# IoT Blockchain Data Vercity with Data Loss Tolerance
## 1 Introduction  
The Internet of Things (IoT) is a new paradigm that enables data-driven analysis in accomplishing productivity improvement in many businesses and industries. IoT provides insights into processes, operations and revealing high yield contributing factors that ultimately lead to better productivity. IoT data-driven approaches utilize data in vital decision making, and enhance processes and operations to produce desired results. Hence, advancement in IoT has provided great opportunities to transform many businesses into data-driven industries. Highly accurate and centralized data-driven models ensure data veracity, authentic and immutable, described as single-source-of-truth. Accurate IoT data-driven analysis can contribute to productivity improvement, as analysis provides insights to yield contributing factors, and productivity improvement is then achieved by maximizing the favorable factors and minimizing the negative factors [1]. Therefore, data-driven approaches with IoT have gained wide adoption in unleashing insights and creating business advantages. IoT systems are commonly designed based on a three-layer architecture, namely a perception/device layer, a network layer, and an application layer [2]. IoT is not a single technology, but a technological agglomeration of sensor/actuator, network and application that work in a highly coupled manner. Sensors and actuators are devices which help in interacting with the physical environment; devices are networked withapplication for the data storage, processing and control. But the incumbents of consistent infrastructure resources requirement and highly coupled IoT architecture created various challenges. IoT systems are often experiencing data loss that affects data veracity, particularly in environments of scarce infrastructure resources. At the perception layer, sensor and actuator devices require power and connectivity to operate, interchange data and control with the application layer. At the network layer, connectivity must accommodate the requirements for consistent connectivity and power efficiency. Any inefficiency at the highly coupled layers and interfaces can result in data loss in IoT, thus impacting data veracity. The IoT application layer ingests data from the sensors to identify the high yield contributing factors. But IoT system failures ranging from the device, network to the application layer are inevitable in scarce infrastructure resources environments and, therefore, lead to data loss. Many IoT systems are deployed for productivity improvement in scarce infrastructure resources environments. One of the use cases is that of a palm oil plantation. Palm oil (Elaeis Guineais) is a splendid oleaginous perennial crop with the highest oil yield among its rivals. Its average fresh fruit bunch (FFB) yield of 18.5 metric ton per hectare per year (MT) (1975-2015), equivalent to 3.7 MT palm oil yield shall be raised to its full potential [3; 4; 5]. Nonetheless, palm oil in many countries is currently encountering stagnated yield. As reported in the European Journal of Agronomy, a potential peak palm oil yield gap of 12.0 MT [1] is foreseeable under favorable conditions. Hence, there are exigent needs to identify the higher yield factors to boost productivity. The yield gap can be distinguished by various yield contributing factors such as genes, nutrient, soil, sun, water/moisture and others. It is therefore vital that accurate and complete data be collected and analyzed to distinguish their performance before the planting commences in the field. Unfortunately, data loss is very often unrecoverable because palm oil growth is a complex combination of genes, nutrient, soil, sun, water, and unique crop cycle conditions, as depicted in Figure 1. Consequences of data loss can eventually lead to negative business impacts. Analysis from inaccurate and incomplete data sources potentially lead to unfavorable conditions set for the operations. Yield will be affected throughout the entire palm oil lifespan due to unfavorable conditions.