Edge computing for big data processing in underwater applications

Underwater data acquisition entities acquire big data that are processed aboard terrestrial data centres. However, processing the big data aboard terrestrial computing entities involves high latency data transfer. In addition, the processing of data in a terrestrial environment is challenging when there is inadequate edge node capacity. These challenges are addressed here. The paper proposes the heterogeneous edge computing paradigm to realize low latency transfer of increasing underwater big data. This is realized via the use of underwater computing entities instead of terrestrial computing entities for processing acquired big data. The proposed heterogeneous edge computing paradigm presents the multi-mode automated teller machine (ATM) as low cost terrestrial edge network entity. The multi-mode ATM is suitable when edge nodes have inadequate computing capacity. Performance evaluation shows that the use of underwater computing entities instead of terrestrial computing entities (existing work) enhances network performance and related capital costs. The number of hops, computing entity access latency and required autonomous underwater vehicle acquisition costs by an average of (5.3–88.4)%, 63.5% and (31.8–95.4)%, respectively. Evaluation shows that the use of the multi-mode ATM in the context of terrestrial cloud computing reduces the number of hops and latency by 44.4% and 37.3% on average, respectively.