Backoff Algorithm Research Articles

Effect of Variable Backoff Algorithms on Age of Information in Slotted ALOHA Networks

Effect of Variable Backoff Algorithms on Age of Information in Slotted ALOHA Networks

Improved Adaptive Backoff Algorithm for Optimal Channel Utilization in Large-Scale IEEE 802.15.4-Based Wireless Body Area Networks

Improved Adaptive Backoff Algorithm for Optimal Channel Utilization in Large-Scale IEEE 802.15.4-Based Wireless Body Area Networks

Optimal Sliding Window Selection in Non-Overlapping Backoff Algorithm for IEEE 802.15.6 WBAN

Optimal Sliding Window Selection in Non-Overlapping Backoff Algorithm for IEEE 802.15.6 WBAN

Energy efficient optimal parent selection with Chameleon Swarm algorithm and non-overlapping backoff algorithm based dynamic slot allocation in wireless body area network

Energy efficient optimal parent selection with Chameleon Swarm algorithm and non-overlapping backoff algorithm based dynamic slot allocation in wireless body area network

MR-SFAMA-Q: A MAC Protocol based on Q-Learning for Underwater Acoustic Sensor Networks

<p>In recent years, with the rapid development of science and technology, many new technologies have made people’s exploration of the ocean deeper and deeper, and due to the requirements of national defense and marine development, the underwater acoustic sensor network (UASN) has been paid more and more attention. Nevertheless, the underwater acoustic channel has the properties of considerable propagation delay, limited bandwidth, and unstable network topology. In order to improve the performance of the medium access control (MAC) protocol in UASN, we propose a new MAC protocol based on the Slotted-FAMA of Multiple Reception (MR-SFAMA) protocol. The protocol uses the Q-Learning algorithm to optimize the multi-receiver handshake mechanism. The current state is judged according to the received node request, and the Q-table is established. Through the multi-round interaction between the node and the environment, the Q-table is continuously updated to obtain the optimal strategy and determine the optimal data transmission scheduling scheme. The reward function is set according to the total back-off time and frame error rate, which can reduce the packet loss rate during network data transmission while reducing the delay. In addition, the matching asynchronous operation and uniform random back-off algorithm are used to solve the problem of long channel idle time and low channel utilization. This new protocol can be well applied to unstable network topology. The simulation results show that the protocol performs better than Slotted-FAMA and MR-SFAMA regarding delay and normalized throughput.</p> <p> </p>

Dynamic adaptation of contention window boundaries using deep Q networks in UAV swarms

In flying ad hoc networks (FANET), medium access layer (MAC) protocols play an essential role in ensuring better network performance. The effective utilization of network resources and providing access fairness are key research issues in this area, and the contention window size directly influences these factors. This paper mainly focuses on addressing the upper and lower boundaries of the contention window in Carrier Sense Multiple Access/Collision Avoidance (CSMA/CA) with a binary exponential back-off algorithm. Existing solutions include static and dynamic window adjustment techniques only to update the upper boundary of the contention window size. However, the static window adjustment technique is unsuitable for changing network conditions, while the dynamic window adjustment technique may need to be more efficient in dense network environments. The proposed solution uses a Deep Q-learning algorithm(DQN) framework to adjust the contention window adaptively based on the reward function, resulting in efficient and dynamic adaptation to changing network conditions among unmanned aerial vehicles (UAV). The proposed methodology is evaluated through simulations, and performance is measured using channel utilization, efficiency, delay, and collision rate. The simulation findings show that, in comparison to generic MAC protocols, the proposed algorithm is 5% more efficient in throughput and incurs a four-fold reduction in delay.

A Rapidly Adaptive Collision Backoff Algorithm for Improving the Throughput in WLANs

In the 802.11 protocol, the fundamental medium access mechanism is called Distributed Coordination Function (DCF). In DCF, before making any transmission attempt, the nodes count down a timer with a value randomly selected from the Contention Window (CW) size. If the transmitted packet is involved in a collision, the node increases the CW size in an attempt to reduce the collision rate. Conversely, if the packet is transmitted successfully, the node reduces the CW size in order to increase the frequency of the transmission attempts. The growth or reduction in the CW size has a critical effect on the network performance. Several backoff algorithms have been proposed to improve the system throughput. However, none of these methods enable the system to approach the theoretical maximum throughput possible under DCF. Accordingly, this study proposes the Rapidly Adaptive Collision Backoff (RACB) algorithm, in which the CW size is adjusted dynamically based on the collision rate, as analyzed by a mathematical model. Notably, RACB requires no knowledge of the number of nodes in the wireless network and is applicable to both lightly loaded and heavily loaded networks. The numerical results show that, by adjusting the CW size such that the collision rate is maintained at a value close to 0.1, RACB enables the system throughput to approach the maximum DCF throughput in wireless environments containing any number of nodes.

Applying the Random Backoff Algorithm for Active Information Exchange Control in Smart Internet Environment Server

The information exchange-as a way of interaction for participants in an Internet environment-is a fundamental function of a distributed system within the computing paradigms of Internet of Things, Cyber-Physical Systems, Ambient Intelligence etc. Participants in an Internet environment are represented by software agents running typically on low-performance edge computing devices. Interaction is implemented through exchange of information collected by the participants in a shared information store that implements the «intelligence» of this environment. The organization of agents access to the shared information and of their interaction uses a semantic information broker that manages information flows between participants. With an increase in the number, diversity, and activity of participants, the workload on the broker and the data transmission network increases. In this paper, a novel method is proposed to reduce the workload by delegating part of the control of the information exchange to the participants themselves. The subscription mechanism is modified for information changes in a shared information store. Instead of the passive op­tion, when the broker must notify all agents who have subscribed to the information being changed, the active option is used, when agents themselves can check for information changes. A new two-component control method based on the well-known backoff algorithm in combination with the well-known adaptive strategy for choosing the time of sending the next request from an agent to a broker for the presence of information changes is proposed. The control capabilities are studied experimentally using a simulated Internet environment.

Performance Evaluation of Triangular Number Sequence Backoff Algorithm for Constrained Application Protocol

In the of the Internet of Things (IoT) realm, congestion is considered a serious issue affecting network throughput due to the requirement of multiple nodes for message exchange. With free-space optical communications, which can help send a message wirelessly, congestion control mechanisms nowadays depend upon the carrier sense of multiple access with collision avoidance (CSMA/CA), using the so-called Backoff Algorithm (BA). These algorithms,

Time-Offset ALOHA With SIC

Internet-of-Things (IoT) applications for real-time control gradually increase and become computationally demanding. To provide better quality-of-service (QoS) in random access (RA) system based on slotted ALOHA (S-ALOHA), this work proposes a novel S-ALOHA system with cross-slot successive interference cancellation (SIC). To facilitate SIC, we design each slot with several time offsets (TOs) and one packet transmission time, where the length of overall TOs is a fraction of a packet transmission time. Users (re)transmit at the boundary of a TO randomly selected. This enables the base station (BS) to distinguish who makes the first and last transmissions in a collision slot and ask immediate retransmissions from them in the subsequent one or two slots. With these retransmitted packets, the BS performs SIC for the previously collided packets. We analyze the system throughput and the distribution of RA delay. The results show that the proposed system can achieve throughput from 0.5 (packets per packet transmission time) at minimum to 0.856 at maximum, depending on the number of TOs and the length of TO. In addition, to run this system stably, we propose a Bayesian-optimized backoff algorithm that enables users to use throughput-optimal (re)transmission probability. It is demonstrated that the proposed backoff algorithms can achieve the throughput close to genie-aided (GA) system.

Reinforcement Learning-based Wi-Fi Contention Window Optimization

The collision avoidance mechanism adopted by the IEEE 802.11 standard is not optimal. The mechanism employs a binary exponential backoff (BEB) algorithm in the medium access control (MAC) layer. Such an algorithm increases the backoff interval whenever a collision is detected to minimize the probability of subsequent collisions. However, the increase of the backoff interval causes degradation of the radio spectrum utilization (i.e., bandwidth wastage). That problem worsens when the network has to manage the channel access to a dense number of stations, leading to a dramatic decrease in network performance. Furthermore, a wrong backoff setting increases the probability of collisions such that the stations experience numerous collisions before achieving the optimal backoff value. Therefore, to mitigate bandwidth wastage and, consequently, maximize the network performance, this work proposes using reinforcement learning (RL) algorithms, namely Deep Q Learning (DQN) and Deep Deterministic Policy Gradient (DDPG), to tackle such an optimization problem. In our proposed approach, we assess two different observation metrics, the average of the normalized level of the transmission queue of all associated stations and the probability of collisions. The overall network’s throughput is defined as the reward. The action is the contention window (CW) value that maximizes throughput while minimizing the number of collisions. As for the simulations, the NS-3 network simulator is used along with a toolkit known as NS3-gym, which integrates a reinforcement-learning (RL) framework into NS-3. The results demonstrate that DQN and DDPG have much better performance than BEB for both static and dynamic scenarios, regardless of the number of stations. Additionally, our results show that observations based on the average of the normalized level of the transmission queues have a slightly better performance than observations based on the collision probability. Moreover, the performance difference with BEB is amplified as the number of stations increases, with DQN and DDPG showing a 45.52% increase in throughput with 50 stations.

Exploiting in-Slot Micro-Synchronism for S-ALOHA

Proliferation of the urban Internet-of-Things (IoTs) for smart cities has fuelled massive amounts of data over wireless cellular networks. Random access (RA) system of wireless cellular networks, e.g., 5G New Radio (NR), based on S-ALOHA system should cope with ever-growing IoT traffic. This work proposes S-ALOHA system with time offsets (TOs), where one slot consists of K TOs and one packet transmission time. The length of the overall TOs is a fraction of a packet transmission time. In the system users (re)transmit to the boundary of a TO randomly selected. This enables the base station (BS) to inform the users of who transmits the first and the last packets in the slot with collision so that the two users can retransmit successfully in the following two slots respectively. Our throughput analysis compared to simulations shows that adopting even with three and four TOs surpasses the throughput limit of S-ALOHA system without TOs. Additionally, we propose two Bayesian-optimized backoff algorithms for S-ALOHA system with TOs, with which users can apply throughput-optimal (re)transmission probability or uniform backoff window even in unsaturated traffic scenarios. Numerical results demonstrate that the proposed backoff algorithms can achieve the throughput close to an ideal system and drastically reduce the access delay compared to S-ALOHA system.

Online Transmission Control for Random Access With Multipacket Reception and Reservation

A larger capacity of random access (RA) channel is demanded to cope with massive accesses from Internet of Things (IoT) devices. To do this, this work proposes multipacket reception (MPR) S-ALOHA with reservation: When making a successful RA, the user can reserve the channel for the next slot transmission with probability <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$r$ </tex-math></inline-formula> , if its queue is not yet empty. This reservation can be continued with probability <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$r$ </tex-math></inline-formula> until its queue becomes empty. In this system, as the number of the reserved channels grows, the number of available capacity for RA reduces. Thus, the number of RA attempting users needs to be controlled to avoid collision, whereas the unbounded growth of other users’ queue should be prevented. This work analyzes the throughput and stability condition of the proposed system and designs a throughput-optimal backoff algorithm based on the backlog size estimation. In the numerical studies, MPR S-ALOHA without reservation and time division multiple access (TDMA) are compared as benchmarks. As a result, it is proven that as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$r\rightarrow 1$ </tex-math></inline-formula> , the MPR channel capacity is fully utilized if the proposed RA algorithm is jointly used with the reservation scheme. Moreover, it is demonstrated that the system can be also stabilized by the proposed backoff algorithm.

A Handshake-Based Low Delay MAC Protocol in Hybrid Topology for Urban Lake Monitoring

Underwater acoustic sensor networks (UASNs), which adopt acoustic communication, have been widely used underwater, such as in oceans and rivers, for monitoring applications. However, the UASNs designed for urban lake monitoring are facing challenges and are seldom regarded. Based on the analysis of the features of the urban lakes and the monitoring requirements, a UASN system is designed in this article. The node deployment strategy is discussed to balance the node density and transmission bandwidth. Then a hybrid topology is proposed to meet the monitoring requirements, in which the nodes on the water surface construct a 2-D ring topology, and the nodes underwater construct a 3-D grid structure. Based on this topology, a media access control (MAC) protocol-improved handshake-based low delay (i-HALO) is designed which improves the existing handshaking mechanism with a dynamic adaptive backoff algorithm to avoid unnecessary back off. As the result, the high delay problem is addressed and the “one handshake, multiple transmission” is realized. The channel access delay is analyzed theoretically and the formula of waiting time for a frame before sending is given. The performance of throughput and transmission delay under different node densities and frame lengths are simulated. The results indicate that a node having about 13 neighbors will have the best performance on throughput, and when the frame length is longer than 100 bytes, the throughput and delay will have fluctuation. In comparison with existing protocols, i-HALO has a better performance on throughput, delay, frame reception rate (FRR), and fairness.

Design and Performance Analysis of 3-D Markov-Chain-Model-Based Fair Spectrum-Sharing Access for IoT Services

The spectrum-sharing access technology using unlicensed spectrum bands is considered as a promising solution to solve the spectrum deficiency problem for various Internet of Things services. But, the efficient and fairness-guaranteed spectrum-sharing access among different systems is challenging by using the listen-before-talk (LBT) technology due to the uncertain channel quality and the channel access collision issues in unlicensed spectrum bands. To solve these problems, a novel three dimension-based Markov chain model is designed to formulate the collision probability of the spectrum-sharing access process using the contention window (CW) back-off algorithm based on the channel quality indicator feedback information. The key reasons for the packet transmission failure are comprehensively analyzed by considering both the channel collision and the channel quality deterioration conditions. A fairness-based spectrum-sharing access algorithm is proposed by assigning the optimal CW for the LBT system to minimize the collision probability and guarantee the fairness among LBT and wireless fidelity coexisted systems. Hardware platform-based evaluation results prove that our proposed algorithms can improve the system throughput and guarantee the fairness among different systems in the unlicensed spectrum band.

Utilization of a Wireless Network Performing CSMA/CA with Random Backoff Algorithm

Optimizing WLAN performance in IEEE 802.11 network with extremely different working environments is a difficult task. We must use a network that can provide the most benefits to its end-users to improve network strength and data transmission. To increase performance in this congested situation, we construct an adaptive backoff algorithm for the IEEE 802.11 DCF that enhances in congested network conditions, system throughput is improved, the collision probability is reduced, and good fairness is maintained. This paper deals with the utilization of networks for different backoff strategies for different network conditions. The results obtained were comparatively much better than the existing methods.

Collision-Based Window-Scaled Back-Off Mechanism for Dense Channel Resource Allocation in Future Wi-Fi

Wireless local area networks (WLANs), known as Wi-Fi, are widely deployed to meet the enhanced needs of data-centric internet applications, such as wireless docking, unified communications, cloud computing, interactive multimedia gaming, progressive streaming, support of wearable devices, up-link broadcasts and cellular offloading. Wi-Fi networks typically adopt the Distributed Coordination Function (DCF)-based Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA), which uses the Binary Exponential Back-off (BEB) algorithm at the MAC layer mechanism to access channel resources. Currently deployed Wi-Fi networks face huge challenges towards efficient channel access for denser environments due to the blind exponential increase/decrease of a contention window (CW) procedure that is inefficient for a higher number of contending stations. Several modifications and amendments have been proposed to improve the performance of the MAC layer channel access based on a fixed or variable CW size. However, a more realistic network density-based channel resource allocation solution is still missing. An efficient channel resource allocation is one of the most critical challenges for future highly dense WLANs, such as High-Efficiency WLAN (HEW). In this paper, we propose a Channel Collision-based Window Scaled Back-off (CWSB) mechanism for channel resource allocation in HEW. In our proposed CWSB, all contending stations select an optimized CW size for each back-off stage for collided or successfully transmitted data frames. We affirm the performance of the proposed CWSB mechanism with the help of an Iterative Discrete Time Markov Chain (I-DTMC) model. This paper evaluates the performance of our proposed CWSB mechanism in HEW Wi-Fi networks using an NS3 simulator in terms of the normalized throughput and channel access delay compared to the state-of-the-art BEB and a recently proposed mechanism.

A Collision Avoidance Based Energy Efficient Medium Access Control Protocol for Clustered Underwater Wireless Sensor Networks

Underwater Wireless Sensor Networks (UWSNs) are typically deployed in energy constrained environments where recharging energy sources and replacing batteries are not viable. This makes energy efficiency in UWSNs a crucial directive to be followed during Medium Access Control (MAC) design. Multiplexing and scheduling based protocols are not ideal for UWSNs because of their strict synchronization requirements, longer latencies and constrained bandwidth. This paper presents the development and simulation analysis of a novel cross-layer communication based MAC protocol called Energy Efficient Collision Avoidance (EECA) MAC protocol. EECA-MAC protocol works on the principle of adaptive power control, controlling the transmission power based on the signal strength at the receiver. EECA-MAC enhances the conventional 4-way handshake to reduce carrier sensing by implementing an enhanced Request to Send (RTS) and Clear to Send (CTS) handshake and an improved back-off algorithm. Simulation analysis shows that the measures taken to achieve energy efficiency have a direct effect on the number of packet retransmissions. Compared to the Medium Access with Collision Avoidance (MACA) protocol, EECA-MAC shows a 40% reduction in the number of packets that are delivered after retransmissions. This reduction, coupled with the reduced signal interference, results in a 16% drop in the energy utilized by the nodes for data transmission.

Singletons for simpletons revisiting windowed backoff with Chernoff bounds

Backoff algorithms are used in many distributed systems where multiple devices contend for a shared resource. For the classic balls-into-bins problem, the number of singletons—those bins with a single ball—is important to the analysis of several backoff algorithms; however, existing analyses employ advanced probabilistic tools. Here, we show that standard Chernoff bounds can be used instead, and the simplicity of this approach is illustrated by re-analyzing some well-known backoff algorithms.

S-ALOHA Systems With Successive Transmission: Emulating CSMA System

In slotted-ALOHA (S-ALOHA) system, users should contend for the channel to transmit a packet in their queue every single time, irrespective of how many packets they have in their queue. The maximum throughput of S-ALOHA system is known as $e^{-1}\approx 0.3679$ (packets/slot) for a large number of users. This work proposes a novel S-ALOHA system, where the user with a successful (re)transmission of the packet at the head of queue is allowed to keep on transmitting his packet with some reservation probability if his queue is not empty. We call this system S-ALOHA system with successive transmission (ST) and show that the throughput of the proposed system becomes one packet per slot as the reservation probability is raised. We analyze the stability region of S-ALOHA systems with ST for two heterogeneous users and also investigate the stability condition of the system with $N$ symmetric users. Finally, we develop a Bayesian-learning backoff algorithm, with which users can control their (re)transmission with real-time estimation on the backlogged users. It is shown that the system with the proposed backoff algorithm can achieve the theoretical limit of the throughput.