Internet of Things (IoTs) refers to a distributed computing architecture that allows devices to be exhaustively connected through protocols of the internet [1]. IRAS, as the advancement of IoTs in contexts including, but not limited to, resource-gateway mapping, became one of the more critical research areas, first of all, because of the given possibilities for a reduction of communication costs [1]. This paper discusses intelligent heuristic algorithms, in particular, the Whale Optimization Algorithm (WOA), and investigates its application in solving resource allocation problems within the IoTs context.

In the last ten years, the IoTs has significantly developed and posed new challenges in resource management and optimization areas [1], [13]. Early works by Sun et al. [11] focused on basic methods for resource allocation. However, the recent explosion in the number of interconnected devices has raised the need for more sophisticated methodologies. According to Changazi et al. [7], traditional resource allocation techniques lose their efficiency when dealing with large IoTs settings.

Gelenbe et al. [13] noted scalability issues, such as the dynamic addition of resources and gateway capacity limitations. Optimization requirements in network topology remain a challenge.

Ibrahim et al. [8] emphasized areas for improvement, including:

I. Algorithm parameter tuning.

II. Convergence rate enhancement.

III. Efficient resource allocation.

Algorithm improvement recent advances include:

I. Machine learning integration [6].

II. Real-time optimization [7].

III. Hybrid optimization methodologies [3].

New fields of application include:

I. Integration of edge computing [12].

II. Optimization in fog computing [7].

III. Cloud resource management [2].

The proposed work introduces a hybrid resource allocation framework for IoTs networks that combines the WOA with Reinforcement Learning (RL) and Bayesian Game Theory to improve scalability, reduce latency, and optimize energy consumption. This hybrid approach addresses the scalability limitations of traditional optimization methods, such as linear and dynamic programming, by leveraging WOA's capability to explore large solution spaces efficiently and RL's adaptability to changing network conditions [1], [2].

The optimization objective for this framework is to minimize the TCC, subject to constraints on resource allocation efficiency and QoS requirements. The optimization problem is formulated as follows:

TCC = Σ (Ci ∗ Pi) + Σ (Bj ∗ Dj),

where: 1) Ci is the communication cost of resource node i, 2) Pi is the processing power allocation for i, 3) Bj is the bandwidth allocation for gateway j, and 4) Dj represents the data transfer rate for j [3], [4].

In the proposed model, the WOA is employed to identify initial optimal positions for search agents, ensuring balanced resource allocation across the network. For each agent i, the position is updated iteratively based on the following WOA position update formulas:

I. Encircling prey

X(t + 1) = X ∗ (t) − A ∗ |C ∗ X ∗ (t) − X(t)|.

II. Bubble-net attack

bl

X(t + 1) = D ∗ e ∗ cos(2πl) + X ∗ (t), where: 1) X(t+1) represents the new position of the agent, 2) X(t) is the optimal position of the prey, and 3) A, C, D, b, and l are control parameters influencing convergence and exploration [5], [6].

RL is then applied to adapt resource allocation dynamically based on the environment's changing demands. At each time step t, the system observes the state s_t and selects an action a_t to maximize a reward R_t for efficient resource allocation. The reward function is designed to balance load distribution while minimizing TCC:

Rt = α ∗ RUE − β ∗ TCC, where: α and β are weights for RUE and TCC, respectively [7], [8].

To ensure fair distribution among IoTs devices, a Bayesian game-theoretic approach is applied, wherein devices strategically allocate resources based on the predicted actions of others. This equilibrium strategy ensures stable resource allocation and reduces conflict, as represented by the Nash equilibrium condition:

E[Ui(a ∗,a )] ≥ E[Ui(a ,a )]for all ai ≠ ai,

i −i i −i

where: 1) Ui is the utility of agent i, 2) ai is the optimal action, and 3) a-i represents actions of other agents [5], [6].

Performance will be evaluated using TCC, RUE, and GLD metrics, comparing results with traditional methods to measure improvements in scalability, convergence rates, and real-time adaptability. This hybrid resource allocation model aims to enhance IoTs network management in cloud-based and edge-enabled environments [2], [9].

The proposed work used WOA to enhance resource allocation within the IoTs network. The acceptance of the proposed approach was done by testing it against some traditional optimization techniques like the GA, PSO, and ACO in different cases of an IoTs network. The major performance metrics tested were TCC, efficiency of resource utilization, GLD, and QoS.

Though the WOA holds significant promise in optimizing the distribution of resources in IoTs networks, many domains require further research. Improvement in the resilience, flexibility, and scalability of the algorithm is required to face the constantly changing challenge that exists in the IoTs environment. The following suggestions point out potential directions in which the current work can be enhanced and further extended.

Improvement of resource distribution in the IoTs became a significant challenge considering the exponentially growing count of connected devices. This paper aims to provide a comprehensive study of advanced optimization methods for resource distribution in IoTs with a special focus on the WOA. Among the inherent challenges of IoTs resource management, heterogeneous capabilities of devices, dynamic network conditions, high performance, low latency, and energy efficiency are addressed. Our work presents how heuristic algorithms such as WOA and hybrid approaches combining machine learning techniques considerably contribute to such challenges.

Well-designed resource allocation strategies form a great and essential part of the IoTs system. While the spread of IoTs devices across industries, such as in healthcare, smart cities, and industrial automation, can ensure optimal performance, energy efficiency, and QoS through efficient management of these resources alone, the landscape of IoTs itself is highly complex due to a multitude of heterogeneity in devices, varying resource requirements, and communication constraints. Traditional resource allocation methods, such as linear programming and greedy algorithms, usually fail to meet the scalability and real-time constraints required for large-scale IoTs networks. They fail here since classical methods are adapted to dynamic environments of an IoTs, where resources may not be available and require adaptation on the fly with the adaptation in device performance, network topology, and communication requirements.

We have researched many advanced optimization techniques to overcome these challenges. In this aspect, GA, PSO, and ACO heuristic algorithms have shown good performance in the context of providing quick near-optimal solutions. However, such heuristics are problems in large, multi-objective optimization problems, such as IoTs complex systems. Machine learning techniques-based approaches of RL and DQNs-can be adaptive and intelligent in solving the resource allocation problem. Such approaches allow an IoT system to learn the best strategies over time, thus enhancing performance and efficiency in dynamic environments. The WOA, which drew inspiration from the hunting behavior of whales, has been selected as a promising approach to tackle the resource allocation problem in IoTs environments. Our study demonstrates how WOA outperforms multiple classical techniques with regard to convergence speed and solution quality, especially in large and complex IoTs scenarios. The ability to maintain an exploration-exploitation balance makes WOA suitable for optimization problems under dynamic and uncertain environments. In addition, this versatility of the proposed algorithm opens up possibilities for tailoring it specifically to solve matters of resource allocation in IoTs systems, such as minimizing energy consumption, optimizing bandwidth usage, ensuring QoS, etc.

Despite such positive outcomes, the WOA has limitations; it needs to be further addressed to have a broader utilization in real IoTs systems. Probably, the most critical issue is the sensitivity of the algorithm to parameter tuning. Actually, WOA has a lot of parameters that need to be fine-tuned appropriately, such as the coefficients of encircling prey and the bubble-net attack approach.

This may be very challenging in the case of large IoTs networks with various types of devices and varying communication conditions. Future work will be oriented towards the automated adaptation process using either self-adaptive mechanisms or machine learning techniques that would dynamically change the parameters based on network conditions. Another issue is that although WOA performs very well on single-objective optimization problems, many real-world IoTs applications involve the concurrent optimization of multiple competing, conflicting objectives, for example, energy efficiency, network throughput, and latency. Multiobjective optimization is still an open challenge in resource allocation to IoTs. Future work should attempt to extend WOA to multiobjective optimization problems and make the algorithm able to cope with trade-offs between competing objectives in a fair and stabilized manner regarding resource distribution. An important dimension of the work in the future will be that of how WOA is integrated with newly arising edge and fog computing, machine learning, and Blockchain technologies. The edge computing technology empowers near-source IoTs device processing, which should eventually lead to significantly reduced latency and improved operational efficiency. This also poses some new challenges related to resource management and the choreography of activities among decentralized nodes. WOA can thus evolve toward optimally allocating resources in such environments, including the distribution and dynamic availability of edge networks along with their nodes. Further improvement in adaptability through the assimilation of machine learning models, such as RL within WOA, allows IoTs to learn from patterns and optimize its resource allocation strategy continuously based on real-time data.

Furthermore, security and privacy are the core concerns of IoTs networks, especially when dealing with sensitive data. The integration of Blockchain with WOA may further provide a decentralized, transparent, and secure way for the allocation of resources in IoTs. Blockchain may guarantee that decisions regarding the allocation of resources will be made in a trustless environment; thus, this improves the security feature and prevents unauthorized access to important resources. Further research may find out how Blockchain can be integrated with WOA to ensure resource management in multi-party IoTs networks. Even though simulation-based analyses suggest that WOA can efficiently work on IoTs resource allocation, real-world tests are necessary to test the performance of the algorithm. Testing WOA live on IoTs testbeds would hence reveal whether the algorithm is scalable and adaptive enough with its efficiency while presented with real-world conditions. Identified potential bottlenecks in the system will further be optimized for ensuring the feasibility of such an algorithm for large scale IoTs networks.

Conclusion: the research presented in the paper makes an excellent ground for future work on IoTs resource allocation. The WOA and other advanced optimizers are truly promising for finding solutions to the complexity of IoTs resource management challenges in networks. In the future, further extension of WOA to solve multi-objective optimization, integration with emerging technologies, and its real-world implementation would pave the way for real applications and improvement in the applicability of resource allocation strategies in IoTs, making IoTs systems more efficient, scalable, and intelligent.

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

The data that support the findings of this study are available from the corresponding author upon reasonable request.

The authors declare that they have no conflicts of interest related to this work.