Efficient cluster head selection using Naïve Bayes classifier for wireless sensor networks

Jafarizadeh, Vahid; Keshavarzi, Amin; Derikvand, Tajedin

doi:10.1007/s11276-015-1169-8

Cited by 50 publications

(16 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These directions-energy-efficient machine learning methods and context-specific designs-are outlined in Table 4. A major drawback with the adoption of a supervised learning approach, such as those presented in [84,85,93,94,97], is the requirement of training data which, for some applications, might be cumbersome. Furthermore, the performance of machine learning methods, though excellent, is not error free.…”

Section: Future Directionsmentioning

confidence: 99%

Energy Efficient Design Techniques in Next-Generation Wireless Communication Networks: Emerging Trends and Future Directions

Ogbebor

Imoize

Atayero

2020

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

The projected rise in wireless communication traffic has necessitated the advancement of energy-efficient (EE) techniques for the design of wireless communication systems, given the high operating costs of conventional wireless cellular networks, and the scarcity of energy resources in low-power applications. The objective of this paper is to examine the paradigm shifts in EE approaches in recent times by reviewing traditional approaches to EE, analyzing recent trends, and identifying future challenges and opportunities. Considering the current energy concerns, nodes in emerging wireless networks range from limited-energy nodes (LENs) to high-energy nodes (HENs) with entirely different constraints in either case. In view of these extremes, this paper examines the principles behind energy-efficient wireless communication network design. We then present a broad taxonomy that tracks the areas of impact of these techniques in the network. We specifically discuss the preponderance of prediction-based energy-efficient techniques and their limits, and then discuss the trends in renewable energy supply systems for future networks. Finally, we recommend more context-specific energy-efficient research efforts and cross-vendor collaborations to push the frontiers of energy efficiency in the design of wireless communication networks.

show abstract

Section: Future Directionsmentioning

confidence: 99%

Energy Efficient Design Techniques in Next-Generation Wireless Communication Networks: Emerging Trends and Future Directions

Ogbebor

Imoize

Atayero

2020

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

show abstract

“…In 2017, Jafarizadeh et al [34] have developed a Naive Bayes that was the division of data mining approaches for solving the issues on CH node's optimal determination. The simulation investigation has been analysed and the resultant outcome has demonstrated that proposed model has better effect than the other to resolve the issues, and therefore it has seemed that these models have turned as obtaining enhanced results on solving these issues.…”

Section: Literature Surveymentioning

confidence: 99%

“…However, pose increased energy expense and nodes are self‐selected to become CHs. Naive Bayes [34] is a cost‐effective method and is suitable for multiple networks. However, needs improvement in the CH output and many more algorithms need to resolve the issue.…”

Section: Literature Surveymentioning

confidence: 99%

Optimal cluster head selection using modified rider assisted clustering for IoT

Poluru

Ramasamy

2020

IET Communications

View full text Add to dashboard Cite

“…Rhaiem et al [20] use non-convex, non-LIP and multicast topology control based on NC in WSNs in order to reduce energy consumption. In the work of Jafarizadeh et al [21] it has been shown that the simulation in MS scenarios and usage of linear and random NC reduces latency. In the work of Jing et al [22] it has been shown that by simulation NC reduces the delay in downloading files compared to non-NC applications.…”

Section: Related Workmentioning

confidence: 99%

“…In the work of Jafarizadeh et al . [21] it has been shown that the simulation in MS scenarios and usage of linear and random NC reduces latency. In the work of Jing et al .…”

Section: Related Workmentioning

confidence: 99%

Increasing the amount of collected data using network coding and continuous movement of mobile sinks in wireless sensor networks

2020

View full text Add to dashboard Cite

This study uses the network coding (NC) and mobile sinks (MSs) for collecting coded data (CCD) of sensor nodes (SNs). MSs move on a steady, direct and predetermined path with constant velocity in wireless sensor networks. The authors present an optimisation model for CCD problem which is a generalisation of the previous works and an optimisation model based on the integrated linear programming model. Solving this problem in polynomial time is not possible. In this model for CCD, each SN and MS are assigned a time slice, and two conditions are presented for transmission range of SNs and at most number time slices for CCD. Then a centralised algorithm with polynomial time complexity is presented in proposed conditions for the problem solving. Finally, by simulating, different sets of SNs are deployed randomly with a fixed position, which was evaluated by influence of the number of SNs, travelled distance of MS at each time slice, the duration of time slice, velocity of MS on delay, the total amount of CCD and network efficiency. The simulation results represent scalability and performance superiority of the proposed algorithm compared to methods with no NC such as C-Schedule and GAP-based approximation algorithms.

show abstract

Efficient cluster head selection using Naïve Bayes classifier for wireless sensor networks

Cited by 50 publications

References 34 publications

Energy Efficient Design Techniques in Next-Generation Wireless Communication Networks: Emerging Trends and Future Directions

Energy Efficient Design Techniques in Next-Generation Wireless Communication Networks: Emerging Trends and Future Directions

Optimal cluster head selection using modified rider assisted clustering for IoT

Increasing the amount of collected data using network coding and continuous movement of mobile sinks in wireless sensor networks

Contact Info

Product

Resources

About