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Found 12 papers, 0 papers with code
On Distributed and Asynchronous Sampling of Gaussian Processes for Sequential Binary Hypothesis Testing
The distributed system is such that the sensors and the FC sample observations periodically, where the sampling times are not necessarily synchronous, i. e., the sampling times at different sensors and the FC may be different from each other.
Distributed Quantized Detection of Sparse Signals Under Byzantine Attacks
The proposed detectors can achieve the detection performance close to the benchmark likelihood ratio test (LRT) detector, which has perfect knowledge of the attack parameters and sparsity degree.
Human-machine Hierarchical Networks for Decision Making under Byzantine Attacks
Consequently, the proposed scheme can effectively defend against Byzantine attacks and improve the quality of human sensors' decisions so that the performance of the human-machine collaborative system is enhanced.
Human-Machine Collaboration for Smart Decision Making: Current Trends and Future Opportunities
Recently, modeling of decision making and control systems that include heterogeneous smart sensing devices (machines) as well as human agents as participants is becoming an important research area due to the wide variety of applications including autonomous driving, smart manufacturing, internet of things, national security, and healthcare.
Loss Attitude Aware Energy Management for Signal Detection
The amount of resource consumption that maximizes the humans' subjective utility is derived to characterize the actual behavior of humans.
Sequential Processing of Observations in Human Decision-Making Systems
In this work, we consider a binary hypothesis testing problem involving a group of human decision-makers.
Efficient Ordered-Transmission Based Distributed Detection under Data Falsification Attacks
In distributed detection systems, energy-efficient ordered transmission (EEOT) schemes are able to reduce the number of transmissions required to make a final decision.
Distributed Estimation in Large Scale Wireless Sensor Networks via a Two Step Group-based Approach
To achieve greater sensor transmission and estimation efficiency, we propose a two step group-based collaborative distributed estimation scheme, where in the first step, sensors form dependence driven groups such that sensors in the same group are highly dependent, while sensors from different groups are independent, and perform a copula-based maximum a posteriori probability (MAP) estimation via intragroup collaboration.
Ordered Transmission-based Detection in Distributed Networks in the Presence of Byzantines
The ordered transmission (OT) scheme reduces the number of transmissions needed in the network to make the final decision, while it maintains the same probability of error as the system without using OT scheme.
A Novel Spectrally-Efficient Uplink Hybrid-Domain NOMA System
This paper proposes a novel hybrid-domain (HD) non-orthogonal multiple access (NOMA) approach to support a larger number of uplink users than the recently proposed code-domain NOMA approach, i. e., sparse code multiple access (SCMA).
Prospect Theory Based Crowdsourcing for Classification in the Presence of Spammers
We consider the $M$-ary classification problem via crowdsourcing, where crowd workers respond to simple binary questions and the answers are aggregated via decision fusion.
Decision Tree Design for Classification in Crowdsourcing Systems
In this paper, we present a novel sequential paradigm for classification in crowdsourcing systems.
