## 1 Introduction
While AmI is a mature discipline that has attracted in the last fifteen years a great deal of attention of scientists and practitioners from various fields, such as human-computer interaction (HCI), AI, and communication networks, the search for on-device lightweight detection solutions is a still-emerging area of study that has barely been around for five years. Both of them, however, have yielded a vast scientific production, as evidenced by the important number of surveys currently present in the related literature. Specifically concerning AmI it is possible to find a wide range of studies: (i) papers of a general nature that overview the field, introducing key concepts, common use cases, and state-of-the-art techniques [6; 7; 8; 9; 10; 11]; (ii) works circumscribed only to a subset of relevant related matters, such as security and privacy [12; 13; 14; 15], sensing, computing, and networking technologies [16; 17], as well as ethical implications and social issues [18], and (iii) research focused on particular application domains (e.g., architecture [19], workplaces [20], educational environments [21; 22], and smart cities [18]), with healthcare being the one that has shown greater treatment [12; 23; 24; 25; 26; 27; 28; 29]. For its part, the on-device object detection corpus is limited to literature published over the last five years (2017-2021). It encompasses (i) surveys that cover the on-device ML field from a generic perspective following a schema similar to the one indicated for AmI surveys [30; 31; 32; 33; 34], (ii) works exclusively circumscribed to the object detection problem [35; 36; 37; 38; 39; 40; 41], and last but not least, (iii) research on problem-specific matters, such as good-performant network architectures [37; 39], appropriate training strategies [36; 39], and techniques to enhance the representational power of neural networks [40]. Beyond the works referenced, the intersection of AmI and AI has already resulted in a substantial number of review articles [42; 43; 44; 17; 45]. They summarize the progress both fields have experienced jointly, detailing the several approaches explored for creating intelligent and context-aware environments (e.g., human activity and behavior recognition techniques [44], and classification methods for more accurate disease prediction and diagnosis [45]). Within such body of works, although some papers can be considered part of the on-device literature since they analyze different IoT communication architectures, as well as hardware technologies for local data processing, there is not any equivalent yet dealing either with the algorithmic part or, in any case, providing an overview of the advanced sensing techniques required for the implementation of the intelligence, context awareness, user adaptability and user privacy preservation principles that, according to [PERSON] et al. [46], should drive the building process of any AmI system. To fill that gap, this paper reviews some of the most relevant research works recently produced in different AmI application areas of interest, aimed at the design and development of efficient, compact, locally executable, and consequently, privacy- and data security-friendly object detection frameworks. In particular, this work is exclusively focused on CNNs, a deep learning algorithm family that has shown to be the current state-of-the-art in relation to the target problem [36; 38; 39]. More specifically, the paper analyzes and provides the reader with a structured presentation of the most salient related approaches and techniques, giving details on both the different challenges or issues that the authors in the field have faced and the various CNN architectures exploited for that purpose, also discussing the specific strategies or approaches adopted and the different configurations or frameworks used to evaluate their performance. The scope of the study is restricted to CNN networks conceived according to the on-device paradigm.