Value of Information (VoI) is a concept to assess the usefulness of information for a specific goal, and has in the last decade experienced a growing interest also for Wireless Sensor Network (WSN) applications and the Internet of Things (IoT). By making the value of information explicit in the form of VoI, WSN and IoT applications should be able to better assess which information to spend their constrained resources on. However, the definition of VoI is highly application-dependent, which has led to a fragmented understanding of VoI, and there is a lack of a comprehensive overview. In this structured review, we first categorize application use cases and examine what VoI is used for, and explore the different approaches to defining VoI. We then provide a well-structured and comprehensive discussion of the specific approaches used in the literature to determine VoI, together with examples of use cases. We categorize the different approaches to calculating VoI, describe their properties systematically and distinguish between observed VoI and expected VoI. We also discuss adaptive VoI approaches and point towards future directions within the field.
We explore the tradeoff between energy consumption and measurement accuracy for noise monitoring and prediction based on continuously collected data by wireless, energyconstrained IoT nodes. This tradeoff can be controlled by the sampling interval between measurements and is of interest for energy-efficient operation, but most often ignored in the literature. We study the influence of the sampling intervals on the accuracy of various noise indicators and metrics. To provide a context for the tradeoff, we consider the use case of noise monitoring in working environments and present a learning algorithm to also predict sound indicators. The results indicate that a proper tradeoff between energy consumption and accuracy can save considerable energy, while only leading to acceptable or insignificant reductions in accuracy, depending on the specific use case. For instance, we show that a system for monitoring and prediction can perform well for users and only uses around 7% of the energy compared to full sampling.
CCS CONCEPTS• Computer systems organization → Sensor networks; • Computing methodologies → Machine learning algorithms.
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