Packet Delivery Performance Research Articles

Hybrid Optimization With Deep Spiking Equilibrium Neural Network for Software Defined Network Based Congestion Prevention Routing

ABSTRACTIn the Internet of Things (IoT) network, within a period of time it constructs a huge network of millions and billions of things that incorporate with each other, leading to various technical and application problems. Also, on‐time delivery of packets is significant in software‐defined networks. In the current software‐defined network, the bandwidth overhead is not considered when an enormous amount of traffic enters the network; this may lead to network congestion. To overcome this issue, a novel method is proposed to detect network congestion based on hybrid optimization, namely, the Hybrid Gannet with Pelican Optimization (HGPO) algorithm. The node‐level congestions and the link‐level congestions, which means the buffer overflow and the multiple nodes trying to utilize the channel at the same time, must be controlled efficiently. Once the network congestion gets controlled, the shortest route path selection and congestion prevention are performed perfectly with the aid of the proposed Deep Spiking Equilibrium Neural Network (DSENN). A few route discovery frequency vectors, such as the interroute discovery time and route discovery time of each node, are determined to prevent congestion. Finally, it is implemented in the Python platform successfully, and the achieved throughput, delay, packet loss ratio, packet delivery ratio, end‐to‐end delay, and performance measures of the proposed method are 140 Mbit/s, 17 ms, 3.8%, 0.35 s, and 100%, respectively, which outperforms the other compared traditional algorithms.

EWFAIGF: Design of an Efficient Model for Enhancing Energy Efficiency in IoT-Based Wireless Sensor Networks through Fuzzy AHP and Iterative Grey Wolf Jelly Fish Optimization

In the realm of Wireless Sensor Networks (WSNs), the quest for enhanced energy efficiency remains paramount, given their pivotal role in facilitating the Internet of Things (IoT) applications. Existing methodologies often grapple with the dual challenge of optimizing energy consumption while ensuring robust data transmission, thereby limiting their efficacy in dynamic, resource-constrained environments. This work introduces a novel paradigm that meticulously addresses these constraints, thereby heralding a significant leap towards optimizing WSNs' operational efficiency. At the core of our proposed model lies the integration of Fuzzy Analytic Hierarchy Process (Fuzzy AHP)-based clustering, which ingeniously segregates nodes by leveraging multi-criteria such as location, energy efficiency, and temporal performance. This clustering serves as a precursor to the application of an innovative Iterative Grey Wolf Jelly Fish Optimizer (GWJFO). The GWJFO stands out by its strategic prowess in delineating optimal routing paths, thus minimizing energy expenditure and enhancing data relay efficiency. Furthermore, our model is fortified with a Q Learning Method, ingeniously designed to identify and execute optimal alternate routing paths through a Make Before Break strategy. This addition not only mitigates potential faults but also significantly boosts computational efficiency and packet delivery performance. Empirical validation through real-time network simulations underscored the model's superiority, demonstrating a 9.5% improvement in communication speed, an 8.5% increase in energy efficiency, a 4.5% improvement in packet delivery performance under fault conditions, a 10.4% rise in throughput, and a 5.9% enhancement in network consistency over existing benchmarks. This groundbreaking work not only paves the way for more energy-efficient WSNs but also sets a new standard in achieving high-performance metrics essential for the next generation of IoT applications. The implications of these advancements extend beyond mere technical enhancements, offering a beacon for future research in the domain, potentially revolutionizing the way we deploy and manage sensor networks in an array of applications.

An Optimized Routing Scheme in SDN-Based IoT Using the Lion Optimization Algorithm

The Internet of Things (IoT) speaks to the current and future state of the Internet. Numerous things (objects) related to the Internet generate a large amount of information that requires a ton of labor and prepare tasks to move to valuable data. In addition, for the association and control of this enormous information, the original ideas in the project and the executives of the IoT system need to expedite and improve its presentation. A software-defined networking (SDN) system is another worldview that has recently emerged to cover all the complexities in the traditional system architecture by summarizing all the controls and management functions from the basic devices (things in IoT). The open-flow protocol is used to make messages between switches and controllers. It is a correspondence conference, through which the controller integrates the path of the network pocket with switches. Therefore, data packets should be directed to the optimum path to improve system operation. The best or optimal path for packet routing in the SDN data plane is found using the Lion Optimization Algorithm (LOA), which is proposed in this study. Response times and packet loss rates can both be decreased by choosing the best route between the hosts. The suggested LOA outperforms current optimal path detection methods in terms of performance, delay, packet delivery rate, performance, and pocket loss rate, according to simulation findings.

Efficient Distance and Connectivity Based Traffic Density Stable Routing Protocol for Vehicular Ad Hoc Networks

Vehicle ad-hoc networks (VANETs) have become a prominent research topic in recent years due to rapid dynamic topology, high vehicle mobility, frequent link failures and significant delay constraints. The mobility of the vehicle nodes increases, and the overhead of control traffic due to the high dynamics of the VANET. Efficient routing algorithms are necessary for VANETs to ensure reliable transmission (VANETs). This work proposes a Traffic Density Stable Routing Protocol based on Connection- and Distance (TDSRP-DC) to avoid data packet collisions at intersections and an adaptive routing schedule based on the selection at every instant. Our approach is based on vehicle-to-vehicle communication. Ground vehicles identify the most appropriate next junction and transfer the data packet to the receiver to find the optimal multi-hop route. It relies on transferring the data between all vehicles to estimate real-time traffic fluctuations sporadically. Network formation, neighbor realization, fitness value prediction, and routing methodology are the conceptual process needed in this research. Valid parameters for finding the optimum path in the conceptual model include node distance, node speed, node azimuth, link stability, and link reliability. The suggested solution’s simulation was performed in comparison to traditional algorithms. Compared to existing TFOR algorithms on complex traffic, the proposed TDSRP-DC provides tremendous improvements based on packet delivery rate (10%) and performance (35%). The demonstrated results and comparisons illustrate the proposed routing protocol’s significance with enhanced quality of service performance.

DFQIoV: Design of a Dynamic Fan-Shaped-Clustering Model for QoS-aware Routing in IoV Networks

Internet of Vehicles (IoV) is a steadily growing field of research that deals with highly ad-hoc wireless networks. These networks require design of high-speed & high-efficiency routing models, that can be applied to dynamically changing network scenarios. Existing models that perform this task are highly complex and require larger delays for estimation of dynamic routes. While, models that have faster performance, do not consider comprehensive parameters, which limits their applicability when used for large-scale network scenarios. To overcome these limitations, this text proposes design of a novel dynamic fan-shaped clustering model for QoS-aware routing in IoV networks. The model initially collects network information sets including node positions, & energy levels, and combines them with their temporal packet delivery & throughput performance levels. These aggregated information sets are processed via a hybrid bioinspired fan shaped clustering model, that aims at optimization of routing performance via deployment of dynamic clustering process. The model performs destination-aware routing process which assists in reducing communication redundances. This is done via a combination of Elephant Herding Optimization (EHO) with Particle Swarm Optimization (PSO), which integrates continuous learning for router level operations. The integrated model is able to reduce communication delays by 5.9%, while improving energy efficiency by 8.3%, throughput by 4.5%, and packet delivery performance by 1.4% under different network scenarios. Due to which the proposed model is capable of deployment for a wide variety of dynamic network scenarios.

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 & deep learning models that propose bioinspired & 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 & 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 & 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 & 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.

BMUDF: Hybrid Bio-inspired Model for fault-aware UAV routing using Destination-aware Fan shaped clustering

Routing data between unmanned aerial vehicles (UAVs) involves identifying node locations, analyzing residual energy levels, evaluating temporal throughput and packet delivery performance, and identifying other network and node parameters. It assists in forming quality of service (QoS) aware routes. Existing routing models require large data samples to find the optimized path or are highly complex, increasing their computational requirements. Low-complexity models showcase low-performance routing QoS when deployed on large-scale networks. To solve these limitations, a hybrid bioinspired model is proposed for fault-aware UAV routing that uses destination awareness with a fan-shaped clustering process (BMUDF). The model initially collects data from different UAV nodes and their node-level and network-level constraints. These parameters are processed through the particle swarm optimization (PSO) model, which enforces fan-shaped clustering (FSC) for effective routing operations. Our scheme uses the PSO model to identify the initial routing paths cascaded with a genetic algorithm (GA) based destination-aware routing model. These routing paths are evaluated through the QoS matrices like maximum temporal throughput, packet delivery ratio (PDR), delay, and energy consumption. A grey wolf optimization (GWO) model further scrutinizes this routing performance, integrating fault tolerance and route optimization during continuous operations. The GWO model evaluates a trust-based fitness function, which helps to identify faulty nodes, and reconfigures the network with non-faulty nodes to improve its QoS performance under node failures and faults. Integrating the bioinspired models into the proposed system maximizes performance under different network scenarios. This performance compares and validated with an analysis of variance (ANOVA) test with various state-of-the-art models. It is observed that the proposed model showcased an 8.3% lower routing delay, 5.9% lower energy consumption, 1.5% higher packet delivery ratio, and 9.1% higher throughput, which makes it useful for a wide variety of real-time UAV routing scenarios.

Resource Allocation in 5G Platoon Communication: Modeling, Analysis and Optimization

New features brought by 5 G New Radio V2X (NR-V2X) support multiple vehicular communication types (unicast, groupcast, and broadcast) to coexist in road scenarios. However, the current standard does not specify the resource scheduling approach for groupcast to support such a new feature, which may severely degrade its packet delivery performance impacted by other communication types. In this paper, we investigate the scheduling and resource allocation approaches for a groupcast-based application, vehicle platooning, under the environment characterizing this new feature. We first analyze two baseline resource allocation approaches, i.e., Semi-persistent Scheduling (SPS) stated in the 3 rd generation partnership project (3GPP) and Random Selection (RS), based on the metric of packet collision probability. Subsequently, considering the issues from baseline approaches, we develop an Improved Random Selection (IRS) scheme to decrease the collision probability. We further propose to employ Deep Deterministic Policy Gradient (DDPG) algorithm to overcome the impact of inter-vehicle collaboration in the platoon based on local information. A Monte Carlo simulator is then used to verify the analytical models' results. The numerical results show that IRS significantly mitigates the packet collision probability compared with the baselines. Meanwhile, DDPG outperforms IRS in terms of the packet collision probability as well as average scheduled delay and is more robust to the change in the environment.

Hybrid Trust Based Reputation Mechanism for Discovering Malevolent Node in MANET

A self-contained connection of wireless links that functions without any infrastructure is known as Mobile Ad Hoc Network (MANET). A MANET’s nodes could engage actively and dynamically with one another. However, MANETs, from the other side, are exposed to severe potential threats that are difficult to counter with present security methods. As a result, several safe communication protocols designed to enhance the secure interaction among MANET nodes. In this research, we offer a reputed optimal routing value among network nodes, secure computations, and misbehavior detection predicated on node’s trust levels with a Hybrid Trust based Reputation Mechanism (HTRM). In addition, the study designs a robust Public Key Infrastructure (PKI) system using the suggested trust evaluation method in terms of “key” generation, which is a crucial component of a PKI cryptosystem. We also concentrate on the solid node authenticating process that relies on pre-authentication. To ensure edge-to-edge security, we assess safe, trustworthy routes to secure computations and authenticate mobile nodes, incorporating uncertainty into the trust management solution. When compared to other protocols, our recommended approach performs better. Finally, we use simulations data and performance evaluation metrics to verify our suggested approach’s validity Our approach outperformed the competing systems in terms of overall end-to-end delay, packet delivery ratio, performance, power consumption, and key-computing time by 3.47%, 3.152%, 2.169%, and 3.527%, 3.762%, significantly.

RPLIE: RPL for indoor environments under midterm link fluctuations

Link dynamics due to environmental change is a critical challenge for wireless networking, and handling it is essential for wireless protocols, especially for low-power and lossy multihop wireless networks (LLNs). Prior work have only studied methods to detect and cope with short-term and long-term link dynamics in LLN. In this work, we tackle the midterm dynamics due to the opening and closing of doors and windows that can happen in a time scale of a few minutes to hours in an indoor LLN. Specifically, we study how midterm link fluctuations impact the performance of standard IPv6 routing protocol for LLN (RPL), and design ‘RPLIE,’ a novel and backward-compatible lightweight enhancement to RPL that detects and overcomes the negative impact of such dynamics. We define ‘opportunistic links’ and propose a novel routing metric, expected breakage cost (EBC), which represents the expected number of link transmissions required from a link breakage to a routing parent change. We implement RPLIE on real embedded devices, and evaluate its performance against the standard RPL via experiments on a 31-node testbed to show that RPLIE achieves significantly better packet delivery performance while using less overhead.

A Defensive Scheduling Scheme to Accommodate Random Selection Devices in 5G NR V2X

The newly completed 3GPP Release 16 for 5G New Radio (NR) vehicle-to-everything (V2X) communication assumes that all participating devices can continually sense the channel activities in terms of resource use. However, many non-vehicular devices such as smartphones of vulnerable road users (VRUs) and road-embedded sensors are constrained by battery, and cannot afford such “always-on” sensing. The deficiency will have undesirable impact on the existing resource allocation algorithm in the envisioned NR V2X integrated framework. As we start the efforts towards Release 17, therefore, there is a pressing need to develop a solution to smoothly integrate the limited-sensing devices into the framework. This letter develops a solution based on the new features available in Release 16 NR Sidelink Mode 2, such as resource reservation pointers and resource pre-emption checks. We demonstrate that a defensive transmission strategy employed by the full-sensing devices that jitters their transmissions around the resource double-booked by the limited-sensing devices can significantly mitigate the packet delivery performance degradation for both the full-sensing and the limited-sensing devices.

Cluster based Certificate Blocking Scheme using Improved False Accusation Algorithm

The aggregation of mobile nodes without the use of a base station is known as Mobile Ad Hoc Networks (MANETS). In nature, the nodes are moving. These networks are not connected and thus subject to security attacks due to their mobility. There are several mechanisms proposed to prevent mishaps while routing of the packets in such networks methods: The methodology outlined in Mobile Ad Hoc Networks to protect against various types of assaults is based on a recent method known as Cooperative Bait Detection Scheme. Its implementation scenario demonstrates that in the event of Sybil assaults, the packet delivery ratio and performance are low. on the network. Our goal is to propose a cluster-based methodology to improve delays, packet delivery ratio, and other performance assessment criteria. Improved Cooperative Bait Detection recommends a disjointed multipath technique to avoid attacks. Until date, the dropped packet delivery ratio has been the key to preventing collaborative and Sybil assaults. In the Hybrid Cooperative Bait Detection Scheme, nodes are verified in two stages: first, on the basis of packet delivery ratio, and then, in the second stage, the exact cause of performance decline is explored to check node behavior. In order to improve security, certifying procedures must be used to clustered networks. For malevolent entities, the false accusation algorithm provided certificate revocation and blocking approaches. An algorithm is proposed that remembers false accusations for a set period of time in order to increase the number of normal nodes in the network and hence improve the system's performance. Results: With the help of NS2 simulation, the clustering approach was evaluated by considering several Sybil-attack network scenarios. When the proposed work is compared to other ways such as Cooperative Bait Detection Scheme, Improve Comparative Bait Detection Scheme, and Hybrid Comparative Bait Detection Scheme, the results show that Packet Delivery Ratio and performance are improved for Sybil attackers over the internet. In conjunction with Certifying authority, the Cluster Head in the network identifies and prevents false complaints. The results of the comparison using several performance parameters reveal that the new strategy outperforms the existing ones. As the number of normal nodes in the system grows, the system will be able to work at its best, preventing various types of attacks.

Distributed Node Scheduling with Adjustable Weight Factor for Ad-hoc Networks.

In this paper, a novel distributed scheduling scheme for an ad-hoc network is proposed. Specifically, the throughput and the delay of packets with different importance are flexibly adjusted by quantifying the importance as weight factors. In this scheme, each node is equipped with two queues, one for packets with high importance and the other for packets with low importance. The proposed scheduling scheme consists of two procedures: intra-node slot reallocation and inter-node reallocation. In the intra-node slot reallocation, self-fairness is adopted as a key metric, which is a composite of the quantified weight factors and traffic loads. This intra-node slot reallocation improves the throughput and the delay performance. Subsequently, through an inter-node reallocation algorithm adopted from LocalVoting (slot exchange among queues having the same importance), the fairness of traffics with the same importance is enhanced. Thorough simulations were conducted under various traffic load and weight factor settings. The simulation results show that the proposed algorithm can adjust packet delivery performance according to a predefined weight factor. Moreover, compared with conventional algorithms, the proposed algorithm achieves better performance in throughput and delay. The low average delay while attaining the high throughput ensures the excellent performance of the proposed algorithm.

Universal Path Tracing for Large-Scale Sensor Networks

Most sensor networks employ dynamic routing protocols so that the routing topology can be dynamically optimized with environmental changes. The routing behaviors can be quite complex with increasing network scale and environmental dynamics. Knowledge on the routing path of each packet is certainly a great help in understanding the complex routing behaviors, allowing effective performance diagnosis and efficient network management. We propose PAT, a universal sensornet path tracing approach. PAT includes an intelligent path encoding scheme that allows efficient decoding at the PC side. To make PAT more scalable, we propose techniques to accurately estimate the degree information by exploiting timing information, allowing more compact path encoding. Moreover, we employ subpath concatenation to infer excessively long paths with a high recovery probability. We propose an analytical model to quantify the benefits of PAT with varying network scale, network density, routing dynamics and packet delivery performance. We evaluate PAT’s performance using testbed experiments, trace-driven study, and extensive simulations. Results show that PAT significantly outperforms existing approaches.

PC-RPL

We present PC-RPL , a transmission power-controlled IPv6 routing protocol for low-power and lossy wireless networks that significantly improves the end-to-end packet delivery performance under heavy traffic compared to the standard RPL. We show through actual design, implementation, and experiments that a multihop wireless network can achieve better throughput and routing stability when transmission power and routing topology are “jointly and adaptively” controlled. Our experiments show that the predominant “fixed and uniform” transmission power strategy with “link quality and hop distance”–based routing topology construction (i.e., RPL) loses significant bandwidth due to hidden terminal and load imbalance problems. We design an adaptive and distributed control mechanism for transmission power and routing topology, named PC-RPL , on top of the standard RPL routing protocol for hidden terminal mitigation and load balancing. We implement PC-RPL on real embedded devices and evaluate its performance on a 49-node multihop testbed. PC-RPL reduces total end-to-end packet losses by approximately sevenfold without increasing hop distance compared to RPL with the highest transmission power, resulting in 17% improvement in aggregate bandwidth and 64% improvement for the worst-case node by successfully alleviating both hidden terminal and load imbalance problems.

Improving Packet Delivery Performance in Water Column Variations through LOCAN in Underwater Acoustic Sensor Network

This paper proposes Lion Optimized Cognitive Acoustic Network (LOCAN) to reduce packet delay and packet loss during packet transmission in Underwater Acoustic Sensor Network (UWASN). Packet delay and packet loss in UWASN are because of water column variations such as Doppler effect and geometric spreading (GS). Doppler effect forms due to sensor node’s motion and sea surface variations such as salinity and temperature. Geometric spreading (GS) occurs due to sediment drift wave fronts and frequent changes in node’s location and depth. Water column variations change the amplitude of sound propagation, causing channel coherence and multipath interference, which affect packet transmission. The existing UWASN algorithms focus only on temperature and salinity variations. In LOCAN, channel selection through Lion Optimization Algorithm solves the problems of water column variation and improves the battery life, network lifetime, and throughput. The proposed algorithms show a better result in terms of efficiency, when compared to existing UWASN algorithms.

On Providing Differentiated Service Exploiting Multi-Instance RPL for Industrial Low-Power and Lossy Networks

The rapid proliferation of low-power wireless devices enables the industrial users to improve the productivity and safety of the plants as well as efficient management of the system. This can be achieved through significant increase in data collection, remote monitoring, and control of the plants and promoting the development of industrial Internet of Things (IoT) applications. However, the industrial environment is typically harsh causing high link quality variations and topology changes. The wireless devices used in this environment are also resource constrained in terms of energy, memory, and processing power. In spite of their low-power and lossy nature, these networks demand provisioning of differentiated services for various industrial applications having diverse quality of service (QoS) requirements. Considering the unique characteristics of low-power and lossy networks (LLN), routing for low-power and lossy networks (RPL) is devised which was standardized by IETF in 2012. To meet the demand of diverse traffic, RPL supports multiple instances in a single network. This paper proposes MI-RPL, a multi-instance solution of RPL for industrial low-power and lossy networks (LLNs). MI-RPL defines four instances for four distinct traffic classes of industrial monitoring applications in terms of delay and reliability. MI-RPL also introduces composite routing metrics and proposes an objective function (OF) to compute the most suitable path for each instance. The performance of MI-RPL is investigated through simulations that exhibit MI-RPL has better delay and packet delivery performance for delay- and reliability-constrained traffic along with lower energy consumption compared to the standard RPL.

Campus network Throughput improvement through metric modification of OSPF routing mechanism

OSPF (Open Shortest Path First) is routing protocol that are widely used in computer networks. Selection of routing protocols is very important in improving network performance. Calculation of OSPF metrics will determine the performance of data packet delivery. It based on Shortest Path Tree. To find the best route from source to destination, it must determine the shortest path between itself and each router in the network. The router creates this perspective by taking the information in the LSDB and transforming it into a shortest path first tree or SPF tree. It is an algorithmic calculation to construct logical network view performed by the computer within the router. To speed the construction of network tree, buffer on next hop, distance (RTT – round trip time), and direction of next hop (getting closer or away) variable is proposed to increase the speed of data packet communication among routers. This research was using OPNet modeler software, simulations were made on a campus network whose PDU variables were modified to obtain the optimum network tree with OSPF routing protocol. The weighted testing parameters in this study is time delay values. Hopefully, the value of redistribution delay is 1% better depending on traffic density. Main route retrieval and alternative package delivery are based on the smallest cost and metric values in the OSPF protocol.

Third Party Based Security Method in Vehicular AD-HOC Networks

Security is the key factor of consideration in the vehicular ad-hoc network (VANET), which is prone to various security dangers. A VANET package gives information on life’s essentials and provides security from detrimental external agencies. This paper presents an outsider-based security approach which secures VANETs condition by verification process, where marks are produced and conveyed to hubs and checked at the measure of any transmission. In the suggested approach, the rise in mobility decreases the packet delivery ratio and performance of proposed protocol is approximately 4% improved as compared to other techniques. Moreover, the escalation in mobility increases the average delay and in case proposed protocol is compared with the group based authentication then the improvement in its performance is approximately 50%.Thus, the proposed approach is completely focused on security and consequently secures the system.

LORA: Load-Balanced Opportunistic Routing for Asynchronous Duty-Cycled WSN

Opportunistic Routing (OR) is adapted to improve the performance of low Duty-cycled Wireless Sensor Networks by exploiting its broadcast nature. In contrast to traditional routing, where packets are transmitted along pre-determined paths, OR uses a prioritization metric to select a set of candidates as potential forwarders. This solves the sender's waiting time problem. However, too many candidates may simultaneously wake-up, generating more duplicate packets, occupying the restricted resources and hinder the packet delivery performance. Consciously, to restrict the number of candidates and to counterbalance between the waiting time problem and the duplicate packets problem, this paper proposed a new protocol that combines two main parts. First, each node defines a Candidates Zone (CZ) by a regular geometric shape of four corners. The packets generated by the node will be routed via any path within the CZ. Expressly, the nodes within the CZ are allowed to be selected as candidates. The size of CZ is controlled by the network density. Second, the candidates within the CZ are prioritized based on the OR metric, which is defined as the multiplication of four-distributions: direction distribution, transmission-distance distribution, perpendicular-distance distribution, and residual energy distribution. Through an extensive performance evaluation study and simulation of large-scale scenarios, the results demonstrated that our protocol achieved better performance compared to the state-of-the-art solutions in terms of network lifetime, energy consumption, routing efficiency, sender waiting time, and duplicate packets.