Parallel Genetic Algorithm to Extend the Lifespan of Internet of Things in 5G Networks

Abstract

With the development and popularization of 5G networks, the coverage problem of the Internet of Things (IoT) will encounter the massive-node problem. In this paper, we design a parallel genetic algorithm that divides the coverage problem of IoTs with massive nodes into many small problems and then solves these problems using Hadoop in parallel. First, the algorithm uses partitioning and grouping operations to degrade the scale of a large IoT and makes the coverage problem solvable. The algorithm then adopts the multi-objective programming-based genetic algorithm (MPGA) to solve the coverage problem. MPGA uses the fast non-dominated sorting to optimize the IoT coverage and node redundancy; it implements the preferential selection of non-critical nodes to maximize the length of the configuration sequence of working nodes. Finally, the parallel genetic algorithm uses uniform mutation and individual pruning to optimize the genetic algorithm internally and force its solving process to quickly converge toward feasible solutions. Experimental results confirm that the MPGA outperforms the existing algorithm on small IoTs in terms of coverage, the number of nodes, computing time, and the IoT lifespan. They also demonstrate that the parallel genetic algorithm successfully solves the coverage problem of IoTs with massive nodes and significantly extends the IoT lifespan.INDEX TERMS 5G networks, Internet of Things, parallel computing, genetic algorithm. II. BACKGROUND, RELATED WORKS AND MOTIVATION OF THIS WORK

A Parallelly Implemented Hybrid Multi-Objective Efficient Persuasion of Coverage and Redundancy Programming Model for Internet of Things in 5G Networks using Hadoop

“… The performance metrics like IoT Lifespan radius Vs Computation Time, IoT Lifespan radius Vs Energy efficiency, IoT Lifespan radius Vs Lifespan, IoT Lifespan radius Vs Lifetime, and IoT Lifespan radius Vs Remaining Nodes are analyzed.  Then the efficiency of the proposed EESS-CRN-GRFOA-BJA method is compared with the existing method such as parallel MPGA-IoT-5GN [21], EDTC-GCN-IoT-5GN [22], and CRAN-IoT-5GN [23].…”

“…These metrics in the proposed system are compared with the 3 existing methods. The 3 existing methods are MPGA-IoT-5GN [21], EDTC-GCN-IoT-5GN [22], and CRAN-IoT-5GN [23]. The parameters utilized in the simulations show in Table 2.…”

The Fellini Museum of Rimini in Italy and the Genetic Algorithms-Based Method to Optimize the Design of an Integrated System Network and Installations

“…GAs have been used for the optimization of a wired data network [16,[56][57][58][59], wireless communication [60][61][62][63], IoT applications [64][65][66][67], electronics circuit [68][69][70], power electronics [71,72], general power distribution [73][74][75][76][77], air conditioning [78][79][80][81], but not specifically for optimizing the design of a wired network, electrical power network, and air conditioning network as a multiple-objectives function of a GA, which represents the scope of the second part of the present paper.…”

A Novel Load Balancing Scheme for Satellite IoT Networks Based on Spatial–Temporal Distribution of Users and Advanced Genetic Algorithms

“…Finally, this paper proposes a novel load balancing scheme of adjacent beams for S-IoT-N based on the modeling of spatial–temporal DoU and advanced GA [ 16 , 17 , 18 , 19 ]. The main contributions of this paper are summarized as follows: In contrast to existing research on load balancing under DoU of uniform, we are the first to improve these schemes by modeling the density variances of users under different moving directions.…”