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Article Citation - WoS: 36Agriculture 4.0: an Implementation Framework for Food Security Attainment in Nigeria's Post-Covid Era(Ieee-inst Electrical Electronics Engineers inc, 2021) Oruma, Samson O.; Misra, Sanjay; Fernandez-Sanz, LuisThe challenge of Nigeria's food insecurity in the era of the Covid-19 pandemic, insecurity, climate change, population growth, food wastage, etc., is a demanding task. This study addresses Nigeria's food insecurity challenges by adopting agriculture 4.0 and commercial farming. Using data from six digital libraries, the Nigerian Bureau of Statistics, and other internet sources, we conducted a Systematic Literature Review (SLR using PRISMA) on Nigeria's agriculture, food security, and agriculture 4.0. Our results show Nigeria's current agricultural state, threats to food security, and modern digital agriculture technologies. We adapted our SLR findings to develop an implementation framework for agriculture 4.0 in solving Nigeria's food insecurity challenge in the post-Covid-19 era. Our proposed framework integrates precision agriculture in Nigeria's food production and the necessary enabling digital technologies in the agri-food supply chain. We analyzed the critical implementation considerations during each agri-food supply chain stage of farming inputs, farming scale, farming approach, farming operation, food processing, food preservation/storage, distribution/logistics, and the final consumers. This study will help researchers, investors, and the government address food security in Nigeria. The implementation of agriculture 4.0 will substantially contribute to SDG 2 (zero hunger), SDG 3 (good health and well-being), and SDG 8 (decent work and economic growth) of #Envision 2030 of the United Nations, for the benefit of Nigeria, Africa, and the entire world.Article Citation - WoS: 75Citation - Scopus: 93Co-Fais: Cooperative Fuzzy Artificial Immune System for Detecting Intrusion in Wireless Sensor Networks(Academic Press Ltd- Elsevier Science Ltd, 2014) Shamshirband, Shahaboddin; Anuar, Nor Badrul; Kiah, Miss Laiha Mat; Rohani, Vala Ali; Petkovic, Dalibor; Misra, Sanjay; Khan, Abdul NasirDue to the distributed nature of Denial-of-Service attacks, it is tremendously challenging to identify such malicious behavior using traditional intrusion detection systems in Wireless Sensor Networks (WSNs). In the current paper, a bio-inspired method is introduced, namely the cooperative-based fuzzy artificial immune system (Co-FATS). It is a modular-based defense strategy derived from the danger theory of the human immune system. The agents synchronize and work with one another to calculate the abnormality of sensor behavior in terms of context antigen value (CAV) or attackers and update the fuzzy activation threshold for security response. In such a multi-node circumstance, the sniffer module adapts to the sink node to audit data by analyzing the packet components and sending the log file to the next layer. The fuzzy misuse detector module (FMDM) integrates with a danger detector module to identify the sources of danger signals. The infected sources are transmitted to the fuzzy Q-learning vaccination modules (FQVM) in order for particular, required action to enhance system abilities. The Cooperative Decision Making Modules (Co-DMM) incorporates danger detector module with the fuzzy Q-learning vaccination module to produce optimum defense strategies. To evaluate the performance of the proposed model, the Low Energy Adaptive Clustering Hierarchy (LEACH) was simulated using a network simulator. The model was subsequently compared against other existing soft computing methods, such as fuzzy logic controller (FLC), artificial immune system (AIS), and fuzzy Q-learning (FQL), in terms of detection accuracy, counter-defense, network lifetime and energy consumption, to demonstrate its efficiency and viability. The proposed method improves detection accuracy and successful defense rate performance against attacks compared to conventional empirical methods. (C) 2014 Elsevier Ltd. All rights reserved.
