Ad Hoc On-Demand Multipath Distance Vector Based Routing in Ad-Hoc Networks

Sarao, Pushpender

doi:10.1007/s11277-020-07511-y

Cited by 16 publications

(5 citation statements)

References 9 publications

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“…The best Q-value is the total rewards obtained by performing allied actions based on the best policy, as described by Eq. (6).…”

Section: Q-learningmentioning

confidence: 99%

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Collaborative Attackers Detection and Route Optimization by Swarm Intelligent-based Q-learning in MANETs

2024

IJIES

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Mobile Adhoc Networks (MANETs) are vulnerable to various attacks such as Black Hole Attack (BHA), Gray Hole Attack (GHA), and Wormhole Attacks (WHA). While researchers have focused on detecting and mitigating individual attacks, protection against collaborative attacks is limited. Therefore, this article introduces a new Tunicate Swarm Optimization Q-learning-based Collaborative Attacker Detection Algorithm (TSOQCADA) to identify and prevent collaborative attackers like BHA, GHA, and WHA, thereby improving routing efficiency. This algorithm utilizes feedback about node properties such as energy, reputation, buffer space, transmission delay, and packet transfer rate from all nodes to determine efficient packet routing. In the TSOQ-learning algorithm, the TSO is adopted to set the Q-table values, resulting in faster convergence of Q-learning. First, a Q-table with prior knowledge is trained to enhance searchability. Additionally, a novel selective search mechanism is adopted to improve exploration efficiency and reduce unwanted explorations by considering the correlation between current and target locations. Furthermore, a nonlinear function is designed to achieve a tradeoff between search and use abilities in Q-learning, dynamically changing ε value in the ε-greedy method according to the number of iterations. Thus, the TSOQ-learning can efficiently obtain a routing path by isolating collaborative attackers with low reputation values. Simulation results show that the TSOQCADA achieves a Packet Delivery Ratio (PDR) of 94.8%, Packet Loss Rate (PLR) of 5.2%, energy consumption of 2.53J energy/packet, throughput of 355Kbps, and End-to-End (E2E) delay of 35ms for a network of 100 nodes with 20 malicious nodes in MANET, outperforming the Efficient Trust-based Routing Scheme (ETRS), Hybrid Trust-based Reputation Mechanism (HTRM) and Deep Neural Learned Projective Pursuit Regression-based Watchdog Malicious Node Detection and Isolation (DNLPPR-WMNDI) algorithms.

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“…The best Q-value is the total rewards obtained by performing allied actions based on the best policy, as described by Eq. (6).…”

Section: Q-learningmentioning

confidence: 99%

“…Proactive protocols update routing tables periodically [5]. Reactive protocols [6] establish routes on-demand. Hybrid protocols merge proactive and reactive elements [7].…”

Section: Introductionmentioning

confidence: 99%

Collaborative Attackers Detection and Route Optimization by Swarm Intelligent-based Q-learning in MANETs

2024

IJIES

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“…Sarao proposed an evaluation work for AODV, AOMDV, DSR, and DSDV routing protocols [23]. Total 31 mobile nodes were simulated on network simulator NS-2.35 based on performance parameters (throughput, end to end delay, normalized routing load).…”

Section: Related Workmentioning

confidence: 99%

Evaluation of Multipath Based Protocols in Wireless Networks

Sarao

2024

JAIT

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In wireless communications, it is very tedious task to achieve the best performance without losing any data and time. It is also a challenging work to select an appropriate routing protocol in different network scenarios in respect of several performance parameters. In this research work reactive and proactive routing protocols are evaluated by considering six performance parameters. Especially multipath-based routing protocols have been compared. Network scenarios are generated by varying the pause time, network size, simulation time, speed, number of nodes, network connections, and packet size. Performance parameters are considered as throughput, packet delivery ratio, average end to end delay, total dropped packets, jitter, and total received packets. Experimental work is conducted on network simulator NS-2.35 and results are tabulated for analytical discussion purpose. In most of the cases simulation results clearly indicate that, Ad-hoc on Demand Multi-path Distance Vector (AOMDV) and Destination Sequenced Distance Vector (DSDV) routing protocols shows excellence performance. In some cases, Multi-path Dynamic Address Routing Techniques (M-DART) routing protocol works well as compared to Ad hoc on Demand Distance Vector (AODV) and DSDV.

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“…Among the existing systems, AOMDV, that is, Ad‐hoc on‐demand multipath distance vector routing 46 protocol, was proposed by the author in 2006, is the advanced version of AODV, that is, ad‐hoc on‐demand distance vector routing protocol. It is a classic reactive routing protocol used in multipath routing by connecting disconnected multiple routes through path finding.…”

Section: Related Workmentioning

confidence: 99%

An intelligentMACprotocol for mission critical applications in wireless sensor networks

Raut

Khandait²

2023

Concurrency and Computation

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SummaryIn the current age, wireless sensor networks (WSN) are growing the attention of researchers. WSNs are significantly used in a broad scope of real‐time monitoring applications. With ongoing advances and requirements in industrial emerging time‐critical applications, WSNs have been accordingly stretched out to help the reliable, time‐bound data delivery. In WSNs, node organization, resource limitations, topology varieties, providing delay guarantees for channel access, and transmission time barrier are the most significant challenges in real‐time handling traffic. Suppose critical data packets arrive from sensor nodes simultaneously. In that case, finding the optimal path for the transmission of data packets so that data are reached within time bounds, taking into account the current network scenario, is a more inspiring task. With this perspective, in order to provide a priority‐based transmission scheme, an improved adaptive media access control (MAC) protocol is proposed to support priority‐based channel access considering the current network scenario, which ensures time‐bounded reliable transfer of critical data packets. The proposed system uses an adaptive learning‐centered approach to find the optimum path for sending critical data packets in varying network scenarios. The proposed mission‐critical MAC (MC‐MAC) protocol is assessed arithmetically to offer reliable and time‐bounded transmission for emergency data packets.

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Ad Hoc On-Demand Multipath Distance Vector Based Routing in Ad-Hoc Networks

Cited by 16 publications

References 9 publications

Collaborative Attackers Detection and Route Optimization by Swarm Intelligent-based Q-learning in MANETs

Collaborative Attackers Detection and Route Optimization by Swarm Intelligent-based Q-learning in MANETs

Evaluation of Multipath Based Protocols in Wireless Networks

An intelligentMACprotocol for mission critical applications in wireless sensor networks

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