An Efficient Software-Defined WiFi-based Multi-Objective Access Point Load Balancing

The growth of WiFi-enabled smart devices and high-bandwidth applications has generated a huge demand for intelligent wireless network management mechanisms that can offer Quality of Service (QoS), seamless mobility and resource efficiency. The conventional WiFi association mechanisms are primarily based on the Received Signal Strength Indicator (RSSI) selection, leading to uneven utilization of Access Points (AP), congestion, excessive handovers and degraded network performance in dense wireless environments. To overcome these challenges, in this paper we propose a multi-objective AP load balancing and handover scheme based on Software-Defined WiFi load balancing (SDW-LB) mechanism for the overlapping wireless coverage regions. The proposed framework leverages the centralized intelligence and global network view of Software Defined Networking (SDN) for dynamic management of user association and handover decision. When the user enters the overlapped area of multiple APs, the SDN controller will initiate an intelligent handover process based on a multi-objective optimization model in terms of RSSI strength, AP load, number of connected users, available bandwidth, channel utilization, packet delay, user mobility and throughput conditions. A weighted utility-based objective function is presented to select the most suitable AP while reducing network congestion and superfluous handovers. Moreover, a centralized SDN controller i.e., Open Network Operating System (ONOS), constantly observes the network environment and makes dynamic adjustments to the AP association decisions for the purpose of balanced resource allocation and enhanced QoS. The proposed scheme can enhance the network throughput, reduce the packet loss, relieve the AP congestion, and improve the user experience in dense WiFi deployments. The experimental results of our proposed approach show the effectiveness of the proposed scheme compared with benchmark methods with respect to QoS metrics such as fairness, delay, throughput, and packet loss ratio.