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Hybrid decentralized Data Analytics in Edge-Computing-Empowered IoT Networks

IEEE INTERNET OF THINGS JOURNAL 2021年第9期8卷 7706-7716页

作者： Zhao, Liang Li, Fangyu Valero, Maria Kennesaw State Univ Dept Informat Technol Marietta GA 30060 USA Kennesaw State Univ Dept Elect & Comp Engn Marietta GA 30067 USA

Edge computing is emerging as a new infrastructure for Internet-of-Things (IoT) networks by placing computation and analytics near to where data are generated. This article presents a novel data analytics framework for edge computing. The framework is based on a new decentralized algorithm, which enables all the nodes to obtain the global optimal model without sharing raw data. The resulting scheme executes in a hybrid mode: local IoT nodes send computed information to edge nodes. The edge nodes cooperate with each other by exchanging analytics with their neighbors only. The presenting approach is analyzed and evaluated on various applications and the experimental results demonstrate the effectiveness of the proposed methodology in providing fast data analytics to edge computing infrastructure.

关键词： Data analytics decentralized algorithm edge computing Internet of Things (IoT)

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A New Method for Peer Matching and Negotiation of Prosumers in Peer-to-Peer Energy Markets

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IEEE TRANSACTIONS ON SMART GRID 2021年第3期12卷 2472-2483页

作者： Khorasany, Mohsen Paudel, Amrit Razzaghi, Reza Siano, Pierluigi Monash Univ Dept Elect & Comp Syst Engn Melbourne Vic 3800 Australia Univ British Columbia Okanagan Fac Appl Sci Sch Engn Kelowna Kelowna BC V1V 1V7 Canada Univ Salerno Dept Management & Innovat Syst I-84084 Fisciano Italy

This article presents a scalable mechanism for peer-to-peer (P2P) energy trading among prosumers in a smart grid. In the proposed mechanism, prosumers engage in a non-mediated negotiation with their peers to reach an agreement on the price and quantity of energy to be exchanged. Instead of concurrent bilateral negotiation between all peers with high overheads, an iterative peer matching process is employed to match peers for bilateral negotiation. The proposed negotiation algorithm enables prosumers to come to an agreement, given that they have no prior knowledge about the preference structure of their trading partners. A greediness factor is introduced to model the selfish behavior of prosumers in the negotiation process and to investigate its impact on the negotiation outcome. In order to recover the costs related to power losses, a transaction fee is applied to each transaction that enables the grid operator to recover incurred losses due to P2P trades. The case studies demonstrate that the proposed mechanism discourages greedy behavior of prosumers in the negotiation process as it does not increase their economic surplus. Also, it has an appropriate performance from the computation overheads and scalability perspectives.

关键词： Peer-to-peer computing Economics Optimization Indexes Computational complexity Smart grids Scalability decentralized algorithm market design multi-issue negotiation peer-to-peer market prosumer smart grids transaction fee

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decentralized Inference With Graph Neural Networks in Wireless Communication Systems

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2023年第5期22卷 2582-2598页

作者： Lee, Mengyuan Yu, Guanding Dai, Huaiyu Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou Peoples R China Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou Peoples R China North Carolina State Univ Dept Elect & Comp Engn Raleigh NC USA

Graph neural network (GNN) is an efficient neural network model for graph data and is widely used in different fields, including wireless communications. Different from other neural network models, GNN can be implemented in a decentralized manner during the inference stage with information exchanges among neighbors, making it a potentially powerful tool for decentralized control in wireless communication systems. The main bottleneck, however, is wireless channel impairments that deteriorate the prediction robustness of GNN. To overcome this obstacle, we analyze and enhance the robustness of the decentralized GNN during the inference stage in different wireless communication systems in this paper. Specifically, using a GNN binary classifier as an example, we first develop a methodology to verify whether the predictions are robust. Then, we analyze the performance of the decentralized GNN binary classifier in both uncoded and coded wireless communication systems. To remedy imperfect wireless transmission and enhance the prediction robustness, we further propose novel retransmission mechanisms for the above two communication systems, respectively. Through simulations on the synthetic graph data, we validate our analysis, verify the effectiveness of the proposed retransmission mechanisms, and provide some insights for practical implementation.

关键词： Wireless communication Robustness Training Predictive models Data models Resource management Prediction algorithms Machine learning graph neural network decentralized algorithm retransmission robustness

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decentralized Radio Resource Adaptation in D2D-U Networks

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IEEE INTERNET OF THINGS JOURNAL 2021年第8期8卷 6720-6732页

作者： Yin, Rui Wu, Zheyi Liu, Shengli Wu, Celimuge Yuan, Jiantao Chen, Xianfu Zhejiang Univ City Coll Sch Informat & Elect Engn Hangzhou 310018 Peoples R China KwaZulu Natal Univ Sch Elect Elect & Comp Engn ZA-4041 Durban South Africa Zhejiang Univ Sch Informat & Elect Engn Hangzhou 310027 Peoples R China Univ Electrocommun Grad Sch Informat & Engn Tokyo 1828585 Japan Valt Teknillinen Tutkimuskeskus Tech Res Ctr Finl Oulu 90570 Finland

Unlike the conventional device-to-device (D2D) networks, the unlicensed D2D (D2D-U) pairs can not only reuse the licensed channels with the base station (BS) but also share the unlicensed channels with the WiFi stations. One challenge arises from the fact that the co-channel interference on licensed channels and the collision probability on unlicensed channels may cause extra power consumption at the terminals. Accordingly, we first propose a channel access method for the D2D-U pairs on unlicensed channels. Then, a decentralized joint spectrum and power allocation scheme is designed to minimize the power consumption at D2D-U pairs. Different from the existing distributed schemes, the proposed scheme can guarantee the global minimization of power consumption across the D2D-U pairs. Simulation results validate the theoretical analysis and verify the performance from the proposed scheme.

关键词： Resource management Device-to-device communication Wireless fidelity 5G mobile communication Power demand Cellular networks Computational complexity decentralized algorithm new radio (NR) radio resource management unlicensed D2D (D2D-U)

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Novel Multidimensional Models of Opinion Dynamics in Social Networks

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2017年第5期62卷 2270-2285页

作者： Parsegov, Sergey E. Proskurnikov, Anton V. Tempo, Roberto Friedkin, Noah E. Russian Acad Sci ICS RAS VA Trapeznikov Inst Control Sci Moscow Russia Univ Groningen Engn & Technol Inst ENTEG Groningen Netherlands Russian Acad Sci IPME St Petersburg Russia ITMO Univ St Petersburg Russia Politecn Torino CNR IEIIT Turin Italy Univ Santa Barbara Santa Barbara CA USA

Unlike many complex networks studied in the literature, social networks rarely exhibit unanimous behavior, or consensus. This requires a development of mathematical models that are sufficiently simple to be examined and capture, at the same time, the complex behavior of real social groups, where opinions and actions related to them may form clusters of different size. One such model, proposed by Friedkin and Johnsen, extends the idea of conventional consensus algorithm (also referred to as the iterative opinion pooling) to take into account the actors' prejudices, caused by some exogenous factors and leading to disagreement in the final opinions. In this paper, we offer a novel multidimensional extension, describing the evolution of the agents' opinions on several topics. Unlike the existing models, these topics are interdependent, and hence the opinions being formed on these topics are also mutually dependent. We rigorously examine stability properties of the proposed model, in particular, convergence of the agents' opinions. Although our model assumes synchronous communication among the agents, we show that the same final opinions may be reached "on average" via asynchronous gossip-based protocols.

关键词： Opinion dynamics social network multi-agent system decentralized algorithm

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decentralized User Scheduling for Rate-Constrained Sum-Utility Maximization in the MIMO IBC

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IEEE TRANSACTIONS ON COMMUNICATIONS 2020年第10期68卷 6215-6229页

作者： Antonioli, Roberto Pinto Fodor, Gabor Soldati, Pablo Maciel, Tarcisio Ferreira Univ Fed Ceara Wireless Telecom Res Grp BR-60440900 Fortaleza Ceara Brazil Ericsson Res Radio Dept S-16480 Stockholm Sweden KTH Royal Inst Technol Div Decis & Control Syst S-10044 Stockholm Sweden Ericsson Res Stand & Technol S-16480 Stockholm Sweden

While the problems of sum-rate maximization and sum-power minimization subject to quality of service (QoS) constraints in the multiple input multiple output interference broadcast channel (MIMO IBC) have been widely studied, most of the proposed solutions have neglected the user scheduling aspect assuming that a feasible set of users has been previously selected. However, ensuring QoS for each user in the MIMO IBC involves the joint optimization of transmit/receive beamforming vectors, transmit powers, and user scheduling variables. To address the full problem, we propose a novel formulation of a rate-constrained sum-utility maximization problem which allows to either deactivate users or minimize the QoS degradation for some scheduled users in infeasible scenarios. Remarkably, this is achieved avoiding the complexity of traditional combinatorial formulations, but rather by introducing a novel expression of the QoS constraints that allows to solve the problem in a continuous domain. We propose centralized and decentralized solutions, where the decentralized solutions focus on practical design and low signaling overhead. The proposed solutions are then compared with benchmarking algorithms, where we show the effectiveness of the joint scheduling and transceiver design as well as the flexibility of the proposed solution performing advantageously in several MIMO IBC scenarios.

关键词： Quality of service MIMO communication Interference Scheduling Transceivers Signal to noise ratio Degradation User scheduling transceiver design decentralized algorithm MIMO IBC sum-rate maximization

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decentralized Equilibrium Seeking of Joint Routing and Destination Planning of Electric Vehicles: A Constrained Aggregative Game Approach

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2022年第8期23卷 13265-13274页

作者： Bakhshayesh, Babak Ghaffarzadeh Kebriaei, Hamed Univ Tehran Coll Engn Sch Elect & Comp Engn Tehran *** Iran Inst Res Fundamental Sci IPM Sch Comp Sci Tehran *** Iran

Increasing the penetration of electric vehicles (EVs) in public transportation, which is also sped up by governments' carbon net-zero policies, will significantly increase the demand for electricity. Therefore, when we face with a large population of selfish EV users, we need a coordination mechanism to manage both the traffic congestion and electricity resource limitations. This paper introduces a novel aggregative game model where heterogeneous EVs simultaneously plan their parking lot as their destination (usually accompanied by battery charging) and the route to the destination. The cost function of users consists of factors such as traveling time, variable costs of congestion and electricity demand, and tolling which is imposed to satisfy coupling constraints such as roads' capacity and stations' power capacity. Since the users are selfish and do not reveal their objectives and personal constraints, we propose a privacy preserving decentralized algorithm with a traffic coordinator and multiple stations' coordinators for generalized Nash equilibrium (GNE) seeking of the game model. Only aggregate information such as traffic on the road and stations' energy demand are available to the traffic coordinator and charging stations' coordinators, respectively. We show that the proposed aggregative game admits a unique variational generalized Nash equilibrium (v-GNE). Then, using the theory of variational inequality (VI), we show that the proposed decentralized algorithm converges to the unique v-GNE of the game. Finally, we carry out comprehensive simulation studies on a simulated Savannah city model to compare and evaluate the proposed method.

关键词： Games Charging stations Roads Transportation Routing Nash equilibrium Planning Aggregative games electric vehicles (EVs) charging stations routing and destination planning generalized Nash equilibrium (GNE) decentralized algorithm

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Spatially-Temporally Collaborative Service Placement and Task Scheduling in MEC Networks

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2023年第12期72卷 16650-16666页

作者： Feng, Chunhui Yang, Qinghai Quek, Tony Q. S. Wu, Weihua Guo, Kun Xidian Univ Sch Telecommun Engn State Key Lab Integrated Serv Network Xian 710071 Peoples R China Singapore Univ Technol & Design Singapore 487372 Singapore Yonsei Univ Yonsei Frontier Lab Seoul 03722 South Korea Shaanxi Normal Univ Sch Phys & Informat Technol Xian 710062 Peoples R China East China Normal Univ Shanghai Key Lab Multidimens Informat Proc Shanghai 200241 Peoples R China

Mobile edge computing provides the opportunity for edge systems to underpin a variety of computation-intensive yet delay-sensitive applications. We acknowledge that the limited computing and storage resource of an individual edge server allows only a subset of services to be placed at a time. This raises the question of service placement, which refers to where to place each type of service. Moreover, which service to place allows which type of tasks to be processed, and thereby affects task scheduling performance, which refers to where/whether to schedule each task and task splitting among edge and cloud. Respect of the interactions between service placement and task scheduling (SPTS), a joint optimized design of SPTS is investigated in this article. Our main contribution is a novel spatial-temporal collaboration of SPTS to minimize the overall cost of edge systems. Spatial collaboration is explored to enable edge servers within specific geographic regions collaboratively serve users' demands, while temporal collaboration is explored to optimize the SPTS in multiple time slots and pursue a long-term performance. We first propose an online and decentralized algorithm for spatial-temporal collaborative of SPTS. We then extend the proposed single-timescale of the proposed algorithm into multiple timescales to have service (re)placed at a larger timescale and hence alleviate service interruption. While proved to preserve the asymptotic optimality, the larger timescale slow down the optimal service placement decision. A learn-and-adapt strategy is further designed to speed up the service placement. Theoretical analysis and simulations are performed to validate the efficiency of the proposed algorithm.

关键词： Mobile edge computing collaborative service placement and task scheduling decentralized algorithm stochastic optimization

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DTA-HMR-TT: Dynamic Task Allocation for a Heterogeneous Team of Mobile Robots With Task Transfer

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IEEE ACCESS 2024年 12卷 177050-177063页

作者： Verma, Ashish Gautam, Avinash Singh Shekhawat, Virendra Mohan, Sudeept Birla Inst Technol & Sci Dept Comp Sci & Informat Syst Pilani 333031 Rajasthan India

Managing time-sensitive deliveries in settings like hospitals is a challenging task, especially when multiple pickup and delivery requests need to be coordinated efficiently within strict time windows. This paper focuses on the Multi-Pickup and Delivery Problem with Time Windows (MPDPTW), where a fleet of autonomous mobile robots works together to fulfill client requests that involve picking up items from specified origins and delivering them to designated destinations. Our objective is to minimize penalties associated with late deliveries while maximizing the number of successfully completed requests. To address this, we introduce a novel approach using a heterogeneous team of robots equipped with an efficient and cost-effective scheduling algorithm. Users submit requests with specific time constraints, and our proposed decentralized algorithm-Dynamic Task Allocation for Heterogeneous Mobile Robots with Task Transfer (DTA-HMR-TT)-optimizes task sharing between robots, ensuring timely service. The algorithm dynamically adjusts to handle rejected or delayed tasks and manages the complex transfer of tasks between robots to improve delivery efficiency. Extensive simulations have demonstrated that our approach significantly outperforms state-of-the-art methods. For smaller task sets (50 to 150 tasks), penalties were reduced by 27%, while for larger sets (150 to 300 tasks), penalties were lowered by 36%. Our results highlight the effectiveness of DTA-HMR-TT in enhancing task scheduling and coordination in multi-robot systems, offering a promising solution for improving delivery performance in structured environments.

关键词： Robots Resource management Robot sensing systems Mobile robots Heuristic algorithms Robot kinematics Hospitals Genetic algorithms Dynamic scheduling Time factors Multi-robot system pickup and delivery temporal constraint online task allocation task transfer decentralized algorithm

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A Novel Prior-Based Channel Estimation and Activity Detection in Cell-Free mMTC Systems

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2024年 72卷 5500-5516页

作者： Rajoriya, Anupama Singh, Nakul Budhiraja, Rohit Chaturvedi, Ajit K. IIT Kanpur Dept Elect Engn Kanpur 208016 India Georgia Inst Technol Atlanta GA 30332 USA

We consider the problem of joint channel estimation (CE) and device activity detection (DAD) in the uplink of a cell-free millimeter wave massive multiple-input multiple output (mMIMO) system for massive machine-type communication. We know that the mMIMO channel is spatially correlated, and is sparse in the angular domain. The correlation and sparsity are captured by tailoring a Gaussian prior. This prior is then used to design a centralized variational Bayesian learning (cVBL) algorithm for CE and DAD. The variational approximation in cVBL algorithm reduces its complexity from cubic to linear in terms of devices. We next propose an asynchronous decentralized VBL (adVBL) algorithm, wherein each AP locally estimates its channel from all the devices. The adVBL algorithm is robust to the AP failures, and its complexity is invariant of the number of APs. The adVBL algorithm is developed by reformulating cVBL updates as global optimization problems, and by deriving their local counterparts using the alternating direction method of multipliers. Through extensive numerical studies, we show that the proposed cVBL and adVBL algorithms i) outperform several existing algorithms;ii) require much less pilot overhead;and iii) estimate large-scale fading, unlike the existing ones.

关键词： Approximation algorithms Signal processing algorithms Correlation Channel estimation Antennas Millimeter wave communication Complexity theory Optimization Bayes methods Vectors decentralized algorithm expectation maximization variational Bayesian learning

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