Edge computing has gained significant attention in recent years due to its ability to provide low-latency services and handle the massive data generated by IoT devices. One of the critical challenges in edge computing...
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In recent years, blockchain technology has received extensive attention and applied in various fields including healthcare, IoT and database systems. Utilizing the decentralization and anti-tampering properties, the b...
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ISBN:
(纸本)9798350381993;9798350382006
In recent years, blockchain technology has received extensive attention and applied in various fields including healthcare, IoT and database systems. Utilizing the decentralization and anti-tampering properties, the blockchain provides potential solutions to achieve verification of data queries, without the assumption of the trusted third parties in traditional data verification studies. However, for kNN queries, a common query type in practical location-based scenarios, few existing solutions can directly support the location-based kNN query processing and result authentication based on blockchain. To address this problem, in this paper, we propose a blockchain-based verifiable kNN query processing method. In this method, we first design a novel authenticated data structure called VMR-Tree, which stores the data objects and their neighboring objects in leaf nodes and stores the hash values used for data verification in non-leaf nodes. To verify the results with the minimum size of verification objects (VOs), we design a query result verification method based on the blockchain, in which the client can verify the query results by processing the VOs generated based on the proposed VMR-Tree index and the blockchain. Besides, we further propose an optimization algorithm to reduce the size of VOs. We theoretically analyze the computational complexity and security guarantees of the proposed approaches. We also conducted extensive experiments on real and synthetic datasets to evaluate the efficiency of the proposed method on the result verification of location-based kNN queries.
This paper proposes an elderly fall detection system based on distributed edge computing and machine learning. The system can identify individual fall events from multiple camera sources and trigger real-time alerts, ...
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ISBN:
(纸本)9798350386851;9798350386844
This paper proposes an elderly fall detection system based on distributed edge computing and machine learning. The system can identify individual fall events from multiple camera sources and trigger real-time alerts, achieving 97% accuracy experimentally.
Timing control while preserving determinism is often a key requirement for ensuring the safety and correctness of distributed cyber-physical systems (CPS). Discrete-event (DE) systems provide a suitable model of compu...
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ISBN:
(纸本)9798350378030;9798350378023
Timing control while preserving determinism is often a key requirement for ensuring the safety and correctness of distributed cyber-physical systems (CPS). Discrete-event (DE) systems provide a suitable model of computation (MoC) for time-sensitive distributed CPS. The high-level architecture (HLA) is a useful tool for the distributed simulation of DE systems, but its techniques can be adapted for implementing distributed CPS. However, HLA incurs considerable overhead in network messages conveying timing information between the distributed nodes and the centralized run-time infrastructure (RTI). This paper gives a novel approach and implementation that reduces such network messages while preserving DE semantics. An evaluation of our runtime demonstrates that our approach significantly reduces the volume of messages for timing information in HLA.
This paper analyzes and researches the problems of poor robustness and high arithmetic complexity that exist in the traditional methods of distributed radar target detection and tracking, and proposes a point track fu...
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Fuzzy Cognitive Maps (FCMs) are powerful tools for the modeling of complex systems, one of which is time series modeling. Although advocated for their interpretability, existing methods measure concept relevance based...
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High speed internet and advanced networking technology contribute to having large number of various edge devices in heterogeneous edge-cloud systems. In conventional cloud computingsystems, all device data is process...
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ISBN:
(纸本)9798350366495;9798350366488
High speed internet and advanced networking technology contribute to having large number of various edge devices in heterogeneous edge-cloud systems. In conventional cloud computingsystems, all device data is processed in the centralized cloud servers. The growing number of devices, i.e., increasing amount of device data, poses a challenge to the cloud servers to process data in a time- and energy-efficient manner. Studies show promise to reduce execution time and energy consumption by introducing collaborative edge-cloud computing paradigm. In this work, we study collaborative edge-cloud computing by introducing a framework of pairing the computations at edge and cloud resources to minimize execution time and energy consumption. First, the cloud servers (CSs) are made about 90% utilized by adjusting the device data i. e., computed data. Then, each edge server (ES) is optimized using 50% or less of the previously generated device data i.e., cloud computed data. Finally, computations (i.e., device data) are distributed among the ESs and CSs, and performance is assessed to obtain the optimal pairing of computations. A heterogeneous system with one CS, two ESs, 10 edges, and 30 devices of five different types is modeled and simulated using VisualSim. Experimental results show that the proposed method helps reduce execution time and energy consumption by 90% and 56%, respectively. The proposed framework holds a promise for enhancing the scalability of heterogeneous systems, an avenue we intend to explore in our upcoming venture.
Edge-assisted federated learning (FedEdge) that integrates an intermediate layer of edge nodes to reduce the workload for central server in traditional federated learning systems has been investigated in this work. Ho...
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The internet of things (IoT) enables heterogeneous devices to independently participate in global communications;however, it exposes the low power devices with minimum capabilities to vulnerability. This has led to th...
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ISBN:
(纸本)9798350361261;9798350361278
The internet of things (IoT) enables heterogeneous devices to independently participate in global communications;however, it exposes the low power devices with minimum capabilities to vulnerability. This has led to the rise of attackers who leverage their attacks on low power devices to collect private data, without the user knowledge. In this paper, we look at the possibility of creating a supervised machine learning mechanism that autonomously detects packet being sent over the systems before it has reached the internet. The proposed model is trained to identify distributed denial of service (DDoS) attacks for outgoing packets, and subsequently inform and send the detected data to a monitoring node. Considering the low power devices, the proposed solution enables a rule-based system where packets can be detected with binary decisions. However, the rules and detection requires decision tree model training with appropriate datasets. Our evaluations show that the proposed mechanism can detect malicious packets without incurring additional delays in the communication by forwarding all packets to intermediate routers or fog nodes for inspection.
Task-based execution frameworks, such as parallel programming libraries, computational workflow systems, and function-as-a-service platforms, enable the composition of distinct tasks into a single, unified application...
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ISBN:
(纸本)9798350365627;9798350365610
Task-based execution frameworks, such as parallel programming libraries, computational workflow systems, and function-as-a-service platforms, enable the composition of distinct tasks into a single, unified application designed to achieve a computational goal and abstract the parallel and distributed execution of those tasks on arbitrary hardware. Research into these task executors has accelerated as computational sciences increasingly need to take advantage of parallel compute and/or heterogeneous hardware. However, the lack of evaluation standards makes it challenging to compare and contrast novel systems against existing implementations. Here, we introduce TAPS, the Task Performance Suite, to support continued research in distributed task executor frameworks. TAPS provides (1) a unified, modular interface for writing and evaluating applications using arbitrary execution frameworks and data management systems and (2) an initial set of reference synthetic and real-world science applications. We discuss how the design of TAPS supports the reliable evaluation of frameworks and demonstrate TAPS through a survey of benchmarks using the provided reference applications.
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