Location Management Cost Optimization using Normalized Percentile Dwell Time

Abstract

This work proposes Normalized Percentile Dwell Time Distribution to be incorporated into various bio-inspired algorithms such as Genetic Algorithm and Binary Particle Swarm Optimization for optimizing the total cost of Location Management. Dwell time parameter is critically dependent on users' behaviour within the network. The innate advantage of Normalized Percentile dwell time over the conventionally used absolute dwell time is that it can take only fixed values depending on the number of cells per network area. This helps tremendously in reducing the computational overhead and can be applied to any shape of the network as long as the total number of cells per network remains constant. Evidently, from the simulation result the proposed technique works well for the reference networks in terms of scaling and cost reduction.

Reporting cell planning-based cellular mobility management using a Binary Artificial Bat algorithm

“…Simulations are carried out for both reference as well as realistic networks of sizes 4 × 4, 6 × 6 and 8 × 8 to gauge the ability of the proposed BABA with respect to the existing ones, such as BDE-PEO (Almeida-Luz et al., 2011; Bettencourt, 2004; Swayamsiddha et al., 2017a,b) and BPSO (Kim et al., 2012). The control parameters used in the BABA, BPSO, and BDE-PEO are outlined in Table 2.…”

“…The location management issue has been addressed in this paper by taking the help of reporting cell planning strategy, which also includes the concept of vicinity factor “v(i)” for reporting and non-reporting cells (Almeida-Luz et al., 2011; Kim et al., 2012; Swayamsiddha et al., 2017a,b). The mobile location management (LM) cost is computed by adding the weighted location update cost and paging cost given in Eq.…”

“…The modified Differential Evolution (DE) developed by incorporating novel probability estimation operator (PEO) based on the distribution of estimation algorithm was found to perform better than ordinary DE because of an improved balance between exploration and exploitation (Swayamsiddha et al., 2017a,b; Wang et al., 2012). Optimum DE parameter values enabled Almeida-Luz et al.…”

“…(2012), reporting cells were of paramount importance, whereas the normalized percentile dwell time distribution incorporated into the bio-inspired optimization techniques, such as GA and BPSO (Parija et al., 2017a,b) had been used for optimizing the location management cost in Swayamsiddha et al. (2017a,b). The Hopfield centric results of Taheri and Zomaya (2007b) were found to be superior as compared to that of GA.…”