期刊：
IEEE Transactions on Fuzzy Systems,2024年:1-13 ISSN：1063-6706
作者机构：
[Khin-Wee Lai] School of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia;[Xiang Wu; Yong-Ting Zhang; Ming-Zhao Yang; Huan-Huan Wang] Institute of Medical Information Security, Xuzhou Medical University, Xuzhou, China;[Ge-Lan Yang] Department of Information Science and Engineering, Hunan City University, Yiyang, China
摘要：
With the advancements in low-power and miniature electronics, various smart devices are deployed and interconnected as the Internet of Things (IoT), collecting a massive amount of data from surrounding environments. Despite the popularity of ZigBee for low-power communications in IoT, WiFi has recently been recommended for data collection in IoT for its high data rate, high reliability, native IP compatibility, and vastly-deployed infrastructures. However, it is well known that WiFi is energy-consuming. Although many schemes have been designed to reduce WiFi energy consumption, they usually suffer from the dilemma that a longer (shorter) sleep of WiFi gives a lower (higher) energy consumption but a larger (smaller) latency, hindering the use of WiFi in a wide range of IoT applications that require a certain level of quality of service (QoS). To this end, we propose a Heterogeneity-aware Dual-interface Scheduling (HDS) scheme to fully exploit the heterogeneity between ZigBee and WiFi to realize energy-efficient and delay-constrained data collection in a tree-based IoT network, where each device is equipped with a ZigBee and a WiFi interface. The low-power feature of ZigBee is utilized as much as possible for high energy efficiency, while the high-reliability advantage of WiFi is leveraged when the ZigBee link quality is low for delay guarantee. Under network dynamics, HDS jointly allocates ZigBee and WiFi schedules to strike a balance between energy and delay for optimized performance. A prototype system is built atop an IoT platform integrated with commercial off-the-shelf ZigBee and WiFi modules. Experiment results show that the energy consumption of HDS is 80.3% and 43.6% lower than the standard power saving protocol and a state-of-the-art dual-interface scheme, respectively, under a moderate delay constraint. Additionally, the percentage of data packets that satisfy the delay constraint is above 98.6%.
摘要：
Driven by emerging wireless applications, numerous smart objects will be deployed, forming things networks interconnected by the Internet of Things (IoT). Many of these networks follow the low-power multihop wireless network (LPMWN) paradigm due to the good scalability of the distributed schemes adopted by LPMWNs. Although distributed schemes may operate satisfactorily most of the time, they are often complex and costly to manage. To simplify network management, the LoRa (long range) wireless technology has been introduced into LPMWNs for centralized network control. Unfortunately, due to LoRa's inherent limitation that a downlink packet must be in response to a precedent uplink packet, the existing protocols typically require every node to periodically send an uplink packet to the gateway for downlink network command dissemination. This causes intensive uplink collisions and quickly depletes LoRa bandwidth, especially when the network is large. In light of these pitfalls, we propose a (cross-interface downlink relay (CDR)) scheme, which leverages the ZigBee communications that are already available in LPMWNs to relay LoRa downlink data traffic for bandwidth-efficient and delay-guaranteed data dissemination. CDR determines the optimal network topology for bandwidth-minimized data relay while adjusting system parameters to ensure each node's delay requirement under network dynamics. A prototype system is implemented by integrating LoRa and ZigBee into an IoT platform. Extensive outdoor experiments show that the bandwidth consumption of CDR is 39.3% lower than a state-of-the-art LoRa data dissemination protocol for ZigBee-based LPMWNs under a moderate data traffic and delay requirement. Besides, CDR improves energy efficiency and network lifetime significantly.
摘要：
With the capability of generating sustainable energy by exploiting the ambient environment (e.g., light, heat, vibrations, etc.), the energy harvesting (EH) technology is increasingly used on low -power smart objects, forming self -powered green Internet of Things (IoTs). Despite targeting different application domains, many of these green IoT systems adopt the distributed network paradigm by forming energy harvesting multi -hop wireless networks (EH-MWNs). While distributed networks have good scalability during the deployment phase, they are often complex, inelastic to change, and costly to manage. To this end, LoRa, a promising long-range wireless technology, has been suggested for centralized single -hop network controls in EH-MWNs. However, the signal propagation of LoRa is severely affected by the surrounding environment (e.g., obstructions, terrains, etc.), which not only creates a significant difference in packet delivery rate (PDR) between nodes but also increases the bandwidth cost for network controls. To address this issue, we propose a C ross -interface D ata T ransfer (CDT) scheme, which leverages the ZigBee interfaces coexisting in EH-MWNs to transfer LoRa data traffic from the nodes with a lower PDR to the nodes with a higher PDR for bandwidth -efficient transmissions. By jointly determining the pairs of nodes performing data transfer and the data transfer rates between them, CDT can reduce the overall LoRa bandwidth consumption while ensuring continuous operation of the EHMWN. A prototype system is implemented by integrating commercial off -the -shelf LoRa and ZigBee interfaces into an IoT platform. Extensive real -world experiments show that under a moderate data traffic input, the bandwidth consumption of CDT is 25.8% and 48.8% lower than the standard LoRaWAN and a state-of-the-art data transfer scheme, respectively. The average energy cost per node is kept as low as 2.29 mW at the same time. Moreover, CDT also shows its performance advantages in terms of PDR and sustainability.
期刊：
Journal of Network and Computer Applications,2023年217:103698 ISSN：1084-8045
通讯作者：
Qin, H
作者机构：
[Chen, Weimin; Li, Ni; Qin, Hua; Yang, Gelan; Qin, H; Wang, Tao; Chen, Hao] Hunan City Univ, Coll Informat & Elect Engn, Yiyang, Hunan, Peoples R China.;[Peng, Yang] Univ Washington Bothell, Div Comp & Software Syst, Bothell, WA USA.
通讯机构：
[Qin, H ] H;Hunan City Univ, Coll Informat & Elect Engn, Yiyang, Hunan, Peoples R China.
关键词：
QoS;Partitioning;Scheduling;Multimedia;IoT
摘要：
In the Internet of Things (IoT), multimedia traffic for audio, image, and video accounts for the largest proportion (over 78.7%) of the total traffic, bringing forward the vision of multimedia IoT (M-IoT). As part of the realization of loT, M-IoT is a general network paradigm that constitutes many smart objects equipped with the capability to collect multimedia data from the physical environment and deliver the data to other things. To satisfy a certain level of user experience, Quality of Service (QoS) is required to be regulated to ensure acceptable delivery of the multimedia content. As the most widely-used wireless technology, WiFi has been recommended for IoT communications for its high data rate, native IP compatibility, and good reusability of the existing infrastructures. However, WiFi suffers from channel contention, especially during multi-hop communications, which degrades the QoS performance and hinders its use for many M-IoT services. Although numerous protocols have been proposed to mitigate WiFi contention, they often consume much WiFi bandwidth for network control, lowering the level of achievable QoS performance. To address this issue, we propose a distributed Cross-interface network Partitioning and Scheduling (CPS) protocol, which leverages the co-existing ZigBee communications to divide the network into partitions and allows only one node in each partition to use its WiFi interface to transmit data at any time, for bandwidth-efficient and delay-constrained data flow delivery in M-IoT. A prototype node is implemented by integrating COTS ZigBee and WiFi interfaces into a BeagleBone Green wireless platform for IoT. Extensive field experiments are conducted in a multi-hop network of 24 prototype nodes that deliver real multimedia data (images and videos). The experiment results show that CPS outperforms the standard WiFi and a state-of-the-art contention control scheme (by 62.6% and 26.4% under high data traffic, respectively) in terms of a QoS metric capturing two basic performance metrics (i.e., bandwidth efficiency and end-to-end delay) of multi-hop communications, while retaining fair QoS performance and high energy efficiency.
通讯机构：
[Chengfeng Long; Xiaoyong Tang] S;School of Information and Intelligence Science and Technology, Hunan Agricultural University, Changsha, China<&wdkj&>School of Computer and Communications Engineering, Changsha University of Science and Technology, Changsha, China
摘要：
Considering the issue with respect to the high data redundancy and high cost of information collection in wireless sensor nodes, this paper proposes a data fusion method based on belief structure to reduce attribution in multi-granulation rough set. By introducing belief structure, attribute reduction is carried out for multi-granulation rough sets. From the view of granular computing, this paper studies the evidential characteristics of incomplete multi-granulation ordered information systems. On this basis, the positive region reduction, belief reduction and plausibility reduction are put forward in incomplete multi-granulation ordered information system and analyze the consistency in the same level and transitivity in different levels. The positive region reduction and belief reduction are equivalent, and the positive region reduction and belief reduction are unnecessary and sufficient conditional plausibility reduction in the same level, if the cover structure order of different levels are the same the corresponding equivalent positive region reduction. The algorithm proposed in this paper not only performs three reductions, but also reduces the time complexity largely. The above study fuses the node data which reduces the amount of data that needs to be transmitted and effectively improves the information processing efficiency.
期刊：
Wireless Communications and Mobile Computing,2020年2020:8815461:1-8815461:10 ISSN：1530-8669
通讯作者：
Zhang, Jing
作者机构：
[Zhang, Jing; Si, Wen; Li, Yu-Dong] Shanghai Business Sch, Fac Business Informat, Shanghai 200235, Peoples R China.;[Si, Wen] Fudan Univ, Huashan Hosp, Dept Rehabil, Shanghai 200433, Peoples R China.;[Tan, Wei] Dongguan Univ Technol, Sch Comp Sci & Technol, Dongguan 523830, Peoples R China.;[Shao, Yi-Fan] Shanghai Jiao Tong Univ, ECE Univ Michigan Shanghai Jiao Tong Univ Joint I, Shanghai 200240, Peoples R China.;[Yang, Ge-Lan] Hunan City Univ, Dept Informat Sci & Engn, Yiyang 413000, Peoples R China.
通讯机构：
[Zhang, Jing] S;Shanghai Business Sch, Fac Business Informat, Shanghai 200235, Peoples R China.
摘要：
<jats:p>Biometric identification has verified its effectiveness in personal identity verification because of the uniqueness and noninvasion. In this research, we tend to apply the detection of biometric information to a remote sensing system for the purpose of security area monitoring. Our system is established by collecting signals from the coming individuals via the remote measurement in the specific condition where both kinds of data are detected to determine the identity. Specifically, the measuring of gait signals and facial images is integrated to provide a way of improving the detection accuracy and the robustness. In addition, the fuzzy association rule (FAR) is employed for data analysis in line with the outcomes of different methods. As such, the signals are integrated and transmitted for further processing and remote identification. Experiments are conducted to demonstrate the capability of the proposed system. With the training data increases, a high detection accuracy of 95.2% is obtained, which makes it a promising basis for the realization of remote identity verification.</jats:p>
摘要：
Chaotic particle swarm optimization algorithm is improved by incorporating antibody concentration, adaptive propagation, optimization mechanism of the multi-population evolution strategy, elite particles chaotic traversal mechanism and constraint processing mechanism. In this paper, an improved adaptive propagation chaotic particle swarm optimization algorithm based on immune selection (IS-APCPSO algorithm for short) is proposed. The performance of several algorithms has been compared by multimodal function, functions with high dimensional and complex constraints, bi-level programming function and a classic example of traffic network optimization. The experimental results prove that the proposed algorithm in accelerating convergence rate, increasing the diversity of particles, and preventing premature phenomenon is effective. The novel algorithm is expected to be used in the model solution of large-scale complex traffic network optimization problem.
摘要：
This paper presents the development of wearable sensing system that can be used to study the gait dynamics of human. A tester wearing sensing shoes participates in this study. Human gait information about standing, jumping and walking is obtained as prior probability based on the train movement model setup theory. For feature extraction of gait, five kinds of features are extracted from foot pressure signals, which are subsequently used for motion analysis. We employ support vector machine and fractal analysis for gait recognition and test the identification performance. Testing outcomes indicate an overall accuracy of 93.57% via radial basis function kernel function. These results demonstrate considerable potential in gait identification.
摘要：
Human detection is a popular topic and difficult problem in surveillance. This paper presents a research on human detection in complex indoor space utilizing a depth sensor. In recent years, target detection methods based on RGB-D data mainly include background learning, and feature detection operator. The former method depends on the initial background knowledge obtained from the first couple of frames in the video, and the length of frames decides detection quality. The latter method is more time intensive, and insufficient training samples will influence the detection result. Thus, in this paper we analyze the complex scene features thoroughly and integrate the color and depth information, proposing a RGBD+ViBe foreground extraction method. Based on the extraction outcome of the foreground, this research utilizes the 3D Mean-Shift method combined with depth constraints to handle multi-person partial occlusion problems. The experiment results indicate that the proposed RGBD+ViBe method outperforms the methods which only consider color or depth information, as well as the RGBD+MoG method. Furthermore, the proposed 3D Mean-Shift method achieves nearly 90% accuracy in multi-person detection result, and the false rate is merely 5%; while the accuracy of HOG, HOD and Comb-HOD methods are less than 75% and the false rate is around 10%. (C) 2018 Elsevier B.V. All rights reserved.
期刊：
Multimedia Tools and Applications,2016年75(23):15601-15617 ISSN：1380-7501
通讯作者：
Zhang, Yudong
作者机构：
[Yang, Gelan] Hunan City Univ, Sch Informat Sci & Engn, Yiyang 413000, Peoples R China.;[Zhang, Yudong; Wang, Shuihua; Ji, Genlin] Nanjing Normal Univ, Sch Comp Sci & Technol, Nanjing 210023, Jiangsu, Peoples R China.;[Zhang, Yudong; Feng, Chunmei; Wang, Qiong; Yang, Jiquan] Jiangsu Key Lab 3D Printing Equipment & Mfg, Nanjing 210042, Jiangsu, Peoples R China.;[Dong, Zhengchao] Columbia Univ, Translat Imaging Div, New York, NY 10032 USA.;[Dong, Zhengchao] Columbia Univ, MRI Unit, New York, NY 10032 USA.
通讯机构：
[Zhang, Yudong] N;[Zhang, Yudong] J;Nanjing Normal Univ, Sch Comp Sci & Technol, Nanjing 210023, Jiangsu, Peoples R China.;Jiangsu Key Lab 3D Printing Equipment & Mfg, Nanjing 210042, Jiangsu, Peoples R China.
作者机构：
[刘琮; 杨格兰] School of Electronics and Information Engineering, Tongji University, Shanghai, 201804, China;[邓晓军] College of Computer and Communication, Hunan University of Technology, Zhuzhou, 412007, China;[杨格兰] School of Information Science and Engineering, Hunan City University, Yiang, 413000, China
通讯机构：
College of Computer and Communication, Hunan University of Technology, Zhuzhou, China
