An Enhanced Erasure Code-Based Security Mechanism for Cloud Storage

Cloud computing offers a wide range of luxuries, such as high performance, rapid elasticity, on-demand self-service, and low cost. However, data security continues to be a significant impediment in the promotion and popularization of cloud computing. To address the problem of data leakage caused by unreliable service providers and external cyber attacks, an enhanced erasure code-based security mechanism is proposed and elaborated in terms of four aspects: data encoding, data transmission, data placement, and data reconstruction, which ensure data security throughout the whole traversing into cloud storage. Based on the mechanism, we implement a secure cloud storage system (SCSS). The key design issues, including data division, construction of generator matrix, data encoding, fragment naming, and data decoding, are also described in detail. Finally, we conduct an analysis of data availability and security and performance evaluation. Experimental results and analysis demonstrate that SCSS achieves high availability, strong security, and excellent performance

Prenatal and Lactational Exposure to Bisphenol A in Mice Alters Expression of Genes Involved in Cortical Barrel Development without Morphological Changes

It has been reported that premature infants in neonatal intensive care units are exposed to a high rate of bisphenol A (BPA), an endocrine disrupting chemical. Our previous studies demonstrated that corticothalamic projection was disrupted by prenatal exposure to BPA, which persisted even in adult mice. We therefore analyzed whether prenatal and lactational exposure to low doses of BPA affected the formation of the cortical barrel, the barreloid of the thalamus, and the barrelette of the brainstem in terms of the histology and the expression of genes involved in the barrel development. Pregnant mice were injected subcutaneously with 20 µg/kg of BPA daily from embryonic day 0 (E0) to postnatal 3 weeks (P3W), while the control mice received a vehicle alone. The barrel, barreloid and barrelette of the adult mice were examined by cytochrome C oxidase (COX) staining. There were no significant differences in the total and septal areas and the patterning of the posterior medial barrel subfield (PMBSF), barreloid and barrelette, between the BPA-exposure and control groups in the adult mice. The developmental study at postnatal day 1 (PD1), PD4 and PD8 revealed that the cortical barrel vaguely appeared at PD4 and completely formed at PD8 in both groups. The expression pattern of some genes was spatiotemporally altered depending on the sex and the treatment. These results suggest that the trigeminal projection and the thalamic relay to the cortical barrel were spared after prenatal and lactational exposure to low doses of BPA, although prenatal exposure to BPA was previously shown to disrupt the corticothalamic projection

Transmission Optimization of Social and Physical Sensor Nodes via Collaborative Beamforming in Cyber-Physical-Social Systems

The recently emerging cyber-physical-social system (CPSS) can enable efficient interactions between the social world and cyber-physical system (CPS). The wireless sensor network (WSN) with physical and social sensor nodes plays an important role in CPSS. The integration of the social sensors and physical sensors in CPSS provides an advantage for smart services in different application areas. However, the dynamics of social mobility for social sensors pose new challenges for implementing the coordination of transmission. Furthermore, the integration of social and physical sensors also faces the challenges in term of improving energy efficiency and increasing transmission range. To solve these problems, we integrate the model of social dynamics with collaborative beamforming (CB) technique to formulate the transmission optimization problem as a dynamic game. A novel transmission scheme based on reinforcement learning is proposed to solve the formulated problem. The corresponding implementation of the proposed transmission scheme in CPSS is presented by the design of message exchange processes. The extensive simulation results demonstrate that the proposed transmission scheme presents lower interference to noise ratio (INR) and better signal to noise ratio (SNR) performance in comparison with the existing schemes. The results also indicate that the proposed method has effective adaptation to the dynamic mobility of social sensor nodes in CPSS

Optimizing Maximum Monitoring Frequency and Guaranteeing Target Coverage and Connectivity in Energy Harvesting Wireless Sensor Networks

Improving the quality of monitoring and guaranteeing target coverage and connectivity in energy harvesting wireless sensor networks (EH-WSNs) are important issues in near-perpetual environmental monitoring. Existing solutions only focus on the utility of coverage or energy efficient coverage by considering target connectivity for battery-powered WSNs. This paper focuses on optimizing the maximum monitoring frequency with guaranteed target coverage and connectivity in EH-WSNs. We first analyzed the factors affecting monitoring quality and the energy harvesting model. Thereafter, we presented the problem formulation and proposed the algorithm for maximizing monitoring frequency and guaranteeing target coverage and connectivity (MFTCC) that is based on graph theory. Furthermore, we presented the corresponding distributed implementation approach. On the basis of the existing energy harvesting prediction model, expensive simulations show that the proposed MFTCC algorithm achieves high average maximum monitoring frequency and energy usage ratio. Moreover, it obtains a higher throughput than existing target monitoring methods

Resource Allocation in Blockchain System Based on Mobile Edge Computing Networks

Blockchain is widely adopted in many applications as a promising distributed data management framework. However, the high demanding on computing and communication resources may pose a bottleneck for blockchain to be applied in wireless connected users, which are assumed a main constituent part for the future digital society. In this paper, in order to solve the problems of insufficient computing resources faced in the “Mining” process, we consider a blockchain system based on the mobile edge computing (MEC) network. The computation-intensive tasks of blockchain users are offloaded to MEC servers, and the calculation tasks offloading problem in the system is formulated as a large-scale mixed integer nonlinear programming (MINLP) problem. The MINLP problem proposed in the blockchain system is solved by an algorithmic framework based on the Benders decomposition method. Meanwhile, we propose the branch-and-bound method and the dichotomy-the alternating direction method of multipliers (ADMM) method instead of the dinkelbach-ADMM to solve the mixed integer programming master problem and the fractional programming sub-problem, respectively. Simulation results demonstrate that the proposed algorithm can save the energy consumption in the blockchain system and reduce computing time of the “Mining” process

Whole-exome sequencing reveals the origin and evolution of hepato-cholangiocarcinoma

Hepatocellular-cholangiocarcinoma (H-ChC) is a rare subtype of liver cancer with clinicopathological features of both hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (iCCA). To date, molecular mechanisms underlying the co-existence of HCC and iCCA components in a single tumor remain elusive. Here, we show that H-ChC samples contain substantial private mutations from WES analyses, ranging from 33.1 to 86.4%, indicative of substantive intratumor heterogeneity (ITH). However, on the other hand, numerous ubiquitous mutations shared by HCC and iCCA suggest the monoclonal origin of H-ChC. Mutated genes identified herein, e.g., VCAN, ACVR2A, and FCGBP, are speculated to contribute to distinct differentiation of HCC and iCCA within H-ChC. Moreover, immunohistochemistry demonstrates that EpCAM is highly expressed in 80% of H-ChC, implying the stemness of such liver cancer. In summary, our data highlight the monoclonal origin and stemness of H-ChC, as well as substantial intratumoral heterogeneity

AI-enabled blockchain framework for dynamic spectrum management in multi-operator 6G networks

Abstract A smart architectural design in 5G with flexibility for various deployment scenarios and service requirements has enabled different business models for mobile network operators in both nationwide and local scales. Future 6G networks will feature even more flexible mobile network deployment driven by spectrum and infrastructure sharing among the operators. In this chapter, we propose a new multi-layer framework for 6G with decoupled operators and infrastructure planes. The proposed framework provides a flexibility of network configuration for multiple operators in condition of open spectrum and infrastructure market by using a multi-dimensional matrix representation of the data flows. In particular, the proposed model supports the dynamic switching of the operator and multi-operator service provision for the end users. As a case study, we have developed an AI-based workflow for the dynamic spectrum allocation among multiple mobile network operators. The key advantage of the proposed workflow is that it can be adjusted to the different combinations of the data flows and thus can be suitable for the spectrum allocation among multiple operators. The intelligent capabilities of the proposed workflow are provided by the deep recurrent neural network based on the long short-term memory architecture. The developed model has been trained over the custom dataset with realistic user mobility in urban area. Simulations results show that the proposed intelligent model provides a stable service quality for end users regardless of the serving operators and outperforms the static and semi-intelligent models