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4th International conference on Consumer Electronics and Computer Engineering, ICCECE 2024

作者： Wan, Weixiang Sun, Wenjian Zeng, Qiang Pan, Linying Xu, Jingyu University of Electronic Science and Technology of China Electronics & Communication Engineering Chengdu China Yantai University Electronic and Information Engineering Shandong Yantai China Zhejiang University Computer Technology Zhejiang Hangzhou China Trine University Information Studies PhoenixAZ United States Northem Arizona University Computer Information Technology FlagstaffAZ United States

ISBN: (纸本)9798350384062

In the era of the Internet of Things, developing a smart sensor system with sustainable power supply, easy deployment, and flexible use has become a challenging problem. Traditional power supplies, prone to frequent replacements or requiring charging during usage, hinder the advancement of wearable devices. The contact-to-separate friction nanogenerator (TEN G), composed of polychotomy ethylene (PTFE) and aluminum (AI) foils, addresses this issue by harvesting human motion energy through body movement arrangement. This energy is then utilized to monitor human motion posture by detecting changes in output electrical signals. In 2012, Academician Wang Zhonglin and his team invented the triboelectric nanogenerator (TENG), which operates as a self-driven sensor primarily powered by Maxwell displacement current. This enables direct conversion of mechanical stimulation into electrical signals during action, making it suitable for self-powered sensor applications. TENG-based sensors boast a simple structure and high instantaneous power density, providing a crucial tool for constructing intelligent sensor systems. Additionally, when combined with machine learning characteristics such as low cost, short development cycles, robust data processing, and predictive capabilities, the processing of the numerous electrical signals generated by TENG has a significant impact. The integration of TENG sensors is poised to drive the rapid evolution of intelligent sensor networks in various fields such as transportation, security, water conservancy, and construction, where urban sound management is essential. The method of seamlessly integrating sensors with Internet access is implemented using the NetBox network development platform, following these steps: 1) Initially, read the default parameter settings from pre-existing storage media, including parameters like IP address, subnet code, gateway address, sensor input and output types, and sensor range. These settings can be modified onl

关键词： Ethylene

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Utilization of IoT-assisted computational strategies in wireless sensor networks for smart infrastructure management

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INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT 2024年第1期15卷 28-34页

作者： Raghunath, K. M. Karthick Koti, Manjula Sanjay Sivakami, R. Kumar, V. Vinoth NagaJyothi, Grande Muthukumaran, V MVJ Coll Engn Dept Comp Sci & Engn Bangalore Karnataka India Sri M Visvesvaraya Inst Technol Dept MCA Bengaluru India Sona Coll Technol Dept CSE Salem Tamil Nadu India Jain Deemed Univ Dept Comp Sci & Engn Bangalore Karnataka India Golden Valley Integrated Campus Madanapalli India REVA Univ Sch Appl Sci Dept Math Bangalore Karnataka India

At the moment, Internet of things (IoT) breakthroughs and implementations are facilitating intelligent city initiatives and operations all over the world. The core theme of IoT has been supporting the design of sustainable building models with standard architecture to boost optimal energy conservation and its efficiency. Wireless sensor networks (WSNs), which are associated with IoT, represent functional networks in assisting the monitoring, tracking and sensing different environmental activities. sensors characteristics play the leading role in designing and applying any WSN. The main problem of coverage factor, predictive event score, dependability ratio, error effect, and energy usage in the sensing node relies on the average node timings in WSN for the power-sharing and processing of communication networks that mainly includes information exchange. This study primarily dealt with formulating and implementing a Hybridized IoT-Assisted Hierarchy-based Computation Strategy (HIHCS) technique and an Active Randomized Optimization Strategy (AROS) to help alleviate the energy problems in a Smart City surveillance using WSN. The energy-controlled sensor node also negotiates many network-related tasks and improves energy utilization and detection precision throughout data processing by adopting an optimal cluster node. The experiments revealed that both the HIHCS and AROS methods could enhance energy savings, especially during cluster node selection in WSN. Ultimately, the result proves that the proposed system improvises the performance of WSN-bound IoT based smart city applications, which are evident through the average resultants: performance ratio (83.97%), event predictive score (77%), dependability ratio (86.23%), energy consumption (58.96%), coverage factor (70.43), and error effect (27.91%).

关键词： HIHCS and AROS Clustering Energy optimization Smart cities WSN IoT Data management Framework

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Classification And Regression Tree (CART) based resource allocation scheme for Wireless sensor networks

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COMPUTER COMMUNICATIONS 2023年 197卷 242-254页

作者： Kori, Gururaj S. Kakkasageri, Mahabaleshwar S. Biluru Gurubasava Mahaswamiji Inst Technol Elect & Commun Engn Dept Mudhol 587313 Karnataka India Basaveshwar Engn Coll Autonomous Elect & Commun Engn Dept Bagalkot 587102 Karnataka India

The 21st century is the era of smart sensors, intelligent computations, and communication technologies. Wireless sensor networks (WSN) play a vital role in performing many remote applications without human intervention. WSNs are an integral part of an ubiquitous computing and Internet of Everything (IoE). A WSN is a decentralized network comprised of several tiny but powerful sensor nodes. sensor nodes are restricted in terms of battery life, communication range, bandwidth, processing latency, and memory. Effective usage of WSN resources is a challenging task to enhance network lifespan, increase throughput, reduce computational delay and minimize control overheads. Several intelligent strategies are proposed to improve WSN performance and enhance the lifespan of the network by adopting intelligent resource management schemes. In WSN, effective and intelligent resource management involves resource discovery, resource scheduling, and resource allocation. In this paper, a Classification and Regression Tree (CART) supervised machine learning algorithm is used to deal with incomplete information about the network i.e., uncertainty and dynamicity for effective resource allocation. The scheme operates as follows: The k-means clustering algorithm is applied to the network, clusters are formed, the Cluster Head (CH) is selected, and the k-NN algorithm is applied to find the number of neighbor nodes i.e., Cluster Members (CM) in the cluster. The attributes of CH like distance from base station, degree of connectivity, congestion rate, data type and size aggregated at CH after performing a task and channel quality are calculated. Aggregation and classification of CM and CH attributes (data sets) use intelligent search and feature selection algorithms. The data sets are then processed for the training and prediction phases. A decision tree model is built using target attribute, which is resource (bandwidth) allocation. A heat map and confusion matrix are generated and a perfor

关键词： WSN Resource allocation Bandwidth CART

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Graph Filter Transfer for Time-Varying Signal Estimation Between Two networks

Graph Filter Transfer for Time-Varying Signal Estimation Bet...

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2024 Asia Pacific Signal and information processing Association Annual Summit and conference

作者： Fukuhara, Tsutahiro Hara, Junya Higashi, Hiroshi Tanaka, Yuichi Osaka Univ Grad Sch Engn Osaka Japan

ISBN: (纸本)9798350367331;9798350367348

This paper presents a filter transfer method for estimating time-varying graph signals, i.e., Kalman filtering between two different networks. In many sensor networks, signals observed are associated with nodes (i.e., sensors), and edges of the network represent the inter-node connectivity. For a large sensor network, measuring the signal values at all nodes requires huge resources, particularly in terms of energy consumption. To alleviate the issue, one may extract one cluster from the network and perform intra-cluster analysis based on the statistics in the cluster. The statistics are then utilized to estimate the signals from another cluster. This leads to the requirement for transferring a set of parameters in the Kalman filter from one cluster to another. In this paper, we propose a cooperative Kalman filter between two networks. The proposed Kalman filter alternately estimates signals in time between the two networks. We formulate a statespace model in the source cluster and transfer it to the target cluster on the basis of optimal transport. In the signal estimation experiments, we validate the effectiveness of the proposed method.

关键词： Kalman filters

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Model-Based Approach for Efficient Data Exploitation Process in IoT Systems 20

Model-Based Approach for Efficient Data Exploitation Process...

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20th IEEE International Wireless Communications and Mobile Computing (IEEE IWCMC) - Green and intelligent Communications

作者： Terracher, Audrey Kocik, Remy Hamouche, Redha Rachedi, Abderrezak Bourouina, Tarik Univ Gustave Eiffel ESYCOM CNRS UMR9007 ESIEE Paris Noisy Le Grand France Univ Gustave Eiffel LIGM Lab CNRS UMR8049 Champs Sur Marne France

ISBN: (纸本)9798350361261;9798350361278

Nowadays, sensor networks are used in various fields and for multiple purposes in medicine, agriculture or industry. In a network, different kinds of sensors are often involved. This heterogeneity makes it complex and time-consuming to implement the exploitation of the data acquired by these sensor networks. We notice a growing need for scalable and rapidly deployable infrastructures to allow for interoperability for the processing, the storage or visualization of collected data. Faced with these challenges, we define a framework to facilitate the automatic deployment of data exploitation applications in heterogeneous sensor networks. We propose a Model-Driven Engineering-based approach that provides a high-level model to describe such infrastructures. Along with automatic code generation, our model aims to facilitate the exploitation of collected data through a graphical interface. The model we define allows for the description of heterogeneous sensor networks with a data exploitation point of view.

关键词： IoT data exploitation Model-Driven Engineering sensor networks Metamodeling Code Generation

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Digital transformation and visual knowledge map analysis of intelligent factory for sensor information of Internet of Things

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intelligent DECISION TECHNOLOGIES-NETHERLANDS 2024年第4期18卷 3437-3451页

作者： Wang, Honglv Shi, Dingke Zhang, Chengting Ding, Nanzhe Cheng, Chao China Tobacco Zhejiang Ind CO LTD Hangzhou 315504 Zhejiang Peoples R China

Industry 4.0 is reshaping conventional factories into "smart factories" via the widespread use of IoT-enabled networks of linked devices, sensors, and software for process optimization and monitoring. intelligent manufacturing facilities may employ IoT-based predictive maintenance to reduce downtime, increase equipment longevity, and avoid machine problems. Manufacturers may get real-time insights into energy consumption patterns, which is a major concern in the business. The primary objective is to optimize energy use during part manufacturing. Hence, this paper proposes the Internet of Things- Low-Power Wide-Area Network Model (IoT-LPWM) to monitor manufacturing and reduce energy consumption. The proposed method's production status component uses visual Knowledge Map Analysis loaded with data from the edge device. A Low-power wide-area network (LPWAN) is the fundamental component of the suggested approach to industrial wireless communication. Using edge computing technology in LPWAN helps reduce computational complexity by shifting high-intensity processing to the periphery, where devices with computing resources are more readily available. Both the energy needed to process and store massive data and the likelihood of cyberattacks may be decreased with this method. The experimental results show that the IoT-LPWM provides useful information to help them make decisions and reduce energy consumption. The experimental results show that our proposed method IoT-LPWM achieves a high performance ratio of 97%, attack prevention ratio of 96.3%, energy management ratio of 93.8%, and data transmission ratio of 98.1% compared to other methods.

关键词： intelligent factory internet of things (IoT) knowledge map (KM) edge computing(EC) sensors

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Distributed Sampling Rate Offset Estimation Over Acoustic sensor networks Based on Asynchronous Network Newton Optimization

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IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE processing 2023年 31卷 301-312页

作者： Hu, De Zhang, Huaiwen Bao, Feilong Wang, Rui Inner Mongolia Univ Coll Comp Sci Hohhot 010021 Peoples R China Natl & Local Joint Engn Res Ctr Intelligent Infor Hohhot 010021 Peoples R China Inner Mongolia Key Lab Mongolian Informat Proc Te Hohhot 010021 Peoples R China Changzhou Univ Sch Microelect & Control Engn Changzhou 213164 Peoples R China

Sampling rate synchronization is an inevitable issue in distributed acoustic sensor networks. In this paper, an analytical sampling rate offset (SRO) estimation approach is first proposed, and then, it is extended to a distributed method that suitable for acoustic sensor networks with arbitrary communication graphs. Specifically, a linear-phase drift model in the short-time Fourier transform domain is used to approximate the SRO between each pair of microphone nodes. Next, after unwrapping the temporally averaged phase information, SROs are recovered analytically via a new weighted-sum criterion. Based on this, a distributed cost function is established at each node to obtain the SROs of all nodes simultaneously in a distributed manner. Finally, a state-of-the-art distributed algorithm named asynchronous network Newton optimization is adopted to carry out the distributed SRO estimation. The proposed method can effectively estimate the SROs among acoustic sensor nodes in noisy and reverberant environments. Compared with the existing approaches, it does not require an external central processor, and only local communications among nodes are needed. Experimental results confirm the validity of the proposed method.

关键词： Estimation Microphones Acoustic sensors Speech processing Location awareness Cost function Correlation Acoustic sensor networks sampling rate offset phase unwrapping asynchronous network Newton optimization

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Energy efficient routing using adaptive elephant herding optimization for IoT-WSN

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JOURNAL OF intelligent & FUZZY SYSTEMS 2023年第4期45卷 5467-5479页

作者： Sivakami, K. Vijayalakshmi, P. Nehru Inst Engn & Technol Dept ECE Coimbatore 641105 Tamil Nadu India Hindusthan Coll Engn & Technol Dept ECE Coimbatore Tamil Nadu India

WSNs(Wireless sensor networks) has been developed with applications in many domains including agriculture, telecommunication, manufacturing industry, healthcare, and surveillance. More specifically, WSN plays a pivotal role in IoT (Internet of Things). The IoT sensors provide information about the physical phenomena in the deployed fields. As the sensors contain only limited resources, the factors like data processing, power consumption, transmission, and storage capabilities adversely affect the efficiency. Thus, the process of routing is necessary for network longevity. The data from IoT-based sensors is routed to the destination through a multi-hop routing system. The Energy aware Routing is motivated by the nature inspired Fuzzy Butterfly Optimization (E2RFBOA). Further a new data aggregation method is introduced in this article customized for IoT based WSN to acquaint higher crop yield in precision farming. Nevertheless, the scalability becomes a primary concern when deployed in larger and denser networks. This is due to the fact that all nodes in IoT and WSN are mostly alive depending on higher usage of bandwidth and power. The primal aim is to build a novel routing protocol developed for IoT-WSN. Apart from this, an Energy aware Clustered Routing that is motivated by Adaptive Elephant Herding Optimization (E2CR-AEHO) is proposed, which sensors collect data and find a group of Cluster Heads (CHs). In the AEHO Algorithm, the formed CH is rotated depending on power consumption. This also prevents frequent re-clustering;at the same time it can effectively adapt to the changes in network topology. According to the AEHOA, the node population comprises of nodes that can choose its CHs among the other nodes. This algorithm takes into account a number of criteria, including power consumption, residual power of sensor Nodes (SN), network reliability, and data reliability. The suggested approach can efficiently represent the network environment, allowing the routing alg

关键词： Internet of things swarm intelligence information fusion integer linear programming adaptive elephant herding optimization wireless sensor networks

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On Solving Area Coverage for Heterogeneous Directional sensor networks 21

On Solving Area Coverage for Heterogeneous Directional Senso...

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21st International conference on Electrical Engineering/Electronics, Computer, Telecommunications and information Technology (ECTI-CON)

作者： Sungvornyothin, Thaschakorn Thammawichai, Mason Navaminda Kasatriyadhiraj Royal Air Force Acad Elect Engn Dept Sara Buri Thailand

ISBN: (纸本)9798350381566;9798350381559

Wireless sensor networks play important roles in the intelligent systems of current technology, such as the Internet of Things (IoT). The heterogeneous directional sensor network (HDSN), which contains a large number of sensor nodes with different capabilities and is limited in sensing direction, has become more realistic in many applications. However, most existing works in the literature focus on homogeneous and omni-directional sensor networks, which cannot be efficiently applied to real-world applications. Therefore, in this study, we propose a novel algorithm called the Van der Waals Force-Based Algorithm for Heterogeneous Directional sensor networks (VW-HDSN) to solve the coverage problem in HDSNs. The simulation results illustrate that our algorithm can achieve a superior coverage ratio compared to that of a random approach by 18.66%. The results also suggest that adding efficient nodes to the network will significantly improve its performance. Another benefit of our algorithm is that it can be generalized to a fully heterogeneous network where all nodes are diverse in angle of view and sensing radius.

关键词： WSNs Area Coverage Heterogeneous Directional sensors Van der Walls

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Wireless sensor Network for Tactical Situation Assessment

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IEEE sensors JOURNAL 2022年第1期22卷 1051-1062页

作者： Pannetier, Benjamin Dezert, Jean Moras, Julien Levy, Raphael CS Grp F-92350 Le Plessis Robinson France Off Natl Etud & Rech Aerosp French Aerosp Lab F-91120 Palaiseau France

This paper describes a complete solution of a new dropped wireless sensor network (called "abandoned" since it is never picked up) dedicated to intelligence operation. The sensor network, named SEXTANT for Smart sensor X for Tactical situation AssessmeNT and presented in this paper is the achievement of several years of research and development in the fields of data fusion and smart sensors at the French Aerospace Lab. The main contribution of this paper is the presentation of experimental results obtained with our Joint Tracking and Classification (JTC) algorithm. The originality of this algorithm relies on the use contextual information in the target tracking processing and data fusion for target classification processing.

关键词： sensors Target tracking Wireless sensor networks sensor phenomena and characterization Classification algorithms Signal processing algorithms intelligent sensors Wireless sensor network mbed protocol data fusion multiple target tracking classification

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