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基于多层次融合框架的军用可穿戴设备系统分析:研究方向。

Systematic Analysis of a Military Wearable Device Based on a Multi-Level Fusion Framework: Research Directions.

机构信息

School of Computer Science and Engineering, Northeastern University, Shenyang 110169, China.

School of Information Science and Engineering, Shenyang University of Technology, Shenyang 110870, China.

出版信息

Sensors (Basel). 2019 Jun 12;19(12):2651. doi: 10.3390/s19122651.

Abstract

With the development of the Internet of Battlefield Things (IoBT), soldiers have become key nodes of information collection and resource control on the battlefield. It has become a trend to develop wearable devices with diverse functions for the military. However, although densely deployed wearable sensors provide a platform for comprehensively monitoring the status of soldiers, wearable technology based on multi-source fusion lacks a generalized research system to highlight the advantages of heterogeneous sensor networks and information fusion. Therefore, this paper proposes a multi-level fusion framework (MLFF) based on Body Sensor Networks (BSNs) of soldiers, and describes a model of the deployment of heterogeneous sensor networks. The proposed framework covers multiple types of information at a single node, including behaviors, physiology, emotions, fatigue, environments, and locations, so as to enable Soldier-BSNs to obtain sufficient evidence, decision-making ability, and information resilience under resource constraints. In addition, we systematically discuss the problems and solutions of each unit according to the frame structure to identify research directions for the development of wearable devices for the military.

摘要

随着战场物联网(IoBT)的发展,士兵已经成为战场上信息收集和资源控制的关键节点。为军队开发具有多种功能的可穿戴设备已经成为一种趋势。然而,尽管密集部署的可穿戴传感器为全面监测士兵的状态提供了一个平台,但基于多源融合的可穿戴技术缺乏一个通用的研究体系来突出异构传感器网络和信息融合的优势。因此,本文提出了一种基于士兵身体传感器网络(BSN)的多层次融合框架(MLFF),并描述了一种异构传感器网络的部署模型。该框架在单个节点上涵盖了多种类型的信息,包括行为、生理、情绪、疲劳、环境和位置,以便使士兵 BSN 在资源受限的情况下获得足够的证据、决策能力和信息弹性。此外,我们根据框架结构系统地讨论了每个单元的问题和解决方案,以确定军用可穿戴设备开发的研究方向。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f7bc/6631929/f9dfe80efdae/sensors-19-02651-g001.jpg

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