Wireless Sensor Network is a distribution of nodes connected wirelessly
to form a wireless topology. Having wide applications, these are prone to
misconduct. The most prevalent misconduct is the ability of a node to send data
under influence of radiation and such nodes are called defective nodes. The
defective node affected due to radiation and a void is created due to this node in
the network topology. Such defective nodes due to its inability to perform triggers
loss of data. Many algorithms/techniques were proposed but none among those
were capable for over thousand sensor nodes. More than thousand nodes are
considered here. Sensor nodes with radio frequency mode and acoustic mode of
communication are considered. Audio mode is implemented in nodes left out with
voids and RF-mode is used in nodes located at the boundary of void-nodes.
Transfer of defective nodes in a network can be foretold depending upon its past
behaviour.
Resourceful data Accretion in radiation in a Hefty WSN
Akash Manish Lad Vellore Institute of Technology Chennai
Abstract: Wireless Sensor Network is a distribution of nodes connected wirelessly to form a wireless topology. Having wide applications, these are prone to misconduct. The most prevalent misconduct is the ability of a node to send data under influence of radiation and such nodes are called defective nodes. The defective node affected due to radiation and a void is created due to this node in the network topology. Such defective nodes due to its inability to perform triggers loss of data. Many algorithms/techniques were proposed but none among those were capable for over thousand sensor nodes. More than thousand nodes are considered here. Sensor nodes with radio frequency mode and acoustic mode of communication are considered. Audio mode is implemented in nodes left out with voids and RF-mode is used in nodes located at the boundary of void-nodes. Transfer of defective nodes in a network can be foretold depending upon its past behaviour.
Keywords: Data Accretion, RF-Communication, Audio-Communication, defective nodes, Prediction technique, testing methods.
Introduction
With advancements in technology and miniaturization, wireless devices could communicate over very-small ranges and due to this wireless sensor networks came into play. Such devices are called as sensor nodes. These nodes are organized in a random fashion to form a topology network so as to cover large area possible geographically. There are large applications of sensor nodes such as sensing important information, sensing ranges and transmitting sensed information to any stations using single/multi hop communication. These nodes which receive and transmit information are connected wirelessly. In a general WSN network several nodes are connected to one another and it can be extended to over a few thousand nodes. WSN network trails IEEE 802.11 format for wireless local area network protocols. The sensor nodes detects , senses and then transmits the message to nearby station wirelessly.
The nodes in WSN are widely used in real-world applications due to its capability to sense various features like temperature , smoke, proportion of chemical substances and other physical quantities. Many other applications include predictions of natural disasters, weather predictions, air-pollution quality index, military application etc. In this technique the data loss is handled due to the presence of defective-nodes. The defective nodes caused due to excessive influence of radiation has been studied and few techniques have been presented. The main reason for data loss is mainly due to the effect of radiation.
The nature of the defective nodes are dynamic as these nodes are created mainly under the course of electromagnetic radiation when under an influence for a long time. The sensor nodes start sensing and transmit communication when the radiation is over. There are cases when the radiations lasts for shorter duration of time. Using the conventional Brute-force approach is not reliable as it involves removal of the defective nodes from a particular network and it is not a solution to the concerned problem. The maintenance cost, using such kinds of approach, is quite high and does not address to the solution of problem. To solve this problem, there is a method involving specialized nodes which communicate using RF and audio means. To obtain the information and details from affected area certain prediction algorithms are used. Most of the testing algorithms are very costly when in failure. One such way to solve the issue is by observing behavior of defective nodes and particular prediction techniques can be applied accordingly.
PREDICTION TECHNIQUE
In this paper, defective node has been incorporated with prediction logic which will predict that if a node is defective or not. The Sink have NET_INFO table which has tuple <node_id, initial_nbr_list, current_nbr_list> besides to every wireless node in observed network. Tuples having count of entries in current_nbr_list than in initial_nbr_list are interesting to sink. Sink looks for tuples in which initial_nbr_list > current_nbr_list, and node besides to this tuple is a boundary node. Sink tells this boundary node to switch to dual communication mode. Sensor nodes besides to tuples in which current_nbr_list is empty are candidates for transfaulty node. The algorithm employs testing techniques to find out communication failure nodes. The applied algorithm is a 2-phase algorithm namely,
i. finding of defective and boundary nodes.
ii. grouping of nodes using special algorithms and bringing out approximate information.
Predict transfaultyness of sensor nodes.
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Observation Phase:
Abbildung in dieser Leseprobe nicht enthalten
REFERENCES
1. H. M. Rehmani, M. Shadaram, S. Zeadally, and P. Bellavista, “Special issue on recent de- velopments in cognitive radio sensor networks,” Pervasive Mobile Comput., 2015.
2. I. F. Akyildiz, S. Weilian, Y. Sankarasubramaniam, and E. Cayirci, “A survey on sensor networks,” Commun. Mag., vol. 40, no. 8, pp. 102-114, Nov. 2002.
3. K. McCusker and N. E. O. Connor, “Low-energy symmetric key distribution in wireless sensor networks,” IEEE Trans. Depend. Secure Comput., vol. 8, no. 3, pp. 363-376, May/Jun. 2011.
4. C. Alippi, G. Anastasi, M. D. Francesco, and M. Roveri, “An adaptive sampling algorithm for effective energy management in wireless sensor networks with energy-hungry sen- sors,” IEEE Trans. Instrum. Meas., vol. 59, no. 2, pp. 335-344, Feb. 2010.
5. I. F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, “Wireless sensor networks: A survey,” Comput. Netw., vol. 38, no. 4, pp. 393-422, 2002.
6. S. S. McClure et al., “Radiation effects in micro-electromechanical systems (MEMS): RF relays,” IEEE Trans. Nucl. Sci., vol. 49, no. 6, pp. 3197-3202, Dec. 2002.
7 H. R. Shea, “Radiation sensitivity of microelectromechanical system devices,” J. Mi- cro/ Nanolithogr. MEMS MOEMS, vol. 8, no. 3, pp. 111, Jul. 2009.
8. Pushpendu Kar, Sudip Misra, “Reliable and Efficient Data Acquisition in Wireless Sensor Networks in the Presence of Transfaulty Nodes”, IEEE Transactions on Network and Ser- vice Management, Vol. 13, No. 1, March 2016
9. M. Ahmed, X. Huang, D. Sharma, and L. Shutao, “Wireless sensor network internal at- tacker identification with multiple evidence by dempster-shafer theory,” in Proc. 12th Int. Conf Algorithms Architect. Parallel Process., 2012, pp. 255-263.
10. R. R. Wilcox, Introduction to Robust Estimation and Hypothesis Testing, 3rd ed. Amster- dam, The Netherlands: Elsevier, 2012, ISBN: 978-0-12-386983-8.
11. T. Kanungo, D. M. Mount, N. S. Netanyahu, C. D. Piatko, R. Silverman, and A. Y. Wu, “An efficient k-means clustering algorithm: Analysis and implementation,” IEEE Trans. Patten Anal. Mach. Intell., vol. 24, no. 7, pp. 881-892, Jul. 2002.
12. S. Rajasegarar, C. Leckie, and M. Palaniswam, “Hyperspherical cluster based distributed anomaly detection in wireless sensor networks,” J. Parallel Distrib. Comput., vol. 74, no. 1, pp. 18331847, 2014.
13. H. Huangshui and Q. Guihe, “Fault management frameworks in wireless sensor networks,” in Proc. Intell. Comput. Technol. Autom., Guangdong, China, March 2011, vol. 2, pp. 1093-1096.
14. C. A. Kamhoua, N. Pissinou, J. Miller, and S. K. Makki, “Mitigating routing misbehavior in multi-hop networks using evolutionary game theory,” in Proc. GLOBECOM Workshops Adv. Commun. Netw., 2010, pp. 1957-1962.
15. E. Soltanmohammadi, M. Orooji, and M. Naraghi-Pour, “Decentralized hypothesis testing in wireless sensor networks in the presence of misbehaving nodes,” IEEE Trans. Inf Fo- rensics Secur., vol. 8, no. 1, pp. 205-215, Jan. 2013.
16. S. Khan, D. Turgut, and L. Boloni, “Bridge protection algorithms-A technique for fault- tolerance in sensor networks,” Ad Hoc Netw., vol. 24, pp. 186-199, Jan. 2015.
17 A. Munir, J. Antoon, and A. R. Gordon, “Modeling and analysis of fault detection and fault tolerance in wireless sensor networks,”
ACM Trans. Embedded Comput. Syst., vol. 14, no. 1, pp. 1-42, Jan. 2015.
Frequently Asked Questions About "Resourceful Data Accretion in Radiation in a Hefty WSN"
What is the main topic of the paper?
The paper focuses on addressing the problem of defective nodes in Wireless Sensor Networks (WSNs) caused by radiation, which leads to data loss. It proposes a prediction technique and specialized communication methods to mitigate this issue.
What are defective nodes in this context?
Defective nodes are sensor nodes in a WSN that malfunction due to excessive radiation exposure. This malfunctioning leads to data loss and creates voids in the network topology.
What is the proposed solution to handle defective nodes?
The paper suggests a prediction technique to identify potentially defective nodes based on their past behavior. It also introduces specialized nodes that communicate using both RF and audio means to maintain connectivity in areas affected by defective nodes.
What is the role of RF and audio communication in the proposed solution?
RF (Radio Frequency) communication is used by nodes at the boundary of void regions created by defective nodes. Audio communication is implemented in nodes left out with voids to ensure data transmission continues despite the presence of malfunctioning nodes.
What is the "NET_INFO" table mentioned in the paper?
The "NET_INFO" table is maintained by the sink and contains information about each node, including its ID, initial neighbor list, and current neighbor list. This information helps identify boundary nodes and potential transfaulty nodes.
What is the observation phase in the prediction technique?
The observation phase is part of the prediction technique where the network is monitored to identify defective and boundary nodes. This information is then used to predict transfaultyness of sensor nodes. The information found in this process is used to identify defective nodes.
Does the paper consider large scale WSN networks?
Yes, the paper addresses the challenges of managing defective nodes in WSNs with over a thousand sensor nodes, a scale often not addressed by existing algorithms.
What are some of the applications of WSNs mentioned in the introduction?
The paper mentions several applications, including sensing important information, sensing ranges, transmitting sensed information, predictions of natural disasters, weather predictions, air-pollution quality index, and military applications.
What is the disadvantage of the Brute-Force Approach?
The Brute-Force approach is not reliable as it involves removing the defective nodes from a particular network and it is not a solution to the concerned problem. The maintenance cost, using such kinds of approach, is quite high and does not address to the solution of problem.
- Citar trabajo
- Akash Lad (Autor), 2019, Resourceful data Accretion in radiation in a Hefty WSN, Múnich, GRIN Verlag, https://www.grin.com/document/505950
