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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]

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]

Due to non-line-of-sight propagation and other effects, the accuracy of indoor positioning based on location fingerprint is not high. Aiming at this problem, this paper proposed a novel indoor positioning algorithm, which based on the reliable AP selection and deep belief network ( DBN) . Firstly, the algorithm used the improved K-means clustering algorithm to divide the locating area into several sub-regions in the off-line phase. Then according to the Fisher criterion and the AP absent frequency, it selected the strongly distinguishable and reliable AP node as the training node of the sub-region. And finally it used the DBN model to train the data of each sub-region. In the on-line phase, the improved algorithm determined the cluster according to the received signal strength, and estimated the location of test point by the trained DBN model. The experimental results show that, compared with WKNN, M-WKNN and PSO-ANN algorithm, the proposed algorithm can effectively improve the accuracy and stability of positioning. [ABSTRACT FROM AUTHOR]

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]

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]