Internet of Things (IoT) is improving human life in a more convenient and simpler way. One of the most promising IoT applications is healthcare. In this paper, an availability model of a healthcare IoT system is proposed which is composed of two groups of structures described by separate Markov state-space models. The two separate models are analyzed and combined to implement the whole IoT system modeling. The system balance equations are solved under a given scenario and some performance metrics of interest, such as probabilities of full service, degraded service, and the system unavailability, are derived. Detailed numerical evaluation of selected metrics is provided for further understanding and verification of the analytic results. An availability performance improving (API) method is also proposed for increasing the probability of system full service and decreasing the system unavailability. The proposed system modeling and performance improving method can serve as a useful reference for general IoT system design and evaluation.

Distributed threat-events are one of the main challenges facing the networks. Although a lot of research has been conducted for these issues, the situation has not been significantly improved. Different from existing victim-centric approaches, in this work we propose a new network-centric approach for the detection of distributed threat-events. The distributed network is treated as a holistic system that consists of spatially interconnected network elements. Network events are detected by the dynamic behavior analysis of the distributed networks. We develop a model consisting of two-layer random fields to describe the time-varying traffic forwarding behavior of the distributed networks. The bottom layer describes the interaction and influence of the network elements under the action of network events. Markovianity is adopted to characterize the spatiotemporal context of each network element's behavior patterns. The top layer describes each network element's traffic features driven by the underlying behavior patterns. A Gaussian mixture model is used to capture the statistical features of the network traffic for each behavior pattern. We derive algorithms for parameter estimation and event detection. Numerical experiments using real datasets and different network scenarios are presented to validate the proposed approach. Performance-related issues and comparison with related works are discussed.

An analytical modeling method is developed for the evaluation of integrated 3G/WLAN networks. To adapt the feature of the emerging wireless networks efficiently and to cover many possible situations in reality, the cell residence times are modeled generally. The channel occupancy times, the horizontal and vertical handoff rates as well as some interesting performance measures are derived and calculated. Both analysis and numerical results show that the performance of the 3G network is significantly increased when the integrated 3G/WLAN network is employed, and that the performance of integrated 3G/WLAN network is improved greatly when the WLANs are in the hot-spot areas. The method developed here is expected to be useful for modeling and performance evaluation of other type of networks, such as the 3G network with the infrastructureless-mode WLANs or ad-hoc networks

A fog-based IoT platform model involving three layers, i.e., IoT devices, fog nodes, and the cloud, was proposed using an open Jackson network with feedback. The system performance was analyzed for individual subsystems, and the overall system was based on different input parameters. Interesting performance metrics were derived from analytical results. A resource optimization problem was developed and solved to determine the optimal service rates at individual fog nodes under some constraint conditions. Numerical evaluations for the performance and the optimization problem are provided for further understanding of the analysis. The modeling and analysis, as well as the optimization design method, are expected to provide a useful reference for the design and evaluation of fog computing systems.