An Intelligent Load Control-Based Random Access Scheme for Space-Based Internet of Things
Random access is one of the most competitive multiple access schemes for future space-based Internet of Things (S-IoT) due to its support for massive connections and grant-free transmission, as well as its ease of implementation. However, firstly, existing random access schemes are highly sensitive...
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doaj-e5ef99b0a8a04fb1a3c5124d9475d0a82021-02-04T00:03:59ZengMDPI AGSensors1424-82202021-02-01211040104010.3390/s21041040An Intelligent Load Control-Based Random Access Scheme for Space-Based Internet of ThingsChangjiang Fei0Bin Jiang1Kun Xu2Lei Wang3Baokang Zhao4College of Information and Communication, National University of Defense Technology, Wuhan 430010, ChinaCollege of Information and Communication, National University of Defense Technology, Wuhan 430010, ChinaCollege of Information and Communication, National University of Defense Technology, Wuhan 430010, ChinaCollege of Information and Communication, National University of Defense Technology, Wuhan 430010, ChinaCollege of Computer, National University of Defense Technology, Changsha 410073, ChinaRandom access is one of the most competitive multiple access schemes for future space-based Internet of Things (S-IoT) due to its support for massive connections and grant-free transmission, as well as its ease of implementation. However, firstly, existing random access schemes are highly sensitive to load: once the load exceeds a certain critical value, the throughput will drop sharply due to the increased probability of data collision. Moreover, due to variable satellite coverage and bursty traffic, the network load of S-IoT changes dynamically; therefore, when existing random access schemes are applied directly to the S-IoT environment, the actual throughput is far below the theoretical maximum. Accordingly, this paper proposes an intelligent load control-based random access scheme based on CRDSA++, which is an enhanced version of the contention resolution diversity slotted ALOHA (CRDSA) and extends the CRDSA concept to more than two replicas. The proposed scheme is dubbed load control-based three-replica contention resolution diversity slotted ALOHA (LC-CRDSA3). LC-CRDSA3 actively controls network load. When the load threatens to exceed the critical value, only certain nodes are allowed to send data, and the load is controlled to be near the critical value, thereby effectively improving the throughput. In order to accurately carry out load control, we innovatively propose a maximum likelihood estimation (MLE)-based load estimation algorithm, which estimates the load value of each received frame by making full use of the number of time slots in different states. On this basis, LC-CRDSA3 adopts computational intelligence-based time series forecasting technology to predict the load values of future frames using the historical load values. We evaluated the performance of LC-CRDSA3 through a series of simulation experiments and compared it with CRDSA++. Our experimental results demonstrate that in S-IoT contexts where the load changes dynamically, LC-CRDSA3 can obtain network throughput that is close to the theoretical maximum across a wide load range through accurate load control.https://www.mdpi.com/1424-8220/21/4/1040random accessspace-based Internet of ThingsInternet of Thingsartificial neural networkssupport vector machines |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Changjiang Fei Bin Jiang Kun Xu Lei Wang Baokang Zhao |
spellingShingle |
Changjiang Fei Bin Jiang Kun Xu Lei Wang Baokang Zhao An Intelligent Load Control-Based Random Access Scheme for Space-Based Internet of Things Sensors random access space-based Internet of Things Internet of Things artificial neural networks support vector machines |
author_facet |
Changjiang Fei Bin Jiang Kun Xu Lei Wang Baokang Zhao |
author_sort |
Changjiang Fei |
title |
An Intelligent Load Control-Based Random Access Scheme for Space-Based Internet of Things |
title_short |
An Intelligent Load Control-Based Random Access Scheme for Space-Based Internet of Things |
title_full |
An Intelligent Load Control-Based Random Access Scheme for Space-Based Internet of Things |
title_fullStr |
An Intelligent Load Control-Based Random Access Scheme for Space-Based Internet of Things |
title_full_unstemmed |
An Intelligent Load Control-Based Random Access Scheme for Space-Based Internet of Things |
title_sort |
intelligent load control-based random access scheme for space-based internet of things |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2021-02-01 |
description |
Random access is one of the most competitive multiple access schemes for future space-based Internet of Things (S-IoT) due to its support for massive connections and grant-free transmission, as well as its ease of implementation. However, firstly, existing random access schemes are highly sensitive to load: once the load exceeds a certain critical value, the throughput will drop sharply due to the increased probability of data collision. Moreover, due to variable satellite coverage and bursty traffic, the network load of S-IoT changes dynamically; therefore, when existing random access schemes are applied directly to the S-IoT environment, the actual throughput is far below the theoretical maximum. Accordingly, this paper proposes an intelligent load control-based random access scheme based on CRDSA++, which is an enhanced version of the contention resolution diversity slotted ALOHA (CRDSA) and extends the CRDSA concept to more than two replicas. The proposed scheme is dubbed load control-based three-replica contention resolution diversity slotted ALOHA (LC-CRDSA3). LC-CRDSA3 actively controls network load. When the load threatens to exceed the critical value, only certain nodes are allowed to send data, and the load is controlled to be near the critical value, thereby effectively improving the throughput. In order to accurately carry out load control, we innovatively propose a maximum likelihood estimation (MLE)-based load estimation algorithm, which estimates the load value of each received frame by making full use of the number of time slots in different states. On this basis, LC-CRDSA3 adopts computational intelligence-based time series forecasting technology to predict the load values of future frames using the historical load values. We evaluated the performance of LC-CRDSA3 through a series of simulation experiments and compared it with CRDSA++. Our experimental results demonstrate that in S-IoT contexts where the load changes dynamically, LC-CRDSA3 can obtain network throughput that is close to the theoretical maximum across a wide load range through accurate load control. |
topic |
random access space-based Internet of Things Internet of Things artificial neural networks support vector machines |
url |
https://www.mdpi.com/1424-8220/21/4/1040 |
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