Energy efficient route discovery for reactive protocols in wireless ad hoc networks

Waqas, Abdullah; Imran, S.; Mahmood, Hasan

doi:10.1109/icet.2012.6375451

Cited by 7 publications

(5 citation statements)

References 22 publications

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“…Waqas et al [20] introduced a path-finding model to mitigate the overhead encountered by reactive route models. This model not only addresses route overhead but also reduces energy consumption.…”

Section: Related Workmentioning

confidence: 99%

Design of Hybrid Snake Optimizer Based Route Selection Approach for Unmanned Aerial Vehicles Communication

Alkahtani,

Mahgoub,

Alotaibi

et al. 2024

IEEE Access

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The effective usage of energy becomes crucial for the successful deployment and operation of unmanned aerial vehicles (UAVs) in different applications, such as surveillance, transportation, and communication networks. The increasing demand for UAVs in different industries such as agriculture, logistics, and emergency response has led to the development of more sophisticated and advanced UAVs. However, the limited onboard energy resource of UAVs poses a major problem for their long-term operation and endurance. In addition, artificial intelligence (AI) and machine learning (ML) could allow UAVs to make more informed and intelligent decisions regarding their operations, resulting in sustainable and more energyefficient UAV deployment. This article designs a Hybrid Snake Optimizer-based Route Selection Approach for Unmanned Aerial Vehicles Communication (HSO-RSAUAVC) technique. The goal of the HSO-RSAUAVC technique is to explore and select optimal routes for UAV communication. In the presented HSO-RSAUAVC technique, the SO algorithm is integrated with Bernoulli Chaotic Mapping and Levy flight (LF) for enhanced performance. In addition, the HSO-RSAUAVC method derives a fitness function including residual energy (RE), distance, and UAV degree. By incorporating the HSO-RSAUAVC technique, we can dynamically adapt UAV paths to overcome obstacles, decrease communication interference, and optimize energy utilization. To validate the performance of the proposed model, a series of simulations were performed. The comparative result analysis illustrates the better performance of the HSO-RSAUAVC technique in improving the performance and reliability of UAV communication.INDEX TERMS Unmanned aerial vehicles; Routing; Snake optimizer; Energy efficiency; Fitness function I. INTRODUCTIONRecently, with the fast growth of unmanned aerial vehicle (UAV) technology, UAVs have been extensively utilized in numerous domains [1]. Various kinds of UAVs can support people to accomplish some comparatively risky, impossible, and urgent tasks, like map reconstruction, ocean exploration, environmental analysis, aerial photography, and material distribution [2]. However, the existing UAVs are inadequately intelligent for performing difficult activities, and still major of them require people's real-time control [3]. A single UAV could only execute moderately easy tasks, nonetheless, the UAV set could effectively perform several laborious and complex tasks after acceptable task planning [4]. The task distribution issue is identical to the combinatorial optimizer decision issue for many UAVs. It is an integration method developed to satisfy UAV efficiency and limitations. The

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Section: Related Workmentioning

confidence: 99%

Design of Hybrid Snake Optimizer Based Route Selection Approach for Unmanned Aerial Vehicles Communication

Alkahtani,

Mahgoub,

Alotaibi

et al. 2024

IEEE Access

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“…The energy consumption of EDS algorithm is measured in terms of number of RREQ packets relayed in the network. Energy consumption of EDS can be calculated as follows [30]:…”

Section: Energy Consumptionmentioning

confidence: 99%

“…The expression in (11) provides the expected broadcast cost for energy consumption of EDS-S as a function of the TTL value, the number of attempts before broadcast, and the network area. The maximum number of attempts required to search the entire network is represented by κ, i.e., κ = x √ πt 1 [30].…”

Section: Disk1mentioning

confidence: 99%

Distributed Destination Search Routing for 5G and beyond Networks

Waqas

Saeed

Mahmood

et al. 2022

Sensors

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Fifth-generation and beyond networks target multiple distributed network application such as Internet of Things (IoT), connected robotics, and massive Machine Type Communication (mMTC). In the absence of a central management unit, the device need to search and establish a route towards the destination before initializing data transmission. In this paper, we proposes a destination search and routing method for distributed 5G and beyond networks. In the proposed method, the source node makes multiple attempts to search for a route towards the destination by expanding disk-like patterns originating from the source node. The source node increases the search area in each attempt, accommodating more nodes in the search process. As a result, the probability of finding the destination increases, which reduces energy consumption and time delay of routing. We propose three variants of routing for high, medium, and low-density network scenarios and analyze their performance for various network configurations. The results demonstrate that the performance of the proposed solution is better than previously proposed techniques in terms of time latency and reduced energy consumption, making it applicable for 5G and beyond networks.

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“…While the algorithm ensures that the SINR does not decrease the threshold SINR value, the resulting route may choose nodes at the network's edges, resulting in higher time delays. Similarly, in [26], the authors defined a routing metric that uses link interference in the network to select the path whose SINR is high to improve the performance of the network. The proposed solution faces a similar problem, i.e., high link length results in high time latency during the routing operation.…”

Section: Literature Reviewmentioning

confidence: 99%

“…As there is no central management unit, the devices themselves manages network operations. Therefore, each node act as router/relay and forwards the data of other nodes in addition to its own data packet [11]. The limitation on the available battery power at each node, requires that the designed algorithm is energy efficient to increase the network life time.…”

Section: Introductionmentioning

confidence: 99%

Interference Aware Cooperative Routing for Edge Computing-enabled 5G Networks

Waqas¹,

Mahmood²,

Saeed³

2022

Preprint

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Recently, there has been growing research on developing interference-aware routing (IAR) protocols for supporting multiple concurrent transmission in next-generation wireless communication systems. The existing IAR protocols do not consider node cooperation while establishing the routes because motivating the nodes to cooperate and modeling that cooperation is not a trivial task. In addition, the information about the cooperative behavior of a node is not directly visible to neighboring nodes. Therefore, in this paper, we develop a new routing method in which the nodes' cooperation information is utilized to improve the performance of edge computing-enabled 5G networks. The proposed metric is a function of created and received interference in the network. The received interference term ensures that the Signal to Interference plus Noise Ratio (SINR) at the route remains above the threshold value, while the created interference term ensures that those nodes are selected to forward the packet that creates low interference for other nodes. The results show that the proposed solution improves ad hoc networks' performance compared to conventional routing protocols in terms of high network throughput and low outage probability.

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Energy efficient route discovery for reactive protocols in wireless ad hoc networks

Cited by 7 publications

References 22 publications

Design of Hybrid Snake Optimizer Based Route Selection Approach for Unmanned Aerial Vehicles Communication

Design of Hybrid Snake Optimizer Based Route Selection Approach for Unmanned Aerial Vehicles Communication

Distributed Destination Search Routing for 5G and beyond Networks

Interference Aware Cooperative Routing for Edge Computing-enabled 5G Networks

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