## 2 Related Work
### Sensor Network Platforms  
Solutions for the sustainable and reusable development of sensor networks have been explored before in the context of industry as well as in academia. Different alternatives have been proposed, which tackle common challenges such as hardware/software (HW/SW) integration and/or the use of heterogeneous devices, being open-source with user-friendly API, etc. Among notable platforms with scalable HW/SW integration is FIT-IoT Lab [12], which is a test-bed available for researchers addressing wireless communications in sensor networks and low power routing protocols, with embedded and distributed applications. However, it is not device-agnostic, as only a limited set of sensors are supported with no extensibility. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Project Name** & **Open-Source** & **Simple API** & **Multimodal** & **Extensible** & **Scalable** & **HW/SW** & **Device-Agnostic** \\ \hline FIT-IoT Lab [12] & ✓ & ✓ & ✓ & \(\times\) & ✓ & ✓ & \(\times\) \\ FIESTA IoT [13] & \(\times\) & \(\times\) & ✓ & \(\times\) & ✓ & ✓ & ✓ \\ Signpost [14] & ✓ & (✓) & ✓ & ✓ & \(\times\) & ✓ & \(\times\) \\ SensorCentral [15] & \(\times\) &? & ✓ & ✓ & ✓ & \(\times\) & ✓ \\ Array of Things [16; 17] & ✓ & (✓) & ✓ & ✓ & \(\times\) & ✓ & \(\times\) \\ WangMode [18] & ✓ & ✓ & ✓ & (✓) & \(\times\) & ✓ & \(\times\) \\ The USC [19] & \(\times\) & \(\times\) &? &? & ✓ & ✓ & ✓ \\ FIWARE [20] & ✓ & (✓) & ✓ & (✓) & \(\times\) & \(\times\) & ✓ \\ DDFlow [21] & ✓ & (✓) & (✓) & (✓) & (✓) & \(\times\) & ✓ \\ EdgeProg [22] & \(\times\) &? & \(\times\) & \(\times\) & (✓) & (✓) & (✓) \\ Caesar [23] & \(\times\) &? & \(\times\) & \(\times\) &? & \(\times\) & (✓) \\ Waggle [24; 25] & ✓ & ✓ & ✓ & ✓ & \(\times\) & ✓ & (✓) \\ Apache Ray [26] & ✓ & ✓ & (✓) & (✓) & (✓) & \(\times\) & (✓) \\ Celery [27] & ✓ & ✓ & (✓) & ✓ & \(\times\) & \(\times\) & ✓ \\ Spotify Luigi [28] & ✓ & ✓ & \(\times\) & \(\times\) & \(\times\) & (✓) \\ GStreamer [29] & ✓ & \(\times\) & (✓) & (✓) & ✓ & \(\times\) & (✓) \\ NVIDIA DeepStream [30] & ✓ & \(\times\) & ✓ & (✓) & ✓ & (✓) & \(\times\) \\ Frmpeg [31] & ✓ & \(\times\) & (✓) & \(\times\) & (✓) & \(\times\) & (✓) \\ \hline REIP (SDK) & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Related works feature comparison. The features include (from left to right): open source availability; user-friendly API/low barrier for entry; support of multiple sensing modalities; ability to easily add new features or sensing modalities (extensible); ability to handle large amounts of data (scalable); sensor level integration (HW/SW); and whether the framework can be used with various computing platforms and sensor devices (device-agnostic). The ✓ and \(\times\) symbols denote whether the feature is present or not in each framework. The parenthesis indicate that the feature is present in the given framework but it does not excel at it, or that the framework could possibly be used but it was not designed to have such feature. A question mark means no assessment. Similarly, FIESTA IoT [13] is a meta-testbed IoT/cloud infrastructure designed for the submission of experiments over interconnected hardware testbeds using a single set of credentials. Although it is scalable, it still lacks an extensible and open-source API and thus relies on proprietary testbed deployments associated with the institutions in the FIESTA IoT Consortium. Other solutions such as Signpost [14], which is an extensible solar energy-harvesting modular platform designed to enable city-scale deployments, are not device-agnostic and only work with the customised sensors they provide. It also requires a considerable amount of engineering to reproduce a sensor network in this configuration. In contrast, SensorCentral [15] is a device-agnostic, multimodal sensor platform but it does not consider HW/SW integration at the sensor level and is not open-source for the research community to use.