A new location update strategy for cellular networks and its implementation using a genetic algorithm

1997 ◽  
Author(s):  
Sajal K. Das ◽  
Sanjoy K. Sen
Keyword(s):  
Genetic Algorithm ◽  
Cellular Networks ◽  
Location Update ◽  
Update Strategy ◽  
New Location

scholarly journals Elimination of generalized ping-pong effects using triple-layers of location areas in cellular networks

2008 ◽  
Vol 5 (1) ◽  
pp. 1-16 ◽  
Author(s):  
Guangbin Fan ◽  
Ivan Stojmenovic ◽  
Jingyuan Zhang
Keyword(s):  
Cellular Networks ◽  
Location Management ◽  
Mobile Terminal ◽  
Location Update ◽  
Triple Layer ◽  
Management Scheme ◽  
Ping Pong ◽  
A Cell ◽  
Location Update Cost ◽  
New Location

Location-areas is a popular location management scheme in cellular networks In the location areas scheme, a service area is partitioned into location areas, each consisting of contiguous cells. A mobile terminal updates its location whenever it moves into a cell that belongs to a new location area. However, no matter how the location areas are designed, the ping-pong location update effect exists when a mobile terminal moves back and forth between two location areas. The paper defines a new kind of ping-pong effect referred to as the generalized ping-pong effect, and shows that it accounts for a nonnegligible portion of the total location update cost. Although several strategies have been proposed to reduce the ping-pong effect in the literature, they either eliminate no generalized ping-pong effect or introduce a larger paging cost. This paper proposes a triple-layer location management strategy to eliminate the generalized ping-pong effect, therefore greatly reducing the total location update cost. Simulation results show that the triple-layer strategy outperforms the existing schemes designed to reduce the ping-pong effect.

scholarly journals Adaptive offloading in mobile-edge computing for ultra-dense cellular networks based on genetic algorithm

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Zhuofan Liao ◽  
Jingsheng Peng ◽  
Bing Xiong ◽  
Jiawei Huang
Keyword(s):  
Genetic Algorithm ◽  
Energy Consumption ◽  
Cellular Networks ◽  
Physical Distance ◽  
Edge Computing ◽  
Base Stations ◽  
Mobile Edge Computing ◽  
Total Energy Consumption ◽  
Average Delay ◽  
Server Selection

AbstractWith the combination of Mobile Edge Computing (MEC) and the next generation cellular networks, computation requests from end devices can be offloaded promptly and accurately by edge servers equipped on Base Stations (BSs). However, due to the densified heterogeneous deployment of BSs, the end device may be covered by more than one BS, which brings new challenges for offloading decision, that is whether and where to offload computing tasks for low latency and energy cost. This paper formulates a multi-user-to-multi-servers (MUMS) edge computing problem in ultra-dense cellular networks. The MUMS problem is divided and conquered by two phases, which are server selection and offloading decision. For the server selection phases, mobile users are grouped to one BS considering both physical distance and workload. After the grouping, the original problem is divided into parallel multi-user-to-one-server offloading decision subproblems. To get fast and near-optimal solutions for these subproblems, a distributed offloading strategy based on a binary-coded genetic algorithm is designed to get an adaptive offloading decision. Convergence analysis of the genetic algorithm is given and extensive simulations show that the proposed strategy significantly reduces the average latency and energy consumption of mobile devices. Compared with the state-of-the-art offloading researches, our strategy reduces the average delay by 56% and total energy consumption by 14% in the ultra-dense cellular networks.

Applying genetic algorithm to a new location and routing model of hazardous materials

2014 ◽  
Vol 53 (3) ◽  
pp. 916-928 ◽  
Author(s):  
Ehsan Ardjmand ◽  
Gary Weckman ◽  
Namkyu Park ◽  
Pooya Taherkhani ◽  
Manjeet Singh
Keyword(s):  
Genetic Algorithm ◽  
Hazardous Materials ◽  
New Location
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