Lagrangian-Relaxation-Based Self-Repairing Mechanism for Wi-Fi Networks

Abstract

Wi-Fi was developed to support software-defined networks that are crucial for achieving high quality of service for next generation wireless networks. In this software-defined networking technology, network resources are utilized to reduce the effects of several influential factors such as the limited number of nonoverlapping channels, co-channel interference, rapidly changing distribution of customers, and access point (AP) failure. These factors cause irregular network traffic and service unavailability in some users. In this paper, we address the problems causing the AP failure and interference. The studied problems were modeled as linear and nonlinear mathematical programming problems. The Lagrangian relaxation approach, which is a type of divide-and-conquer method, was used to solve the load distribution problems near optimally. A self-repairing heuristic multiple-level load-balancing traffic adjustment was proposed to manage the problems pertaining to the AP failure. The delay difference metric, which is defined as the difference between tolerable delay and transmission delay, was used to evaluate the difference between the upper bound and lower bound of the delay. Moreover, the metric was used to estimate the improvement ratio between the existing methods and the proposed method. Modifiable transmission power ranges (which involve the cell breathing method) and association managements are adjusted to minimize the narrow gap and improve the self-repairing performance. Thus, resources are appropriately allocated to users, and adequate QoS is attained.

Software defined wireless sensor load balancing routing for internet of things applications: Review of approaches

“…The method is simple; however, it can not make precise decisions for evenly distributing the load due to the lack of a global network view. Centralized-based systems show better load-balancing decisions due to their centralized network knowledge and automatic adjustment of the coverage area of specific AP [ 9 ]. Several studies [ [10] , [11] , [12] ] leverage the centralized-based method to balance the load among AP efficiently.…”

Towards QoS-Aware Load Balancing for High Density Software Defined Wi-Fi Networks

“…To reach the ideal load balancing condition a number of repeated associations are required which adds to the latency factor [6], [7]. The centralized methods on the other hand contribute well in the load balancing decisions by either controlling the association control mechanism [8], [9] or by adjusting the coverage area of a specific AP [10], [11]. Some of the centralized methods are based on a distributed approach, where an extensive message exchange is required between the custom hardware and the APs.…”

An Improved Lagrange Particle Swarm Optimization Algorithm and Its Application in Multiple Fault Diagnosis

“…Subsequently, the multiple fault diagnosis model is solved based on the Lagrange relaxation algorithm [20,21]. Apart from being applied in multiple fault diagnosis, the Lagrange relaxation algorithm is also applied for other applications as partitioning problem [22], scheduling problem [23], the adjacent only quadratic minimum spanning tree problem [24], the supply chain network problem [25], and so on [26][27][28].…”