Design and analysis of genetic fuzzy backoff algorithm for contention window optimization of flying ad-hoc network under channel error

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.

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.

The Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) procedure of IEEE 802.15.6 Medium Access Control (MAC) protocols for the Wireless Body Area Network (WBAN) use an Alternative Binary Exponential Backoff (ABEB) procedure. The backoff algorithm plays an important role to avoid collision in wireless networks. The Binary Exponential Backoff (BEB) algorithm used in different standards does not obtain the optimum performance due to enormous Contention Window (CW) gaps induced from packet collisions. Therefore, The IEEE 802.15.6 CSMA/CA has developed the ABEB procedure to avoid the large CW gaps upon each collision. However, the ABEB algorithm may lead to a high collision rate (as the CW size is incremented on every alternative collision) and poor utilization of the channel due to the gap between the subsequent CW. To minimize the gap between subsequent CW sizes, we adopted the Prioritized Fibonacci Backoff (PFB) procedure. This procedure leads to a smooth and gradual increase in the CW size, after each collision, which eventually decreases the waiting time, and the contending node can access the channel promptly with little delay; while ABEB leads to irregular and fluctuated CW values, which eventually increase collision and waiting time before a re-transmission attempt. We analytically approach this problem by employing a Markov chain to design the PFB scheme for the CSMA/CA procedure of the IEEE 80.15.6 standard. The performance of the PFB algorithm is compared against the ABEB function of WBAN CSMA/CA. The results show that the PFB procedure adopted for IEEE 802.15.6 CSMA/CA outperforms the ABEB procedure.

UAVs are flying in the air at different speeds and continuously forwarding the collected information to other UAVs or IoT devices in FANET. UAVs are playing an important role in data collection from places where humans can’t reach them easily. The UAVs are intelligent devices, and these devices have sufficient bandwidth and memory for data forwarding and storing. The role of UAVs is specific, and they have the reflexibility to change the battery and control the data interval to control the congestion in network. The IoT devices with FANET can transfer the valuable data to other IoT devices for verification and matching. The proper utilization of bandwidth, memory, energy and processing capability are able to increase the Quality of Service (QoS) in FANET. In this paper, proposed the Memory, channel and Process utilization for Fuzzy based (MCPFB) for congestion detection and avoidance scheme to improve bandwidth utilization, energy consumption in FANET with the IoT network. primarily aims to identify and prevent network congestion, which is crucial for maintaining the QoS requirements and ensuring reliable communication. Congestion is a phenomenon that arises when the volume of data transmitted across a network exceeds its capacity. These factors can lead to disruptions, reduced efficiency, and potential data loss in communication networks such as Flying Ad Hoc Networks (FANETs). To effectively handle congestion in FANET and provide reliable communication in challenging and dynamic environments, it is crucial to employ efficient resource management, intelligent algorithms, and adaptable protocols. The process of designing fuzzy rules for Flying Ad Hoc Networks (FANET) entails developing a set of guidelines that utilize fuzzy logic to make decisions pertaining to different parts of the network. The MCPFB is better than the previous BARS approach in terms of different performance metrics.

The Flying Ad-hoc Networks (FANETs) is relatively a new research area that governs the autonomous movement of multiple and small-sized unmanned aerial vehicles (UAVs). Compared to the traditional ad-hoc networks, FANETs are more efficient in completing their task in catastrophic situations to deliver data communication services from the height. However, type of the mission and sensitivity of the application requires an adaptive, efficient, and scalable communication network for data transmission among the UAVs. Such network are challenged by the high mobility and frequent topology changes, resulting connectivity loss problem. In this context, the UAVs in FANETs have been deployed with omnidirectional antennas, which leads to restrict the overall performance of the network. Alternately, directional antennas have the potential to enhance the transmission range, spatial reuse, and capacity of the network effectively. Nevertheless, these benefits also bring some unique challenges, particularly for the MAC layer. In order to overcome such constraints, we propose a new directional antennas based medium access control (MAC) protocol to adapt the FANETs architecture. The idea is to use multiple directional antennas on a single UAV with IEEE 802.11 protocol. The design goal is to improve the overall performance of the network and to provide solutions to some of the unique challenges of using directional antennas. The integrated solution is modeled in OPNET and evaluated for throughput, end-to-end delay, and retransmission attempts. The simulation results authenticate that the proposed IEEE 802.11-based directional antennas protocol could significantly increase the performance of FANETs as compared to omnidirectional antennas.

Based on the standardized IEEE 802.11 Distributed Coordination Function (DCF) protocol, this paper proposes a new backoff algorithm, called Smart Exponential-Threshold-Linear (SETL) Backoff Algorithm to enhance the system performance of contention-based wireless networks. As we know, the smaller contention window (CW) will increase the collision probability, but the larger CW will delay the transmission. Hence, in the SETL scheme, a threshold is set to determine the network load. When the CW is smaller than the threshold, a light network load, the CW size is self-adjusted exponentially. Conversely, if the CW is larger than the threshold, a heavy network load, the CW size is tuned linearly. In addition, the SETL takes a more conservative measure by decrease the CW after ldquoSrdquo times consecutive successful transmission to reduce the collision probability, especially when the competing station is large. By simulation, the numerical results show that the SETL provides a better system throughput and collision rate in both light and heavy network load than the related backoff algorithm schemes, including binary exponential backoff (BEB), exponential increase exponential decrease (EIED) and linear increase linear decrease (LILD). The SETL is very easy to implement, as it dose not require any changes in DCF procedures. Every station will self-adjust CW well with high performance and low collision rate.

Considering the high-density and high-dynamic feature of cooperative unmanned aerial vehicles (UAVs) swarm, also referred to as flocking of flying ad hoc networks (FANETs), reliable medium access control (MAC) protocol design for network connectivity maintaining and network information sharing is a challenging issue. In this article, we propose a self-adaptive carrier sense multiple access with collision avoidance (CSMA/CA)-based MAC protocol for flocking of FANET, namely, FMAC, to provide reliable broadcast information service under density-varying flocking scenarios. To represent the varying trend of UAV density during flocking, we define the collective neighboring potential (CNP) in the FMAC protocol. Specifically, at the beginning of each period, each UAV computes the current CNP based on available neighbors' motion states. Then, the value of CNP at the start of the next period regarding the same neighbors is predicted using UAV's kinetic equation. After that, each UAV can update the contention window (CW) size by comparing the current CNP and the predicted CNP, and CW will be decreased (increased) if the current CNP is larger (smaller) than the predicted one for enough period. The simulation results show that the proposed FMAC protocol can ensure high successful transmission probability under density-varying flocking scenarios and outperforms the typical MAC solutions.

Due to the efficient use of UAVs in several military and rescue applications, FANET (Flying Ad Hoc Network) offer a broad field for research and deployment. Drones have high maneuverability in 3D (three-dimensional) environments, and the low battery power of drones creates some problems, such as short usage time and ineffective routing. Providing a node routing protocol in FANET can effectively solve these problems. DSR (Dynamic Source Routing Protocol), as a classic on-demand routing protocol, can effectively save network resources in FANET. Therefore, this paper proposes a dynamic source routing protocol (WOA-DSR) based on the whale optimization algorithm (WOA) to provide an optimal route for FANET while saving energy. Finally, use OPNET modeler 14.5 to simulate WOA-DSR. The simulation results show that WOA-DSR is superior to DSR and FA-DSR in terms of average end-to-end delay, routing overhead and energy utilization.

Nowadays, drones became very popular with the enhancement of the technological progress of moving devices with a connection to each other, known as Flying Ad Hoc Network (FANET). It is used in most worldwide necessary life scenarios such as video recording, search and rescue, military missions, moving items between different areas, and many more. This leads to the necessity to evaluate different network strategies between these flying drones, which are essential to improve their quality of performance in the field. Several challenges must be addressed to effectively use FANET, to provide stable and reliable transmission for different types of data during vast changing topologies, such as different video sizes, different types of mobility models, recent Wireless Fidelity standards, types of routing protocols used, security problems, and many more. In this paper, a fully comprehensive analysis of FANET will be done to evaluate and enhance these challenges that concern different video types, mobility models, and IEEE 802.11n standards for best performance, by measuring throughput, retransmission attempt, and delay metrics. The result shows that Gauss–Markov mobility model gives the highest result using Ad Hoc On-Demand Vector and lowest delay, whereas for retransmission attempts, 2.4 GHz frequency has the lowest as it can reach more coverage area than 5 GHz.

Optimized link state routing protocol performance in flying ad-hoc networks for various data rates of Un manned aerial network

FANET (flying ad-hoc network) has provided broad area for research and deployment due to efficient use of the capabilities of drones and UAVs (unmanned ariel vehicles) in several military and rescue applications. Drones have high mobility in 3D (3 dimensional) environment and low battery power, which produce various problems such as small journey time and infertile routing. The optimal routing for communication will assist to resolve these problems and provide the energy efficient and secure data transmission over FANET. Hence, in this paper, we proposed a whale optimization algorithm based optimized link state routing (WOA-OLSR) over FANET to provide optimal routing for energy efficient and secure FANET. The efficiency of OLSR is enhanced by using WOA and evaluated performance shows the better efficiency of WOA-OLSR in terms of some parameters such as a packet delivery ratio, end to end delay, energy utilization, throughput, and time complexity against the previous approaches OLSR, MP-OLSR, P-OLSR, ML-OLSR-FIFO and ML-OLSR-PMS.

In this paper, a backoff mechanism, Exponential Linear Backoff Algorithm (ELBA), is proposed to improve system performance over contention-based wireless networks. In the ELBA, the variation of contention window size is combined both exponentially and linearly, dependent on the network load, as indicated by the number of consecutive collisions. In the ELBA scheme, a threshold is set to determine the network load. If the contention window size is smaller than the threshold, a light network load, the contention window is tuned exponentially. Conversely, if the contention window size is larger than the threshold, a heavy network load, the contention window size is tuned linearly. The numerical results show that the ELBA provides a better system throughput and collision rate in both light and heavy network loads than the related backoff schemes, including binary exponential backoff (BEB), exponential increase exponential decrease (EIED) and linear increase linear decrease (LILD).

Flying Ad hoc NETwork (FANET) refers to a self-organizing wireless network that facilitates easy, flexible and inexpensive deployment of flying nodes termed as Unmanned Aerial Vehicles (UAVs). These UAVs communicate with one another without the presence of any fixed network infrastructure. The routing process is responsible for achieving reliable coordination and cooperation among flying nodes to establish reliable routes towards radio access infrastructure that corresponds to Base Station (BS) of FANET. Routing protocols in FANETs play an anchor role in preventing network partitions and link disconnections to guarantee prolonged route lifetime with minimized energy utilization rate. In this paper, an Adaptive Opposition Learning-Improved Slime Mould Algorithm (AOLISMA)-based optimization routing is proposed for ensuring reliable data dissemination among UAVs with extended network lifetime and minimized energy consumption. This AOLISMA routing approach utilizes two randomly selected search agents for determining feasible direction and displacement that aid in better routing process. It adopts random selection of search agents for restricting the limits of exploration and exploitation to establish better balance during the process of routing. It also helps in attaining a near-optimal or optimal route that prolongs network route lifetime. It specifically utilizes opposition learning for adaptive increase in the exploration rate for identifying feasible routes from which, optimal route can be selected based on an objective function for attaining reliable routing. The simulation experiments of the proposed AOLISMA scheme conducted based on throughput, control overhead, mean delay and energy consumption for varying mobility rates of UAVs confirm better performance on par with the baseline GAR, ACOAR and ABCAR approaches taken for comparison.

The Medium Access Control (MAC) layer of Wireless Local Area Networks (WLANs) undergoes several amendments since the birth of the legacy standard IEEE 802.11. The legacy standard IEEE 802.11 uses Distributed Coordination Function (DCF) for the MAC layer protocol. The DCF algorithm performs better in low dense WLAN networks where only one station (STA) can send data at one time. The selection of Contention Window (CW) plays a vital role in getting optimum WLANs performance. The legacy IEEE 802.11 and most of its amendments use Binary Exponential Backoff (BEB) algorithm to resolve collision in WLANs. In a conventional BEB algorithm, the size of CW is adjusted forward and backward i.e., the CW is initialized at minimum CW (<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CW_min</i>) and for each collision the CW is exponentially incriminated to an optimal CW size where the packets can be transferred successfully. Hence the BEB mechanism is not suitable for dense environment as after every successful transmission the CW size is resumed to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CW_min</i> value where collision is obvious due to many contending stations. Finding the optimum CW is the key factor to maximize throughput and minimize delay. Moreover, Orthogonal Frequency Division Multiple Access (OFDMA) technology is used in next-generation IEEE 802.11ax WLANs to accommodate more active stations in a dense environment. To solve these issues, we proposed adaptive algorithm. In the proposed algorithm, First, we calculate the number of estimated active stations using a probabilistic approach. Second, we use Markov chain model to find the throughput ratio of the previous Contention Window (CW). We will consider the aforementioned parameters as input to the Fuzzy system, which adjust the optimum CW size to enhance throughput and minimize delay. The proposed approach enhances the throughput performances of IEEE 802.11ax and IEEE 802.11 medium access protocols up to 10% and 40% respectively.

In order to improve the tactical coordination ability of cluster unmanned aerial vehicles, extend their application scopes and effectively enhance the reliability and survivability in flying ad hoc network, a novel adaptive backoff algorithm based on active nodes is proposed in this paper. The backoff algorithm adopts a connection window adaptive mechanism in order for adapting loads dynamic change. Therefore, nodes connection window can be adaptively adjusted for reducing collisions under heavy loads. The two-dimensional Markov chain of the backoff algorithm is established and the node state transition probability under different loads are solved. Moreover, the expression of system throughput and mean delay are also deduced. Simulations show that the algorithm not only maintains a higher information success transmission rate under light loads, but also possesses the ability to effectively resolve connections under heavy loads.