We formulate the problem of fake news detection using distributed inexpert agents. We consider the source for news/statements as a binary source (to model true vs. false statements). Upon observing news, each agent labels the news as true or false, which equals the validity of the statement with some probability depending on the reliability of the agent. In other words, each agent is viewed as a Binary Symmetric Channel (BSC) that misclassifies each statement with some error probability. For an algorithm that estimates the validity by thresholding a linear combination of the individual agents’ labels, we characterize the optimal weights and threshold to minimize the probability of error. We establish an upper bound on this probability of error as well as the naive majority rule.

This paper proposes a generic scheme VaCoChain, that fuses blockchain (BC) and unmanned aerial vehicles (UAVs) underlying fifth-generation (5G) communication services for timely vaccine distribution during novel coronavirus (COVID-19) and future pandemics. The scheme offers 5G-tactile internet (5G-TI) based services for UAV communication networks (UAVCN) monitored through ground controller stations (GCs). 5G-TI enabled UAVCN supports real-time dense connectivity at ultra-low round-trip time (RTT) latency of [Formula: see text] and high availability of 99.99999%. Thus, it can support resilient vaccine distributions in a phased manner at government-designated nodal centers (NCs) with reduced round trip delays from vaccine production warehouses (VPW). Further, UAVCNs ensure minimizes human intervention and controls vaccine health conditions due to shorter trip times. Once vaccines are supplied at NCs warehouses, then the BC ensures timestamped documentation of vaccinated persons with chronology, auditability, and transparency of supply-chain checkpoints from VPW to NCs. Through smart contracts (SCs), priority groups can be formed for vaccination based on age, healthcare workers, and general commodities. In the simulation, for UAV efficacy, we have compared the scheme against fourth-generation (4G)-assisted long term evolution-advanced (LTE-A), orthogonal frequency division multiplexing (OFDM) channels, and traditional logistics for round-trip time (RTT) latency, logistics, and communication costs. In the BC setup, we have compared the scheme against the existing 5G-TI delivery scheme (Gupta et al.) for processing latency, packet losses, and transaction time. For example, in communication costs, the proposed scheme achieves an average improvement of 9.13 for block meta-information. For 4000 transactions, the proposed scheme has a communication latency of 16 s compared to 36 s. The packet loss is significantly reduced to 2.5% using 5G-TI compared to 16% in 4G-LTE-A. The proposed scheme has a computation cost of 1.6 ms and a communication cost of 157 bytes, which indicates the scheme efficacy against conventional approaches.

A universal compression code can act as an estimator for the distribution of a finite alphabet finite-order Markov source. This property of universal codes was exploited by Jacob and Bansal in [5] to propose a modification of the CUSUM test in order to solve the change detection problem when the post-change distribution is not known. The performance of this test was proven to be asymptotically optimal for a memoryless sources and class of sources with memory under Lorden's minimax formulation. This study was further extended in [9] where performance of the modified CUSUM for an i.i.d. setting under Lai's criterion involving a constraint on the probability of false alarm within a window (PFAW) was analyzed. In this paper, we introduce a modified version of the window limited CUSUM (WL-CUSUM) test by incorporating strongly universal code. We closely follow the work of Lai [8] in order to prove the asymptotic optimality for the test under the PFAW criterion. We further prove the asymptotic optimality of the modified WL-CUSUM test in the Bayesian setting.