Cyber-Physical Systems (CPS) and Internet of Thing (IoT) generate large amounts of data spurring the rise of Artificial Intelligence (AI) based smart applications. Driven by rapid advancements in technologies that support smart devices, agriculture and farming sector is shifting towards IoT connected ecosystem to balance the increase in demand for food supply. As the number of smart farms reach critical mass, it is now possible to include AI assisted systems at a cooperative (co-op) farming level. Today, in the United States alone there are about 1,871 coops serving 1,890,057 member farmers. Hence, such advanced technologies and infrastructure when incorporated in the coop farming ecosystem can immensely benefit small member farmers who operate and maintain these independent coop entities. In this paper, we develop a connected cooperative ecosystem which defines sensors and their communication among different entities along with cloud supported coop hub. We develop member farm and coop ontologies to capture data and various interactions that happen between shared resources, member farms, and the coop that are stored in the cloud. These can then help generate AI supported insights for farmers and the cooperative. Several coop farming use case scenarios have been discussed to demonstrate the functioning of our smart cooperative ecosystem. Finally, the paper describes various AI applications that can be deployed at the coop level to aid member farmers.
With the advent of smart farming, individual farmers have started adopting the concepts of agriculture 4.0. Modern smart farms leverage technologies like big data, Cyber Physical Systems (CPS), Artificial Intelligence (AI), blockchain, etc. The use of these technologies has left these smart farms susceptible to cyber-attacks. In order to help secure the smart farm ecosystem in this paper, we develop a smart farming ontology. Our ontology helps represent various physical entities like sensors, workers on the farm, and their interactions with each other. Using the expressive ontology we implement an Attribute Based Access Control (ABAC) system to dynamically evaluate access control requests. Furthermore, we discuss various use cases to showcase our access control model in various scenarios on a smart farm.
The rise in popularity of Internet of Things (IoT) devices has opened doors for privacy and security breaches in Cyber-Physical systems like smart homes, smart vehicles, and smart grids that affect our daily existence. IoT systems are also a source of big data that gets shared via cloud. IoT systems in a smart home environment have sensitive access control issues since they are deployed in a personal space. The collected data can also be of highly personal nature. Therefore, it is critical to build access control models that govern who, under what circumstances, can access which sensed data or actuate a physical system. Traditional access control mechanisms are not expressive enough to handle such complex access control needs, warranting the incorporation of new methodologies for privacy and security. In this paper, we propose the creation of the PALS system, that builds upon existing work in attribute based access control model, captures physical context collected from sensed data (attributes), and performs dynamic reasoning over these attributes and context driven policies using Semantic Web technologies to execute access control decisions. Reasoning over user context, details of information collected by cloud service provider and device type our mechanism generates as a consequent access control decisions. Our system's access control decisions are supplemented by another sub-system that detects intrusions into smart home systems based on both network and behavioral data. The combined approach serves to determine indicators that a smart home system is under attack, as well as limit what data breach such attacks can achieve.
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