The explosion in human population has left researchers scrambling for solutions on how to feed the world. Furthermore, rural-urban immigration has on the one hand left the farms in the rural areas devoid of farmers and on the other hand has left the urban areas over-populated. Hydroponics is a form of agriculture where crops are grown without soil. This technique allows the farms to follow the farmers to the urban area. In addition, the fact that no soil is needed, allows hydroponic system to be stacked vertically (also known as vertical farming) to save space. The final frontier in hydroponics is automation. It will allow one farmer to work more than one job and cultivate more than one farm simultaneously. This paper provides a comprehensive survey on smart hydroponic system developed to date.

Researchers have associated agriculture and food processing with adverse environmental impacts like; falls in the underground freshwater table, energy consumption, and high carbon emission. These factors have the worst effect on developing countries. Therefore, there is a need for on-demand food production techniques that require minimum resource utilization. For these reasons, scientists are now focusing their attention on hydroponics. Hydroponics is the process of growing crops without the use of soil. However, different components of the system need to be closely monitored and controlled. In this paper, we compared the performance of an automated hydroponic system using cluster-based wireless sensor networks against a multihop-based one. We used Simponics for the simulation. It is a simulator based on the OMNET++ framework. Simulation results show that both latency and energy overhead of the multihop network increases with the number of nodes. However, they stay constant on a cluster-based network.

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The traditional password-based authentication systems are easy to breach because they rely on what the user knows and not on who is the user. This makes them prone to impersonation, as the culprit can gain access to the system once they knew the user's password, which led to the development of biometric-based authentication systems. Nonetheless, noisy data, intra-, and inter-class variations render these systems inaccurate. However, combining multiple biometric traits (multi-biometric systems) promises to increase the accuracy of biometric systems. But, this leads to an overhead in energy and latency in the system. These overheads are frowned at in embedded systems, especially those deployed in remote areas. Here, the authors propose a trust management-based multi-biometric system. The system switches between a uni-biometric and a multibiometric system to achieve high accuracy and reduced overhead (i.e. energy and latency). The system uses the uni-biometric technique for trusted users and the multibiometric method for untrustworthy users who are zero-effort impostors. It is found to reduce the false positive rate by 0.29 of Fingerprint uni-biometric system and the false negative rate by a factor of 0.5 of the multi-biometric system. Also, the energy consumption is reduced by a factor of 0.2 of the multi-biometric system.