![]() ![]() In the field of smart agriculture health monitoring of livestock is an important field of research. For a system with 20 ECUs, for example, our proposed framework only requires 6.5% of the number of CAN frames that is required by the existing approach to successfully authenticate every ECU. The improvements in our proposed framework result in major reduction in the number of CAN frames that must be sent during operation. ![]() The framework does not require any modification to the standard CAN protocol while also minimizing the amount of additional message overhead required for its operation. In this work, we propose a security framework that is based on physically unclonable functions (PUFs) and lightweight cryptography (LWC). Smart vehicles have a multitude of communication interfaces an attacker could exploit to gain access to the networks. The lack of security becomes even more concerning as vehicles continue to adopt smart features. Replacing the protocol is another option, but it is subject to many of the same problems. Adding security features to CAN is difficult due to the limited resources available to the individual ECUs and the costs that would be associated with adding the necessary hardware to support any additional security operations without overly degrading the performance of standard communication. The fact that so many critical systems can be accessed through an insecure communication network presents a major security concern. The CAN protocol was not designed to include much support for secure communication. Within vehicles, the Controller Area Network (CAN) allows efficient communication between the electronic control units (ECUs) responsible for controlling the various subsystems. AI, ML, the blockchain as a DLT, and Physical Unclonable Functions (PUF) based hardware security fall under the technology group, whereas any network related attacks, fake data injection and similar threats fall under the network research problem group. We have divided open research problems of smart agriculture as future research work in two groups - from a technological perspective and from a networking perspective. ![]() After an in-depth study of different architectures, we also present a smart agriculture framework which relies on the location of data processing. We focus on the technologies, such as Artificial Intelligence (AI) and Machine Learning (ML) which support the automation, along with the Distributed Ledger Technology (DLT) which provides data integrity and security. Agriculture 4.0 is also discussed as a whole. How Agro Cyber Physical Systems are built upon the Internet-of-Agro-Things is discussed through various application fields. In this survey paper the authors present the applications, technological trends, available datasets, networking options, and challenges in smart agriculture. Traditional agriculture can be remade to efficient, sustainable, eco-friendly smart agriculture by adopting existing technologies. To cater to the needs of the increasing population, the agricultural industry needs to be modernized, become smart, and automated. Vulnerable groups of people will suffer malnutrition. Socio-economic and well being fallout will affect the food security. ![]() A recent projection shows that the world is lagging behind accomplishing the "Zero Hunger" goal, in spite of some advancements. The world population is anticipated to increase by close to 2 billion by 2050 causing a rapid escalation of food demand. ![]()
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