BSBL-Based Auxiliary Vehicle Position Analysis in Smart City Using Distributed MEC and UAV-Deployed IoT

Wang, Huafei; Wang, Xianpeng; Lan, Xiang; Su, Ting; Wan, Liangtian

doi:10.1109/jiot.2022.3204986

Cited by 17 publications

(11 citation statements)

References 33 publications

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“…The Results section of this paper presents the outcomes of extensive experiments conducted to evaluate the performance of the proposed fusion model for Predictive Maintenance in IoT-enabled Smart Cities. Three existing methods, [5], [9], and [14], serve as benchmarks for comparison. The evaluation metrics include precision, accuracy, recall, and the 1 showcases the precision comparison between the proposed model and existing methods.…”

Section: Results Analysismentioning

confidence: 99%

“…Their study provides valuable insights into the complex interplay of security factors within smart urban environments. Wang et al [9] explore vehicle position analysis in Smart Cities using block sparse Bayesian learning and distributed mobile-edge computing. Their research showcases the importance of location-awareness in optimizing urban services.…”

Section: Review Of Existing Modelsmentioning

confidence: 99%

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Predictive Maintenance for IoT-Enabled Smart Cities using Recurrent Neural Networks

Jabakumar,

Dhablia

2022

RJCSE

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This work delves into the pressing need for more efficient Predictive Maintenance solutions in the context of IoT-enabled Smart Cities. Existing methodologies often fall short, lacking the precision and accuracy required to keep these rapidly evolving urban environments running smoothly for different use cases. The limitations of current approaches become apparent when considering their inability to cope with the intricacies of IoT data. They struggle to harness the wealth of information generated by countless interconnected devices and systems, resulting in suboptimal performance. This issue is further compounded by their relatively sluggish response times, hindering the timely detection of critical maintenance needs. In response to these challenges, this paper presents an innovative approach that leverages the fusion of Bidirectional Long Short-Term Memory (BiLSTM) with Autoencoders in conjunction with Recurrent Neural Networks (RNNs). This combination of advanced techniques brings forth a powerful predictive maintenance model process. The utilization of BiLSTM and Autoencoders adds a layer of sophistication, allowing for a deeper understanding of the underlying data patterns. BiLSTM's ability to capture contextual information from both past and future data points enriches the model's predictive capabilities. The inclusion of Autoencoders aids in feature extraction and reconstruction, enhancing the model's ability to discern relevant information sets. The advantages of this proposed model are profound. It exhibits 4.9% higher precision, ensuring that maintenance actions are precisely targeted, thus reducing unnecessary interventions. Moreover, the model achieves a remarkable 5.5% increase in accuracy, guaranteeing more reliable predictions. Its 4.5% boost in recall ensures that potential issues are identified promptly. The model's 3.9% increase in speed means that maintenance responses are faster and more effective, minimizing downtemporal instance sets. Lastly, an 8.5% improvement in the Area Under the Curve (AUC) underscores its superior performance compared to existing methodologies. The impacts of this work extend far beyond the realm of predictive maintenance levels. It contributes significantly to the realization of truly smart and efficient cities, where resources are optimized, disruptions are minimized, and the quality of life for citizens is enhanced for different use cases. This research marks a pivotal step towards the seamless integration of IoT technologies into urban environments, with the potential to revolutionize the way we manage and maintain our cities in the future scenarios.

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Section: Results Analysismentioning

confidence: 99%

Section: Review Of Existing Modelsmentioning

confidence: 99%

Predictive Maintenance for IoT-Enabled Smart Cities using Recurrent Neural Networks

Jabakumar,

Dhablia

2022

RJCSE

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“…Tasks can also be relayed to the BS server for computation through UAVs, which function as Amplify and Forward(AF) relays. Examples of such network scenario can be applied to industrial smart park, emergency communication, cooperation strike [19]. The entire system's service cycle is divided into time slots, assuming that each TD generates a compute-intensive task in each time slot.…”

Section: Wireless Link Uav Trajectorymentioning

confidence: 99%

Joint Offloading Decision and Trajectory Optimization for Multi-UAV-Assisted Mobile Edge Computing System

Qiu,

Li,

2024

Preprint

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Multi-unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) has recently emerged as a cost-effective solution to provide computational services to Internet of Thing (IOT) terminal devices (TDs) in the limited or no available infrastructures. However, irrational trajectory of UAVs and offloading decision by TDs contribute to increased delay and energy consumption in the UAV-MEC system. In this paper, we present a two-stage optimization algorithm that jointly considers computation-offloading decision and trajectory to minimize the weighted sum of delay and energy consumption in the multi-UAV-assisted MEC system. Specifically, a Stackelberg Game-based computation offloading (SG-CO) algorithm is designed to obtain the optimal offloading decision for TDs. Furthermore, under the given TDs offloading decision, we develop the Simulated Annealing-Beetle Antennae Search (SA-BAS) algorithm to achieve the optimal trajectory for UAVs, in which SA-BAS algorithm accelerates convergence and enhances global optimization performance. Extensive simulation analysis demonstrates that the proposed scheme outperforms the benchmark schemes, showcasing its effectiveness in minimizing the weight sum of delay and energy consumption.

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“…Hundreds of BC-IoT integrated solutions [14] were proposed in the literature, where BC is deployed to enhance the security of distributed ledger handled in smart environments [15,16,17]. Meanwhile, Things are deployed to process (e.g.…”

Section: A Blockchain and Iotmentioning

confidence: 99%

Machine Learning for Alternative Mining in PoW-based Blockchains: Theory, Implications and Applications

Baniata¹,

Prodan²,

Kertész³

2022

Preprint

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<p>Blockchain (BC) technology provides a secure distributed transactional database, that can enhance the security and privacy of decentralized systems and applications, e.g. distributed identity, supply chain and Internet of Things (IoT). The most known secure consensus mechanism for permissionless BCs is the Proof-of-Wok (PoW) algorithm. Many argue that the fastest approach to mine new blocks in PoW-based BC networks (although too energy consuming) is Brute-forcing the nonce. In this paper, we demonstrate how a well-trained Machine Learning (ML) model can find more accurate initial nonce values for this problem aiming to decrease the total energy consumption. We attempt to identify linear relationships between inputs and outputs of the classical mining processes, which typically use pure Brute-force techniques to solve the problem. Then, we integrate two ML models, namely SGDRegressor and LinearRegressor with PolynomialFeatures, into a classical mining method to predict the solution. For this, we use more than 780k+ real Bitcoin blocks for training and testing. We mathematically formalize our analysis and propose equations to predict and score the total enhancement, for any ML model deployment, compared to classical mining. We experimentally prove that our method can mine faster than the classical method. Furthermore, we discuss the implications on the node level and the network level, including the allowance for taking over the system by controlling a portion of only 35.5% out of the total computational power of the network. Finally, we apply our method in an integrated IoT-Fog-Blockchain system to enhance the fairness among participating miners.</p>

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BSBL-Based Auxiliary Vehicle Position Analysis in Smart City Using Distributed MEC and UAV-Deployed IoT

Cited by 17 publications

References 33 publications

Predictive Maintenance for IoT-Enabled Smart Cities using Recurrent Neural Networks

Predictive Maintenance for IoT-Enabled Smart Cities using Recurrent Neural Networks

Joint Offloading Decision and Trajectory Optimization for Multi-UAV-Assisted Mobile Edge Computing System

Machine Learning for Alternative Mining in PoW-based Blockchains: Theory, Implications and Applications

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