Brief summary on wireless sensor networks
Abstract. This paper provides a brief summary on wireless sensor networks research.
Summary
The Mobile Sensor Networks are constituted on ad-hoc basis by tiny mobile sensor nodes deployed
randomly over an area. The automatic random localization of mobile nodes may serve many potential
applications where dynamicity and continuity are critically important. In this paper we envision one such
application of health management. In most of the severe health risk, the patient is kept under intensive
observation to collect post treatment health data about the patient. Sometime the observation period is
prolonged, and just contributes to the traffic in the hospital. The housing in the central health care unit
also imposes stressful cost to the patient. We propose a viable solution for the benefit of both. Post
treatment under the observation period a patient - equipped with various body sensors - may be shifted to
a nearby affordable locality where a patient may perform unguided longer walk also. The body sensors
will continuously be disseminating the various health data to a data repository in the hospital where the
concerned doctor has the access to look it up. As the sensor networks are formed on ad-hoc basis, so they
are prone for loss. A network loss causes the loss of data and absence of the health data may put the
patient on severe risk. In our solution, we propose a random movement of a set of mobile sensor nodes
between the body sensors and the repository and responsible to strengthen the ad-hoc wireless sensor
network, the transmitting medium between them. The sensors will randomly but unobtrusively be mobile
and unnoticeably strengthening the data transmission from a patient to doctor. Mobile Sensor Networks
are constituted on ad-hoc basis by tiny mobile sensor nodes deployed randomly over an area. The
automatic random localization of mobile nodes may serve many potential applications where dynamicity
and continuity are critically important. In this paper we envision one such application of health
management. In most of the severe health risk, the patient is kept under intensive observation to collect
post treatment health data about the patient. Sometime the observation period is prolonged, and just
contributes to the traffic in the hospital. The housing in the central health care unit also imposes stressful
cost to the patient. We propose a viable solution for the benefit of both. Post treatment under the
observation period a patient - equipped with various body sensors - may be shifted to a nearby affordable
locality where a patient may perform unguided longer walk also. The body sensors will continuously be
disseminating the various health data to a data repository in the hospital where the concerned doctor has
the access to look it up. As the sensor networks are formed on ad-hoc basis, so they are prone for loss. A
network loss causes the loss of data and absence of the health data may put the patient on severe risk. In
our solution, we propose a random movement of a set of mobile sensor nodes between the body sensors
and the repository and responsible to strengthen the ad-hoc wireless sensor network, the transmitting
medium between them. The sensors will randomly but unobtrusively be mobile and unnoticeably
strengthening the data transmission from a patient to doctor.
1
Conclusion. This paper was a one pager wireless sensor networks research.
References
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
Sharma, S. (2016). Expanded cloud plumes hiding Big Data ecosystem. Future Generation Computer
Systems, 59, 63-92.
Sharma, S., Chang, V., Tim, U. S., Wong, S, Gadia, S. (2016). Cloud-based Emerging Services Systems.
International Journal of Information Management, Elsevier.
Sharma, S. (2016). Concept of Association Rule of Data Mining Assists Mitigating the Increasing
Obesity. International Journal of Information Retrieval Research (IJIRR), IGI Global, 2016.
Sharma, S., Chang, V., Tim, U. S., Wong, S, Gadia, S. (2016). Growing Cloud Density & as-a-Service
Modality and OTH-Cloud Classification in IOT Era.
Sharma, S., Tim, U. S., Gadia, S., & Wong, J. (2016). Proliferating Cloud Density through Big Data
Ecosystem, Novel XCLOUDX Classification and Emergence of as-a-Service Era.
Sharma, S. (2015). Evolution of as-a-Service Era in Cloud. arXiv preprint arXiv:1507.00939.
Sharma, S., Tim, U. S., Payton, M., Cohly, H., Gadia, S., Wong, J., & Karakala, S. (2015). Contextual
motivation in physical activity by means of association rule mining. Egyptian Informatics Journal,
16(3), 243-251.
Sharma, S., Gadia, S., Tim, U. S. (2016). SubVizCon: Visual Subsetting, Conversion and Complex
Queries Exploitation in Spatio-Temporal Compound of Big Data.
Sharma, S., Tim, U. S., Gadia, S., Wong, J., Shandilya, R., & Peddoju, S. K. (2015). Classification and
comparison of NoSQL big data models. International Journal of Big Data Intelligence, 2(3), 201-221.
Sharma, S., Shandilya, R., Patnaik, S., & Mahapatra, A. (2015). Leading NoSQL models for handling
Big Data: a brief review. International Journal of Business Information Systems, Inderscience, 18(4).
Sharma, S., Tim, U. S., Wong, J., Gadia, S., & Sharma, S. (2014). A brief review on leading big data
models. Data Science Journal, 13(0), 138-157.
Sharma, S., Tim, U. S., Gadia, S., & Wong, J. (2014). Does SNAP eligibility have racial or ethnic
gradients: a geospatial social exploratory. International Journal of Information and Communication
Technology, 6(2), 189-212.
Sharma, S. (2014). Racial disparity in SNAP? No: a geospatial study for Iowa. International Journal of
Indian Culture and Business Management, 9(3), 340-352.
SUGAM, S., SHASHI, G., & UDOYARA SUNDAY, T. (2013). Parametric database approach
integration for handling temporal data in GIS. Geo-spatial Information Science, 16(2), 91-99.
SHARMA, S., TIM, U. S., & GADIA, S. (2012). AutoConViz: automating the conversion and
visualization of spatio-temporal query results in GIS. Geo-spatial Information Science, 15(2), 85-93.
Sharma, S., Wong, J., Tim, U. S., & Gadia, S. (2013). Bidirectional migration between variability and
commonality in product line engineering of smart homes. International Journal of System Assurance
Engineering and Management, 4(1), 1-12.
Sharma, S., Goyal, S. B., Shandliya, R., & Samadhiya, D. (2012). Towards XML Interoperability. In
Advances in Computer Science, Engineering & Applications (pp. 1035-1043). Springer Berlin
Heidelberg.
Shandilya, R., Sharma, S., & Qamar, S. (2012). A Domain Specific Indexing Technique for Hidden Web
Documents. Communications in Information Science and Management Engineering.
Sharma, S., Tim, U. S., Gadia, S., & Smith, P. (2011). Geo-spatial Pattern Determination for SNAP
Eligibility in Iowa Using GIS. In Advances in Computing and Communications (pp. 191-200).
Springer Berlin Heidelberg.
Sharma, S., Yang, H. I., Wong, J., & Chang, C. K. (2011). Wrenching: transient migration from
commonality to variability in product line engineering of smart homes. In Toward Useful Services for
Elderly and People with Disabilities (pp. 230-235). Springer Berlin Heidelberg.
Meghanathan, N., Sharma, S., & Skelton, G. W. (2010). On Energy Efficient Dissemination in Wireless
sensor Networks using Mobile Sinks. Journal of Theoretical and Applied Information Technology,
19(2), 79-91.
Sharma, S., Gadia, S., Kumar, N., Narayanan, V., & Zhao, X. (2010). Climate Analysis in IOWA Using
XML and Spatio-Temporal Dataset-NC94. Int. J. Database Manage. Syst, 2(3), 82-93.
Sharma, S., & Gadia, S. (2010). Perl Status Reporter (SRr) on Spatiotemporal Data Mining.
International Journal Computer Science and Engineering Survey, AIRCC-IJCSES (August 2010).
2
24. Sharma, S., & Gadia, S. K. (2010). On analyzing the degree of coldness in Iowa, a north central region,
United States: An XML exploitation in spatial databases. In Recent Trends in Network Security and
Applications (pp. 613-624). Springer Berlin Heidelberg.
25. Sharma, S., Gadia, S., & Goyal, S. B. (2010, September). On the Calculation of Coldness in Iowa, a North
Central Region, United States: A Summary on XML Based Scheme. In Information and Communication
Technologies (pp. 400-405). Springer Berlin Heidelberg.
26. Sharma, S., & Gadia, S. K. (2010, April). An XML-based range variation approach to render the coldness
in Iowa, a North Central region, United States. In Information Management and Engineering (ICIME),
2010 The 2nd IEEE International Conference on (pp. 567-571). IEEE.
27. Sharma, S., Goyal, S. B., & Qamar, S. (2009, December). Four-Layer Architecture Model for Energy
Conservation in Wireless Sensor Networks. In Embedded and Multimedia Computing, 2009. EMCom 2009. 4th International Conference on (pp. 1-3). IEEE.
28. Sharma, S., & Cohly, H. (2009, December). On enhancing the energy conservation by ATIM window
in wireless sensor networks. In Internet Multimedia Services Architecture and Applications (IMSAA),
2009 IEEE International Conference on (pp. 1-4). IEEE.
29. Sharma, S., & Rani, M. (2009, October). Three-layer architecture model (TLAM) for energy
conservation in wireless sensor networks. In Ultra Modern Telecommunications & Workshops, 2009.
ICUMT'09. International Conference on (pp. 1-5). IEEE.
30. Sharma, S., Rani, M., & Goyal, S. B. (2009, October). Energy efficient data dissemination with ATIM
window and dynamic sink in wireless sensor networks. In Advances in Recent Technologies in
Communication and Computing, 2009. ARTCom'09. International Conference on (pp. 559-564).
IEEE.
31. Sharma, S., Cohly, H., & Pei, T. (2010). On Generation of Firewall Log Status Reporter (SRr) Using
Perl. arXiv preprint arXiv:1004.0604.
32. Sharma, S., Pei, T., & Cohly, H. (2010). On Utilization and Importance of Perl Status Reporter (SRr)
in Text Mining. arXiv preprint arXiv:1001.3277.
33. Meghanathan, N., Sugam, S., & Skelton, G. W. (2008). Use of Mobile Sinks to Disseminate Data in
WSNs. International Journal of Information Processing, 2(2).
34. SHARMA, S., PEI, T., & COHLY, H. (2008). ToAccess PubMed Database to Extract Articles Using
Perl_Su. IJCSES, 2(2), 92.
35. Challa, V. S., Indracanti, J., White, L. D., Sharma, S., Baham, J. M., Rabarison, M. K., & Anjaneyulu,
Y. (2007, September). Study of Meso-Scale Coastal Circulations in Mississippi Gulf coast with
Mesonet Observations and Modeling. In Seventh Conference on Coastal Atmospheric and Oceanic
Prediction and Processes.
36. Singh, S. P., Bhanot, K., & Sharma, S. (2016). Critical Analysis of Clustering Algorithms for Wireless
Sensor Networks. In Proceedings of Fifth International Conference on Soft Computing for Problem
Solving (pp. 783-793). Springer Singapore.
37. Sharma, S. (2015). An Extended Classification and Comparison of NoSQL Big Data Models. arXiv
preprint arXiv:1509.08035.
3