Time-constrained data harvesting in WSNs: Theoretical foundation and algorithm design

Data harvesting using mobile data ferries has recently emerged as a promising alternative to the traditional multi-hop transmission paradigm. The use of data ferries can significantly reduce energy consumption at sensor nodes and increase network lifetime. However, it usually incurs longer data delivery latency as the data ferry needs to travel through the network to collect data, during which some delay-sensitive data may become obsolete. Therefore, optimizing the trajectory of the data ferry with data delivery latency bound is important for this approach to be effective in practice. To address this problem, we formally define the time-constrained data harvesting problem, which seeks an optimal data harvesting path in a network to collect as much data as possible within a time duration. We first characterise the performance bound given by the optimal data harvesting algorithm and show that the optimal algorithm significantly outperforms the random algorithm, especially when network scales. Motivated by the theoretical analysis and proving the NP-completeness of the time-constrained data harvesting problem, we then devise polynomial-time approximation schemes (PTAS) and mathematically prove the output being a constant-factor approximation of the optimal solution.