At present, the wide range of critical applications encompassing healthcare and financial management are inhabited by wireless sensor networks (WSN). The foremost challenge associated with it is the resource constrain...
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In industrial Internet of Things, feedback control loops employed over wireless sensor-actuator network (WSAN) for various process monitoring and control applications require real-time communication for stability. In ...
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ISBN:
(纸本)9781450397964
In industrial Internet of Things, feedback control loops employed over wireless sensor-actuator network (WSAN) for various process monitoring and control applications require real-time communication for stability. In the real world, most complex control systems are, de facto, Mixed-Criticality (MC) system, meaning that all control loops are not equally critical for the system's correct operation. While the notion of mixed-criticality has been studied widely in CPU scheduling, it still remains largely unexplored for wireless domain. For MC CPU scheduling, the key challenge stems from the uncertainty of worst-case execution times, while the uncertainty in WSAN comes from unpredictable channel conditions and plant dynamics. In this paper, we formulate the MC scheduling problem, formally define the MC semantics for WSAN, and propose MC real-time scheduling in multihop WSAN that allows co-scheduling of the loops for handling dynamic criticality changes. This proposed approach exploits the capture effects of the radios for dynamic resource allocation and reclamation when criticality changes. Then, by exploiting the unused channel capacity of WSAN, we propose a technique to minimize redundancy in high criticality control loop scheduling while preserving the communication reliability and MC constraints, thereby enhancing MC schedulability.
We revisit the design of self-adjusting single-source tree networks. The problem can be seen as a generalization of the classic list update problem to trees, and finds applications in reconfigurable datacenter network...
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ISBN:
(数字)9781665471770
ISBN:
(纸本)9781665471770
We revisit the design of self-adjusting single-source tree networks. The problem can be seen as a generalization of the classic list update problem to trees, and finds applications in reconfigurable datacenter networks. We are given a balanced binary tree T connecting n nodes V = {v(1), ...,v(n)}. A source node v(0), attached to the root of the tree, issues communication requests to nodes in V, in an online and adversarial manner;the access cost of a request to a node v, is given by the current depth of v in T. The online algorithm can try to reduce the access cost by performing swap operations, with which the position of a node is exchanged with the position of its parent in the tree;a swap operation costs one unit. The objective is to design an online algorithm which minimizes the total access cost plus adjustment cost (swapping). Avin et al. [12] (LATIN 2020) recently presented RANDOM-PUSH, a constant competitive online algorithm for this problem, based on random walks, together with a sophisticated analysis exploiting the working set property. This paper studies analytically and empirically, online algorithms for this problem. In particular, we explore how to derandomize RANDOM-PUSH. In the analytical part, we consider a simple derandomized algorithm which we call ROTOR-PUSH, as its behavior is reminiscent of rotor walks. Our first contribution is a proof that ROTOR-PUSH is constant competitive: its competitive ratio is 12 and hence by a factor of five lower than the best existing competitive ratio. Interestingly, in contrast to RANDOM-PUSH, the algorithm does not feature the working set property, which requires a new analysis. We further present a significantly improved and simpler analysis for the randomized algorithm, showing that it is 16-competitive. In the empirical part, we compare all self-adjusting single-source tree networks, using both synthetic and real data. In particular, we shed light on the extent to which these self-adjusting trees can exploit t
Large-scale parallelizable data processing jobs, executed by frameworks like Spark, frequently have heavily skewed response time distributions with long tail times. This phenomenon is known as straggling and it occurs...
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This paper presents the design of reconfigurable green PM2.5 sensor module deployment scheme based on blockchain technology. The proposed scheme proposes classes with hierarchical layers implemented by name registry s...
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ISBN:
(纸本)9783031054914;9783031054907
This paper presents the design of reconfigurable green PM2.5 sensor module deployment scheme based on blockchain technology. The proposed scheme proposes classes with hierarchical layers implemented by name registry smart contract that could be reconfigured for solving the green deployment problem among PM2.5 sensor modules. This proposed scheme design three functions made by smart contracts including Registration smart contract, Hierarchical Class Mapping Based on Name Registry Smart Contract Function, and Reconfigurable Working Grouping Smart Contract Function. The results shown that the modules which are measured the similar data will be distributed into the candidate group instead of the current working group, where the total corresponding energy consumption will be reduced for the BC transaction operations in this blockchain network. Moreover, the relevant data storages will be also avoided to store the duplication data as well as redundant data.
Federated Learning (FL) is a distributed learning scheme that enables deep learning to be applied to sensitive data streams and applications in a privacy-preserving manner. This paper focuses on the use of FL for anal...
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Advances in single-cell RNA sequencing (scRNA-seq) have dramatically enhanced our understanding of cellular functions and disease mechanisms. Despite its potential, scRNA-seq faces significant challenges related to da...
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When commonly used two-step localization techniques, such as TOA/TDOA (Time-Of-Arrival, Time-Difference-of-Arrival), DOA (Direction-of-Arrival) or RSSI (Received-Signal-Strength-Indication)/Fingerprint-based, are appl...
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The purpose of this study is to present agile, intelligent, and efficient computer vision architectures, operating on quantum neuromorphic computing, as part of a Space Situational Awareness (SSA) network. Quantum neu...
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ISBN:
(纸本)9781665481021
The purpose of this study is to present agile, intelligent, and efficient computer vision architectures, operating on quantum neuromorphic computing, as part of a Space Situational Awareness (SSA) network. Quantum neuromorphic vision paired with polarimetric Dynamic Vision sensors p(DVS) principles, would give rise to the next generation of highly efficient neuromorphic engineering vision systems for SSA, at fast speeds, while operating at reduced bandwidth, low-power, and low-memory. A deep-learning network has been designed with high accuracy to classify different target speeds and shapes, by means of a p(DVS) neuromorphic sensor. The neural network relies on a limited number of events, within a fixed time window, instead of full frame images. In addition, it makes use of two classifiers, which practically take a single input and independently classify both its speed and shape. The outcome of this study indicates that both high computational efficiency and target classification accuracy results.
作者：
Patel, AnandPatel, MiralDharmsinh Desai University
Faculty of Technology and Engineering Department of Information Technology Nadiad India Cvm University
G H.Patel College of Engineering and Technology Department of Information Technology Vallabh Vidyanagar Gujarat Anand India
The distributed ledger aspect of blockchain technology has transformed traditional trade;each record in this ledger is secured by cryptographic technique, making it more protected and tamper-proof, and the technology ...
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