Sensor data computing in an IoT environment incorporates various issues’ that determine the level and quality of services provided. An important element associated with IoT and sensor networks is the ability to handle large volumes of data [8]. Data management in cloud computing environment is a sophisticated task especially considering that clients need to update and access the data on a regular basis. In an IoT environment, data should be available seamlessly otherwise service delivery will be affected. This can be catastrophic for critical services such as in transport and the health sector.
The data generated by sensor devices portrays multiple characteristics, which makes it difficult to manage. Even though most of the data represents time series constraints, it is difficult to manage any form of manipulations that apply to this data [9]. The quality of data generated by sensor nodes is also an issue of concern. In most cases the data generated has a lot of noise thus making it difficult to represent accurate measurements [9]. It is also difficult to manage data interoperability, especially if the data comes from different architectures.
A sensory network is designed in such a way that it is supposed to exhibit self-organizing features. In other words, a WSN is supposed to resume normal operations even after reconfiguration of major settings. Also, a WSN is supposed to establish automatic communication with the WSNs in the surrounding environment. The problem is that not many WSN harbors the ability to self-organize. This not only affects service delivery but also the security of the WSN [8].