Internet of Things

Session IoT-04

Resource Allocation

10:30 AM — 12:00 PM CST
Aug 10 Mon, 10:30 PM — 12:00 AM EDT

Dynamic Task Offloading and Resource Allocation for Heterogeneous MEC-enable IoT

Shichao Xia, Wen Xingxing and Yao Zhixiu (Chongqing University of Posts and Telecommunications, China); Yun Li (ChongQing University of Posts and Telecommunications of China, China)

With the rapid development and convergence of the mobile Internet and the Internet of Things (IoT), the diverse IoT applications with requirements of ultra-low latency and heterogeneous connectivity have been increasing sharply in recent years. In this work, we consider the problem of diverse task offloading and computation resource allocation in the dynamic and heterogeneous MEC-enable network, wherein heterogeneous characterized by the MEC servers with different computing capacities and multi-type applications with different computation requirements. Firstly, to drive the long-term system-wide effectiveness towards a near-optimum, joint task offloading and computation resource allocation is proposed by invoking the Lyapunov optimization theory, and we analyze the tradeoff between task offloading revenue and latency. Besides, a heuristic task offloading methodology based on a search tree is developed to get the optimal offloading strategy in a heterogeneous environment. Moreover, to improve processing efficiency and reduce unnecessary communication overhead, the Offloading Priority Selection Criterion (OPSC) is designed. Finally, the effectiveness and rationality of the algorithm are verified by experimental simulations.

Hypergraph Based Resource Allocation and Interference Management for Multi-Platoon in Vehicular Networks

Hewen Cui (BUPT, China); Lianming Xu, Qing Wei and Li Wang (Beijing University of Posts and Telecommunications, China)

Platoon communications in vehicular networks is considered to have broad application prospects in improving road capacity and traffic safety. This paper considers a multi-platoon vehicles scenario, in which platoon vehicles (PVs) transmit cooperative awareness messages (CAMs) to other PVs in the same platoon. The leader vehicles of platoons (L-PVs) use dedicated resource blocks (RBs) to communicate with base station (BS) and broadcast their CAMs to their member vehicles (M-PVs). Meanwhile, to increase spectral efficiency and satisfy the communication demands of PVs, we consider M-PVs reuse RBs with each other to transmit their CAMs. Finally, due to conventional graph theory can only model pairwise relations, we propose a Hypergraph-based Resource Allocation and Interference Management (HRAIM) for multi-platoon scheme to maximize spectral efficiency. Simulation results illustrate that, the proposed scheme shows a better performance in terms of spectral efficiency and sum data rate when compared with traditional graph coloring algorithm and random selection scheme.

A Prediction-Based Spectrum Allocation Scheme for Two-Layer Cellular Vehicular Networks

Qian Li (Northeastern University, China); Weijing Qi and Lei Guo (Chongqing University of Posts and Telecommunications, China)

As the number of vehicles and applications in vehicular networks increase, an efficient spectrum allocation method plays an important role in improving resource utilization ratio and relieving network congestion. In this paper, we propose a prediction-based spectrum allocation scheme for two-layer cellular vehicular networks, in order to balance resource satisfaction of users in the whole network. Specifically, considering that the number of vehicles in an area is predictable, we introduce a convolutional long short-term memory (Conv_LSTM) network model to predict the number of vehicles within the range of a small base station (SBS). In addition, since the resource demand of the SBS is related to the number of vehicles, we allocate spectrum resources for SBSs based on the prediction result. The spectrum allocation problem is transformed into a multi-coloring (MC) problem and solved by our proposed spectrum allocation algorithm. Numerical results show that our scheme has high accuracy in predicting the number of vehicles and availability in balancing resource satisfaction.

Mobility Improves the Performance of Collaborated Spectrum Sensing

Huijun Xing (Beihang University, China); Zheng Dezhi and Wang Shuai (Beihang Unicersity, China)

Spectrum sharing is a promising technology to solve the problem of the shortage and low utilization of spectrum resources in the future mobile communication systems. Spectrum sensing, as a critical step to discover the available spectrum holes in spectrum sharing, attracts wide attention in academia and industry. The collaborative spectrum sensing allocates sensing tasks to secondary users (SUs), such that the spectrum opportunity of SUs can be guaranteed. However, the impact of mobility on the collaborative spectrum sensing is still unknown. In this paper, we study the impact of mobility on the performance of collaborative spectrum sensing. The multi-user diversity introduced by mobility is applied in the collaborative spectrum sensing, and the performance of spectrum sensing is improved. The detection time and spectrum opportunity of SUs are derived with closed form. We discover that when exploiting the mobility of SUs, the detection time of SUs is reduced and the spectrum opportunity of SUs is improved. Thus the performance of spectrum sensing and spectrum sharing can be improved. This paper provide fundamental guidelines for the design of spectrum sensing mechanisms in the mobile environment.

Multi-user Cooperative Spectrum Sensing Based on the Mean Value of Cumulative Power

Yufei Dai (Beijing Institute of Technology & School of Information and Electronics, China); Liang Liu (China Mobile Communications Corporation & China Mobile Research Institute, China); Dongfang Hu (Beijing Institute of Technology & School of Information and Electronics, China); Han Yang (School of Information and Electronics Beijing Institute of Technology, China)

In the practical application of cognitive radio systems, spectrum sensing requires accurate signal detection without any prior knowledge and in complex channel background. Energy detection algorithms perform poorly in complex channel environments, and single-user spectrum sensing is susceptible to problems such as multipath effects, shadow fading, and hidden terminals. This paper analyzes the spectrum sensing algorithm based on the average value of the cumulative power spectral density, and compares it with the traditional energy detection algorithm. In view of the limitations of single-user spectrum sensing, this paper applies multi-user cooperative spectrum sensing to the algorithm, which improves the performance of spectrum sensing.

Session Chair

Qing Wei, Hanlin Mou

Session IoT-05


1:30 PM — 3:00 PM CST
Aug 11 Tue, 1:30 AM — 3:00 AM EDT

A Novel Virtual Small Cell-Based Group-Paging Scheme for Massive MTCs over LTE Networks

Linh T. Hoang and Anh-Tuan Hoang Bui (The University of Aizu, Japan); Chuyen T. Nguyen (Hanoi University of Science and Technology, Vietnam); Anh T. Pham (The University of Aizu, Japan)

Group paging (GP) is one of the improvements proposed by 3GPP on the radio access network (RAN) of LTE/LTE-A to enable machine-type communications (MTCs). Nevertheless, the conventional GP might still pose an overload on RAN when a massive number of paged MTC devices trigger the LTE's contention-based random access procedure (RAP) in a highly synchronized manner. This paper, based on the concept of Virtual Small Cells (VSCs), introduces a new RAP for MTC devices to solve the RAN overload issue in the group-paging process caused by the massive access. The proposed VSC-based RAP is designed based on the group-based access manner and an adaptive access barring algorithm, which efficiently controls the access rate in each VSC during the paging process. Computer simulation shows that, in the context of massive MTCs, the proposed VSC-based scheme can significantly outperform the conventional GP in terms of success access rate and average delay of successfully-accessed devices.

Preamble Split Transmission and Joint Active User Detection for Massive Connectivity

Lin jie Yang, Pingzhi Fan, Li Li and Li Hao (Southwest Jiaotong University, China)

Low-power delay-tolerable services are very important applications of future large-scale IoT. Active user detection is its critical challenge. By exploiting the sparsity of the received signal, this challenge could be converted to a compressive sensing problem and hence solved by the approximated message passing (AMP) algorithm. In order to improve the active user detection performance, a preamble split transmission (PST) random access (RA) scheme is proposed, in which each partition of the preamble is sent in different coherent time duration to achieve the time diversity. Correspondingly, a joint active user detection (JAUD) algorithm is proposed to jointly detect the distributed split preambles at the base station. Simulation results show that the proposed access scheme achieves a higher detection accuracy at the cost of a longer access delay. According to the delay-tolerable characteristic of the target network, this cost could be acceptable.

Maximum Sum Rate of Slotted Aloha for mMTC with Short Packet

Weihua Liu (Sun Yat-sen University, China); Xinghua Sun, Wen Zhan and Xijun Wang (Sun Yat-sen University, China)

As one of three generic services to be supported by the fifth-generation (5G), massive machine type communication (mMTC) is envisioned to support short-packet transmissions, which leads to 1) advantage of grant-free access due to a small signalling overhead; 2) loss of information encoding rate in the finite blocklength region. As one of the representative grant-free schemes, slotted Aloha has gained renewed interests in MTC networks recently, yet its optimal performance in the finite blocklength region remains largely unexplored.

Toward the above issue, this paper focuses on optimizing the sum rate performance of a slotted Aloha network with retry limit, where each node encodes k information bits to a packet with the blocklength N, and transmits over an Additive white Gaussian noise (AWGN) channel. The probability of successful transmissions of data packets is derived, based on which the network sum rate is obtained as an explicit function of key system parameters. Further by jointly tuning both the transmission probabilities of nodes and the blocklength of packets, the maximum sum rate is characterized. The analysis reveals the effect of the number of information bits per packet k and the retry limit M on the optimal sum rate performance. It is found that the retry limit M does not affect the maximum sum rate, while a larger number of information bits per packet ameliorates the maximum sum rate in the finite blocklength region.

An Incentive Mechanism for Nondeterministic Vehicular Crowdsensing with Blockchain

Fan Li, Changle Li, Yuchuan Fu and Pincan Zhao (Xidian University, China)

With the increase in the number of on-board sensors, vehicles have shown great potential in mobile crowdsensing. To ensure the capability of the vehicular crowdsensing system, it is necessary to inspire sufficient vehicles to participate. However, due to personal interests and privacy protection, this goal is not easy to achieve. In addition, uncertain mobility of vehicles also brings challenges to the design of incentive mechanisms. In this paper, we propose an incentive mechanism for nondeterministic vehicular crowdsensing with blockchain (INVCB), which can effectively incentivize vehicles while protecting user privacy. We first propose a framework for nondeterministic vehicular crowdsensing with blockchain and design a series of smart contracts to automate the crowdsensing process. Then, in order to improve the quality of sensing data, we add reputation attribute to each user, and provide an incentive mechanism that considers reputation. Extensive simulation results show the performance of our proposal is reliable and effective.

User Scheduling for Information Freshness over Correlated Markov Channels

Yanzhi Huang, Xijun Wang, Xinghua Sun and Xiang Chen (Sun Yat-sen University, China)

There is a surge of need for fresh information with the overwhelming proliferation of the Internet of Things (IoT) applications. To characterize the information freshness perceived by the destination, age of information (AoI) has been proposed. In this paper, we consider an IoT system with multiple IoT devices sending time-sensitive information to a central controller through time-correlated Markov channels. Our goal is to design a user scheduling policy that minimizes the time-average of AoI under a scheduling constraint. We formulate the AoI minimization problem by the restless multi-armed bandit. With Lagrangian relaxation, we establish the indexability and obtain Whittle's index in closed-form. A scheduling policy is further proposed based on the Whittle's index. Simulation results show that the proposed scheduling policy can achieve comparable performance with the optimal policy.

Session Chair

Neng Ye, Hao Liu

Session IoT-06

Localization, Recognition and Detection

3:10 PM — 4:40 PM CST
Aug 11 Tue, 3:10 AM — 4:40 AM EDT

Indoor CSI fingerprint localization based on tensor decomposition

Yuexin Long (Chongqing University of posts and Telecommunications, China); Liang Bo Xie, Mu Zhou and Yong Wang (Chongqing University of Posts and Telecommunications, China)

Indoor Wi-Fi localization methods based on the Received Signal Strength (RSS) are widely used because of the low computational complexity and strong applicability. Compared with the RSS, the Channel State Information (CSI) can provide the multi-channel subcarrier phase and amplitude information to better describe the signal propagation path. Thus, the CSI becomes one of the most commonly used signal features in indoor Wi-Fi localization. Compared to the CSI-based geometric localization method, the fingerprint-based localization method has advantages of easy implementation and high accuracy. Based on this, this paper proposes an indoor CSI fingerprint localization approach based on tensor decomposition. Specifically, we combine the tensor decomposition algorithm based on the Parallel Factor (PARAFAC) analysis model with the Alternating Least Squares (ALS) iterative algorithm to reduce the interference of the environment. Then, we use the tensor wavelet decomposition algorithm for feature extraction and obtain the CSI fingerprint. Finally, distinguishing from the traditional localization algorithm based on machine learning, this paper establishes a localization model based on the Partial Least Squares Regression (PLSR) algorithm to predict position coordinates. Experimental results show that the proposed approach is with the high localization accuracy and good fingerprint collection efficiency.

A Novel Cost-Effective IoT-Based Traffic Flow Detection Scheme for Smart Roads

Zhao Liu, Changle Li, Hui Wang, Yunpeng Wang, Yilong Hui and Guoqiang Mao (Xidian University, China)

Autonomous driving is expected to be realized in the future with the development of information and communication technology (ICT). However, the reliability of autonomous vehicles (AVs) in complex environments needs major improvement. To facilitate the autonomous driving, high-precision and low-cost traffic flow detection is essential for driving decision and traffic surveillance, and has drawn increasing attention from both academia and industry. In this paper, we propose a novel cost-effective IoT-based traffic flow detection scheme with particular focuses on vehicle counting and speed measurement. To this end, microwave Doppler radar sensors with a unit price of $2.3 are utilized to collect the traffic data of passing vehicles on the road. Then, a multi-threshold detection algorithm is proposed to extract features for vehicle counting and speed measurement. After this, experiments are carried out in different scenarios to evaluate the proposed traffic flow detection scheme. The results validate the high-precision detection with average 98.3% vehicle counting accuracy and 95.8% speed measurement accuracy.

Basketball Footwork Recognition using Smart Insoles Integrated with Multiple Sensors

Min Peng and Zhong Zhang (Hefei University of Technology, China); Qingfeng Zhou (Dongguan University of Technology,China)

In the basketball training, statistical data of basketball footwork can be used to improve training level.However, most basketball motion recognition systems are with high cost, and the recognition of footwork is often overlooked.In this paper, we propose a system to recognize basketball footwork using smart insoles. The system collects data through the three-axis accelerometer and three-axis angular velocity meter embedded in smart insoles.Then, five kinds of basic basketball footwork such as sideslip step, back step, cross step, jab step and jump step can be recognized.In this paper, K-Nearest Neighbors (KNN), Support Vector Machines (SVM) and Convolutional Neural Networks (CNN) are used for footwork recognition.The experimental results show that the proposed system can recognize five kinds of basketball footwork effectively.

A Dynamic Continuous Hand Gesture Detection and Recognition Method with FMCW Radar

Aihu Ren, Yong Wang, Xiaobo Yang and Mu Zhou (Chongqing University of Posts and Telecommunications, China)

In this paper, a continuous dynamic hand gesture detection and recognition method is proposed using a frequency modulated continuous wave (FMCW) radar. Specifically, we collect the raw radar data to estimate the radar intermediate frequency (IF) signal, and construct the range-time map (RTM) and Doppler-time map (DTM) with 2-Dimensional Fast Fourier Transform (2D-FFT). Then, we propose a hand gesture detection method, which obtains the amplitude of the normalized hand gesture and uses a threshold to effectively segment the continuous hand gesture. Finally, the hand gesture is recognized by the proposed Fusion Dynamic Time Warping (FDTW) algorithm based on the central time-frequency trajectory. Experiments with radar data show that the accuracy of the proposed hand gesture detection method can reach 96.17%, and compared with the traditional recognition algorithm, the proposed recognition algorithm can significantly improve the recognition accuracy rate (hand gesture average recognition accuracy rate can reach 94.50%) with the time complexity reduced by more than 50%.

Intelligent Emotion Detection Method in Mobile Edge Computing Networks

Zhidu Li, Ji Lv and Dapeng Wu (Chongqing University of Posts and Telecommunications, China)

How to detect emotions of different people in time is significant in various areas, such as healthcare, VR/AR and etc. In this paper, an intelligent emotion detection method is proposed to transmit individual emotion data in real-time with as little energy as possible in a resource-limited mobile edge computing (MEC) networks. First, a data compression method is designed based on Convolutional Auto-Encoder (CAE) to compress emotion data on the user side. Then, to guarantee the requirements of data transmission delay and the data distortion rate simultaneously, an optimal channel allocation algorithm is proposed. Thereafter, the emotion data is recovered and analyzed by the edge personal emotion model EEGNET on the edge computing server where the real-time emotions of users can be monitored. The effectiveness of the proposed method is finally verified by extensive simulation experiments.

Session Chair

Xiaozheng Gao, Hang Yuan

Made with in Toronto · Privacy Policy · © 2020 Duetone Corp.