Towards Large-Scale and Privacy-Preserving Contact Tracing in COVID-19 Pandemic: A Blockchain Perspective.

Activity-tracking applications and location-based services using short-range communication (SRC) techniques have been abruptly demanded in the COVID-19 pandemic, especially for automated contact tracing. The attention from both public and policy keeps raising on related practical problems, including 1) how to protect data security and location privacy? 2) how to efficiently and dynamically deploy SRC Internet of Thing (IoT) witnesses to monitor large areas? To answer these questions, in this paper, we propose a decentralized and permissionless blockchain protocol, named Bychain. Specifically, 1) a privacy-preserving SRC protocol for activity-tracking and corresponding generalized block structure is developed, by connecting an interactive zero-knowledge proof protocol and the key escrow mechanism. As a result, connections between personal identity and the ownership of on-chain location information are decoupled. Meanwhile, the owner of the on-chain location data can still claim its ownership without revealing the private key to anyone else. 2) An artificial potential field-based incentive allocation mechanism is proposed to incentivize IoT witnesses to pursue the maximum monitoring coverage deployment. We implemented and evaluated the proposed blockchain protocol in the real-world using the Bluetooth 5.0. The storage, CPU utilization, power consumption, time delay, and security of each procedure and performance of activities are analyzed. The experiment and security analysis is shown to provide a real-world performance evaluation.

Blockchain localization spoofing detection based on fuzzy AHP in IoT systems.

Location spoof detection is a major component of location proofing mechanisms in internet of things (IoT), and it is significant for the system to assess the trustworthiness of the location data associated with the user. Unlike the work that employs physical layer features, we interest in building the infrastructure for a solution to establish location spoofing detection capabilities in blockchain-based IoT systems. In detail, at the node and the mobile trajectory level, we create an IoT system for evaluating the trustworthiness of location proofs with blockchain location system features. A blockchain-based multilayer fuzzy hierarchical analysis process (AHP) evaluation method is contemplated to detect location spoofing in the IoT system. Simulation results indicate the proposed method has a superior performance and provides a basis for the trustworthiness assessment of location proofs.

Emergency Communications Based on Throughput-Aware D2D Multicasting in 5G Public Safety Networks.

Emergency communications need to meet the developing demand of equipment and the complex scenarios of network in public safety networks (PSNs). Heterogeneous Cloud Radio Access Network (H-CRAN), an important technology of the 5th generation wireless systems (5G), plays an important role in PSN. H-CRAN has the features of resource sharing and centralized allocation which can make up for resource shortage in emergency communications. Therefore, an emergency communications strategy based on Device-to-device (D2D) multicast is proposed to make PSN more flexible and rapid. Nearby users can communicate directly without a base station through D2D. This strategy may guarantee high speed data transmission and stable continuous real-time communications. It is divided into three steps. Firstly, according to the distance between users, the alternative cluster head is divided. Secondly, two kinds of cluster head user selection schemes are developed. One is based on terminal power and the other is based on the number of extended users. Last but not least, the Hungarian Algorithm based on throughput-aware is used to channel multiplexing. The numerical results show that the proposed scheme can effectively extend the coverage of PSN and optimize the utilization of resources.

Distributed Fault Detection Based on Credibility and Cooperation for WSNs in Smart Grids.

Due to the increasingly important role in monitoring and data collection that sensors play, accurate and timely fault detection is a key issue for wireless sensor networks (WSNs) in smart grids. This paper presents a novel distributed fault detection mechanism for WSNs based on credibility and cooperation. Firstly, a reasonable credibility model of a sensor is established to identify any suspicious status of the sensor according to its own temporal data correlation. Based on the credibility model, the suspicious sensor is then chosen to launch fault diagnosis requests. Secondly, the sending time of fault diagnosis request is discussed to avoid the transmission overhead brought about by unnecessary diagnosis requests and improve the efficiency of fault detection based on neighbor cooperation. The diagnosis reply of a neighbor sensor is analyzed according to its own status. Finally, to further improve the accuracy of fault detection, the diagnosis results of neighbors are divided into several classifications to judge the fault status of the sensors which launch the fault diagnosis requests. Simulation results show that this novel mechanism can achieve high fault detection ratio with a small number of fault diagnoses and low data congestion probability.

Data fault detection in medical sensor networks.

Medical body sensors can be implanted or attached to the human body to monitor the physiological parameters of patients all the time. Inaccurate data due to sensor faults or incorrect placement on the body will seriously influence clinicians' diagnosis, therefore detecting sensor data faults has been widely researched in recent years. Most of the typical approaches to sensor fault detection in the medical area ignore the fact that the physiological indexes of patients aren't changing synchronously at the same time, and fault values mixed with abnormal physiological data due to illness make it difficult to determine true faults. Based on these facts, we propose a Data Fault Detection mechanism in Medical sensor networks (DFD-M). Its mechanism includes: (1) use of a dynamic-local outlier factor (D-LOF) algorithm to identify outlying sensed data vectors; (2) use of a linear regression model based on trapezoidal fuzzy numbers to predict which readings in the outlying data vector are suspected to be faulty; (3) the proposal of a novel judgment criterion of fault state according to the prediction values. The simulation results demonstrate the efficiency and superiority of DFD-M.

An uncertainty-based distributed fault detection mechanism for wireless sensor networks.

Exchanging too many messages for fault detection will cause not only a degradation of the network quality of service, but also represents a huge burden on the limited energy of sensors. Therefore, we propose an uncertainty-based distributed fault detection through aided judgment of neighbors for wireless sensor networks. The algorithm considers the serious influence of sensing measurement loss and therefore uses Markov decision processes for filling in missing data. Most important of all, fault misjudgments caused by uncertainty conditions are the main drawbacks of traditional distributed fault detection mechanisms. We draw on the experience of evidence fusion rules based on information entropy theory and the degree of disagreement function to increase the accuracy of fault detection. Simulation results demonstrate our algorithm can effectively reduce communication energy overhead due to message exchanges and provide a higher detection accuracy ratio.

The prognostic value of phosphorylated AKT expression in non-small cell lung cancer: a meta-analysis.

OBJECTIVES: Phosphorylated AKT (p-AKT), constitutive activation of AKT, is a potentially interesting prognostic marker and therapeutic target in non-small cell lung cancer (NSCLC). However, the available results of p-AKT expression in NSCLC are heterogeneous. Therefore, a meta-analysis of published researches investigating the prognostic relevance of p-AKT expression in patients with NSCLC was performed. MATERIALS AND METHODS: A literature search via PubMed, EMBASE and CNKI (China National Knowledge Infrastructure) databases was conducted. Data from eligible studies were extracted and included into meta-analysis using a random effects model. RESULTS: A total of 1049 patients from nine studies were included in the meta-analysis. Nine studies investigated the relationship between p-AKT expression and overall survival using univariate analysis, and five of these undertook multivariate analysis. The pooled hazard ratio (HR) for overall survival was 1.49 (95% confidence interval (CI): 1.01-2.20) by univariate analysis and 1.02 (95% CI: 0.54-1.95) by multivariate analysis. CONCLUSION: Our study shows that positive expression of p-AKT is associated with poor prognosis in patients with NSCLC. However, adequately designed prospective studies need to perform.

CD226 Gly307Ser association with multiple autoimmune diseases: a meta-analysis.

BACKGROUND: Recently, there has been increasing evidence shown that a non-synonymous exchange (Gly307Ser/rs763361) of the CD226 gene on chromosome 18q22 is linked to several autoimmune diseases (ADs) including type 1 diabetes (T1D), celiac disease (CED), rheumatoid arthritis (RA), multiple sclerosis (MS), Grave's disease, Wegener's granulomatosis (WG), psoriasis, and primary sicca syndrome (pSS). Taking into consideration that different autoimmune diseases may share some common pathogenic pathways and in order to assess the overall relationship between CD226 Gly307Ser (rs763361) polymorphism and multiple autoimmune diseases, we performed this meta-analysis. METHOD: All eligible case-control studies were searched in the US National Library of Medicine's PubMed and Embase database. Crude odds ratios (OR) with 95% confidence intervals (CI) were conducted to assess the association. RESULTS: 7876 cases and 8558 controls from 7 published studies which were selected from 149 articles identified by a search of the US National Library of Medicine's PubMed and Embase databases for the period up to 25th April 2012. The total OR for ADs associated with the T allele was 1.19 (95%CI=1.12-1.27) by random effects model. Significantly increased risks were also observed in the South American (OR=1.72, 95%CI=1.34-2.20), Asian (OR=1.46, 95%CI=1.01-2.10), and European (OR=1.29, 95%CI=1.07-1.58). Similarly, significant associations were observed in two genetic models (OR=1.41, 95%CI=1.23-1.62 in a codominant model; OR=1.33, 95%CI=1.18-1.50 in a recessive model). CONCLUSION: This meta-analysis provided evidence that CD226 Gly307Ser (rs763361) is significantly associated with the risk of multiple autoimmune diseases.

Prognostic impact of Raf-1 and p-Raf-1 expressions for poor survival rate in non-small cell lung cancer.

Overexpression of Raf-1 has commonly been observed in solid tumors including non-small cell lung cancer (NSCLC). The objective of this study was to investigate whether overexpression of Raf-1, phosphorylated-Raf-1 (p-Raf-1) or both correlates with poor survival rate in NSCLC patients and to explore associations between expression of these proteins and NSCLC cell fate both in vitro and in vivo. Expression of Raf-1 and p-Raf-1 were detected by immunohistochemistry in tumor specimens from 152 NSCLC patients and associations between their expression and the clinicopathological characteristics were assessed. Five-year median survival rate of patients were analyzed by Kaplan-Meier method, log-rank test and Cox regression. Cell fate was compared between normal tumor cells and those with Raf-1 silencing, in both the adenocarcinoma cell line A549 and xenografted mice that were infected with the A549 cell line. The incidence of overexpression of both Raf-1 and p-Raf-1 in NSCLC was much higher than normal control (P < 0.05), and the survival rate of patients with positive expression of Raf-1, p-Raf-1 or both was found to be significantly lower than the negative group (P < 0.05). Both univariate and multivariate analyses showed Raf-1 (P = 0.000, P = 0.010), p-Raf-1 (P = 0.004, P = 0.046), or both (P = 0.001, P = 0.016) was good prognostic markers for poor survival rate in NSCLC patients. Suppression of Raf-1 inhibited tumorigenesis by inducing apoptosis both in vitro and in vivo. These findings demonstrate that overexpression of Raf-1, p-Raf-1 or both could be considered as a new independent prognostic biomarker for poor survival rates for NSCLC patients.

Simple random sampling-based probe station selection for fault detection in wireless sensor networks.

Fault detection for wireless sensor networks (WSNs) has been studied intensively in recent years. Most existing works statically choose the manager nodes as probe stations and probe the network at a fixed frequency. This straightforward solution leads however to several deficiencies. Firstly, by only assigning the fault detection task to the manager node the whole network is out of balance, and this quickly overloads the already heavily burdened manager node, which in turn ultimately shortens the lifetime of the whole network. Secondly, probing with a fixed frequency often generates too much useless network traffic, which results in a waste of the limited network energy. Thirdly, the traditional algorithm for choosing a probing node is too complicated to be used in energy-critical wireless sensor networks. In this paper, we study the distribution characters of the fault nodes in wireless sensor networks, validate the Pareto principle that a small number of clusters contain most of the faults. We then present a Simple Random Sampling-based algorithm to dynamic choose sensor nodes as probe stations. A dynamic adjusting rule for probing frequency is also proposed to reduce the number of useless probing packets. The simulation experiments demonstrate that the algorithm and adjusting rule we present can effectively prolong the lifetime of a wireless sensor network without decreasing the fault detected rate.