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Next-generation stream processing systems for community scale IoT applications must handle complex nonfunctional needs, e.g. scalability of input, reliability/timeliness of communication and privacy/security of captured data. In many IoT settings, efficiently batching complex workflows remains challenging in resource-constrained environments. High data rates, combined with real-time processing needs for applications, have pointed to the need for efficient edge stream processing techniques. In this work, we focus on designing scalable edge stream processing workflows in real-world IoT deployments where performance and privacy are key concerns. Initial efforts have revealed that privacy policy execution/enforcement at the edge for intensive workloads is prohibitively expensive. Thus, we leverage intelligent batching techniques to enhance the performance and throughput of streaming in IoT smart spaces. We introduce BatchIT, a processing middleware based on a smart batching strategy that optimizes the trade-off between batching delay and the end-to-end delay requirements of IoT applications. Through experiments with a deployed system we demonstrate that BatchIT outperforms several approaches, including micro-batching and EdgeWise, while reducing computation overhead. 

CTE Journal . Fall2023, Vol. 11 Issue 2, p26-41. 

Smart space administration and application development is challenging in part due to the semantic gap that exists between the high-level requirements of users and the low-level capabilities of IoT devices. The stakeholders in a smart space are required to deal with communicating with specific IoT devices, capturing data, processing it, and abstracting it out to generate useful inferences. Additionally, this makes reusability of smart space applications difficult, since they are developed for specific sensor deployments. In this article, we present a holistic approach to IoT smart spaces, the SemIoTic ecosystem, to facilitate application development, space management, and service provision to its inhabitants. The ecosystem is based on a centralized repository, where developers can advertise their space-agnostic applications, and a SemIoTic system deployed in each smart space that interacts with those applications to provide them with the required information. SemIoTic applications are developed using a metamodel that defines high-level concepts abstracted from the smart space about the space itself and the people within it. Application requirements can be expressed then in terms of user-friendly high-level concepts, which are automatically translated by SemIoTic into sensor/actuator commands adapted to the underlying device deployment in each space. We present a reference implementation of the ecosystem that has been deployed at the University of California, Irvine and is abstracting data from hundreds of sensors in the space and providing applications to campus members.