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In this paper, we study the fabricated report with false votes attack and the false votes on real reports attack in wireless sensor networks. Since most of the existing works addresses the first attack while leaving an easy way for the attackers to launch the second attack, we propose a probabilistic voting-based filtering scheme (PVFS) to deal with both of them simultaneously. On the basis of the en-route filtering scheme, PVFS combines cluster-based organization, probabilistic key assignment, and voting methods. Through both analysis and simulation, we demonstrate that PVFS could achieve strong protection against both attacks while maintaining a sufficiently high filtering power.

The American Heart Association aims to improve cardiovascular health by encouraging the general population to meet 7 cardiovascular health behaviors and factors. The atherogenic index of plasma (AIP) is an important index. Our aim is to evaluate the relationship between ideal cardiovascular health and the atherogenic index of plasma (AIP) in middle-aged Chinese men.A cross-sectional study was performed. A total of 27,824 middle-aged Chinese men were enrolled. The association between ideal cardiovascular health behaviors and factors and AIP was determined. The 7 cardiovascular health metrics were scored as follows: 0, poor; 1, general; and 2, ideal. The cardiovascular health status was classified according to the total score, as follows: 0 to 4, inadequate; 5 to 9, average; and 10 to 14, optimum. Analyses assessed the prevalence of 7 cardiovascular health metrics, its association with AIP. Logistic regression models were used to calculate odds ratios (ORs), adjusting for age.All 7 cardiovascular health metrics were shown to correlate with AIP (all P values < 0.05), and the strongest correlation existed between body mass and AIP, followed by total cholesterol and AIP. The mean AIP level increased with the decrease in the score of each of the 7 cardiovascular health metrics (all P values < 0.05). The subjects with poor cardiovascular health status had a 4.982-fold increase in the high risk of developing atherosclerosis, whereas a 1-point increase in the cardiovascular health score resulted a 0.046 reduction in AIP and a 22.3% reduction in the high-risk of developing atherosclerosis (OR = 0.777, 95% CI: 0.768–0.787).The ideal cardiovascular health score correlated significantly with AIP, and a 1-point increase in the cardiovascular health score led to a 0.046 reduction in AIP and a 22.3% reduction in the high risk of developing atherosclerosis. These validated the value of ideal cardiovascular health behaviors and factors in the prediction of high risk of developing cardiovascular diseases. Ideal cardiovascular health metrics are of great realistic significance for the prevention and control of atherosclerosis and cardiovascular diseases.

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The Internet of Things (IoT) is a network of interconnected objects, in which every object in the world seeks to communicate and exchange information actively. This exponential growth of interconnected objects increases the demand for wireless spectrum. However, providing wireless channel access to every communicating object while ensuring its guaranteed quality of service (QoS) requirements is challenging and has not yet been explored, especially for IoT-enabled mission-critical applications and services. Meanwhile, Cognitive Radio-enabled Internet of Things (CR-IoT) is an emerging field that is considered the future of IoT. The combination of CR technology and IoT can better handle the increasing demands of various applications such as manufacturing, logistics, retail, environment, public safety, healthcare, food, and drugs. However, due to the limited and dynamic resource availability, CR-IoT cannot accommodate all types of users. In this paper, we first examine the availability of a licensed channel on the basis of its primary users' activities (e.g., traffic patterns). Second, we propose a priority-based secondary user (SU) call admission and channel allocation scheme, which is further based on a priority-based dynamic channel reservation scheme. The objective of our study is to reduce the blocking probability of higher-priority SU calls while maintaining a sufficient level of channel utilization. The arrival rates of SU calls of all priority classes are estimated using a Markov chain model, and further channels for each priority class are reserved based on this analysis. We compare the performance of the proposed scheme with the greedy non-priority and fair proportion schemes in terms of the SU call-blocking probability, SU call-dropping probability, channel utilization, and throughput. Numerical results show that the proposed priority scheme outperforms the greedy non-priority and fair proportion schemes.

Multi-tenancy and resource sharing are essential to make a Databaseas-a-Service (DaaS) cost-effective. However, one major consequence of resource sharing is that the performance of one tenant's workload can be significantly affected by the resource demands of co-located tenants. The lack of performance isolation in a shared environment can make DaaS less attractive to performance-sensitive tenants. Our approach to performance isolation in a DaaS is to isolate the key resources needed by the tenants' workload. In this paper, we focus on the problem of effectively sharing and isolating CPU among co-located tenants in a multi-tenant DaaS. We show that traditional CPU sharing abstractions and algorithms are inadequate to support several key new requirements that arise in DaaS: (a) absolute and fine-grained CPU reservations without static allocation; (b) support elasticity by dynamically adapting to bursty resource demands; and (c) enable the DaaS provider to suitably tradeoff revenue with fairness. We implemented these new scheduling algorithms in a commercial DaaS prototype and extensive experiments demonstrate the effectiveness of our techniques.

Tactile Internet-based nanonetwork is an emerging field that promises a new range of e-health applications, in which human operators can efficiently operate and control devices at the nanoscale for remote-patient treatment. Haptic feedback is inevitable for establishing a link between the operator and unknown in-body environment. However, haptic communications over the terahertz band may incur significant path loss due to molecular absorption. In this paper, we propose an optimization framework for haptic communications over nanonetworks, in which in-body nano-devices transmit haptic information to an operator via the terahertz band. By considering the properties of the terahertz band, we employ Brownian motion to describe the mobility of the nano-devices and develop a time-variant terahertz channel model. Furthermore, based on the developed channel model, we construct a stochastic optimization problem for improving haptic communications under the constraints of system stability, energy consumption, and latency. To solve the formulated non-convex stochastic problem, an improved timevarying particle swarm optimization algorithm is presented, which can deal with the constraints of the problem efficiently by reducing the convergence time significantly. The simulation results validate the theoretical analysis of the proposed system.