1 Introduction

Recent advancements in IoT using telecommunication and intelligence have become crucial in shaping the future of Internet of Things in various sectors such as transportation, remote monitoring, logistics, agriculture, and automation. According to Ericsson, it is projected that by the end of 2022, over 28 billion devices could be interconnected globally (Akpakwu et al. 2017). According to the research, it has been observed that a single person is carrying out approximately 5–6 devices in 2022 (Chamara et al. 2022; Ramachandran et al. 2022; Pathmudi et al. 2023). These devices generate vast quantities of data. The surge in data usage and data rate requirements has been remarkable over the past two years, primarily driven by the impact of COVID-19. Ejaz et al. 2016; delves into the realm of IoT technology, examining its potential for addressing challenges and opportunities in the field of agriculture (Ejaz et al. 2016). A decrease in network bandwidth was found to result in a decrease in spectrum efficiency (Shrivastava et al. 2022). The experimental results showed that utilizing suboptimal machine learning and deep learning detectors led to a notable decrease in complexity while enhancing the quality of service. The results indicate that the proposed system shows potential for improving BER performance and reducing complexity in 5G communications (Subalatha et al. 2021; Danandeh Mehr et al. 2023; Nayak et al. 2023). Presented in Fig. 1 is the architecture of the 5G network, as referenced in (Gupta and Jha 2015).

Fig. 1
figure 1

A general 5G cellular network architecture

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The research focuses on the integration of cognitive radio spectrum efficiency into IoT networks for agricultural monitoring. This article explores the challenges faced by 5G systems in maintaining energy efficiency and scalability in Centralized Radio Access Networks. It also discusses the use of advanced multi-carrier modulations such as massive MIMO and full-duplex transfer. Dealing with the growing intricacy of 5G Heterogeneous Networks, a proposed approach in studies recommends a cluster-based strategy that utilizes GFDM modulation (Rajappa et al. 2020; Kumar and Prasad 2021). This strategy aims to establish dynamic connections between multiple eNBs, ensuring reliable data transmission pathways and efficient residual energy usage. This innovative approach aims to enhance network performance and meet the demands of 5G networks by minimizing energy consumption, data traffic, and latency (Idrissi et al. 2022). Managing network traffic and resources remains a challenging task due to the increasing demand and limited availability. Efficient allocation of resources is crucial for minimizing network congestion and enhancing service quality (Akpakwu et al. 2017). There are several reviews available on resource distribution, but unfortunately, none of them specifically focus on the topic of 5G. To address this issue, this paper explores various resource allocation strategies for 5G and evaluates their impact on service quality by analyzing network performance metrics (Gebre et al. 2021; Zhang et al. 2024).

This study reveals a significant decrease in spectrum efficiency as network bandwidth increases, posing significant challenges for agricultural IoT applications. In addition, the lack of thorough reviews in current literature regarding 5G resource allocation strategies hinders a detailed understanding of their impact on service quality. Therefore, there is a pressing need for further research efforts to enhance the allocation of resources in 5G networks. In addition, our research addresses ongoing challenges in maintaining effective communication, establishing consistent standards, and optimizing data management for a wide range of 5G IoT devices. It is crucial to conduct focused research to tackle these unresolved matters and improve the overall efficiency of 5G IoT networks.

2 Related work

The Internet of Things (IoT) provides a seamless connection, allowing devices to communicate at any time and in any location. Every device that is connected requires its own spectrum in order to achieve faster data transfer rates, whether it is through wired or wireless communication. Previous research has examined the issue of multipath propagation causing Inter-symbol Interference (ISI) in wireless systems (Storck and Duarte-Figueiredo 2020). This research explores three different equalization techniques: pilot signal-based, training-based, and blind equalization without pilots. Examining four equalization algorithms, which is crucial for fine-tuning for future 5G and 6G technologies (Ahmed et al. 2019). There is a growing need for higher data rates and user capacity, leading to a more efficient utilization of the existing frequency spectrum. The most recent studies have centered around utilization. Due to the small wavelengths at mmWave bands, 5G cellular systems can achieve improved spectral utilization in those frequencies. However, the mmWave Frequencies limit the communication range and lead to increased propagation losses. In order to address these issues of losses, hardware costs, and power consumption in 5G NR networks, large-scale antenna arrays are employed (Dilli 2022). It is quite challenging to maintain the desired Quality of Service (QoS) while ensuring optimal spectrum utilization. The concept of Mobile IP is centered around a decentralized server that controls connected devices. There are numerous applications of the IoT, including smart building, e-health care, smart industry, smart tree, smart home, smart transportation, and smart agriculture (Borgia 2014; Shi et al. 2016; Gardašević et al. 2017; Morshed et al. 2018; Nawandar and Satpute 2019; Rahman et al. 2022; Sanida et al. 2023). Optimizing spectrum utilization is crucial for a wide range of applications that heavily depend on communication technology, particularly in the realm of wireless mobile communication. Seamless wireless communication in these applications necessitates the use of spectrum. Thus, it is crucial to maximize the use of spectrum resources to ensure the seamless operation and efficiency of these technologies. To ensure that unlicensed users have access to the widest range of spectrum without causing any disruptions to the licensed network. The unlicensed bands utilize the dynamic spectrum access strategy. Obtaining the blocking probability of licensed users, the mean dwell time of unlicensed users, the total carried traffic, and the overall service quality for both licensed and unlicensed users. The results indicate that the proposed method effectively preserves the quality of service and optimizes the use of spectrum for unlicensed users in IoT applications (Moon 2017). An essential focus of research in 5G and IoT connectivity revolves around the seamless integration and efficient management of diverse IoT devices. There are still challenges that need to be addressed in order to ensure seamless communication and standardization among different 5G devices, protocols, and platforms. These issues can cause difficulties in connecting IoT devices that utilize varying communication protocols and security measures, resulting in a lack of seamless connectivity. In addition, optimizing network architectures to meet the requirements of different devices while also ensuring resource efficiency remains a challenge. An area that has been somewhat overlooked is the management of the substantial volume of data produced by IoT devices, as well as the enhancement of connectivity and data processing in 5G networks. In order to address these gaps, the research approach will focus on evaluating the probability of forced termination, handover, and blocking, with a particular emphasis on data management, security, scalability, and interoperability within 5G frameworks.

Table 1 showcases various applications that make use of spectrum utilization. These applications include voice and data services with data rates ranging from 10Kbps to 200Kbps, which have a spectrum utilization between 40% and 85%. In the case of 3G and 4G, where data rates are higher, spectrum utilization exceeds 90%. However, in 5G communications, spectrum utilization can reach nearly 100% at data rates of 10-100Gbps.

Table 1 Application with spectrum utilization Jon et al. 2023
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The Internet of Things covers a combination of different technologies and standards, such as 2G to 5G and beyond cellular networks, Bluetooth, Wi-Fi, and more (Marcus 2015). In recent years, there has been a significant increase in the number of mobile device subscriptions, particularly smart devices. The networks used in the Internet of Things (IoT) rely on different technologies such as Wi-Fi, 3G, 4G, and 5G. However, these technologies may not be ideal for IoT applications that prioritize low power consumption and lower data rates (Akpakwu et al. 2017; Li et al. 2018). Addressing these challenges requires the adoption of 5G technology, which offers a promising solution, particularly in terms of spectrum usage (Rajiv et al. 2024). However, when it comes to agriculture, wireless communication based on IoT faces several challenges. These challenges revolve around device intelligence, computational capabilities, low latency, and cost-effective availability. Several efforts have been made to establish a uniform framework for 5G-enabled IoT (Zhao et al. 2022), yet the issue of spectrum requirement remains a significant concern. This problem can be solved by utilizing different frequency bands that are currently accessible. When it comes to massive machine-type communication and indoor applications, opting for a low-frequency band below 2 GHz is the way to go. This choice offers a broader coverage and wider channel availability, making it a preferred option. In addition, Table 2 provides detailed information on the specifications of the New Radio for 5G, which is divided into frequency range 1 and frequency range 2.

Table 2 Frequency range 1 spectrum (Chen et al. 2015)
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The frequency range provided in Table 2 presents a wider range of possibilities (Jon et al. 2023), making it well-suited for investigating the utilization of untapped spectrum in 5G. In addition, 5G deployment has identified other frequency bands including 24 GHz, 28 GHz, 37 GHz, 39 GHz, and 47 GHz. Cellular network architectures of today heavily depend on hardware-based solutions to handle the growing traffic. However, in order to effectively adapt to new network technologies, it is crucial to transition towards software-defined radio (SDR) (Akpakwu et al. 2017; Kobo et al. 2017; Ndiaye et al. 2017). SDR provides enhanced flexibility for expanding and deploying cellular technology in wireless sensor networks. SDR streamlines network management, optimizes resource utilization, and facilitates node deployment through a decentralized control and centralized mechanism. For IoT, ensuring extensive connectivity and optimal resource utilization is of utmost importance. Therefore, effective spectrum management through spectrum sharing becomes crucial. Cognitive radio (CR) effectively tackles the issue of spectrum scarcity by cleverly sharing spectrum resources opportunistically (Akpakwu et al. 2017). CR technology has the capability to adjust its transmitter parameters, allowing it to efficiently utilize the spectrum by reallocating unused portions, referred to as white spaces, to unlicensed users. This enhances overall spectrum utilization. The interference between licensed and unlicensed users is minimized through the use of various techniques, including spectrum hopping, power control, and modulation changes (Akyildiz et al. 2006). Utilizing CR Technology, the spectrum allocation is optimized to determine the most suitable data rate considering factors such as hardware, bandwidth, and transmission mode. This approach guarantees that the Quality of Service (QoS) requirements of the cellular network are met. Through intelligent sensing and analysis of the spectrum, even faint signals can be utilized, resulting in improved spectrum efficiency (Zheng and Cao 2005; Mukhopadhyay et al. 2021).

3 Materials and methods

This paper presents results based on various channel allocation schemes in 5G networks, particularly for mobile communication. Fixed Channel Allocation (FCA) involves allocating a fixed number of channels to a specific cell, assuming uniform traffic distribution. However, real-time scenarios rarely exhibit uniform traffic conditions in cellular networks. Dynamic Channel Allocation (DCA) addresses this by dynamically allocating channels to users in the homogeneous or heterogeneous network without a fixed number of channels in a cell (Zhao et al. 2005). Here, following are the algorithms deployed and analysed for the channel allocation for Agriculture IoT application scenarios.

3.1 Non-Priority Algorithm (NPA)

The Non-Priority Algorithm (NPA) is a channel allocation scheme that does not prioritize, or reserve channels based on specific criteria such as handovers or permanent allocations. In the NPA approach, all cells are treated equally in terms of channel access, and channel allocation decisions are made dynamically based on real-time traffic conditions within the network cell (Khan et al. 2020).

Channel Allocation Process:

  1. i.

    Dynamic Allocation: NPA dynamically allocates channels to new call requests within a cell based on the current traffic conditions. There is no predetermined reservation of channels for specific purposes like handovers or permanent assignments.

  2. ii.

    Equal Treatment: All cells within the network are considered equal, and there is no prioritization of channel access based on factors such as location or user priority.

  3. iii.

    Real-Time Traffic Conditions: The allocation decision is influenced by the ongoing traffic in the cell, and channels are assigned based on availability and current demand.

3.2 Reserved Channel Algorithm (RCA) - no permanent channels

The Reserved Channel Algorithm (RCA) with no permanent channels is a channel allocation scheme designed to prioritize the reservation of channels for handovers without allocating specific channels for permanent use. The primary objective is to ensure seamless connectivity during ongoing calls by reserving a pool of channels specifically for accommodating handovers (Akbar and Safaei 2020).

Channel Allocation Process:

  1. i.

    Reserved Pool Setup: A certain percentage of available channels in the cellular network is designated as a reserved pool exclusively for handling handovers. Let R represent the total number of channels reserved for handovers.

  2. ii.

    Dynamic Channel Allocation for Ongoing Calls: When a new call request is initiated within the cell, the algorithm dynamically allocates channels based on real-time traffic conditions. The allocation is performed from the pool of non-reserved channels, ensuring flexibility in handling ongoing calls without compromising the availability of channels for handovers.

  3. iii.

    Borrowing from the Reserved Pool: In situations where all non-reserved channels are occupied, the algorithm borrows channels from the reserved pool to fulfill the new call request. The borrowed channel is temporarily assigned for the new call while maintaining a focus on maintaining the ongoing calls.

  4. iv.

    Handover Utilization: The reserved channels are primarily utilized for facilitating handovers, ensuring a dedicated set of resources is available to maintain connectivity during transitions between cells.

Mathematically:

  1. i.

    Let N be the total number of available channels in the cellular network.

  2. ii.

    Let n be the number of non-reserved channels.

  3. iii.

    The number of channels allocated for ongoing calls (Congoing) is determined dynamically based on real-time traffic conditions. The number of reserved channels for handovers (R) is predefined.

  4. iv.

    The total number of channels available for new call allocation (Navailable) is given by: Navailable = n + R.

The algorithm allocates channels based on traffic conditions, and the specific mathematical expressions for channel allocation can vary based on the dynamic conditions and criteria set by the network operator.

3.3 Reserved Channel Algorithm (RCA) - permanent channels

The Reserved Channel Algorithm (RCA) with permanent channels is a channel allocation strategy in cellular networks designed to prioritize and reserve specific channels for permanent use. In this context, certain channels are exclusively assigned and dedicated to particular users or applications, ensuring consistent and uninterrupted connectivity for these designated entities (Taherkhani et al. 2021; Sousa et al. 2023).

Channel Allocation Process:

The channel allocation process under the RCA with permanent channels involves two main steps: the assignment of channels for permanent use and the dynamic allocation of the remaining channels based on real-time traffic conditions. Let’s delve into each step:

  1. i.

    Permanent Channel Assignment: A subset of available channels, denoted as K, is permanently assigned to specific users or applications. This allocation is static and remains constant, providing dedicated resources for the entities with permanent channel assignments. Mathematically, this can be expressed as: Np=K where Np represents the number of permanently assigned channels.

  2. ii.

    Dynamic Channel Allocation: The remaining available channels, Nr, are dynamically allocated based on traffic conditions within the network cell. The allocation process considers real-time parameters such as the number of ongoing calls, call durations, and the overall network load. Mathematically, the dynamic allocation can be represented as: Nd=Nr−Nc where Nd is the number of dynamically allocated channels, Nr is the total remaining channels, and Nc is the number of currently occupied channels.

  3. iii.

    Overall Channel Allocation: The total number of channels available for the RCA with permanent channels is the sum of the permanently assigned and dynamically allocated channels: Ntotal=Np+Nd.

The RCA with permanent channels is particularly suitable for scenarios where certain users or applications require consistent and dedicated channel resources. This allocation strategy ensures that these designated entities have priority access to channels, resulting in improved Quality of Service (QoS) for specific connections.

Figure 2 demonstrate the dynamic channel allocation method deployed for the simulation and analysis of the proposed algorithms.

Fig. 2
figure 2

Dynamic channel allocation flow chart

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The initialization has been done by selecting a channel and setting the frame time and amount of data available for transmission as data time, from this we compute number of frame to be transmitted and we select 1st frame, and check if data is available on the frame then channel is checked and if the data is not available then the next frame checked after that the channel is checked, if it is free the this channel is allotted as last channel and data is transmitted using this channel and if the channel is not free then the random channel is selected from the available channel and data was transmitted and last used channel was assigned as a channel for further process as shown in flow chart considering, all channels are clustered at Mobile Switching Centre (MSC) or Base Stations (BS), and they are assigned as needed. When no free channel is available from the clustered channels, a channel is borrowed from a neighbouring cell, considering minimal co-channel interference. Nevertheless, under high traffic conditions, DCA may not be the most efficient scheme (Zhao et al. 2005).

The Hybrid Channel Allocation (HCA) scheme divides the total available channels into two parts. Firstly, a Fixed Channel Allocation (FCA) is used to allocate channels to all users within the network cell, while the remaining channels form clusters. When all allocated channels are occupied in FCA, new call requests within the cell are dynamically allocated channels based on traffic conditions. Additionally, non-priority algorithms exist where all cells have equal priority for accessing available channels. Another channel allocation scheme, the Reserved Channel Algorithm (RCA), prioritizes permanent channel users, granting them the highest access priority (Lee and Yang 2023).

If we consider total N channels in the network and from these N channels, some S channels are reserved for handovers and K channels for permanent allocation. Then the remaining available channels can be assigned to new calls originating in the cell. In a cellular network, the probability of blocking (Pb) and the probability of handover (Ph), Forced termination (Pft) and incomplete call (Pnc) are crucial performance metrics that impact the quality of service for mobile users (Habibi and Beyranvand 2022). Pb represents the likelihood of new calls being blocked due busy channels, while Ph indicates the likelihood of ongoing calls requiring handovers to maintain seamless connectivity.

$${\rm{Probability\,of\,Blocking }}\left( {{{\rm{P}}_{\rm{b}}}} \right){\rm{ = }}\left( {{{\rm{N}}_{\rm{b}}}} \right){\rm{ / }}\left( {{{\rm{T}}_{{\rm{Nn}}}}} \right)$$
(1)
$${\rm{Probability\,of\,Handover }}\left( {{{\rm{P}}_{\rm{h}}}} \right){\rm{ = }}\left( {{{\rm{N}}_{\rm{h}}}} \right){\rm{ / }}\left( {{{\rm{T}}_{{\rm{No}}}}} \right)$$
(2)
$${\rm{Probability\,of\,Forced\,Termination }}\left( {{{\rm{P}}_{{\rm{ft}}}}} \right){\rm{ = }}\left( {{{\rm{N}}_{{\rm{ft}}}}} \right){\rm{ / }}\left( {{{\rm{T}}_{{\rm{No}}}}} \right)$$
(3)
$${\rm{Probability\,of\,Incomplete\,Call }}\left( {{{\rm{P}}_{{\rm{nc}}}}} \right){\rm{ = }}\left( {{{\rm{N}}_{\rm{i}}}} \right){\rm{ / }}\left( {{{\rm{T}}_{{\rm{No}}}}} \right)$$
(4)

Where, Nb is Number of Blocked Calls, TNn is Total Number of New Calls, Nh is Number of Handovers, TNo is the Total Number of Ongoing Calls, Nft is the Number of Calls Forced to Terminate, Ni is the Number of Incomplete Calls (Rao et al. 2016; Saini and Wason 2016). These metrics collectively provide a comprehensive evaluation of the network performance, reflecting aspects such as call success rates, seamless handover management, and the rate of forced terminations and incomplete calls.

4 Results and analysis

The analytical method is used for analysis of probability of call blockage (Pb), call handover failure (Ph), forced termination of connected call (Pft) and incomplete/not completed calls (Pnc) within a cell (El Azaly et al. 2021). When all the channels in a cell are busy there is no channel free/available within the cell for arrival of unique users and this state is considered as Pb. The condition is when the users will not be allowed to access the channel to make a call, and hence, users receive network busy message. There is another possibility that call may be dropped due to a busy channel condition and is known as Pn (Zhang et al. 2015). The other cases are those in which the accepted and ongoing calls may be terminated due to handover failure even after successful handover to the neighbouring cell. Pnc is the probability condition in which for any of the above condition the call remains incomplete (Bany Salameh and Badarneh 2013). The simulation was performed keeping actual scenario by increasing number of channels for maximum of twelve channels in which two channels reserved for handovers and permanent assigned channels to user is two.

The numerical values for the probability of call blocking (Pb) using three different algorithms (NPA, Reserved Channel Algorithm with no Permanent Channels, Reserved Channel Algorithm with permanent Channels shown in Table 3.

Table 3 Numerical values for probability of call blocking pb
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These values in Fig. 3 represent the probability of call blocking for different numbers of busy channels. The RCA with permanent channels generally has the highest probability of call blocking, followed by RCA with no permanent channels, and NPA has the lowest probability of call blocking.

Fig. 3
figure 3

Probability of call blocking

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The numerical values for the probability of handover failure (Ph) using three different algorithms (NPA, Reserved Channel Algorithm with no Permanent Channels, Reserved Channel Algorithm with Permanent Channels are shown in Table 4. These values represent the probability of handover failure for different numbers of channels using the specified algorithms (Hamad et al. 2011). The NPA generally has the lowest probability of handover failure, followed by the RCA with no permanent channels, and the RCA with permanent channels has the highest probability of handover failure.

Table 4 Numerical values for probability of handover failure Ph
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Figure 4 presents the probability of handover failure (Ph) v/s number of busy channels. Here the observation is that the probability of handover failure is increasing with the increase in the number of busy channel and maximum failures are reported in case of NPA, followed by RCA (No permanent channel) and RCA (permanent channel) respectively (Huang et al. 2010).

Fig. 4
figure 4

Probability of handover failure

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The Table 5 below presents the numerical values for Pft using with NPA, Reserved Channel Algorithm with no Permanent Channels, Reserved Channel Algorithm with Permanent Channels. These values represent the probability of forced termination for different numbers of busy channels using the specified algorithms. The NPA generally has the lowest probability of forced termination, followed by the RCA with no permanent channels, and the RCA with permanent channels has the highest probability of forced termination (Yeo and Jun 2002).

Table 5 Numerical values for probability of forced termination Pft
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It was observed from Fig. 5 that the probability of forced termination increases with the increase in the number of busy channels. The highest increase was demonstrated by the NPA followed by RCA (No permanent channel) and RCA (permanent channel) respectively.

Fig. 5
figure 5

Probability of forced termination

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5 Discussion

This Paper focuses on the utilization of IoT technology in the context of the emerging 5G-IoT landscape, particularly for cognitive radio applications within cellular networks for agricultural monitoring. The validation of the spectrum usage involves the examination of numerous channels utilized in real-world cellular network scenarios, with a particular emphasis on the available channels in 5G networks. The chapter assesses the system’s performance in terms of call blocking, terminations, and handovers within specified cells of the cellular network (as depicted in Figs. 2, 3 and 4). Notably, in the fixed channel allocation scheme, there is a reduction in the number of call blocking instances, handover failures, and terminations compared to non-prioritized allocations, especially when the number of busy channels increases.

The discussion underscores the critical role of channel allocation schemes and spectrum usage in optimizing 5G networks for Internet of Things (IoT) applications, especially in cellular networks. In this context, the trade-offs between efficiency and adaptability become evident when comparing Fixed Channel Allocation (FCA), Dynamic Channel Allocation (DCA), and Hybrid Channel Allocation (HCA) (Yadav et al. 2022; Vishnoi et al. 2024). Each scheme has its advantages and limitations, emphasizing the need for further research to identify the most effective approach.

Cognitive Radio (CR) spectrum sharing emerges as a promising solution to address spectrum scarcity, although its full potential requires more in-depth exploration (Gupta and Jha 2015). The study introduces important metrics such as Pb, Ph, Pft, and Pnc probabilities, which play a crucial role in evaluating network performance across various scenarios (Al-Saud et al. 2020). These metrics, as highlighted in Table 1, provide insights into dynamic channel allocation plans that ensure uninterrupted connectivity and high-quality service under different traffic scenarios.

Comparatively, the work contributes significantly to the advancement of 5G networks for IoT deployment in cellular environments (Huang et al. 2004). Insights derived from various channel allocation schemes, combined with performance metrics like call blocking, handover failure, and forced termination, offer valuable information for maximizing network efficiency (Abdulkarem et al. 2023; Jon et al. 2023). This study underscores the importance of considering diverse scenarios to maintain reliable connectivity in dynamic IoT environments.

Drawing on the findings, the incorporation of the best allocation strategies identified in this analysis can greatly enhance 5G spectrum allocation schemes for IoT applications. However, it is crucial to acknowledge the limitations of the study and the ongoing need for further research in this rapidly evolving field. The work aligns with the literature on ultra-dense networks and other 5G enabling technologies, adding empirical evidence to the broader discourse on efficient spectrum utilization (Balachander and Krishnan 2022; Khedkar et al. 2023; Mishra 2023; Silva et al. 2024).

6 Conclusion

In conclusion, this research contributes valuable insights into the challenges faced by agricultural IoT applications within the evolving landscape of 5G networks. The study, with a focus on spectrum efficiency and resource allocation, recognizes the critical need for optimizing communication and data management for diverse 5G IoT devices. The evaluation of three distinct models for spectrum allocation in agricultural scenarios provides a nuanced understanding of their implications for service quality.

However, the study acknowledges limitations, particularly the static nature of the channel allocation scheme without dynamic adaptation to IoT application demands. The absence of real-world verification in a 5G scenario also highlights the need for further research. The fixed channel allocation scheme demonstrates improvements in call blocking, handover failures, and terminations compared to non-prioritized allocations, emphasizing the potential benefits of spectrum optimization.

Future work is crucial to address these limitations and enhance network performance. Researchers should explore the combination of various channel distribution strategies, considering the dynamic requirements of IoT devices. Additionally, developing more intelligent methods for channel selection and speeding up IoT applications in agriculture can contribute to the effective connectivity of IoT devices. This ongoing research aims to propel the advancement of 5G networks and fortify agricultural IoT services through innovative spectrum allocation techniques, ensuring the optimal utilization of limited spectrum resources.