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The application of Wireless Sensor Networks (WSNs) with a large number of tiny, cost-efficient, battery-powered sensor nodes that are able to communicate directly with each other poses many challenges.
Due to the large number of communicating objects and despite a used CSMA/CA MAC protocol, there may be many signal collisions.
In addition, WSNs frequently operate under harsh conditions and nodes are often prone to failure, for example, due to a depleted battery or unreliable components.
Thus, nodes or even large parts of the network can fail.
These aspects lead to reliable data dissemination and data storage being a key issue.
Therefore, these issues are addressed herein while keeping latency low, throughput high, and energy consumption reduced.
Furthermore, simplicity as well as robustness to changes in conditions are essential here.
In order to achieve these aims, a certain amount of redundancy has to be included.
This can be realized, for example, by using network coding.
Existing approaches, however, often only perform well under certain conditions or for a specific scenario, have to perform a time-consuming initialization, require complex calculations, or do not provide the possibility of early decoding.
Therefore, we developed a network coding procedure called Broadcast Growth Codes (BCGC) for reliable data dissemination, which performs well under a broad range of diverse conditions.
These can be a high probability of signal collisions, any degree of nodes' mobility, a large number of nodes, or occurring node failures, for example.
BCGC do not require complex initialization and only use simple XOR operations for encoding and decoding.
Furthermore, decoding can be started as soon as a first packet/codeword has been received.
Evaluations by using an in-house implemented network simulator as well as a real-world testbed showed that BCGC enhance reliability and enable to retrieve data dependably despite an unreliable network.
In terms of latency, throughput, and energy consumption, depending on the conditions and the procedure being compared, BCGC can achieve the same performance or even outperform existing procedures significantly while being robust to changes in conditions and allowing low complexity of the nodes as well as early decoding.