The concept of Internet of Things (IoT) is a reality now. This paradigm shift has caught everyone's attention in a large class of applications, including IoT-based video analytics using smart doorbells. Due to its growing application segments, various efforts exist in scientific literature and many video-based doorbell solutions are commercially available in the market. However, contemporary offerings are bespoke, offering limited composability and reusability of a smart doorbell framework. Second, they are monolithic and proprietary, which means that the implementation details remain hidden from the users. We believe that a transparent design can greatly aid in the development of a smart doorbell, enabling its use in multiple application domains.To address the above-mentioned challenges, we propose a distributed framework to orchestrate video analytics across Edge and Cloud resources. We investigate trade-offs in the distribution of different software components over a bespoke/full system, where components over Edge and Cloud are treated generically. The proposal looks at the smart doorbell case study and uses AWS as a base platform for implementation. To showcase the composability and reusability of the proposed framework, the state of the art ML-and DL-based models are implemented on the affordable Raspberry Pi in the form of an Edge device. This also demonstrates the feasibility of running these models on IoT devices. Moreover, this paper evaluates the proposed framework as well as the state-of-the-art models and presents comparative analysis of them on various metrics (such as overall model accuracy, latency, memory and CPU usage). The evaluation result demonstrates our intuition very well, showcasing that the AWS-based approach exhibits reasonably high object-detection accuracy, low memory and CPU usage when compared to the state-of-the-art approaches, but high latency.

The early Internet of Things stream processing platforms were mainly designed to collect and display real‐time raw sensor measurements. Data often has to go through several phases of processing to lead to actionable automatic or human decision making. This chapter discusses five main operations performed on streaming data: compression, dimensionality reduction, summarization, learning, and visualization. It distinguishes two main types of stream data processing systems. The first type, also called a data stream management system, is based on relational database principles and introduced the concept of continuous queries. The second type does not enforce a relational view and enables the creation of custom operators. Based on batch and stream processing paradigms, two data processing architectures emerged: the Lambda architecture, which enables both batch and stream processing, and the simpler Kappa architecture, which enables stream processing.

Most advanced Internet of Things solutions today are more than just technology. This chapter first introduces crowdsourcing and human‐in‐the‐loop (HITL), two related approaches for realizing use cases and devises data science pipelines that seamlessly combine machine with human and collective intelligence. Broadly speaking, there are two main categories of crowdsourcing activity: microtask and macrotask crowdsourcing. These can be distinguished based on task granularity, or the amount of work required for – and by extension, the complexity of – the task assigned to individual workers. HITL and crowdsourcing are related, but there are important differences: crowdsourcing is a distributed problem‐solving approach. An important part of a crowdsourcing project is how to allocate the tasks to participants and how to validate and aggregate their contributions. HITL does not necessarily involve decentralization. The chapter also discusses two instances of crowdsourcing for location data: spatial crowdsourcing and citizen sensing, which are particularly relevant in a smart city context.