Traffic-Aware Sensor Grouping for IEEE 802.11ah Networks: Regression Based Analysis and Design

Chang, Tung-Chun; Lin, Chi-Han; Lin, Kate Ching-Ju; Chen, Wen-Tsuen

doi:10.1109/tmc.2018.2840692

Cited by 51 publications

(23 citation statements)

References 27 publications

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“…The regression models can also be employed for solving both estimation and detection problems in the upper layers of the seven-layer OSI model. For example, Chang et al derived a regression-based analytical model for the sake of estimating the contention success probability considering heterogeneous sensor-traffic demands, which beneficially improved the channel's exploitation in IoT [199]. Moreover, in [200], Chen et al employed a regression model for reconstructing the radio map with the aid of signal strength models for the path planning and UAV-location design in UAV-assisted wireless networks.…”

Section: N We Are Capable Of Estimating the Regression Coefficienmentioning

confidence: 99%

“…Fortunately, there are a range of efficient heuristic algorithms, which converge quickly to a local optimum. [195] interference estimate regression strike a trade-off between the overhead and accuracy of interference measurement [196] spectrum sensing regression reduce the number of parameters and maintain a high detection accuracy [197] wireless coexistence regression estimate the likelihood of the wireless coexistence of Wi-Fi and ZigBee [198] PHY authentication regression do not need the assumption on the accurate known channel model [199] traffic estimation regression estimate the contention success probability considering sensors' heterogeneous traffic demands [200] map reconstruction regression reconstruct the wireless radio map for UAV path planning and location design [201] wireless localization regression logistic regression classifier for counteracting the negative influence relying on fingerprint signals [203] traffic prediction KNN explore both the temporal and spatial characteristics of radio resources [204] anomaly detection KNN rely on the hypergrid intuition in the context of WSN applications [206] missing data estimation KNN rely on the temporal and spatial correlation feature of sensor data [207] modulation classification KNN combine the genetic programming and KNN for improving the modulation classification accuracy [208] interference elimination KNN extract environmental interference from Wi-Fi signal and reduce computational complexity [221] data estimation SVM provide an efficient estimation procedure in a distributed manner [222] localization estimation SVM yield fast convergence performance and efficiently use the communication resources [223] user location SVM without knowledge about base station location and environmental propagation characteristics [224] data prediction SVM provide location-specific interface configuration for HetNets [225] behavior learning SVM combine both the superior accuracy of SVM and fast convergence speed of FDA [226] signal classification SVM classify acoustic signals emitted by vehicles rely on feature extraction [227] channel selection SVM propose a control channel selection mechanism for a cognitive radio network [228] attacker counting SVM develop a cluster-based SVM mechanism for determining the number of attackers [232] antenna selection Bayes enhance the physical layer security relying on Bayes-based optimal antenna selection [233] network association Bayes schedul...…”

Section: A K-means Clustering and Its Applications 1)mentioning

confidence: 99%

See 1 more Smart Citation

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Wang

Jiang

Zhang

et al. 2020

IEEE Commun. Surv. Tutorials

469

200

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Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (Het-Nets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.

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Section: N We Are Capable Of Estimating the Regression Coefficienmentioning

confidence: 99%

Section: A K-means Clustering and Its Applications 1)mentioning

confidence: 99%

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Wang

Jiang

Zhang

et al. 2020

IEEE Commun. Surv. Tutorials

469

200

View full text Add to dashboard Cite

show abstract

“…In addition, the two categorising algorithms are computational complex. Grouping based on STAs' traffic, Chang et al [14] propose a traffic-aware grouping scheme to improve channel utilisation. STAs are grouped based on their greedy need to access the channel.…”

Section: Related Workmentioning

confidence: 99%

“…Chang et al [14] only consider UL traffic in grouping STAs, and its model is solely analysed in terms of channel utilisation. In the aspect of the grouping scheme, the geographical grouping proposed in this paper is different from [13,14] in terms of simplicity. AP obtains the physical location of STAs through their requests during the authentication/association process.…”

Section: Related Workmentioning

confidence: 99%

Extreme power saving directional MAC protocol in IEEE 802.11ah networks

2020

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In this study, a novel directional medium access control scheme with an extreme power saving mechanism is developed for traffic indication map stations in the IEEE 802.11ah networks. To improve the aggregate throughput, stations are managed based on their geographical locations in combination with the employment of directional operation mode on the access point. In addition to the restricted access window mechanism used in the IEEE 802.11ah standard for power saving, an adaptive transmission power scheme is proposed for uplink transmission to leverage the power-saving efficiency. With the proposed access scheme, the network performance is significantly enhanced compared to the IEEE 802.11ah standard. The analytical models for throughput and energy consumption are formulated using Markov Chains under unsaturated conditions. The simulation results indicate the effectiveness of the proposed directional access scheme under various network scenarios.

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“…However, their utility is limited for real-time station grouping under dynamic and realistic traffic conditions. Chang et al went a step further to support more diverse traffic demands [21]. They used the results of two extreme cases (i.e., with infinite traffic and with only a single packet) to extrapolate a regression-based analytical model that can accurately fit the contention success probability of any traffic patterns.…”

Section: Related Work On Ieee 80211ah Rawmentioning

confidence: 99%

Optimization-Oriented RAW Modeling of IEEE 802.11ah Heterogeneous Networks

Tian

López-Aguilera

García-Villegas

et al. 2019

IEEE Internet Things J.

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The new medium access method of IEEE 802.11ah, called Restricted Access Window (RAW), divides stations into different groups, and only allows stations in the same group to access the channel simultaneously, in order to reduce collisions and thus achieve better performance (e.g., throughput). However, the existing station grouping strategies only support homogeneous scenarios where all stations use the same modulation and coding scheme (MCS) and packet size. A surrogate model is an efficient mathematical model that represents the behavior of a complex system, trained with a limited set of labeled input-output data samples. In this paper, we present a surrogate model that can accurately predict RAW performance under a given Restricted Access Window (RAW) configuration in heterogeneous networks. Different from the homogeneous scenario, heterogeneous networks are defined by a large number of parameters, leading to an enormous design space, i.e., the order of 10 17 possible data points. This is too big to achieve feasible training convergence. In this paper, we present a novel training methodology that leads to a new design space with highly reduced size, i.e., the order of 10 5 data points. The surrogate model converges when less than 6000 labeled data points are used for training, which is only a tiny portion of the whole design space. The results show that, the relative error between model prediction and simulation results is less than 0.1 for 95% of the data points, in the areas of the design space studied. Its low complexity and high precision make the proposed model a valuable tool to develop real-time RAW optimization algorithms for heterogeneous IEEE 802.11ah networks.Index Terms-IEEE 802.11ah, RAW, surrogate model, heterogeneous networks.

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Traffic-Aware Sensor Grouping for IEEE 802.11ah Networks: Regression Based Analysis and Design

Cited by 51 publications

References 27 publications

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Extreme power saving directional MAC protocol in IEEE 802.11ah networks

Optimization-Oriented RAW Modeling of IEEE 802.11ah Heterogeneous Networks

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