Recent advancements and challenges of Internet of Things in smart agriculture: A survey

Holzinger A., Malle B., Saranti A., Pfeifer B.

AI is remarkably successful and outperforms human experts in certain tasks, even in complex domains such as medicine. Humans on the other hand are experts at multi-modal thinking and can embed new inputs almost instantly into a conceptual knowledge space shaped by experience. In many fields the aim is to build systems capable of explaining themselves, engaging in interactive what-if questions. Such questions, called counterfactuals, are becoming important in the rising field of explainable AI (xAI). Our central hypothesis is that using conceptual knowledge as a guiding model of reality will help to train more explainable, more robust and less biased machine learning models, ideally able to learn from fewer data. One important aspect in the medical domain is that various modalities contribute to one single result. Our main question is “How can we construct a multi-modal feature representation space (spanning images, text, genomics data) using knowledge bases as an initial connector for the development of novel explanation interface techniques?”. In this paper we argue for using Graph Neural Networks as a method-of-choice, enabling information fusion for multi-modal causability (causability – not to confuse with causality – is the measurable extent to which an explanation to a human expert achieves a specified level of causal understanding). The aim of this paper is to motivate the international xAI community to further work into the fields of multi-modal embeddings and interactive explainability, to lay the foundations for effective future human–AI interfaces. We emphasize that Graph Neural Networks play a major role for multi-modal causability, since causal links between features can be defined directly using graph structures. • How multi-modal representations enable joint learning of a single outcome. • How embeddings can be learned in a distributed manner securely & efficiently. • How to use counterfactual paths for intuitive explainability and causability.

Kumar R., Mishra R., Gupta H.P., Dutta T.

Smart sensing for agriculture (SSA) is an emerging paradigm to facilitate quantitative and qualitative improvement in the productions. It incorporates a large number of low cost and low energy consuming sensors for inducing automation and reducing human efforts. SSA not only increases the monetary benefits of farmers but also ensures the delivery of fresh products. SSA uses a variety of consumer electronics (CE) devices that can automate multiple processes, monitor the soil health, growth of the crops, animal husbandry, detect anomalies in crop growth or livestock health, and enhance agriculture product quality and volumes. These devices incorporate various short-range and long-range communication protocols for relaying the sensory data. SSA finds several applications and provides a futuristic perspective for developing efficient and economical CE devices. This encourages us to present a taxonomy of different applications and advancements in agriculture through smart sensing. We also discuss different challenges that we incur while developing CE devices for smart agriculture.

Babun L., Denney K., Celik Z.B., McDaniel P., Uluagac A.S.

The Internet of Things (IoT) redefines the way how commodity and industrial tasks are performed every day. The integration of sensors, lightweight computation, and the proliferation of different wireless technologies on IoT platforms enable human beings to easily interact with their surrounding physical world thoroughly. With the recent rise of IoT, several different IoT platforms have been introduced for researchers and developers to ease the management and control of various IoT devices. In general, the IoT platforms act as a bridge between core IoT functionalities and users by providing APIs. Due to their wide variety of applications, IoT platforms are mostly unique in their architectures and designs. Thus, IoT administrators, developers, and researchers (i.e., IoT users) are challenged with substantial configuration differences in the proper configuration, implementation, and protection of the IoT solutions. In this survey, we conduct an in-depth analysis of popular IoT platforms from different application domains. More specifically, we define a comprehensive evaluation framework that considers seven different technical comparison criteria: (1) topology design, (2) programming languages, (3) third-party support, (4) extended protocol support, (5) event handling, (6) security, and (7) privacy. Then, we use the framework to evaluate the different IoT platforms highlighting their distinguishing attributes on communications, security, and privacy. First, we describe the communication protocols supported by the different IoT platforms surveyed. Then, rather than uncovering novel threats affecting IoT, we aim to analyze how the different IoT platforms handle security and privacy vulnerabilities affecting the most common security services of confidentiality, integrity, availability, and access control. Further, we present possible solutions that these platforms could implement to strengthen security and privacy within the IoT solution. Finally, we discuss the advantages and disadvantages of every IoT platform, so IoT administrators, developers, and researchers (i.e., IoT users) can make an informed decision on the use of specific platforms to implement their IoT solutions. To the best of our knowledge, this is the first comprehensive survey to evaluate different IoT platforms using the criteria defined in this work.

Kuo Y., Wen W., Hu X., Shen Y., Miao S.

This paper presents a long-range (LoRa)-based Internet of Things (IoT) system that consists of a series of IoT units in the field, as well as several servers for agriculture monitoring and pump control in water bamboo fields. Four types of IoT units were developed in accordance with the application’s need based on LoRa technology. Front- and back-end servers were constructed for data delivery, storage, and visualization, forming a complete solution. Another key feature is making a traditional submersible pump programmable with one IoT unit attached to the magnetic contactor in the electricity distribution box. Moreover, this paper presents design details of the proposed system and field experiment results for function validation. This IoT system is the first step in our project of revealing the relationship between farming and environmental reaction. This makes proposing a new farming procedure possible in the future.

Podder A.K., Bukhari A.A., Islam S., Mia S., Mohammed M.A., Kumar N.M., Cengiz K., Abdulkareem K.H.

The recent seen intelligent technologies like the internet of things (IoT), computer vision etc. facilitates farming activities and also provides flexible farm operations. On the other side, farming has become feasible even in urban areas, especially building roofs, open gardens, and indoor agriculture. In this context, farm management and appropriate monitoring of farm parameters are now indispensable for productive farming in smart cities or rural areas. In this paper, an IoT based Smart AgroTech system is proposed in the context of urban farming that considers humidity, temperature, and soil moisture as necessary farming parameters. The proposed system decides whether the irrigation action should begin or stop depending on the farming land condition and provides the monitoring facility and remote control to the farm owner. The system's reliability is verified by determining the error percentage between actual data and observed data at different observations. The average error rate for humidity and soil moisture is below 3% and for temperature is below 1.5%. The system ascertains a feasible Smart AgroTech system that provides advantages to the farming activities in future cities than other conventional methods.

Friha O., Ferrag M.A., Shu L., Maglaras L., Wang X.

This paper presents a comprehensive review of emerging technologies for the internet of things (IoT)-based smart agriculture. We begin by summarizing the existing surveys and describing emergent technologies for the agricultural IoT, such as unmanned aerial vehicles, wireless technologies, open-source IoT platforms, software defined networking (SDN), network function virtualization (NFV) technologies, cloud/fog computing, and middleware platforms. We also provide a classification of IoT applications for smart agriculture into seven categories: including smart monitoring, smart water management, agrochemicals applications, disease management, smart harvesting, supply chain management, and smart agricultural practices. Moreover, we provide a taxonomy and a side-by-side comparison of the state-of-the-art methods toward supply chain management based on the blockchain technology for agricultural IoTs. Furthermore, we present real projects that use most of the aforementioned technologies, which demonstrate their great performance in the field of smart agriculture. Finally, we highlight open research challenges and discuss possible future research directions for agricultural IoTs.

Njah Y., Cheriet M.

In recent years, the Industrial world has been embracing new digital technology, including the internet of things (IoT) paradigm that promises revolutionizing-prospects in numerous industrial applications. However, many deployment challenges related to real-time big data analytics, service assurance, resource optimization, energy consumption, and security awareness are raised. In this work, we focus on service assurance and resource optimization, including energy consumption challenges over Industrial Internet of Things (IIoT)-based environments since the existing network routing algorithms cannot meet the strict heterogeneous quality of service (QoS) requirements of industrial communications while optimizing resources. We take advantage of the flexibility and programmability offered by the promising software-defined networking paradigm, and we propose a centralized route optimization and service assurance scheme, named ROSA, over a multi-layer programmable industrial architecture. The proposed solution supports a wide range of heterogeneous flows, such as ultra-reliable low-latency communications (URLLC) and bandwidth-sensitive services. The routing optimization problems are formulated as multi-constrained shortest path problems. The Lagrangian Relaxation approach is used to solve the . Hence, we deploy a pair of parallel routing algorithms run according to the flow type to ensure QoS requirements, efficiently allocate constrained resources, and enhance the overall network energy consumption. We conduct extensive simulations to validate the proposed ROSA scheme. The experimental results show promising performance in terms of reducing bandwidth utilization by up to 22%, end-to-end delay at least by 21%, packet loss by more than 19%, flow violation by about 16%, and energy consumption up to 14% as compared to well-known benchmarks in QoS provisioning and energy-aware routing problem.

Casas R., Hermosa A., Marco Á., Blanco T., Zarazaga-Soria F.J.

Extensive unsupervised livestock farming is a habitual technique in many places around the globe. Animal release can be done for months, in large areas and with different species packing and behaving very differently. Nevertheless, the farmer’s needs are similar: where livestock is (and where has been) and how healthy they are. The geographical areas involved usually have difficult access with harsh orography and lack of communications infrastructure. This paper presents the design of a solution for extensive livestock monitoring in these areas. Our proposal is based in a wearable equipped with inertial sensors, global positioning system and wireless communications; and a Low-Power Wide Area Network infrastructure that can run with and without internet connection. Using adaptive analysis and data compression, we provide real-time monitoring and logging of cattle’s position and activities. Hardware and firmware design achieve very low energy consumption allowing months of battery life. We have thoroughly tested the devices in different laboratory setups and evaluated the system performance in real scenarios in the mountains and in the forest.

Li M., Jwo K., Yi W.

• Soil water content can be determined by simply measuring resonant frequency. • Only monopole antenna to frequency scan soil, emitting and receiving EM waves. • Easy operation and high accuracy; air gap has little effect on measurement results. • Pick 2.4–2.5 GHz frequency range to tradeoff penetration depth and energy efficiency. • Potential to implement measuring soil particle size and material. Soil water content measurement is indispensable to precision agriculture. A novel soil water content measuring method based on frequency scanning is proposed in this research. The measuring system consists of a monopole antenna to emit and receive electromagnetic waves as well as a network analyzer. The soil equivalent circuit model is presented and its spectroscopy is obtained by comparing incident and reflected waves. The results indicate that the resonance frequency shifts towards the low frequency region as soil moisture increases, therefore soil water content can be obtained simply by detecting resonance frequency. The linear fitting curve has a determination coefficient of 0.9834 and a root mean square error of 1.4%. The measuring range from 5.0% to 37.4% is wide enough for actual agricultural production. The method offers a productive solution for soil water content measurement and provides an idea to sense other soil characteristics.

Zhao W., Wang X., Qi B., Runge T.

Autonomous agricultural systems are a promising solution to bridge the gap between labor shortage for agriculture tasks and the continuing needs for increasing productivity in agriculture. Automated mapping and navigation system will be a cornerstone of most autonomous agricultural system. Accordingly, we propose a ground-level mapping and navigating system based on computer vision technology (Mesh Simultaneous Localization and Mapping algorithm, Mesh-SLAM) and Internet of Things (IoT), to generate a 3D farm map on both the edge side and cloud. The innovation of this system includes three layers as sub-systems that are 1) ground-level robot vehicles' layer for conducting frames collection only with a monocular camera, 2) edge node layer for image feature data edge computing and communication, and 3) cloud layer for general management and deep computing. High efficiency and speed of mapping stage are enabled by making the robot vehicles directly stream continuous frames to their corresponding edge node. Then each edge node, that coordinate a certain range of robots, applies a new Mesh-SLAM frame by frame, whose core is reconstructing the features map by a mesh-based algorithm with scalable units and reduce the feature data size by a filtering algorithm. Additionally, the cloud-computing allows comprehensive arrangement and heavily deep computing. The system is scalable to larger-scale fields and more complex environment by taking advantage of dynamically distributing the computation power to edges. Our evaluation indicates that: 1) this Mesh-SLAM algorithm outperforms in mapping and localization precision, accuracy, and yield prediction error (resolution at centimeter); and 2) The scalability and flexibility of the IoT architecture make the system modularized, easy adding/removing new functional modules or IoT sensors. We conclude the trade-off between cost and performance widely augments the feasibility and practical implementation of this system in real farms.

Demestichas K., Peppes N., Alexakis T.

The agriculture sector has held a major role in human societies across the planet throughout history. The rapid evolution in Information and Communication Technologies (ICT) strongly affects the structure and the procedures of modern agriculture. Despite the advantages gained from this evolution, there are several existing as well as emerging security threats that can severely impact the agricultural domain. The present paper provides an overview of the main existing and potential threats for agriculture. Initially, the paper presents an overview of the evolution of ICT solutions and how these may be utilized and affect the agriculture sector. It then conducts an extensive literature review on the use of ICT in agriculture, as well as on the associated emerging threats and vulnerabilities. The authors highlight the main ICT innovations, techniques, benefits, threats and mitigation measures by studying the literature on them and by providing a concise discussion on the possible impacts these could have on the agri-sector.

El-Basioni B.M., El-Kader S.M.

The Internet of Things (IoT) relates to many billions of various applications and devices scattered around the world talk to each other and can exchange data and perform cooperative tasks without the intervention of humans. Towards efficiently realizing this, many things are needed to be achieved in advance, such as common language, basis of work and cooperation, roles distribution, resources availability, and security. Here comes the role of humans to build a reference architecture represents the common communication framework among the Internet things. There is no doubt that in order for the IoT to meet expectations, it needs to follow standardization; therefore, this paper addresses the IoT standardization by formulating the basis of an IoT reference architecture for the agriculture domain. The proposed Agricultural IoT Reference Architecture (AITRA) is based on a defined architecture generation process incorporates analysis of the IoT and the application domain ecosystems. AITRA is composed of three tiers: Device, Cloud, and Business, described in the paper including architectures, conventions, frame format, applications and services, and illustrative examples for utilizing the architecture at its highest abstraction level. The proposed design resulted in a foundation for a reference architecture combines the three main required features: best practices, common vocabulary, and reusable designs; characterized over the other architectures by its efficient low abstraction level meanwhile giving design freedom, lower time-to-market, standardization in its interfaces and communication protocol. It connects to its outside world with authorization rules and at any scale: individual, company, government(s), and global levels.

Yanes A.R., Martinez P., Ahmad R.

Aquaponics is a farming method that promises to be a good alternative against the food and environmental problem the world is facing. It is a combination between aquaculture (farming of fish) and hydroponics (growing plants without soil), being a technique to grow plants with the aquaculture effluent. This technique claims to have high water use efficiency, does not use pesticides and reduce the use of fertilizers, which make this technology green and sustainable. Since the interest in aquaponics is increasing, the major challenge is to do it feasible and reliable at commercial scale. The concept of precision farming usually applied in the traditional farming sense is now being introduced, leading to the need to adopt sensing, smart and IoT systems for monitoring and control of its automated processes. Lately, valuable contributions have been made towards the introduction of fully and semi-automated systems in small-scale aquaponics systems by automation and manufacturing experts. This paper aims to support research towards a viable commercial aquaponics solution by identifying, listing, and providing an in-depth explanation of each of the parameters sensed in aquaponics, and the smart systems and IoT technologies in the reviewed literature. Further, the proposed review highlights potential gaps in the research literature and future contributions to be made in regards of automated aquaponics solutions.

Huang M., Liu A., Xiong N.N., Wang T., Vasilakos A.V.

The development of a 5G-enabled Internet of Things has led to a dramatic increase in network traffic load, which has presented tremendous challenges to network management. In this paper, a service-oriented network architecture is proposed to support the effective management of 5G-enabled IoT systems. This architecture effectively reduces the traffic load and simplifies network management by introducing a service aggregation and caching (SAaC) scheme. Specifically, SAaC first breaks through the data-centric network architecture by converting data into services. Then, SAaC significantly reduces traffic load and energy consumption by service aggregation. Finally, SAaC introduces service caching, and each content router caches new services locally after aggregating received services so that user requests are handled at the network layer. Experimental results demonstrate that compared with traditional solutions, the SAaC scheme improves the request response time by 20.52%–56.09%, reduces the traffic load by 10.85%–37.67%, and reduces energy consumption by more than 50%.

Haseeb K., Ud Din I., Almogren A., Islam N.

Wireless sensor networks (WSNs) have demonstrated research and developmental interests in numerous fields, like communication, agriculture, industry, smart health, monitoring, and surveillance. In the area of agriculture production, IoT-based WSN has been used to observe the yields condition and automate agriculture precision using various sensors. These sensors are deployed in the agricultural environment to improve production yields through intelligent farming decisions and obtain information regarding crops, plants, temperature measurement, humidity, and irrigation systems. However, sensors have limited resources concerning processing, energy, transmitting, and memory capabilities that can negatively impact agriculture production. Besides efficiency, the protection and security of these IoT-based agricultural sensors are also important from malicious adversaries. In this article, we proposed an IoT-based WSN framework as an application to smart agriculture comprising different design levels. Firstly, agricultural sensors capture relevant data and determine a set of cluster heads based on multi-criteria decision function. Additionally, the strength of the signals on the transmission links is measured while using signal to noise ratio (SNR) to achieve consistent and efficient data transmissions. Secondly, security is provided for data transmission from agricultural sensors towards base stations (BS) while using the recurrence of the linear congruential generator. The simulated results proved that the proposed framework significantly enhanced the communication performance as an average of 13.5% in the network throughput, 38.5% in the packets drop ratio, 13.5% in the network latency, 16% in the energy consumption, and 26% in the routing overheads for smart agriculture, as compared to other solutions.

Adamo T., Caivano D., Colizzi L., Dimauro G., Guerriero E.

Dubey P., Kumar M.

Zheng Q., Chen S., Wang G., Li L., Peng S., Yao Z.

Thakur V., Sharma S., Kumar A., Himanshu, Ankit, Prachi, Kumar A., Kumar R., Sharma N., Thakur S., Sharma S.

Mba P.C., Njoku J.N., Uyeh D.D.

Singh G., Sharma S.

Carvalho N., Adão T., Morais R., Costa A.R., Peres E.

Durand T.G., Booysen M.J.

Research into, and the usage of, Low-Power Wide-Area Networks (LPWANs) has increased significantly to support the ever-expanding requirements set by IoT applications. Specifically, the usage of Long-Range Wide-Area Networks (LoRaWANs) has increased, due to the LPWAN’s robust physical layer, Long-Range (LoRa), modulation scheme, which enables scalable, low-power consumption, long-range communication to IoT devices. The LoRaWAN Medium Access Control (MAC) protocol is currently limited to only support single-hop communication. This limits the coverage of a single gateway and increases the power consumption of devices which are located at the edge of a gateway’s coverage range. There is currently no standardised and commercialised multi-hop LoRa-based network, and the field is experiencing ongoing research. In this work, we propose a complementary network to LoRaWAN, which integrates mesh networking. An ns-3 simulation model has been developed, and the proposed LoRaMesh network is simulated for a varying number of scenarios. This research focuses on the design decisions needed to design a LoRa-based mesh network which maintains the low-power consumption advantages that LoRaWAN offers while ensuring that data packets are routed successfully to the gateway. The results highlighted a significant increase in the packet delivery ratio in nodes located far from a centralised gateway in a dense network. Nodes located further than 5.8 km from a gateway’s packet delivery ratio were increased from an average of 40.2% to 73.78%. The findings in this article validate the concept of a mesh-type LPWAN network based on the LoRa physical layer and highlight the potential for future optimisation.

Zhang S., Chen R., Wu J., Zhu N.

Nandal V., Dahiya S.

ABSTRACTIn agriculture production, Internet of Things–based wireless sensor network (IoT‐based WSN) technology is used to observe yield conditions and automatic precision agriculture through different sensors. As a part of intelligent farming decisions, sensors are used in the agricultural field to collect information regarding plants, crops, measurements, temperature, humidity, and irrigation systems. IoT‐based WSNs are proposed for smart agriculture that have different levels of design. Initially, significant information is captured by agricultural sensors. Then, an optimized trilevel K‐means clustering (KMA) is introduced to cluster the agriculture data. The trilevel KMA is effectively used to extract knowledge in the agriculture field. After that, the cluster head is selected using the arithmetic optimization algorithm (AOA) by considering the general factors, including energy, delay, and distance, improving the lifetime of the nodes. This method proposes a new chain‐based routing method for optimizing information transmission in IoT‐based WSN frameworks. A comparison between the proposed and existing methods is performed to prove their effectiveness. The experimental results show that the proposed protocol outperforms the other existing methods in terms of throughput (96 kbps), packet delivery ratio (37%), network latency (0.028 ms), and average energy consumption (0.52 J).

Sadul O., Phirake S., Sonawwanay P.D.

The growing need for sustainable and effective farming methods has led to increased demand and integration of Intelligent Systems in the digitalization and automation of agricultural equipment. This review paper explores the transformative impact of the Internet of Things (IoT), Artificial Intelligence (AI), Machine Learning (ML), and Data Analytics in the realm of smart agriculture. The synergy of these technologies not only enhances precision farming but also facilitates data-driven decision-making for optimizing resource utilization and crop management. The paper delves into both current and emerging applications of these Intelligent Systems in agriculture, emphasizing their capacity to transform conventional farming methodologies. Additionally, the review includes a comprehensive case study comparing the effectiveness of Drone-mounted pesticide spraying with traditional manual pesticide spraying methods. This comparative analysis sheds light on the operational efficiency, cost-effectiveness, and environmental impact of each approach. By evaluating the real-world implications of these technologies, the paper aims to provide valuable insights into the practical implementation and benefits of Intelligent Systems in the agricultural sector, laying the groundwork for a future in farming practices that is both technologically advanced and sustainable.

Thilakarathne N.N., Abu Bakar M.S., Abas P.E., Yassin H.

Huang W., Yang H., Wang Y., Ding P., Nawi N.M., Zhang X.

Dhanasekar S.

Thilakarathne N.N., Bakar M.S., Abas P.E., Yassin H.

The rapid proliferation of Internet of Things (IoT) devices has brought about a profound transformation in our daily lives and work environments. However, this proliferation has also given rise to significant security challenges, as cybercriminals increasingly target IoT devices to exploit vulnerabilities and gain access to sensitive data. This escalating threat landscape poses a severe issue across diverse domains where IoT is deployed, including agriculture, healthcare, and surveillance. In the realm of agriculture, where farmers have historically contended with pests and environmental challenges, a new adversary has emerged in the form of cyber criminals. The agriculture sector has witnessed a surge in cyber-attacks targeting smart agriculture solutions despite being a relatively recent addition to the industry. Farmers may not have control over the actions of cyber adversaries, but they possess the ability to make informed purchasing decisions when adopting smart farming solutions and implementing fundamental security measures, such as robust user credentials and regular system updates. In this regard, this research introduces a groundbreaking approach to addressing the cybersecurity concerns associated with smart agriculture–deception technology. Overall, deception technology involves the creation of deceptive elements, including decoys, traps, and false information, designed to divert cybercriminals away from genuine data and systems where this research presents a novel cyber threat intelligence platform that leverages deception technology to assess and mitigate the risks associated with smart agriculture as the first of its kind research. Based on the insights derived from the experimental work, actionable recommendations would be provided to relevant stakeholders on how to mitigate cyber risks and bolster the security posture of IoT-enabled smart agriculture. Overall, this innovative approach represents a significant step towards safeguarding the increasingly interconnected world of smart agriculture, offering a promising avenue for defending against the escalating cyber threats faced by this vital industry.