Abstract
Over the coming years, the foresighted enormous increase in smart devices supporting Internet-of-Things (IoT) applications demand novelty in network design. A promising solution to the ever-increasing low-latency requirement of IoT applications is the development of fog network architecture. However, the presence of an enormous number of smart devices in fog networks affects the performance of the network. To harvest the benefits of fog networking necessitates finding optimal cloudlet selection strategies. This article formulates a mixed-integer non-linear programming (MINLP) problem that has the objective of latency minimization. An exhaustive search on our cache-enabled (CE) fog architecture cannot be applied because of the problem’s combinatorial and NP-hard nature. Similarly, the genetic algorithm (GA) cannot be used to find the solution because of the calculation of the number of generations. The increase in the number of IoT and fog nodes increases the solution search space, hence an Outer Approximation Algorithm (OAA) is proposed to arrive at the solution. Low complexity, convergence, and effectiveness of the proposed algorithm ensures the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\epsilon $ </tex-math></inline-formula> -optimal solution = 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−3</sup> , obtained through standard problem solvers.
Highlights
Exponential industrial development in all fields expected to result in an enormous number of smart devices deployment
To the best of our knowledge and comparison of some previous work mentioned in Table 1, our contributions in this article are: 1) We propose a mathematical framework for latency minimization in the CE-fog network
There are some other algorithms to solve the mixed-integer non-linear programming (MINLP) problem used in literature namely the branch and reduce (BR) algorithm [38], and the method by Lawler and Bell [39]
Summary
Exponential industrial development in all fields expected to result in an enormous number of smart devices deployment. User association in [33], [34], [35], authors have studied cloud-fog integrated Industrial Internet of Things (CF-IIoT) network to get ultra-low latency For this objective, they have proposed a real-coded genetic algorithm for constrained optimization problem (RCGA-CO) algorithm. There will be a transmission delay and there will be a backhaul delay, as the cloudlet has to fetch the requested files from the cloud via backhaul links These delays will cause an overall increase in latency experienced by an IoT node. For downlink transmission under cache-storage capacity, power allocation and QoS constraints, the formulated problem of joint cloudlet selection and latency minimization for CE-fog network, with objective function J , can be mathematically stated as: J(x,y,z,p) = min xgnlgn g∈G n∈N subject to constraints C1 to C9: C1 : xgn ≤ 1; ∀n ∈ N g∈G. There are some other algorithms to solve the MINLP problem used in literature namely the branch and reduce (BR) algorithm [38], and the method by Lawler and Bell [39]
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