Massive Data Communication Research Articles

Enhanced stretchability of porous PDMS/CIP composites via weak interfacial bonding and their electromagnetic noise suppression properties

The development of wearable devices operated through massive data communication necessitates the use of lightweight, highly efficient stretchable composites to suppress electromagnetic (EM) noise. In this study, highly stretchable porous polydimethylsiloxane (PDMS)/carbonyl iron powder (CIP) composite-based EM noise suppressors were explored. The porous structure reduced the volume fraction of PDMS in the composites, leading to a reduction in the total weight of the composites. The introduction of CIP to porous PDMS initially degraded stretchability owing to stress concentration at the strongly bonded interfaces between the CIP surface and PDMS. However, reducing the interfacial bonding strength by removing the SiO2 layer on the CIP surface significantly enhanced stretchability, causing the resulting composite to display 1.2–1.6 times higher rupture strain values at the same CIP loading contents than those with high interfacial bonding strength. The EM absorption properties of the porous and nonporous PDMS/CIP composites were characterized, and the porous PDMS/CIP composites exhibited ∼1.5 times higher EM noise suppression performance than the nonporous composites. This improvement is attributed to the vacant space within the porous framework filled with air, which enhances impedance matching and extends the transmission route of EM waves.

Modified Carbonyl Iron Powders to Have Partial Interface Bonding for Stretchable Polymer Matrix Composites

With the technological progress of wearable electronics operated in 5G network communication system, a stretchable noise suppressor at GHz bandwidth that is reliable under severe deformation coming from the user’s motion should be developed. However, current material systems for the noise suppressor showed a limited stretchability. In this study, the CIP/PDMS composite with partial interface bonding between particle and polymer matrix was introduced as a solution to enhance the stretchability dramatically. Modulation of surface condition of CIPs to have the partial interface bonding (CIP-PID) provided the large freedom for PDMS matrix in a composite to elongate similarly to the pure PDMS. The CIP-PID based composites showed an ultra large strain of ~165% even at the 40 vol% of CIP-PIDs, while only ~75% of strain was observed for CIPs with the full interface bonding (CIP-FID) at the same particle contents. Microstructural analysis of the strained composites and FEM simulation revealed that PDMS matrix partially bonded to CIPs formed the free surface that can be deformed as pure PDMS, thereby resulting in the similar stretchability to those of pure PDMS even in the form of composites. Moreover, CIP-PID based composites showed a proper disperionability and high noise suppressing efficiencty, which are comparable to those of CIP-FIDs. Our work provides ways to the increase in stretchability of polymer-based composite films with a proper noise suppressing efficiency at GHz bandwidth in application to the next generation wearable devices requiring massive data communication.

An Overview on Active Transmission Techniques for Wireless Scalable Networks

Currently, massive data communication and computing pose a severe challenge on existing wireless network architecture, from various aspects such as data rate, latency, energy consumption and pricing. Hence, it is of vital importance to investigate active wireless transmission for wireless networks. To this end, we first overview the data rate of wireless active transmission. We then overview the latency of wireless active transmission, which is particularly important for the applications of monitoring services. We further overview the spectral efficiency of the active transmission, which is particularly important for the battery-limited Internet of Things (IoT) networks. After these overviews, we give several critical challenges on the active transmission, and we finally present feasible solutions to meet these challenges. The work in this paper can serve as an important reference to the wireless networks and IoT networks.

Void-hole aware and reliable data forwarding strategy for underwater wireless sensor networks

Abstract Reliable data transfer and energy efficiency are the essential considerations for network performance in resource-constrained underwater environments. One of the efficient approaches for data routing in underwater wireless sensor networks (UWSNs) is clustering, in which the data packets are transferred from sensor nodes to the cluster head (CH). Data packets are then forwarded to a sink node in a single or multiple hops manners, which can possibly increase energy depletion of the CH as compared to other nodes. While several mechanisms have been proposed for cluster formation and CH selection to ensure efficient delivery of data packets, less attention has been given to massive data communication processes with sink node. As such, failure in communicating nodes would lead to a significant network void-holes problem. Considering the limited energy resources of nodes in UWSNs along with the heavy load of CHs in the routing process, this paper proposes a void-holes aware and reliable data forwarding strategy (VHARD-FS) in a proactive mode to control data packets delivery from CH nodes to the sink in UWSNs. In the proposed strategy, each CH node is aware of its neighbor’s performance ranking index to conduct a reliable packet transmission to the sink via the most energy-efficient route. Extensive simulation results indicate that the VHARD-FS outperforms existing routing approaches while comparing energy efficiency and network throughput. This study helps to effectively alleviate the resource limitations associated with UWSNs by extending network life and increasing service availability even in a harsh underwater environment.

The global energy footprint of information and communication technology electronics in connected Internet-of-Things devices

Information and communication technology (ICT) has been critical to the emergence of a new Internet-of-Things (IoT) era, as a rapidly increasing number and variety of IoT electronic devices in a wide range of end-use sectors require massive data communication in terms of volume, velocity/speed, and variety. Semiconductor technology advancements have played a key role in ICT and unlike the past ICT energy footprint analysis, which focused on the final electronic product, an attempt has been made to examine the global energy impacts for the period 2016–2025 based on the market forecasts of three major IoT IC types, i.e., sensors and actuators, connectivity, and processor ICs distributed among four major IoT device components. Total global IoT semiconductor primary energy demand is projected to increase from 2 EJ in 2016 to 35 EJ by 2025, resulting mainly from a substantial projected increase energy needed in the manufacturing of ICs, with growth in the use of sensors and energy-intensive next fab generation manufacturing. Unlike the trend in manufacturing energy, total global operational energy use is projected to decrease significantly with the development of smaller transistor size, low-power devices, and faster wireless data communication technology. Total operational energy is projected to significantly decrease from 118 TWh/year (1,2 EJ/year) to less than 1 TWh/year (0.003 EJ/year) in spite of a significantly higher anticipated growth in its market size during the 2016–2025 forecast period. Its annual share of total primary energy use is projected to reach an insignificant level of 0.01% by the end of 2025, consistent with the past limited electronic product life cycle analyses besides the analysis focus here limited to ICs in IoT devices.

A NoC-based simulator for design and evaluation of deep neural networks

The astonishing development in the field of artificial neural networks (ANN) has brought significant advancement in many application domains, such as pattern recognition, image classification, and computer vision. ANN imitates neuron behaviors and makes a decision or prediction by learning patterns and features from the given data set. To reach higher accuracies, neural networks are getting deeper, and consequently, the computation and storage demands on hardware platforms are steadily increasing. In addition, the massive data communication among neurons makes the interconnection more complex and challenging. To overcome these challenges, ASIC-based DNN accelerators are being designed which usually incorporate customized processing elements, fixed interconnection, and large off-chip memory storage. As a result, DNN computation involves large memory accesses due to frequent load/off-loading data, which significantly increases the energy consumption and latency. Also, the rigid architecture and interconnection among processing elements limit the efficiency of the platform to specific applications. In recent years, Network-on-Chip-based (NoC-based) DNN becomes an emerging design paradigm because the NoC interconnection can help to reduce the off-chip memory accesses while offers better scalability and flexibility. To evaluate the NoC-based DNN in the early design stage, we introduce a cycle-accurate NoC-based DNN simulator, called DNNoC-sim. To support various operations such as convolution and pooling in the modern DNN models, we first propose a DNN flattening technique to convert diverse DNN operation into MAC-like operations. In addition, we propose a DNN slicing method to evaluate the large-scale DNN models on a resource-constraint NoC platform. The evaluation results show a significant reduction in the off-chip memory accesses compared to the state-of-the-art DNN model. We also analyze the performance and discuss the trade-off between different design parameters.

A realized on-demand cross-layer connection strategy for wireless self-organization link based on WLAN

For massive data communication, the wireless network is often needed to support data transmission in complex environment. In this case, a portable, inexpensive and efficient wireless device is especially helpful. In order to realize this function, we propose a wireless self-organized connection strategy in this paper. We focus on a multihop wireless network with single relay at each hop. One of the challenges over this system is to set up the communication link optimally and automatically, which is also the main contribution of this paper. So we designed an On-Demand Cross-Layer Connection Strategy (ODCLCS) based on WLAN to achieve the data transmission between source and destination point. This strategy crosses application layer, transport layer, network layer and MAC layer. By using active scanning in MAC layer, proper nodes can be found and connected. The automatic routing method can get benefit from active scanning message, automatically allocate IP address for each node. At last, we develop a link maintenance function which works after the link is established. The link maintenance function using heartbeats communication mechanism to find link errors. Moreover, the link maintenance message is very short which can be neglected compared with the data stream. The ODCLCS is designed for Wireless Self-organized Link (WSOL) system. WSOL system aims to establish a stable and user-friendly communication link by using ODCLCS and other necessary programs. All programs are properly written and debugged repeatedly by us, which can work well on testbed devices. Performance evaluation using NS-3 simulator shows the characteristic of WSOL. The WSOL program for testbed is based on cfg80211 and mac80211 in Linux Kernel. And some API based on libnl library is invoked. The testing data provided verify the good performance of ODCLCS in reality.

Characterization of heterogeneous InP-on-Si optical modulators operating between 77 K and room temperature

Heterogeneous integration of InP modulators on a silicon photonic platform, fabricated by bonding III–V wafer on patterned silicon waveguides, are proved to work between 77 K and 295 K. The performance of modulators based on the Franz-Keldysh effect (bulk) and the quantum confined Stark effect (quantum-well) is investigated for wavelengths ranging within 1460 nm–1580 nm. The bulk modulator is preferred when operating over a wide range of temperatures. The demonstration of such integrated optical components at low temperatures is especially attractive for applications that demand massive data communication between cryogenic and room temperatures requiring photonic interconnect, as well as applications with extreme environmental conditions, such as outer space exploration.

IMFlexCom

In this article, we propose an <u>I</u>n-<u>M</u>emory <u>Flex</u>ible <u>Com</u>puting platform (IMFlexCom) using a novel Spin Orbit Torque Magnetic Random Access Memory (SOT-MRAM) array architecture, which could work in dual mode: memory mode and computing mode. Such intrinsic in-memory logic (AND/OR/XOR) could be used to process data within memory to greatly reduce power-hungry and long distance massive data communication in conventional Von Neumann computing systems. A comprehensive reliability analysis is performed, which confirms ∼90mV and ∼10mV (worst-case) sense margin for memory and in-memory logic operation in variations on resistance-area product and tunnel magnetoresistance. We further show that sense margin for in-memory logic computation can be significantly increased by increasing the oxide thickness. Furthermore, we employ bulk bitwise vector operation and data encryption engine as case studies to investigate the performance of our proposed design. IMFlexCom shows ∼35× energy saving and ∼18× speedup for bulk bitwise in-memory vector AND/OR operation compared to DRAM-based in-memory logic. Again, IMFlexCom can achieve 77.27% and 85.4% lower energy consumption compared to CMOS-ASIC- and CMOL-based Advanced Encryption Standard (AES) implementations, respectively. It offers almost similar energy consumption as recent DW-AES implementation with 66.7% less area overhead.

Low-Complexity Iterative Detection Algorithm for Massive Data Communication in IIoT

As the fundamental problem of Industrial Internet of Things, massive data communication based on non-orthogonal multiple access is attractive. An iterative multiuser receiver provides a substantial performance improvement, but suffers from a distortion that the overestimation of output reliability values for bad channels. Furthermore, the main challenge lies in the high computational complexity. This paper develops an improved iterative multiuser receiver with independent channel information. In order to analyze its performance, JS-divergence is introduced to measure the correlation of exchanged information between the detector and the decoder. Low-complexity iterative detection algorithm based on JS-divergence values is proposed in this paper. The simulation results demonstrate that the proposed iterative multiuser receiver reduces the overestimation of reliability values and improves the system performance when Eb/N0 is less than 3 dB. The low-complexity iterative detection algorithm can terminate in advance when JS-divergence values of all users reach to a threshold and reduce the number of outer-loop iterations and computational complexity greatly.

Performance optimization and evaluation for parallel processing of big data in earth system models

Big data and high performance computing in Earth System Models (ESMs) are receiving increased attention in earth science research. When scaling to large-scale multi-core computing, efficient parallelization of an ESM, which demands fast parallel computing for long-term integration or climate simulation, becomes extremely challenging because of time-consuming internal big data communication. In this paper, an optimization algorithm for the massive data communication between the Weather Research and Forecasting model and Coupler version 7 in the Chinese Academy of Sciences-Earth System Model (CAS-ESM) is proposed. The optimization strategy is to transmit data from a small packet into a larger packet. Through experiments on a multi-core cluster, the efficiency of the algorithm is confirmed. Then, the parallel performance of the CAS-ESM is evaluated fully. Results show that the parallel efficiency of the CAS-ESM on 1024 CPU cores reaches nearly 70%, indicating that the CAS-ESM has desirable parallel performance and strong scalability. In addition, a generic performance evaluation method for ESMs from perspectives of optimal load balance and efficiency is proposed. Results show that the computing speed is the fastest and computational efficiency is the highest when the CAS-ESM runs on a certain number of cores.

Asymmetrical topology and entropy-based heterogeneous link for many-core massive data communication

As the need for data processing and communication increases, and likewise, as the number of processing cores placed on a given single chip increases, improving the performance of interconnection networks is vital. In the present work, traditional topologies are re-examined. Torus is shown to be a good structure in terms of average latency and symmetry. When using torus in combination with high process levels, it is possible to design new, yet asymmetrical topologies that can meet the high communication performance requirements of many-core processors and also suit a large variety of traffic patterns. Firstly, this paper presents two novel and torus-like topologies called xtorus and xxtorus, which are evaluated by using both theoretical analysis and experimental simulation methods. For theoretical analysis, an algorithm for computing link path diversity and link entropy is given. The analysis shows that, compared with mesh, xmesh and torus, the proposed topologies have better properties in terms of diameter, average latency, throughput, and path diversity. Although more links are added, the number of links is of the same order of magnitude with that of mesh, xmesh, and torus. Proposed topologies also take advantage of increasingly higher levels of the VLSI process. Simulations on GEM5 reveal that xtorus has better scalability, and that its average latency is less than that of mesh, xmesh and torus by significant proportions respectively, particularly when the network scale is larger. Moreover, for different traffic patterns, its performance swing is less than that of mesh. Furthermore, in the present work, the proposed topologies are both asymmetrical and based on the entropy difference of the links in the topology. A strategy for heterogeneous link design is presented, which enables designers to trade off between delay, power and area according to a concrete integrated circuit design scene.

The speed-controlled interpolator for machining parametric curves

Modern CNC systems are designed with the function of machining arbitrary parametric curves to save massive data communication between CAD/CAM and CNC systems and improve their machining quality. Although available CNC interpolators for parametric curves generally achieve contouring position accuracy, the specified feedrate, which dominates the quality of the machining processes, is not guaranteed during motion. Recently, some approximation results concerning motion speed have been reported [Shpitalni M, Koren Y, Lo CC. Computer-Aided Design 1994;26(11):832–838; Bedi S, Ali I, Quan N. Trans ASME J Engng Industry 1993;115:329–336; Chou JJ, Yang DCH. Trans. ASME J Engng Industry 1991;113:305–310; Chou JJ, Yang DCH. Trans ASME J Engng Industry 1992;114:15–22]. In this paper, by deriving a compensatory parameter, the proposed interpolation algorithm has significantly improved curve speed accuracy. In real applications, the proposed algorithm results in: (1) constant speed; and (2) specified acceleration and deceleration (ACC/DEC) to meet the feedrate commands. The motion accuracy and feasibility of the present interpolator have been verified with a provided non-uniform rational B-spline (NURBS) example.

Modelling flexible manufacturing systems using mean-value analysis

Modern CNC systems are designed with the function of machining arbitrary parametric curves to save massive data communication between CAD/CAM and CNC systems and improve their machining quality. Although available CNC interpolators for parametric curves generally achieve contouring position accuracy, the specified feedrate, which dominates the quality of the machining processes, is not guaranteed during motion. Recently, some approximation results concerning motion speed have been reported [Shpitalni M, Koren Y, Lo CC. Computer-Aided Design 1994;26(11):832–838; Bedi S, Ali I, Quan N. Trans ASME J Engng Industry 1993;115:329–336; Chou JJ, Yang DCH. Trans. ASME J Engng Industry 1991;113:305–310; Chou JJ, Yang DCH. Trans ASME J Engng Industry 1992;114:15–22]. In this paper, by deriving a compensatory parameter, the proposed interpolation algorithm has significantly improved curve speed accuracy. In real applications, the proposed algorithm results in: (1) constant speed; and (2) specified acceleration and deceleration (ACC/DEC) to meet the feedrate commands. The motion accuracy and feasibility of the present interpolator have been verified with a provided non-uniform rational B-spline (NURBS) example.