Routing Path Research Articles

Link attributes based multi-service routing for software-defined satellite networks

As the potential complement to terrestrial networks, satellite networks are expected to facilitate full coverage and broadband access at anytime and anywhere. However, satellite networks are characterized by highly dynamic topology and load, and how to design an adaptive routing algorithm to meet diverse application needs faces significant challenges. In this paper, we propose a Link-Attributes-based multi-service On-Demand Routing (LAODR) algorithm to provide highly reliable and service-aware data forwarding for Software -Defined Satellite Networks (SDSN). Specifically, we construct a quantitative model of link reliability which provides a fine-grained state description of the dynamic topology. Furthermore, we select the K-shortest path as the solution space and plan routing paths based on NSGA-II to allocate link resources reasonably. Extensive experiments on the Iridium validate that LAODR outperforms state-of-art routing algorithms in terms of end-to-end average latency, packet loss, throughput and node congestion degree, as well as meets the requirements of diverse services.

A Novel Energy-Efficient Routing Protocol for Industrial WSN Using Hybrid Coot-Ls Algorithm with LSTM-Based Dom Prediction

In industrial Wireless Sensor Networks (WSNs), energy efficiency and reliable data transmission are critical challenges that need to be addressed to ensure sustainable and robust network operations. This paper proposes a novel energy-efficient routing protocol that integrates a Hybrid COOT-LS (Coot- Levy Search) algorithm with Long Short-Term Memory (LSTM)-based Dominant Object Motion (DOM) prediction. The routing protocol leverages the strengths of Hybrid Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) to enhance routing efficiency and reduce energy consumption. The Hybrid PSO and ACO algorithms are employed to optimize routing paths by balancing exploration and exploitation, considering multiple factors such as energy levels, node distance, and reliability. The COOT-LS algorithm further refines these paths by incorporating a Levy flight mechanism to enhance the search process. Additionally, the LSTM-based DOM prediction provides accurate forecasts of network conditions, enabling dynamic adjustments to routing strategies in real time. Simulation results demonstrate that the proposed protocol significantly improves network lifetime, reduces energy consumption, and enhances data transmission reliability compared to traditional routing protocols. This approach provides a robust and scalable solution for industrial WSN applications, ensuring efficient and reliable network performance in dynamic and complex industrial environments.

NxtSPR: A deadlock-free shortest path routing dedicated to relaying for Triplet-Based many-core Architecture

Deadlock-free routing is a significant challenge in Network-on-Chip (NoC) design as it affects the network’s latency, power consumption, and load balance, impacting the performance of multi-processor systems-on-chip. However, achieving deadlock-free routing will routinely result in expensive overhead as previous solutions either sacrifice performance or power efficiency to proactively avoid deadlocks or impose high hardware complexity to resolve deadlocks when they occur reactively. Utilizing the various characteristics of NoC to implement deadlock-free routing can be significantly more cost-effective with less impact on performance. This paper proposes a relay routing algorithm (NxtSPR) with a shortest path property and a deadlock prevention mechanism based on a synchronized Hamiltonian ring. The proposal is based on an in-depth study of the characteristics of a Triplet-Based many-core Architecture (TriBA) NoC. We establish various important topology-related theories and perform a formal verification (proof-based) for them. By utilizing the critical subgraph and apex of TriBA, NxtSPR can pre-calculate downstream nodes forwarding ports for packets by using a concise judgment strategy. This significantly reduces the computational overhead required for data transmission while optimizing the pipeline design of routers to decrease packet transmission latency and power consumption compared to other TriBA routing algorithms. We group the data transmissions according to the levels of maximum Hamiltonian edges a packet will traverse during its transmission life cycle. Independent data transmissions between groups can avoid mutual interference and resource competition, eliminating potential deadlocks. Gem5 simulation results show that, under the synthetic traffic patterns, compared to the representative (Table) and up-to-date (SPR4T) routing algorithms, NxtSPR achieves a 20.19%, 14.76%, and 5.54%, 4.66% reduction in average packet latency and per-packet power consumption, respectively. Moreover, it has an average of 18.50% and 4.34% improvement in throughput, as compared to them. PARSEC benchmark results show that NxtSPR reduces application runtime by up to a maximum of 22.30% and 12.82% compared to Table and SPR4T, and running the same applications with TriBA results in a maximum runtime reduction of 10.77% compared to 2D-Mesh.

SD-GPSR: A Software-Defined Greedy Perimeter Stateless Routing Method Based on Geographic Location Information

To mitigate the control overhead of Software-Defined Mobile Ad Hoc Networks (SD-MANETs), this paper proposes a novel approach, termed Software-Defined Greedy Perimeter Stateless Routing (SD-GPSR), which integrates geographical location information. SD-GPSR optimizes routing functions by decentralizing them within the data plane of SD-MANET, utilizing the geographic location information of nodes to enhance routing efficiency. The controller is primarily responsible for providing location services and facilitating partial centralized decision-making. Within the data plane, nodes employ an enhanced distance and angle-based greedy forwarding algorithm, denoted as GPSR_DA, to efficiently forward data. Additionally, to address the issue of routing voids in the data plane, we employ the A* algorithm to compute an optimal routing path that circumvents such voids. Finally, we conducted a comparative analysis with several state-of-the-art approaches. The evaluation experiments demonstrate that SD-GPSR significantly reduces the control overhead of the network. Simultaneously, there is a notable improvement in both end-to-end latency and packet loss rate across the network.

Routing on heavy path WSPD spanners

In this article, we present a construction of a spanner on a set of n points in Rd that we call a heavy path WSPD spanner. The construction is parameterized by a constant s>2 called the separation ratio. The size of the graph is O(sdn) and the spanning ratio is at most 1+2/s+2/(s−1). We also show that this graph has a hop spanning ratio of at most 2lg⁡n+1.We present a memoryless local routing algorithm for heavy path WSPD spanners. The routing algorithm requires a vertex v of the graph to store O(deg⁡(v)log⁡n) bits of information, where deg⁡(v) is the degree of v. The routing ratio is at most 1+4/s+1/(s−1) and at least 1+4/s in the worst case. The number of edges on the routing path is bounded by 2lg⁡n+1.We then show that the heavy path WSPD spanner can be constructed in metric spaces of bounded doubling dimension. These metric spaces have been studied in computational geometry as a generalization of Euclidean space. We show that, in a metric space with doubling dimension λ, the heavy path WSPD spanner has size O(sλn) where s is the separation ratio. The spanning ratio and hop spanning ratio are the same as in the Euclidean case.Finally, we show that the local routing algorithm works in the bounded doubling dimension case. The vertices require the same amount of storage, but the routing ratio becomes at most 1+(2+ττ−1)/s+1/(s−1) in the worst case, where τ≥11 is a constant related to the doubling dimension.

Clustered Routing Using Chaotic Genetic Algorithm with Grey Wolf Optimization to Enhance Energy Efficiency in Sensor Networks.

As an alternative to flat architectures, clustering architectures are designed to minimize the total energy consumption of sensor networks. Nonetheless, sensor nodes experience increased energy consumption during data transmission, leading to a rapid depletion of energy levels as data are routed towards the base station. Although numerous strategies have been developed to address these challenges and enhance the energy efficiency of networks, the formulation of a clustering-based routing algorithm that achieves both high energy efficiency and increased packet transmission rate for large-scale sensor networks remains an NP-hard problem. Accordingly, the proposed work formulated an energy-efficient clustering mechanism using a chaotic genetic algorithm, and subsequently developed an energy-saving routing system using a bio-inspired grey wolf optimizer algorithm. The proposed chaotic genetic algorithm-grey wolf optimization (CGA-GWO) method is designed to minimize overall energy consumption by selecting energy-aware cluster heads and creating an optimal routing path to reach the base station. The simulation results demonstrate the enhanced functionality of the proposed system when associated with three more relevant systems, considering metrics such as the number of live nodes, average remaining energy level, packet delivery ratio, and overhead associated with cluster formation and routing.

Energy Efficient Data Aggregation in Wireless Sensor Networks Using Meta Heuristic Based Feed Forward Back Propagation Neural Network Approach

Sensor nodes are low-cost, low-power, tiny devices that make up the majority of WSNs, or distributed, self-organizing systems. These sensor nodes are able to exchange, perceive, and interpret data. The sensor nodes are equipped with a wide variety of sensors, such as chemical, touch, motion, temperature, and weather sensors. Because of its adaptability, sensors are used in a variety of applications such as automation, tracking, monitoring, and surveillance. Despite the enormous number of sensor applications, WSNs continue to suffer from common challenges like as low memory, slow processing speed, and short network lifetime. The feed forward back propagation neural network mode (FFBPNN) based on meta heuristics aims to create many paths for effective data aggregation in wireless sensor networks. This model handled the process of identifying and selecting the optimum route path. The distributed sensor nodes are utilized to create the various route paths. In this research paper, data aggregation is done using meta-heuristic firefly algorithm that helped in identifying an optimal route from among the found routes. After selecting the operative ideal route choice, the data aggregation procedure practices a rank-based approach to accomplish lower latency and a better packet delivery ratio(PDR). In addition to throughput, simulation was done to improve and measure performance in terms of packet delivery ratio, energy consumption, and end-to-end latency.

Graph Theoretical Models for Enhancing Highway Connectivity and Safety in Vehicular Networks

Vehicular networks play a crucial role in modern transportation systems, significantly impacting connectivity and safety on highways. This paper explores the application of graph theoretical models to enhance both connectivity and safety in vehicular networks. Graph theory, a branch of discrete mathematics, provides a robust framework for modeling and analyzing complex networks, including those formed by vehicles on highways. Our study begins by defining the vehicular network as a graph where nodes represent vehicles, and edges denote communication links between them. We employ various graph theoretical concepts such as connectivity, centrality, and network flow to evaluate and improve the network's performance. Key metrics, including the degree of nodes, clustering coefficients, and shortest path lengths, are utilized to quantify network connectivity and identify critical nodes and edges that influence overall network efficiency. One of the primary objectives is to ensure uninterrupted connectivity in the presence of dynamic and often unpredictable vehicular movement. To this end, we analyze the network's resilience to node failures and propose strategies to enhance robustness using redundancy and alternative routing paths. By incorporating concepts like k-connectivity and network diameter, we develop models that maintain high levels of connectivity despite the removal or failure of multiple nodes or edges. Safety is addressed through the lens of network stability and reliability. We investigate the impact of vehicular density, speed, and communication range on the network's ability to sustain reliable communication channels. Techniques such as dynamic topology management and adaptive power control are proposed to mitigate the risks associated with network fragmentation and communication delays. Furthermore, we introduce optimization algorithms that leverage graph partitioning and community detection to improve the management of vehicular clusters, facilitating efficient data dissemination and reducing the likelihood of congestion-related incidents. The proposed models are validated through simulations that mimic real-world highway conditions, demonstrating significant improvements in both connectivity and safety metrics. In conclusion, the application of graph theoretical models offers a promising approach to enhancing highway connectivity and safety in vehicular networks

Dynamic Cooperative Communications with Mutual Information Accumulation for Mobile Robots in Industrial Internet of Things.

Mobile robots play an important role in the industrial Internet of Things (IIoT); they need effective mutual communication between the cloud and themselves when they move in a factory. By using the sensor nodes existing in the IIoT environment as relays, mobile robots and the cloud can communicate through multiple hops. However, the mobility and delay sensitivity of mobile robots bring new challenges. In this paper, we propose a dynamic cooperative transmission algorithm with mutual information accumulation to cope with these two challenges. By using rateless coding, nodes can reduce the delay caused by retransmission under poor channel conditions. With the help of mutual information accumulation, nodes can accumulate information faster and reduce delay. We propose a two-step dynamic algorithm, which can obtain the current routing path with low time complexity. The simulation results show that our algorithm is better than the existing heuristic algorithm in terms of delay.

Research on AdHoc network routing algorithm for link stability prediction

Abstract This study aims to improve network communication efficiency and reliability by researching routing algorithms for predicting link stability in AdHo networks. AdHoc network is a self-organizing, decentralized network structure in which nodes establish temporary connections through wireless communication, making the network resilient and flexible. However, due to the continuous changes in network topology and the instability of wireless links, routing problems in AdHoc networks have become particularly complex. This article proposes a backup path routing algorithm LS-BPR based on link stability prediction. This mechanism takes the path with the minimum delay as the main path, uses the cost function as the standard for selecting backup paths, and then uses the ARIMA model to predict links with good link stability as backup paths. We simulate and compare LS-BPRAODV and AODV protocols using the QualNet simulation platform. The simulation results show that the LS-BPRAODV, with the addition of a prediction mechanism, improves network performance, reduces the number of source node routing discoveries, reduces the number of route breaks, significantly reduces the packet loss rate and information reception delay in the network, and improves the average throughput of the network. This study provides new theoretical and empirical support for the routing algorithm for link stability prediction in AdHo networks, which is of great significance for promoting the development of wireless self-organizing networks.

Effects of packetization on communication dynamics in brain networks.

Computational studies in network neuroscience build models of communication dynamics in the connectome that help us understand the structure-function relationships of the brain. In these models, the dynamics of cortical signal transmission in brain networks are approximated with simple propagation strategies such as random walks and shortest path routing. Furthermore, the signal transmission dynamics in brain networks can be associated with the switching architectures of engineered communication systems (e.g., message switching and packet switching). However, it has been unclear how propagation strategies and switching architectures are related in models of brain network communication. Here, we investigate the effects of the difference between packet switching and message switching (i.e., whether signals are packetized or not) on the transmission completion time of propagation strategies when simulating signal propagation in mammalian brain networks. The results show that packetization in the connectome with hubs increases the time of the random walk strategy and does not change that of the shortest path strategy, but decreases that of more plausible strategies for brain networks that balance between communication speed and information requirements. This finding suggests an advantage of packet-switched communication in the connectome and provides new insights into modeling the communication dynamics in brain networks.

Detection of sequence number attacks using enhanced AODV protocol in MANETs

A Mobile Adhoc NETwork (MANET) operates without dedicated routing hardware, relying instead on dynamic wireless connections among nodes that can join or leave the network at any time. This dynamic nature demands a flexible routing protocol to manage variable number of nodes effectively. However, the absence of centralized infrastructure in MANETs poses security challenges, leaving nodes vulnerable to attacks. While several security solutions have been proposed, many face practical limitations. This paper introduces a novel approach to detect attacks on the Ad Hoc On-Demand Distance Vector (AODV) routing protocol within MANETs, specifically those involving malicious nodes masquerading as best routing paths by manipulating AODV's sequence numbers. An extension to the AODV protocol is introduced to ensure secure communication between different nodes in MANET. The proposed security mechanism includes a signature verification mechanism based on the Edwards digital signature curve (Ed25519), which is demonstrated on the Objective Modular Network Testbed in C++ (OMNet++) simulator. Our solution employs an extension to AODV protocol where asymmetric cryptography is introduced to generate and authenticate digital signatures, ensuring a high level of security. Additionally, a novel detection mechanism is introduced to identify malicious nodes within the network as a supplementary security layer. Simulation results demonstrate a high detection rate, accurately identifying between 83 % and 100 % of malicious nodes based on their presence in the network.

High-Performance Wave Union Time-to-Digital Converter Implementation Based on Routing Path Delays of FPGA

Time-to-digital converters (TDCs) with superior performance are in high demand in application domains like light detection and ranging (LIDAR), nuclear physics, and time interval counters. One of the interesting architectures for field-programmable gate array (FPGA)-based TDCs is the tapped delay line (TDL) approach with carry chains as delay elements. However, the resolution of TDL-TDCs is limited, and linearity is weakened by the ultra-wide bins that correspond to the FPGA’s long routing wires crossing into another clock area. This paper presents wave union TDC using FPGA internal routing wires as delay elements to subdivide ultra-wide bins. The Zynq Evaluation and Development (ZED) board is used to implement and test the wave union types: A (WU-A) and B (WU-B) TDCs. According to experimental data, the WU-A TDC based on an 8 × 128 matrix of counters has a resolution of 5.7 ps, an integral nonlinearity (INL) of 1.1170 LSB (RMS), and a differential nonlinearity of 0.329 LSB (RMS). WU-A TDC improves DNL and INL by 19% and 57%, respectively, over ordinary TDC. The WU-B TDC uses an average of sixteen different time measurements, resulting in an effective resolution of up to 0.356 ps, a DNL of 0.60 LSB (RMS), and an INL of 1.04 LSB (RMS). These characteristics make the TDC suitable for time-of-flight applications such as LIDAR and for other general-purpose scientific instruments.

Optimizing Charging Pad Deployment by Applying a Quad-Tree Scheme

The recent advancement in wireless power transmission (WPT) has led to the development of wireless rechargeable sensor networks (WRSNs), since this technology provides a means to replenish sensor nodes wirelessly, offering a solution to the energy challenges faced by WSNs. Most of the recent previous work has focused on charging sensor nodes using wireless charging vehicles (WCVs) equipped with high-capacity batteries and WPT devices. In these schemes, a vehicle can move close to a sensor node and wirelessly charge it without physical contact. While these schemes can mitigate the energy problem to some extent, they overlook two primary challenges of applied WCVs: off-road navigation and vehicle speed limitations. To overcome these challenges, previous work proposed a new WRSN model equipped with one drone coupled with several pads deployed to charge the drone when it cannot reach the subsequent stop. This wireless charging pad deployment aims to deploy the minimum number of pads so that at least one feasible routing path from the base station can be established for the drone to reach every SN in a given WRSN. The major weakness of previous studies is that they only consider deploying a wireless charging pad at the locations of the wireless sensor nodes. Their schemes are limited and constrained because usually every point in the deployed area can be considered to deploy a pad. Moreover, the deployed pads suggested by these schemes may not be able to meet the connected requirements due to sparse environments. In this work, we introduce a new scheme that utilizes the Quad-Tree concept to address the wireless charging pad deployment problem and reduce the number of deployed pads at the same time. Extensive simulations were conducted to illustrate the merits of the proposed schemes by comparing them with different previous schemes on maps of varying sizes. In the case of large maps, the proposed schemes surpassed all previous works, indicating that our approach is more suitable for large-scale network environments.

Hierarchical porous spindle-shaped MIL-88B@Fe2O3 with core-shelled structure for conductometric trimethylamine detection

Hierarchical porous spindle-shaped MIL-88B (HPS-MIL-88B) with core-shelled structure was prepared by citric acid-assisted. The material exhibits open channels on its outer surface and a hierarchical internal cavity structure with meso-/macro-pores. The samples were then pretreated at 200 °C to form an oxide film, aiming to improve the conductivity of material. The as-prepared HPS-MIL-88B-6 h@Fe2O3 material with core-shelled structure exhibits superior selectivity and high sensitivity to trimethylamine (TMA) at 200 °C (detection limit as low as 1 ppm) with fast response and excellent repeatability. The outstanding sensing performance was owing to the open and hierarchical porous that enhances the reaction between the target gas and the active sites of the material. In addition, the metal unsaturated active sites of MIL-88B (Lewis’s acid) can selectively undergo acid-base reactions with TMA (Lewis’s base), enhancing the generation of oxygen species and improving the selective adsorption capacity towards the target gas. This work provides a new path route for the application of MOF materials in sensors.

QoS-based energy-efficient hybrid routing protocols in RSU assisted VANET using clustering mechanism

ABSTRACT Vehicular ad hoc networks (VANET) communication framework are employed to improve communication between vehicles on the highway and urban roads. Evaluating the best routing path by satisfying the QoS (Quality of Service) plays a substantial role in networking. Researchers for maximising the routing efficiency have employed numerous methods. But these criteria cannot be attained effectively due to certain limitations, including frequent route failure, high overhead, increased packet loss rate, less throughput and packet delivery rate. Hence, this work presents an effective strategy to offer better communication services with less energy consumption and high flexibility using QoS-based energy efficient routing framework with clustering (QoS_EErcF) network model. Initially, different clusters are formed by considering the constraints like node direction, link reliability, speed and distance. The cluster heads (CH) are chosen from the clusters with the dissemination of gateway nodes and cluster members using an Amended rapid cosine similarity-founded clustering mechanism (Arc_SimC). The best routing strategy for transmitting the data from source to destination is performed by using Integrated Pelican with grasshopper optimisation algorithm (IPel_Ghop). Constraints such as bandwidth, node distance, energy, packet loss rate, network traffic and end-to-end delay are considered to find the optimal path. The results are simulated using MATLAB, whereas the energy efficiency of a proposed model is obtained to be 98.37%, and the packet delivery ratio is attained as 99.60%.

A multi path routing protocol with efficient energy consumption in IoT applications real time traffic

The extensive utilization of IoT applications leads to the aggregation of a substantial volume of data, presenting a crucial challenge in terms of data routing within these networks. RPL intentionally surpasses the limitations sometimes observed in low-power and lossy networks, which are particularly prevalent in IoT networks. The RPL protocol is designed specifically for static networks that do not involve mobility or topological changes. The RPL protocol guarantees continuous connectivity between nodes and mitigates the risk of data loss in stationary IoT applications that do not involve mobility or alterations in network configuration. The article utilizes a mobility aid technology known as network performance stability using the intelligent routing protocol (nPSIR), which expands upon RPL. The Mobility Support Entity (nPSIR) facilitates the displacement of all nodes, with the exception of the root node, and ensures uninterrupted connection during mobility. Moreover, it deals with the situation where there is a physical barrier between two interconnected nodes in a changing environment. In order to achieve this objective, it employs a dynamic trickle timer that operates within two distinct ranges. Furthermore, it utilizes a neighbor link quality table, a mechanism for selecting the most beneficial parent node in the event of migration, a measure of confidence, the identification of crucial regions, and a blacklist. Multiple simulations validate that nPSIR effectively decreases hand-off delay and improves packet delivery, despite the minor drawbacks of increased signaling costs and power consumption. The delivery ratio decreases the quantity of lost data packets and surpasses both RPL as a responsive protocol and mRPL as a proactive protocol in relation to mobility.

Energy-Efficient Secure Routing for a Sustainable Heterogeneous IoT Network Management

The Heterogeneous Internet of Things (H-IoT) is considered as the upcoming industrial and academic revolution in the technological world, having billions of things and devices connected to the Internet. This H-IoT has a major issue of energy consumption during data transmission which leads to low scalability. Additionally, anomalies in the data create a serious threat to energy in H-IoT. To overcome these issues, a novel approach has been proposed in this study termed as the Energy-Efficient Memetic Clustering Method (EEMCM), which combines the Parallelized Memetic Algorithm (PMA) with the AlexNet architecture to improve anomaly detection efficiency in IoT WSNs. Initially, cluster formation and CH selection are carried out using PMA. This is followed by routing path generation, and the data are prepared for high-level feature extraction. The extracted features are classified to identify anomalies. For anomaly detection, high-level features were collected that contain data relevant to the model given as input into the AlexNet architecture, which detects anomalies and identifies normal or potential attacks within the IoT WSNs. The proposed EEMCM model has been implemented in the MATLAB platform and obtained an accuracy of 99.11%. As a result, the overall performance of the network is improved.

Spatial Optimization for Energy Island Location and Cable Routing Considering Offshore Zoning

The energy hub concept presents a compelling opportunity to channel electricity from offshore wind farms to the grid, facilitating cross-border energy trading and conversion. However, one of the main challenges lies in the strategic identification of a suitable energy island location and the establishment of an interconnected cable routing path. Part of the complexity arises from the need to navigate offshore zoning regulations and accommodate various spatial uses within the offshore area. This paper exhibits a case study to explore the spatial optimization for a potential offshore energy hub in the North Sea. It aims at finding out an optimal spatial configuration of the offshore energy hub, such that the island location and the associated cable routing do not infringe upon keep-out zones while strategically positioning the hub closer to high-capacity wind farms. To achieve this goal, detailed geographical data incorporating offshore zoning information is leveraged for spatial optimization. The Dijkstra’s algorithm is then applied to identify the optimal island location and cable routing path. The effectiveness of this spatial optimization methodology is demonstrated through a case study involving offshore wind farm sites in the North Sea. Given the real geographical data, simulation results underscore the efficacy of the Dijkstra’s algorithm in determining the optimal energy hub layout. In particular, an optimal spatial design is achieved, based on cable lengths, asset capacities, offshore zoning and island/platform location for a potential offshore energy hub in the North Sea while ensuring compliance with keep-out zoning regulations.

A Performance Efficient Quadrant-Based Scheme for Multiple Assets Location Preservation in Wireless Sensor Networks

The usefulness of wireless sensor networks has grown throughout numerous areas, many of which demand constant monitoring of the event of interest by the sensor nodes. The wireless transfer of information and the connection of the sensors, however, present the network with a number of security risks. A real-world concern is a danger to source location privacy for networks when several events of interest are monitored. In this research, we present the quadrant-based boundary routing (QBR) method for multiple source location privacy protection. In the first phase, the packet is routed arbitrarily away from the source nodes by random routing, and then quadrant routing begins. Before being sent to the base station using a forward random walk, the packet is transmitted to either side of the network border. We present a theoretical analysis of the maximum delay for the proposed technique. Extensive simulations are carried out and results demonstrate that in contrast to Shortest Path Routing, QBR elevates security by 880.95%, entropy by 623.28%, and capture rate by 71.42% for multiple assets, whereas contemporary PRLPRW increases the corresponding metrics by 692%, 390%, and 49.28%, respectively.