Data aggregation and data fusion techniques in WSN/SANET topologies - a critical discussion

WSN and SANET topologies generate huge amount of heterogeneous data, which has to be transmitted in a dynamically changing network infrastructure. Especially in the domain of wireless low-power applications, the energy-efficiency and the prioritisation of communication tasks is critical. Several research areas deal with this issue. They optimising the respective hardware components as well as the protocols within the PHY, MAC or network layer. But for an optimised media transport in the topology also the data management and the task scheduling on the application layer is essential. Here, the key challenge is to minimise the data amount without decreasing the information quality. Related research work in the field of data aggregation and data fusion offers interesting techniques for an efficient data handling. In this paper, we discuss usual ways for data aggregation, including the adapted communication process. We critically analyse the benefits in theory and compare these conceptual advantages with measured real-world results. The evaluation was done in two steps. The first one is based on simulation scenarios of typical WSN/SANET applications. In a second step, we implement a demonstrator platform for a respective real-world environment. The test bed configuration is similar to the simulation scenario and provides comparable data. Based on the results and the respective analysis, we propose feasible methods for optimising data aggregation techniques. We clarify, that these improvements are essential for an efficient usage in resource-limited, embedded sensor network environments.

[1]  Sesh Commuri,et al.  Dynamic Data Aggregation in Wireless Sensor Networks , 2007, 2007 IEEE 22nd International Symposium on Intelligent Control.

[2]  Raghupathy Sivakumar,et al.  ATP: a reliable transport protocol for ad hoc networks , 2003, IEEE Transactions on Mobile Computing.

[3]  Gustavo de Veciana,et al.  Minimizing energy consumption in large-scale sensor networks through distributed data compression and hierarchical aggregation , 2004, IEEE Journal on Selected Areas in Communications.

[4]  Pravin Varaiya,et al.  WTRP - wireless token ring protocol , 2002, IEEE Transactions on Vehicular Technology.

[5]  Wei Hong,et al.  Proceedings of the 5th Symposium on Operating Systems Design and Implementation Tag: a Tiny Aggregation Service for Ad-hoc Sensor Networks , 2022 .

[6]  Leen Stougie,et al.  Data Aggregation in Sensor Networks: Balancing Communication and Delay Costs , 2007, SIROCCO.

[7]  Yong Yao,et al.  The cougar approach to in-network query processing in sensor networks , 2002, SGMD.

[8]  Matthias Vodel,et al.  SimANet - A Large Scalable, Distributed Simulation Framework for Ambient Networks , 2008, J. Commun..

[9]  Ian F. Akyildiz,et al.  NeXt generation/dynamic spectrum access/cognitive radio wireless networks: A survey , 2006, Comput. Networks.

[10]  S. Sitharama Iyengar,et al.  Distributed Sensor Networks — a Review of Recent Research , 2001, J. Frankl. Inst..

[11]  Dipak Ghosal,et al.  Multipath Routing in Mobile Ad Hoc Networks: Issues and Challenges , 2003, MASCOTS Tutorials.

[12]  Deborah Estrin,et al.  The impact of data aggregation in wireless sensor networks , 2002, Proceedings 22nd International Conference on Distributed Computing Systems Workshops.

[13]  Bengt Ahlgren,et al.  Ambient networks: bridging heterogeneous network domains , 2005, 2005 IEEE 16th International Symposium on Personal, Indoor and Mobile Radio Communications.

[14]  Matthias,et al.  Distributed High-Level Scheduling Concept for Synchronised, Wireless Sensor and Actuator Networks , 2010 .

[15]  Andreas Willig,et al.  Protocols and Architectures for Wireless Sensor Networks , 2005 .

[16]  Matthias Vodel,et al.  Wake-Up-Receiver Concepts - Capabilities and Limitations , 2012, J. Networks.