Long-Term Routing Stability of Wireless Sensor Networks in a Real-World Environment

Lim, Cheng Leong; Goh, Cindy; Li, Yun

doi:10.1109/access.2019.2920248

Cited by 11 publications

(4 citation statements)

References 19 publications

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“…Another important point to make is that latency and reliability go together. Packets have to wait for an alternate path to be selected by the routing centre when the node fails during a communication process [5] - [6]. Load balancing is another key feature seen where, in the case of dynamic traffic situations, pathways may suffer low traffic rates because certain active nodes not involved in the communication process may utilize power, resulting in increased energy consumption.…”

Section: Fig 1 Illustration Of Typical Wsn Applicationsmentioning

confidence: 99%

A Metaheuristic Approach Towards Enhancement of Network Lifetime in Wireless Sensor Networks

2023

KSII TIIS

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Sensor networks are now an essential aspect of wireless communication, especially with the introduction of new gadgets and protocols. Their ability to be deployed anywhere, especially where human presence is undesirable, makes them perfect choices for remote observation and control. Despite their vast range of applications from home to hostile territory monitoring, limited battery power remains a limiting factor in their efficacy. To analyze and transmit data, it requires intelligent use of available battery power. Several studies have established effective routing algorithms based on clustering. However, choosing optimal cluster heads and similarity measures for clustering significantly increases computing time and cost. This work proposes and implements a simple two-phase technique of route creation and maintenance to ensure route reliability by employing nature-inspired ant colony optimization followed by the fuzzy decision engine (FDE). Benchmark methods such as PSO, ACO and GWO are compared with the proposed HRCM's performance. The objective has been focused towards establishing the superiority of proposed work amongst existing optimization methods in a standalone configuration. An average of 15% improvement in energy consumption followed by 12% improvement in latency reduction is observed in proposed hybrid model over standalone optimization methods.

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Section: Fig 1 Illustration Of Typical Wsn Applicationsmentioning

confidence: 99%

A Metaheuristic Approach Towards Enhancement of Network Lifetime in Wireless Sensor Networks

2023

KSII TIIS

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“…Due to the limited processing capabilities of nodes, optimizing transmission costs, data gathering, and load tolerance of nodes to extend their life is a difficult challenge. Clustering optimization, which involves selecting the best energy path for routing, can assist in extending the network's lifespan [67].…”

Section: Network Stabilitymentioning

confidence: 99%

Bio-Inspired Hybrid Optimization Algorithms for Energy Efficient Wireless Sensor Networks: A Comprehensive Review

2022

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Researchers are facing significant challenges to develop robust energy-efficient clustering and routing protocols for Wireless Sensor Networks (WSNs) in different areas such as military, agriculture, education, industry, environmental monitoring, etc. WSNs have made an everlasting imprint on everyone’s lives. The bulk of existing routing protocols has focused on cluster head election while disregarding other important aspects of routing including cluster formation, data aggregation, and security, among others. Although cluster-based routing has made a significant contribution to tackling this issue, the cluster head (CH) selection procedure may still be improved by integrating critical characteristics. Nature-inspired algorithms are gaining traction as a viable solution for addressing important challenges in WSNs, such as sensor lifespan and transmission distance. Despite this, the sensor node batteries cannot be changed when installed in a remote or unsupervised area due to their wireless nature. As a result, numerous researches are being done to lengthen the life of a node span. The bulk of existing node clustering techniques suffers from non-uniform cluster head distribution, an imbalanced load difficulty within clusters, concerning left-out nodes, coverage area, and placement according to a recent study. Metaheuristic algorithms (DE, GA, PSO, ACO, SFO, and GWO) have the advantages of being simple, versatile, and derivation-free, as well as effectively utilizing the network’s energy resource by grouping nodes into clusters to increase the lifespan of the entire network. In this paper, we explore recently used hybridization techniques (DE-GA, GA-PSO, PSO-ACO, PSO-ABC, PSO-GWO, etc.) for bio-inspired algorithms to improve the energy efficiency of WSNs. This paper also discusses how critical issues can be addressed by speeding up the implementation process, how more efficient data can be transferred, as well as how energy consumption can be reduced by using bio-inspired hybrid optimization algorithms.

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“…In [40], the authors examine the routing stability of the WSN nodes implemented by ZigBee PRO in a real world environment. They introduce three metrics; switching frequency count, use rate of unique next hop, relative routing path usage count; as routing stability indicators.…”

Section: Related Workmentioning

confidence: 99%

Stable routing and energy-conserved data transmission over wireless sensor networks

Shukry

2021

J Wireless Com Network

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Stable routing and energy conservation over a wireless sensor network (WSN) is a major issue in Internet of Things applications. The network lifetime can be increased when studying this issue with interest. Data transmission is a dominant factor in IoT networks for communication overhead and energy consumption. A proposed efficient node stable routing ($$ENSR$$ ENSR ) protocol is introduced to guarantee the stability of transmission data between the source and destination nodes, in a dynamic WSN conditions. $$ENSR$$ ENSR minimizes energy consumption and selects more stable nodes for packets forwarding. Stability becomes the most important factor that qualifies the node's centrality. A node’s stability is characterized by residual energy, link quality, and number of hops needed to reach the destination from the node. To calculate node's stability, an enhanced centrality concept, known as stable betweenness centrality ($$SBC$$ SBC ) is introduced. In $$ENSR$$ ENSR , at first, some nodes will be selected as the stable forwarding nodes, usually with maximum $$SBC$$ SBC between their neighbors within a limited communication radio range of a particular region. Furthermore, each stable forwarding node then broadcasts its identity, including $$SBC$$ SBC , to the source node separately. The source node can compute a stable path to forward packets to the corresponding stable forwarding node, based on a proper designed stable path routing metric ($$SPRM$$ SPRM ). Then, the stable forwarding node will behave as a new source node and start another stable path routing process until the packets are forwarded and reached to the destination node. In addition, the change of stable nodes over time balances and conserves node energy consumption, thereby mitigating “hot spots”. The proposed routing protocol is validated through simulation. The numerical results show that the proposed protocol outperforms the existing algorithms, global and local reliability-based routing ($$GLRR$$ GLRR ) and reliable energy-aware routing protocol $$(RER)$$ ( R E R ) , in terms of network efficiency and reliability.

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Long-Term Routing Stability of Wireless Sensor Networks in a Real-World Environment

Cited by 11 publications

References 19 publications

A Metaheuristic Approach Towards Enhancement of Network Lifetime in Wireless Sensor Networks

A Metaheuristic Approach Towards Enhancement of Network Lifetime in Wireless Sensor Networks

Bio-Inspired Hybrid Optimization Algorithms for Energy Efficient Wireless Sensor Networks: A Comprehensive Review

Stable routing and energy-conserved data transmission over wireless sensor networks

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