Computational Intelligence for Wireless Sensor Networks: Applications and Clustering Algorithms

Solaiman, Basma; Sheta, Alaa

doi:10.5120/12814-9940

Cited by 25 publications

(22 citation statements)

References 41 publications

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“…John Holland in 1970 [22] introduced the GA is on the basis of evolutionary Charles Darwin theory. This heuristic adaptive algorithm depends on the biological genetic evolution.…”

Section: Ch Selection Using Genetic Algorithm (Ga)mentioning

confidence: 99%

Optimized cluster head selection using krill herd algorithm for wireless sensor network

Karthick

Palanisamy²

2019

Automatika

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Wireless Sensor Network (WSNs) can perform transmission within themselves and examination is performed based on their range of frequency. It is quite difficult to recharge devises under adverse conditions. The main limitations are area of coverage, network's lifetime and aggregating and scheduling. If the lifetime of a network should be prolonged, then it can become a success along with reliability of the data transferred, conservation of sensor and scalability. Through many research works, this challenge can be overcome which are being proposed and the network's lifespan improved which can preserve the sensor's energy. By schemes of clustering, a low overhead is provided and the resources are efficiently allocated thus increasing the ultimate consumption of energy and reducing interfaces within the sensor nodes. Challenges such as node deployment and energy-aware clustering can be considered as issues of optimization with regards to WSNs, along with data collection. An optimal solution can be gotten through evolutionary and SI algorithm, pertaining to Non-deterministic Polynomial (NP)-complete along with a number of techniques. In this work, Krill Herd Algorithm based clustering is proposed.

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“…John Holland in 1970 [22] introduced the GA is on the basis of evolutionary Charles Darwin theory. This heuristic adaptive algorithm depends on the biological genetic evolution.…”

Section: Ch Selection Using Genetic Algorithm (Ga)mentioning

confidence: 99%

Optimized cluster head selection using krill herd algorithm for wireless sensor network

Karthick

Palanisamy²

2019

Automatika

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“…Computational intelligence (CI) model is appropriate to be used for WSN which is dynamic in nature. Solaiman and Sheta (2013) explore the benefits of CI technique and exploit it to solve different types of issues linked to WSN. The paper concluded with further research directions.…”

Section: Related Workmentioning

confidence: 99%

Fuzzy rule selection using artificial bee colony optimisation algorithm

Kiran

Dethe

2019

IJAIP

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Wireless sensor network (WSN) brings an innovative model with embedded system with restrictions of computing ability, intercommunication, storage capacity, and energy resource which is applied for a high range of applications in the situations when constructing the network based on conventional infrastructure is not feasible. Clustering with WSN is a successful technique to reduce the rate of energy use of sensor node. The fuzzy logic calculates the cluster head (CH) selection probability depending on the node's earlier communication history to choose the CH. The set of rules applied to the fuzzified input is the fuzzy rule base. The output of the inference engine is changed to crisp output by defuzzification. Artificial bee colony (ABC) is an optimisation protocol owes its inspiration to the exploration behaviour of honey bees. It is a comparatively innovative optimisation algorithm which has proven to be on par with classical bio-inspired protocols. This work on ABC optimisation algorithm is suggested for selecting fuzzy rules. Rule selection methods combine different rules from fuzzy rule set to decrease the rules while maintaining the performance of the system. The rules that decrease the performance of the system are removed, to get a fuzzy rule set with improved performance.

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“…[1] GA is characterized by heuristic search algorithm that models biological genetic evolution. It demonstrate to be a robust optimizer that searches among a population of solutions, and showed easy going in solving dynamic problems.…”

Section: Genetic Algorithmmentioning

confidence: 99%

“…WIRELESS sensor network is a network of distributed autonomous devices in which physical conditions can be sensed [1]. WSNs are helpful in different various applications such as environmental monitoring, prediction, agriculture applications House and office buildings, medical monitoring and structural health monitoring [2].…”

Section: Introductionmentioning

confidence: 99%

Computational Intelligence Techniques for Wireless Sensor Network: Review

Chawla¹,

Manju²,

Singh³

2015

IJCA

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Wireless sensors (nodes) in the network sense extract data from the various surrounding environment, sequence the sensed data locally, and then transfer the data to a base station for further processing through wireless communication. WSNs face various problem, communication failures, storage and computational constraints and limited power supply. The main challenge occur in WSN to save energy and prolong the network lifetime. Clustering is a technique that is used for managing energy consumption. However, clustering is NP hard optimization problem that can't be solved effectively by traditional methods. Different Computational Intelligence (CI) techniques are used to alter WSN dynamic nature.

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Computational Intelligence for Wireless Sensor Networks: Applications and Clustering Algorithms

Cited by 25 publications

References 41 publications

Optimized cluster head selection using krill herd algorithm for wireless sensor network

Optimized cluster head selection using krill herd algorithm for wireless sensor network

Fuzzy rule selection using artificial bee colony optimisation algorithm

Computational Intelligence Techniques for Wireless Sensor Network: Review

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