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Blockchain is an innovative technology which enabled new applications for solving numerous problems in distributed environments such as the internet of things (IoT) equipped food supply chains (FSCs). In FSCs, the large volume of IoT data such as audio, video, images, and sensor data will be transferred to ensure the traceability of the food to its source. When blockchain technology is used in an FSC, the storage requirements in the nodes will grow with time, since blockchain only allows information adding, without deleting the already stored information. Therefore, it is evident that offchain storage offers more flexibility than onchain storage in IoT equipped supply chains. This paper proposes a scalable storage scheme using offchain storage, where the data from IoT devices in the supply chain will store offchain. To reduce the growth of offchain storage, we exploit the fact that some information regarding a particular food item may not be required after the expiration date. The scalability of the proposed scheme is validated through numerical and experimental results. The 2021 Joint EuCNC & 6G Summit, Porto, Portugal (held online due to Coronavirus outbreak), 8-11 June 2021 University College Dublin 6Genesis Flagship 5GEAR

In precision agriculture (PA), soil sampling and testing op-eration is prior to planting any new crop. It is an expensive operationsince there are many soil characteristics to take into account. This papergives an overview of soil characteristics and their relationships with cropyield and soil profiling. We propose an approach for predicting soil pHbased on nearest neighbour fields. It implements spatial radius queriesand various regression techniques in data mining. We use soil dataset containing about 4,000 fields profiles to evaluate them and analyse theirrobustness. A comparative study indicates that LR, SVR, andGBRTtechniques achieved high accuracy, with the R2 values of about 0.718 and MAEvalues of 0.29. The experimental results showed that the pro-posed approach is very promising and can contribute significantly to PA. The 39th SGAI International Conference on Artificial Intelligence (AI 2019), Cambridge, United Kingdom, 17-19 December 2019 Science Foundation Ireland Insight Research Centre Origin Enterprises

The mobile service platform envisaged by emerging IoT and 5G is guaranteeing gigabit-level bandwidth, ultra-low latency and ultra-high storage capacity for their subscribers. In spite of the variety of applications plausible with the envisaged technologies, security is a demanding objective that should be applied beyond the design stages. Thus, Security as a Service (SECaaS) is an initiative for a service model that enable mobile and IoT consumers with diverse security functions such as Intrusion Detection and Prevention (IDPaaS), Authentication (AaaS), and Secure Transmission Channel (STCaaS) as a Service. A well-equipped edge computing infrastructure is intrinsic to achieve this goal. The emerging Multi-Access Edge Computing (MEC) paradigm standardized by the ETSI is excelling among other edge computing flavours due to its well-defined structure and protocols. Thus, in our directive, we intend to utilize MEC as the edge computing platform to launch the SECaaS functions. Though, the actual development of a MEC infrastructure is highly dependent on the integration of virtualization technologies to enable dynamic creation, the deployment, and the detachment of virtualized entities that should feature interoperability to cater the heterogeneous IoT devices and services. To that extent, this work is proposing a security service architecture that offers these SECaaS services. Further, we validate our proposed architecture through the development of a virtualized infrastructure that integrates lightweight and hypervisor-based virtualization technologies. Our experiments prove the plausibility of launching multiple security instances on the developed prototype edge platform. The 2020 IEEE International Conference on Communications (ICC), Dublin, Ireland, 7-11 June 2020 European Commission Horizon 2020 University College Dublin

Smart agriculture (SmartAg) has emerged as a rich domain for AI-driven decision support systems (DSS); however, it is often challenged by user-adoption issues. This paper reports a case-based reasoning system, PBI-CBR, that predicts grass growth for dairy farmers, that combines predictive accuracy and explanations to improve user adoption. PBI-CBR’s key novelty is its use of Bayesian methods for case-base maintenance in a regression domain. Experiments report the tradeoff between predictive accuracy and explanatory capability for different variants of PBI-CBR, and how updating Bayesian priors each year improves performance. The 29th International Joint Conference on Artificial Intelligence - 17th Pacific Rim International Conference on Artificial Intelligence (IJCAI-PRICAI-20), Yokohama, Japan, January 2021 (Conference postponed due to COVID-19 pandemic) Science Foundation Ireland Insight Research Centre

This paper proposes “An Emergency Situation Detection System for Ambient Assisted Living (AAL)”, to support elderly people and patients with chronic conditions and potential health-related emergencies to live independently. It implements an Internet of Things (IoT) network that continuously monitors the health conditions of these people. The network includes mobile phones, to transmit the data generated by the IoT sensors to the cloud server. Especially, the paper proposes the 3 rd party unknown mobile relays instead of dedicated gateways as opposed to many existing solutions for IoT healthcare applications. The wireless communication technology used to provide the connectivity between the sensor nodes and mobile relays is Bluetooth Low Energy (BLE). To establish a secure end-to-end connectivity between low power IoT sensor nodes and cloud servers, the paper proposes several techniques. After the medical data transmission to the cloud server, it is responsible for emergency detection and alert generation accordingly. The type of emergency is not limited to a specific health issue, but new emergency situations can be defined and added to the proposed system. Ultimately, the interested parties such as family members, caretakers and doctors receive these alerts. The development of a prototype of the system as a part of the work using commercial off-the-shelf devices verifies the validity of the proposing system and evaluates the performance advantage over the existing systems. The 2020 IEEE International Conference on Communications Workshops (ICC Workshops), Dublin, Ireland, 7-11 June 2020 University College Dublin European Commission Academy of Finland

The inherent factor of poor site productivity in western peatland forests combined with the reduction in management intensity from increased environmental considerations has brought some new challenges into forest management. Our study investigates new, alternative forest management models in the area chosen for this study, Cloosh forest, Co. Galway, to assess how these forests should be managed under future impacts of climate change and dynamic timber prices due to an expanding bioeconomy, and to quantify the impact this will have on forest ecosystem services (ES). The Environmental and Sustainable Resource Management (ESRM) Post-graduate Research Day, University College Dublin, Ireland, 6 December 2019 Department of Agriculture, Food and the Marine European Commission Horizon 2020

As the supporting technologies for Ambient Assistant Living (AAL) in Internet of Things (IoT) domain have become more powerful and more attractive, the related systems will be widely deployed and put into action. With all associated embedded IoT sensing devices, how to maintain users’ privacy and data security is a highly concerned problem. There are generally two approaches to protect privacy. One is to implement complex security protocols to guarantee the safety of sensing, storage and data transmission. Another one is to prevent the privacy issues and concerns from the source. This proposed research will provide a concept of a framework that can support behaviour monitoring through noninvasive and privacy preserving sensing. The data collected, transmitted and used for analysing in this framework is sensing information with low richness. This framework aims to increase the users’ perceived privacy in existing monitoring systems to avoid data over-collection and over exposure problems. IEEE 5th World Forum on Internet of Things, Limerick, Ireland, 15-18 April 2019

Policymakers, farm advisors and water agencies require up-to-date national maps of critical source areas (CSAs) of nitrogen (N) and phosphorus (P) losses from agricultural land to improve catchment management decisions. The DiffuseTools project aimed to achieve this in Ireland by updating the existing Catchment Characterisation Tool and sub-model NCYCLE_IRL, which predicts environmental losses of N and P from the farm via surface runoff, leaching, denitrification and volatilisation. Updates included (i) using improved national maps of farm-scale source loadings as inputs, (ii) sub-field scale modelling of surface transport risk using soil topographic indices derived from 1 m and 5 m NEXTMap digital elevation models (DEMs), (iii) modelling hydrological disconnectivity from microtopography (HSA Index) and reinfiltration (SCIMAP), (iv) improving the national ditch and stream channel network used by the model by DEM extraction, and (v) using SCIMAP to improve predictions of erosion risk. The improved national source loading maps included mean nationally weighted farm-gate N and P imports (fertilizer, feed and livestock) and balance surpluses (kg/ha) calculated for each stocking rate and soil group (land use potential) category within each sector type (dairy, mixed livestock, suckler cattle, non-suckler cattle, sheep and tillage), using annual Teagasc National Farm Survey data (2008-15). Furthermore, updated national maps of soil P and atmospheric N and P deposition inputs were also used within the national source loading maps to improve model performance. National CSA maps for N and P for each pathway were then produced and evaluated using water quality monitoring data and field observations from the Environmental Protection Agency and Teagasc Agricultural Catchments Programme. These maps will be able to support sustainable intensification by informing farm and catchment management decisions such as where to cost effectively target mitigation measures to reduce environmental losses, where to distribute nutrient surpluses (to non-CSAs in nutrient deficit), and improving functional land management. LUWQ 2019: International Interdisciplinary Conference on Land Use and Water Quality. Agriculture and the Environment. Aarhus, Denmark, 3-6 June 2019 Check for published version during checkdate report - AC Environmental Protection Agency

Data is central to the Internet of Things IoT ecosystem. Most of the current IoT systems are using centralized cloud-based data sharing systems. Involvement of such third-party service provider requires also trust from both sensor owner and sensor data user. Moreover, the fees need to be paid for their services. To tackle both the scalability and trust issues and to automatize the payments, this paper presents a blockchain based proxy re-encryption scheme. The system stores the IoT data in a distributed cloud after encryption. To share the collected IoT data, the system establishes runtime dynamic smart contracts between the sensor and the data user without the involvement of a trusted third party. It also uses an efficient proxy re-encryption scheme which allows that the data is only visible by the owner and the person present in the smart contract. The proposed system is implemented in an Ethereum based testbed to analyze the performance and security properties. The 2019 IEEE International Conference on Blockchain and Cryptocurrency, Seoul, South Korea, 14-17 May 2019 SECUREConnect, 6Genesis Flagship Enterprise Ireland

Internet of Things (IoT) systems produce large amounts of raw data in the form of log files. This raw data must then be processed to extract useful information. Machine Learning (ML) has proved to be an efficient technique for such tasks, but there are many different ML algorithms available, each suited to different types of scenarios. In this work, we compare the performance of 22 state-of-the-art supervised ML classification algorithms on different IoT datasets, when applied to the problem of anomaly detection. Our results show that there is no dominant solution, and that for each scenario, several candidate techniques perform similarly. Based on our results and a characterization of our datasets, we propose a recommendation framework which guides practitioners towards the subset of the 22 ML algorithms which is likely to perform best on their data. 2019 IEEE 5th World Forum on Internet of Things (WF-IoT'19)2019 IEEE 5th World Forum on Internet of Things (WF-IoT), Limerick, Ireland, 15-18 April 2019 European Commission - European Regional Development Fund Science Foundation Ireland