## 3 Communication in IoT
### HPC on Edge (HPCE)  
This new High-Performance Computing (HPC) solution seeks to move beyond the agricultural services offered on edge and provide a comprehensive platform for precision farming and animal husbandry and furnish with utility not only for farmers but also for stakeholders. The HPCE architecture is adapted from CYBELE conceptual framework [2]. The HPCE model uses open and proprietary vast amounts of datasets, including sensor readings, as well as satellite data and historic climatic and environmental information for ready reference. While this would be the most effective way to use HPC technology, it only uses the latest software platforms and projects that are being developed by HPCE's e-controlled services, as well as increased HPC e-infrastructure to enable huge heterogeneous data processing to be done and find modern solutions to complex problems using dedicated algorithms. Due to the interconnection of large-oriented approaches, varying datasets, and available big data techniques, it is possible to scale distributed big data research to enormous scales when holding many types of datasets together in one place [2]. In doing so, it enables the aggregated data and metadata to be aligned semantically to a standard scheme and data model and enables advanced data analytics to take secret information into account. In addition to this, the HPCE architecture will also help in gaining insights from adaptive data visualization services. With reference to CYBELE [2], the architectural approach of the HPC on edge is illustrated in Figure 12. By organizing a product component based on interdependencies, this is intended to highlight the importance of pipelines being constructed to promote compatibility and show how to maintain the integrity of interdependent services. It is worth noting that CYBELE resonates well with the EdgeX platform architecture. EdgeX platform comprises four core services, i.e., device services, core services, supporting services, and application services to enable smooth workflow optimization [188]. In addition, it will be interesting to see a synchronization of EdgeX with a dedicated HPC framework for faster batch processing of data over edge. Big, heterogeneous data are made available through repositories powered by HPC which is responsible for the processing at the edge layer. In this regard, as can be seen in Figure 11, HPC frameworks such as Spark, Hadoop, YARN, Big Deep Learning (BigDL), Directed Acyclic Graph (DAG), and Kubernetes are deployed for the batch processing of data using distributed framework attached to the edge layer [189]. It is worth noting that Spark and BigDL are the widely used frameworks in many organizations for their open source and high degree of interoperability features. Spark and BigDL are based on MapReduce framework which has high room for tuning for smooth workflow optimization [190]. The transmission of the application process interface along with data from the cloud layer to the edge layer is conducted using 4G/5G or fiber/DOCSIS/DSL communication system. This is seen on the middle section of the architecture as shown in Figure 12. At first,  
Figure 12: High Performance Computing on Edge. the data are processed in the background prior to being passed on to the check-in stage for data validator or timestamp validator for resolution of data verification and timing problems. Once data are obtained in edge layer, quality checks are conducted to identify anomalies and any other data irregularities, maintaining their accuracy and validity, which are accompanied by a series of measures aligned with processes of data cleansing. Finally, the HPE data provenance service provides the mechanisms required for recording all relevant information concerning incoming data of interest. With HPE, the data provenance platform is inherently connected to the data policy and asset brokerage engine that enables the platform to bind data providers and data users with data share and business features.