Agricultural traceability model based on IoT and Blockchain: Application in industrial hemp production

Facilities based on the Internet of Things and embedded systems along with the application of ambient intelligence paradigms offer new scenarios for optimization services in agronomic processes, specifically in the hemp industry. The traceability of products and activities demonstrates the scope of these technologies. However, the technologies themselves introduce integration-related problems that can affect the planned benefits. This article proposes a model that balances agricultural expert knowledge (user-centered design), value chain planning (through blockchain implementation), and digital technology (Internet of Things protocols) for providing tamper proof, transparent, and secure traceability in this agricultural sector. The proposed approach is backed by a proof-of-concept implementation in a realist scenario, using embedded devices and a permissioned blockchain. The model and its deployment fully integrate a set of services that other proposals only partially integrate. On one hand, the design creates a permissioned blockchain that contemplates the different actors in the value chain, and on the other hand, it develops services that use applications with human-machine interfaces. Finally, it deploys a network of embedded devices with Internet of Things protocols and control algorithms with automated access to the blockchain for traceability services. Combining digital systems with interoperable human tasks it has been possible to deploy a model that provides a new approach for the development of value-added services

Cautious Weight Tuning for Link State Routing Protocols

Link state routing protocols are widely used for intradomain routing in the Internet. These protocols are simple to administer and automatically update paths between sources and destinations when the topology changes. However, finding link weights that optimize network performance for a given traffic scenario is computationally hard. The situation is even more complex when the traffic is uncertain or time-varying. We present an efficient heuristic for finding link settings that give uniformly good performance also under large changes in the traffic. The heuristic combines efficient search techniques with a novel objective function. The objective function combines network performance with a cost of deviating from desirable features of robust link weight settings. Furthermore, we discuss why link weight optimization is insensitive to errors in estimated traffic data from link load measurements. We assess performance of our method using traffic data from an operational IP backbone