Internet of Things (IoT) and Sensors Technologies in Smart Agriculture: Applications, Opportunities, and Current Trends

Mehmood, Muhammad Zeeshan; Ahmed, Mukhtar; Afzal, Obaid; Aslam, Muhammad; Zoq-ul-Arfeen, Raja; Qādir, Ghulām; Komal, Saida; Shahid, Muhammad Adnan; Awan, Adeem Arshad; Awale, Mohamed Ali; Sameen, Aashir; Kalsoom, Tahira; Nasim, Wajid; Fayyaz-ul-Hassan,; Ahmad, Shakeel

doi:10.1007/978-3-030-79408-8_21

Cited by 7 publications

(4 citation statements)

References 99 publications

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“…IoT has emerged as a transformative technology with the potential to revolutionize the world of work and bring massive improvement in the daily lives of humans [1][2][3]. Notably, its application spans several sectors like in the monitoring and control of machinery through advanced technologies such as digital twins [4], smart factories, and cities [5,6], healthcare [7,8], agriculture [9,10] and in many other fields. IoT's success in all the above application areas, however, depends heavily on the ability to connect with many devices, collect, store, and process their data, and respond to changing conditions in real-time and this requires significant processing power and computational resources [11][12][13] In a typical cloud architecture for IoT data processing, raw data emanating from end-user devices are transmitted through high bandwidth data networks to remote servers for processing, storage, knowledge extraction, decision making, and finally, sending results to the same or other nodes for actuation [14,15].…”

Section: Introductionmentioning

confidence: 99%

A Real-Time Task Balancing Strategy for IoT Networks Using Ant Colony Optimization

Wilson,

Nunoo-Mensah,

Boateng

et al. 2024

Preprint

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IoT devices relying on cloud-based computing resources for data processing are causing unsustainable growth and latency, thus making their dependence on these computing resources a foremost challenge to real-time and time-critical IoT applications. Researchers have proffered several approaches, but notable amongst them are the bio-inspired optimisation methods due to their ability to model stochastic environments efficiently. This paper proposes a bio-inspired load-balancing optimisation approach for reducing latency in IoT networks. The paper leverages the concept of “stigmergy” in social ants to create a metric for assessing the computational capacity, reliability, and availability of a node’s computing resources in a decentralised Internet of Things (IoT) network. The main contribution of this work is the design of a decentralised serverless computing paradigm for stateful(reliable) task offloading and data processing among end-user nodes in an IoT environment. A novel bio-inspired algorithm is designed for latency reduction and optimal load-balancing in any collaborative data processing environment without a centralised server. This work also introduces the concept of computing pheromones as a metric for assessing computation resource capacity and availability in decentralised data processing environments, which paves the way for more efficient and reliable IoT data processing solutions. Experimental results show the effectiveness of the proposed IoT data processing approach, highlighting improvements in response time and turnaround time compared to existing approaches.

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Section: Introductionmentioning

confidence: 99%

A Real-Time Task Balancing Strategy for IoT Networks Using Ant Colony Optimization

Wilson,

Nunoo-Mensah,

Boateng

et al. 2024

Preprint

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“…Due to several reasons, including insect attacks, plant disease, a lack of knowledge about essential nutrients for crops, and other problems, farmers are no longer able to enjoy the benefits of their work. To eliminate these obstacles and make farming more profitable, smart, and enjoyable for farmers, technological advancement is needed [2]. In every way, smart farming and conventional farming are diametrically opposed.…”

Section: Introductionmentioning

confidence: 99%

Authenticated Wireless Links between a Drone and Sensors Using a Blockchain: Case of Smart Farming

Alqarni

Almalki

Soufiene

et al. 2022

Wireless Communications and Mobile Computing

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Agriculture is confronted with several significant difficulties, such as rising air temperatures and population growth, causing the implementation of smart farming operations as an optimum solution. This research aims to contribute to the growing knowledge of the potential role of blockchain technology in promoting the concept of smart farming by enhancing the efficiency of farming operations by boosting agricultural production, lowering environmental impact, and automating the work of farmers. It proposes a secure blockchain-based framework to establish trust among smart farming users. The framework utilizes asymmetric key exchange mechanism using an ECC authentication algorithm and SHA-256 hash function cryptography to secure communication between sensors and drones in the farm field. The SHA-256 hashing function ensures data integrity as attempts to tamper with data result in a different hash value, breaking the chain of blocks. To demonstrate the feasibility of the proposed framework, a proof-of-concept implementation was developed on the Ethereum blockchain, in which smart contracts were used to model the framework operations. The proof of concept’s performance was examined using Hyperledger Caliper for latency, throughput, and transaction success rate. The findings clearly indicate that blockchain technology can provide an efficient and scalable mechanism to advance smart farming and address some of the barriers that inhibit smart farming, particularly regarding to data integrity and availability.

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“…Due to the advancement in sensors and smart technology, we can now readily access numerous previously inaccessible areas and collect more data samples at an appealing price point. 22,23…”

Section: Introductionmentioning

confidence: 99%

“…Due to the advancement in sensors and smart technology, we can now readily access numerous previously inaccessible areas and collect more data samples at an appealing price point. 22,23 Despite the excellent findings and potential of ML and AI applications, one of the primary challenges that need additional exploration and experimentation is the safety and robustness of the AI-based systems. These applications have potential vulnerabilities that might result in severe consequences and possibly costly damage.…”

Section: Introductionmentioning

confidence: 99%

The robustness of popular multiclass machine learning models against poisoning attacks: Lessons and insights

Maabreh

Qolomany

et al. 2022

International Journal of Distributed Sensor Networks

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Despite the encouraging outcomes of machine learning and artificial intelligence applications, the safety of artificial intelligence–based systems is one of the most severe challenges that need further exploration. Data set poisoning is a severe problem that may lead to the corruption of machine learning models. The attacker injects data into the data set that are faulty or mislabeled by flipping the actual labels into the incorrect ones. The word “robustness” refers to a machine learning algorithm’s ability to cope with hostile situations. Here, instead of flipping the labels randomly, we use the clustering approach to choose the training samples for label changes to influence the classifiers’ performance and the distance-based anomaly detection capacity in quarantining the poisoned samples. According to our experiments on a benchmark data set, random label flipping may have a short-term negative impact on the classifier’s accuracy. Yet, an anomaly filter would discover on average 63% of them. On the contrary, the proposed clustering-based flipping might inject dormant poisoned samples until the number of poisoned samples is enough to influence the classifiers’ performance severely; on average, the same anomaly filter would discover 25% of them. We also highlight important lessons and observations during this experiment about the performance and robustness of popular multiclass learners against training data set–poisoning attacks that include: trade-offs, complexity, categories, poisoning resistance, and hyperparameter optimization.

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Internet of Things (IoT) and Sensors Technologies in Smart Agriculture: Applications, Opportunities, and Current Trends

Cited by 7 publications

References 99 publications

A Real-Time Task Balancing Strategy for IoT Networks Using Ant Colony Optimization

A Real-Time Task Balancing Strategy for IoT Networks Using Ant Colony Optimization

Authenticated Wireless Links between a Drone and Sensors Using a Blockchain: Case of Smart Farming

The robustness of popular multiclass machine learning models against poisoning attacks: Lessons and insights

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