Energy aware fault tolerant clustering with routing protocol for improved survivability in wireless sensor networks

Mansour, Romany F.; Alsuhibany, Suliman A.; Abdel‐Khalek, S.; Alharbi, Randa; Vaiyapuri, Thavavel; Gupta, Deepak

doi:10.1016/j.comnet.2022.109049

Cited by 25 publications

(8 citation statements)

References 21 publications

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“…The data transmission process within a chain is designed to ensure that each sensor is able to deliver its data successfully to the CL 19 . It means that even if a non‐CL node fails, the protocol has mechanisms in place to handle it, as proposed in references 20,21.…”

Section: Proposed Fault Tolerance Mechanismsmentioning

confidence: 99%

Markov chain‐based analysis and fault tolerance technique for enhancing chain‐based routing in WSNs

Jalili,

Alzubi,

Rezaei

et al. 2024

Concurrency and Computation

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SummaryWireless sensor networks (WSNs) are faced with the challenge of energy conservation, which makes efficient routing protocols crucial for prolonging network lifetime. In addition, delay time from sensors to the base station is critical in applications such as military, medical, and security monitoring systems. Chain‐based protocols like PEGASIS, CCBRP, and CCM have been developed to address routing in WSNs, with the aim of reducing energy consumption and delay. However, node failures are inevitable due to energy reduction and node mobility. Therefore, fault tolerance techniques must be integrated into routing protocols. A new fault tolerance method has been proposed to prevent early chain failures in WSNs by formulating network availability and reliability using Markov chain analysis. The results indicate that chain‐based routing protocols with one spare node are more reliable than those with two spare nodes.

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Section: Proposed Fault Tolerance Mechanismsmentioning

confidence: 99%

Markov chain‐based analysis and fault tolerance technique for enhancing chain‐based routing in WSNs

Jalili,

Alzubi,

Rezaei

et al. 2024

Concurrency and Computation

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“…EAFTC‐RIS 24 protocol built on a fault‐tolerant mechanism. To choose cluster heads (CHs), the EAFTC‐RIS strategy uses an MFO and an SSO‐based routing approach.…”

Section: Literature Surveymentioning

confidence: 99%

MH‐ARO: Meta‐heuristic based adaptive routing for large scale opportunistic networks

Chaurasia,

Kumar

2024

Concurrency and Computation

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SummaryIn wireless sensor networks, sensor nodes (SNs) are placed throughout a wide area and gather information from the surroundings. SN used to detect and send data consumes a lot of energy and dies instantly, which causes network overhead issues. Due to network overhead, network faults occur and do not cover a significant region for data transmission. A meta‐heuristic‐based adaptive routing for large‐scale opportunistic networks (MH‐ARO) is proposed in this work to overcome these problems. The network exploits the dragonfly approach (DA) with opportunistic routing in this protocol. The DA is based on local and global search (GS) optimization. In local search (LS), each node assigns/uses a rank for data transmission and selects the forwarder node after applying the decision level matrix in a single group. In GS, an opportunistic network has multiple groups. Each group sends optimal data to the next group. Each group assigns a relay ranking based on the forwarder node's highest rank. The BS receives critical data from the relay and increases the survival of the node. Based on LS and GS, MH‐ARO is categorized into two parts: (1) optimal forwarder opt (OFO) and (2) optimal route selection (ORS). In OFO, forwarder node selection is based on the following factors: relay ranking, node density, group ranking, residual energy, forwarder distance, and relay distance. ORS follows the adaptive routing and sends the data to BS using the best optimal route. The BS receives critical data without network failure and increases the survival of the node. The maximum active node participates in the network and consumes less energy so that a node covers a large region for communication. Comparing MH‐ARO's performance to competing routing protocols, key performance indicators such as PDR, MSR, alive node, survivability, AEC, AD, and throughput are examined. The results demonstrate that the MH‐ARO performs noticeably better than its rivals regarding energy efficiency.

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“…The applications that may suffer causalities from data loss require fault-tolerant nodes. The self-organized WSNs solve this problem by re-clustering, but re-clustering not only interrupts the current process but also increases overhead [24,30].…”

Section: Objectivementioning

confidence: 99%

An In-depth Analysis of Uneven Clustering Techniques in Wireless Sensor Networks

Zhang¹

2023

IJACSA

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The low-cost and convenient feature of Wireless Sensor Networks (WSNs) has made them popular in many sectors over the last decade. The WSNs are now widely used as a result of recent advancements in low-power communication and being energy-efficient. The WSNs typically use batteries to power sensor nodes. The finite stored energy in batteries and the hassle of battery replacement have led to a critical focus on energy efficiency for WSNs. Clustering and data aggregation are the most efficient methods to address the energy concerns of WSNs. This paper comprehensively reviews several uneven clustering methods and compares the various uneven clustering algorithms. The methods are described in terms of their goals, attributes, categories, advantages and disadvantages. Probabilistic clustering is used when there is a need of simplicity and speed. As a result, this study compared all these types of protocols based on their clustering properties, CHs properties, and on the type of clustering process; and current research gap effective techniques are also addressed.

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Energy aware fault tolerant clustering with routing protocol for improved survivability in wireless sensor networks

Cited by 25 publications

References 21 publications

Markov chain‐based analysis and fault tolerance technique for enhancing chain‐based routing in WSNs

Markov chain‐based analysis and fault tolerance technique for enhancing chain‐based routing in WSNs

MH‐ARO: Meta‐heuristic based adaptive routing for large scale opportunistic networks

An In-depth Analysis of Uneven Clustering Techniques in Wireless Sensor Networks

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