Energy Aware Q-learning AODV (EAQ-AODV) routing for cognitive radio sensor networks

Joon, Ranjita; Tomar, Parul

doi:10.1016/j.jksuci.2022.03.021

Cited by 13 publications

(11 citation statements)

References 40 publications

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“…Multi-hop clustering routing protocols for CRSNs are further categorized into uniform and uneven clustering protocols, and majority of them select CHs and form clusters in a distributed manner. In uniform clustering protocols, all CRSNs nodes exchange information within the same range (i.e., cluster radius) during CHs selection and cluster construction, such as DSAC 14 , EACRP 15 , SACR 16 , RFMCRP 17 and EAQ-AODV 18 . In DSAC, each CRSNs node is clustered separately at the beginning, and then neighboring clusters are merged according to inter-cluster distance and channel information until the optimal number of clusters which is obtained by minimizing the total network energy consumption is achieved.…”

Section: Single-hop Clustering Routing Protocols For Crsns Single-hop...mentioning

confidence: 99%

An imperfect spectrum sensing-based multi-hop clustering routing protocol for cognitive radio sensor networks

Wang

Liu

2023

Sci Rep

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Multi-hop clustering routing protocols are potential solutions to achieve effective and energy-efficient data delivery in cognitive radio sensor networks (CRSNs). Current clustering routing protocols for CRSNs generally assume perfect spectrum sensing, i.e., false alarm and missed detection are ignored, which may result in collisions with primary users and transmission failure or constrained spectrum usage. To alleviate the impact of imperfect spectrum sensing on network performance, an imperfect spectrum sensing-based multi-hop clustering routing protocol (ISSMCRP) is proposed for CRSNs in this paper. Cluster head (CH) and relay selection criteria are defined based on detection level function of available channels to help select CHs and relays with high spectrum sensing capability. Idle detection accuracy-based intra-cluster and inter-cluster channel selection criterion is proposed to promote successful intra-cluster and inter-cluster data delivery. In addition, control overhead introduced by CHs selection and cluster formation is strictly controlled, and reasonable cluster radii are set to manage the range of control information exchange, so as to reduce the energy consumption caused by control overhead. Simulation results show that compared with the existing clustering routing protocols for CRSNs, ISSMCRP gains obvious advantages in expanding the network lifespan and enhancing the network surveillance capability.

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Section: Single-hop Clustering Routing Protocols For Crsns Single-hop...mentioning

confidence: 99%

An imperfect spectrum sensing-based multi-hop clustering routing protocol for cognitive radio sensor networks

Wang

Liu

2023

Sci Rep

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“…In Reference28, a stabilized Q‐learning with the AODV routing model has been presented to obtain effective clustering in WSN. The cluster leader selection is performed using the Q‐learning method.…”

Section: Literature Surveymentioning

confidence: 99%

An artificial intelligence approach for energy‐aware intrusion detection and secure routing in internet of things‐enabled wireless sensor networks

Aruchamy,

Gnanaselvi,

Sowndarya

et al. 2023

Concurrency and Computation

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SummaryIn today's world, the Internet of things (IoT) and wireless sensor networks (WSNs) have been integrated into various applications to support reliable communication systems. The IoT nodes collect the sensed data with the aid of WSN and transfer them to the cloud environment. Nevertheless, these nodes become vulnerable when Denial of Service attacks occurred in the IoT‐enabled WSN (IWSN). The insecure route also affects the energy efficiency of IWSN. Therefore, robust intrusion detection along with secure routing is necessitated to protect the IWSN from various security threats. In this paper, a novel Artificial Intelligence‐based Energy‐aware Intrusion Detection and Secure Routing model has been proposed to develop a secured IWSN. Initially, the proposed model executes the intrusion detection system to identify the various attacks. Afterward, a game strategy‐based decision mechanism is integrated with the proposed intrusion detection model to decide whether protection is needed or not. In the last phase, an energy‐aware ad‐hoc on‐demand distance vector algorithm has been established to offer secured routing among multiple nodes. The performance results show that the proposed model achieves superior detection accuracy of 95% and reduced energy utilization of 38% as compared with the existing intrusion detection models.

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“… Joon & Tomar (2022) , suggested the Energy Efficient Q-learning AODV (EAQ-AODV) protocol for efficient routing. Cluster head selection is done through the reward mechanism based on Q-learning.…”

Section: Literature Reviewmentioning

confidence: 99%

Systematic review on modification to the ad-hoc on-demand distance vector routing discovery mechanics

Alameri

Komárková

Al-Hadhrami

et al. 2022

PeerJ Computer Science

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Mobile ad-hoc networks (MANETs) and wireless mesh networks (WMNs) are used in a variety of research areas, including the military, industry, healthcare, agriculture, the Internet of Things (IoT), transportation, and smart cities. The swift advancement in MANET technology is the driving force behind this rising adoption rate. Routing over MANET is a critical problem due to the dynamic nature of the link qualities, even when nodes are static. A key challenge in MANETs is the need for an efficient routing protocol that establishes a route according to certain performance metrics related to the link quality. The routing protocols utilised by the nodes in WMNs and MANETs are distinct. Nodes in both types of networks exchange data packets through the routing protocols. For this highly mobile network, the ad-hoc On-Demand Distance Vector (AODV) routing protocol has been suggested as a possible solution. Recent years have attracted researchers’ attention to AODV since it is a routing technique for ad-hoc networks that prevents looping. The architecture of this routing protocol considers several factors, including the mobility of nodes, the failure of connection links, and the loss of packets. In this systematic review, one of the key focuses is bringing attention to the classic AODV, which was developed after discussing the recent development of several versions of AODV. The AODV routing protocol performs a path strength check to generate a more reliable and secure route between the source and destination nodes. In AODV, investigations demonstrate advances in both the format protocol approach and the network simulation-2 (NS-2), and these improvements were made in the same scenario used to revitalise AODV. It has been discovered that the AODV is more effective in several aspects, such as throughput, end-to-end delay, packet delivery ratio (PDR), energy consumption, jitter, packet loss ratio, and network overhead. Furthermore, this paper presents this systematic review based on AODV modifications in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). It also provides a methodological framework for the papers’ selection.

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Energy Aware Q-learning AODV (EAQ-AODV) routing for cognitive radio sensor networks

Cited by 13 publications

References 40 publications

An imperfect spectrum sensing-based multi-hop clustering routing protocol for cognitive radio sensor networks

An imperfect spectrum sensing-based multi-hop clustering routing protocol for cognitive radio sensor networks

An artificial intelligence approach for energy‐aware intrusion detection and secure routing in internet of things‐enabled wireless sensor networks

Systematic review on modification to the ad-hoc on-demand distance vector routing discovery mechanics

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