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WiFi-based fingerprint indoor positioning technology has been widely concerned, but it has been facing the challenge of lack of robustness to signal changes, and the positioning service requires fast and accurate positioning estimation. Therefore, an random forest-kernel extreme learning machine (RF-KELM) positioning algorithm with good comprehensive performance is proposed in this paper. Both offline and online phases are included by this algorithm. In the offline phase, the original data of WiFi fingerprint is first transformed into a form more suitable for positioning. Then, access point (AP) selection is performed on the fingerprint database containing many useless APs, in which an RF which can evaluate the importance of features is used. Finally, the KELM is trained with the sub-database that have undergone data transformation and AP selection. In the online phase, firstly, the obtained signal is processed, and then the trained KELM is used to predict the position of the data processed signal. In this paper, the performance of the proposed RF-KELM positioning algorithm is thoroughly tested on a publicly available dataset, and the experimental results demonstrate that the proposed algorithm not only has high positioning accuracy and robustness, but also takes only 0.08 s to position online. [ABSTRACT FROM AUTHOR]

WiFi-based indoor positioning technology has gradually become a hot research topic in the field of indoor positioning, but the development of this technology has been facing the challenge of susceptibility to environmental interference. Therefore, in this paper, the kernel function method (KFM) with stronger interference resistance is used for positioning, and the adaptive σ algorithm is proposed for the time-consuming and laborious problem of manual parameter tuning, which incorporates the ideas of cross-validation and iteration. In addition, too many wireless access points (APs) mean higher computational cost and longer positioning time, so it is necessary to choose reasonable APs for positioning. In this paper, we use the random forest (RF) algorithm to assess the importance of APs and filter out a small number of APs with high importance. Considering the obvious differences in the WiFi signals received on different floors, a system framework for positioning by floors based on WiFi fingerprints is proposed. In the offline phase, the fingerprint library is first divided according to floors, and then perform separately AP selection and parameter tuning for each sub-fingerprint library. In the online phase, support vector machine is used to discriminate the floors first, and then KFM is used for planar positioning. Experiments are conducted on the public dataset, and the results show that the proposed algorithm has higher positioning accuracy, more robustness, and less time-consuming compared to several common algorithms. [ABSTRACT FROM AUTHOR]

The proliferation of mobile devices has fueled the demand for indoor location-based services. Consequently, a plethora of techniques have emerged to facilitate object and device localization in indoor environments. Among these, fingerprint-based indoor localization systems, which leverage machine learning, stand out as a promising solution for providing accurate localization. Nonetheless, their performance is inherently reliant on the accuracy of the underlying database, and any changes in the indoor layout can significantly impact the wireless signals, subsequently affecting the localization accuracy. To circumvent this issue, this work proposes a novel access point (AP) selection framework to enhance the robustness of fingerprint-based indoor positioning systems in dynamic indoor environments. More specifically, a hybrid Wi-Fi and BLE fingerprint database is constructed by collecting received signal strength (RSS) from the pre-defined reference points (RPs). To ensure that the fingerprint database remains relevant over time, some RPs are designated as known points so that the system can periodically collect new RSS at the known points. Subsequently, the proposed scheme computes the differences between the RSS of the database and the updated RSS from the new layout to account for the changes occurred. The building-based, floor-based, and zone-based implementation modes determine which APs are reliable to be utilized during localization based on the RSS discrepancies observed in building, floor, and zone, respectively. Results demonstrate that the proposed building-based, floor-based, and zone-based AP selection schemes could achieve reduction in positioning error up to 28.86%, 33.53%, and 39.66%, respectively, compared to the baseline technique without AP selection schemes.

Distributed MIMO systems leverage multiple access points (APs) distributed across a geographical area to enhance system performance and provide robust connectivity to users by transmitting independent data streams simultaneously, thus improving coverage and capacity. The nonlinear power amplifiers (PAs) employed at the APs can significantly distort the transmitted signal and degrade the spectral efficiency (SE), as PAs are more efficient when operating close to saturation. In this paper, we present an efficient power allocation framework in a multi-stream distributed massive MIMO system with nonlinear PAs, where APs send multiple spatial streams to the user equipment (UE). With the objective of maximizing the SE while adhering to total input power constraints, we first formulate the power allocation problem as nonlinear programming, which is first solved using the multiplier punitive function-based method. Moreover, we focus on the asymptotic analysis of the SE when multi-stream interference among APs is negligible. This can be achieved through ideal spatial multiplexing with interference nulling, assuming a sufficiently large antenna at the UE, or through interference-free frequency multiplexing. We examine the optimality of the water-filling method in power allocation and extend it by considering the PA’s nonlinearity. Simulation results show that the proposed power allocation method can maximize the achievable rate while considering the PA nonlinearity, thereby preventing degradation in performance.

With the emergence of various Internet of Things (IoT) technologies, energy-saving schemes for IoT devices have been rapidly developed. To enhance the energy efficiency of IoT devices in crowded environments with multiple overlapping cells, the selection of access points (APs) for IoT devices should consider energy conservation by reducing unnecessary packet transmission activities caused by collisions. Therefore, in this paper, we present a novel energy-efficient AP selection scheme using reinforcement learning to address the problem of unbalanced load that arises from biased AP connections. Our proposed method utilizes the Energy and Latency Reinforcement Learning (EL-RL) model for energy-efficient AP selection that takes into account the average energy consumption and the average latency of IoT devices. In the EL-RL model, we analyze the collision probability in Wi-Fi networks to reduce the number of retransmissions that induces more energy consumption and higher latency. According to the simulation, the proposed method achieves a maximum improvement of 53 % in energy efficiency, 50 % in uplink latency, and a 2.1 -times longer expected lifespan of IoT devices compared to the conventional AP selection scheme. [ABSTRACT FROM AUTHOR]

Pilot contamination due to pilot reuse seriously mitigates the performance of the cell-free massive multiple-input multiple-output (MIMO) systems. In this paper, we propose a joint pilot assignment scheme that employs user clustering and graph coloring (UC-GC) to reduce pilot contamination. The proposed method consists of two steps: firstly, we utilize AP selection to classify all users; secondly, we assign pilots to users with more severe pilot contamination using the graph coloring algorithm and then assign pilots to the remaining users. The numerical simulation results show that the proposed scheme outperforms existing pilot assignment schemes and significantly improves throughout with low complexity. [ABSTRACT FROM AUTHOR]

Optimal selection of features leads to the increase in perceptibility of the predictor procedure and thereby in turn increases the accuracy. In the domain of WiFi-based indoor positioning, access points are a ubiquitous source of features with which the positioning process is carried out. The selection of a proper subset of access points is crucial for sustainable localization performance. Most of the existing works in the literature focus on important AP selection whereas, the stability of the feature sets w.r.t varying context remains mostly ignored. Thus, to impart sustainable localization performance, in this work, the problem of identification of a stable set(s) of APs is investigated. Thus, the contribution of this work is two-fold. A genetic algorithm based feature selection technique for indoor localization is proposed first. The motive behind the approach is to capture different subsets of APs that best contribute to the localization process. Accordingly, our second contribution is the design of a Feature Association Based Ensemble Learning (FABEL) model that utilizes the selected feature sets in order to retain generality for sustainable localization performance. Experimentation has been carried out on real-world data and analysis has been presented. The proposed training pipeline has been tested for device heterogeneity. It has been found that even for an unknown test device, the localization accuracy is significantly good and 80% of the errors lies within 4m. [ABSTRACT FROM AUTHOR]

Pilot contamination due to pilot reuse seriously mitigates the performance of the cell-free massive multiple-input multiple-output (MIMO) systems. In this paper, we propose a joint pilot assignment scheme that employs user clustering and graph coloring (UC-GC) to reduce pilot contamination. The proposed method consists of two steps: firstly, we utilize AP selection to classify all users; secondly, we assign pilots to users with more severe pilot contamination using the graph coloring algorithm and then assign pilots to the remaining users. The numerical simulation results show that the proposed scheme outperforms existing pilot assignment schemes and significantly improves throughout with low complexity.

With the emergence of various Internet of Things (IoT) technologies, energy-saving schemes for IoT devices have been rapidly developed. To enhance the energy efficiency of IoT devices in crowded environments with multiple overlapping cells, the selection of access points (APs) for IoT devices should consider energy conservation by reducing unnecessary packet transmission activities caused by collisions. Therefore, in this paper, we present a novel energy-efficient AP selection scheme using reinforcement learning to address the problem of unbalanced load that arises from biased AP connections. Our proposed method utilizes the Energy and Latency Reinforcement Learning (EL-RL) model for energy-efficient AP selection that takes into account the average energy consumption and the average latency of IoT devices. In the EL-RL model, we analyze the collision probability in Wi-Fi networks to reduce the number of retransmissions that induces more energy consumption and higher latency. According to the simulation, the proposed method achieves a maximum improvement of 53% in energy efficiency, 50% in uplink latency, and a 2.1-times longer expected lifespan of IoT devices compared to the conventional AP selection scheme.

Received signal strength indicator (RSSI) of WiFi access point (AP) is a primary sensor data used for indoor fingerprint localization. User sends its online RSSI to server to estimate its position by matching with RSSI fingerprints database which built in the offline phase. An important goal of indoor fingerprint localization is to increase the accuracy while reduce the storage cost. Meanwhile, APs perform different effects on target estimation and mapping metric in RSSI fingerprint localization. In this paper, a novel localization model based on the nonuniform quantization RSSI entropy (NQRELoc) is proposed to address these problems. First, to select the APs that contribute more to the localization, the nonuniform quantization RSSI entropy (NQRE) is introduced to quantify AP’s discernibility and select APs whose signals show sufficient differentiation to construct an offline fingerprints database. Then, the entropy-weighted euclidean distance (EWED) is used as a metric to measure the similarity of online RSSI vectors and offline RPs fingerprints. Finally, NQRELoc locates the target by the improved entropy-weighted K nearest neighbor (IEWKNN) algorithm, which takes the APs effect into target estimation. To validate the proposed algorithm, a large scale of experiments and simulations are implemented. The results demonstrate that NQRELoc can not only reduce the storage overhead but also improve the positioning accuracy compared to the other existing techniques. [ABSTRACT FROM AUTHOR]

Cell-Free massive MIMO (mMIMO) offers promising features such as higher spectral efficiency, higher energy efficiency and superior spatial diversity, which makes it suitable to be adopted in beyond 5G (B5G) networks. However, the original form of Cell-Free massive MIMO requires each AP to be connected to CPU via front haul (front-haul constraints) resulting in huge economic costs and network synchronization issues. Radio Stripe architecture of cell-free mMIMO is one such architecture of cell-free mMIMO which is suitable for practical deployment. In this paper, we propose DNN Based Parallel Decoding in Radio Stripe (DNNBPDRS) to decode the symbols of User Equipments (UEs) in the uplink in a parallel fashion to reduce computational complexity by reducing delay in processing. Moreover, to solve the issue of Access Point (AP) selection in radio stripe networks, we propose a Channel link-based AP selection (CLBAPS) algorithm to choose the best APs in terms of channel link quality. The proposed DNNBPDRS framework not only improves Symbol Error Rate (SER) performance when compared to counterparts but is also proved to be comparatively far lesser computational complex. Moreover, the numerical result indicates the proposed AP selection algorithm CLBAPS performs better than random selection of AP in radio stripe networks. [ABSTRACT FROM AUTHOR]

Next-generation Home WiFi networks have to step forward in terms of performance. New applications such as on-line games, virtual reality or high quality video contents will further demand higher throughput levels, as well as low latency. Beyond physical (PHY) and medium access control (MAC) improvements, deploying multiple access points (APs) in a given area may significantly contribute to achieve those performance goals by simply improving average coverage and data rates. However, it opens a new challenge: to determine the best AP for each given station (STA). This article studies the achievable performance gains of using secondary APs, also called Extenders, in Home WiFi networks in terms of throughput and delay. To do that, we introduce a centralized, easily implementable channel load aware selection mechanism for WiFi networks that takes full advantage of IEEE 802.11k/v capabilities to collect data from STAs, and distribute association decisions accordingly. These decisions are completely computed in the AP (or, alternatively, in an external network controller) based on an AP selection decision metric that, in addition to RSSI, also takes into account the load of both access and backhaul wireless links for each potential STA-AP/Extender connection. Performance evaluation of the proposed channel load aware AP and Extender selection mechanism has been first conducted in a purpose-built simulator, resulting in an overall improvement of the main analyzed metrics (throughput and delay) and the ability to serve, at least, 35% more traffic while keeping the network uncongested when compared to the traditional RSSI-based WiFi association. This trend was confirmed when the channel load aware mechanism was tested in a real deployment, where STAs were associated to the indicated AP/Extender and total throughput was increased by 77.12%.

In this paper, we investigate the selection problem of access points (APs) in cell-free massive multiple-input multiple-output (MIMO) systems, where APs equipped with a large number of antennas are geographically distributed over a wide area with no cell border. These APs simultaneously serve many users, which are randomly distributed all over the area. We first derive formulas to calculate two proposed metrics used to measure the effective channel gain from all users to all APs and the channel quality of each user. Moreover, these metrics are only based on large-scale fading coefficients, which change very slowly in time. Next, we propose an algorithm to effectively sort and connect users to each AP in a sequential manner using these proposed metrics. Simulation results show that cell-free massive MIMO systems using proposed scheme have better performance compared to existing schemes.

To ensure an efficient coverage and allow transparent mobility, it is common to find a number of access points (APs) deployed in the neighborhood of each other in current WLANs. A mobile terminal (MT) often has multiple potential APs that it can connect to, and the selection of an appropriate AP becomes an important factor affecting both the network performance and user experience. This paper proposes a new AP selection scheme, which is based on the analysis of achievable normalized throughputs (ANTs) that the MT could obtain in the network. Specifically, we first compute the normalized throughput that characterizes the channel competition among multiple basic service sets (BSSs) as well as MTs within the same BSS. By evaluating ANTs in situations where the MT associates with different APs, the most appropriate AP for the MT can then be selected. Then, we present the implementation of the proposed AP selection scheme under the SDN-WiFi framework, in which the AP selection process can be performed in a software manner. The experimental results show that our computations on ANTs are of high accuracy and the proposed AP selection scheme can achieve a better performance than the existing schemes in terms of both the quality of service of MTs and the fairness among MTs.

How to meet the demand for increasing number of users, higher data rates, and stringent quality-of-service (QoS) in the beyond fifth-generation (B5G) networks? Cell-free massive multiple-input multiple-output (MIMO) is considered as a promising solution, in which many wireless access points cooperate to jointly serve the users by exploiting coherent signal processing. However, there are still many unsolved practical issues in cell-free massive MIMO systems, whereof scalable massive access implementation is one of the most vital. In this paper, we propose a new framework for structured massive access in cell-free massive MIMO systems, which comprises one initial access algorithm, a partial large-scale fading decoding (P-LSFD) strategy, two pilot assignment schemes, and one fractional power control policy. New closed-form spectral efficiency (SE) expressions with maximum ratio (MR) combining are derived. The simulation results show that our proposed framework provides high SE when using local partial minimum mean-square error (LP-MMSE) and MR combining. Specifically, the proposed initial access algorithm and pilot assignment schemes outperform their corresponding benchmarks, P-LSFD achieves scalability with a negligible performance loss compared to the conventional optimal large-scale fading decoding (LSFD), and scalable fractional power control provides a controllable trade-off between user fairness and the average SE. [ABSTRACT FROM AUTHOR]

An efficient pilot assignment scheme based on graph coloring is proposed to mitigate the severe pilot contamination effect in cell-free massive multiple-input multiple-output systems. By exploiting the large-scale fading coefficients between the access points (APs) and the user equipments, we first utilize an AP selection algorithm to construct an interference graph. Then, the optimal pilot assignment can be achieved by updating the interference graph. In addition, we consider a fair policy for pilot reuse to improve the system performance. Numerical results verify that, compared with conventional pilot assignment schemes, the proposed graph coloring based pilot assignment scheme can significantly increase the 95 $\%$ -likely per-user throughput and reduce the complexity of assigning pilot sequences. [ABSTRACT FROM AUTHOR]

An RSS transform–based weighted k-nearest neighbor (WKNN) indoor positioning algorithm, Q-WKNN, is proposed to improve the positioning accuracy and real-time performance of Wi-Fi fingerprint–based indoor positioning. To smooth the RSS fluctuation difference caused by acquisition equipment, time, and environment changes, base Q is introduced in Q-WKNN to transform RSS to Q-based RSS, based on the relationship between the received signal strength (RSS) and physical distance. Analysis of the effective range of base Q indicates that Q-WKNN is more suitable for regions with noticeable environmental changes and fixed access points (APs). To reduce the positioning time, APs are selected to form a Q-WKNN similarity matrix. Adaptive K is applied to estimate the test point (TP) position. Commonly used indoor positioning algorithms are compared to Q-WKNN on Zenodo and underground parking databases. Results show that Q-WKNN has better positioning accuracy and real-time performance than WKNN, modified-WKNN (M-WKNN), Gaussian kernel (GK), and least squares-support vector machine (LS-SVM) algorithms.

As the deployment of WLANs becomes extensive, users commonly have to select one access point (AP) for association among multiple available APs. However, the existing AP selection policies are not sufficient enough due to their drawbacks (e.g., imbalanced load and time consuming for association). In this paper, we present a collaborative filtering (CF) recommendation-based AP selection strategy in wireless local area networks (WLANs) with differentiated access service. At first, a differentiated AP selection strategy by utilizing analytic hierarchy process (AHP) is leveraged to optimize AP selection metric with multiple objectives for different users. Furthermore, an improved differentiated AP selection approach by CF recommendation is investigated. Based on our method, the new user can associate with the recommended AP promptly instead of channel scanning and selection metric computation. Meanwhile, load prebalancing module periodically excludes the AP considered to be overloaded from the recommendation list, while the pioneer user (PU) updates the corresponding module periodically with an adaptive method to replace the AP with low recommendation efficiency. Simulation results demonstrate that our presented strategies can provide differentiated quality-of-service (QoS) assurance to users, improve overall network throughput, balance network traffic, and degrade association time cost. [ABSTRACT FROM PUBLISHER]

In traditional IEEE 802.11 protocols, the RSSI is usually taken as the criterion for AP selection, by which the AP with the greatest signal strength may be heavily burdened. In this paper, A SDN-based AP selection scheme called TPR is proposed. Through centralized control from SDN controller which has a full view of the network, our TPR enables AP selection more reasonable and efficient. In addition, the OpenFlow protocol embedded in the AP firmware is revised to efficiently collect the measurement data for AP selection. Besides, since the data processing task is shifted to the SDN controller, the burden of each STA is greatly alleviated thus resulting in fewer energy consumption and overheads at STAs. Experimental results demonstrate the ability of our TPR to increase the throughput of the WLAN system in multi-AP scenario and to reduce the number of handoffs among different APs. [ABSTRACT FROM AUTHOR]

With the development of communication technology, the demand for location-based services is growing rapidly. This paper presents an algorithm for indoor localization based on Received Signal Strength (RSS), which is collected from Access Points (APs). The proposed localization algorithm contains the offline information acquisition phase and online positioning phase. Firstly, the AP selection algorithm is reviewed and improved based on the stability of signals to remove useless AP; secondly, Kernel Principal Component Analysis (KPCA) is analyzed and used to remove the data redundancy and maintain useful characteristics for nonlinear feature extraction; thirdly, the Affinity Propagation Clustering (APC) algorithm utilizes RSS values to classify data samples and narrow the positioning range. In the online positioning phase, the classified data will be matched with the testing data to determine the position area, and the Maximum Likelihood (ML) estimate will be employed for precise positioning. Eventually, the proposed algorithm is implemented in a real-world environment for performance evaluation. Experimental results demonstrate that the proposed algorithm improves the accuracy and computational complexity. [ABSTRACT FROM AUTHOR]

More mobile devices can obtain WiFi Received Signal Strength (RSS) for indoor positioning, so selecting reasonable Access Points (APs) for rapid positioning is important. Furthermore, RSS is vulnerable to diverse interference, making the positioning accuracy decrease. To solve the above mentioned issues, two novel algorithms, AP selection and positioning algorithms, are proposed, in this paper, which both use the location distance between RPs to construct a circle and treat the RSS from each AP individually. RPs in the same circle are seen as having similar RSS. 1) For each received signal, the proposed selection algorithm leverages the RSS extreme values (maximum and minimum values) of circles to find out RPs which may surround Test Point (TP), then it uses the degree of RSS difference and location concentration to evaluate the importance of each AP. 2) For the positioning algorithm, it uses the extreme values to find the useful APs and determines the weight of RPs. Extensive experiments are conducted on simulation and two real-world sites, results show the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

With the wide deployment of IEEE 802.11 Wireless Local Area Networks, it has become common for mobile nodes (MNs) to have multiple access points (APs) to associate with. With the Received Signal Strength Indicator (RSSI)-based AP selection algorithm, which is implemented in most commercial IEEE 802.11 clients, the AP with the best signal strength is selected regardless of the candidate AP’s available throughput, resulting in unbalanced distribution of clients among the APs in the network. Several studies have shown performance improvement in not just the new MN (nMN), but also the network as a whole when the selection process considers the current load status of candidate APs. However, the proposed algorithms in these studies assume that there are no hidden terminal problems that severely affect the performance of the network. Hidden terminal problems frequently occur in wireless networks with unlicensed frequencies, like IEEE 802.11 in the 2.4 GHz band. Moreover, none of the previous studies have considered frame aggregation, a major improvement in transmission efficiency introduced and widely deployed with the IEEE 802.11n standard. In this paper, we propose a new AP selection algorithm based on the estimation of available throughput calculated with a model based on the IEEE 802.11 distributed coordination function in consideration of hidden terminal problems and frame aggregation. The proposed algorithm is evaluated through extensive simulation, and the results show that the nMN with the proposed AP selection algorithm can achieve up to 55.84% and 22.31% higher throughput compared to the traditional RSSI-based approach and the selection algorithm solely based on the network load, respectively. [ABSTRACT FROM AUTHOR]

Based on various existing wireless fingerprint location algorithms in intelligent sports venues, a high-precision and fast indoor location algorithm improved weighted k-nearest neighbor (I-WKNN) is proposed. In order to meet the complex environment of sports venues and the demand of high-speed sampling, this paper proposes an AP selection algorithm for offline and online stages. Based on the characteristics of the signal intensity distribution in intelligent venues, an asymmetric Gaussian filter algorithm is proposed. This paper introduces the application of the positioning algorithm in the intelligent stadium system, and completes the data acquisition and real-time positioning of the stadium. Compared with traditional WKNN and KNN algorithms, the I-WKNN algorithm has advantages in fingerprint positioning database processing, environmental noise adaptability, real-time positioning accuracy and positioning speed, etc. The experimental results show that the I-WKNN algorithm has obvious advantages in positioning accuracy and positioning time in a complex noise environment and has obvious application potential in a smart stadium. • This paper puts forward the application scheme of WIFI location in the sports field. • The scheme of AP signal selection is established. • This paper proposes a new algorithm I-WKNN to improve the positioning accuracy and speed. • The performance of the algorithm is analyzed and compared from the perspective of delay and positioning accuracy. [ABSTRACT FROM AUTHOR]

With the development of IEEE 802.11 MAC (Medium Access Control) protocols, efficient utilization of wireless local area networks (WLANs) has become a very important issue. In typical IEEE 802.1] networks, the association between mobile users (MUs) and access point (AP) is based on the signal strength information. As a result, it often results in the extremely unfair bandwidth allocation among MUs. In this paper, we propose a distributed association algorithm to achieve load balancing among the APs. The proposed algorithm gradually balances the AP loads for the available multiple bit rate choices in a distributed manner. We analyze the stability and overhead of the proposed algorithm, and show the improvement of the fairness via computer simulation. Additionally, we have implemented a prototype on a testbed to prove its feasibility. [ABSTRACT FROM AUTHOR]

An RSS transform–based weighted k-nearest neighbor (WKNN) indoor positioning algorithm, Q-WKNN, is proposed to improve the positioning accuracy and real-time performance of Wi-Fi fingerprint–based indoor positioning. To smooth the RSS fluctuation difference caused by acquisition equipment, time, and environment changes, base Q is introduced in Q-WKNN to transform RSS to Q-based RSS, based on the relationship between the received signal strength (RSS) and physical distance. Analysis of the effective range of base Q indicates that Q-WKNN is more suitable for regions with noticeable environmental changes and fixed access points (APs). To reduce the positioning time, APs are selected to form a Q-WKNN similarity matrix. Adaptive K is applied to estimate the test point (TP) position. Commonly used indoor positioning algorithms are compared to Q-WKNN on Zenodo and underground parking databases. Results show that Q-WKNN has better positioning accuracy and real-time performance than WKNN, modified-WKNN (M-WKNN), Gaussian kernel (GK), and least squares-support vector machine (LS-SVM) algorithms. [ABSTRACT FROM AUTHOR]

Abstract In 802.11 wireless networks, a complete handoff procedure for a mobile node requires access point (AP) selection, AP switch, call admission control (CAC), IP address re-allocation, and network re-configuration. Most current handoff schemes deal only with either AP selection or IP address re-allocation. In this paper, an integrated handoff procedure is proposed. First, AP selection is accomplished by choosing an AP with the lowest channel utilization and smaller number of associated users. The information about load of each AP is reported through modified beacon frames. In the case of adopting load-based AP selection, the average throughput can be increased up to 56%, as opposed to pure SNR-based AP selection. Next, both CAC and IP address pre-fetch are performed simultaneously through the simplified DHCP procedure. Specifically, efficient limited fractional guard channel policy (ELFGCP) is proposed for the CAC phase. By adopting ELFGCP, the failure probability can be reduced as much as 45% from conventional LFGCP. Finally, the simulation results demonstrate the applicability of the integrated approach, and the overall disconnection time due to handoff can be reduced from 2.9 to 0.004 s using traditional handoff procedures.

With the development of communication technology, the demand for location-based services is growing rapidly. This paper presents an algorithm for indoor localization based on Received Signal Strength (RSS), which is collected from Access Points (APs). The proposed localization algorithm contains the offline information acquisition phase and online positioning phase. Firstly, the AP selection algorithm is reviewed and improved based on the stability of signals to remove useless AP; secondly, Kernel Principal Component Analysis (KPCA) is analyzed and used to remove the data redundancy and maintain useful characteristics for nonlinear feature extraction; thirdly, the Affinity Propagation Clustering (APC) algorithm utilizes RSS values to classify data samples and narrow the positioning range. In the online positioning phase, the classified data will be matched with the testing data to determine the position area, and the Maximum Likelihood (ML) estimate will be employed for precise positioning. Eventually, the proposed algorithm is implemented in a real-world environment for performance evaluation. Experimental results demonstrate that the proposed algorithm improves the accuracy and computational complexity., Competing Interests: The authors declare no conflict of interest.