Data Gathering Algorithms in Sensor Networks Using Energy Metrics
IEEE Transactions on Parallel and Distributed Systems
Directed diffusion for wireless sensor networking
IEEE/ACM Transactions on Networking (TON)
Simultaneous optimization for concave costs: single sink aggregation or single source buy-at-bulk
SODA '03 Proceedings of the fourteenth annual ACM-SIAM symposium on Discrete algorithms
Proceedings of the 5th ACM international symposium on Mobile ad hoc networking and computing
Gateway Placement for Latency and Energy Efficient Data Aggregation
LCN '04 Proceedings of the 29th Annual IEEE International Conference on Local Computer Networks
The Critical Transmitting Range for Connectivity in Mobile Ad Hoc Networks
IEEE Transactions on Mobile Computing
Scalable data aggregation for dynamic events in sensor networks
Proceedings of the 4th international conference on Embedded networked sensor systems
Structure-Free Data Aggregation in Sensor Networks
IEEE Transactions on Mobile Computing
Minimum data aggregation time problem in wireless sensor networks
MSN'05 Proceedings of the First international conference on Mobile Ad-hoc and Sensor Networks
An application-specific protocol architecture for wireless microsensor networks
IEEE Transactions on Wireless Communications
DCTC: dynamic convoy tree-based collaboration for target tracking in sensor networks
IEEE Transactions on Wireless Communications
Evaluation of multicast routing algorithms for real-time communication on high-speed networks
IEEE Journal on Selected Areas in Communications
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A wireless sensor network consists of many energy-autonomous micro-sensors distributed throughout an area of interest. Each node has a limited energy supply and generates information that needs to be communicated to a sink node. To reduce costs, the data sent via intermediate sensors to a sink, are often aggregated. The existing energy-efficient approaches to in-network aggregation in sensor networks can be classified into two categories, the centralized and distributed approaches, each having its unique strengths and weaknesses. In this paper, we introduce PMDA (Prediction based Mobile Data Aggregation) scheme which uses a novel data aggregation scheme to utilize the knowledge of the mobile node and the infrastructure (static node tree) in gathering the data from the mobile node. This knowledge (geo-location and transmission range of the mobile node) is useful for gathering the data of the mobile node. Hence, the sensor nodes can construct a near-optimal aggregation tree by itself, using the knowledge of the mobile node, which is a similar process to forming the centralized aggregation tree. We show that the PMDA is a near-optimal data aggregation scheme with mobility support, achieving energy and delay efficiency. This data aggregation scheme is proven to outperform the other general data aggregation schemes by our experimental results.