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IoT technologies for livestock management: A review of present status, opportunities, and future trends

Akhigbe, Bernard Ijesunor; Munir, Kamran; Akinade, Olugbenga; Akanbi, Lukman; Oyedele, Lukumon O.


Bernard Ijesunor Akhigbe

Olugbenga Akinade
Associate Professor - AR/VR Development with Artificial Intelligence

Dr Lukman Akanbi
Associate Professor - Big Data Application Developer


The world population currently stands at about 7 billion amidst an expected increase in 2030 from 9.4 billion to around 10 billion in 2050. This burgeoning population has continued to influence the upward demand for animal food. Moreover, the management of finite resources such as land, the need to reduce livestock contribution to greenhouse gases, and the need to manage inherent complex, highly contextual, and repetitive day-to-day livestock management (LsM) routines are some examples of challenges to overcome in livestock production. The Internet of Things (IoT)’s usefulness in other vertical industries (OVI) shows that its role will be significant in LsM. This work uses the systematic review methodology of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) to guide a review of existing literature on IoT in OVI. The goal is to identify the IoT’s ecosystem, architecture, and its technicalities—present status, opportunities, and expected future trends—regarding its role in LsM. Among identified IoT roles in LsM, the authors found that data will be its main contributor. The traditional approach of reactive data processing will give way to the proactive approach of augmented analytics to provide insights about animal processes. This will undoubtedly free LsM from the drudgery of repetitive tasks with opportunities for improved productivity.


Akhigbe, B. I., Munir, K., Akinade, O., Akanbi, L., & Oyedele, L. O. (2021). IoT technologies for livestock management: A review of present status, opportunities, and future trends. Big Data and Cognitive Computing, 5(1), Article 10.

Journal Article Type Article
Acceptance Date Feb 19, 2021
Online Publication Date Feb 26, 2021
Publication Date Mar 1, 2021
Deposit Date Jun 25, 2021
Publicly Available Date Jul 1, 2021
Journal Big Data and Cognitive Computing
Electronic ISSN 2504-2289
Publisher MDPI
Peer Reviewed Peer Reviewed
Volume 5
Issue 1
Article Number 10
Public URL


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