Internet of Things

Session IoT-01

Space-air-ground IoT

1:30 PM — 3:00 PM CST
Aug 10 Mon, 1:30 AM — 3:00 AM EDT

Reverse Auction for Cloud-Based Traffic Offloading in Hybrid Satellite-Terrestrial Networks

Cui-Qin Dai, Lan Jin and Qianbin Chen (Chongqing University of Posts and Telecommunications, China)

Hybrid Satellite-Terrestrial Networks (HSTNs) are expected to support extremely high traffic demands. In HSTN, cellular cells and satellite network are operated by the mobile network operator (MNO) and satellite network operator respectively, traffic offloading strategies need to be designed joint considering the throughput of HSTN and economic profits of the MNO. In this paper, a reverse auction with cell grouping algorithm (RA-CG) is proposed for cloud-based traffic offloading in HSTN. Firstly, a HSTN model is constructed and the problem of co-channel interference is analyzed. Then, the auction model is adopted to analyze the economic profits of traffic offloading in HSTN, and the optimization is formulated to maximum the MNO's profits. Following that, RA-CG is proposed. In RA-CG, cell grouping is proposed to solve the co-channel interference problem during the offloading process. A Hungarian-based reverse auction algorithm is performed to find the optimal cell-satellite association solution corresponds with the economic profits within each group. Simulation results show that RA-CG can maximize the MNO's profits on the basis of improving the network offloading rate, and simultaneously achieve higher spectrum efficiency.

Towards Ubiquitous Coverage of High Altitude Platforms Aided 5G+ for Massive Internet of Things: A Cell Perspective

Wei Wu (Peng Cheng Laboratory & Beijing Institute of Technology, China); Qinyu Zhang (Peng Cheng Laboratory & Harbin Institute of Technology (Shenzhen), China); Kai Wang and Weizhi Wang (Peng Cheng Laboratory, China)

High altitude platforms (HAPs) is promising for providing low-delay and high-throughput wireless communications for massive wireless internet of things networks in non-terrestrial scenarios. In this paper, we propose a heterogeneous layering cell configuration scheme for seamless uniform coverage of HAPs-aided 5G+. To overcome the drawbacks of the existing schemes, we design the two-layer architecture with three and six circular cells considering Preamble Format 2 in 5G NR, and the beam parameters vary across layers. We provide a parameter configuration method to combat the geometrical enlargement of the distance from the projection point of a HAP to its users on the normal direction. The simulation results confirm the superiority of our scheme to the existing schemes in terms of the received signal level in the service area. We finally discuss the open challenges to deploy HAPs-aided 5G+ and the opportunities.

Sum Rate Maximization via Reconfigurable Intelligent Surface in UAV Communication: Phase Shift and Trajectory Optimization

Jingyi Li (Xidian University, China); Jiajia Liu (Northwestern Polytechnical University, China)

Facing the fast-growing application demands in future wireless communication networks, unmanned aerial vehicles (UAVs) have emerged to provide users with highly cost-effective deployment and flexible access services. Due to the complex communication environment, the line-of-sight transmissions of aerial-ground networks are probably blocked, which seriously affects the communication quality. Reconfigurable intelligent surface (RIS) is known as a promising solution to improve the wireless environment through reconfigurable passive units. Motivated by this, we explore a novel RIS-assisted aerial-ground communication scenario, investigating the UAV trajectory design and RIS's phase shift optimization problem aiming at maximizing the sum rate. Considering that the problem is non-convex, we develop an alternating optimization method that decomposes our formulated problem into two blocks when another variable is fixed. With the given optimal phase shifts, the trajectory design subproblem is well solved by resorting to the successive convex approximation method. The experimental results show the remarkable performance of our proposed scheme compared with other schemes.

Drift compensation algorithm based on Time-Wasserstein dynamic distribution alignment

Tao Yang, Ke wei Zeng and Zhi fang Liang (Chongqing University of Posts and Telecommunications, China)

The electronic nose (E-nose) is mainly used to detect different types and concentrations of gases. At present, the average life of the electronic nose is relatively short, mainly due to the drift of the sensor resulting in a decrease in the effect. Therefore, it is the focus of research in this field to propose an effective solution to sensor drift. This paper proposes Time-Wasserstein dynamic distribution alignment (TWDDA) to solve drift compensation according to the domain adaptive thought. This method simultaneously adapts the dynamic distribution according to the data distribution and the collection time span. Finally, this paper uses the public data set provided by the University of California San Diego (UCSD) to conduct experiments. The experimental results show that TWDDA is superior to other comparison algorithms and achieves better results.

A Robust Timing Synchronization Method for OFDM systems Over Multipath Fading Channels

Han Yang (School of Information and Electronics Beijing Institute of Technology, China); Lintao Li (School of Computer&Communication Engineering, University of Science and Technology Beijing, China); Jiaxuan Li (School of Information and Electronics Beijing Institute of Technology, China)

Timing synchronization is premise and foundation of reliable receiving for OFDM signals, especially when multipath fading and Doppler shifting exists. In this work, a robust timing synchronization method based on fast timing search window and double threshold for decision is proposed. The fast timing search window can effectively reduce the false alarm probability and the double threshold decision can reduce the acquisition lost probability. Also, simulations to testify the effectiveness of the proposed strategy are carried out.

Session Chair

Xiulong Liu, Jihong Yu

Session IoT-02

Green IoT

3:10 PM — 4:40 PM CST
Aug 10 Mon, 3:10 AM — 4:40 AM EDT

Protocol-Aware Backscatter Communication Using Commodity Radios

Longzhi Yuan (University of Science and Technology of China, China); Rongrong Zhang (Capital Normal University, China); Kai Yang and Jianping An (Beijing Institute of Technology, China); Si Chen (Simon Fraser University, Canada); Wei Gong (University of Science and Technology of China, China)

Backscatter is one of the important techniques of IoTs as it can offer low-cost and low-energy wireless communication. With the help of widely available ambient signals, backscatter communication can even work without specialized carrier generation. The current solutions, however, are unable to identify various ambient signals. So, we introduce a backscatter system to do this. In particular, we realize the identification of OFDM WiFi (802.11a/g/n), 802.11b WiFi, Bluetooth Low Energy, and ZigBee. Further, we implement our backscatter in the FPGA hardware to evaluate the design. Comprehensive field studies show that our backscatter can identify four protocols at an average identification accuracy of about 90%. We also demonstrate that our identification is compatible with different backscatter modulation for all four signals in 2.4GHz band.

Joint Power Control and Time Allocation for WBANs with RF Energy Harvesting

Rongrong Zhang and Xinglong Li (Capital Normal University, China)

Energy harvesting Wireless Body Area Network (WBANs) where sensor nodes can harvest energy from their ambient environment have attracted considerable attention recently. However, how to control transmission power of energy source and charging duration to maximizing the sum of transmission rate has not been addressed systematically. In order to bridge this gap, we devote this paper to developing an optimal joint power control and time allocation scheme with the objective to maximize the sum of data transmission rate for WBANs with RF energy harvesting. To this end, we first formulate an optimization problem maximizing the sum of data transmission rate with constraint of the transmission power and interference power. The formulated problem is non-convex optimization, so we transform it into three solvable convex subproblems. We then design an optimal scheme by jointing power control and time allocation and compare it with two reference schemes. The simulation results demonstrate that our proposed optimal scheme performs best in terms of the sum of data transmission rate.

Joint Beamforming and Phase-Shifting Optimization in MISO with RIS-Assisted Communication

Kehao Wang (WHUT, China); Zhenhua Xiong, Zhixin Hu and Xueyan Chen (Wuhan University of Technology, China); Lin Chen (Sun Yat-Sen University, China)

In this paper, we jointly investigate beamforming and phase shift of reconfigurable intelligent surface (RIS) for energy efficiency (EE) maximization in an MISO downlink system with multiple users. Specially, we assume that we only know the channel statistics information between RIS to users. The formulated problem is formulated to a non-convex one such that it is difficult to obtain the optimal solution. To tackle this non- convexity, we decouple the original problem to two subproblems and further propose two iterative suboptimal algorithms based on the successive convex approximation (SCA) to solve the two subproblems, respectively. Then we propose an iterative algorithm to obtain a suboptimal solution of original problem in polynomial time. The numerical results verifies the effectiveness of our proposed algorithm.

A Novel Link-Selection Strategy for DCSK-SWIPT Relay System with Buffer

Mi Qian, Guofa Cai, Yi Fang and Guojun Han (Guangdong University of Technology, China)

Adaptive link selection for buffer-aided relaying can achieve significant performance gain compared with the conventional relaying with fixed transmission criterion. However, most of the existing link-selection strategies are designed based on perfect channel state information (CSI), which are very complex by requiring channel estimator. To solve this issue, in this paper, we investigate a buffer-aided differential chaos-shift-keying based simultaneous wireless information and power transfer (DCSK-SWIPT) relay system, where a decode-and-forward (DF) protocol is considered and the relay is equipped with a data buffer and an energy buffer. In particular, we propose a novel link-selection protocol for the proposed system based on harvested energy, data-buffer status and energy-shortage status, where the CSI is replaced by the harvested energy to avoid the channel estimation and the practical problem of the decoding cost at the relay is considered. Furthermore, the bit-error-rate (BER) and average-delay closed-form expressions of the proposed protocol are derived over multipath Rayleigh fading channels, which are validated via simulations.

Performance Analysis of A SLIPT-Based Hybrid VLC/RF System

Huijie Peng and Qiang Li (Huazhong University of Science and Technology, China); Ashish Pandharipande (Signify, The Netherlands); Xiaohu Ge (Huazhong University of Science & Technology, China); Jiliang Zhang (The University of Sheffield, United Kingdom (Great Britain))

To alleviate the energy constraint and coverage limitations in Internet of Things (IoT) applications, in this paper we investigate a dual-hop hybrid visible light communication (VLC)/radio frequency (RF) cooperative communication system based on simultaneous lightwave information and power transfer (SLIPT). The proposed system consists of a light-emitting diode (LED) source (S), an off-the-grid relay (R) that moves randomly within the coverage of S, and a destination (D) that is located outside the coverage of S. To facilitate communications between S and D, the received optical signal at R in the first phase can be separated into alternating current (AC) and direct current (DC) components for information decoding and energy harvesting, respectively. Then the energy harvested by R is used to forward the decoded information to D using RF in the second phase. In view of the distinct channel conditions across the two hops, the decoding status and the energy available at R, the end-to-end system outage probability is analytically derived. Simulation results are used to evaluate the impact of different system parameters and demonstrate the efficiency of the proposed system.

Session Chair

Rongrong Zhang, Ye Yu

Session IoT-03

Signal and Information Processing

4:50 PM — 6:20 PM CST
Aug 10 Mon, 4:50 AM — 6:20 AM EDT

An Intelligent Prediction Method for Device Status Based on IoT Temporal Knowledge Graph

Shujuan You and Xiaotao Li (China Mobile Research Institute, China); Wai Chen (CMRI, USA)

With the development of Internet of Things (IoT) technology, unattended operation of devices has become an important feature of IoT, which requires devices to perform proper actions to provide services without human intervention. To achieve the unattended operation of IoT, a major challenge is how to accurately predict the actions that the device will perform and meet the personalized requirements of users. To address this challenge, we propose a novel method to predict device status based on the IoT temporal knowledge graph (TKG) and the long short term memory (LSTM) model. We firstly build a TKG for the IoT to provide rich semantic information for the objects and the continuously changing time series data in the IoT. Then, leveraging the advantages of LSTM in sequence learning, the timing characteristics of semantic information in TKG are learned to realize intelligent prediction of the equipment status. To verify the effectiveness of our method, we conducted an experimental verification of device status prediction in a smart home use case, and the experimental results show that our method achieves the state-of-the-art performance.

Access Authentication Architecture Design of Industrial Internet Identification and Resolution System

Baoluo Ma (CAICT, China)

Identification and resolution system is an important part of the industrial Internet, is the key hub to realize the information exchange of all elements and links of industry, and is one of the core infrastructure of the safe operation of the industrial internet. As an important content of industrial internet security, Identification and resolution security is an important guarantee for the healthy development of industrial Internet. It has become the most important issue in the deployment of identification and resolution system. This article is oriented to identity resolution node access authentication, an industrial internet identification and resolution node access authentication architecture is proposed, and analyzes the authentication business process of identity application, identity registration and identity resolution, to provide ideas and references for the construction of industrial internet identification and resolution security certification capacity.

Interactive Attention Encoder Network with Local Context Features for Aspect-Level Sentiment Analysis

Ruyan Wang and Zhongyuan Tao (Chongqing University of Posts and Telecommunications, China)

The sentiment analysis of the data collected by Internet of Things devices has been widely concerned by researchers. Aspect sentiment analysis is a fine-grained task of sentiment analysis, which aims to identify the sentiment polarity of specific aspects in a given context. Most of the previous studies have modelled context and aspect terms using RNN and attention mechanisms. However, the RNN model is difficult to process in parallel, taking into account only the global context features, not the correlation between the sentiment polarity and the local context. To address this issue, this paper proposes an Interactive Attention Encoder Network Model with Local Context Features (IAEN-LCF) for identifying aspect-level sentiment polarity. First, the word embedding and aspect term embedding are pre-trained by Bidirectional Encoder Representations from Transformers (BERT). Secondly, the attention-over-attention (AOA) module in the machine reading comprehension task is applied to the attentional encoder network, and a network model named Interactive Attention Encoder (IAEN) is proposed to extract global context features. By setting a fixed text window, the local context features are captured in a dynamic weighted manner. Finally, the performance of the proposed model is verified in three public data sets. Experimental results show that the proposed model can outperform state-of-the-art methods in aspect sentiment analysis tasks.

Fast Recovery of Low-Rank and Joint-Sparse Signals in Wireless Body Area Networks

Yanbin Zhang (Beijing University of Posts and Telecommunications, China); Longting Huang (Wuhan University of Technology, China); Yangqing Li, Kai Zhang and Changchuan Yin (Beijing University of Posts and Telecommunications, China)

E-health monitoring signals collected from wireless body area networks (WBANs) usually have some highly correlated structures in a certain transform domain (e.g., discrete cosine transform (DCT)). We exploit these structures and propose a fast recovery algorithm for low-rank and joint-sparse (L&S) structured WBAN signal in the framework of compressed sensing (CS). By using a simultaneously L&S signal model, we employ the number of the bigger singular values and Bayesian learning which incorporates an L&S-inducing prior over the signal and the appropriate hyperpriors over all hyperparameters to recover the signal. Experiments show that the proposed algorithm has a superior performance to state-of-the-art algorithms.

Multi-sensor Data Fusion Algorithm Based on Adaptive Trust Estimation and Neural Network

Xuexin Zhao, Junhua Wu, Maoli Wang, Guangshun Li, Haili Yu and Wenzhen Feng (Qufu Normal University, China)

Multi-sensor data fusion technique plays a key role in the agricultural services such as data collection and processing. However, the collected data usually is featured by redundancies and errors, which deteriorate the reliability of network. In this paper, based on adaptive trust estimation, we propose a multi-sensor data fusion algorithm (T-NN) in neural network, aiming to solve the problem of low accuracy and poor stability of multi-sensor data fusion. In particular, the original data collected by the sensors first are pre-processed by exponential smoothing. Then, trust estimation model is applied to calculate the value of trust among the sensing nodes and optimize the data, and the performance of redundancy and reliability are enhanced. Furthermore, the data optimized is introduced into BP neural network for training and fusion. Extensive simulations show that the algorithm proposed in this paper greatly outperforms adaptive weighted average model and traditional BPNN model, in terms of the accuracy of data fusion.

Session Chair

Kehao Wang, Hui Cao

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