An Adaptive Data Gathering Scheduler Based on Data Variance for Energy Efficiency in Mobile Social Networks

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Опубліковано в::The International Journal of Networked and Distributed Computing vol. 13, no. 2 (Dec 2025), p. 23
Автор: Alilu, Elham
Інші автори: Derakhshanfard, Nahideh, Ghaffari, Ali
Опубліковано:
Springer Nature B.V.
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100 1 |a Alilu, Elham  |u Islamic Azad University, Department of Computer Engineering, Tabriz Branch, Tabriz, Iran (GRID:grid.459617.8) (ISNI:0000 0004 0494 2783) 
245 1 |a An Adaptive Data Gathering Scheduler Based on Data Variance for Energy Efficiency in Mobile Social Networks 
260 |b Springer Nature B.V.  |c Dec 2025 
513 |a Journal Article 
520 3 |a Mobile Social Networks (MSNs) consist of numerous mobile nodes that exhibit social characteristics such as gender, age, and more. Nowadays, with the rise in popularity of smartphones, these devices serve as nodes in mobile social networks, inheriting their users’ characteristics. These networks utilize a “store-carry-and-forward” mechanism for transmitting and delivering packets. New applications, like smart city monitoring, necessitate the deployment of sensors in urban areas to gather relevant information. The sensed data must be collected through mobile nodes (in this case, smartphones) and transmitted to a base station or other interested nodes. In these applications, if the data collection period is brief, smartphones will experience high energy consumption, and a significant amount of redundant data will be produced. Conversely, if the collection period is extended, some data may be lost. Several schemes have been proposed for data collection in wireless sensor networks. Unfortunately, in these schemes, nodes are either constantly in the data collection phase or gather data at fixed time intervals through simple scheduling. It appears that adaptive data collection based on the differences in the collected data could be more effective. This paper proposes a new Data Gathering Scheduler based on the Differences in collected data, DGSD. In this method, if the difference between the last two data points is low, the next time slot is set to be longer; as the difference between the last two data points increases, the time slot is shortened. Simulation results indicate that energy consumption with DGSD improves when compared to related works in discovering the same number of events. 
653 |a Radio equipment 
653 |a Smartphones 
653 |a Social networks 
653 |a Protocol 
653 |a Mobile communications networks 
653 |a Sensors 
653 |a Wireless sensor networks 
653 |a Nodes 
653 |a Energy efficiency 
653 |a Data collection 
653 |a Packet transmission 
653 |a Algorithms 
653 |a Energy consumption 
653 |a Data compression 
653 |a Data points 
700 1 |a Derakhshanfard, Nahideh  |u Islamic Azad University, Department of Computer Engineering, Tabriz Branch, Tabriz, Iran (GRID:grid.459617.8) (ISNI:0000 0004 0494 2783) 
700 1 |a Ghaffari, Ali  |u Islamic Azad University, Department of Computer Engineering, Tabriz Branch, Tabriz, Iran (GRID:grid.459617.8) (ISNI:0000 0004 0494 2783); Istinye University, Department of Computer Engineering, Faculty of Engineering and Natural Science, Istanbul, Turkey (GRID:grid.508740.e) (ISNI:0000 0004 5936 1556); Khazar University, Department of Computer Science, Baku, Azerbaijan (GRID:grid.442897.4) (ISNI:0000 0001 0743 1899) 
773 0 |t The International Journal of Networked and Distributed Computing  |g vol. 13, no. 2 (Dec 2025), p. 23 
786 0 |d ProQuest  |t Computer Science Database 
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