Classification of Packet Transmission Outcomes in Wireless Sensor Networks

Energy and security are very important issues in Wireless Sensor Networks (WSN) which need to be handled. These issues are interrelated because of limited energy there are some restrictions on implementation of security. Insider packet drop attack is one of the dangerous attacks for wireless sensor network that causes a heavy damage to WSN functionalities by dropping packets. It becomes necessary to identify such attack for secure routing of data in WSN. To detect this attack, trust mechanism has been proven as a successful technique. In this mechanism, each node verifies the trustworthiness of its neighbor node before packet transmission so that packets can only be transmitted to trustworthy nodes. But there is a problem of False Alarm with such trust-aware scheme. False alarm occurs when a good node’s trust value goes down due to natural packet dropping and being eliminated from the routing paths. This wastes network’s resources that further shortens network lifetime. In this paper, we have proposed a system for identification and recovery of false alarms (IRFA) which is the optimization of existing trust based system. But security solution needs to be energy efficient due to scarcity of energy resources in WSN. To provide energy efficiency, we have implemented proposed IRFA system in cluster based environment which detects insider packet drop attackers in an energy efficient manner. We have conducted OMNET++ simulation and results demonstrate that the proposed system performance is better than existing trust-based system in terms of packet delivery rate and energy efficiency which improves network lifetime.

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Towards a classification of energy aware MAC protocols for wireless sensor networks

Wireless Communications and Mobile Computing ◽

10.1002/wcm.743 ◽

2009 ◽

Vol 9 (12) ◽

pp. 1572-1607 ◽

Cited By ~ 99

Author(s):

Bashir Yahya ◽

Jalel Ben-Othman

Keyword(s):

Wireless Sensor Networks ◽

Sensor Networks ◽

Mac Protocols ◽

Wireless Sensor ◽

Energy Aware

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

Sensor Technology ◽

10.4018/978-1-7998-2454-1.ch074 ◽

2020 ◽

pp. 1580-1600

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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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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