Energy-Saving Algorithm and Simulation of Wireless Sensor Networks Based on Clustering Routing Protocol

He, Wei

doi:10.1109/access.2019.2956068

Cited by 33 publications

(18 citation statements)

References 27 publications

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“…Zamry et al [103] applied an energy saving hierarchical network protocol based on low-energy adaptive clustering hierarchy for WSNs. He [104] suggested an energy-saving algorithm and simulation of WSNs based on clustering routing protocol. Vo [105] designed a method to optimize the energy consumption for sensor processing layer in IoT.…”

Section: Miscellaneous Algorithmsmentioning

confidence: 99%

Energy Coherent Fog Networks Using Multi-Sink Wireless Sensor Networks

et al. 2021

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Fog computing (FC) models the cloud computing paradigm expedient by bridging the breach between centralized data servers and diverse terrestrially distributed applications. It wields various wireless sensor networks (WSNs) that sprawl in the core of any IoT applications. Consequently, the operation of fog network turns on the efficiency of WSNs operation, while the all-inclusive network energy consumption depends on both FC and WSNs operation. This paper addresses how dissimilar organizations of a fog network can influence its effectiveness and energy savings. Chiefly, it appraises whether deploying multisink nodes in close enough vicinity to the fog nodes can give in energy savings and foster coherent data communication between WSNs and fog networks. To assess the multi-sink assignment problem the following four criteria are used: (i) Distance from the fog network nodes; (ii) Nodes degree; (iii) Sink nodes energy; and (iv) Sink nodes processing capabilities. This paper suggests four novel solutions to the multisink connectivity for some challenges of fog networks deeming: (i) Window Nondominant Set (WNS); (ii) Evaluation Based Approach (EBA); (iii) Harris Hawks Optimizer (HHO); and (iv) Modified HHO (MHHO). Distinct sets of experiments are conducted to check out algorithmic performance. The performance of all algorithms is measured and then compared to each other in terms of power consumption, runtime, packet loss, and localization error. One of the key supremacies of our approaches is the utilization of fog network for sensor networks data processing, principally with the large-scale networks. Yet, the communication challenges could need further study due to the limited communication range of the sensors.

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Section: Miscellaneous Algorithmsmentioning

confidence: 99%

Energy Coherent Fog Networks Using Multi-Sink Wireless Sensor Networks

et al. 2021

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“…In the uplink transmission phase, the sensing nodes randomly distributed within the monitoring range complete the sensing of the monitoring target and gather the sensing outcomes to the head of the cluster. The cluster head node collects relevant information from the sensing nodes in the cluster and uploads the information to the gateway node within a direct or multihop manner [33]. The gateway node summarizes the information through every cluster head and transmits it to the user for further analysis and processing.…”

Section: Emwsn Clustering Modelmentioning

confidence: 99%

A Chaotic Elite Niche Evolutionary Algorithm for Low‐Power Clustering in Environment Monitoring Wireless Sensor Networks

Liu

Yang

et al. 2021

Journal of Sensors

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In recent years, as people’s demand for environmental quality has increased, it has become inevitable to monitor sensitive parameters such as temperature and oxygen content. Environmental monitoring wireless sensor networks (EMWSNs) have become a research hotspot because of their flexibility and high monitoring accuracy. This paper proposes a chaotic elite niche evolutionary algorithm (CENEA) for low-power clustering in EMWSNs. To verify the performance of CENEA, simulation experiments are carried out in this paper. Through simulation experiments, CENEA was compared with shuffled frog leaping algorithm (SFLA), differential evolution algorithm (DE), and genetic algorithm (GA) in the same conditional parameters. The results show that CENEA balances node energy and improved node energy usage efficiency. CENEA’s network energy consumption is reduced by 8.3% compared to SFLA, 3.9% lower than DE, and 4.6% lower than GA. Moreover, CENEA improves the precision and minimizes the computation time.

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“…Simulation results also demonstrate that this scheme has improved the end to end delay, energy efficiency and packet delivery ratio as compared to the previous schemes. Various challenges, i.e., low throughput, short life cycle and other energy constraints of the nodes in a network are tackled by the K-means and fuzzy analytic hierarchy process [18]. Where, clustering is done by the K-means algorithm in the network and cluster head selection is done by the fuzzy analytic hierarchy process based on the considered factors in the system.…”

Section: Related Workmentioning

confidence: 99%

On Modeling Optimizations and Enhancing Routing Protocols for Wireless Multihop Networks

Al-Zahrani

2020

IEEE Access

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The contribution of this paper is two fold. It first analyses the flooding strategies for the Wireless Multihop Networks (WMNs) then it enhances the reactive and proactive routing protocols. For analysis purpose, we select four widely used flooding techniques for routing: i. traditional flooding, ii. Time-To-Live based Expanding Ring Search (TTL-based ERS) flooding scheme, iii. TTL-based Scope Routing (SR) flooding and iv. Multi-Point Relays (MPR) flooding. These techniques play a vital role and act as a backbone for routing protocols. Therefore, we compare efficiency of these techniques for six widely used routing protocols: Ad-hoc On-demand Distance Vector (AODV), Destination Sequenced Distance Vector (DSDV), Dynamic Source Routing (DSR), DYnamic MANET On-demand (DYMO), Fish-eye Scope Routing (FSR) and Optimized Link State Routing (OLSR). DSDV uses traditional flooding, AODV, DSR and DYMO use TTL-based ERS flooding, FSR uses TTL-based SR flooding and OLSR uses MPR flooding. This paper also presents mathematical models for flooding techniques and studies the affects of these techniques on their respective protocols in terms of energy and time consumption. This is done to measure the cost incurred by the routing protocols in the form of routing overhead and latencies. A novel contribution of this work is the enhancement in search set values and intervals of routing algorithms to improve the efficiency of selected existing protocols. A detailed comparison analysis of selected protocols with their default and enhanced routing algorithms in NS-2 is also a part of this work.INDEX TERMS Time-To-Live, expanding ring, destination sequenced distance vector, DYnamic MANET On-demand, ad-hoc on-demand distance vector, wireless multihop networks.

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Energy-Saving Algorithm and Simulation of Wireless Sensor Networks Based on Clustering Routing Protocol

Cited by 33 publications

References 27 publications

Energy Coherent Fog Networks Using Multi-Sink Wireless Sensor Networks

Energy Coherent Fog Networks Using Multi-Sink Wireless Sensor Networks

A Chaotic Elite Niche Evolutionary Algorithm for Low‐Power Clustering in Environment Monitoring Wireless Sensor Networks

On Modeling Optimizations and Enhancing Routing Protocols for Wireless Multihop Networks

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