Priority-based queuing and transmission rate management using a fuzzy logic controller in WSNs

Bouazzi, Imen; Bhar, Jamila; Atri, Mohamed

doi:10.1016/j.icte.2017.02.001

Cited by 19 publications

(5 citation statements)

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“…Fuzzy logic application is widespread in automotive and decision making engineering, as well as it applied in routing protocols, such as reactive routing [30], cluster-based routing [31], geographic routing, broadcast routing [32].To select a route, a reinforcement learning method has been adopted [33], that maximizes the network performance in terms of end-to-end delay and packet delivery ratio. A routing decision protocol based on fuzzy logic [34] has considered speed difference, transmission distance and link stability time. But, they have not considered the link quality factor, which is the most effective parameter to increase the efficiency of routing.…”

Section: Related Workmentioning

confidence: 99%

Fuzzy logic-based VANET routing method to increase the QoS by considering the dynamic nature of vehicles

et al. 2021

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Vehicular ad hoc network usually operates in various challenging situations like frequent topology changes, high vehicular mobility and the wide range of communication networks. Due to this it is very hard to maintain a higher data rate and also to achieve low latency during data communication. To overcome these problems, given the dynamic natures of all the vehicles in a given network in the proposed routing method, we have defined two fundamental parameters to determine the forwarding vehicle. The first parameter, which we developed, we call it "Channel quality factor (CQF)" or 'Z'. The other parameter known as "Communication expiration time" or 'T' together with CQF is used in the present method to determine the forwarding vehicle. Fuzzy logic is also used to optimize various Quality of Service matrices. This proposed routing method involves two main parts; one is for forwarding Vehicle selection in the road based on the fuzzy logic. The second one is Road selection at the Road Junction to select the right path to reach the signal to the destination vehicle. The simulation results show that our proposed method performs well compare to other well-known protocols (MoZo, BRAVE, OFAODV) in terms of the average end to end delay, packet delivery ratio and control packet overhead, given any number of vehicles in a set of streets. While we are comparing with VEFR protocol, our proposed method shows higher performance in terms of average E2E delay and control packet overhead. However, it is interesting to see that VEFR gives ∼ 5% better result than our proposed method when the number of vehicles in the streets are lower. But in the limit, when the number of vehicles reaches close to ∼ 1900 the difference between the proposed method and method in VEFR goes to zero. At last we compare our proposed method with junction based two V2I protocols. In every cases, it shows better result even though we change the speed of the vehicles, beacon interval, channel data rate and transmission region.

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Section: Related Workmentioning

confidence: 99%

Fuzzy logic-based VANET routing method to increase the QoS by considering the dynamic nature of vehicles

et al. 2021

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“…33 A reinforcement learning algorithm 33 was used to identify a path that enhances network efficiency based on endto-end latency and packet delivery ratio. A route selection system based on fuzzy logic 34 took into account gearbox distance, speed difference, and link stability instance. However, disregarded the most critical issue in enhancing routing performance: link quality.…”

Section: Introductionmentioning

confidence: 99%

A novel swarm intelligence‐based fuzzy logic in efficient connectivity of vehicles

Kumaragurubaran,

Vijayakumar

2024

Int J Communication

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SummaryA vehicular ad hoc network (VANET) is a wireless network composed of a collection of mobility devices that link via a wireless channel without the need for permanent infrastructure or centralized administration. Multicasting is a method of sending data from a single node to multiple recipients at the same time. Because of its energizing network topology and constricted resources, a mobile ad hoc network (MANET) presents a number of issues. Because network execution measurements such as latency, bandwidth, throughput, and energy fluctuate so rapidly owing to node movement, these network properties in a wireless mobile ad hoc network are ambiguous. Because of the influence of these uncertainty issues, determining the top‐grade path from the originating node to a set of recipient nodes is challenging. In order to preserve network resources, we used a Swarm Intelligence‐based fuzzy logic strategy in this study that includes channel caliber factor and connection termination time to try to alleviate these uncertainty issues. This strategy integrates all of the network indicators available for the routes into an individual quantity named as fuzzy cost or cost of communication. The routes with the lowest fuzzy cost will be regarded optimum, and data will be delivered along this route from the originating point to a set of receivers. The simulation was carried out using NS‐3.38 and MATLAB, and the results reveal that the suggested protocol, HACOFLM, outperforms ODMRP, OFAODV, MAODV, and DFMCRP by means of packet delivery ratio, latency, control packet overhead, and throughput. DFMCRP outperforms MAODV by 3% to 6%, OFAODV by 13% to 19%, and ODMRP by 16% to 23%. According to HACOSMO, based on the number of vehicles and velocities, routing overhead is 7% to 10% less than DFMCRP, 9% to 13% less than MAODV, 17% to 29% less than OFAODV, and 22% to 36% less than ODMRP. It improvise throughput by 4% to 6% when contrasted to DFMCRP, 5% to 9% when contrasted to MAODV, 10% to 14% when contrasted to OFAODV, and 14% to 23% when contrasted to ODMRP.

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“…In [13] (Sen, K., Chakraborty, B., Gayen, A., & Dey, C.), the proposed technique is Fuzzy rule-based set point weighting with a PID controller (PIDC), this technique showed a great efficacy in fulfilling important responses to the traditional PIDC used in closed-loop control applications. In [14], the authors (Bouazzi, I., Bhar, J., & Atri, M.) are proposed to use a fuzzy logic algorithm to solve some problems that happen in Wireless sensor networks (WSNs). Such as the reliability of transmitting data preserving the latency of message, and prolonging the battery life of sensor nodes, the results showed a better power consumption compared to basic application of CSMA/CA.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy type 1 PID controllers design for TCP/AQM wireless networks

Oudah

Sulttan

Shneen

2021

IJEECS

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<p><span>The search of FLC_PID controller for TCP/AQM Wireless Networks, to deal with congestion for Internet users and to get good performance and capabilities of TCP / IP networks. Neglect of controlling the network delay and the number of continuous users that leads to a problem in the transmission process. Recently, automatic control units are adapted to solve this problem with the difficulty of controlling congestion in the presence of wireless links. This modest research presents one of the traditional PID controller methods with fuzzy logic so that wireless networks and congestion can be controlled by various configurations. The proposed methods were simulated with the required comparisons in the adoption of nonlinear systems to determine the best performance and it was found that the use of the Fuzzy logic control can achieve the best performance (reducing the delay time of delivering packets and packets loss). The simulation of the current work shows through its results the possibility of controlling the behavior of the system and through testing the balance when changed with time delay through the impact of communication time and its relationship with the stability of the work of the system.</span></p>

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Priority-based queuing and transmission rate management using a fuzzy logic controller in WSNs

Cited by 19 publications

References 1 publication

Fuzzy logic-based VANET routing method to increase the QoS by considering the dynamic nature of vehicles

Fuzzy logic-based VANET routing method to increase the QoS by considering the dynamic nature of vehicles

A novel swarm intelligence‐based fuzzy logic in efficient connectivity of vehicles

Fuzzy type 1 PID controllers design for TCP/AQM wireless networks

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