Congestion Free Path Research Articles

INFED: Enhancing fire evacuation dynamics through 3D congestion-aware indoor navigation framework

This paper introduces Indoor Navigation Framework for Fire Evacuation Dynamics (INFED), a novel indoor navigation framework that combines dynamic fire constraints and path congestion management. INFED considers the three-dimensional (3D) attributes of both the agents (speed, volume, location, count) and the environment (3D volume, congestion, corridor height, and corridor length) to estimate navigation routes that avoid fire-affected evacuation paths. It achieves this by integrating various proposed algorithms as modules: Environment Establisher, Fired/Safe Node Identifier, Pre-processor, Weighted Graph Generator, and Path Generator. The 3D features of the agent and environment are used to effectively estimate the capacity of the corridors in an indoor environment for the estimation of path congestion. The path congestion so computed is used during evacuation to identify the safest and congestion-free path. We discuss the performance of INFED by implementing it on various realistic scenarios in a commercial floor setup. We found that the incorporation of safety constraints results in longer evacuation routes, ranging from a 6% increase under mild fire and congestion conditions to a 40% increase under severe fire and congestion conditions. In the event of a worst-case scenario where fire-free paths are scarce, INFED utilizes congestion to reduce agent speed along the recommended evacuation route. This mechanism is activated when congestion surpasses a threshold of 0.3. The system can be used by stakeholders to test various evacuation hypotheses, which can lead to better preparedness and rescue operations, ultimately saving lives in the event of a fire.

An adaptive congestion and energy aware multipath routing scheme for mobile ad-hoc networks through stable link prediction

Advancement in mobile ad-hoc network (MANET's) has offered solutions to wide range of real-time applications. MANET's consist of group of battery operated wireless mobile nodes that forms a dynamic topology and functions autonomously without infrastructure. Mobile nodes are limited to resource, bandwidth, energy and memory. Due to mobile nature of the nodes, mobility and resource limitations are the two major factors that affect MANET's performance. Frequent topology changes and mobility affects the link stability between two nodes, frequent link interruption results in network congestion, lead to packet drop and increase retransmission delays. During multihop data transmissions, the wireless channel is shared among neighbour nodes. Determining the channel status and predicting the quality of link is still a challenging issue. Existing MANET's routing scheme considers optimal hop count as a metric for data transmission and selects the shortest path. Due to nodes movement, frequent link failure occurs, resulting in packet loss and retransmission of packet, which restablished path avoids packets loss, and alternate paths can be discovered within time such that congestion is controlled and delay is minimised. Therefore, it is required to design routing scheme for MANET's that can adapt to network changes and conditions to support quality of service (QoS). In this paper, we propose an Adaptive Congestion and Energy Aware Multipath Routing Scheme (ACEAMR) to provision QoS requirements in MANET's. ACEAMR discovers stable, energy aware, and congestion free paths towards destination. The congestion level at node is determined by computing queue utilization through adaptive feedback mechanism. ACEAMR integrates stable link prediction (SLP) to estimate the link quality and channel availability for reliable data transmission. Energy aware nodes are computed using fitness function of nodes having sufficient energy to extend network lifetime. Simulation results show that proposed ACEAMR achieves 30 % of higher throughput, 25 % more in packet delivery ratio, 27 % less delay and 28 % energy efficient compared to LLECP-AOMDV, OLEADM and AOMDV schemes.

An Integrated Approach to Scheduling B-CAVs in Container Terminals considering Battery Management

With the aim of promoting environmental sustainability and enhancing transport efficiency, battery-powered connected and automated vehicles (B-CAVs) are employed to replace diesel-powered ones in horizontal transport systems (HTSs) of container terminals. The operational efficiency of an HTS can be increased by the cooperation of B-CAVs. However, it is time-consuming to charge them. Therefore, their battery management becomes a critical issue of a transport schedule. To run container terminals more economically and efficiently, this work proposes an integrated scheduling approach to B-CAVs tasks’ dispatch and route planning, where battery management is taken into account. An integer programming model is constructed with the goal of minimizing the total travel distance. Then, a sustainable charging policy is designed to ensure the consistent transport capacity of an HTS. Furthermore, a congestion-free path plan-based improved genetic algorithm is presented to obtain a near-optimal plan for dispatching B-CAVs to perform transporting and charging operations. A series of experiments are carried out to verify the effectiveness and efficiency of our approach.

Congestion-Free Cluster Formation and Energy Efficient Path Selection in Wireless Sensor Networks using ButPCNN

Today, network congestion is a common occurrence that needs to be focused on and effectively addressed, particularly in Wireless Sensor Networks (WSN) for packed type networks. The main causes of congestion in WSN are a lack of channel capacity and energy waste. This study's major goal is to develop Energy Efficient Congestion Free Path Selection Protocol (ECFPSP) protocol, which aims to reduce network congestion. By selecting the most appropriate main cluster head (PCH) and secondary cluster head (SCH), the ECFPSP protocol is proposed to decrease end-to-end delay time and extend the network lifetime. The suggested protocol implements a routing protocol that provides security by avoiding hostile nodes and reducing data loss. It also routes the nodes. Hence, a Congestion-Free Cluster Formation is provided to increase the lifetime of the network by proposed ButPCNN approach. To decrease packet loss and conserve energy, this research also uses brand-new cluster-based WSNs. In comparison to other standard protocols, the simulation results reveal that ButPCNN has a reduced packet drop rate, which increases the ratio of packet distribution, network life, and residual energy. As a result, the suggested method enhances congestion control performance while using less energy and a recently developed strategy is suggested to successfully enhance network performance. The proposed ButPCNN gives 25 percent improvement to optimize traffic on overloaded node than the other traditional approaches.

An Energy-Efficient Clustering and Fuzzy-Based Path Selection for Flying Ad-Hoc Networks

Flying Ad-hoc Networks (FANET) allow for an ad-hoc networking among Unmanned Aerial Vehicles (UAV), have recently gained popularity in a variety of military and non-militant applications. The existing work used the Glowworm Swarm Optimization (GSO) algorithm to create a self-organization depending on clustering technique for FANET. Owing to UAV increased mobility, network topology might vary over time, providing route discovery and maintenance is one of the most difficult tasks. And also, the network throughput is still more worsened by the network congestion. To solve this problem, the proposed work designed an energy efficient clustering and fuzzy-based path selection for FANET. In this work, initially, the clustering is performed using the UAV distance. For efficient communication and energy consumption, optimal selection of Cluster Head (CH) is performed by using Adaptive Mutation with Teaching-Learning-Based Optimization (AMTLBO) algorithm. To improve the optimal selection of CH nodes, the best fitness values are calculated. The fitness function depends on Link capacity, remaining energy and neighbor UAV distance. Next to that, nodes begin communications as well as transmit their information to their CH. Improved Fuzzy-based Routing (IFR) is introduced for improving the route discovery process. The goal is to find routes that have a high level of flying autonomy, minimal mobility, and a higher Received Signal Strength Indicator (RSSI). As a result, the energy usage of network is decreased, as well as the cluster’s lifespan is extended. Finally, an adaptive and reliable congestion detection mechanism is introduced to transmit the packets with congestion free path. The experimental result shows that the proposed AMTLBO system attains higher performance compared to the existing system in terms of energy usage, throughput, delay, overhead and packet delivery ratio.

Effective and Enhanced Congestion Control Technique using Adaptive Routing Protocol to Improve the Performance of Crammed WSN

Nowadays, congestion on the network becomes a usual fact which is to be focused and to be addressed appropriately especially in Wireless Sensor Networks (WSN) for crammed type networks. Limited capacity on channel and wastage of energy are the root cause of congestion in WSN. The effects of congestion implies on QoS parameters, queue length, data arrival rate etc. Furthermore, data packets should be transmitted energy-efficiently to the sink node. In this regard, an Energy-Efficient Routing Protocol is offered to efficiently transmit the nodes to their end node or destination. To control congestion, an Adaptive Buffer trade-off and Improved Trust-based Energy Efficient Routing protocol are first presented, this method identifies the congestion free paths and the Buffer trade-off handles the buffer effectively. To route the protocol, a Cross-Layer Security-Based Fuzzy Logic Energy Efficient Packet Loss Preventive Routing Protocol has been developed. The proposed protocol routes the nodes and the protocol adopts a routing protocol that imparts security in terms of avoiding malicious nodes and preventing data loss. Consequently, to improve the lifetime of the network, a Density Aware Optimal Clustering Approach is presented. The proposed method is evaluated based on the Matlab software and the QoS performance metrics are Energy Consumption, Packet Delivery Ratio, Trust Value Computation, latency, reliability, energy efficiency, end-to end delay, Average Throughput, accuracy and network lifetime. The effectiveness of the research is evaluated by comparing it with other existing techniques, including Trust Aware Secure Routing Protocol (TASRP), Artificial Flora Algorithm Based Support Vector Machine (SVM-AF), Well-Organized Trust Estimation Based Routing Scheme (ETERS), Lion Fuzzy Bee, and Bat Fuzzy Bee Algorithm. Accordingly, the suggested method’s performance is higher than the existing methods for Packet delivery ratio, throughput, network lifetime, energy efficiency, and reliability. Consequently, the proposed method improves the congestion control performance in an energy-efficient manner, in future; a recently advanced technique is proposed to effectively improve the network performance respectively.

Congestion Aware MANET Routing Using Evolutionary Game Theory and Cross-Layer Design

A wireless network without infrastructure is called MANET. In MANET, a node can move in any direction, at any speed, and at any time. There is no separate router on this network. Instead, the nodes act as a router. To transfer data in the network in an efficient manner, the network should be congestion-free or less congested. If the network has congestion, it would affect the performance of the network. In the routing process, if a node is aware of the neighbour node’s congestion and selects a congestion-free path, it leads to better performance of the network. In this paper, we have designed two approaches for improving the performance of the network. One is the cross-layer protocol and the other is the evolutionary game theory approach to find the least congestion path. Cross-layer involves transport layer and MAC layer of MANET to avoid congestion. The Linear Rank Selection method is one of the evolutionary game theory approaches used to find the Least Congested Node. We have enhanced this strategy in the GPSR protocol. The proposed EVO GPSR protocol performs 4.65% better throughput, 0.54% less network delay, 2.10% less routing overhead, and 8.20% better packet delivery ratio compared with GPSR protocol. Based on simulation, the performance of the proposed protocol is better than the GPSR protocol.

Coefficient of Restitution based Cross Layer Interference Aware Routing Protocol in Wireless Mesh Networks

In Multi-Radio Multi-Channel (MRMC) Wireless Mesh Networks (WMN), Partially Overlapped Channels (POC) has been used to increase the parallel transmission. But adjacent channel interference is very severe in MRMC environment; it decreases the network throughput very badly. In this paper, we propose a Coefficient of Restitution based cross layer interference aware routing protocol (CoRCiaR) to improve TCP performance in Wireless Mesh Networks. This approach comprises of two-steps: Initially, the interference detection algorithm is developed at MAC layer by enhancing the RTS/CTS method. Based on the channel interference, congestion is identified by Round Trip Time (RTT) measurements, and subsequently the route discovery module selects the alternative path to send the data packet. The packets are transmitted to the congestion free path seamlessly by the source. The performance of the proposed CoRCiaR protocol is measured by Coefficient of Restitution (COR) parameter. The impact of the rerouting is experienced on the network throughput performance. The simulation results show that the proposed cross layer interference aware dynamic routing enhances the TCP performance on WMN.

Energy Efficient Data Distribution on Cloud with Optimal Routing Path Based Congestion Control in WSN Environment

Presently, Wireless Sensor Network (WSN) is the extremely standard services utilized in commercial in addition to industrial applications, owing to its technical development in the embedded computing devices’ processor, communication, along with less-power usage. Moreover, in the WSN’s advantages, the sensor data’s energy-efficient distribution onto the cloud data centre is amid the core challenges caused by higher data loads and also congestion. Aimed at overcoming these problems, this work proffers an energy-efficient Data Packet (DP) distribution protocol comprising an optimal routing path centred congestion control technique in the WSN environment. This system not just ponders the energy-efficient DP distribution nevertheless increments the sensor’s lifetime by choosing the Cluster Head (CH) centred on the sensor’s residual energy, packet loss factor, data forwarding rate, and minimal distance. Next, the numerous routing paths prevalent in inter-cluster and intra-clusters are detected utilizing the QLA2ODV method; the congested nodes are detected as of these paths utilizing LM-NN classifier; if congestion happens, the QLA2ODV distributes the DP onto the Base Station (BS) via the optimum congestion-free routing path. The optimal routing paths were elected as of the congestion-free paths utilizing the WFCSO method. Past obtaining the sensor data, the BS transmits the DPs onto the Fog Nodes (FNs). At last, the sensor data are effectively distributed onto the cloud data centres grounded on the data’s features and cloud features utilizing the BD-SBO technique. The experiential outcomes determine the proposed system’s effectiveness.

Optimal Routing based Load Balanced Congestion Control using MAODV in WANET Environment

A decentralized sort of network that can allow the nodes to communicate with them lacking any central controller is Wireless Ad hoc Networks (WANET). Network Congestions can befall on account of nodes' restricted Bandwidth (BW) together with dynamic topology. Network Congestions brings about data loss as it makes the Data Packets (DP) be dropped on the network. Therefore, in order to lessen Network Congestions, it is necessary to model Congestion Control (CC) systems aimed at the WANET. Thus, this paper offers an optimal routing centered CC scheme utilizing the Modified Ad hoc on-demand Distances Vector (MAODV) Routing Protocol (RP) aimed at the WANET. Here, primarily, the Source Node (SN) together with Destination Nodes (DN) is initialized, and after that, the MAODV discovers the multiple routing paths. Subsequently, Stochastic Gradients Descent Deep Learning Neural Network (SGD-DLNN) identifies the Congestion Status (CS) of every node in the discovered paths. In addition, the MAODV allocates the traffic over the optimum congestion-free routing path if congestion befalls. The Levy Flight Based Black Widow Optimization (LF-BWO) algorithm chooses the optimal routing paths as of congestion-free paths. Centered upon path lifetime, residual energy, link cost, together with path distance, this algorithm enhances the Data Transmission (DT) performance by means of discovering a path. The experimentation’s outcomes are rendered to exhibit the proposed RP’s effectiveness.

Traffic and Energy Aware Optimization for Congestion Control in Next Generation Wireless Sensor Networks

Congestion in wireless sensor networks (WSNs) is an unavoidable issue in today’s scenario, where data traffic increased to its aggregated capacity of the channel. The consequence of this turns in to overflowing of the buffer at each receiving sensor nodes which ultimately drops the packets, reduces the packet delivery ratio, and degrades throughput of the network, since retransmission of every unacknowledged packet is not an optimized solution in terms of energy for resource‐restricted sensor nodes. Routing is one of the most preferred approaches for minimizing the energy consumption of nodes and enhancing the throughput in WSNs, since the routing problem has been proved to be an NP‐hard and it has been realized that a heuristic‐based approach provides better performance than their traditional counterparts. To tackle all the mentioned issues, this paper proposes an efficient congestion avoidance approach using Huffman coding algorithm and ant colony optimization (ECA‐HA) to improve the network performance. This approach is a combination of traffic‐oriented and resource‐oriented optimization. Specially, ant colony optimization has been employed to find multiple congestion‐free alternate paths. The forward ant constructs multiple congestion‐free paths from source to sink node, and backward ant ensures about the successful creation of paths moving from sink to source node, considering energy of the link, packet loss rate, and congestion level. Huffman coding considers the packet loss rate on different alternate paths discovered by ant colony optimization for selection of an optimal path. Finally, the simulation result presents that the proposed approach outperforms the state of the art approaches in terms of average energy consumption, delay, and throughput and packet delivery ratio.

Safe mobile robot navigation in human-centered environments using a heat map-based path planner

Safe robot navigation in human-centered environments is important to avoid collisions. A major limitation of the traditional path planning algorithms is that the global path is planned only with the knowledge of static obstacles in the map. This paper presents a novel ‘HMRP (heat map-based robot path planner)’ which uses fixed external cameras to generate a heat map of different passages based on congestion, so that robots can generate congestion-free paths at the global planning stage itself. The congestion values are maintained in a database and the paths are classified into hot and cold regions. Robot navigation is affected by the direction of movement of people. Hence, in this work, the HMRP-based planner also considers the direction of movement of people in passages which improves robot navigation. The proposed HMRP is compared with traditional path planning algorithms in real environment. Results show that the proposed HMRP algorithm generates congestion-free paths for safe robot navigation.

A Dominance of the Channel Capacity in Load Balancing of Software Defined Network

Software defined networks concept is used to transfer the responsibility of route management from forwarding devices to a centralized control application which is called a controller. The controller is used to decide the congestion-free path between source and destination. In data transfer, channel capacity plays an important role. In many situations channel capacity is below some threshold value which causes congestion and decreases the reliability of the network. Several requests may contact the controller for getting the optimal path. This increases the load at the controller side. The controller decides the path to improve the reliability of the network as well as to maximize the uses of channel capacity in the network. In this paper, we have proposed an algorithm for load balancing. We have considered our network as a graph, the vertices of the graph are switches and the channels of the network are represented as edges and considered the dominance of the channel capacity over server load for deciding the optimal path. We have demonstrated our algorithm using a mathematical example.

Software-Defined Networking (SDN) based VANET Architecture: Mitigation of Traffic Congestion

In VANETs (Vehicular Ad-hoc Networks), the num-ber of vehicles increased continuously, leading to significant traffic problems like traffic congestion, a feasible path, and associated events like accidents. Though, the Intelligent Transportation System (ITS) providing excellent services, such as safety appli-cations and emergency warnings. However, ITS has limitations regarding traffic management tasks, scalability, and flexibility because of the enormous number of vehicles. Therefore, extending the traditional VANET architecture is indeed a must. Thus, in the recent period, the design of the SD-VANETs (Software-Defined Networking defined VANETs) has gained significant interest and made VANET more intelligent. The SD-VANET architecture can handle the aforesaid VANET challenges. The centralized (logically) SDN architecture is programmable and also has global information about the VANET architecture. Therefore, it can effortlessly handle scalability, traffic management, and traffic congestion issues. The traffic congestion problem leads to longer trip times, decreases the vehicles’ speed, and prolong average end-to-end delay. Though, somewhere, some routes in the network are available with capacity, which can minimize the congestion problem and its characteristics. Therefore, we proposed heuristic algorithms called Congestion-Free Path (CFP) and Optimize CFP (OCFP), in SD-VANET architecture. The proposed algorithms address the traffic congestion issue and also provide a feasible path (less end-to-end delay) for a vehicle in VANET. We used the NS-3 simulator to evaluate the performance of the proposed algorithms, and for generating a real scenario of VANET traffic; we use the SUMO module. The results show that the proposed algorithms decrease road traffic congestion drastically compared to exiting approaches.

CONGESTION AVOIDANCE AND CONTROL MECHANISMS FOR MANET USING AODV PROTOCOL: A SURVEY

Mobile Ad-hoc Network (MANET) comprises of self-organized mobile nodes, which communicate wirelessly without any fixed infrastructure.Congestion is one of the most common factors that affects functioning of MANET. When congestion encounters in the network the victim nodesfail to transmit the data, in turndeteriorates the network throughput. Appropriate congestion control algorithm is necessary in controlling the congestion, which reduces the load over the network, utilize the resources efficiently and provide a congestion free path for transmission of data.The Ad hoc On-Demand Distance Vector (AODV) protocol, one of the popular routing protocols defined for MANET, enables dynamic, self-starting, multihop routing between participating mobile nodes wishing to establish and maintain an ad hoc network. As AODV protocol is not adaptive to congestion, several researchers have suggested upgradations in the AODV protocolto control the congestion.This review paper aims to deliberate about some of the bettercongestion avoidance and control mechanisms for MANET using AODV Protocol by different researchers, their comparative analysis and performance assessment.

Congestion Free Vehicular Path Planning System: A Real-Time Cloud-Enabled ITS Application

Traffic congestion is one of the major issues in urban areas. To mitigate this problem a centralized and personalized traffic control system is essential. This paper proposes a cloud-enabled real-time congestion free vehicular path planning system using Vehicular Ad-hoc Networks (VANETs). In this work, a novel algorithm has been proposed for finding the fastest (i.e. less density) congestion free path using real-time traffic information. This algorithm also predicts the future road traffic density and takes into account in order to find the near future congestion free path. This algorithm can be made available as Software as a Service (SaaS) on the cloud.

Preemptive MACO (MACO-P) Algorithm for Reducing Travel Time in VANETs

ABSTRACTVehicular Ad-hoc NETworks (VANETs) are extremely flexible and dynamic ad-hoc networks that are used to provide smooth, safe and comfortable journey to commuters. The commuters spend significant time of their journey either for their turn to cross the intersections or in the congestion on the path. For reducing the total travel time, it is essential to minimize the waiting time at intersections and find best/optimal congestion-free paths for smoother movement of vehicular traffic on roads. A novel Preemptive Modified Ant Colony Optimization (MACO-P) algorithm has been proposed in this paper for reducing the total travel time. The Modified Ant Colony Optimization (MACO) algorithm is used in literature to avoid the congested path by sensing the pheromone trail. Adding preemption to the existing MACO algorithm will result in the reduction of the average queue length at intersections, meaning thereby, less waiting time ensuring smooth mobility of vehicles. For implementation, various open source softwares, i.e., OSM, Simulation of Urban MObility (SUMO), MObility model generator for VEhicular networks (MOVE), Python and Traffic Control Interface (TraCI) are being used. Real-time maps are being fetched by OSM. Traffic simulation is done using SUMO and the Mobility model is generated through MOVE. Python is used for writing client scripts that initiate and control the simulation, with traffic control interface provided by TraCI. Experimental results confirm that the the total travel time is reduced using the proposed MACO-P algorithm, resulting in significant reduction in consumption of fuel.

Design of Fuzzy-Based Traffic Provisioning in Software Defined Network

This Software defined networks helps to realize extraordinary services that can be easily embedded in network operations of switch. It provokes the decomposition of the control and data planes. The control plane is more extensible, as it is unproblematic to change or introduce any new functionality into the network. It is studied that any new integration can be easily added up with a very low line of code (LOC). The work proposes a fuzzy based approach for traffic provisioning in SDN. Fu zzy Logic Control System (FLCS) is a controller co mp rising of two fuzzy systems - Label Switched Path setup System (LsS) and Traffic Splitting System (TSS). The co mputation of dynamic status of Load and Delay is utilized by LsS to arrange the paths in preference order. The attained Link Capacity and Utilizat ion Rate are emp loying by TSS for maintain ing congestion free path. Created three different topologies and performed ping reachability test and executed iperf testing tool for performance analysis on Mininet framework.The impact of this is to facilitate better decision making for splitting the traffic for different capable paths. Simulat ion setup is deployed using OpenFlow Switches and Controllers to study their performance. The packet delivery rat io remained above 98% , showing rare chances of congestion and delay was below than 2.6 seconds with TTL in range of 60-80 milliseconds.

Congestion Control Using Fuzzy Based LSPS in Multiprotocol Label Switching Networks

In this paper, we have proposed a fuzzy based decision making component for high volume traffic MPLS networks, by implementing Traffic Engineering, Quality of Service and Multipath routing. The approach explicitly proves to be successful in solving the issues and challenges pertaining to stability, scalability in high volume and dynamic traffic. Furthermore, it will work to handle congestion by higher link utilization and provides efficient rerouting of traffic along with fault tolerance in the network. In this research work, fuzzy calculations are done for fixing the attributes of the MPLS label(s), which is put on particular packet representing the Forwarding Equivalence Class. Fuzzy controller consists of two sub fuzzy systemsLabel Switched Path setup System (LsS) and Traffic Splitting System (TSS). The computation of dynamic status of Load and Delay is utilized by LsS to arrange the paths in preference order. The attained Link Capacity and Utilization Rate are employing by TSS for maintaining congestion free path. The impact of this is to facilitate, we have better decision making for splitting the traffic for different promising paths. This was apparent from the series of traffic scenarios. Observations are obtained using this realization.

Agent based Intelligent Routing Algorithm for Energy Efficient Routing in Wireless Sensor Network

In wireless sensor network, massive sensor nodes are deployed to sense the environment for various applications, so that there is a reduction in energy when performing routing which leads to decrease in network lifetime. However, if routing is performed through all the nodes, energy will be wasted. To avoid the wastage of resources such as energy or battery lifetime of nodes, various energy efficient routing algorithms were formulated to find the suboptimal path to reach the destination nodes with shortest time. Hence, finding an optimal routing with low energy is an important challenge in wireless sensor networks. Therefore, we propose a new routing algorithm in this paper by integrating agent based intelligent decision making with energy efficient routing algorithm which provides freedom to choose the optimized path over the network traffic with limited energy consumption. In all available routes, intelligent agents are deployed in nodes which help to predict the best path by making a suitable decision on whether to take congestion free path or shortest path. Moreover, every node is considered as an independent node to choose flexible route in a distributed environment with the help of moving average (MA) model. The Moving Average model is used in this paper, since it is a parametric time series model which can predict the network traffic based on historical traffic data and current traffic data. The main advantage of the proposed algorithm is the reduction in energy consumption, time taken to reach destination and packet drop ratio.