Categorization of Data Mining Systems and Data Secrecy Towards Wireless Sensor Networks

A wireless sensor network may contain hundreds or even tens of thousands of inexpensive sensor devices that can communicate with their neighbors within a limited radio range. By relaying information on each other, they transmit signals to a command post anywhere within the network. Worldwide market for wireless sensor networks is rapidly growing due to a huge variety of applications it offers. In this chapter, we discuss application of computational intelligence techniques in wireless sensor networks on the coverage problem in general and area coverage in particular. After providing different types of coverage encountered in WSN, we present a possible classification of coverage algorithms. Then we dwell on area coverage which is widely studied due to its importance. We provide a survey of literature on area coverage and give an account of its state-of-the art and research directions.

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Application of Computational Intelligence Techniques in Wireless Sensor Networks the State of the Art

Handbook of Research on Computational Intelligence Applications in Bioinformatics - Advances in Bioinformatics and Biomedical Engineering ◽

10.4018/978-1-5225-0427-6.ch018 ◽

2016 ◽

pp. 441-461

Author(s):

Subhendu Kumar Pani

Keyword(s):

Wireless Sensor Networks ◽

Sensor Networks ◽

Computational Intelligence ◽

State Of The Art ◽

Area Coverage ◽

Wireless Sensor ◽

Coverage Problem ◽

Sensor Devices ◽

Huge Variety

A wireless sensor network may contain hundreds or even tens of thousands of inexpensive sensor devices that can communicate with their neighbors within a limited radio range. By relaying information on each other, they transmit signals to a command post anywhere within the network. Worldwide market for wireless sensor networks is rapidly growing due to a huge variety of applications it offers. In this chapter, we discuss application of computational intelligence techniques in wireless sensor networks on the coverage problem in general and area coverage in particular. After providing different types of coverage encountered in WSN, we present a possible classification of coverage algorithms. Then we dwell on area coverage which is widely studied due to its importance. We provide a survey of literature on area coverage and give an account of its state-of-the art and research directions.

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Hierarchical classification of time series data aggregation in underwater wireless sensor networks

Underwater Technology The International Journal of the Society for Underwater ◽

10.3723/ut.37.053underwater ◽

2020 ◽

Vol 37 (2) ◽

pp. 53-64

Author(s):

Durairaj Ruby ◽

Jayachandran Jeyachidra

Keyword(s):

Time Series ◽

Wireless Sensor Networks ◽

Energy Consumption ◽

Sensor Networks ◽

Error Rate ◽

Data Aggregation ◽

Hierarchical Classification ◽

Wireless Sensor ◽

Underwater Wireless Sensor Networks

Environmental fluctuations are continuous and provide opportunities for further exploration, including the study of overground, as well as underground and submarine, strata. Underwater wireless sensor networks (UWSNs) facilitate the study of ocean-based submarine and marine parameters details and data. Hardware plays a major role in monitoring marine parameters; however, protecting the hardware deployed in water can be difficult. To extend the lifespan of the hardware, the inputs, processing and output cycles may be reduced, thus minimising the consumption of energy and increasing the lifespan of the devices. In the present study, time series similarity check (TSSC) algorithm is applied to the real-time sensed data to identify repeated and duplicated occurrences of data for reduction, and thus improve energy consumption. Hierarchical classification of ANOVA approach (HCAA) applies ANOVA (analysis of variance) statistical analysis model to calculate error analysis for realtime sensed data. To avoid repeated occurrences, the scheduled time to read measurements may be extended, thereby reducing the energy consumption of the node. The shorter time interval of observations leads to a higher error rate with lesser accuracy. TSSC and HCAA data aggregation models help to minimise the error rate and improve accuracy.

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