Binary Exponential Backoff Algorithm Research Articles

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.

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.

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.

Modeling and Online Adaptation of ALOHA for Low-Power Wide-Area Networks (LPWANs)

Unslotted ALOHA protocol has been adopted as a channel access mechanism in commercial low-power wide-area networks (LPWANs), such as Sigfox and long-range (LoRa) alliance. This work examines the throughput and random access (RA) delay distribution of unslotted ALOHA systems by considering exponential random backoff (ERB) or uniform random backoff (URB) algorithm. We further characterize the operating region of the systems as unsaturated stable, bistable, and saturated regions in terms of the new packet arrival and retransmission rates. To run the system stably with the maximum throughput, we propose a Bayesian online backoff algorithm that estimates the number of backlogged devices. Its performance is compared with other algorithms, such as particle filter (PF)-based algorithm, binary exponential backoff (BEB) algorithm, and the algorithm of exploiting exact backlog size information. Through extensive simulations, it is demonstrated that the performance of the proposed algorithm is very close to the upper bound and robust to time-varying traffic condition.

Sliding Group Window with Rebacking off for Collision Avoidance in High-Efficiency Wireless Networks

It is difficult for wireless local area networks (WLANs), IEEE 802.11ax high-efficiency WLAN (HEW), to join next-generation innovations such as 5th generation (5G) and Internet of Things (IoT) because they still have their conventional channel access mechanism as their essential medium access control (MAC) protocol. The MAC protocol uses a traditional binary exponential backoff (BEB) algorithm to access channel resources that depend on the noncognitive increment of contention parameters for collision avoidance. In BEB, the collision issue increases with the increase in connected devices in the network due to a fixed contention window size. The larger the size of the network, the larger the collision in the network. To avoid such a circumstance, in this paper, we propose a sliding group window (sGW) mechanism dependent on collision-point assessment in order to improve the performance of MAC protocol for HEW. The proposed algorithm additionally presents a rebacking off for collision avoidance (ReBOCA) system for sGW, which combines the uniform dispersion of the contention parameters. This variation of an ordinary backoff algorithm permits the reasonable sliding of the user groups in the case of collision. The algorithm explicitly accounts for the peculiarities of dense environments and backward compatibility. Key aspects of the proposed solution include collision-point estimation, rebacking off for collision distribution convergence for fair treatment, and adaptive sliding of group windows to mitigate contention unfairness. We further formulated a closed-form Markov chain model for the performance analysis of our proposed sGW with ReBOCA scheme. Theoretical and practical results prove that our proposed scheme achieved maximal efficiency, even under dense environments. An increase in throughput with a lower packet collision probability was achieved with the proposed mechanism, and the efficiency increased as the number of contending stations increased than compared to traditional BEB performance. Our proposed ReBOCA mechanism enhanced network throughput by 38.18% than compared to the conventional BEB mechanism.

Enhanced collision resolution and throughput analysis for the 802.11 distributed coordination function

SummaryThe IEEE 802 standards rely on the distributed coordination function (DCF) as the fundamental medium access control method. DCF uses the binary exponential backoff (BEB) algorithm to regulate channel access. The backoff time determined by BEB depends on a contention window (CW) whose size is doubled if a station suffers a collision and reset to its minimum value after a successful transmission. Doubling the size of CW reduces channel access time, which decreases the throughput. Resetting it to its minimum value harms fairness since the station will have a better chance of accessing the channel compared to stations that suffered a collision. We propose an algorithm that addresses collisions without instantly increasing the CW size. Our algorithm aims to reduce the collision probability without affecting the channel access time and delay. We present extensive simulations for fixed and mobile scenarios. The results show that, on average, our algorithm outperforms BEB in terms of throughput and fairness. Compared to exponential increase exponential decrease (EIED), our algorithm improves, on average, throughput and delay performance. We also propose analytical models for BEB, EIED and our algorithm. Our models extend Bianchi's popular Markov chain‐based model by using a collision probability that is dependent on the station transmission history. Our models provide a better estimation of the probability that a station transmits in a random slot time, which allows a more accurate throughput analysis. Using our models, we show that both the saturation throughput and maximum throughput of our algorithm are higher than those of BEB and EIED.

AI-empowered IoT Security for Smart Cities

Smart cities fully utilize the new generation of Internet of Things (IoT) technology in the process of urban informatization to optimize the urban management and service. However, in the IoT system, while information exchange and communication, wireless sensor network devices may not be able to resist all forms of attacks, which may lead to security issues such as user data disclosure. Aiming at the information security risks in smart city, the typical technologies in IoT is analyzed from the perspective of IoT perception layer and provides corresponding security solutions for the existing security threats. Regarding the communication security, the emerging wireless technology, long range (LoRa), is discussed, and the performance of wireless communication protocol is analyzed through simulation experiments, to verify that the IoT technology based on LoRa communication technology can improve the security of the system in the construction of smart city. The results show that REBEB, a new backoff algorithm, is similar to the binary exponential backoff algorithm in terms of throughput performance. REBEB focuses more on fairness, which is up to 0.985, and to a certain extent, its security is significantly improved. The fairness of REBEB algorithm is more than 0.4 in different nodes and competing windows, and the fairness of the system is better when the number of nodes is small. To sum up, the IoT system based on LoRa communication can effectively improve the security performance of the system in the construction of smart city and avoid the security threats in the IoT signal transmission.

Intelligent CW Selection Mechanism Based on Q-Learning (MISQ)

Mobile ad hoc networks (MANETs) consist of self-configured mobile wireless nodes capable of communicating with each other without any fixed infrastructure or centralized administration using the medium radio. Wireless technology is based on standard IEEE.802.11. The IEEE 802.11 Distributed Coordination Function (DCF) MAC layer uses the Binary Exponential Backoff (BEB) algorithm to deal with wireless network collisions. BEB is considered effective in reducing the probability of collisions but at the expense of numerous network performance measures, such as throughput and packets delivery ratio, mainly in high traffic load. Deep Reinforcement Learning (DRL) is a DL technique in which an agent can achieve a goal by interacting with the environment. In this paper, using one of the DRL models, we propose Q-learning (QL) to optimize MAC protocols' performance based on the contention window (CW) in MANETs. The intelligent proposed MISQ takes into account the number of packets to be transmitted and the collisions committed by each station to select the appropriate contention window. The performance of the proposed mechanism is evaluated by using in-depth simulations. The outputs indicate that the intelligent proposal mechanism learns various MANETS environments and optimizes performance over standard MAC protocol. The performance of MISQ is evaluated in various networks with throughput, channel access delay, and packets delivery rate as performance measures.

Performance analysis of an adaptive OFDMA‐based CSMA/CA scheme on a wireless network

The conventional carrier-sense multiple access/collision avoidance (CSMA/CA) IEEE 802.11 protocol uses the rigid binary exponential backoff algorithm of the distribution coordination function (DCF) for channel access. The protocol poorly performs under loss by wireless links with high packet error rates and dense networks with numerous collisions from hidden nodes. Furthermore, the scheme is not cognisant of the state of the wireless link in its operation. This work develops hybrid adaptive orthogonal frequency division multiple access (OFDMA) CSMA/CA (hybrid AO–CSMA/CA) protocol that adapts the backoff and congestion window size according to the knowledge of the wireless channel conditions to mitigate against the effects of the loss by wireless links. The protocol utilises OFDM for effective resource allocation and solving the problem of the hidden terminal node on an OFDMA IEEE 802.11-based wireless network. The teletraffic performance analysis of the developed protocol is investigated in terms of throughput and delay metrics on a real dynamic Markovian wireless channel. The performance results indicate that the developed hybrid protocols perform better than the DCF protocol on the lossy wireless communication links.

Pseudorandom sequence contention algorithm for IEEE 802.11ah based internet of things network.

The IEEE 802.11ah standard relies on the conventional distributed coordination function (DCF) as a backoff selection method. The DCF is utilized in the contention-based period of the newly introduced medium access control (MAC) mechanism, namely restricted access window (RAW). Despite various advantages of RAW, DCF still utilizes the legacy binary exponential backoff (BEB) algorithm, which suffers from a crucial disadvantage of being prone to high probability of collisions with high number of contending stations. To mitigate this issue, this paper investigates the possibility of replacing the existing exponential sequence (i.e., as in BEB) with a better pseudorandom sequence of integers. In particular, a new backoff algorithm, namely Pseudorandom Sequence Contention Algorithm (PRSCA) is proposed to update the CW size and minimize the collision probability. In addition, the proposed PRSCA incorporates a different approach of CW freezing mechanism and backoff stage reset process. An analytical model is derived for the proposed PRSCA and presented through a discrete 2-D Markov chain model. Performance evaluation demonstrates the efficiency of the proposed PRSCA in reducing collision probability and improving saturation throughput, network throughput, and access delay performance.

Performance Characterization of IEEE 802.11s Compatible MAC Protocols

Wireless Mesh Network (WMN) [1] deployment based on IEEE 802.11s [2] standard is a popular choice in setting up cost-efficient alternative to support broadband internet services to a larger population. They can provide network connectivity over large geography when compared to WLANs. WMN makes use of Distributed Coordination Function (DCF) Medium Access Control (MAC) protocol with Binary Exponential Backoff (BEB) algorithm to avoid collision due to simultaneous transmissions by more than one user at the same time. These collisions bring the network performance drastically down if not handled properly. Thus this work tries to evaluate the suitability of the better efficient CA algorithms (EIED, EILD, MILD, PB, CCW, EBO, HBA…) which can replace BEB to support higher throughput for IEEE 802.11s networks (WMNs). The NS-3 [3] simulation results indicate that Polynomial Backoff (PB) performs better than others against the BEB algorithm in about 52 % of network scenarios and it is suggested to operate with TWO radios enabled with all access points.

PRIORITY BASED OPTIMIZED BACKOFF ALGORITHM FOR COLLISION AVOIDANCE IN WIRELESS AD HOC NETWORKS

In wireless ad hoc networks, designing a backoff (BO) algorithm is crucial to avoid collision and to enhance the network throughput. The majority of current MAC protocols have altered the Binary Exponential Backoff (BEB) algorithm to provide relative priority among multiple traffic classes. But the BO interval should be adaptively altered depending on the residual energy and traffic load of contending nodes. Hence this paper designs a priority based optimized BO (PBOB) algorithm for collision avoidance. This algorithm works in standard IEEE 802.11 MAC protocol in which the contending nodes are prioritized based on their residual energy, load and traffic type. Then during contention period, the backoff interval is adaptively adjusted based on the priority of each contending node. By simulation results, it has been show that the PBOB algorithm has better packet delivery ratio and throughput with reduced energy consumption.

Improved MAC layer protocol of Wifi for satellite network.

With the development and universal application of satellite technology, an important way to expand the function of satellites is setting up inter-satellite networks to make them work together. Traditional satellite networking methods generally adopt a fixed time slot allocation method, which is not suitable for small satellite groups with low latency and high throughput requirements. In order to solve this problem, it has been proposed to apply the traditional Wifi protocol in satellite networking. As there are differences between satellite networks and terrestrial networks, it’s necessary to improve the traditional 802.11 protocol. The Media Access Control (MAC) protocol in 802.11 is improved in this paper, which mainly includes the adaptive algorithm of maximum contention window size and the growth algorithm of Contention Window (CW) size. The maximum contention window is adjusted according to the conflict state of the current network, which makes the network accommodate more satellite nodes. The CW growth algorithm improves the traditional Binary Exponential Back-off (BEB) algorithm, where the CW is designed according to the priority of the data frame or the network load. In this way, high-priority satellite accusation information will have higher reliability or tolerate greater network load.

Guaranteed cost control of networked systems with stochastic medium‐access protocols: methodology and applications

This study investigates a switching control problem for a set of plants which share communication channels, and for which medium-access control is achieved based on contention-based protocols such as the binary exponential back-off algorithm. The medium-access procedure can be modelled by a Markov chain with completely or partially known transition probabilities. The entire networked system operates as a stochastic switching system (SSS). Based on the average dwell time technique, guaranteed cost control method, and SSS theory, sufficient conditions are derived to establish the relation between the mean-square exponential stability and the property of the Markov chain-driven medium-access control for the SSS. Depending on these conditions, the designed feedback controller can stabilise the SSS using the mean-square exponential and guarantee the desired robust performance. The proposed control framework is extended and applied to the cooperative adaptive cruise control of vehicles within the context of intelligent vehicle highway systems, which can reject the disturbance resulting from the fluctuating acceleration of preceding vehicles. Numerical simulations and experiments using laboratory-scale cars show the effectiveness of the proposed control method.

A QoS Based Adaptive Backoff Scheme for Vehicular Ad Hoc Networks.

The media access control (MAC) protocol is a key element in the design of vehicular ad hoc networks (VANETs) that directly affects the network performance. The backoff schemes of existing MAC protocols apply the single backoff process and therefore are not suitable for multi-class data transmission. Additionally, they cannot satisfy the delay requirements of emergency data in the case of varying number of vehicles, causing an adverse effect to the intelligent transportation system (ITS). This paper presents a priority-based adaptive backoff scheme that can enhance the binary exponential backoff (BEB) algorithm as well as the polynomial backoff (QB) algorithm. This system distinguishes priority data with different delay requirements first and designs different backoff schemes for each type of data later. The two-dimensional Markov Chain is used to analyze the backoff scheme and determine the expressions for throughput and delay. The simulation results show that the backoff scheme provided by this paper can reduce the average data delay and regulate each kind of data delay adaptively, according to the varying number of vehicles and different delay requirements.

MDA-SMAC: An Energy-Efficient Improved SMAC Protocol for Wireless Sensor Networks

In sensor medium access control (SMAC) protocol, sensor nodes can only access the channel in the scheduling and listening period. However, this fixed working method may generate data latency and high conflict. To solve those problems, scheduling duty in the original SMAC protocol is divided into multiple small scheduling duties (micro duty MD). By applying different micro-dispersed contention channel, sensor nodes can reduce the collision probability of the data and thereby save energy. Based on the given micro-duty, this paper presents an adaptive duty cycle (DC) and back-off algorithm, aiming at detecting the fixed duty cycle in SMAC protocol. According to the given buffer queue length, sensor nodes dynamically change the duty cycle. In the context of low duty cycle and low flow, fair binary exponential back-off (F-BEB) algorithm is applied to reduce data latency. In the context of high duty cycle and high flow, capture avoidance binary exponential back-off (CA-BEB) algorithm is used to further reduce the conflict probability for saving energy consumption. Based on the above two contexts, we propose an improved SMAC protocol, micro duty adaptive SMAC protocol (MDA-SMAC). Comparing the performance between MDA-SMAC protocol and SMAC protocol on the NS-2 simulation platform, the results show that, MDA-SMAC protocol performs better in terms of energy consumption, latency and effective throughput than SMAC protocol, especially in the condition of more crowded network traffic and more sensor nodes.

Communication parameter design for networked control systems with the slotted ALOHA access protocol

This paper investigates the co-design problem of communication parameters and controller for a linear networked control system (NCS). The distributed sensors of the NCS communicate with the controller via a single wireless channel where a slotted ALOHA protocol with binary exponential backoff algorithm is used for media access control. The main contributions are two-fold: the lower bound of network throughput satisfying the mean square stability of the NCS is derived based on a designed feedback control gain; the other is that the key parameters of the slotted ALOHA protocol are designed so as to provide a viable media access control protocol for the sensors. Numerical examples are given to illustrate the effectiveness of the proposed design method and to further show the relationship between the communication part and the control part.

Improving Performance by a Dynamic Adaptive Success-Collision Backoff Algorithm for Contention-Based Vehicular Network

The distributed coordination function (DCF) is the core of the IEEE 802.11 standard and is applied routinely to contention-based vehicular networks. Many existing backoff algorithms based on DCF are applied to develop the performance of wireless networks and most of them work in saturated traffic load conditions, which are unlikely to be valid in practical wireless network. The study of the backoff algorithm under unsaturated traffic load conditions is still an open issue. In this paper, a dynamic adaptive success-collision (DASC) backoff algorithm is proposed to improve the performance of contention-based vehicular network. As we know that the probability of collision reflects system performance, and the probability of collision is restricted by the variation of contention window ( $CW$ ) size and it depends on the traffic loads. A threshold is defined to judge the low or high traffic loads. In addition, the DASC backoff algorithm takes proactive measures to optimize throughput and delay through decreasing $CW$ after “ $S$ ” times consecutive successful transmissions and increasing $CW$ after “ $C$ ” times consecutive collisions. The simulation results indicate that the proposed DASC backoff algorithm provides better performance in terms of throughput and delay than dynamic control backoff time algorithm, exponential linear backoff algorithm, and binary exponential backoff algorithm.

Cognitive Radio-Based Smart Grid Traffic Scheduling With Binary Exponential Backoff

This paper develops the traffic models of smart grid electronic data ( ${E}$ data) and multimedia video over cognitive radio (CR). Unlike the traditional “Poisson” arrival model, each arrival monitoring stream follows fixed time triggered Gaussian distribution which approximates to the reality, and the video data is classified as key frame data with higher priority than nonkey frame data to reduce communication burden. To enhance the delivery probability of ${E}$ data and multimedia data, we adopt a buffer mechanism to store the “sending fail” data and try to resend them together with new coming data by using the new data’s sending opportunity. To avoid buffer overflow, the unsent data should be compressed and some should be removed, and the new coming data rate should be reduced to alleviate the congestion of the CR communication network. In this paper, we propose a new binary exponential backoff (NBEB) algorithm to “compress” the unsent data which can keep key information but recover the electronic tendency as much as possible. With NBEB, the new coming data can be temporally selected and thrown into the buffer and more new data can be put in the buffer. The algorithm can reduce the arrival traffic rate exponentially related with the sending failure times. The results show that NBEB can significantly decrease the blocking/dropping probability, increase the communication success probability, and improve the communication performance.

A Multi-Priority Service Differentiated and Adaptive Backoff Mechanism over IEEE 802.11 DCF for Wireless Mobile Networks

Backoff mechanism serves as one of the key technologies in the MAC-layer of wireless mobile networks. The traditional Binary Exponential Backoff (BEB) mechanism in IEEE 802.11 Distributed Coordination Function (DCF) and other existing backoff mechanisms poses several performance issues. For instance, the Contention Window (CW) oscillations occur frequently; a low delay QoS guarantee cannot be provided for real-time transmission, and services with different priorities are not differentiated. For these problems, we present a novel Multi-Priority service differentiated and Adaptive Backoff (MPAB) algorithm over IEEE 802.11 DCF for wireless mobile networks in this paper. In this algorithm, the backoff stage is chosen adaptively according to the channel status and traffic priority, and the forwarding and receding transition probability between the adjacent backoff stages for different priority traffic can be controlled and adjusted for demands at any time. We further employ the 2-dimensional Markov chain model to analyze the algorithm, and derive the analytical expressions of the saturation throughput and average medium access delay. Both the accuracy of the expressions and the algorithm performance are verified through simulations. The results show that the performance of the MPAB algorithm can offer a higher throughput and lower delay than the BEB algorithm.