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Energy-efficient blockchain-integrated IoT and AI framework for sustainable urban microclimate management

Sustainable Computing: Informatics and Systems 2025年 47卷

作者： Krishnaraj, N. Alsolai, Hadeel Al-Wesabi, Fahd N. Said, Yahia Alqazzaz, Ali Priya, S. Gayathri Shanmathi, S. Narmada, B. Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur 603203 India Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh 11671 Saudi Arabia Department of Computer Science Applied College at Mahayil King Khalid University Saudi Arabia Center for Scientific Research and Entrepreneurship Northern Border University Arar 73213 Saudi Arabia Department of Computer Science and Artificial Intelligence College of Computing and Information Technology University of Bisha Bisha 67714 Saudi Arabia Department of ECE R.M.D. Engineering College Chennai India Department of Electronics and Communication Engineering GMR Institute of Technology Andhra Pradesh Rajam 532127 India Department of Information Technology Dhirajlal Gandhi College of Technology Tamil Nadu Salem 636309 India

The increase of urban areas has difficulties in managing microclimate conditions in green city to improve data security, resource efficiency, and predictive accuracy. This research integrates IoT, blockchain, and AI technologies with nature-inspired solutions like Sponge City and Biophilic Design precept to tackle these challenges. The proposed model utilizes blockchain meshwork to vouch unattackable and tamper-proof data direction. The fog calculation facilitates localized data pre- processing, reduced response time and reduction in bandwidth usage. The module for predictive analytics, AI and LSTM network enhances the preciseness of mastery actions in superintend microclimates, providing significant improvements in environmental control systems. This study ensures urban sustainability and management in real-time and adaptive environmental control. © 2025

关键词： AI Biophilic Design Blockchain Fog computing IoT Microclimate control Smart city management Sponge City Urban sustainability

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Pseudonym Authentication on Network Layer in Information-Centric Networks 2

Pseudonym Authentication on Network Layer in Information-Cen...

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2nd IEEE International Conference on Hot Information-Centric networking, HotICN 2019

作者： Jiang, Zuoqi Luo, Jiangtao Zhang, Fei He, Chen Wang, Mengnan Zhang, Yanyong School of Communications and Information Engineering Electronic Information and Networking Research Institute Chongqing University of Posts and Telecommunication Chongqing400065 China Rutgers University Department of Electrical and Computer Engineering North BrunswickNJ08902 United States

ISBN: (纸本)9781728157818

The current data-centric security architecture for the Information Centric Network (ICN) does not provide efficient user authentication mechanisms, thus causing undesirable issues such as interest flooding a ttacks (IFA). I no rder to address them, this paper proposes a pseudonym authentication scheme on network layer based on the system of identity-based cryptography (IBC). In the proposed approach, each consumer needs to be authenticated before sending Interest packets into the network. After successful authentication, the consumer will be assigned a temporary nickname to be used in later data request. During the procedure, IBC is adopted to protect real names of consumers and verify the interest origin. As a result, this approach can complete user authentication and protect his real identity simultaneously. The proposed approach is case studied in IFA depression and expected results are achieved. © 2019 IEEE.

关键词： Network layers

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Distributed Antenna Selection and Beamforming for ELAA based Downlink ISAC Systems

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IEEE Transactions on Vehicular Technology 2025年

作者： Gao, Qian Zhong, Ruikang Mu, Xidong Peng, Mugen Shin, Hyundong Liu, Yuanwei Queen Mary University of London School of Electronic Engineering and Computer Science LondonE1 4NS United Kingdom School of Electronics Electrical Engineering and Computer Science BelfastBT3 9DT United Kingdom Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Kyung Hee University Dept. of Electronics and Information Convergence Eng. Yongin-si17104 Korea Republic of The University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong Kyung Hee University Department of Electronic Engineering Gyeonggi-do Yongin-si17104 Korea Republic of

An extremely large-scale antenna array (ELAA) based integrated sensing and communication (ISAC) system is investigated. Focusing on a practical user-roaming environment, the ISAC beamforming design and antenna selection for ELAA are studied. In particular, two antenna selection schemes, namely, movable subarray selection (MSS) and adjustable antenna selection (ASS), are proposed to provide low complexity and flexible beamforming design. For each proposed scheme, a long-term sum rate maximization problem is formulated via jointly optimizing the antenna selection and beamforming, subject to the constraints of transmitting power and beampattern gain for target sensing. To address this problem, a dimension reduction assisted multi-agent proximal policy optimization (DR-MAPPO) algorithm, which can be used for different antenna selection schemes, is developed. An OrderConv layer is designed, which reduces the input dimension by removing the channel state information (CSI) of unselected antennas while keeping the order information of selected ones. Moreover, multiple agents are employed to execute antenna selection and beamforming in a distributed manner according to current channel state information and previous antenna selection, which can reduce the complexity by decomposing the problem. Numerical results demonstrate that MSS outperforms the fixed subarray selection by providing more degrees of freedom and ASS further surpasses MSS through adopting changeable subarray shape. Moreover, the proposed DR-MAPPO algorithm improves the computational efficiency and achieves preferable data rates compared to original MAPPO algorithm. © 1967-2012 IEEE.

关键词： Beamforming

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Design and Implementation of Autonomous Driving Robot Car Using SoC FPGA

Design and Implementation of Autonomous Driving Robot Car Us...

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IEEE International Conference on Field-Programmable Technology (FPT)

作者： Akira Kojima Yuya Osawa Department of Computer Engineering and Networking Hiroshima City University Hiroshima City Japan

In this paper, we describe the design and implementation of our autonomous driving robot car for FPT'19 FPGA design contest. The controller of our robot car is implemented on Avnet Ultra96 board using Xilinx UltraScale+ MPSoC, which includes ARM processor and programmable logic. Its object detection uses neural network hardware on programmable logic. On the other hand, its lane-keeping and navigation are implemented as software which uses the processor part.

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Pushing AI to wireless network edge: An overview on integrated sensing, communication, and computation towards 6G

arXiv

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arXiv 2022年

作者： Zhu, Guangxu Lyu, Zhonghao Jiao, Xiang Liu, Peixi Chen, Mingzhe Xu, Jie Cui, Shuguang Zhang, Ping Shenzhen Research Institute of Big Data Shenzhen518172 China Shenzhen518172 China State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University Beijing100871 China Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral Gables33146 United States State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China Peng Cheng Laboratory Shenzhen518066 China

Pushing artificial intelligence (AI) from central cloud to network edge has reached board consensus in both industry and academia for materializing the vision of artificial intelligence of things (AIoT) in the sixth-generation (6G) era. This gives rise to an emerging research area known as edge intelligence, which concerns the distillation of human-like intelligence from the huge amount of data scattered at wireless network edge. In general, realizing edge intelligence corresponds to the process of sensing, communication, and computation, which are coupled ingredients for data generation, exchanging, and processing, respectively. However, conventional wireless networks design the sensing, communication, and computation separately in a task-agnostic manner, which encounters difficulties in accommodating the stringent demands of ultra-low latency, ultra-high reliability, and high capacity in emerging AI applications such as auto-driving. This thus prompts a new design paradigm of seamless integrated sensing, communication, and computation (ISCC) in a task-oriented manner, which comprehensively accounts for the use of the data in the downstream AI applications. In view of its growing interest, this article provides a timely overview of ISCC for edge intelligence by introducing its basic concept, design challenges, and enabling techniques, surveying the state-of-the-art development, and shedding light on the road ahead. Copyright © 2022, The Authors. All rights reserved.

关键词： Artificial intelligence

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Upper-Bounded Price of Anarchy in D2D Communications over 5G Edge-Computing Mobile Wireless Networks

Upper-Bounded Price of Anarchy in D2D Communications over 5G...

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2019 INFOCOM IEEE Conference on computer Communications Workshops, INFOCOM WKSHPS 2019

作者： Xu, Xiaohua Lee, Benjamin Chen, Yuanfang Zhang, Xi Department of Computer Science Kennesaw State University Marietta United States School of Cyberspace Hangzhou Dianzi University Hangzhou China Networking and Information Systems Laboratory Department of Electrical and Computer Engineering Texas A&M University College StationTX77843 United States

ISBN: (纸本)9781728118789

Device to Device (D2D) communications provide a means of boosting communication capacity. As there is a gap between the D2D requests and the available spectrum resource, it is critical to design an allocation rule for user equipment and payment. We design two mechanisms from a linear programming perspective and the maximum in range perspective, respectively. Both mechanisms can achieve the upper-bounded approximation ratios in terms of price of anarchy for D2D communications over 5G edge-computing mobile networks. These two mechanisms have found various applications in other disciplines as well. We conduct numerical studies and compare our work with a greedy method. We show that the proposed algorithm outperforms the greedy method. © 2019 IEEE.

关键词： Edge computing

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Incorporating distributed DRL into storage resource optimization of space-air-ground integrated wireless communication network

arXiv

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arXiv 2022年

作者： Wang, Chao Liu, Lei Jiang, Chunxiao Wang, Shangguang Zhang, Peiying Shen, Shigen Qingdao266580 China State Key Laboratory of Integrated Services Networks Xidian University Xi'An710071 China Xidian Guangzhou Institute of Technology Guangzhou510555 China Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China Tsinghua Space Center Tsinghua University Beijing100084 China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China Department of Computer Science and Engineering Shaoxing University Shaoxing312000 China

Space-air-ground integrated network (SAGIN) is a new type of wireless network mode. The effective management of SAGIN resources is a prerequisite for high-reliability communication. However, the storage capacity of space-air network segment is extremely limited. The air servers also do not have sufficient storage resources to centrally accommodate the information uploaded by each edge server. So the problem of how to coordinate the storage resources of SAGIN has arisen. This paper proposes a SAGIN storage resource management algorithm based on distributed deep reinforcement learning (DRL). The resource management process is modeled as a Markov decision model. In each edge physical domain, we extract the network attributes represented by storage resources for the agent to build a training environment, so as to realize the distributed training. In addition, we propose a SAGIN resource management framework based on distributed DRL. Simulation results show that the agent has an ideal training effect. Compared with other algorithms, the resource allocation revenue and user request acceptance rate of the proposed algorithm are increased by about 18.15% and 8.35% respectively. Besides, the proposed algorithm has good flexibility in dealing with the changes of resource conditions. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep learning

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Efficient Mining Cluster Selection for Blockchain-based Cellular V2X Communications

arXiv

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arXiv 2020年

作者： Jameel, Furqan Javed, Muhammad Awais Zeadally, Sherali Jäntti, Riku Department of Communications and Networking Aalto University Espoo02150 Finland Electrical and Computer Engineering Department COMSATS University Islamabad Islamabad Pakistan College of Communication and Information University of Kentucky LexingtonKY40506-0224 United States

Cellular vehicle-to-everything (V2X) communication is expected to herald the age of autonomous vehicles in the coming years. With the integration of blockchain in such networks, information of all granularity levels, from complete blocks to individual transactions, would be accessible to vehicles at any time. Specifically, the blockchain technology is expected to improve the security, immutability, and decentralization of cellular V2X communication through smart contract and distributed ledgers. Although blockchain-based cellular V2X networks hold promise, many challenges need to be addressed to enable the future interoperability and accessibility of such large-scale platforms. One such challenge is the offloading of mining tasks in cellular V2X networks. While transportation authorities may try to balance the network mining load, the vehicles may select the nearest mining clusters to offload a task. This may cause congestion and disproportionate use of vehicular network resources. To address this issue, we propose a game-theoretic approach for balancing the load at mining clusters while maintaining fairness among offloading vehicles. Keeping in mind the low-latency requirements of vehicles, we consider a finite channel blocklength transmission which is more practical compared to the use of infinite blocklength codes. The simulation results obtained with our proposed offloading framework show improved performance over the conventional nearest mining cluster selection technique. Copyright © 2020, The Authors. All rights reserved.

关键词： Blockchain

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Predicting Carbon Residual in Biomass Wastes Using Soft Computing Techniques

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Adsorption Science & Technology 2022年 2022卷

作者： Preety Verma J. Godwin Ponsam Rajeev Shrivastava Ajay Kushwaha Neelabh Sao AL Chockalingam Leena Bojaraj JaikumarR S. Chandragandhi Assefa Alene Department of Computer Science & Engineering Greater Noida Institute of Technology (GNIOT) Greater Noida India Department of Networking and Communications SRM Institute of Science and Technology SRM Nagar Kattankulathur 603203 India Princeton Institute of Engineering and Technology for Women Hyderabad Telangana India Computer Science and Engineering Rungta College of Engineering and Technology Bhilai India Department of Electrical and Electronics Engineering M.Kumarasamy College of Engineering Karur Tamilnadu 639113 India Department of Electronics and Communications Engineering KGiSL Institute of Technology Coimbatore India Department of ECE KGiSL Institute of Technology Coimbatore India Department of Computer Science and Engineering JCT College of Engineering and Technology Coimbatore India Department of Chemical Engineering College of Biological and Chemical Engineering Addis Ababa Science and Technology University Ethiopia

In recent decades, the development of complex materials developed a class of biomass waste-derived porous carbons (BWDPCs), which are used for carbon capture and sustainable waste management. It is difficult in understanding the adsorption mechanism of CO2 in the air as it has a wide range of properties associated with its diverse textures, functional group existence, pressure, and temperature of varying range. These properties influence diversely the adsorption mechanism of CO2 and pose serious challenges in the process. To resolve this multiobjective formulation, we use a machine learning classifier that maps systematically the CO2 adsorption as a function of compositional and textural properties and adsorption parameters. The machine learning classifier helps in the classification of various porous carbon materials during the time of training and testing. The results of the simulation show that the proposed method is more efficient in classifying the porous nature of the CO2 adsorption materials than other methods.

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Federated learning in multi-RIS aided systems

arXiv

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arXiv 2020年

作者： Ni, Wanli Liu, Yuanwei Yang, Zhaohui Tian, Hui Shen, Xuemin The State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China The School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom The Centre for Telecommunications Research Department of Engineering King's College London London United Kingdom The Department of Electrical and Computer Engineering University of Waterloo Waterloo Canada

This paper investigates the problem of model aggregation in federated learning systems aided by multiple reconfigurable intelligent surfaces (RISs). The effective integration of computation and communication is achieved by over-the-air computation (AirComp), which can be regarded as one of uplink non-orthogonal multiple access (NOMA) schemes without individual information decoding. Since all local parameters are transmitted over shared wireless channels, the undesirable propagation error inevitably deteriorates the performance of global aggregation. The objective of this work is to i) reduce the signal distortion of AirComp;ii) enhance the convergence rate of federated learning. Thus, the mean-square-error (MSE) and the device set are optimized by designing the transmit power, controlling the receive scalar, tuning the phase shifts, and selecting participants in the model uploading process. To address this challenging issue, the formulated mixed-integer non-linear problem (P0) is decomposed into a non-convex problem (P1) with continuous variables and a combinatorial problem (P2) with integer variables. In an effort to solve the MSE minimization problem (P1), the closed-form expressions for transceivers are first derived, then the multi-antenna cases are addressed by the semidefinite relaxation. Next, the problem of phase shifts design is tackled by invoking the penalty-based successive convex approximation method. In terms of the combinatorial optimization problem (P2), the difference-of-convex programming is adopted to optimize the device set for convergence acceleration, while satisfying the aggregation error demand. After that, an alternating optimization algorithm is proposed to find a suboptimal solution for the original non-convex problem (P0), where the corresponding convergence and complexity are analyzed. Finally, simulation results demonstrate that i) the designed algorithm can converge faster and aggregate model more accurately compared to baselines;ii) t

关键词： Mean square error

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