Too much, too little or just right: Designing data fusion for situation awareness

2004 ◽  
Author(s):  
Geoffrey B. Duggan ◽  
Simon Banbury ◽  
Andrew Howes ◽  
John Patrick ◽  
Samuel M. Waldron
Keyword(s):  
Data Fusion ◽  
Situation Awareness ◽  
Just Right

Too Much, Too Little or Just Right: Designing Data Fusion for Situation Awareness

2004 ◽  
Vol 48 (3) ◽  
pp. 528-532 ◽  
Author(s):  
Geoffrey B. Duggan ◽  
Simon Banbury ◽  
Andrew Howes ◽  
John Patrick ◽  
Samuel M. Waldron
Keyword(s):  
Data Fusion ◽  
Situation Awareness ◽  
Just Right

A hierarchical P2P model and a data fusion method for network security situation awareness system

2016 ◽  
Vol 21 (2) ◽  
pp. 126-132
Author(s):  
Fangfang Guo ◽  
Yibing Hu ◽  
Longting Xiu ◽  
Guangsheng Feng ◽  
Shuaishuai Wang
Keyword(s):  
Network Security ◽  
Data Fusion ◽  
Situation Awareness ◽  
Fusion Method ◽  
Awareness System

scholarly journals Multi-sensor Data Fusion for Cyber Security Situation Awareness

2011 ◽  
Vol 10 ◽  
pp. 1029-1034 ◽  
Author(s):  
Yan Zhang ◽  
Shuguang Huang ◽  
Shize Guo ◽  
Junmao Zhu
Keyword(s):  
Data Fusion ◽  
Situation Awareness ◽  
Cyber Security ◽  
Sensor Data ◽  
Sensor Data Fusion ◽  
Multi Sensor Data Fusion

scholarly journals Situation awareness via Internet of things and in-network data processing

2017 ◽  
Vol 13 (1) ◽  
pp. 155014771668657 ◽  
Author(s):  
Johannes Ehala ◽  
Jaanus Kaugerand ◽  
Raido Pahtma ◽  
Sergei Astapov ◽  
Andri Riid ◽  
...  
Keyword(s):  
Signal Processing ◽  
Wireless Sensor Network ◽  
Internet Of Things ◽  
Data Fusion ◽  
Situation Awareness ◽  
Wireless Sensor ◽  
Network Data ◽  
Distributed Data ◽  
Sensor Signal ◽  
Data Validity

Computing on the edge of the Internet of things comprises among other tasks in-sensor signal processing and performing distributed data fusion and aggregation at network nodes. This poses a challenge to distributed sensor networks of low computing power devices that have to do complex fusion, aggregation and signal processing in situ. One of the difficulties lies in ensuring validity of data collected from heterogeneous sources. Ensuring data validity, for example, the temporal and spatial correctness of data, is crucial for correct in-network data fusion and aggregation. The article considers wireless sensor technology in military domain with the aim of improving situation awareness for military operations. Requirements for contemporary intelligence, surveillance and reconnaissance applications are explored and an experimental wireless sensor network, designed to enhance situation awareness to both in-the-field units and remote intelligence operatives, is described. The sensor nodes have the capability to perform in-sensor signal processing and distributed in-network data aggregation and fusion complying with edge computing paradigm. In-network data processing is supported by service-oriented middleware which facilitates run-time sensor discovery and tasking and ad hoc (re)configuration of the network links. The article describes two experiments demonstrating the ability of the wireless sensor network to meet intelligence, surveillance and reconnaissance requirements. The efficiency of distributed data fusion is evaluated and the importance and effect of establishing data validity is shown.

scholarly journals Data Fusion through Fuzzy-Bayesian Networks for Belief Generation in Cognitive Agents

2019 ◽  
Vol 26 (2) ◽  
pp. 69-80
Author(s):  
Munyque Mittelmann ◽  
Jerusa Marchi ◽  
Aldo Von Wangenheim
Keyword(s):  
Data Fusion ◽  
Situation Awareness ◽  
Situational Awareness ◽  
Physical World ◽  
Autonomous Agent ◽  
Environmental Aspects ◽  
Sensory Data ◽  
Multiple Sensors ◽  
Physical Sensors ◽  
Different Types

Situation Awareness provides a theory for agents decision making to allow perception and comprehension of his environment. However, the transformation of the sensory stimulus in beliefs to favor the BDI reasoning cycle is still an unexplored subject. Autonomous agent projects often require the use of multiple sensors to capture environmental aspects. The natural variability of the physical world and the imprecision contained in linguistic concepts used by humans mean that sensory data contain different types of uncertainty in their measurements. Thus, to obtain the Situational Awareness for Agents with physical sensors, it is necessary to define a data fusion process to perform uncertainty treatment. This paper presents a model to beliefs generation using fuzzy-bayesian inference. An example in robotics navigation and location is used to illustrate the proposal.

Sign in / Sign up

Export Citation Format

Share Document