Access Point Selection Problem Research Articles

Toward Practical Access Point Deployment for Angle-of-Arrival Based Localization

The access point (AP) deployment is a fundamental task for constructing an accurate localization system. Existing literature mainly deals with the AP placement problem using optimal geometry analysis since the target-AP geometry will affect the localization performance. However, some non-ideal phenomena in practical scenario, e.g., the existence of obstacles, array orientation and path loss, will degrade the accuracy of angle-of-arrival (AoA) estimation as well as the localization accuracy. In this article, we reformulate the AP planning incorporating these factors. We decompose the problem into two subproblems, namely AP selection problem and error minimization problem. The AP selection problem selects the minimum number of APs to satisfy a desired localization accuracy, aided by a refined orientation updating procedure. We design a centralized and a distributed error minimization algorithm to further decrease the localization error. The centralized algorithm shows superiority in time efficiency. Nevertheless, the case with large number of APs may lead to excessive computational cost. Accordingly, we further devise the distributed algorithm which is adaptive to large-scale deployment. Numerical studies in indoor environments with barriers are conducted to verify our proposed approach.

Radius based domain clustering for WiFi indoor positioning

PurposeNowadays, WiFi indoor positioning based on received signal strength (RSS) becomes a research hotspot due to its low cost and ease of deployment characteristics. To further improve the performance of WiFi indoor positioning based on RSS, this paper aims to propose a novel position estimation strategy which is called radius-based domain clustering (RDC). This domain clustering technology aims to avoid the issue of access point (AP) selection.Design/methodology/approachThe proposed positioning approach uses each individual AP of all available APs to estimate the position of target point. Then, according to circular error probable, the authors search the decision domain which has the 50 per cent of the intermediate position estimates and minimize the radius of a circle via a RDC algorithm. The final estimate of the position of target point is obtained by averaging intermediate position estimates in the decision domain.FindingsExperiments are conducted, and comparison between the different position estimation strategies demonstrates that the new method has a better location estimation accuracy and reliability.Research limitations/implicationsWeighted k nearest neighbor approach and Naive Bayes Classifier method are two classic position estimation strategies for location determination using WiFi fingerprinting. Both of the two strategies are affected by AP selection strategies and inappropriate selection of APs may degrade positioning performance considerably.Practical implicationsThe RDC positioning approach can improve the performance of WiFi indoor positioning, and the issue of AP selection and related drawbacks is avoided.Social implicationsThe RSS-based effective WiFi indoor positioning system can makes up for the indoor positioning weaknesses of global navigation satellite system. Many indoor location-based services can be encouraged with the effective and low-cost positioning technology.Originality/valueA novel position estimation strategy is introduced to avoid the AP selection problem in RSS-based WiFi indoor positioning technology, and the domain clustering technology is proposed to obtain a better accuracy and reliability.

Access point selection game with mobile users using correlated equilibrium.

One of the most important issues in wireless local area network (WLAN) systems with multiple access points (APs) is the AP selection problem. Game theory is a mathematical tool used to analyze the interactions in multiplayer systems and has been applied to various problems in wireless networks. Correlated equilibrium (CE) is one of the powerful game theory solution concepts, which is more general than the Nash equilibrium for analyzing the interactions in multiplayer mixed strategy games. A game-theoretic formulation of the AP selection problem with mobile users is presented using a novel scheme based on a regret-based learning procedure. Through convergence analysis, we show that the joint actions based on the proposed algorithm achieve CE. Simulation results illustrate that the proposed algorithm is effective in a realistic WLAN environment with user mobility and achieves maximum system throughput based on the game-theoretic formulation.

Energy Efficiency Oriented Access Point Selection for Cognitive Sensors in Internet of Things

This paper studies the distributed energy efficient access point (AP) selection for cognitive sensors in the Internet of Things (IoT). The energy consumption is critical for the wireless sensor network (WSN), and central control would cause extremely high complexity due to the dense and dynamic deployment of sensors in the IoT. The desired approach is the one with lower computation complexity and much more flexibility, and the global optimization is also expected. We solve the multisensors AP selection problem by using the game theory and distributed learning algorithm. First, we formulate an energy oriented AP selection problem and propose a game model which is proved to be an exact potential game. Second, we design a distributed learning algorithm to obtain the globally optimal solution to the problem in a distributed manner. Finally, simulation results verify the theoretic analysis and show that the proposed approach could achieve much higher energy efficiency.

A theoretical approach to optimal association control in vehicular Wi-Fi networks

Abstract The recent ubiquitous deployment of Wi-Fi access points (APs) has offered vehicles to use the high-speed and low-cost Internet service via the roadside APs. However, the high mobility of vehicles and the limited coverage of APs render some challenges. First, it results in frequent handoffs, thus leading to long delay and low service availability. Second, the available AP sets and their channel quality change dynamically, making the AP selection problem even harder. Therefore, there is a strong need to develop an efficient association control mechanism that provides efficient vehicular Wi-Fi access. In this paper, we present a theoretical framework to formulate the optimal association problem through non-linear integer programming, whose objective function is to maximize the throughput or to minimize the handoff overhead. We show that this problem holds the totally unimodular (TU) property and is thus solvable in polynomial time. Then, we study the optimality of association control by comparing existing online algorithms through real trace-based simulations. The results show that there exists a large performance gap between the performance of existing online algorithms and the optimal one. We also observe that the association control algorithm can be further improved if it has access to future knowledge. Particularly, the offline optimal with future AP information improves the performance of the local optimal by up to 10%.

SmartAssoc: Decentralized Access Point Selection Algorithm to Improve Throughput

As the first step of the communication procedure in 802.11, an unwise selection of the access point (AP) hurts one client's throughput. This performance downgrade is usually hard to be offset by other methods, such as efficient rate adaptations. In this paper, we study this AP selection problem in a decentralized manner, with the objective of maximizing the minimum throughput among all clients. We reveal through theoretical analysis that the selfish strategy, which commonly applies in decentralized systems, cannot effectively achieve this objective. Accordingly, we propose an online AP association strategy that not only achieves a minimum throughput (among all clients) that is provably close to the optimum, but also works effectively in practice with reasonable computation and transmission overhead. The association protocol applying this strategy is implemented on the commercial hardware and compatible with legacy APs without any modification. We demonstrate its feasibility and performance through real experiments and intensive simulations.

Green Access Point Selection for Wireless Local Area Networks Enhanced by Cognitive Radio

In wireless local area networks (WLANs) made up of Extended Service Sets, access point (AP) selection is a key issue to improve the network performance and balance the traffic load. WLANs operating in the shared Industrial, Scientific and Medical band can benefit from the use of cognitive radio (CR) techniques to enable dynamic access to spectrum holes that are free from interference. In this paper, we propose an optimal Green AP Selection (GAPS) scheme, in which AP selection is optimized to maximize the system throughput while minimizing the energy consumption, for multi-rate WLANs enhanced by CRs. Different from most existing AP selection schemes, GAPS takes into account of the state transition tendency of APs and the influence of Automatic Rate Fallback mechanism in the Distributed Coordination Function. The AP selection problem is formulated as a restless bandit problem and solved by the primal-dual index heuristic algorithm based on first order relaxation to yield the GAPS scheme with the "indexability" property and hence a low complexity. GAPS is further divided into offline computation, which accounts for the bulk of the computations, and online selection, with a low complexity to facilitate implementation. Extensive simulation results illustrate the significant performance improvements of GAPS compared with existing AP selection schemes in different scenarios.

Joint Access Point Selection and Power Allocation for Uplink Wireless Networks

We consider the distributed uplink resource allocation problem in a multi-carrier wireless network with multiple access points (APs). Each mobile user can optimize its own transmission rate by selecting a suitable AP and by controlling its transmit power. Our objective is to devise suitable algorithms by which mobile users can jointly perform these tasks in a distributed manner. Our approach relies on a game theoretic formulation of the joint power control and AP selection problem. In the proposed game, each user is a player with an associated strategy containing a discrete variable (the AP selection decision) and a continuous vector (the power allocation among multiple channels). We provide characterizations of the Nash Equilibrium of the proposed game, and present a set of novel algorithms that allow the users to efficiently optimize their rates. Finally, we study the properties of the proposed algorithms as well as their performance via extensive simulations.

AN ACTIVE ACCESS-POINT SELECTION APPROACH FOR DEPENDABLE WIRELESS MESH NETWORKS

As an inexpensive, flexible, and scalable Internet-access wireless network, we have studied the architecture, protocols, and design optimizations of the Wireless Internet-access Mesh NETwork (WIMNET) that adopts multiple access-points (APs) connected through wireless links. WIMNET can improve the dependability to failures of APs and/or their links by allocating APs redundantly in the network field. Because redundant APs may increase the operational cost and degrade the throughput due to increasing radio interferences, only the necessary APs for connectivity between the hosts and the Internet gateway should be activated in communications. In this paper, we first define the active AP selection problem of selecting the minimum number of active APs, and prove the NP-completeness of its decision version through reduction from the known minimum set cover problem. Then, we propose an active AP selection algorithm of deactivating APs one by one until no more AP can be deactivated. Due to the limited transmission capacity in WIMNET, we further present an algorithm extension for throughput maximization. We verify the effectiveness of our proposal through simulations in three instances, where the number of active APs is reduced by more than 40% and the throughput is improved by more than 10% from the original in any instance.

Performance analysis of multi‐criteria CAC for distributed access point selection in WLANs

PurposeWireless local area networks (WLANs) are the predominant option for broadband wireless access network, and multiple access points (APs) will be much more available for wireless stations (WSTAs). Call admission control (CAC) on AP selection problem over 802.11 WLAN is a critical issue. In the existing architecture, strongest‐signal‐first is the default AP selection mechanism in 802.11 WLAN which uses the single criterion, received signal strength indicator, to select AP. However, this method suffers from bandwidth deficiency and unbalanced load among APs due to the uneven distribution of user load, thus degrading the system throughput. Instead, the purpose of this paper is to propose a multi‐criteria CAC on AP selection algorithms.Design/methodology/approachThe distributed multi‐criteria considered in order are RSSI, minimum required bandwidth of WSTA, estimated effective bandwidth (EEB) and AP‐WSTA distance. A semiMarkov model considering both packet retransmission limit, packet error rate and collision effect is proposed to predict the system throughput and validated through simulation results. Two multi‐criteria AP selection algorithms after EEB is evaluated are proposed and compared based on this analytical model.FindingsThe proposed algorithms outperform the traditional SSF algorithm in terms of the balance index for AP and the average system throughput.Originality/valveThe paper presents performance analysis for multi‐criteria CAC for distributed access point selection in WLANs.

Joint Optimal Access Point Selection and Channel Assignment in Wireless Networks

In wireless cellular networks or in other networks with single-hop communication, the fundamental access control problem pertains to access point (AP) selection and channel allocation for each user. For users in the coverage area of one AP, this involves only channel allocation. However, users that belong in the intersection of coverage areas of more than one AP can select the appropriate AP to establish connection and implicitly affect the channel assignment procedure. We address the joint problem of AP selection and channel assignment with the objective to satisfy a given user load vector with the minimum number of channels. Our major finding is that the joint problem reduces to plain channel allocation in a cellular network that emerges from the original one after executing an iterative and provably convergent clique load balancing algorithm. For linear cellular networks, our approach leads to minimum number of required channels to serve a given load vector. For 2D cellular networks, the same approach leads to a heuristic algorithm with a suboptimal solution due to the fact that clique loads cannot be balanced. Numerical results demonstrate the performance benefits of our approach in terms of blocking probability in a dynamic scenario with time-varying number of connection requests. The presented approach constitutes the basis for addressing more composite resource allocation problems in different context.