A cluster-based trusted routing method using fire hawk optimizer (FHO) in wireless sensor networks (WSNs)

Hosseinzadeh, Mehdi; Yoo, Joon; Ali, Saqib; Lánský, Jan; Mildeová, Stanislava; Yousefpoor, Mohammad Sadegh; Ahmed, Omed Hassan; Rahmani, Amir Masoud; Tightiz, Lilia

doi:10.1038/s41598-023-40273-8

Cited by 17 publications

(2 citation statements)

References 32 publications

(38 reference statements)

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“…The important aim of CH is to acquire the data from the Cluster Member (CM) as well as transform it to the sink node for further data processing. The WSN network lifetime can be enhanced by utilizing the two most significant approaches such as clustering and routing [8] [9].…”

Section: Introductionmentioning

confidence: 99%

Multi-objective Trust-aware Dynamic Weight Pelican Optimization Algorithm for Secure Cluster Head and Routing Selection in WSN

Srinivasan R.

2024

jes

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Wireless Sensor Networks (WSN) is a group of Sensor Nodes (SNs) which are performed to intellect a normal singularity from an environment. The minimum resources and computational power of WSN creates vulnerable to number of security attacks. To addresses this problem, this paper proposes a Multi-objective Trust-aware Dynamic Weight Pelican Optimization Algorithm (M-TDWPOA)-based secure and energy aware multi-hop routing in WSN with the Base Station (BS). The proposed method comprises of two significant steps: Initially, dynamic energy-efficient Cluster Head (CH) selection by utilizing the multi-objective functions such as Distance between neighbor nodes, Distance between BS to CH, Energy, Centrality and Trust Threshold. Then, dynamic secure multi-hop routing is selected by the multi-objective functions such as Distance between BS to CH, Energy, Trust Threshold which is dynamically reducing the network overhead. Furthermore, a security aware multi-hop routing is employed through the different trust classes like direct, indirect, recent and authentication. The proposed M-TDWPOA approach is estimated with various performance metrics like alive nodes, dead nodes, energy consumption, Throughput and so on. The proposed M-TDWPOA achieves the minimum energy consumption of 0.4927J, 0.4851J, 0.4771J, 0.4693J and 0.4617J when compared to the existing methods like Fractional Artificial Lion (FAL).

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Section: Introductionmentioning

confidence: 99%

Multi-objective Trust-aware Dynamic Weight Pelican Optimization Algorithm for Secure Cluster Head and Routing Selection in WSN

Srinivasan R.

2024

jes

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“…FANET in precision agriculture.high speed, and applying a low-density network. These challenges should be taken into consideration to achieve a reliable and stable data transmission process 13,14 . A suitable routing approach must provide extensive communication coverage, high reliability, and strong communication links in FANET.…”

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confidence: 99%

A local filtering-based energy-aware routing scheme in flying ad hoc networks

Hosseinzadeh,

Husari,

Yousefpoor

et al. 2024

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Flying ad hoc network (FANET) is a new technology, which creates a self-organized wireless network containing unmanned aerial vehicles (UAVs). In FANET, routing protocols deal with important challenges due to limited energy, frequent failures in communication links, high mobility of UAVs, and limited communication range of UAVs. Thus, a suitable path is always essential to transmit data between UAVs reliably. In this paper, a local filtering-based energy-aware routing scheme (LFEAR) is proposed for FANETs. LFEAR improves the template of the route request (RREQ) packet by adding three other fields, namely the energy, reliable distance, and movement similarity of the relevant route to create stable and energy-efficient paths between UAVs. In the routing process, LFEAR presents a local filtering construction technique to avoid the broadcasting storm issue. This filter limits the broadcasting range of RREQs in the network. Accordingly, only UAVs inside this local filtered area can rebroadcast RREQ and other UAVs must eliminate this packet. Upon the end of the route discovery process, the destination begins the route selection phase and extracts information about each discovered route, including the number of hops, route energy, reliable distance, and movement similarity from the relevant RREQ. Then, the destination node calculates a score for each path based on the extracted information, selects the route with the highest score, and sends a route reply (RREP) packet to the source node through this route. Finally, the simulation process of LFEAR is performed using the NS2 simulator, and two simulation scenarios, namely change in network density and change in the speed of UAVs, are defined to evaluate network performance. In the first scenario, LFEAR improves energy consumption, packet delivery rate, network lifespan, and delay by 1.33%, 1.77%, 6.74%, and 1.71%, while its routing overhead is about 16.51% more than EARVRT. In the second scenario, LFEAR optimizes energy consumption and network lifetime by 5.55% and 5.67%, respectively. However, its performance in terms of routing overhead, packet delivery rate, and delay is 23%, 2.29%, and 6.67% weaker than EARVRT, respectively.

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