{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T18:04:39Z","timestamp":1754157879841,"version":"3.41.2"},"reference-count":18,"publisher":"Emerald","issue":"4","license":[{"start":{"date-parts":[[2012,11,23]],"date-time":"2012-11-23T00:00:00Z","timestamp":1353628800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2012,11,23]]},"abstract":"<jats:sec><jats:title content-type=\"abstract-heading\">Purpose<\/jats:title><jats:p>Energy constraint is always a serious issue in wireless sensor networks, as the energy possessed by the sensors is limited and non\u2010renewable. Data aggregation at intermediate base stations increases the lifespan of the sensors, whereby the sensors' data are aggregated before being communicated to the central server. This paper proposes a query\u2010based aggregation within Monte Carlo simulator to explore the best and worst possible query orders to aggregate the sensors' data at the base stations. The proposed query\u2010based aggregation model can help the network administrator to envisage the best query orders in improving the performance of the base stations under uncertain query ordering. Furthermore, it aims to examine the feasibility of the proposed model to engage simultaneous transmissions at the base station and also to derive a best\u2010fit mathematical model to study the behavior of data aggregation with uncertain querying order.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Design\/methodology\/approach<\/jats:title><jats:p>The paper considers small and medium\u2010sized wireless sensor networks comprised of randomly deployed sensors in a square arena. It formulates the query\u2010based data aggregation problem as an uncertain ordering problem within Monte Carlo simulator, generating several thousands of uncertain orders to schedule the responses of M sensors at the base station within the specified time interval. For each selected time interval, the model finds the best possible querying order to aggregate the data with reduced idle time and with improved throughput. Furthermore, it extends the model to include multiple sensing parameters and multiple aggregating channels, thereby enabling the administrator to plan the capacity of its WSN according to specific time intervals known in advance.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Findings<\/jats:title><jats:p>The experimental results within Monte Carlo simulator demonstrate that the query\u2010based aggregation scheme show a better trade\u2010off in maximizing the aggregating efficiency and also reducing the average idle\u2010time experienced by the individual sensor. The query\u2010based aggregation model was tested for a WSN containing 25 sensors with single sensing parameter, transmitting data to a base station; moreover, the simulation results show continuous improvement in best\u2010case performances from 56 percent to 96 percent in the time interval of 80 to 200 time units. Moreover, the query aggregation is extended to analyze the behavior of WSN with 50 sensors, sensing two environmental parameters and base station equipped with multiple channels, whereby it demonstrates a shorter aggregation time interval against single channel. The analysis of average waiting time of individual sensors in the generated uncertain querying order shows that the best\u2010case scenario within a specified time interval showed a gain of 10 percent to 20 percent over the worst\u2010case scenario, which reduces the total transmission time by around 50 percent.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Practical implications<\/jats:title><jats:p>The proposed query\u2010based data aggregation model can be utilized to predict the non\u2010deterministic real\u2010time behavior of the wireless sensor network in response to the flooded queries by the base station.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Originality\/value<\/jats:title><jats:p>This paper employs a novel framework to analyze all possible ordering of sensor responses to be aggregated at the base station within the stipulated aggregating time interval.<\/jats:p><\/jats:sec>","DOI":"10.1108\/17427371211283010","type":"journal-article","created":{"date-parts":[[2012,11,22]],"date-time":"2012-11-22T05:18:06Z","timestamp":1353561486000},"page":"329-343","source":"Crossref","is-referenced-by-count":1,"title":["Empirical analysis of query\u2010based data aggregation within WSN through Monte Carlo simulation"],"prefix":"10.1108","volume":"8","author":[{"given":"Sami J.","family":"Habib","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Paulvanna N.","family":"Marimuthu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"140","reference":[{"key":"key2022032120360385800_b1","doi-asserted-by":"crossref","unstructured":"Ahvar, E. (2010), \u201cEDQD: an energy\u2010distance aware query\u2010based data aggregation technique for wireless sensor networks\u201d, Proceedings of International Workshop on Data Management for Wireless and Pervasive Communications, Perth, Australia, 13\u201020 April.","DOI":"10.1109\/WAINA.2010.160"},{"key":"key2022032120360385800_b2","doi-asserted-by":"crossref","unstructured":"Buttyan, L. and Holczer, T. (2010), \u201cPerfectly anonymous data aggregation in wireless sensor networks\u201d, Proceedings of IEEE 7th International Conference on Mobile Adhoc and Sensor Systems, San Francisco, CA, USA, 8\u201012 November.","DOI":"10.1109\/MASS.2010.5663911"},{"key":"key2022032120360385800_b3","doi-asserted-by":"crossref","unstructured":"Habib, S.J. 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