Low Packet Delivery Ratio Research Articles

Improved junction-based routing for VANETs using a Bio-inspired route stability approach

The Junction-Based Routing (JBR) is established as the adequate solution to data packet routing within an urban Vehicular Ad Hoc Network (VANET). The latter is characterized by radio obstacles, unreadable traffic density change, and unstable vehicular mobility speed. Existing JBR routing protocols have not defined specific evaluation mechanisms to anticipate and detect stable road segments in large urban areas yet. Consequently, the connectivity of selected streets for data forwarding may not be regularly reliable for long periods, especially for remote destinations. As a matter of fact, stable long-lifespan JBR paths cannot be discovered; hence, generating low Packet Delivery Ratio (PDR) performances. In this paper, we introduce swarm-inspired optimization as a novel solution to reduce the number of lost data packets on the basis of a new route stability notion. A bio-inspired metaheuristic technique named the Intelligent Water Drops (IWD) algorithm is applied to track reactively and proactively the routing consistency of road segments. IWD seeks to gather accurately unreachable road segments’ up-to-date connectivity information in order to predict progressively their stability degree. The latter is deduced according to predefined routing parameters featuring the history of route traffic and data forwarding, and vehicles’ speed. These parameters are utilized to trace direct JBR paths to data packets’ destinations with minimum disconnected road segments following IWD-inspired Dijkstra JBR paths. The proposed bio-inspired JBR protocol is compared to a few notable JBR schemes in a city map scenario. The simulation results showed a consistent mean PDR progress between 2.55% and 33.47% of all tested simulation variants.

A Cooperative Attack Detection Framework for MANET-Iot Network Using Optimized Gradient Boosting Convolutional Neural Network

The implementation of large-scale Internet of Things (IoT) devices results in smart cities. Using standard mobile ad-hoc networks and IoT, developers establish the communication model for a smart city. The rapid growth of IoT devices based on smart cities poses different Quality of Service (QoS) and security problems. This research presents a novel Modified Elephant Herd Optimization (MEHO) method and a Gradient Boosting Convolutional Neural Network (CNN) strategy to address these issues. The cooperative attacks with varied disruption probabilities are initially assessed at the edge nodes of the IoT network. The MEHO-based Gradient Boosting CNN (MEHO-CNN) approach effectively detects cooperative attacks, ensuring the identification of malicious entities. For traditional cloud access, both bandwidth utilization as well as expected latency are minimized in edge computing. By using the IoT network, the proposed MEHO-CNN model identifies and eliminates malicious nodes. To establish the claimed trustworthy background, the legitimate accusations are based on an examination of trust-based allegations. When compared to existing methodologies, the proposed approach lowers the impact of cooperative attacks, resulting in increased throughput, reduced attack detection rates, lower packet loss ratio, lower packet delivery ratio, and other benefits.

Mutation Based Hybrid Routing Algorithm for Mobile Ad-hoc Networks

Mobile Adhoc NETworks (MANETs) usually present challenges such as a highly dynamic topology due to node mobility, route rediscovery process, and packet loss. This leads to low throughput, a lot of energy consumption, delay and low packet delivery ratio. In order to ensure that the route is not rediscovered over and over, multipath routing protocols such as Adhoc Multipath Distance Vector (AOMDV) is used in order to utilize the alternate routes. However, nodes that have low residual energy can die and add to the problem of disconnection of network and route rediscovery. This paper proposes a multipath routing algorithm based on AOMDV and genetic mutation. It takes into account residual energy, hop count, congestion and received signal strength for primary route selection. For secondary path selection it uses residual energy, hop count, congestion and received signal strength together with mutation. The simulation results show that the proposed algorithm gives better performance results compared to AOMDV by 11% for residual energy, 45% throughput, 3% packet delivery ratio, and 63% less delay.

Performance Evaluation of LoRa in Farm Irrigation System with Internet of Things

Long Range (LoRa) Communication is one of the emerging Internet of Things (IoT) technologies and has been widely discussed by researchers. LoRa is also part of the Low Power Wide Area Networks (LPWAN) technology where this technology focuses on communication systems on energy efficiency, wide coverage, low data rates, and long battery life. LoRa uses industrial, scientific, and medical (ISM) radio frequencies. These frequencies can be used for free without paying for a license. Theoretically and under ideal conditions, the LoRa range can reach < 3 km in urban areas and > 3 km in rural areas. However, only a few studies discuss the evaluation and analysis of LoRa performance, which is implemented in the real world with particular case studies. This article aims to evaluate and analyze the performance of LoRa, which is applied to a case study of an IoT-based agricultural irrigation system. Several parameters will be assessed and analyzed, including distance, received signal strength indication (RSSI), spreading factor, coding rate, power transmission, and packet delivery ratio (PDR). Experimental and measurement results show that LoRa can transmit data packets up to a distance of 2.5 km but with a very low PDR rate of around 5-7%. The results also show that LoRa can work optimally if the distance is > 1 km with a PDR rate of about 70-100%.

Secure Spectrum Access, Routing, and Hybrid Beamforming in an Edge-Enabled mmWave Massive MIMO CRN-Based Internet of Connected Vehicle (IoCV) Environments.

A cognitive radio network (CRN) is integrated with the Internet of Connected Vehicles (IoCV) in order to address spectrum scarcity and communication reliability issues. However, it is limited, possessing less throughput, a low packet delivery ratio, high latency, and high mobility in the spectrum. In this research study, the existing issues are addressed by proposing a 6G cognitive radio network–Internet of connected vehicles (6GCRN–IoCV) approach. Initially, all the entities such as secondary users (SUs), primary users (PUs), and pedestrians are authenticated in blockchain to ensure security. The edge-assisted roadside units (ERSU) initiate clustering only for authenticated SUs using the improved DBSCAN algorithm in consideration of several metrics. The ERSU then generates an intersection-aware map using the spatial and temporal-based logistic regression algorithm (STLR) to reduce collisions in the intersection. The spectrum utilization is improved by performing spectrum sensing in which all the SUs involved in spectrum sensing use lightweight convolutional neural networks (Lite-CNN) in consideration of several metrics and provide the sensing report to the fusion center (FC) in an encrypted manner to reduce the spectrum scarcity and security issues. The communications between the SUs are necessary to avoid risks in the IoCV environment. Hence, optimal routing is performed using the Dingo Optimization Algorithm (DOA), which increases throughput and packet delivery ratio. Finally, communication reliability is enhanced by performing hybrid beamforming, and this exploits the multi-agent-based categorical Deep-Q Network (categorical DQN), which increases spectral efficiency based on its adaptive intelligent behavior. The proposed study is simulated using the SUMO and OMNeT++ simulation tools and the performances are validated with existing works using several performance metrics. The result of the simulation shows that the proposed work performs better than the existing approaches.

A position-based reliable emergency message routing scheme for road safety in VANETs

Reliable emergency message (EM) transmission in vehicular adhoc networks (VANETs) necessitates an effective routing scheme. Position-based routing is considered more suitable for VANETs for not having to maintain any routing table or sharing connection states with neighbors. However, position-based routing is challenging in VANETs because vehicles change their positions instantly, and the next-hop can often go out of the communication range in greedy forwarding mode. This unstable behavior of the next-hop triggers route redundancy and leads to a high end-to-end delay (ED) and lower packet delivery ratio (PDR). Moreover, routing decisions based on a next-hop (relay) vehicle may be less optimal if we do not consider the stability and predict the position of a next-hop vehicle in such dynamic environments. To that end, we propose a position-based reliable emergency message routing (REMR) scheme based on our mobility metrics, which exploits the vehicle moving behaviors to enhance EM delivery. We describe how the choice of next-hop in greedy forwarding can be enhanced by leveraging neighbor’s future location information. By taking into account the Euclidean distance and position information, REMR predicts the relative positions of neighbor vehicles to exclude unstable neighbors from the list of candidate next-hops. In addition, REMR employs the vehicles’ movement information (e.g., position, speed variation, and moving angle) to minimize a possible link disruption and to choose an optimal next-hop for robust routing of EMs. REMR also offers a beaconing control strategy to enhance message reliability and to deal with the problem of beacons congestion. To minimize beacons congestion, REMR adjusts the beacon interval based on the neighborhood density. By consolidating mobility metrics and beacon control strategy, REMR can respond adequately to variation in the network traffic and frequent topology changes as validated by our simulation results.

Investigation of RPL in 6LoWPAN based elderly care monitoring system

Wireless Sensor Network (WSN) is a self-structured network containing small, energy-constrained wireless nodes that act together to accomplish difficult tasks. Wearable sensors, one of the WSNs play a significant role in healthcare applications, especially patient monitoring. With a miniature size, wearable sensors have less space dedicated for energy sources. So it is important for wearable sensors to be manufactured as energy efficient and reliable and it must ensure quality of service in providing the data. Remote health care monitoring has two limitations such as adoption of mobility and the usage of low power consumption devices. To overcome these limitations, appropriate routing protocol can be used in Low Power Lossy Networks (LLNs). IPV6 Routing Protocol for Low Power Lossy Networks (RPL) is one of the routing protocols standardized to be applied in Internet of things network with wireless sensors. The current research article investigates the performance of RPL with three Objective Functions (OF), Minimum Rank with Hysteresis Objective Function (MRHOF) with Energy as metric, MRHOF with Expected Transmission count (ETX) as metrics and Objective Function zero(OF0) with hop count as metric, in elderly health care monitoring system. The study considered two scenarios case 1 has all static nodes while case 2 has few dynamic nodes. The performance was evaluated in terms of metrics control overhead, convergence time, Packet Delivery Ratio (PDR), Latency and energy consumption and the OF optimum for reliability, mobility and energy consumption is determined. The results of the simulation showed that, in mobile scenario OF0 converged at a fast rate than the MRHOF, which increases the life time. OF0 also consumed the least energy and it increased the life time of the node. As far as PDR is concerned, OF0 had low PDR when the nodes were mobile and ETX performed well.

Energy efficient routing in IOT based UWSN using bald eagle search algorithm

AbstractThe underwater wireless sensor network (UWSN) faces many problems such as limited bandwidth, large path loss, high attenuation, and limited battery power. In recent times, different routing protocols have been proposed in the Internet of Things (IoT) enabled UWSN (IoT‐UWSNs) to explore the underwater environment for different purposes, that is, scientific and military purposes. However, high energy consumption (EC), end to end (E2E) delay, low packet delivery ratio (PDR) and minimum network lifetime make the energy efficient communication a challenging task in UWSN. The high E2E delay, EC, and reliable data delivery are the critical issues, which play an important role to enhance the network throughput. For effective achievement of EC, proposed a new routing algorithm called bald eagle search (BES). It is a nature‐inspired optimization algorithm that copied bald eagle hunting behavior. The proposed routing protocol includes three stages: initialization stage, construction stage, and the data transmission phase. The nodes identify the location and the sector during the initialization process. The construction phase includes two steps, neighbor node selection, and identification of energy efficient path. Then the last phase includes the transmission, in this the broadcasting node chooses the nodes with high residual energy (RE) in comparison to the average RE of the entire network. The proposed method is implemented in MATLAB. The outcomes of simulation confirm that the BES algorithm has better performance on EC, average RE, and network lifetime than the three existing algorithm in UWSN.

Energy Efficient Proactive Routing Scheme for Enabling Reliable Communication in Underwater Internet of Things

The Underwater Internet of Things (UIoT) is a network of smart interconnected devices operating under various aqueous environments. In UIoT, due to aqueous feature of signal absorption, data signals are transmitted at low frequency. Similarly, significant interference and collisions degrade transmission quality, resulting in a low Packet Delivery Ratio (PDR) and a long End-to-End (E2E) delay. Moreover, a significant amount of energy is being wasted due to the void hole problem, which arises when the source node does not identify an instant forwarder node. Thus, a reliable communication with enhanced network lifetime is highly desired in UIoT. Therefore, this work proposes a proactive energy Efficient Layer-by-Layer Watchman-based Collision Free Routing (ELW-CFR) scheme to address the above mentioned issues. The proposed scheme provides the low E2E delay and high PDR while avoiding the void hole problem. Extensive simulations have been performed to show the performance superiority of the proposed scheme against the state-of-the-art schemes.

Energy-Aware Path Selection to Improve Packet Delivery Ratio and Throughput in RPL Networks

Internet of things (IoT) is a concept that motivates researchers to develop their ideas into an innovative application. RPL stands for Routing protocols for low power and lossy networks. RPL is the routing protocol, designed for constrained devices. IPv6 and Low power and Lossy network (LLN) techniques make this protocol more flexible with IoT. It is used to route the packets from the one node to another node. Various issues are observed during the routing. In RPL, Node failure, Mobility, energy consumption and congestion are few problems that are observed during the routing. The above problems lead to some issues like Packet loss, path loss, low packet delivery ratio, High energy consumption, and so on. Existing objective functions OF0 and OF1 are used in the RPL protocols to support static nodes in Destination-Oriented Directed Acyclic Graph (DODAG). In this paper, a solution is introduced to overcome node failure in RPL. The node failure in RPL is addressed by using the energy-aware routing approach in the network. The proposed technique has two mechanisms: finding the node with minimum lifetime and parent selection process. This proposed work is evaluated using COOJA simulator.

Multi-hop Energy-Efficient Reliable Cluster-based Sectoring Scheme Using Markov Chain Model to Improve QoS Parameters in a WSN

The data dissemination toward the sink node in a wireless sensor network in one of three ways: single-hop data dissemination, multi-hop dissemination, and multi-hop dissemination, using the nearest cluster head or sector head (SH). This study focuses on third multi-hop data dissemination in which SHs are selected in the network, other nodes disseminate data toward these SHs, and then, the SHs transfer the collected data to the sink node. This paper proposes a novel approach to SH selection called Energy-Efficient Reliable Sectoring-Scheme (EERSS). It is supported by an optimization algorithm called Cat Swarm Optimization (CSO), which proves that the elected number of SH is optimal. This CSO based EERSS algorithm is implemented to rank quality of service parameters, such as network lifetime maximization, with reliability and to minimize energy consumption. A path reliability prediction-based Markov chain model is applied to the CSO-based-EERSS algorithm to achieve results that are superior to that of traditional models. After the construction of the proposed prediction algorithms CSO-based-EERSS (P-CSO-EERSS), its performance is compared with that of the traditional CSO-based-EERSS, EERSS, and Particle Swarm Optimization based EERSS, concerning packet delivery ratio(PDR), end-to-end delay, and energy consumption. The SH selection performance of the proposed P-CSO-EERSS is superior to that of the traditional algorithms based on low energy consumption and high PDR.

Comprehensive Survey of Radio Resource Allocation Schemes for 5G V2X Communications

In recent years, intelligent transportation systems, which provide safety and autonomous services on the road using the Internet and cellular technologies, have become increasingly important. Vehicles are equipped with sensors that can transmit data to other vehicles or infrastructure according to different types of services. In order to ensure robust connectivity for safety services, the transmission among vehicles requires low latency, high reliability, and high packet delivery ratio. With the increased number of vehicles, more frequency resources are needed to establish fast and reliable services. In the area of high vehicle density, the demand of frequency resources is increasing to establish stable connectivity in a short time. However, the amount of frequency resources is limited; the resource allocation scheme (RAS) should effectively assign a resource block to each user to ensure the quality of service. In this paper, we present a comprehensive survey of RASs for the 5G-based vehicular networks known as 5G V2X. We also discuss the challenges and opportunities for resource allocation in modern vehicular networks and present numerous promising future research directions.

Routing Protocol Design for Underwater Optical Wireless Sensor Networks: A Multiagent Reinforcement Learning Approach

Underwater optical wireless sensor networks (UOWSNs) have been attracting many interests for the advantages of high transmission rate, ultrawide bandwidth, and low latency. However, due to limited energy resources and highly dynamic topology caused by the water flow movement, it is challenging to provide a low-consumption and reliable routing in UOWSNs. To tackle this issue, in this article, we propose an efficient routing protocol based on multiagent reinforcement learning, termed as DMARL, for UOWSNs. The network is first modeled as a distributed multiagent system, and residual energy and link quality are considered into the routing protocol design to improve the adaptation to a dynamic environment and the support of prolonging network life. Additionally, two optimization strategies are proposed to accelerate the convergence of the reinforcement learning algorithm. On the basis, a reward mechanism is provided for the distributed system. The simulation results show that the DMARL-based routing protocol has low energy consumption and high packet delivery ratio (over 90%), and it is suitable for networks where the average number of neighbor nodes is less than 14.

EAR: Energy-aware risk-averse routing for disaster response networks

Disaster Response Networks (DRNs) are specific delay-tolerant networks (DTNs) designed to support temporary communications during disaster recovery with limited energy resources. Existing research on DRNs routing addresses routing metrics like packet delivery delay (PDD), packet delivery ratio (PDR), and total transmission energy (TTE). However, the complex interplay among these metrics remains unclear and makes their control difficult. Moreover, the standard deviation of packet delivery delay (PDS), which is overlooked by the previous works, is an important metric in DRNs. In this paper, we propose the Energy-Aware Risk-averse routing framework (EAR) for DRNs. EAR models the mobility in DRNs using a stochastic multigraph and computes the risks associated with routing paths using the Mean-Risk and Max-Probability models. A “λ-optimal” algorithm is used for finding routing paths with the minimum risks. A parameter L restricting the number of packet replicas and a differentiated service model are utilized to improve packet delivery efficiency (PDE) while maintaining other metrics at an adequate level. We show via extensive simulations that EAR provides flexible control of the routing risks and delivers packets to the destinations in a more energy-efficient way (up to 8x higher PDE with 4% lower PDR) than well-known routing protocols Prophet, MaxProp, RAPID and Spray&Wait.

Secure Routing Protocols for Source Node Privacy Protection in Multi-Hop Communication Wireless Networks

Traffic analysis attacks are common in monitoring wireless sensor networks (WSNs). In the attacks, adversaries analyze the traffic pattern to obtain critical information such as the location information of a source node. Fake source packet routing protocols are often used to ensure source location privacy (SLP) protection. The protocols rely on broadcasting fake packets from fake sources concurrently with the transmission of real packets from the real source nodes to obfuscate the adversaries. However, fake source packet routing protocols have demonstrated some performance limitations including high energy consumption, low packet delivery ratio (PDR), and long end-to-end delay (EED). In this study, two existing fake source packet routing protocols are considered. Then two new phantom-based SLP routing protocols are proposed to address the limitations. Each proposed protocol introduces a two-level phantom routing strategy to ensure two adversary confusion phases. When the adversaries perform traffic analysis attacks on the packet routes, they encounter two levels of obfuscation. Simulation results establish that the proposed protocols have superior performance features. The protocols guarantee strong SLP protection throughout the WSN domain with controlled energy consumption, PDR, and EED. Furthermore, the proposed protocols achieve more practical results under varied network configurations.

Towards Void Hole Alleviation: Enhanced GEographic and Opportunistic Routing Protocols in Harsh Underwater WSNs

Internet of Things enabled Underwater Wireless Sensor Networks (IoT-UWSNs) are quite useful in monitoring different tasks including: from instrument monitoring to the climate recording and from pollution control to the prediction of natural disasters. However, there are some challenges, which affect the performance of a network, i.e., void hole occurrence, high Energy Consumption (EC) and low Packet Delivery Ratio (PDR). Therefore, in this work, two energy efficient routing protocols are proposed to maximize the PDR by minimizing the ratio of void hole occurrence. Scalability analysis of the proposed routing protocols is also performed. Additionally, feasible regions are computed to check the optimality of the proposed protocol in terms of EC. Furthermore, proposed protocols are compared with benchmark routing protocols in counterparts. Simulation results clearly show that proposed routing protocols achieved 80-81% higher PDR than GEographic and opportunistic routing with Depth Adjustment based topology control for communication Recovery (GEDAR) and Transmission Adjustment Neighbor-node Approaching Distinct Energy Efficient Mates (TA-NADEEM). Moreover, the ratio of void hole occurrence is minimized upto 30% approximately.

Aggregator based RPL for an IoT-fog based power distribution system with 6LoWPAN

Advanced Metering Infrastructure (AMI) forms an important part in Smart Grids. Routing the data effectively from smart meters to the Edge/Fog node requires an efficient routing protocol. Routing Protocol for Low Power Lossy Area Network (RPL) is a standard routing protocol for IPv6 over Low Power Personal Area Network (6LoWPAN). In a Power Distribution system all the smart meters together form 6LoWPAN network. They communicate with the fog router, which acts as the 6LoWPAN gateway. ContikiRPL was evaluated using Cooja Network simulator for a power distribution network topology. The nodes which were far away from the fog node gave low Packet Delivery Ratio (PDR) and large End to End delay. This paper proposes an aggregation RPL scheme by modifying the existing Contiki RPL. The smart meter nodes communicate to the aggregator, which communicates to the fog node. The results show that the aggregation scheme has 35.6% increase in PDR, lesser hop count and 13.24% decrease in End to End delay on an average compared to existing RPL.

Multi-Token Based MAC-Cum-Routing Protocol for WSN: A Distributed Approach

A wireless sensor network is a collection of batterypowered sensor nodes distributed in a geographical area. Inmany applications, such networks are left unattended for along period of time. These networks suffer from the problemslike high energy consumption, high latency time, and end- to-end low packet delivery ratio. To design a protocol that findsa trade-off between these problems is a challenging task. Inorder to mitigate energy consumption issue, different existingMedia Access Control (MAC) protocols such as S-MAC, RMAC,HEMAC, and Congestion-less Single Token MAC protocols havebeen proposed which ensure better packet delivery but fail toensure energy efficiency due to high end-to-end latency. Theproblem of high end-to-end latency is resolved with the existingrouting protocols such as Fault Tolerant Multilevel Routingprotocol (FMS)and Enhanced Tree Routing (ETR) protocol.AS2-MAC and Multi Token based MAC protocol are able toimprove the end-to-end packet delivery ratio. However, thehierarchical network structure used in these protocols increasestime and energy consumption during network reconstruction.This problem was further resolved in Distributed HierarchicalStructure Routing protocol by constructing the network structurein a distributed manner. In all these existing protocols, efficienttoken management and reliable data delivery ratio was notproperly addressed, which in turn consume more energy. So,it is clear that MAC and routing protocols both together cangive better results related to data transmission in WSN. Inorder to achieve the same, in this paper, we propose a reliabledata transmission algorithm that satisfies both routing and MACprotocol to improve the end-to-end data delivery. The proposedprotocol uses different control message exchange that ensures datapacket delivery in each individual levels and it ultimately uses oftokens to ensure reliable data transmission along with reducedtraffic congestion during end-to-end data delivery. The algorithmconsiderably improves the packet delivery ratio along with reduceenergy consumption of each sensor node. Simulation studies ofthe proposed approach have been carried out and its performancehas been compared with the Multi Token based MAC protocol,AS-MAC protocol and ETR routing protocol. The experimentalresults based on simulation confirms that the proposed approachhas a higher data packet delivery ratio.

A Novel Hybrid MAC Protocol for Basic Safety Message Broadcasting in Vehicular Networks

Basic Safety Messaging plays a crucial role to provide road safety in vehicular ad-hoc networks (VANETs). To avoid potential accidents, vehicles periodically broadcast safety information to neighboring vehicles. However, due to transmission collisions, fading channels and other factors, vehicular networks usually suffer a low packet delivery ratio (PDR) and a large delay, which are intolerant of many safety applications. To tackle these issues, this paper proposes a hybrid medium access control (MAC) protocol for basic safety message (BSM) dissemination based on the framework of Dedicated Short-Range Communication (DSRC). Its partially centralized and partially distributed characteristic not only can effectively suppress the collisions, but keep compatibility with IEEE 802.11p. In addition, the integration of Physical-Layer Network Coding (PNC) and Random Linear Network Coding (RLNC) further strengthens the reliability and efficiency for BSM dissemination. Both the theoretical analysis and comprehensive simulations indicate that, compared with existing schemes, the proposed protocol can significantly improve the PDR by a range of 20% to 300%. Meanwhile, in terms of normalized throughput, it increases by varying percent between 20% and 160% in different scenarios.

Minimizing transmission loss using inspired ant colony optimization and Markov Chain Monte Carlo in underwater WSN environment

In Underwater Wireless Sensor Networks (UWSNs), the most important challenging issues are propagation delay, high error probability, high latency, high communication cost, limited bandwidth, limited memory, low packet delivery ratio, and transmission loss. In our proposed work, the various efforts are taken to minimize the propagation delay and transmission loss during data transmission in an underwater environment. A hybrid mechanism is implemented to improve energy efficiency for faster data transmission in underwater WSN. In the underwater environment of acoustic channel condition, propagation delay and transmission loss lead to high complexity in accessing the information and also it is difficult to obtain the Channel Status Information (CSI). To address this problem, Ant Colony Optimization (ACO) routing with Markov Chain Monte Carlo (MCMC) algorithm is used and to capture the transmission loss in the MCMC approach, CSI Forecast Prediction (FP) algorithm is used. The experimental simulations are evaluated by utilizing the performance evaluation metrics such as Transmission Loss, Probability Density Function, Average Delay and Throughput. From the simulation results it is evident that the proposed algorithm, ACO-MCMC has recorded the minimum transmission loss, increase in probability density function, minimum average delay and maximum throughput of the network when compared to the existing algorithms.