Distributed Low-Latency Data Aggregation Scheduling in Wireless Sensor Networks

2015 ◽  
Vol 11 (3) ◽  
pp. 1-36 ◽  
Author(s):  
Miloud Bagaa ◽  
Mohamed Younis ◽  
Djamel Djenouri ◽  
Abdelouahid Derhab ◽  
Nadjib Badache
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Aggregation ◽  
Wireless Sensor ◽  
Low Latency ◽  
Latency Data ◽  
Data Aggregation Scheduling

scholarly journals Distributed Low-Latency Data Aggregation for Duty-Cycle Wireless Sensor Networks

2018 ◽  
Vol 26 (5) ◽  
pp. 2347-2360 ◽  
Author(s):  
Quan Chen ◽  
Hong Gao ◽  
Zhipeng Cai ◽  
Lianglun Cheng ◽  
Jianzhong Li
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Duty Cycle ◽  
Data Aggregation ◽  
Wireless Sensor ◽  
Low Latency ◽  
Latency Data

CONFLICT-AWARE DATA AGGREGATION SCHEDULING IN WIRELESS SENSOR NETWORKS WITH ADJUSTABLE TRANSMISSION RANGE

2012 ◽  
Vol 04 (03) ◽  
pp. 1250013 ◽  
Author(s):  
YI HONG ◽  
HONGWEI DU ◽  
DEYING LI ◽  
WENPING CHEN
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Aggregation ◽  
Base Station ◽  
Sensor Nodes ◽  
Performance Ratio ◽  
Wireless Sensor ◽  
Transmission Range ◽  
Minimum Latency ◽  
Data Aggregation Scheduling

The minimum latency data aggregation schedule is one of the fundamental problems in wireless sensor networks. Most existing works assumed that the transmission ranges of sensor nodes cannot be adjusted. However, sensors with adjustable transmission ranges have advantages in energy saving, reducing transmission interference and latency. In this paper, we study the minimum latency conflict-aware data aggregation scheduling problem with adjustable transmission radii: given locations of sensors along with a base station, all sensors could adjust their transmission radii and each sensor's interference radius is α times of its transmission radius, we try to find a data aggregation schedule in which the data from all sensors can be transmitted to the base station without conflicts, such that the latency is minimized. We first partition the set of all nodes into two parts: the major set and the minor set. Then, we design different scheduling strategies for the two sets, respectively. Finally, we propose an approximation algorithm for the problem and prove the performance ratio of the algorithm is bounded by a nearly constant. Our experimental results evaluate the efficiency of the proposed algorithm.

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