This work performs a rigorous, comparative analysis of the fog computing paradigm and the conventional cloud computing paradigm in the context of the Internet of Things (IoT), by mathematically formulating the parameters and characteristics of fog computing -one of the first attempts of its kind. With the rapid increase in the number of Internetconnected devices, the increased demand of real-time, low-latency services is proving to be challenging for the traditional cloud computing framework. Also, our irreplaceable dependency on cloud computing demands the cloud data centers (DCs) always to be up and running which exhausts huge amount of power and yield tons of carbon dioxide (CO2) gas. In this work, we assess the applicability of the newly proposed fog computing paradigm to serve the demands of the latency-sensitive applications in the context of IoT. We model the fog computing paradigm by mathematically characterizing the fog computing network in terms of power consumption, service latency, CO2 emission, and cost, and evaluating its performance for an environment with high number of Internet-connected devices demanding realtime service. A case study is performed with traffic generated from the 100 highest populated cities being served by eight geographically distributed DCs. Results show that as the number of applications demanding real-time service increases, the fog computing paradigm outperforms traditional cloud computing. For an environment with 50% applications requesting for instantaneous, real-time services, the overall service latency for fog computing is noted to decrease by 50.09%. However, it is mentionworthy that for an environment with less percentage of applications demanding for low-latency services, fog computing is observed to be an overhead compared to the traditional cloud computing. Therefore, the work shows that in the context of IoT, with high number of latency-sensitive applications fog computing outperforms cloud computing.

In this study, the authors focus on theoretical modelling of the fog computing architecture and compare its performance with the traditional cloud computing model. Existing research works on fog computing have primarily focused on the principles and concepts of fog computing and its significance in the context of internet of things (IoT). This work, one of the first attempts in its domain, proposes a mathematical formulation for this new computational paradigm by defining its individual components and presents a comparative study with cloud computing in terms of service latency and energy consumption. From the performance analysis, the work establishes fog computing, in collaboration with the traditional cloud computing platform, as an efficient green computing platform to support the demands of the next generation IoT applications. Results show that for a scenario where 25% of the IoT applications demand real-time, low-latency services, the mean energy expenditure in fog computing is 40.48% less than the conventional cloud computing model. 2 Fog computing architecture As stated earlier, fog computing is a non-trivial extension of cloud computing [7], and typically serves as a platform that bridges IET Networks

No abstract

In critical medical emergency situations, wireless body area network (WBAN) equipped health monitoring systems treat data packets with critical information regarding patients' health in the same way as data packets bearing regular healthcare information. This snag results in a higher average waiting time for the local data processing units (LDPUs) transmitting data packets of higher importance. In this paper, we formulate an algorithm for Priority-based Allocation of Time Slots (PATS) that considers a fitness parameter characterizing the criticality of health data that a packet carries, energy consumption rate for a transmitting LDPU, and other crucial LDPU properties. Based on this fitness parameter, we design the constant model hawk-dove game that ensures prioritizing the LDPUs based on crucial properties. In comparison with the existing works on priority-based wireless transmission, we measure and take into consideration the urgency, seriousness, and criticality associated with an LDPU and, thus, allocate transmission time slots proportionately. We show that the number of transmitting LDPUs in medical emergency situations can be reduced by 25.97%, in comparison with the existing time-division-based techniques.

Large-eddy simulations (LESs) of flow past a circular cylinder in the vicinity of a flat plate have been carried out for three different gap-to-diameter (G/D) ratios of 0.25, 0.5, and 1.0 (where G signifies the gap between the flat plate and the cylinder, and D signifies the cylinder diameter) following the experiment of Price et al. (2002, “Flow Visualization Around a Circular Cylinder Near to a Plane Wall,” J. Fluids Struct., 16, pp. 175–191). The flow visualization along with turbulent statistics are presented for a Reynolds number of Re=1440 (based on D and the inlet free-stream velocity U∞). The three-dimensional time-dependent, incompressible Navier–Stokes equations are solved using a symmetry-preserving finite-difference scheme of second-order spatial and temporal accuracy. The immersed-boundary method is employed to impose the no-slip boundary condition at the cylinder surface. An attempt is made to understand the physics of flow involving interactions of shear layers shed from the cylinder and the wall boundary layer. Present LES reveals the shear layer instability and formation of small-scale eddies apart from their mutual interactions with the boundary layer. It has been observed that G/D ratio has a large influence on the modification of wake dynamics and evolution of the wall boundary layer. For a low gap ratio, it is difficult to identify the boundary layer because of its strong interactions with the shear layers; however, a rapid transition to turbulence of the boundary layer, which is similar to bypass transition, is observed for a large gap ratio.