SAP: Smart Access Point with seamless load balancing multiple interfaces

Chen, Xi; Zhao, Yue; Peck, Brian; Qiao, Daji

doi:10.1109/infcom.2012.6195511

Cited by 22 publications

(12 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As another essential problem of load balancing, association control aims to intelligently associate clients with APs to achieve optimal proportional fairness in a network of APs [14], [21]- [23]. For associations between practical APs and stations, handoff delays may last for 6 seconds [24]. If the corresponding costs of handoff delays are not considered when computing new associations, the outputs may contain some unnecessary associations with profits less than their handoff losses.…”

Section: B Handoff Delay Handlingmentioning

confidence: 99%

Fairness and Load Balancing in SDWN Using Handoff-Delay-Based Association Control and Load Monitoring

Lin

Che

et al. 2019

IEEE Access

View full text Add to dashboard Cite

Traffic loads in any 802.11 WLAN are distributed unevenly. This imbalance implies that some access points (APs) suffer from traffic congestions, while others are underutilized. The unbalanced load distributions cause annoying packet delay and throughput degradation which is unacceptable in current and future networks. A load-balancing algorithm should solve two challenges. The first is to accurately identify the APs' loads to timely find traffic imbalances. And the second is to associate clients with APs to achieve optimal proportional fairness intelligently. Network metrics such as throughput, delay, jitter, and client amount cannot be used individually to accurately identify APs' loads, because of the complexities of wireless communications. Which metrics to use and how to combine those network metrics to represent AP load are controversial. For intelligent association control, handoff delay (time to move a station from an AP to another) may last for 6 seconds. If the algorithm designers do not consider this delay in their optimization processes, unnecessary re-associations generated in their algorithm will offset the optimization profits. In this paper, we propose novel learning-based methods to monitor the network load to discover realtime load unbalances. We also model the load balancing problem as a utility maximization problem in which costs caused by handoff delay are considered. Then we utilize discretized linear programming theory and general assignment problem theory to solve it. We also compute the approximation ratio of our algorithm. We implement the whole load balancing system and evaluate the performances which show that our method outperforms a state-of-art algorithm in terms of throughput by up to12.7%, and it outperforms the received signal strength indicator (RSSI) based method by up to 28.13%.INDEX TERMS Load balancing, association control, load monitoring, general assignment problems.

show abstract

Section: B Handoff Delay Handlingmentioning

confidence: 99%

Fairness and Load Balancing in SDWN Using Handoff-Delay-Based Association Control and Load Monitoring

Lin

Che

et al. 2019

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Smart access point: The usage of programmable smart APs in wireless technologies have also received great attention. Applying smart APs, SAP in [8] provides seamless handoff experience to users, while smartly balancing the network load across multiple interfaces based on users' time-varying traffic load conditions. Bottigliengo et al in [4] propose an LLC-layer algorithm implemented at both AP and WSs to guarantee fair access to the medium to every user.…”

Section: Related Workmentioning

confidence: 99%

Mmloc

Ren

Nahrstedt

2019

Proceedings of the 16th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services

View full text Add to dashboard Cite

Indoor localization based on Wi-Fi fingerprints has been an active research topic for years. However, existing approaches do not consider the instability of access points (APs) which may be unreliable in practice, particularly the ones deployed by individual users. This instability impacts the localization accuracy severely, due to the unreliable or even wrong Wi-Fi fingerprints. Ideally, the localization should be done using only the well-deployed APs (e.g., deployed by facility teams). However, in many places the number of these APs is too few to achieve a good localization accuracy. To solve this problem, we leverage emerging smart APs equipped with multimode antennas, and build a new indoor localization system called MMLOC to reduce the number of necessary APs. The key idea is controlling the modes of AP antennas to generate more fingerprints with fewer APs. A clustering based localization strategy is designed to enable a mobile terminal to figure out the RSSI (Received Signal Strength Indicator) for different antenna modes without requiring any synchronization. We have implemented a prototype system using smart APs and commercial smartphones. Experimental results demonstrate that MMLOC can reduce the number of necessary APs by 50%, and achieve the same or even better localization accuracy. CCS CONCEPTS• Human-centered computing → Ubiquitous and mobile computing systems and tools; • Computer systems organization → Special purpose systems.

show abstract

“…However, the AP with the highest RSSI can be overloaded by a number of users, and thus may not ensure the proper load balancing among APs. [22] presents a smart AP solution to balance the loads across multiple Wi-Fi interfaces. However, the implementation of this approach is difficult since each AP must be equipped with multiple interfaces operating on different channels simultaneously.…”

Section: Related Workmentioning

confidence: 99%

Dynamic Access-Point Configuration Approach for Elastic Wireless Local-Area Network System and Its Implementation Using Raspberry Pi

Islam

Funabiki

Kuribayashi

et al. 2018

IJNC

View full text Add to dashboard Cite

Recently, Wireless Local Area Networks (WLANs) have increased popularity around the world, where users can easily access to the Internet through associations with access points (APs) using mobile devices like smart phones, tablets, and laptops. In a WLAN, it is common that the number of users is always changing by time and users are not evenly distributed in the field. To optimize the number of active APs and the host associations in the network depending on traffic demands from users, previously, we proposed the AP configuration algorithm for the elastic WLAN system. Unfortunately, this algorithm can find the solution for the fixed user state in the network, although users often repeat joining and leaving the network. In this paper, we propose the extension of the AP configuration algorithm to deal with this dynamic nature. Here, as practical situations, this extension considers that at most one host may join or leave at the same time, and any communicating AP cannot be suspended and any communicating host cannot change its associated AP. Through numerical experiments using the WIMNET simulator in two network instances, the effectiveness of the proposal is demonstrated. Furthermore, it is implemented in the elastic WLAN system testbed using Raspberry Pi for the AP. The performance of this implementation is verified through experiments in four scenarios.

show abstract

SAP: Smart Access Point with seamless load balancing multiple interfaces

Cited by 22 publications

References 16 publications

Fairness and Load Balancing in SDWN Using Handoff-Delay-Based Association Control and Load Monitoring

Fairness and Load Balancing in SDWN Using Handoff-Delay-Based Association Control and Load Monitoring

Mmloc

Dynamic Access-Point Configuration Approach for Elastic Wireless Local-Area Network System and Its Implementation Using Raspberry Pi

Contact Info

Product

Resources

About