Access Point Load Research Articles

WiMA: Towards a Multi-Criterion Association in Software Defined Wi-Fi Networks

Despite the planned installation and operations of the traditional IEEE 802.11 networks, they still experience degraded performance due to the number of inefficiencies. One of the main reasons is the received signal strength indicator (RSSI) association problem, in which the user remains connected to the access point (AP) unless the RSSI becomes too weak. In this paper, we propose a multi-criterion association (WiMA) scheme based on software defined networking (SDN) in Wi-Fi networks. An association solution based on multi-criterion such as AP load, RSSI, and channel occupancy is proposed to satisfy the quality of service (QoS). SDN having an overall view of the network takes the association and reassociation decisions making the handoffs smooth in throughput performance. To implement WiMA extensive simulations runs are carried out on Mininet-NS3-Wi-Fi network simulator. The performance evaluation shows that the WiMA significantly reduces the average number of retransmissions by 5%–30% and enhances the throughput by 20%–50%, hence maintaining user fairness and accommodating more wireless devices and traffic load in the network, when compared to traditional client-driven (CD) approach and state of the art Wi-Balance approach.

AP Association Algorithm Based on VR User Behavior Awareness

With the rapid development of virtual reality (VR) technology, this paper proposes an access point (AP) correlation method based on VR user behavior awareness to address the problem of how current AP correlation methods only focus on the performance improvements of ordinary users and ignore the impact of VR user behavior on service quality. This paper analyzes the AP association method under the coverage scenario of a multi-access point (multi-AP) scenario environment and controls the performance improvement of VR user APs or APs under the access controller (AC) by association. Firstly, the VR network application scenario and system model were constructed, and secondly, the user behavior was sensed by analyzing the viewing habits of users. Then, the VR user association problem based on VR user behavior perception was transformed into a “many-to-many” matching problem between VR user devices and APs, and the generalized multidimensional multiple choice knapsack (GMMKP) model was established to solve the problem using the backpack problem theory; the suboptimal solution algorithm was selected to obtain the best VR user AP association strategy. The experimental results show by simulation that the proposed algorithm in this paper performed better in terms of the AP load balancing and average network download latency compared to the comparison algorithms.

Inference of Wi-Fi busy time fraction based on Markov chains

IEEE 802.11 has emerged as a vital wireless network access technology for mobile devices. By providing the potential for high connectivity speeds, this technology has led to a huge rise in the number of access points (APs). In such environments, mobile devices have the choice to join several Wi-Fi networks. Despite its importance to user Quality of Experience (QoE), the AP selection is still trivial since it focuses at best on the received signal strength if not only the user’s history. Crucial metrics that capture the overall dynamics of the AP load condition, such as the network load, are not taken into account. In this paper, we propose to use the Busy Time Fraction (BTF) as a metric to choose the best AP to attach to. The BTF level of a given channel is inferred based on the frame aggregation scheme proposed since the 802.11n standard. In this regard, we build a proof of concept system, FAM (Frame Aggregation based method) that leverages the theoretical frame aggregation levels of a probe traffic returned by two analytical Markovian models and the measured ones in order to estimate not only the BTF but also the nature of the traffic. We validate the accuracy of our proposed approach against ns-3 simulations under several scenarios.

Learning-aided client association control for high-density WLANs

As wireless local area network (WLAN) continues to become popular, there is an increasing number of clients with huge data traffic demands. Especially, some high client-density environments are emerging, such as industrial plants, stadiums, and event centers, which poses significant challenges in terms of client association control. Under such environments, conventional client-side solutions that select access points (APs) according to simple indicators such as signal strength may result in poor network performance, and although some centralized association control mechanisms are proposed, it is still difficult that a large amount of complex global network status information needs to be effectively and efficiently utilized. To meet these challenges, we investigate the online centralized association control problem that aims to improve user quality of experience (QoE), and propose a deep reinforcement learning (DRL) aided solution, called Wi-OAC, where an image-like state pattern is designed to achieve state reformulation for deep Q-network (DQN), and the double DQN and dueling DQN strategies are combined to improve convergence speed. On the basis of offline training, Wi-OAC can determine the proper AP-client associations for the arriving clients. Both simulation experiments and real-world experiments have been conducted to validate the effectiveness of Wi-OAC. In real-world experiments, we build a Wi-OAC testbed including 3 APs and 54 clients in less than 10.5 m2 area, and the results show that Wi-OAC can significantly improve the performance on the client throughput, AP load balancing and user QoE.

A New Method of User Association in Wireless Mesh Networks

The IEEE 802.11 based wireless mesh networks (WMNs) are becoming the promising technology to provide last-mile broadband Internet access to the users. In order to access the Internet through the pre-deployed WMN, the user has to associate with one of the access points (APs) present in the network. In WMN, it is very common that the user device can have multiple APs in its vicinity. Since the user performance majorly depends on the associated AP, how to select the best AP is always remaining as a challenging research problem in WMN. The traditional method of AP selection is based on received signal strength (RSS) and it is proven inefficient in the literature as the method does not consider AP load, channel conditions, etc. This paper proposes a new method of user association in WMN such that the user selects the AP based on achievable end-to-end throughput measured in the presence of other interfering APs. The proposed association metric is independent of routing protocol and routing metric used in WMN. The simulation results show that our method outperforms the RSS based AP selection method in WMN.

Distributed user-to-multiple access points association through deep learning for beyond 5G

Future wireless networks will be facing unprecedented difficulties arising from mobile traffic growth, network densification, as well as diversification of applications and services. Indeed, future user devices are expected to integrate diverse radio interfaces such as 5G, WBAN or IoT, enabling each user to be served a wide range of applications at any time. This poses significant challenges in terms of wireless resource sharing and interference management, as more and more stringent Quality of Service (QoS) constraints should be jointly satisfied in dense interfering environments. Furthermore, future networks are expected to be highly autonomous and decentralized. To meet these challenges, this work proposes distributed user-to-multiple Access Points (AP) association methods, where the objective is to maximize the long-term sum-rate subject to application QoS constraints, as well as to AP load constraints. Our distributed methods enable each user to leverage their Deep Reinforcement Learning (DRL) capabilities, in particular Deep Q-Learning (DQL), to self-optimize their APs’ selection solely based on their local network state knowledge, so as to best satisfy their diverse requirements. Numerical results show that, compared to baseline schemes, the proposed methods enable global throughput enhancements while reducing user QoS outage probabilities, even in large and dense networks.

A Technique for Order of Preference by Similarity to Ideal Solution-Based Method for Access Point Selection in a Software-Defined Wi-Fi Network

The traditional wireless networks are not designed to support several network constraints, like scalability, high traffic load, and high mobility, making it necessary to switch towards new architectures to overcome these challenges. The Software-Defined Network-based solutions are presented as a future technology for Internet of things networks because of their ability to make the system more flexible, programmable, and automatic. Therefore, they improve both the management and the performance of communicating devices. In a Wi-Fi network, the association or re-association process is based on the received signal strength; this is not always efficient because other parameters must be taken into account, such as access point load and bandwidth. This paper proposes a new algorithm based on the Technique for Order of Preference by Similarity to Ideal Solution method to select the best access point for association/re-association in a Software-Defined Wi-Fi network. The proposed algorithm is implemented and evaluated through simulation using mininet- Wi-Fi. The results indicate that the proposed method improves Wi-Fi network performance.

Evaluation of load balancing algorithms on overlappiing wireless accesspoints

<span>Wireless networks came into the computing world replacing the costlier and more complex wired method of connecting numerous equipment in the same or different location via the use of cables. It provides the user devices a connection to one another and the greater internet via connections to access points. Generally, 802.11 access point products follow a default strongest signal first approach in selecting user devices or nodes to connect to the access point or overlapping access points. This standard does not make provisions for even distribution of load and hence the quality of service and the throughput in areas of congestion would be reduced. This article brings forward two algorithms used in load balancing and they include round-robin technique and the weighted round-robin technique to be used in the simulation of the distribution of the load amongst the access points with the results collated and compared to clearly show which algorithm is best suited to be used as a standard for access point load distribution.</span>

Multicast via Point to Multipoint Transmissions in Directional 5G mmWave Communications

mmWave communications have been shown to be feasible in future networks. Thanks to their very large available bandwidth, they have captured the interest of academia and industry alike as a key enabler to solve the spectrum crunch problem. Nevertheless, the new characteristics of mmWaves call for a paradigm change in wireless protocols and many challenges still remain unaddressed. Among these, an important feature to analyze is the use of multicast via point-to-multipoint communications to further increase spectrum efficiency, improve reliability and reduce the access point load. The combination of multicast and mmWaves presents many issues, starting with how the beams should be shaped to communicate to the users. This article will cover some important aspects at the intersection of the two technologies and, in particular, it will show that restricting the wireless links to be unicast only may be strongly suboptimal.

A user application-based access point selection algorithm for dense WLANs.

The current commercial access point (AP) selection schemes are mostly based on received signal strength, but perform poorly in many situations. To address this problem, a number of alternative schemes collect and analyze the actual load of every candidate AP. However, these schemes may incur significant latency and signaling overhead in dense wireless local area networks (WLANs). To mitigate such overhead, we propose a user application-based AP selection scheme that considers historical information about AP performance. Without inducing any signaling activity, our scheme monitors the amount of network traffic used by applications and estimates the achievable throughput of APs. Our scheme employs the characteristics of application traffic with the intent of accurately predicting AP performance. Using a measurement study in dense WLAN environments, we show that our scheme achieves higher throughput and lower association latency than those of existing schemes in places highly accessible to the user.

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

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 real-time 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%.

Access point backhaul capacity aggregation as a matching game in the context of Wireless Local Area Networks

We propose, in this paper, a novel approach of Access Point (AP) backhaul capacity aggregation in the context of Wireless Local Area Networks (WLAN). Our purpose is to optimize the Available Backhaul Resource (ABR) allocation between APs belonging to different Basic Service Sets (BSSs), so as to relieve pressure of traffic offloaded from non-WiFi networks. In this context, we define two different sets of APs within the same WLAN: the ones with spare backhaul resources (Providers) and those in lack of it (Beneficiaries). Considering the two sided nature of this system, we model our assignment problem using matching games: one-to-one, many-to-one and many-to-many, and solve it using the so-called Deferred Acceptance (DA) algorithm leading to an optimal pairwise stable matching. Our results show that, compared to a legacy WLAN, the overall performance is substantially improved in terms of AP throughput gain, AP satisfaction percentage and load balancing between different APs.

Handover based on AP load in software defined Wi-Fi systems

Existing wireless systems do not work efficiently under changing environment. Due to its inflexible and proprietary hardware based architectural limitations it's not easy for an operator to change the network strategy under heavy loads, which enables vendors to try and implement new networking protocols. The software defined network (SDN) is proposed to bring flexibility and programmability, which allows the control plane of switch to be controlled and managed remotely using open-flow channels. Taking the advantage of SDN in wireless networks, a new SDN based Wi-Fi architecture is introduced and an access point (AP) load balance based handover algorithm is proposed. Mininet Wi-Fi emulator is used to construct the desired topology for experiments and performance analysis. Simulation results show a successful handover from an overloaded AP to a lightly loaded AP. A significant improvement observed in the throughput with low latency.

Liberal method of access point selection in wireless mesh networks

IEEE 802.11-based wireless mesh networks (WMNs) are emerging as the promising technology to provide wireless broadband internet access. In order to access the internet via a pre-deployed WMN, a user has to associate with one of the access points (APs) present in the network. Received signal strength (RSS)-based association method defined by the IEEE 802.11 standard does not consider the channel conditions and the AP load into association process. This paper proposes a novel method of AP selection which considers the channel conditions, AP load and wireless mesh backhaul conditions. As lower-rate user association drops down the performance of higher-rate users already associated with the same AP, our method tries to minimise this negative impact. The proposed AP selection method is scalable and also independent of routing protocol and routing metric used in WMN. Through the simulations, we show that our method performs better than RSS-based AP selection method in terms of improving system throughput in WMNs.

Liberal method of access point selection in wireless mesh networks

IEEE 802.11-based wireless mesh networks (WMNs) are emerging as the promising technology to provide wireless broadband internet access. In order to access the internet via a pre-deployed WMN, a user has to associate with one of the access points (APs) present in the network. Received signal strength (RSS)-based association method defined by the IEEE 802.11 standard does not consider the channel conditions and the AP load into association process. This paper proposes a novel method of AP selection which considers the channel conditions, AP load and wireless mesh backhaul conditions. As lower-rate user association drops down the performance of higher-rate users already associated with the same AP, our method tries to minimise this negative impact. The proposed AP selection method is scalable and also independent of routing protocol and routing metric used in WMN. Through the simulations, we show that our method performs better than RSS-based AP selection method in terms of improving system throughput in WMNs.

An efficient hybrid algorithm for balancing the load of wireless local area network

Although the IEEE 802.11 wireless local area network (WLAN) provides users of mobile device a convenient method to connect to the Internet, the load of access points (APs) may have a strong impact on the performance of a WLAN, such as the congestion problem. To avoid this issue and to maximize the performance and scalability of a WLAN, an effective method for balancing the load of APs, called a modified multiple-search multi-start framework based micro-genetic algorithm, is presented in this paper. Simulation results show that the proposed algorithm gives a much better solution than the original genetic algorithm does in terms of both the quality and the computation time.

Physical constraint and load aware seamless handover for IPTV in wireless LANs

In wireless LANs (WLANs), handover is usually performed based on either signal power or congestion level. However, considering only the congestion level could be insufficient for handover since it may cause traffic loss. Therefore, besides the load of access points (APs), it is necessary to consider the physical conditions of different WLANs for performing a seamless handover. This article introduces a novel scheme for seamless handover of IPTV streams in WLAN carrying IPTV traffic, called Physical Constraint and Load Aware (PCLA) handover. The PCLA can compute the load of APs for congestion detection purposes. In PCLA, a mobile node chooses the best network considering signal strength, bit error rate in the relevant environment, and the congestion of APs for making a seamless handover. The simulation results show the appropriateness of PCLA in improving handover performance.

A Novel WLAN Roaming Decision and Selection Scheme for Mobile Data Offloading

The existing IEEE and 3GPP standards have laid the foundation for integrating cellular and WiFi network to deliver a seamless experience for the end-users when roaming across multiple access networks. However, in recent studies, the issue of making roaming decision and intelligently selecting the most preferable Point of Service to optimize network resource and improve end user’s experience has not been considered properly. In this paper, we propose a novel cellular and WiFi roaming decision and AP selection scheme based on state of the art, 3GPP TS24.312 and IEEE 802.11u, k standards. Our proposed scheme assists the mobile nodes to decide the right timing to make roaming decision and select preferable point of service based on the operator’s policies and real-time network condition. We also introduce our simulation model of a heterogeneous network with cellular and WiFi interworking as well as 3GPP ANDSF, TS24.312. It is a complete end-to-end system model from application to physical layer with considering user’s mobility and realistic traffic model. The proposed scheme outperformed the conventional WiFi selection scheme in terms of dynamically steering mobile node’s data traffic from macrocell to available Access Points. The proposed scheme increased the utilization and balanced the traffic load of access points and improved user’s experienced throughput.

An adaptive handover prediction scheme for seamless mobility based wireless networks.

We propose an adaptive handover prediction (AHP) scheme for seamless mobility based wireless networks. That is, the AHP scheme incorporates fuzzy logic with AP prediction process in order to lend cognitive capability to handover decision making. Selection metrics, including received signal strength, mobile node relative direction towards the access points in the vicinity, and access point load, are collected and considered inputs of the fuzzy decision making system in order to select the best preferable AP around WLANs. The obtained handover decision which is based on the calculated quality cost using fuzzy inference system is also based on adaptable coefficients instead of fixed coefficients. In other words, the mean and the standard deviation of the normalized network prediction metrics of fuzzy inference system, which are collected from available WLANs are obtained adaptively. Accordingly, they are applied as statistical information to adjust or adapt the coefficients of membership functions. In addition, we propose an adjustable weight vector concept for input metrics in order to cope with the continuous, unpredictable variation in their membership degrees. Furthermore, handover decisions are performed in each MN independently after knowing RSS, direction toward APs, and AP load. Finally, performance evaluation of the proposed scheme shows its superiority compared with representatives of the prediction approaches.

Novel association control strategies for multicasting in relay-enabled WLANs

Multicast is a fundamental communication service in Wireless Local Area Networks (WLANs). Unfortunately, multicast packets are transmitted at the base rate needlessly, which prevents rich multimedia contents from being delivered to users. To this end, a number of prior studies have proposed to increase multicast capacity whereby an Access Point (AP) transmits at the highest data rate that can be decoded by all subscribers. Another approach is by controlling subscriber-AP associations such that a given objective, e.g., AP load, is met. Moreover, they have also proposed using relays to help boost stations with weak channel condition. In this paper, we propose an approach that exploits association control, high number of APs and relays simultaneously to improve the performance of multicast in WLANs. We show that the problem is NP-hard by reducing it from the Maximum Coverage with Group Budget (MCG) problem. We then propose a number of heuristics that consider two types of relays: non-subscribers and subscribers. The former are stations that are not part of any multicast groups whereas the latter comprise of stations that are actively receiving multicast packets. Our extensive simulation studies with varying APs, station numbers, and multicast transmission rates show the efficacy of our heuristics in reducing the average and maximum AP load, and are able to support a higher number of users at high multicast rates. Specifically, they result in up to 83% reduction in AP load, and support up to 17% and 53% more users in low and high rate multicast scenarios respectively.