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Smart monitoring systems in orchards can automate agriculture monitoring processes and provide useful information about the presence of insects, such as the Brown Marmorated Stink Bug (BMSB), that threaten the production quantity and quality of fruit such as pears. Unlike other approaches in the literature, we propose a low-cost image monitoring system which exhibits a very low power consumption without compromising much of the accuracy that existing expensive systems incorporating significant computing and processing capability can achieve in such applications. The proposed system relies on a microcontroller unit and a camera which can take pictures of a double-sided sticky insect trap which, with the help of novel machine learning algorithms, can report on the presence of BMSB via a long-range communication link. The Internet of Things data capture and analysis system has recently been deployed in a real orchard in Italy which is subject to BMSB infestation and the first images have been analysed. This paper presents how the system works, the image processing, detection and classification algorithms, as well as a demonstration of the memory and energy consumption associated with the processing algorithms. The system achieves an accuracy of over 90% with multiple times less memory and energy consumption compared to other similar approaches in the literature. • Low-Power Edge-based insect monitoring system. • Insect detection and classification on resource-constrained microcontroller units (MCUs). • Extremely lightweight classification model for Halyomorpha Halys detection. • Joint improvement of hardware metrics without considerably compromising accuracy. [ABSTRACT FROM AUTHOR]

Cooperative, connected and automated mobility (CCAM) has a great potential to change drastically the mobility landscape and improve safety on the roads by assisting drivers to take the best decisions in given circumstances or by supporting autonomous driving features aboard the car, among other expected benefits. Yet, providing CCAM services poses not only technical but also business challenges, especially in cross-border environments. In this paper, we present a general techno-economic methodology intended first, to identify the cross-border ecosystem and the potential business models to enable CCAM services provision and second, to derive the Total Cost of Ownership (TCO) of deploying the required Vehicle to Infrastructure (V2I) and Vehicle to Network (V2N) infrastructure. As an illustration, we then apply this methodology to a canonical Cooperative Lane Merging (CLM) scenario in a representative border environment (i.e., the Brenner pass between Italy and Austria). On this occasion, the impact of Multi-access Edge Computing (MEC) placement on the deployment cost is also discussed and finally, a cost allocation model is developed to link these deployment costs to the key stakeholders within the identified business models. Results aim to give insights into the different deployment strategies in typical cross-border environments and settings. • This paper presents a comprehensive techno-economic analysis of providing CCAM services in cross-border environment considering both technical and business challenges. • Proposed business models based on the adoption of new 5G technologies namely network slicing and MEC to ensure seamless service provision. • Collaborative business model is the most promising but needs review of regulations. • The collaborative business model is particularly attractive when considering the implementation of passive and active network sharing strategies. • Cost modelling results provide TCO insights for 5G network deployment scenarios. [ABSTRACT FROM AUTHOR]