The emerging Internet of Things (IoT) is facing significant scalability and security challenges. On the one hand, IoT devices are "weak" and need external assistance. Edge computing provides a promising direction addressing the deficiency of centralized cloud computing in scaling massive number of devices. On the other hand, IoT devices are also relatively "vulnerable" facing malicious hackers due to resource constraints. The emerging blockchain and smart contracts technologies bring a series of new security features for IoT and edge computing. In this paper, to address the challenges, we design and prototype an edge-IoT framework named "EdgeChain" based on blockchain and smart contracts. The core idea is to integrate a permissioned blockchain and the internal currency or "coin" system to link the edge cloud resource pool with each IoT device' account and resource usage, and hence behavior of the IoT devices. EdgeChain uses a credit-based resource management system to control how much resource IoT devices can obtain from edge servers, based on pre-defined rules on priority, application types and past behaviors. Smart contracts are used to enforce the rules and policies to regulate the IoT device behavior in a non-deniable and automated manner. All the IoT activities and transactions are recorded into blockchain for secure data logging and auditing. We implement an EdgeChain prototype and conduct extensive experiments to evaluate the ideas. The results show that while gaining the security benefits of blockchain and smart contracts, the cost of integrating them into EdgeChain is within a reasonable and acceptable range.

Although anaplasmosis cases have been nationally identified in China, no human isolates of A. phagocytophilum have been obtained, which limits the analysis of any molecular and genetic contributions to patients' severe clinical manifestations and the study of the bacteria's pathogeneses in China. Given this situation, a joint project was conducted in 2009–2010. A total of 421 febrile cases of unknown etiology were collected and the patients' blood samples were collected for laboratory diagnoses including serologic diagnosis based on the four-fold rise in the anti- A. phagocytophilum IgG titer by indirect micro-immunofluorescence assay (IFA), positive PCR assay and confirmation of A. phagocytophilum DNA and positive culture of A. phagocytophilum and confirmed by amplification and sequencing of the 16S rRNA and ank A genes of the A. phagocytophilum isolates. A total of 570 ticks were collected from the patients' domestic animals (456) and from wild fields (114) for culturing and amplifying and sequencing the 16S rRNA gene of A. phagocytophilum. Phylogenetic analyses were performed on the 16S rRNA and ank A gene sequences of the isolates and the ticks tested in the study. A total of 46 (10.9%) confirmed and 16 (3.8%) probable cases were diagnosed and severe clinical features and higher mortality rates were observed in these Chinese patients. Five isolates were obtained and the 16S rRNA genes of the 5 isolates were conserved but variety for ank A genes. Two human isolates and 1 tick isolate from Shandong Peninsula, where all patients exhibited severe clinical manifestations, were grouped as one clan based on the phylogenetic analyses, while 2 other human isolates were clustered in a second clan. 43.5% of H. longicornis were infected with A. phagocytophilum.The present study is the first to obtain clinical isolates of A. phagocytophilum in China. The diversity of the ank A genes of Chinese isolates will help us to further discern the relationship between the variations in the ank A genes and the severity of the disease's clinical manifestations in China.

BackgroundSevere fever with thrombocytopenia syndrome (SFTS) caused by the SFTS virus is an emerging infectious disease that was first identified in the rural areas of China in 2011. Severe cases often result in death due to multiple organ failure. To date, there are still numerous problems remain unresolved in SFTS, including unclear pathogenesis, lack of specific treatment, and no effective vaccines available.AimTo analyze the clinical information of patients with early-stage SFTS and to establish a nomogram for the mortality risk.MethodsBetween April 2011 and December 2018, data on consecutive patients who were diagnosed with SFTS were prospectively collected from five medical centers distributed in central and northeastern China. Multivariable Cox analyses were used to identify the factors independently associated with mortality. A nomogram for mortality was established using those factors.ResultsDuring the study period, 429 consecutive patients were diagnosed with SFTS at the early stage of the disease (within 7 days of fever), among whom 69 (16.1%) died within 28 days. The multivariable Cox proportional hazard regression analysis showed that low lymphocyte percentage, early-stage encephalopathy, and elevated concentration of serum LDH and BUN were independent risk factors for fatal outcomes. Received-operating characteristic curves for 7-, 14-, and 28-days survival had AUCs of 0.944 (95% CI: 0.920–0.968), 0.924 (95% CI: 0.896–0.953), and 0.924 (95% CI: 0.895–0.952), respectively. Among low-risk patients, 6 patients died (2.2%). Among moderate-risk patients, 25 patients died (24.0%, hazard ratio (HR) = 11.957). Among high-risk patients, the mortality rate was 69.1% (HR = 57.768).ConclusionWe established a simple and practical clinical scoring system, through which we can identify critically ill patients and provide intensive medical intervention for patients as soon as possible to reduce mortality.

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The selfishness and randomness of users in the mobile crowd sensing network could cause them unwilling to participate in sensing activities and lead to lower completion rates of sensing tasks. In order to deal with these problems, this paper proposes a novel incentive mechanism based on a new auction model for mobile crowd sensing, which consists of two consecutive stages. In the first stage, a novel Incentive Method based on Reverse Auction for Location-aware sensing (IMRAL) is proposed to maximize user utility. By introducing a task-centric method to determine the winning bids, it can provide higher user utility and higher task coverage ratio. To ensure the truthfulness of IMRAL, we design a unique payment determination algorithm based on critical payment for the incentive platform. In the second stage, we propose a user-interaction incentive model (UIBIM) to cover the situation that a user may drop out of the sensing activity. This new incentive model includes a dynamic double auction framework prompting users' interaction and a user matching algorithm based on a bipartite graph. The proposed new mechanism achieves the goal of improving task completion rates without increasing the cost of the incentive platform. The simulation results show that comparing with other solutions, such as a truthful auction for location-aware collaborative sensing in mobile crowdsourcing and incentive mechanism for crowdsourcing in the single-requester single-bid-model, IMRAL can achieve better performance in terms of average user utility and tasks coverage ratio, and the UIBIM can significantly improve task completion rates.INDEX TERMS Mobile crowd sensing, incentive mechanism, task coverage, double auction.