Furthermore, by selecting the optimal node and path for data flow, it can reduce network traffic, a valuable additional benefit. The suggested method achieved a 95% energy efficiency, 97% throughput, 50% end-to-end latency, 96% accuracy, and a 94% packet delivery ratio. Liu et al.7 investigate the integration of blockchain technology with 6G networks from the perspectives of security and performance, thereby proposing a technique that focuses on building secure mechanisms and improving performance. The potential applications of 6G networks and how blockchain technology can enhance them must be highlighted to underscore the need to incorporate blockchain technology into 6G networks. Therefore, it is essential to assess security needs in the construction of 6G networks to ensure the security of data flow and communication using blockchain technology. Furthermore, to address ways to maximize communication and storage to meet the needs of 6G networks, a Directed Acyclic Graph (DAG) solution is proposed for the asynchronous consensus mechanism used by blockchains. This will be implemented to maximize the integration of blockchain and 6G networks. Ultimately, it is equally important to provide valuable suggestions for future studies that will advance blockchain technology and facilitate the integration of 6G networks.
This study employs various IoMT devices with distinct initial energy levels in monitoring scenarios utilizing 6G-enabled Internet of Things (IoT) capabilities. N 6G-enabled IoMT devices constitute the network, which comprises three levels of energy heterogeneity. Furthermore, Fig. 1 illustrates the methodology of the proposed prototype.
The 6G-enabled IoMT devices that comprise the network range from highly sophisticated to relatively simple, with varying degrees of initial energy. As a result of the energy disparity, advanced 6G-enabled IoMT devices start with more energy than intermediate 6G-enabled IoMT devices, which in turn have more energy than regular nodes . The overall number of every type of 6G-enabled IoMT devices is cutting-edge (), intermediate (), and normal () reflects a descending orders, with standard 6G-enabled IoMT devices being the most frequent, filling the inequality . The first energy for every 6G-enabled IoMT device types are represented as E0, and the overall energy in the network is signified by . The progressive 6G-enabled IoMT devices have their energy described as , and intermediate 6G-enabled IoMT devices as , with normal 6G-enabled IoMT device at . Therefore, the aggregate energy of all 6G-enabled IoMT devices — advanced, intermediate, and normal — makes up the network’s total energy . The overall energy is , combining the energy contributions from all levels. The energy ratios and correspond to the energy fractions of the advanced and intermediate 6G-enabled IoMT device, correspondingly. The proportion of advanced and intermediate 6G-enabled IoMT devices within the network are indicated by and . A diverse range of 6G-enabled IoMT devices, categorized as advanced, intermediate, and basic nodes, each with its unique energy capacity, are integrated into the network architecture. To ensure that energy-intensive processes, such as CH selection, are handled by competent devices, a hierarchy is established, where advanced nodes have the most energy reserves, intermediate nodes have moderate energy, and regular nodes have the least. Normal nodes are most numerous, followed by intermediate nodes, and advanced nodes make up the smallest group; this distribution also represents practical deployments. This network architecture allows the system to use high-energy nodes for mission-critical activities and lightweight devices for routine sensing. To optimize CH selection in 6G settings, an energy-aware setup is essential. It allows balanced energy consumption, extends the total lifespan of the network, and ensures optimal integration with blockchain for safe, low-latency data transfer.
There are several assumptions about the network setup of the suggested work, which are as follows:
The purpose of this endeavor is to utilize the same radio energy utilization model as prior studies, to focus on the transmission and receipt of data between 6G-enabled IoT devices and sink connections. Three elements define the architecture: the administrative domain, the network of the 6G-enabled IoMT, and the blockchain network. Utilizing 6G technology for fast and low-latency connectivity, devices located in homes, hospitals, and ambulances comprise the IoMT network. By utilizing smart contracts, the blockchain network ensures secure and transparent data management. Using this will enhance data security and facilitate tracking. Monitoring the network, the administrator ensures the domain achieves optimal performance and resolves any newly emerging issues. Gathering data, a “sink” sends it to the “blockchain” for processing. The IoT devices send their data to the sink. Monitoring the network and ensuring regulatory compliance helps the administrator keep the system running as intended.
The primary objective of the suggested research is to reduce energy consumption in IoMT networks by leveraging 6G technology, thereby promoting sustainable communication in these systems. Achieving this goal depends on the CH’s performance in supervising the network’s energy consumption. The ADCGRO method produces an ideal answer, which guides the choice of CH. Establishing a comprehensive fitness function with several key elements will help determine the CH precisely. Each of these factors significantly impacts the CH choosing process. This all-encompassing solution ensures effective energy management in 6G IoT systems. This work sought to integrate four different fitness metrics: residual energy, node-to-sink closeness, cluster energy, and remaining node energy levels. This comprehensive approach ensures a more thorough and efficient decision-making process.
To provide effective energy management and balanced load distribution across the network, the node density must be high, allowing a suitable candidate node to be selected as the CH.
Our method involves combining multiple fitness criteria to convert a function into a single-objective one. To achieve this, we multiply these parameters using linear weight functions, as described in our technique. This integration makes it easier to evaluate the system’s performance while still considering all the important factors.
where . It provides a weighted aggregate of multiple separate elements, where each element is multiplied by its corresponding characteristics. The suggested method is to evaluate each node based on the value of its fitness function. The node with the greatest fitness value upon completion of this calculation is designated as the CH.
Three separate groups constitute an artificial bee colony: onlooker bees, employed bees, and scout bees. The bee’s inherent curiosity enables it to identify food sources, which it then shares with other nearby bees. Employing the information relayed by the foraging bee, the observer bee surveys its surroundings to locate a food source that better meets its needs. The observing bees will transition to scout bees and leave the food source when the food supply reaches a specific threshold. Expansion of the food supply will result in this occurrence. The method by which bees search for new food sources is chaotic. As per Eq. (6), scout bees are responsible for monitoring the exploration procedure, whereas paid and bystander bees are tasked with exploiting the ABC:
The new location of the i-th worker bee is represented by , whereas the former position is indicated by . The variable k represents an arbitrary integer ranging from 1 to the total count of bees utilized. Each issue possesses an arbitrary dimension represented by j, and the variable is a randomly assigned value within the range of – 1 to 1. The vigilant bee uses Eq. (7) to ascertain which items to consume, taking into account the likelihood of each worker bee’s actions:
In addition to being governed by Eq. (8), fit represents the fitness values of the worker bee’s answer.
fit is fitness values of the working bee’s answer, Eq. (8) is utilized to regulate it.
Bees employ it to enhance their understanding of the chosen food supply, whereas worker bees utilize it for navigation purposes (9). The crucial decision in optimizing with the Cuckoo algorithm is the formulation of the goal function. Depending on the objective function, it is feasible to optimize for scheme efficiency or to reduce scheme errors. This study aims to identify the most efficient approach for mitigating errors in system or cost functions. The cost function (fc) may be computed using the following equations to calculate the integral of square error (ISE):
Like other optimization strategies, the cuckoo technique commences with an initial population, whereby each cuckoo occupies a unique spatial location. The optimization findings will get more accurate as the population of cuckoos rises. As the density of the cuckoo population increases, the convergence rate diminishes. Nonetheless, even with offline parameter optimization, the convergence rate remains invariant. Consequently, enumerating the cuckoos that will deposit eggs is the initial essential phase. The variables of the problem are presented using an array. I will refer to these arrays as “habits” when implementing the Cuckoo approach. This issue space encompasses several habitats, each proposing a distinct viable answer. The variable values pertinent to the situation must be encapsulated within an array. The “Habitat” array, an element of the cuckoo optimization process, serves this purpose. In addressing an optimization problem, the subsequent position of the habitat, indicated by the symbol Nvar, is determined by the cuckoo’s current location, expressed as 1 * Nvar. Equation (11) defines these components.
The parameter values () each characterises the sum points. The profit functions habitat is assessed to determine (Eq. 12).
The cuckoo strategy utilizes a notably superior profit function. To incorporate this strategy into the minimization algorithm, it is essential to apply a negative sign to the cost function. The GSO algorithm’s mechanisms were inspired by the natural behavior of glowworms, which are attracted to brighter sources of light. To maximize multimodal functions, swarm intelligence (SI) methods, such as the GSO technique, operate in a manner analogous to that of glowworms. Swarm intelligence algorithms may effectively tackle complicated optimization problems that prior methods are either incapable of addressing or unable to resolve comprehensively. The typical procedure is impractical. In the realm of statistics, several algorithms provide numerous advantages and disadvantages. You may be able to observe glowworms during a nocturnal stroll in the meadows. Throughout the night, these timeless beings shimmer exquisitely, illuminating the darkness. All glowworms possess a light substance called luciferin. Consequently, every glowworm will radiate light, irrespective of the surrounding darkness or cave environment. A decision range and a sensor range define the community, allowing any two glowworms in the area to interact with each other.
For the sake of reproduction and nutrition, glowworms relentlessly scour their environments for glowing luciferin. The function value that depends on the location of the glowworm has a substantial impact on the luciferin updating phase. The luciferin level of each glowworm is adjusted by increasing its previous value. While the glowworm is in its positionally effective state, this change occurs. A portion of its value is subtracted from its total value to mimic the steady deterioration of luciferin. The following suggestions are offered for updating glowworm luciferin using Eq. (13):
where where li(t) is the luciferin level of glowworm i at time t, p is the decay constant (0 < p < 1), y is the constant, and is the value of the objective function at agent i's position at time t. The GSO principle dictates that the agent or glowworm will approach or encircle a neighbour with a higher luciferin level than its own. During this phase, the glowworm will also use the probabilistic approach given by Eq. (14) to travel to a neighbour with a higher intensity:
The collection of time t is Eq. (14). A sensor variety that bounded surrounds the capricious. Equation (15) elasticities the probability of neighbour for each glowworm .
Equation (16) then characterizes the glowworm's movement model.
where || || where s is the step size and is an operator for the Euclidean norm. Then, in the m-dimensional real space R m, the position at time t is denoted by . Equation (17) illustrates a neighbor range update phase used to identify multiple peaks in a landscape of multimodal functions. Then, set V_0 to be the initial range charge for each glowworm. The subsequent rule is used to update each glowworm's neighbourhood variety:
If there is less than a 4-meter gap between a Grey Wolf (GW) and its nearest neighbour, the GW is allowed to roam. Additionally, the coverage rate may be slower with a smaller step size. Finding the optimal step size is thus a challenging task. This investigation will not have a constant step size; rather, it may vary with each cycle according on the GW. There will be several adjustments to GW's step size. One of these is the introduction of a dynamic step size, which should increase computational precision in velocity stage. What follows is an explanation of the ROA, or remora optimisation method.
Unrestricted Journeys: The location update mechanism for the SFO strategy was developed using Eq. (18), which produces the algorithm's elite idea.
where is a random location.
An experience attack works in a manner comparable to how knowledge grows: it checks in with the host regularly to determine if it needs to be updated. To represent these ideas, to use the following expression in Eq. (19):
where is a step and is the prior iteration's site, distinct by Eq. (20).
Equation (21) designates updated formulas with alterations.
where D is the distance between the remora and the meal. To further enhance the solution, the remora can utilize the encircling prey mechanism in WOA to take a small step, as illustrated in Eq. (25).:
where the i-th remora's freshly created site is characterised by . The value of the remora factor, denoted as C in ROA, is fixed at 0.1. Finding its food is something the remora is capable of. With this in mind, to enhance the remora's search efficiency by including a novel autonomous foraging mechanism into the core algorithm. A fair balance between exploration and exploitation is achieved by the upgraded remora optimisation algorithm, which features a more adaptive mode. Note that this approach does not increase the computational complexity of the original program. Since the proposed approach is compatible with various optimisation algorithms, it has some generalisability.
The proposed approach significantly enhances data security and integrity by integrating blockchain technology with IoT environments facilitated by 6G. The fundamental concept of the system's blockchain layer is a permissioned blockchain network utilizing nodes with substantial computational capacity within the network. This indicates that pre-authentication has occurred and that all nodes inside the network have developed a certain level of trust among themselves. The nodes employ Proof of Authority (PoA), a streamlined consensus mechanism. The technique depends on a consortium of trustworthy nodes, referred to as authorities, to authenticate transactions and produce blocks. At each instance when data is transmitted from the CH to the primary blockchain layer, a blockchain node should validate the transaction, authorize it, and generate a new block. All the blockchain nodes in the network receive the newly created block, making maintenance easier. There are two main components to a block on this blockchain: the header and the transaction data. This block's version, genesis timestamp, nonce, difficulty target, Merkle root, and hash of the previous block are all essential pieces of information that are included in the header. The hash of the preceding block is included in the hash of the current block, ensuring the integrity of the blockchain and rendering it immutable. Altering the content of one block will initiate a domino effect, resulting in changes to the hashes of succeeding blocks. Secure, decentralized data exchange and automated decision-making among diverse devices and service providers are two ways in which blockchain is facilitating the adoption of intelligent, context-aware services in 6G networks. Without relying on a centralized authority, 6G apps such as smart healthcare, autonomous vehicles, or adaptive content distribution may utilize smart contracts to negotiate resources like bandwidth and processing power dynamically. They can also make choices based on real-time context. Blockchain technology ensures the integrity, immutability, and auditability of all contextual data, including user movement patterns, network load, and service priority. This enables the network's artificial intelligence (AI) agents to make reliable predictions and adaptive improvements. For instance, in a 6G smart city network, blockchain technology can enable vehicle edge nodes to safely exchange traffic data and establish low-latency priority lanes for emergency vehicles using smart contracts, ensuring that decisions are made transparently and securely.
A variety of verifications are employed to guarantee the accuracy of each block. The verifier initiates the process by assessing if the current value exceeds its predecessors and confirming the presence and legitimacy of the preceding block. Furthermore, it has been verified that additional components, such as the transaction root and difficulty, are authentic. All transactions within the block undergo independent verification. An error notice appears if any transaction fails. If all transactions have been properly authenticated and the final state matches the current root state, the block is deemed validated. A formula exists to compute the hash of a block:
where Bn signifies the block for which the hash is being calculated, is the prior block's hash, and represents the current block data.
The utilization of the current block in hash calculations significantly bolsters the blockchain's durability against assaults. Implementing data modifications in any block becomes exceedingly challenging as the number of blocks in the chain increases significantly. Should a single block be altered, it would create a discrepancy in the hashes of all following blocks, jeopardizing the whole blockchain and facilitating manipulation. In this method, cluster chiefs diminish the transaction volume sent to the blockchain and alleviate the processing burden on nodes by consolidating data from IoMT sensor devices. A permissioned blockchain utilizing Proof of Authority (PoA) consensus, with a limited number of pre-approved validators, might expedite transaction validation, reduce latency, and improve throughput compared to conventional methods such as Proof of Stake (PoS) or Proof of Work (PoW). Scalability and efficacy for IoT devices are attained by mitigating network congestion and energy consumption through the restriction of the total number of validator nodes. By replacing trust in centralised authority with a distributed, immutable ledger, blockchain technology enhances 6G decentralized authentication and access management. Decentralized consensus systems, instead of a central server, manage authentication for each device, user, or service node by assigning them a unique cryptographic identity and storing it on the blockchain. By automatically enforcing role- or attribute-based access controls, smart contracts can limit access to 6G services and network slices to approved organizations exclusively. For example, a blockchain can be utilized in an IoMT system enabled by 6G to restrict access to patient data to authorized healthcare practitioners and to record all transactions for transparent audit purposes. In addition to improving reliability and security, this eliminates frequent problems associated with conventional methods, including centralized authentication methods such as identity spoofing and single points of failure.
