the serial link is presented. In an energy proportional link, energy consumed by serial link is proportional to the amount of data communicated. Energy proportionality can be achieved by scaling the serial link power linearly with the link utilization, and fine grained rapid power state transition (rapid-on/off) is one such technique which can achieve this objective. In this paper, architecture and circuit techniques to achieve rapid-on/off in PLL, transmitter and receiver are discussed. Background phase calibration technique in PLL and CDR phase calibration logic in receiver enable instantaneous lock on power-on. The proposed transceiver demonstrates power scalability with a wide range of link utilization and, therefore, helps in improving overall system efficiency. Fabricated in 65 nm CMOS technology, the 7 Gb/s transceiver achieves power-on-lock in less than 20 ns. Proposed PLL achieves power-on-lock in 1 ns. The transceiver achieves power scaling by 44 $\times$ (63.7 mW-to-1.43 mW) and energy efficiency degradation by only 2.2 $\times$ (9.1 pJ/bit-to-20.5 pJ/bit), when the effective data rate (link utilization) changes by 100 $\times$ (7 Gb/s-to-70 Mb/s). The proposed transceiver occupies an active die area of 0.39 mm $^{2}$ .

Tunable second-order, birefringent interferometric filter for dynamic chirp management

A tunable second-order birefringent interferometric filter for minimizing signal degradation due to chirping in modulated lasers is proposed and demonstrated. Compared with commonly used filters with standard Gaussian and super-Gaussian filter profiles, a high-order loop mirror filter of similar bandwidth has a much sharper filtering slope. This provides more effective, specific filtering of desired components of a signal’s spectrum, minimizing signal power loss experienced during filtering. The steep filtering slope also provides significant improvement in transmission performance even with a change in data rate. Thus, the same filter can be reused for different bit rates. Furthermore, the tuning capability of the loop mirror filter enables good transmission performance when a variation in signal wavelength is present. Moreover, the unique, periodic filter profile potentially enables chirp management for multiple channels at different wavelengths. We experimentally verify the chirp management performance of the second-order birefringent loop mirror filter. Improvements to the transmission performance of both directly modulated lasers and electro-absorption modulated lasers at different data rates are achieved.

A Clustering Sleep Scheduling Mechanism Based on Sentinel Nodes Monitor for WSN

This paper proposes a clustering sleep scheduling mechanism based on sentinel nodes monitoring for WSN. The mechanism combines the network clustering strategy with the node dormancy strategy, and improves the method of selecting the candidate cluster heads randomly in Energy-Efficient Unequal Clustering (EEUC) algorithm. The conception of sentinel node is introduced based on EEUC, and the neighbor node set of sentinel node will be dormant when the sentinel node’s data change rate is lower than the setting threshold. Simulation results show that this mechanism can effectively balance the energy consumption of the entire network, and significantly extend the network lifetime.

An 8 Gb/s–64 Mb/s, 2.3–4.2 mW/Gb/s Burst-Mode Transmitter in 90 nm CMOS

A burst-mode transmitter achieves 6 ns turn-on time by utilizing a fast frequency settling ring oscillator in digital multiplying delay-locked loop and a rapid on-off biasing scheme for current mode output driver. The resistor tuning-based ring oscillator avoids the use of bias voltages and thereby eliminates the related settling time overhead. The calibrated rapid on-off biasing circuit utilizes a fast charging technique to achieve bias voltage settling time of 4 ns, resulting in 30X improvement over a diode-connected bias circuit. Fabricated in 90 nm CMOS process, the prototype achieves 2.29 mW/Gb/s energy efficiency at peak data rate of 8 Gb/s. A 125X (8 Gb/s to 64 Mb/s) change in effective data rate results in 67X (18.29 mW to 0.27 mW) change in transmitter power consumption corresponding to only 2X (2.29 mW/Gb/s to 4.24 mW/Gb/s) degradation in energy efficiency for 32 byte long data bursts. We also present an analytical bit error rate (BER) computation technique for rapid on-off links, which uses MDLL settling measurement data in conjunction with always-on transmitter measurements. This technique indicates that the BER bathtub width for 10 -12 BER is 0.65 UI and 0.72 UI during rapid on-off operation and always-on operation, respectively.

A 100-Channel 1-mW Implantable Neural Recording IC

This paper presents a fully implantable 100-channel neural interface IC for neural activity monitoring. It contains 100-channel analog recording front-ends, 10 multiplexing successive approximation register ADCs, digital control modules and power management circuits. A dual sample-and-hold architecture is proposed, which extends the sampling time of the ADC and reduces the average power per channel by more than 50% compared to the conventional multiplexing neural recording system. A neural amplifier (NA) with current-reuse technique and weak inversion operation is demonstrated, consuming 800 nA under 1-V supply while achieving an input-referred noise of 4.0 μVrms in a 8-kHz bandwidth and a NEF of 1.9 for the whole analog recording chain. The measured frequency response of the analog front-end has a high-pass cutoff frequency from sub-1 Hz to 248 Hz and a low-pass cutoff frequency from 432 Hz to 5.1 kHz, which can be configured to record neural spikes and local field potentials simultaneously or separately. The whole system was fabricated in a 0.18-μm standard CMOS process and operates under 1 V for analog blocks and ADC, and 1.8 V for digital modules. The number of active recording channels is programmable and the digital output data rate changes accordingly, leading to high system power efficiency. The overall 100-channel interface IC consumes 1.16-mW total power, making it the optimum solution for multi-channel neural recording systems.

RELIABILITY OF OPTIMAL LINEAR PROJECTION OF GROWING SCALE-FREE NETWORKS

Singular Value Decomposition (SVD) is a technique based on linear projection theory, which has been frequently used for data analysis. It constitutes an optimal (in the sense of least squares) decomposition of a matrix in the most relevant directions of the data variance. Usually, this information is used to reduce the dimensionality of the data set in a few principal projection directions, this is called Truncated Singular Value Decomposition (TSVD). In situations where the data is continuously changing, the projection might become obsolete. Since the change rate of data can be fast, it is an interesting question whether the TSVD projection of the initial data is reliable. In the case of complex networks, this scenario is particularly important when considering network growth. Here we study the reliability of the TSVD projection of growing scale-free networks, monitoring its evolution at global and local scales.

Routing TCP Flows in Underwater Mesh Networks

Due to the growing importance of coastline surveillance and protection, underwater communication is playing an increasingly important role in military networks. As compared to terrestrial networks, large propagation delays and low data rates are fundamental characteristics of underwater communication. Furthermore, due to some key differences in the factors causing fluctuations in the quality of the underwater channels, the corresponding communication links experience more prolonged data rate changes as compared to those in terrestrial networks. Large propagation delays and prolonged link data rate deteriorations severely degrade the end-to-end performance of Transmission Control Protocol (TCP) based applications. Since military applications often require the reliable data delivery provided by TCP, it is important to devise solutions to alleviate this problem. In this paper, we propose a new routing scheme called Linear Coded Digraph Routing (LCDR) to enhance the end-to-end throughput of TCP based packet flows in underwater mesh networks. LCDR is a fully distributed scheme designed to locally respond to changes in the link data rates. In LCDR, each ingress node forwards packets after network coding. Each intermediate node adaptively uses network coding before forwarding the packets to the outgoing links. Each terrestrial gateway decodes the network coded packets before forwarding them to terrestrial networks. Each node adapts its packet forwarding rate based on the available bandwidth on the outgoing links, such that the terrestrial gateway can successfully receive packets with higher probability without significantly affecting cross-traffic. The effectiveness of the proposed scheme is evaluated using simulation. The simulation results show that the proposed scheme uses the spare bandwidth on each link efficiently and it significantly improves end-to-end throughput of TCP flows.