Set Of Paths Research Articles

Software Defined Security Flow Scheduling Method Based on PSO Algorithm

To solve the problem of link load imbalance and bandwidth waste in software defined security, a secure traffic scheduling method based on particle swarm optimization is proposed in this paper. This method combines the software defined security controller to obtain the network topology information and the current link bandwidth resource status, aiming at the matching degree between the link bandwidth and the network flow size and the function matching degree between the link node and the network flow service chain, combined with specific constraints to establish the objective function. The optimized particle swarm optimization algorithm finds out the best scheduling path of network traffic from the shortest path set. The experiment results show that compared with other algorithms, the proposed algorithm can improve the average throughput of the network and maintain a low packet loss rate in the process of traffic transmission, thus reducing the waste of bandwidth resources and realizing the load balancing of software defined network security traffic better.

Improving readability of static, straight-line graph drawings: A first look at edge crossing resolution through iterative vertex splitting

We present a novel vertex-splitting approach to iteratively resolve edge crossings in order to improve the readability of graph drawings. Dense graphs, even when small in size (10 to 15 nodes in size) quickly become difficult to read with increasing numbers of edges, and form so-called “hairballs”. The readability of a graph drawing is measured using many different quantitative aesthetic metrics. One such metric of particular importance is the number of edge crossings. Classical approaches to improving readability, such as the minimization of the number of edge crossings, focus on providing overviews of the input graph by aggregating or sampling vertices and/or edges. However, this simplification of the graph drawing does not allow for detailed views into the data, as not all vertices or edges are rendered, and also requires sophisticated interaction approaches to perform well. To avoid this, our locally optimal vertex splitting approach aims to minimize the number of remaining edge crossings while also minimizing the number of vertices that need to be split. In each iteration, we identify the vertex contributing the largest number of edge crossings, remove it, locate the embedding locations of said vertex’s two split copies, and determine each copy’s unique adjacency. We conduct a user study with 52 participants to evaluate whether vertex splitting affects users’ abilities to conduct a set of graph analytical tasks on graphs 12 nodes in size. Users were tasked with identifying a vertex’s adjacency, determining the shared neighbors of two vertices, and checking the validity of a set of paths. We ultimately conclude that within the context of small, dense graphs, systematic vertex splitting is preferred by participants and even positively impacts user performance, though at the cost of the time taken per task.

A Graph reinforcement learning based SDN routing path selection for optimizing long-term revenue

Software-Defined Network (SDN) paradigm decouples control plane from data plane and provides a logically-centralized control to whole underlying network, which enables the controller to centrally configure the network, and has demonstrated great potential in fully utilizing network capacity. However, the traditional SDN does not consider the accumulative long-term revenue in routing configuration for network service provider (SP). For example, it is prone to choosing the path passing through important nodes, which may increase the possibility of future congestion, and hamper the SP’s long-term revenue. Deep reinforcement learning (DRL) has brought about the transformative advances in decision-making when facing uncertainty. As a well-known type of DRL, Deep Q-network (DQN) can explore optimal routing schemes from high dimensional network state. However, most of existing DRL based methods fail to provide satisfactory configuration when confronted with a topology unseen during training. Meanwhile, many existing network optimization schemes ignore the impact of nodes and global network attributes on the routing configuration. Consequently, they may perform poorly in real scenarios. In this paper, we propose a graph reinforcement learning (GRL) based SDN routing selection scheme, GN-DQN for optimizing SP’s accumulative long-term revenue. First, besides the link embeddings, GN-DQN explicitly learns the representations of the routers and globe network topology with full graph neural network block. Then, through utilizing the formed network representation as the state, DQN is used to learn to sequentially choose the optimal routing path from the candidate path set to maximize the accumulative long-term revenue. Thorough experiments on multiple real network topologies show that our proposed GN-DQN outperforms other GRL based and traditional routing selection schemes, and can generalize over arbitrary topologies. Moreover, GN-DQN also demonstrates great robustness to the link failure scenario.

A Single–Multi-Path Combinatorial RMSA Algorithm with Least Resource Consumption in Semi-Filterless Optical Networks

Filterless optical networks (FONs) have become a cost-effective solution for optical network deployment due to their low-cost characteristics. However, eliminating active switching elements causes signals to propagate over unintended links, wasting spectral resources. Therefore, semi-filterless optical networks (Semi-FONs) have become a more cost-effective solution. This paper mainly studies the routing, modulation, and spectrum assignment (RMSA) problem in semi-filterless optical networks. It proposes a single–multi-path combination (LR-SMPC) RMSA algorithm with the least resource consumption. The algorithm first obtains the K shortest paths that satisfy the conditions according to the K short path (KSP) algorithm and re-orders the paths according to the resource consumption path re-ordering strategy, selecting the three paths that consume the least resources as the set of candidate paths. Then, based on the single–multi-path combination scheme of the set of candidate paths, the resource consumption of each scheme and the maximum number of available spectrum blocks for each path is calculated, from which the single path or multi-path with the least resource consumption is selected to serve the request. We perform simulation experiments on two network topologies using Poisson traffic models and compare them with existing single-path algorithms (S-P), fixed spectrum assignment granularity algorithms (g = 1), and adaptation spectrum assignment algorithms (g adaptation) to evaluate the performance of the proposed algorithm. The simulation results show that the proposed algorithm exhibits better performance in terms of both blocking rate and spectrum utilization.

Optimal Antenna Locations for Coverage Extension in Sub-Terahertz Vehicle-to-Vehicle Communications

Sub-terahertz (sub-THz, 100-300 GHz) communications promise to bring extraordinary rates in future 6G systems. High path loss and blockage effects will limit the coverage of base stations (BS) to a few hundred meters making deployment of such systems along the roads expensive. As a way to decrease the BS density, relaying has been proposed. However, vehicle-to-vehicle (V2V) propagation is characterized by different sets of communications paths depending on the antenna locations raising the question of their optimal positions. In this paper, by utilizing IEEE 802.15.3d parameters and 300 GHz propagation measurements, we develop a mathematical framework for comparison of multi-hop relaying systems with different antenna locations. We utilize coverage, BS availability, and the data rate over a multi-hop path as metrics of interest. Our results show that the windshield location is characterized by lower data rates and larger coverage while bumper and engine levels are similar in terms of these metrics. For the windshield location, the coverage is extended by 50% with BS availability 0.95. The windshield location is recommended as it is less sensitive to the technology penetration rate and is characterized by larger coverage. The proposed approach shows gains of up to 32% in terms of required BS density for the range of vehicles density (10-40 units/km).

Performance Analysis Model and Deterministic Routing Decision Algorithm for Broadband Real-Time Services in Wireless Multi-Hop Networks

Wireless multi-hop networks (WMN) are considered as one of promising solutions for broadband real-time service transmission in the Internet of Everything. Due to the timevarying wireless channels, the deterministic communication of services becomes extremely challenging for WMN. In order to comprehensively evaluate the service provisioning capability of WMN, we first propose an end-to-end performance analysis model for reliable transmission of broadband real-time services in WMN based on stochastic network calculus (SNC). We define a general expression for the effective service process that a changing channel provides. The end-to-end delay violation bound and hop-delay relationship are obtained by applying the effective service process and the cascade theorem. Then based on this analysis model, we propose a deterministic routing search algorithm, where the initial path search sets are reduced through the hopdelay relationship. And the routes are obtained from the reduced path sets. The deterministic routing decision algorithm can ensure the bounded end-to-end delay of services with a certain probability. Theoretical and simulation results demonstrate the correctness of the proposed performance analysis model and the effectiveness of the routing algorithm.

DA-DMPF: Delay-aware differential multi-path forwarding of industrial time-triggered flows in deterministic network

The time-sensitive network (TSN) utilizes gating technology to achieve mixed traffic transmission across the common network, replacing dedicated networks with time division multiplexing, simplifying the interconnection architecture. TSN fundamentally changes the uncertainty of traditional Ethernet through clock synchronization, traffic scheduling, and network configuration. TSN can meet the requirements of industrial communication for low delay, low jitter, and high-reliable data transmission. It is also fully compatible with the existing Ethernet system and is an important communication platform supporting the development of the industrial Internet. Time-triggered flow is the key flow responsible for transmitting real-time data in time-sensitive networks. Its transmission should strictly follow the gating schedule. Selecting different transmission paths will affect the success rate of scheduling. To solve the delay optimization problem of industrial time-triggered (TT) flow path selection, this paper proposes a novel forwarding mechanism for industrial TT flow, namely delay-aware differential multi-path forwarding (DA-DMPF). This mechanism first designs a multipath forwarding index based on sending delay and processing delay by analyzing the end-to-end delay, then converts the multipath forwarding problem into a 0-1 integer linear planning problem, and finally solves the forwarding path set of the stream through differential multipath forwarding algorithm to achieve low delay parallel transmission of the stream. The simulation results show that compared with load-balanced multi-path forwarding and Hop-Load forwarding mechanisms, this scheme reduces the end-to-end average delay of industrial TT flows by 22.55% and 17.32% respectively, meeting the low delay transmission requirements of TT flows.

Component-Based Test Case Generation and Prioritization Using an Improved Genetic Algorithm

Developing test cases is the most challenging and crucial step in the software testing process. The initial test data must be optimized using a strong optimization technique due to many testing scenarios and poor testing effectiveness. Test prioritization is essential for testing the developed software products in a production line with a restricted budget in terms of time and money. A good understanding of the trade-off between costs (e.g. time and resources needed) and efficiency (e.g. component coverage) is necessary to prioritize test case scenarios for one or more software products. So, this paper proposes an efficient Multi-objective Test Case Generation and Prioritization using an Improved Genetic Algorithm (MTCGP-IGA) in Component-based Software Development (CSD). A random search-based method for creating and prioritizing multi-objective tests has been employed utilizing numerous cost and efficacy criteria. Specifically, the multi-objective optimization comprises maximizing the Prioritized Range of test cases (PR), Pairwise Coverage of Characteristics (PCC), Fault-Finding Capability (FFC), and minimizing Total Implementation Cost (TIC). For this test prioritizing problem, a unique fitness function is constructed with cost-effectiveness metrics. IGA is a robust search technique that exhibits excellent benefits and significant efficacy in resolving challenging issues, including ample space, multiple-peak, stochastic, and universal optimization. Relying on the use of IGA, this paper classifies, computes the objective function, introduces the Nondominated Sorting Genetic Algorithm-II (NSGA-II) method, evaluates each branch’s proximity on the handling route, and arranges the path set to get the best answer. The outcomes demonstrate that the proposed MTCGP-IGA with NSGA-II performed the best than other baseline algorithms in terms of prioritizing the test cases (mean value of 195.2), PCC (mean score of 0.7828), and FFC (mean score of 0.8136).

From Competition Law to Platform Regulation – Regulatory Choices for the Digital Markets Act

Abstract The digital landscape is characterized by the concentration of power in the hands of a few “gatekeepers.” Europe’s legal answer, the Digital Markets Act (DMA), constitutes a new legal approach. It aims at restricting this power and ensuring “fairness and contestability.” Traditionally, the tools of competition law were used to set the basic rules of how to operate in markets. The shift to a different technique, regulatory law, prompts three questions: First, why is it necessary to deviate from the path set in competition law? Second, what are the differences between the competition law approach and the regulatory approach of the DMA? Third, what are key decisions to be taken within the new regulatory framework? At the outset, the DMA is presented.

Flight Path Setting and Data Quality Assessments for Unmanned-Aerial-Vehicle-Based Photogrammetric Bridge Deck Documentation.

Imagery from Unmanned Aerial Vehicles can be used to generate three-dimensional (3D) point cloud models. However, final data quality is impacted by the flight altitude, camera angle, overlap rate, and data processing strategies. Typically, both overview images and redundant close-range images are collected, which significantly increases the data collection and processing time. To investigate the relationship between input resources and output quality, a suite of seven metrics is proposed including total points, average point density, uniformity, yield rate, coverage, geometry accuracy, and time efficiency. When applied in the field to a full-scale structure, the UAV altitude and camera angle most strongly affected data density and uniformity. A 66% overlapping was needed for successful 3D reconstruction. Conducting multiple flight paths improved local geometric accuracy better than increasing the overlapping rate. The highest coverage was achieved at 77% due to the formation of semi-irregular gridded gaps between point groups as an artefact of the Structure from Motion process. No single set of flight parameters was optimal for every data collection goal. Hence, understanding flight path parameter impacts is crucial to optimal UAV data collection.

Hitchhiker: Accelerating ORAM With Dynamic Scheduling

Oblivious RAM (ORAM) remains a bittersweet protection of memory access patterns because of its prohibitively high overhead. The root cause is that ORAM hides intended accesses among a sufficiently large number of dummy accesses. Most existing optimizations mitigate memory accesses using architectural enhancements (e.g., cache) yet few of them improve the efficiency of ORAM primitives per se. In this paper, we identify path-grained static scheduling as a fundamental ORAM performance bottleneck. We propose level-grained dynamic scheduling that directly optimizes ORAM primitives to boost efficiency. It enables ORAM to service more than one request per path and write paths batch wise. We can thus boost ORAM efficiency through handling queued requests as soon as possible and remove as many redundant accesses as possible. Since optimized memory accesses still target the same set of paths, dynamic scheduling preserves ORAM security. We implement dynamic scheduling through Hitchhiker ORAM. In comparison with the state-of-the-art primitive-optimized Fork Path ORAM, Hitchhiker ORAM yields 31.5% fewer memory accesses, 60.2% shorter latency, and 40.7% less energy consumption, being 2.5× faster. In comparison with the state-of-the-art architecture-optimized <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\rho$</tex-math></inline-formula> , Hitchhiker ORAM is 1.5× faster and the integrated version— <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\rho$</tex-math></inline-formula> -Hitchhiker ORAM is 2.0× faster.

Pengoptimalan Jaringan Pipa Primer PDAM Tirtanadi Cabang Tuasan Dengan Menggunakan Algoritma Kruskal

Kruskal's algorithm in searching for minimum spanning trees can be applied to pipelines installed at the location of PDAM Tirtanadi Tuasan where problem identification starts with the water discharge reaching the consumer is small but the discharge flowing from the reservoir is sufficient, so this research is used as a solution to this problem and also as an optimization of the clean water distribution network in the Tirtanadi Regional Drinking Water Company (PDAM) of the Tuasan Branch with the intention of cutting the direction of the pipe flow to overcome this problem. The data obtained from PDAM Tirtanadi Tuasan Branch is in the form of a floor plan and formed into a weighted graph. After the data is obtained, then it is calculated manually that the length of the installed water pipe is 32,645 m with 86 vertices and 100 edges, then the pipe length is represented as a set of paths and the pipe connection ends are represented as nodes. The pipe length obtained using Kruskal's algorithm and inspection of iterations using the QM for windows software is 22,095 m, with 86 vertices and 85 edges. So, using the Kruskal Algorithm and the help of the QM for windows software, the difference in pipe length obtained is 10,610 m.

Multiple fault localization based on ant colony algorithm via genetic operation

Debugging programs involves a considerable amount of time and resources spent on identifying the source of errors. Spectrum-based fault localization techniques are becoming increasingly popular due to their quick and effective performance when dealing with programs that have a single fault. However, these methods do not take into account the reality that most programs tend to have multiple faults. To address the above challenges, we propose an automatic approach using ant colony algorithm via genetic operation for multiple fault localization (ACOMFL). Our approach first constructs the search graph by the coverage information. Secondly, it designs the fitness function to select the best paths and dynamically adjusts the pheromone updating. Finally, it computes the suspiciousness ranking of each statement by the ant path information in the optimal path set. Four open-source benchmarks are utilized to validate the performance of the ACOMFL approach. The experimental results show ACOMFL achieves better results for multi-fault programs in comparison with six spectrum-based fault localization approaches and a genetic algorithm-based multiple fault localization approach. The ACOMFL is effective at locating multi-fault, serving as a helpful method for locating faults.

Criticality-based allocation of active redundancies for asymmetric components in coherent systems

The active redundancy technique is widely used in reliability engineering to enhance the performance of reliability systems. In this paper, we utilize the criticality ordering to study optimal allocations of active redundancies in coherent systems comprising independent components. Firstly, we establish optimal allocation strategies for two heterogeneous active redundancies for components ranked by the criticality ordering, which is characterized by the structure relationships induced from the associated minimal cut sets. Our findings demonstrate that, under certain conditions where the criticality ordering (and reliability performances) between any two original components is required, the stronger redundancy should be allocated to the more critical component. Secondly, we investigate the best allocation policies for a batch of homogeneous active spares for different components while preserving the criticality ordering. It is shown that allocating more active redundancies to the more critical component results in a more reliable coherent system. Additionally, we prove that under certain conditions, an imbalance in the numbers of allocated spares leads to even greater reliability in the system. Thirdly, we study optimal allocations for any two components in coherent systems when their locations are quantified by minimal path sets. The optimal allocation policies are further derived, most of which are similar to those obtained by applying minimal cut sets. Numerical examples are provided to illustrate the main findings, and we apply our findings to a real-world scenario in an airplane cockpit system to enhance its reliability.

A Combinatorial Bijection between Ordered Trees and Lattice Paths

This work presents a combinatorial bijection between the set of lattice paths and the set of ordered trees, both counted by the central coefficients of the expansion of the trinomial (1+x+x^2)^n. Moreover, using a combinatorial interpretation of Catalan numbers, we establish a new set of ordered trees counted by a new sequence.

Bi-objective optimization of last-train timetabling with multimodal coordination in urban transportation

When urban rail transit (URT) does not provide 24-hour services, passengers who travel at late night may not be able to reach their destinations with only URT trains. As a result, passengers have to find alternative transport means, or combine URT trains with other transport services to fulfill their journeys. This paper investigates the integrated optimization of last train timetabling and bridging service design with consideration of passenger path choices. Two bridging services are considered: taxis and buses. Based on pre-constructed path sets, a bi-objective mixed-integer nonlinear programming (MINLP) model is developed, aiming at minimizing total passenger travel time and total passenger travel cost. To reduce the model scale and improve solution efficiency, three path dominance principles are proposed to remove redundant passenger paths without loss of optimality. An adaptive iterative algorithm is designed to obtain the Pareto frontier curve. The proposed model and solution methods are demonstrated on the Chengdu URT network. Results indicate that passenger travel costs and travel times can be significantly reduced by the integrated optimization. It also provides passengers with a safer night travel environment due to the reduction in passenger travel times in taxis.

Tectonic evolution of Southeast Asia documented by two periods of high-grade metamorphism in the Kon Tum Massif, Central Vietnam

The Kon Tum Massif in the eastern Indochina Block (ICB) contains extensive Ordo-Silurian and Permo-Triassic metamorphic rocks. These two episodes of metamorphism not only affected the Kon Tum Massif, but were also widely developed in the whole of Southeast and East Asia, including the South China Block (SCB). Comparisons of the metamorphic characteristics among these blocks can reveal the crustal evolution and their affinity in supercontinents. Two gneiss samples in the eastern part of the Kan Nack Complex of the Kon Tum Massif have similar peak metamorphic ages of 488–482 Ma and mineral assemblages of garnet + plagioclase + K-feldspar + biotite + sillimanite + ilmenite + quartz. P-T calculations and thermodynamic modelling constrain a pressure peak (M1) at 770–820 °C and ∼1.1–1.2 GPa, and a temperature peak (M2) at 790–850 °C and 0.85–0.98 GPa, suggesting a near-isothermal decompression. The post-peak M3 assemblage developed at 650–760 °C and 0.48–0.74 GPa. The clockwise P-T-t path with medium and variable geothermal gradients probably reflects an orogeny process from compression to extension, which was triggered by the closure of the Tam Ky-Phuoc Son Ocean in the northern flank of the Kon Tum Massif. The P-T-t path and tectonic setting of this period of metamorphism in the Kon Tum Massif is similar to that in the SCB, but occurred a little earlier than that in the SCB, suggesting that the ICB and the SCB were adjacent on the northern margin of East Gondwana and together experienced a diachronous early Paleozoic orogeny.A granulite sample from the western Kan Nack Complex contains garnet + orthopyroxene + plagioclase + quartz + ilmenite ± K-feldspar. Decomposition of garnet to plagioclase and orthopyroxene suggests a decompression process. Coupled with features of a gneiss sampled at the same spot, P-T calculations and thermodynamic modelling indicate a pressure peak of 885–960 °C and 0.83–0.95 GPa and a temperature peak at 890–970 °C and 0.61–0.73 GPa, corresponding to ultrahigh-temperature (UHT) metamorphism. LA-ICP-MS monazite U-Th-Pb dating constrains a peak metamorphic age of 254 Ma, and Sm-Nd mineral isochrons date the retrograde stage at 243–236 Ma. This UHT metamorphism with a clockwise P-T-t path probably resulted from the excess heat supplied by asthenospheric upwelling following the break-off of the subducting slab of the Paleo-Tethyan Ocean. The Permo-Triassic metamorphism and other geological records in the ICB are significantly different from those in the SCB, implying that the geological processes in the two blocks were controlled by different geodynamic regimes during Late Permian to Early Triassic.

Path-Based Delay Variation Models for Parallel-Prefix Adders

State-of-the-art static timing analysis algorithms can evaluate worst-case delay in statistical terms. In this paper, a modeling framework is introduced for the evaluation of the maximum-delay Cumulative Density Function (CDF) of an ensemble of parallel-prefix adder topologies. For moderate variations and close-to-nominal supply voltages, the maximum delay of parallel-prefix adders is practically determined by the maximum of a set of near-critical-delay paths around the nominal maximum-delay path. These paths end to the most significant and neighboring bit positions. Matrix-based path delay formulations are derived for the particular set of paths. The introduced matrix formulations are exploited to assess the maximum-delay CDF by means of a multivariate Gaussian CDF. To validate the accuracy of the introduced models, a quantitative comparison of the proposed probabilistic delay models against Spice-level Monte-Carlo simulations is offered for certain parallel-prefix adders. Threshold-voltage variations summarize several process-dependent variation-inducing mechanisms and are modeled as Gaussian variations, introduced to BSIM-4 transistor models for a 16-nm technology node. For the nominal voltage case and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$10\%$</tex-math></inline-formula> threshold-voltage variations, the introduced models estimate the 0.95 timing yield point with a mean absolute error below <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$1\%$</tex-math></inline-formula> compared to Spice-level simulations for the 16 bit-length case. Furthermore, an extension of the proposed approach to account for multiple end points is investigated that reduces the error for the estimation of maximum delay, demonstrated for a unit delay model and certain bit-lengths of Kogge-Stone adder.

Optimization Model of Regional Green Wave Coordination Control for the Coordinated Path Set

An optimization model of regional green wave coordination control for the coordinated path set is proposed based on the network’s directional and asymmetric actual coordination control demands. The model in this paper selects the coordinated path set as the optimization object and takes the best regional coordination control effect as the optimization objective. By selecting signal timing parameters, such as stage time, stage center point, offset, and cycle length, as optimization variables, the relationship between signal timing parameters and the effect of green wave coordination was established. The stage time of the coordinated stage at the upstream and downstream intersections is used to calculate the evaluation index of the coordination control benefit of the whole road network. The case study demonstrates that this model can optimize coordinated path chains with left-turn traffic flows under green wave coordination management. In terms of network performance, compared with Transyt and Synchro, the signal control scheme obtained by the proposed model can reduce the average delay by about 30% and the average number of stops by about 40%. To meet the requirements of regional green wave coordination control, the proposed model can optimize different signal timing parameters to obtain a wide range of applications of green wave coordinated control in road networks.

IARMTS: Design of an Interference-Aware Routing Model with Time Synchronization Capabilities for Dense Wireless Sensor Network Deployments

Performance of dense wireless sensor networks is often degraded due to communication interference and time synchronization issues. Existing machine learning &amp; deep learning models that propose bioinspired &amp; pre-emptive packet-analysis solutions for these tasks either have high complexity, or high deployment costs. Moreover, these models cannot be scaled for heterogeneous node &amp; traffic types, which limits their applicability when applied to real-time scenarios. To overcome these issues, this text proposes design of an interference-aware routing model with time synchronization capabilities for dense wireless sensor network deployments. The network initially collects temporal clock states &amp; packet delivery performance of different nodes on heterogeneous traffic scenarios. These traffic patterns are converted into frequency, entropy, Gabor, and Wavelet components. The converted components are used to train an ensemble set of Naïve Bayes (NB), k Nearest Neighbour (kNN), Multilayer Perceptron (MLP), and Support Vector Machine (SVM) classifiers. These classifiers assist in identification of optimal clock deviations and set of routing paths. These routing paths are further fine-tuned via use of a Bacterial Foraging Optimization (BFO) Model, which assists in identification of interference-aware paths. The BFO Model uses a temporal fitness function that fuses throughput, communication delay, energy levels, and packet delivery performance for different set of contextual communications. Due to which, the model is able to showcase lower end-to-end delay, higher throughput, lower energy consumption, and higher packet delivery performance when compared with existing routing methods under high density nodes &amp; heterogeneous network scenarios. The model showcases 99% PDR, 18.3% lower delay, 19.5% higher energy efficiency and 10.4% lower delay levels when compared with existing methods.