sensor data is susceptible to faults, noise, and malicious attacks, posing a significant operational and security threat. Therefore, ensuring reliability of sensor data is critical for real-time monitoring systems. Pr...
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ISBN:
(纸本)9798350336672
sensor data is susceptible to faults, noise, and malicious attacks, posing a significant operational and security threat. Therefore, ensuring reliability of sensor data is critical for real-time monitoring systems. Prior research on sensor data reliability relies on edge or upper-layer devices for data fusion from multiple sensors, employing architectures with major overheads and latency due to transmission and storage demands. An alternative approach is to have the sensor estimate and declare its own reliability. While some methods involve sensors computing data confidence and including it in payloads, limitations arise in the absence of neighboring sensor data, and communication overheads are incurred. To address this problem, this paper proposes an innovative approach to enhance the reliability of sensor data using an intelligent self-declaration process. Proposed reliability estimation is evaluate with three lightweight estimation algorithms, namely, Kalman Filter, Holt-Winters Method, and Mahalanobis Distance using sensor's historical data. The reliability level is then added to the three reserved bits of a TCP packet header which results in zero additional overhead. Experiments conducted using real-world sensor data (from water quality monitoring systems) obtained from our IoT lab demonstrate the effectiveness of our proposal and the potential for application in real-world sensor-based applications.
This study focuses on the design and implementation of a distributed online video management system based on cloud computing. First, this study introduces the basic concepts and development history of distributed syst...
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This paper investigates a bipolar structure voltage sensor based on the principle of capacitive coupling. The non-contact voltage sensor, based on electric field coupling, offers advantages such as simple structure, e...
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Recent years have witnessed the emergence of a new class of cooperative edge systems in which a large number of edge nodes can collaborate through local peer-to-peer connectivity. In this paper, we propose CoEdge, a n...
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ISBN:
(纸本)9798400701184
Recent years have witnessed the emergence of a new class of cooperative edge systems in which a large number of edge nodes can collaborate through local peer-to-peer connectivity. In this paper, we propose CoEdge, a novel cooperative edge system that can support concurrent data/compute-intensive deep learning (DL) models for distributed real-time applications such as city-scale traffic monitoring and autonomous driving. First, CoEdge includes a hierarchical DL task scheduling framework that dispatches DL tasks to edge nodes based on their computational profiles, communication overhead, and real-time requirements. Second, CoEdge can dramatically increase the execution efficiency of DL models by batching sensor data and aggregating the inferences of the same model. Finally, we propose a new edge containerization approach that enables an edge node to execute concurrent DL tasks by partitioning the CPU and GPU workloads into different containers. We extensively evaluate CoEdge on a self-deployed smart lamppost testbed on a university campus. Our results show that CoEdge can achieve up to 82.32% reduction on deadline missing rate compared to baselines.
In this paper we propose a new algorithm for robots searching a hazardous, communications-denied area to gather information using a robot fleet that has a limited number of agents. The centralized algorithm uses robot...
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ISBN:
(纸本)9798350377712;9798350377705
In this paper we propose a new algorithm for robots searching a hazardous, communications-denied area to gather information using a robot fleet that has a limited number of agents. The centralized algorithm uses robot survival along search paths as a sensor event for a distributedsensor network. As agents are lost to hazards, the search behavior adjusts to prioritize agent longevity in order to maximize information gain. In the past, related work solving this problem has assumed an infinite number of agents. In contrast, we assume that the number of agents is finite. We use Bayesian inference to update target and hazard belief maps of an area using data from the probability of survival of prior agents' paths as well as sensor readings from the agents along those paths. Using those belief maps, the algorithm can construct paths that maximize information gain, in expectation, while taking into account the predicted decrease in future information collected when losing an agent. This behavior increases the likelihood that agents survive longer, allowing them to collect more data. Using simulations with various fleet sizes and probabilities for hazards disabling agents, we compare our algorithm to work that does not account for attrition. The results show an increase in the longevity of the fleet when hazards are more effective at disabling agents. In nearly all cases, this contributes to an increased rate in information gain when the fleet size is small. Small sized fleets, in our case 10 or less agents, do not meet a threshold of collected information necessary to direct agents away from hazards. Large fleets, over 200 agents in our scenario, collect most of the information before Our algorithm causes a noticeable change in agent behavior (as compared to existing techniques). We find that the proposed method provides the greatest advantage for mid-sized fleets, between 20 and 100 agents, and when hazards have an increased probability of immobilizing agents.
The paper presents a logical time-triggered distributedcomputing environment for cyber-physical systems utilizing V2X (Vehicle to Everything) wireless communication based on ieee802.11p protocol. Logical time-trigger...
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With the advancement of modern robotics, autonomous agents are now capable of hosting sophisticated algorithms, which enables them to make intelligent decisions. But developing and testing such algorithms directly in ...
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ISBN:
(纸本)9781665495127
With the advancement of modern robotics, autonomous agents are now capable of hosting sophisticated algorithms, which enables them to make intelligent decisions. But developing and testing such algorithms directly in real -world systems is tedious and may result in the wastage of valuable resources. Especially for heterogeneous multi -agent systems in battlefield environments where communication is critical in determining the system's behavior and usability. Due to the necessity of simulators of separate paradigms (co -simulation) to simulate such scenarios before deploying, synchronization between those simulators is vital. Existing works aimed at resolving this issue fall short of addressing diversity among deployed agents. In this work, we propose SynchroSim, an integrated co -simulation middleware to simulate a heterogeneous multi-robot system. Here we propose a velocity difference-driven adjustable window size approach with a view to reducing packet loss probability. It takes into account the respective velocities of deployed agents to calculate a suitable window size before transmitting data between them. We consider our algorithm specific simulator agnostic but for the sake of implementation results, we have used Gazebo as a Physics simulator and NS -3 as a network simulator. Also, we design our algorithm considering the Perception -Action loop inside a closed communication channel, which is one of the essential factors in a contested scenario with the requirement of high fidelity in terms of data transmission. We validate our approach empirically at both the simulation and system level for both line -of-sight (LOS) and non -line-of-sight (NLOS) scenarios. Our approach achieves a noticeable improvement in terms of reducing packet loss probability (approximate to 11%), and average packet delay (approximate to 10%) compared to the fixed window size-based synchronization approach.
Cyber-physical systems (CPS) are susceptible to physical attacks, and researchers are exploring ways to detect them. One method involves monitoring the system for a set duration, known as the time-window, and identify...
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ISBN:
(纸本)9798350339024
Cyber-physical systems (CPS) are susceptible to physical attacks, and researchers are exploring ways to detect them. One method involves monitoring the system for a set duration, known as the time-window, and identifying residual errors that exceed a predetermined threshold. However, this approach means that any sensor attack alert can only be triggered after the time-window has elapsed. The length of the time-window affects the detection delay and the likelihood of false alarms, with a shorter time-window leading to quicker detection but a higher false positive rate, and a longer time-window resulting in slower detection but a lower false positive rate. While researchers aim to choose a fixed time-window that balances a low false positive rate and short detection delay, this goal is difficult to attain due to a trade-off between the two. An alternative solution proposed in this paper is to have a variable time-window that can adapt based on the current state of the CPS. For instance, if the CPS is heading towards an unsafe state, it is more crucial to reduce the detection delay (by decreasing the time-window) rather than reducing the false alarm rate, and vice versa. The paper presents a sensor attack detection framework that dynamically adjusts the time-window, enabling attack alerts to be triggered before the system enters dangerous regions, ensuring timely detection. This framework consists of three components: attack detector, state predictor, and window adaptor. We have evaluated our work using real-world data, and the results demonstrate that our solution improves the usability and timeliness of time-window-based attack detectors.
This paper presents a miniaturized semiconductor toluene gas sensor with an integrated diffusion-based preconcentrator. The proposed design, featuring a micro-scale integrated and suspended structure, enhances gas det...
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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.
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