Secure IoT Routing through Manifold Criterion Trust Evaluation using Ant Colony Optimization

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I. INTRODUCTION
Sensors are an integral part of the IoT landscape.They acquire environmental data and perform real-time transmission over the hosted IoT network [1].These compact electronic devices can sense various environmental attributes, such as chemical composition, motion, sound, light, pressure, humidity, and temperature, depending on their application or the environment in which they are deployed [2][3][4][5].In the context of IoT, sensors acquire information from the physical world and forward it to a sink node for analysis [6].This acquired information might be used for research purposes or to trigger specific actuators for automated actions [7][8][9][10].However, despite their capabilities, these sensors often have limited processing and computing abilities, and ensuring their extended lifespan remains challenging [11].
Among the various issues associated with sensors, security is the most critical concern for IoT sensors [12].The first issue is data privacy, ensuring data is stored securely and transmitted to the intended terminal without unauthorized access [13].The second pertains to vulnerabilities in IoT devices; sensors often fall prey to cyber-attacks due to unpatched vulnerabilities, outdated software, or weak passwords [14].The third challenge relates to malware attacks in the form of Trojans, worms, and viruses, which can hinder data transmission, steal data, or corrupt device functionalities [15].Physical security represents the fourth concern; unauthorized access to a sensor can lead to data tampering, malware introduction, or functionality disruption [16].The fifth challenge involves Distributed Denial-of-Service (DDoS) attacks, where overwhelming traffic incapacitates sensors, disrupting their communication or services [17].The sixth pertains to Man-in-The-Middle attacks, where attackers can intercept and potentially modify or steal data [18].Numerous studies have proposed security mechanisms for IoT to address these vulnerabilities [19][20][21][22][23], but comprehensive solutions that tackle all these challenges remain elusive, with each approach having its strengths and weaknesses.
Securing routing in IoT has become a complex endeavor in today's landscape, marked by an ever-increasing array of known and emerging threats [24,25].One core challenge arises from the use of resource-constrained devices in IoT, characterized by limited battery life, memory, and computing power.This limitation precludes the deployment of robust cryptographic algorithms on such devices [26].The dynamic topology of IoT, where devices can spontaneously join or leave the network, further complicates security protocols.Incorporating diverse IoT devices with specific service requirements complicates implementing universal security protocols.Challenges also arise from issues with mobility and localization; accurate localization information is hard to obtain, and the legitimacy of mobile nodes is difficult to verify.The need for standardization in IoT devices and security protocol management further complicates matters.Most current IoT security research is conducted in controlled environments, distinct from real-world scenarios.Thus, the reliability of their applications in practical deployments remains to be determined.
The manuscript is structured as follows: Section II delves into current methodologies for secure routing in IoT, emphasizing various bioinspired approaches and their *Corresponding Author This research was supported by the INTI IU Research Seeding Grant Phase 1/2023 initiative under Project No: INTI-FDSIT-01-01-2023.www.ijacsa.thesai.orgcontributions.Section III outlines the research problem identified from insights gleaned from these existing methodologies.The proposed methodology to address these issues is detailed in Section IV.An analysis of the results is presented in Section V, while Section VI concludes the paper.

II. EXISTING APPROACHES
Over time, several schemes have been developed to investigate secure routing within IoT.Among these, trust-based schemes have emerged as an essential tool for a simplified defense against security breaches [27,28].Liu et al. [29] devised a secure aggregation model for a Wireless Sensor Network (WSN), ensuring increased trust when operated with a single mobile sink node.Additionally, some research has focused on optimizing trust factors through bioinspired approaches.For instance, Mangalampalli et al. [30] employed a whale optimization scheme for enhancing task scheduling.Muzammal et al. [32] introduced a trust-based protocol to counter blackhole attacks in static and mobile IoT environments.However, these approaches often need to pay more attention to the dynamic nature of IoT networks and may not be efficient in real-time scenarios.
Awan et al. [33] embraced a blockchain-based model for secure routing in WSN, aiming to refine trust management.Notably, their model incorporated the Rivest Shamir Algorithm to safeguard data propagation.However, blockchain's inherent latency issues could limit its practicality in specific IoT setups.Nagaraju et al. [34] combined energy optimization with a traditional hybrid approach for secure IoT routing within heterogeneous WSNs.While energy-efficient, such models might compromise on real-time response.Bakhtiari et al. [35] proposed a two-way trust strategy using Bayesian learning automata for fog computing in IoT.It improves efficiency, reduces latency, and enhances trust calculations compared to existing methods.However, it's sensitive to initial trust accuracy, potentially impacting performance in dynamic networks.Also, implementing this two-way trust management strategy may introduce increased computational complexity as a potential limitation.Concurrently, Rakesh and Sultana [36] designed a neural network-empowered quantum scheme for mobile sink selection, employing the sailfish optimization approach for enhanced route security.However, this approach might demand more computational resources, impacting resource-constrained IoT devices.
Additionally, its effectiveness may vary based on network conditions and the presence of malicious nodes, with reliance on initial trust assessments potentially limiting performance in dynamic environments.Gladkov et al. [37] championed a novel routing technique merging the residual number of redundant systems with a secret sharing scheme.However, the complexity associated with such hybrid approaches might lead to higher computational overheads.These diverse strategies enhance security in IoT and WSNs, but careful consideration of their computational requirements and adaptability to dynamic networks is crucial during implementation.Balancing security with resource constraints remains an ongoing challenge in these technologies.
Ramaswamy and Norman [38] introduced a trust model targeting network longevity and internal attacks in IoT.The exploration of bioinspired methods for secure IoT routing has seen algorithms like ACO applied in secured routing (Wang [39], Saleem & Ahmad [40]), decentralized traffic management (Nguyen and Jung [41]), electric vehicle selection (Ajinappa and Prabhakar [42]), and malware detection (El-Ghamry et al. [43]).While ACO's adaptive nature is commendable, it may struggle with large-scale, dynamic IoT environments due to its iterative nature.Particle Swarm Optimization (PSO), as documented by Alterazi et al. [44], Lin et al. [45], and Rajeshwari & Ramakrishnan [46], has also been harnessed for secure data transmission.Other notable bioinspired techniques include the Mayfly Optimization Algorithm (MOA) by Janani and Ramamoorthy [47], the Dragonfly Algorithm (DA) by Hosseinzadeh et al. [48], and Glowworm Swarm Optimization (GSO) by Selvaraj et al. [49].Although these bioinspired strategies offer unique solutions, their scalability and adaptability in diverse IoT ecosystems might be limiting factors.The summary of strength and weakness of the reviewed literature is presented in Table I to state that existing methodologies towards secure routing in IoT is associated with beneficial features as well as shortcomings, which are required to be addressed in future upcoming series of research work.Bakhtiari et al. [35] Trust issues in Fog and IoT Bayesian-based learning automata • Faster response time.
• Enhanced trust management.
• Induces complexity for an extensive network.From the highlights of the methodologies, their advantages, and limitations in the table above, it can be noted that existing secured routing methodologies are associated with various shortcomings in the perspective of deployment in an IoT environment.Following are some of the significant research problems related to the existing methods towards secure routing in IoT:  Issues in Trust-based IoT Security: Various trust-based secure routing schemes are formulated in existing systems.Most of these techniques offer better throughput; however, the majority are also witnessed by their non-sustainability towards dynamic threats.Furthermore, the existing trust management schemes are usually designed considering the predefined information of adversaries.This makes the model robust in one environment but not applicable if the adversary environment is altered.

 Imbalance between Energy and Security Demands:
Existing approaches favor energy or security retention.
The security schema presented in existing schemes uses various sophisticated operations that can offer more security but at the cost of uncertain resource consumption.
There has yet to be a benchmarked study model to prove this in the presence of the dynamic environment of IoT.
 Usage of Bioinspired Approach: The adoption of bioinspired approaches towards secure routing is less abundant in archives of technical publications.However, the available publications on the use of bioinspired strategies contribute towards findings of the secured path by adopting the varied cognitive principles of organisms to attain optimal security.Unfortunately, the issues related to premature convergence and higher sensitivity towards parameters by conventional bioinspired approaches have not yet been addressed.Moreover, there is no report of any study model where novel features of organisms' cognitive behavior have been attempted to be modified and investigated.
 Non-inclusion of Constraint in Trust Management: The existing trust management scheme needs to be more reportedly designed considering the restricted resources in IoT, e.g., processing power, energy, etc., which makes it quite challenging even to execute the sophisticated security protocols in sensors.This yet-tosolve challenge acts as a potential impediment to computing and establishing trust between all the entities and IoT devices in large and dynamic environments.
For secure routing to occur correctly, it is necessary to formulate a better form of constraint modeling with a clear definition of the attributes that need to be added to the existing system.

 The tradeoff between Model Effectiveness and Scalability: Scalability is a different set of problems in
IoT to be resolved.It demands a smart and highly planned processing for secure routing in IoT.
Unfortunately, none of the existing study models has been designed considering its optimization parameter or problem space mapping with the large environment of an IoT with more interconnected devices.For this purpose, the outcome of model effectiveness doesn't match the sensors' scalable performance in increased traffic flow over an IoT.
From the above-stated highlights of the identified research problem, it can be inferred that trust modeling is one of the complex issues that demand the inclusion of various intrinsic and extrinsic attributes and effective constraint modeling.Apart from this, it is necessary to optimize the problem space to increase the change of optimal outcome of the secured path.Bioinspired algorithms are a potential alternative to address this problem, but they demand a novel inclusion of characteristics that can balance security and resource demands.The proposed solution addresses all these research issues, and its associated methodology towards implementation is discussed next.

III. RESEARCH PROBLEM
The rapid proliferation of the IoT heralds both transformative opportunities and notable security challenges.With its expansive network of devices, IoT underscores the urgency for reliable, secure routing.Emerging as a promising avenue, trust-based secure routing strategies aim to buttress IoT's security framework.However, IoT's dynamic and resource-limited nature has often rendered traditional trustbased methods inadequate.
A significant limitation of extant trust-based IoT security methodologies is their inability to adapt to evolving threats, often designed around known adversarial models.The quest to harmonize energy conservation with security amplifies this challenge, as current systems lean towards one, often sacrificing the other.This compromise becomes more palpable when high-security measures, despite their efficacy, devour substantial resources.
The contemporary research landscape needs to display bioinspired strategies for secure IoT routing.Drawing inspiration from natural systems to fortify security, these approaches appear promising.Nevertheless, prevalent bioinspired models grapple with issues like early convergence and parameter sensitivity.This highlights a pressing need to refine and adapt these methods, tailoring them for IoT's unique challenges.
Further complicating the scenario is the design of trust management systems.The intrinsically limited resources of IoT devices, in terms of energy and processing power, hinder the execution of comprehensive security protocols.This challenge intensifies in the sprawling IoT ecosystems, where fostering trust amongst various devices is vital and daunting.www.ijacsa.thesai.orgScalability further accentuates these problems.While effective in a controlled setting, an approach might need to be revised under IoT's expansive and interconnected structure, especially when encountering unexpected traffic surges.
While trust modeling offers a promising foundation for IoT security, its practical deployment is beset with multifaceted challenges.This study aspires to address these gaps, employing the bioinspired ACO technique to architect an adaptive and secure IoT routing paradigm adeptly poised to navigate the intricate challenges of IoT.

IV. MATERIALS AND METHODS
The prime agenda of the proposed study model is to introduce a novel computational model that ensures robust trust-based security while transmitting data among the sensor nodes in IoT.The prime basis of the proposed study model is based on the fact that the severity degree of security threat for sensor nodes deployed in an IoT environment is comparatively higher in contrast to conventional WSN.Therefore, the security aspect of WSN deployed in IoT is subjected to improvement by balancing the demand for increased security along with energy consumption.Hence, various criterion-based schemes is implemented, harnessing ACO along with constraint consideration towards secure trust evaluation.
From the exhibited methodology in Fig. 1, it can be noted from the declaration of the proposed scheme that it uses a manifold criterion modeling towards trust-based secure routing in an IoT environment.Adopting an optimization agenda towards manifold criteria is simultaneously challenging due to the surfacing possibilities of various conflicts.Moreover, unlike managing unit criterion-based routing improvement strategy, the multiple criterion schemes usually seeks to obtain compromised outcomes, yet another sub-optimal solution towards secure routing.Therefore, the proposed system constructs a manifold criterion-based trust modeling in IoT to address the issue of energy drainage and security threats as follows: In Eq. ( 1), the manifold criterion function ( ) is represented by two sub-criteria, i.e., ( ) and ( ), representing the mean criterion for remnant energy and the mean value of trust of the sensor nodes associated with the routing path in IoT.Two processes follow for this purpose:  The above expression is required to be satisfied for its remnant energy score ( ) for the the sensor at instance of time such that acquired data by the sensor is ( ) from the sensor to the sensor while forwarding data ( ) from the sensor to the sensor.
 This task is carried out considering .From Eq. ( 1), it can be noted that the proposed scheme considers − ( ) as a function for mean remnant energy, − ( ) as a function for trust score that is considered towards the selection of an optimal path, − Multiple constraints as a maximal time of communication , the quantity of forwarding data ( ), the quantity of acquired data ( ), and remnant energy ( ).

Compute Euclidean distance
Assessing the sustenance factor The complete formulation of the proposed study is based on two essential functions toward meeting the objectives, i.e., ( ) and ( ) towards energy and trust, respectively.It also includes four conditions of constraint.It should be noted that adopting these two essential functions is deployed for opting for the best path for data propagation, where the performance of nodes is selected as an indicator.This section further elaborates on the formulation of the proposed scheme.Further discussion of the formulation of the proposed modeling is carried out in subsequent sections.

A. Formulation of Manifold Criterion
Energy is one of the essential attributes considered in the proposed scheme whose consumption is generally recorded for multiple events of a sensor being in an idle state, sleep state, or either in receiving or transmission state.The formulation of energy attribute ( ) is carried out as follows: To obtain information on the consumed energy of IoT nodes, any conventional mechanism can be adopted considering software and hardware components.The proposed scheme considers this an energy input value and contributes towards further optimized routing.Besides energy, the proposed method chooses to initiate its design implementation considering any conventional trust evaluation scheme that can vary based on application and use case.However, the system relies not much on device-specific trust computation mechanisms but more on behavioral analysis based on historical reports of security breaches, violations /adherence to security protocols, and compliance with access control protocols.Like the consumed energy metric, the proposed scheme also considers this trust score to act as an input to its model toward working in the direction of proof-of-concept.In Eq. ( 2), the computation of energy attribute ( ) is carried out using two sub-entities, i.e., and .

 (
) As shown in Eq. ( 3), the formulation of is carried out by differentiating the product of ( ) and ( ) from ( ), where the entities ( ) and ( ) represent remnant energy of the sensor at instant and ( ), respectively.The entity ( ) represents the data acquisition rate from the sensor to the sensor.In contrast, ( ) represents the energy consumed for data acquisition for the node at theinstance.
On the other hand, the formulation of shown in ( 4) is carried out by differentiating energy dissipated by the sensor, ( ) from the product of the data acquisition rate from the sensor to the sensor, ( ) and energy consumed while forwarding data for the sensor, ( ).Therefore, the criterion function for mean energy associated with the path of routing considering α number of sensors in IoT can be represented as: In Eq. ( 5), the suffix resided between (1, α).After formulating the energy attribute, the next task is developing the trust attribute of a sensor deployed in an IoT environment.The notion of trust attribute represents the degree of consistency in data packet transmission and receiving by the sensors when exposed to different severity levels of attacks in IoT (e.g., DDoS).The trust attribute is computed based on the evidential traces furnished by the neighboring sensors or by observing trust attributes from the adjacent sensors.Therefore, the proposed scheme formulated two types of trust attributes, i.e., primary and secondary.

1) Evaluation of primary trust:
The primary trust is the value obtained directly from the sensor to the sensor.In contrast, the secondary trust is the value obtained from the node to different relay sensors, and then it reaches the sensor.The initial assessment towards the trust modeling is carried out for primary trust that considers that every possibility of evaluation of the sensor's trust consists of information associated with the degree of consistency associated with data transmission, rate of transmission, and rate of acquiring the data packet.The proposed scheme considers the evaluation of the core trust score of the target sensor on the sensor to be assessed at -instance of time as follows: In Eq. ( 6), the computation of core trust is carried out based on sensor positive trust, sensor negative trust, and sensor uncertain trust represented by ( ) ( ), and ( ), respectively.It can be noted that all these three types of trust variables are equivalent to probability factors associated with multiple forms of assigned probability ( ), ( ), and ( ), where represents a possible secured route for data propagation.It will eventually mean that the variable ( ) represents the summation of the success rate of data acquiring, i.e., ( ).In contrast, the success rate of data forwarding ( ) and ( ) denotes consistency in data packet transmission.Therefore, the expression in Eq. ( 6) can now be rewritten as: where, In Eq. ( 7) in its expanded form, the power variable of the expression, i.e., and , represents successful and unsuccessful transmission in the IoT environment, while the variables , and represent weight values associated with different modes of transmission, which are subjected to training using first-order iterative optimization to arrive at the local value of the defined function.With the aid of Eq. ( 7), the formulation of the primary trust between the sensor and sensor can be carried out as follows:

B. Formulation of Proposed ACO
After the trust computation, the next part of implementing the proposed scheme consists of applying the ACO scheme to arrive at an optimal solution for enhancing network lifetime and accomplishing a higher degree of resiliency from varied threats in the IoT environment.It should be noted that the proposed scheme introduces dual criteria and optimal solutions to amend the conventional ACO scheme to be used in secured routing in IoT.The novelty of the proposed ACO approach is that it constructs logic of manifold heuristic data and manifold pheromones to accomplish the discussed concept of manifold criterion in IoT routing by sensor nodes.The proposed scheme makes use of the idea of crowding distance to boost the algorithm to yield optimal solution diversity.The idea of the proposed ACO approach using crowding distance is to perform www.ijacsa.thesai.organ optimal selection of solutions required for the next generation to map with secure and energy-efficient routing among the sensors in IoT.If the crowding distance is more for a specific set of solutions, then the algorithm considers that set of solutions further for the next generation.Therefore, the proposed ACO approach offers a diversified and optimal solution to accomplish both secured trustworthy routes for data transmission with higher residual energy.According to the proposed scheme, the first set of operations is towards selecting the sensor by number of ants in the form of the sensor present in the next hop.This computation offers the probability of change from one state to another.The proposed scheme constructs dual heuristic information that is associated with remnant energy and trust value, which can be empirically exhibited as follows: From Eq. ( 14), it should be noted that the first heuristic information ( ), i.e., ( ), is evaluated considering energy dissipated by the data acquired divided by the initialized energy of the sensor.In contrast, the second heuristic information ( ) depends on a set of candidate routes for optimal data transmission performance with higher trust.The proposed scheme implements two types of pheromone information to fit the model with two heuristic information.The idea is to jointly study the value of trust and the associated remnant energy of a sensor.When an ant constructs a candidate solution using Eq. ( 15), the scheme instantly updates the pheromone associated with each heuristic.The following are the empirical expressions: In Eq. ( 15), the first attribute of pheromone, i.e., ( ), is associated with the criterion for remnant energy, i.e., .In contrast, the second attribute of pheromone, i.e., ( ), is related to the criterion for the mean value of trust, i.e., .It should be noted that the maximization of the pheromone score is relative to the mean quantity of resource availability and trust score associated with the propagation routes.The inference is that increased resource availability on the communication routes can only ensure a higher degree of pheromone retention.The conclusive remark of this logic is that nodes with reported higher value of trust and resources will eventually have higher feasibility to be opted as participating routing nodes.However, this empirical expression is in abstract form, and it demands more clarity in terms of the actual updating mechanism of pheromone in ACO, which is given as follows: In Eq. ( 16), the updating of local pheromone is stated considering the unique function of coefficient of pheromone evaporation mainly.The function performs extractions of two operators using arguments of and , i.e., i) ( ) ( ) and ii) ( ), where the values of suffixes and are associated with , while the value of power variable is (1, 2) owing to consideration of two heuristic information.The variable ( ) is incremented, further proportional to the heuristic information of mean trust and energy score.The prominent inference of this implementation concept is that the maximum score of the function associated with mean remnant energy will mean higher retention of pheromone on the routing path.It will also mean that if the value of the function associated with the mean trust value is found to be maximal, then it will mean that a large quantity of pheromone will be retained.Hence, the routing operation in IoT will always choose only those sensors with a higher value of remnant energy and higher trust.The implementation concept of updating the local pheromone is completed when the data reaches the sink node, followed by further updating of enhanced crowding distance by sub-optimal solutions in the sink node.The system finally yields a backward-moving ant from the forward-moving ant upon completion of the local updating operation of the pheromone.
Finally, the proposed scheme performs updating of the global pheromone by subjecting all the sub-optimal solutions presented by the sink node.The backward-moving ant in the proposed ACO approach carries out this task of updating the sub-optimal solution.An empirical expression for this global pheromone updating is similar to that of Eq. ( 12) only with the difference of ( ) equivalent to ⁄ , where the variable represents the quantity of the sensors traversed by the k number of backward-moving ants considering a set of sub-optimal solutions as routing paths to update the global pheromone.
The contributions of this methodology can be seen by two significant results: (a) The primary contribution of the proposed method is to design and develop a simplified yet robust secure data propagation scheme in IoT.It is simplified as it doesn't consist of any sophisticated mechanism or involve a higher number of complex processing routing schemes.It is robust as the method can realize the dynamic vulnerabilities present in links connecting IoT nodes without any dependencies on the apriori information of an attacker.The scheme is highly secured as the formulation of the system is carried out considering manifold criteria in the form of an essential function associated with trust and resources of IoT nodes as well as various practical constraints related to time, forwarding, and receiving data, and remnant resources of IoT nodes, (b) The second prime contribution is associated with the mechanism of deploying an ACO approach where novel pheromone management is presented.The credibility of the data propagation and exchange among the IoT nodes is carried out by proposed ACO-based routing, where the selection of cost-effective and secured routes is based on heuristic and pheromone information of manifold type.It should be noted that the proposed scheme considers heuristic information and pheromone information derived from remanent information and the trust score of IoT nodes.The main contributions of the study can be briefly summarized as: 1) The proposed scheme balances trust computation with the selection of optimal nodes possessing substantial residual energy, ensuring they have the requisite resources for extensive secure data propagation within IoT.
2) The trust evaluation mechanism of the proposed system is scalable and viable for both compact and expansive IoT environments.Its resilience is evident as it can function even www.ijacsa.thesai.org in the presence of unknown-origin attackers.This resilience stems from the fact that, regardless of an attacker's strategy, the proposed scheme ensures all standard sensors compute an optimal solution that remains inscrutable to potential intruders.
3) The proposed ACO approach addresses and rectifies the traditional issues of slow convergence and parameter sensitivity, which plague conventional ACOs.By providing a broader problem scope, it aptly aligns with the expansive nature of IoT.
The ensuing section delves into the results derived from implementing the proposed scheme.

V. RESULT
This section presents the results achieved after implementing the proposed model.Since the proposed implementation introduces a novel ACO-based secure routing method, emphasis has been placed on investigating data transmission performance.Additionally, the trust management scheme has been executed for the system.The primary objective of the result analysis is to establish an extensive test environment using variable performance metrics.This is done to gauge the impact of the proposed secure routing conducted by sensors within the IoT environment.The results are then analyzed to provide insights into how the model's performance compares to existing secure routing schemes.

A. Assessment Strategy
The entire implementation is conducted in MATLAB on a standard 64-bit Windows machine.The simulation environment selected for the experiment spans an area of 1000x1000 m 2 , with the specific simulation parameters detailed in Table II.This environment replicates a smart city setting where numerous clusters of wireless sensor nodes are interconnected, facilitating data aggregation with a predetermined energy level.While the initial energy assigned to each node is 10 J, it can be adjusted based on the specific IoT application in use.Thus, the proposed simulation environment offers considerable flexibility, accommodating modifications to parameter values to fit various scenarios.To gauge the effectiveness of the proposed scheme, a benchmarked analysis against existing secure routing schemes in the IoT environment is essential.This comparative analysis examines specific performance metrics across the proposed and existing secure routing schemes.The conventional secured routing schemes selected for comparative analysis in IoT include:  Routing Protocol for Low-Power and Lossy Network (RPL): This standard IoT routing scheme is tailored for networks with lossy features and low-powered nodes.Its secure variant, Secure RPL (SRPL), employs authentication of messages and encryption operations to ensure data freshness, integrity, and confidentiality [32].
 Routing using 6LoWPAN: Another conventional IoT routing method closely aligned with RPL, this protocol leverages the IPv6 scheme for routing.It adopts 6LoSec, primarily designed to guard against replay attacks and ensure data integrity and confidentiality [50].
 Secured routing using Zigbee Cluster Library (ZCL): Zigbee's prevalence in IoT-based wireless communication systems is notable.However, ZCL is designed to provide secure routing exclusively for Zigbee-based networks.This scheme uses authentication and encryption to ensure authentic communication, data integrity, and confidentiality [51].
 Secure routing using Constrained Application Protocol (CoAP): This protocol facilitates data transmission for resource-constrained devices within the IoT framework.It incorporates Datagram Transport Layer Security (DTLS) to provide authentication, data integrity, and confidentiality [52].
The aforementioned secure routing schemes and proposed method have been implemented in comparable test environments and under similar simulation parameters.Moreover, the conventional ACO and PSO algorithms have been employed to evaluate the performance enhancements ushered in by the proposed scheme relative to existing bioinspired approaches.

B. Discussion of Result
The initial performance metric examined is network throughput, calculated as the volume of data packets transmitted from one node to another within the IoT environment over a specific time.A detailed examination of the proposed secure routing scheme reveals that it encompasses various mathematical and logical operations based on the manifold criterion-based model.This design ensures a robust defense against manipulation or unauthorized access.Given these intricacies, potential delays or added computational demands might impact throughput.
Consequently, benchmarking based on throughput provides a clear insight into whether the inherent security operations compromise network performance and data transmission.Additionally, thorough throughput assessment can highlight potential bottleneck areas within the routing path.Fig. 3 presents the average throughput observed during a series of evaluations over specified simulation duration.It indicates that the proposed scheme outperforms existing methods in terms of throughput.The RPL protocol emerges as the next best performer in current systems, primarily due to its dynamic path selection capabilities.However, it needs help to balance traffic load during dynamic events within the IoT landscape.Following RPL, conventional ACO, PSO, and www.ijacsa.thesai.orgCoAP algorithms rank next in terms of throughput.It's worth noting that traditional ACOs have a limitation, needing help to provide optimal solutions amidst heavy traffic flow.PSO faces a similar challenge, grappling with increased memory dependencies as traffic surges, reducing throughput.The CoAP protocol, because it utilizes User Datagram Protocol (UDP), is prone to packet loss, primarily when data packets are transmitted in an unordered sequence within IoT.Moreover, 6LoWPAN and ZCL underperform in throughput within the IoT environment due to complexities arising from header compression.In contrast, the proposed system remains unaffected by such challenges.Its constraints concerning energy and trust, combined with its modeling, are adept at identifying vulnerabilities and pinpointing alternative optimal paths for propagation based on the manifold criterion.Consequently, even without encryption, the proposed scheme achieves superior secure data transmission throughput compared to conventional secure IoT routing strategies that predominantly depend on encryption and authentication.
The subsequent performance metric evaluated is a delay, calculated as the latency encountered during the data bit transmission across the network from one sensor to another within the IoT framework.Evaluating delay is paramount for secure data transmission, given the diverse applications and services housed within the IoT environment.It's essential to highlight that the proposed scheme incorporates several mathematical procedures for trust calculation, where local and global parameters play pivotal roles.most extant secure routing schemes lean heavily on encryption and message authentication to ensure data security.Thus, it becomes imperative to ascertain that these intrinsic security processes don't detrimentally influence network performance by augmenting delay.For optimal network efficiency, it's crucial to maintain low delay, as extended latency often signals network bottlenecks or areas compromised by security vulnerabilities, consequently impinging on data transmission durations.Examining Fig. 4 reveals that the proposed scheme considerably reduces delay compared to existing systems.ZCL displays a higher delay due to its conventional architecture's slower transmission rate tailored to meet Zigbee transmission requirements.6LoWPAN outperforms ZCL in delay, but it sacrifices some security; it possesses a weaker immunity to interference.A significant cause of delay in 6LoWPAN is its repeated retransmissions due to packet loss from IPv6 data chunks.CoAP, being optimized for peer-to-peer communication, has an inevitable delay.However, RPL shines the best in reducing delay compared to other secure transmission methods.This is attributed to its autoconfiguration capabilities and dynamic path selection, vital for large-scale IoT devices with resource-constrained sensors.Yet, RPL's delay becomes significant in IoT's mobile environments.Conventional ACO and PSO also underperform due to i) increasing iteration counts as they seek optimal solutions in constrained problem areas and ii) premature convergence resulting in sub-optimal routing paths, leading to increased delay.In contrast, our proposed scheme excels in delay performance, being more progressive, less iterative, and encompassing a broader problem area, thereby reducing the effort needed for optimal solutions and achieving a superior delay score.
The third performance metric is energy consumption.The rationale behind selecting this metric hinges on two factors: i) our mathematical model prioritizes residual energy as a primary constraint, with manifold criterion modeling also considering residual energy apart from the trust attribute as a heuristic.This necessitates evaluating the model's impact on energy consumption, and ii) our scheme primarily focuses on sensors as essential IoT devices.Given their limited energy resources, it becomes crucial to ascertain the energy expended during secure data transmission in IoT.The objective is to balance trust-based security and energy efficiency, ensuring a prolonged network lifespan.Here, "energy" denotes the power expended on cumulative sensor operations, which encompasses data transmission and reception, data processing, and internal circuit functions.We rely on the first-order radio energy model [53] to assess this, which provides a comprehensive formula for sensors' total energy consumption.The data presented in Fig. 5 reveals that our proposed scheme significantly reduces power consumption compared to most existing systems.What stands out is that both conventional ACO and PSO demonstrate slightly higher energy consumption.The main reasons for this are: i) the increased use of attributes to achieve higher convergence performance and ii) a stronger focus on the local search optimization problem, often at the expense of global search space.The distinction between the proposed ACO and the conventional ACO lies in the former's formulation of pheromone management based on multiple criteria intertwined with an adaptive operational principle.A detailed examination of our ACO scheme further shows that their functionalities are mostly preserved despite the utilization of numerous parameters.The only variation arises from their use cases to enhance trust and energy retention.Due to the calculated global update formulation, local operatives' reliance on extensive operations diminishes as simulation time increases, leading to energy conservation.IoT secure routing schemes, such as RPL, ZCL, CoAP, and 6LoWPAN, incorporate encryption in their security variants.This inclusion demands a significant energy allocation for the ciphering and deciphering processes, which our proposed secure routing scheme avoids.
The final performance metric assessed for the proposed scheme is the processing time, an essential measure that reflects the time complexity inherent in algorithmic processing during secure routing.This metric is determined by evaluating a sensor's time to complete its operations.An efficient system model, especially one with lightweight characteristics, should display reduced processing times.If the processing time is extensive, the security scheme in use might benefit from some refinement to boost its overall efficiency.It is also crucial to note that, given sensors' limited computational capabilities, their processing time can increase when more complex operations are introduced.As a result, gauging the system's efficacy in terms of computational complexity by assessing its processing time becomes vital.Fig. 6 illustrates that the proposed scheme significantly reduces processing time compared to other secured routing schemes.The processing time for ZCL is notably higher, as this scheme necessitates performing extensive iterative operations for massive data transmission in IoT.On closer examination, 6LoWPAN, CoAP, and conventional PSO performance reveal similar processing times.This similarity arises because these approaches segment and chunk data into smaller portions, leading to packet loss in a heterogeneous IoT network that necessitates retransmission.Additionally, the conventional PSO requires iterative computation of particles and velocities to identify optimal results.While this is effective for homogeneous systems, it is less so for heterogeneous ones, causing them to amalgamate all data packets and conduct routing.Such operations demand significant processing time and deliver sub-optimal data quality.Furthermore, RPL displays a longer processing time, slightly more than CoAP.This is attributed to RPL's formation of a directed acyclic graph, eventually resulting in a singular link from the leaf node to the route.While this might be suitable for smaller IoT networks, the RPL graph operation must be repeated to achieve data transmission within the context of more extensive IoT networks.This repetition expends excessive resources and consumes considerable processing time for data transmission.Therefore, the proposed scheme promises minimal processing time, primarily due to diminished resource dependencies and fewer iterative operations, as emphasized in the proposed mathematical modeling.

VI. CONCLUSION
The landscape of the IoT is both exciting and challenging, marked by tremendous opportunities and, in parallel, considerable security vulnerabilities.In the modern age, when digital interconnectedness is both a boon and a bane, the urgency to fortify IoT against burgeoning threats cannot be overstated.The paper has addressed this urgency, providing a novel and streamlined approach that leverages a modified version of ACO toward achieving optimal security in the vast and dynamic IoT ecosystem.This bioinspired approach symbolizes our attempt to mimic nature's intuitive problemsolving methodologies.Through our process, not only is data Approaches of Secured Routing in IoT www.ijacsa.thesai.orgtransmission secured, but the dual challenges of optimizing sensor energy and ensuring high trust scores are simultaneously addressed.A particular innovation in our work is the unique pheromone management system, which holistically considers residual energy and trust scores.Coupled with our manifold criterion and the dual-tiered trust evaluation system, the methodology provides an unparalleled framework for IoT security.Our research has showcased its merits, delivering impressive performance metrics compared to prevailing secure routing protocols.With a 35% improvement in throughput, 89% reduction in delay, 54% decrease in energy consumption, and 73% surge in processing speed, the approach is theoretically sound and practically efficacious.The current work, however, has its limitations.While it has achieved a balance between energy conservation and security, nuances to this balance need exploration.The inherently dynamic nature of IoT means that newer devices with diverse capacities are continually entering the ecosystem, presenting evolving challenges for security protocols.The future work of this study model will be to formulate a hybrid modeling of a bioinspired approach to optimize the security and resource management performance in a large IoT environment.Furthermore, integrating Artificial Intelligence and Machine Learning algorithms could further enhance our initial results.These can help the system to adaptively learn from emerging threats and respond proactively, ensuring a more agile and dynamic security mechanism.