scholarly journals CoRL: Collaborative Reinforcement Learning-Based MAC Protocol for IoT Networks

Electronics ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 143 ◽  
Author(s):  
Taegyeom Lee ◽  
Ohyun Jo ◽  
Kyungseop Shin
Keyword(s):  
Reinforcement Learning ◽  
Mac Protocol ◽  
Wireless Channel ◽  
System Level ◽  
Convergence Time ◽  
Value Functions ◽  
Medium Access ◽  
Essential Requirement ◽  
Q Learning ◽  
Collision Problem

Devices used in Internet of Things (IoT) networks continue to perform sensing, gathering, modifying, and forwarding data. Since IoT networks have a lot of participants, mitigating and reducing collisions among the participants becomes an essential requirement for the Medium Access Control (MAC) protocols to increase system performance. A collision occurs in wireless channel when two or more nodes try to access the channel at the same time. In this paper, a reinforcement learning-based MAC protocol was proposed to provide high throughput and alleviate the collision problem. A collaboratively predicted Q-value was proposed for nodes to update their value functions by using communications trial information of other nodes. Our proposed protocol was confirmed by intensive system level simulations that it can reduce convergence time in 34.1% compared to the conventional Q-learning-based MAC protocol.

scholarly journals Packet Flow Based Reinforcement Learning MAC Protocol for Underwater Acoustic Sensor Networks

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2284
Author(s):  
Ibrahim B. Alhassan ◽  
Paul D. Mitchell
Keyword(s):  
Sensor Networks ◽  
Mac Protocol ◽  
Propagation Delay ◽  
Basic Function ◽  
Acoustic Sensor ◽  
Underwater Acoustic Sensor Networks ◽  
Medium Access ◽  
Underwater Acoustic ◽  
Q Learning ◽  
Acoustic Sensor Networks

Medium access control (MAC) is one of the key requirements in underwater acoustic sensor networks (UASNs). For a MAC protocol to provide its basic function of efficient sharing of channel access, the highly dynamic underwater environment demands MAC protocols to be adaptive as well. Q-learning is one of the promising techniques employed in intelligent MAC protocol solutions, however, due to the long propagation delay, the performance of this approach is severely limited by reliance on an explicit reward signal to function. In this paper, we propose a restructured and a modified two stage Q-learning process to extract an implicit reward signal for a novel MAC protocol: Packet flow ALOHA with Q-learning (ALOHA-QUPAF). Based on a simulated pipeline monitoring chain network, results show that the protocol outperforms both ALOHA-Q and framed ALOHA by at least 13% and 148% in all simulated scenarios, respectively.

scholarly journals The Impact of Dynamic RTS Threshold Adjustment for IEEE 802.11 MAC Protocol

2009 ◽  
Vol 5 (1) ◽  
pp. 5-20 ◽  
Author(s):  
Mostafa Mjidi ◽  
Debasish Chakraborty ◽  
Naoki Nakamura ◽  
Norio Shiratori
Keyword(s):  
Ieee 802.11 ◽  
Data Transmission ◽  
Mac Protocol ◽  
Threshold Value ◽  
Local Area ◽  
Wireless Channel ◽  
Main Element ◽  
Medium Access ◽  
Hidden Terminal ◽  
The Impact

In recent years, wireless technologies and application received great attention. The Medium Access Control (MAC) protocol is the main element that determines the efficiency in sharing the limited communication bandwidth of the wireless channel in wireless local area networks (WLANs). IEEE 802.11 introduced the optional RTS/CTS handshaking mechanism to address the hidden terminal problem as well as to reduces the chance of collision in case of higher node density and traffic. RTS Threshold (RT) determines when RTS/CTS mechanism should be used and proved to be an important parameter for performance characteristics in data transmission. We first investigate to find a meaningful threshold value according to the network situation and determine the impact of using or disengaging the RTS/CTS optional mechanism and dynamically adjust the RTS Threshold to maximize data transmission. The results show a significant improvement over existing CSMA/CA and RTS/CTS schemes. Our adaptive scheme performed even better when data rate increases. We verify our proposed scheme both analytically and with extensive network simulation using ns-2.

scholarly journals A Hybrid Algorithm in Reinforcement Learning for Crowd Simulation

2020 ◽  
Vol 8 (6) ◽  
pp. 5251-5255
Keyword(s):  
Reinforcement Learning ◽  
Value Function ◽  
Crowd Simulation ◽  
Value Functions ◽  
Q Learning ◽  
Efficient Measurement ◽  
Multi Agent ◽  
Hybrid Agent ◽  
Multiple Value ◽  
Transportation Applications

Exploiting the efficiency and stability of Dynamic Crowd, the paper proposes a hybrid crowd simulation algorithm that runs using multi agents and it mainly focuses on identifying the crowd to simulate. An efficient measurement for both static and dynamic crowd simulation is applied in tracking and transportation applications. The proposed Hybrid Agent Reinforcement Learning (HARL) algorithm combines the Q-Learning off-policy value function and SARSA algorithm on-policy value function, which is used for dynamic crowd evacuation scenario. The HARL algorithm performs multiple value functions and combines the policy value function derived from the multi agent to improve the performance. In addition, the efficiency of the HARL algorithm is able to demonstrate in varied crowd sizes. Two kinds of applications are used in Reinforcement Learning such as tracking applications and transportation monitoring applications for pretending the crowd sizes.

scholarly journals Reinforcement-Learning Based Dynamic Transmission Range Adjustment in Medium Access Control for Underwater Wireless Sensor Networks

Electronics ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1727
Author(s):  
Dmitrii Dugaev ◽  
Zheng Peng ◽  
Yu Luo ◽  
Lina Pu
Keyword(s):  
Energy Efficiency ◽  
Reinforcement Learning ◽  
Access Control ◽  
Medium Access Control ◽  
Network Performance ◽  
Energy Savings ◽  
Learning Algorithm ◽  
Mac Protocol ◽  
Transmission Range ◽  
Medium Access

In this paper, we propose a reinforcement learning (RL) based Medium Access Control (MAC) protocol with dynamic transmission range control (TRC). This protocol provides an adaptive, multi-hop, energy-efficient solution for communication in underwater sensors networks. It features a contention-based TRC scheme with a reactive multi-hop transmission. The protocol has the ability to adjust to network conditions using RL-based learning algorithm. The combination of TRC and RL algorithms can hit a balance between the energy consumption and network performance. Moreover, the proposed adaptive mechanism for relay-selection provides better network utilization and energy-efficiency over time, comparing to existing solutions. Using a straightforward ALOHA-based channel access alongside “helper-relays” (intermediate nodes), the protocol is able to obtain a substantial amount of energy savings, achieving up to 90% of the theoretical “best possible” energy efficiency. In addition, the protocol shows a significant advantage in MAC layer performance, such as network throughput and end-to-end delay.

scholarly journals Learning nodes: machine learning-based energy and data management strategy

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Yunmin Kim ◽  
Tae-Jin Lee
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Mac Protocol ◽  
Energy Resource ◽  
Machine Learning Algorithms ◽  
Medium Access ◽  
Q Learning ◽  
Use Of Resources ◽  
Viable Solution ◽  
The Internet Of Things

AbstractThe efficient use of resources in wireless communications has always been a major issue. In the Internet of Things (IoT), the energy resource becomes more critical. The transmission policy with the aid of a coordinator is not a viable solution in an IoT network, since a node should report its state to the coordinator for scheduling and it causes serious signaling overhead. Machine learning algorithms can provide the optimal distributed transmission mechanism with little overhead. A node can learn by itself by utilizing the machine learning algorithm and make the optimal transmission decision on its own. In this paper, we propose a novel learning Medium Access Control (MAC) protocol with learning nodes. Nodes learn the optimal transmission policy, i.e., minimizing the data and energy queue levels, using the Q-learning algorithm. The performance evaluation shows that the proposed scheme enhances the queue states and throughput.

scholarly journals Cross-Layer Analysis of Multi-Static RFID Systems Exploiting Capture Diversity

2021 ◽  
Author(s):  
Roberto Valentini ◽  
Piergiuseppe Di Marco ◽  
Roberto Alesii ◽  
Fortunato Santucci
Keyword(s):  
Mac Protocol ◽  
Wireless Channel ◽  
Cross Layer ◽  
Mac Layer ◽  
Medium Access ◽  
Rfid Systems ◽  
Layer Analysis ◽  
Passive Rfid ◽  
The Impact ◽  
Collision Mechanism

In this paper, we propose a framework for cross-layer analysis of multi-static passive RFID systems. The model takes into account details of the shared wireless channel, including fading and capture effect, whereas, at the medium access control (MAC) layer, the anti-collision mechanism proposed in the EPC Generation 2 standard is taken as a reference.<br>To address the complexity of the system model, we rely on a semi-analytical approach, that combines a moment matching approximation method to abstract the physical layer and Monte-Carlo simulations to describe the MAC dynamics.<br>Furthermore, based on the space diversity feature offered by the multi-static settings, we introduce the concept of capture deiversity and propose a modification to the standard to fully support this form of diversity.<br>Numerical results show the impact of deployment conditions and the relative positions of interrogator, tags, and detection points on the performance of tags' identification. We show how the number of detection points impacts the system performance under various channel conditions and MAC parameters' settings. Finally, we validate the proposed update of the MAC protocol, showing substantial performance improvement with respect to the standard collision resolution policy.

scholarly journals Cross-Layer Analysis of Multi-Static RFID Systems Exploiting Capture Diversity

2021 ◽  
Author(s):  
Roberto Valentini ◽  
Piergiuseppe Di Marco ◽  
Roberto Alesii ◽  
Fortunato Santucci
Keyword(s):  
Mac Protocol ◽  
Wireless Channel ◽  
Cross Layer ◽  
Mac Layer ◽  
Medium Access ◽  
Rfid Systems ◽  
Layer Analysis ◽  
Passive Rfid ◽  
The Impact ◽  
Collision Mechanism

In this paper, we propose a framework for cross-layer analysis of multi-static passive RFID systems. The model takes into account details of the shared wireless channel, including fading and capture effect, whereas, at the medium access control (MAC) layer, the anti-collision mechanism proposed in the EPC Generation 2 standard is taken as a reference.<br>To address the complexity of the system model, we rely on a semi-analytical approach, that combines a moment matching approximation method to abstract the physical layer and Monte-Carlo simulations to describe the MAC dynamics.<br>Furthermore, based on the space diversity feature offered by the multi-static settings, we introduce the concept of capture deiversity and propose a modification to the standard to fully support this form of diversity.<br>Numerical results show the impact of deployment conditions and the relative positions of interrogator, tags, and detection points on the performance of tags' identification. We show how the number of detection points impacts the system performance under various channel conditions and MAC parameters' settings. Finally, we validate the proposed update of the MAC protocol, showing substantial performance improvement with respect to the standard collision resolution policy.

scholarly journals A Survey on Scheduling Based Mac Protocol for Congestion Minimization in Internet of Things (IoT)

2021 ◽  
Vol 12 (2) ◽  
pp. 963-969
Author(s):  
Abhijit Biswas, Et. al.
Keyword(s):  
Wireless Sensor Networks ◽  
Internet Of Things ◽  
Mac Protocol ◽  
Wireless Channel ◽  
Wireless Sensor ◽  
Smart Environment ◽  
Wireless Technology ◽  
Medium Access ◽  
Simulated Performance ◽  
Conventional Algorithm

Recently, in Internet era the most common technology ubiquitous to develop smart environment is Internet of things (IoT) and Wireless Sensor Networks (WSNs).These technologies deployed enormously to formulate wide applications in area of Smart homes, Industrial automation, and security destined applications and information trailing. The huge development in wireless technology is due to great exploration in MEMS concept and Embedded Systems. Huge evolution in this technique leads to access different Medium Access Control (MAC) protocol and this protocol used to access multiple nodes peculiarly in wireless channel. The projected MAC protocol designed to enhance network lifetime. Essentially, the network leads to lot of congestion due to non-availability of IoT equipment and less available resources for various environmental applications. The simulated performance ensures that the conventional algorithm limits their dynamic service quality for IoT based applications. The above setbacks motivate the researches to develop the survey in existing scheduling based MAC protocol by highlighting their parameters.

scholarly journals Collision Prevention for Duty-Cycle Receiver-Initiation MAC Protocol via Multiple Access Reservation (MAR-RiMAC)

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 127
Author(s):  
Omer Gurewitz ◽  
Oren Zaharia
Keyword(s):  
Mac Protocol ◽  
Wireless Channel ◽  
The Internet ◽  
Wireless Devices ◽  
Medium Access ◽  
Daily Lives ◽  
Channel One ◽  
Multiple Devices ◽  
Time Utilization ◽  
Intended Receiver

The prevalence of the Internet of Things (IoT) paradigm in more and more applications associated with our daily lives has induced a dense network in which numerous wireless devices, many of which have limited capabilities (e.g., power, memory, computation), need to communicate with the internet. One of the main bottlenecks of this setup is the wireless channel. Numerous medium access control (MAC) protocols have been devised to coordinate between devices that share the wireless channel. One prominent approach that is highly suitable for IoT and wireless sensor networks (WSNs), which rely on duty cycling, is the receiver-initiated approach, in which, rather than the transmitter, the receiver initiates the transaction. The problem with this approach is that when many devices are trying to respond to the receiver’s transmission invitation and transmit simultaneously, a collision occurs. When the network is highly loaded, resolving such collisions is quite tedious. In this paper, we devise an enhancement to the receiver-initiated approach that aims at preventing this inherent collision scenario. Our modification relies on multiple devices sending a short predefined signal, informing their intended receiver of their intention to transmit simultaneously. The data transaction is done via a four-way handshake in which, after all backlogged devices have informed their designated receiver of their desire to transmit simultaneously, the receiver identifies them and polls them one by one, avoiding the collision. We compare the performance of Receiver-Initiated-MAC protocol (RI-MAC), which is one of the prevalent receiver-initiated protocols, with and without the suggested enhancement, and show superior air-time utilization under high traffic loads, especially in the presence of hidden terminals.

TRIPLEPR-MAC: A Triple Queue Priority Based Medium Access Control Protocol for Wireless Sensor Network

2019 ◽  
Vol 09 ◽  
Author(s):  
Rinkuben N. Patel ◽  
Nirav V. Bhatt
Keyword(s):  
Sensor Network ◽  
Data Transmission ◽  
Energy Efficient ◽  
Power Efficiency ◽  
Mac Protocol ◽  
Medium Access Control Protocol ◽  
Quality Factors ◽  
Medium Access ◽  
Critical Fields ◽  
Storage Unit

Background: WSN is a network of smart tiny electromechanical devices named as sensors. Sensors perform various tasks like sensing the environment as per its range, transmit the data using transmission units, store the data in the storage unit and perform an action based on captured data. As they are installed in an unfriendly environment, to recharge the sensors are not possible every time which leads to a limited lifetime of a network. To enhance the life of a sensor network, the network required energy-efficient protocols. Various energy-efficient MAC protocols are developed by Research community, but very few of them are integrated with the priority-based environment which performs the priority-based data transmission. Another challenge of WSN is, most of the WSN areas are delay-sensitive because it is implemented in critical fields like military, disaster management, and health monitoring. Energy, Delay, and throughput are major quality factors that affect the sensor network. Objective: In this paper, the aim is to design and develop a MAC Protocol for a field like the military where the system requires energy efficiency and priority-based data transmission. Method: In the proposed model, the cluster-based network with priority queues are formed that can achieve higher power efficiency and less delay for sensitive data. Results: In this research simulation of Proposed MAC, TMAC and SMAC are done with different numbers of nodes, same inter-packet intervals, and variant inter-packet intervals. Based on the script simulation, result graphs are generated. Conclusion: The proposed work achieves greater lifetime compared to TMAC and SMAC using priority-based data transmission.

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