Data aggregation in wireless sensor networks using ant colony algorithm
Journal of Network and Computer Applications
ATS-DA: Adaptive Timeout Scheduling for Data Aggregation in Wireless Sensor Networks
Information Networking. Towards Ubiquitous Networking and Services
Aggregation Protocols for High Rate, Low Delay Data Collection in Sensor Networks
NETWORKING '09 Proceedings of the 8th International IFIP-TC 6 Networking Conference
Near-lifetime-optimal data collection in wireless sensor networks via spatio-temporal load balancing
ACM Transactions on Sensor Networks (TOSN)
A real-time data aggregation method for fault-tolerant wireless sensor networks
Proceedings of the 27th Annual ACM Symposium on Applied Computing
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Wireless sensor network monitoring is important for network maintenance, since it keeps the observer aware of node failures, resource depletion etc. Since communication overheads increase if the sink collects data individually from all sensor nodes, in-network data aggregation methods have been proposed which reduce the overheads. They form a routing tree and data follows up from the edge of the tree to the sink. However, in the event of heavy packet loss, the error margin of the collected data received by the sink grows. Furthermore, when the assumed hop count of the edge of the tree is smaller than the actual count, data can not be followed up from the edge. For the reasons mentioned above, observers find it problematic to assess the state of the network, since the error margin increases as the accuracy of the collected data falls. In this paper, we propose a new in-network aggregation method for sensor network monitoring. The method provides fault tolerance for packet loss by forming a Directed Acyclic Graph (DAG), which allows a node to have multiple parent nodes. In addition, the method can ensure correct data transmission timing, according to the actual hop count of the edge of the DAG. Furthermore, we evaluated the proposed method in comparison with the existing methods, from the perspective of the error margin of the collected data.