Volume 2, Spl. Issue 2 (2015)
e-ISSN: 1694-2310 | p-ISSN: 1694-2426
Analysis Of Multi-Path Routing in Vanets with Elastic and Inelastic Traffic Monika Pathania, Vinay Bhatia Department of ECE, Baddi University of Emerging Sciences and Technology, Baddi, H.P monikapathania999@gmail.com, rvinay4research@yahoo.com
Abstract— In Recent years, the Vehicular Ad hoc Networks (VANETs) have emerged as an area to support transportation system and improves road traffic safety. In VANETs, cars are used as mobile nodes in case of Mobile Ad hoc Networks (MANETs). When misbehaving node is present, it becomes difficult to meet better network performance, throughput and fair allocation of resources among users with elastic and inelastic traffic. However, VANETs are vulnerable to misbehaving node as compared to MANETS [1]. Elastic traffic is usually coming from non-real-time applications and while inelastic traffic from real-time applications. Multi-path routing will increase overall network utility, throughput and fairness among users as compared to single path routing. Multi-path routing network balance the load of traffic across multiple paths in the network and are fault tolerant network. Different approaches Dynamic Rate Power Allocation Algorithm (DRPAA), Stochastic Dual Theory, Particle Swarm Optimization (PSO) and Fault Correlated Flow Control and Routing ((FC)2R) are proposed [1-4] to solve the optimization problem. To each network state link powers and flow rate will divide by using DRPAA approach. DRPAA and fault aware flow control algorithm will use network resources efficiently and offer high throughput.
communication shows that figure 1. RSU provide communication deal with the infrastructure. RSUs are similar to get access to points in wireless networks. These are very expensive to install. V2V communication uses multi-hop multicast/broadcast to send information to a group of receivers while vehicle to RSU communication represents a single hop broadcast where the RSU broadcasts a message to all the vehicles equipped in the vicinity.
Fig. 1.
Keywords—ad hoc network; elastic and inelastic traffic; real and non-real time applications; utility function; VANETs.
I. INTRODUCTION The modern communication systems are evolving in form of Ad-hoc networks. Ad-hoc network is a term associated with decentralized type of wireless network. Decentralization means it does not rely on pre-existing infrastructure as in wireless and wired networks. In wireless networks and wired networks, access points and routers are responsible for routing omit. In ad hoc networks nodes are mobile or movable. Thus, mobiles used as movable nodes. Hence, sometimes called as Mobile Ad hoc Networks(MANETs). However, using vehicles instead of mobile nodes make it a VANET.
VANET structure
II. MULTI-PATH ROUTING In multi-path routing, multi-path exists between the networks in the internetwork as in figure 2. Thus, in case of fault occurrence in one path, signal follows another path easily. Moreover, multipath routing can improve the packet delivery ratio in VANETs. However, multi-path routing is more complex to configure when using distance vector routing protocols. Thus it becomes difficult task for researchers to improve performance of ad hoc networks. Multi-path routing is a solution to improve the packet delivery ratio, throughput and fairness of resources.
VANET is the sub-class of MANET used for intelligent transportation system of the future. Routing is a critical issue in VANETs because of absence of central entity as in other ad hoc networks. Thus routing of information throughout the network is difficult to control. Moreover, if there exists a faulty node in single path routing then information is not received at destination node. Thus multipath routing will use instead of single path routing. VANET serves as one of the most enabling technologies which will carry out various applications related to transportation systems. It has potential to improve traffic efficiency, road and vehicle safety, and convenience to driver as well as passenger. VANETs offer Vehicle to Vehicle (V2V) communication, Vehicle to Road Side Unit (RSU) communication, RSU to RSU
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Fig. 2. Multi-path routing
BUEST, Baddi
RIEECE -2015
Volume 2, Spl. Issue 2 (2015)
e-ISSN: 1694-2310 | p-ISSN: 1694-2426
A. Misbehaving Node Sometimes, a node refuses its own resources and may attempt to help from other nodes. Such nodes are misbehaving nodes. Because of their selfishness behavior, sometimes called as selfish node. However, ad hoc network rely on mobile nodes but their selfishness behavior may degrade the network performance. Misbehaving nodes have selfish behavior and are unwilling to send the packets. Since misbehaving node does not coöperate in the sending packets.
B. Inelastic traffic Inelastic traffic cannot easily adapt to changes in delay/throughput across an internet. Inelastic traffic came from real-time multimedia applications. They are application such as VoIP, IPTV, audio and video conference. Inelastic traffic is vulnerable to delay and jitter. Inelastic traffic is not friendly with TCP protocol and direct to RTP protocol a discrete set of data (i.e. image sent in the form of video frames every 40 ms). Inelastic traffic has not ability to adjust over wide ranges to changes in delay and throughput. Inelastic traffic requires special treatment to avoid network congestion, collisions or noise environments. IV. LITERATURE SURVEY
Fig. 3.
Misbehaving node
Thus, detecting misbehaving node is essential for the overall performance of the network. To detect a selfish node, requires collaborative approach. As shown in figure 3, source S is sending information packets to the destination node D. Here, each node participates in sending packets to neighboring node but node 5 misbehaves, drop the information packets and due to packet loss, reduction in network performance. III. ELASTIC AND INELASTIC TRAFFIC Elastic traffic is insensitive to delay/throughput. This type of traffic associates with application that involves sending of information using Transmission Control Protocol (TCP) protocol. Inelastic traffic is sensitive to delay/throughput. This kind of traffic associates with application that involves sending of data using RTP protocol. A. Elastic Traffic Elastic traffic can easily adapt, over wide ranges across an internet, to changes in delay/throughput. Elastic traffic came from non real-time application. They are application such as email, World Wide Web (www), file transfer. Elastic traffic is not vulnerable to delay and jitter. They direct to TCP protocol a continuous stream of data (message, email, file etc.). Reliable transport protocol such as TCP achieves correct data transmission. Elastic traffic has ability to adjust over wide ranges to changes in delay and throughput. Elastic traffic adjusts its throughput between end hosts. Throughput adjusts with respect to network conditions. Packet loss may occur due network congestion. Network congestion usually occurs when the demand for resources exceeds available resources. To avoid network congestion, TCP involves its congestion avoidance algorithm and it decreases the rate at which information packets send over the network.
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To maximize the network utility, use a fault correlated flow control and routing approach to maximize the network utility. They investigate the optimization problem via the joint design of rate control and multipath routing in fault aware VANETs. Algorithm provides largest throughput and better fairness of resources among different users [1-2]. The various features of vehicular networks provide tremendous applications. The properties of vehicular network also address various challenges faced by network along their solutions. The architectures and protocol suites supported by International Transport System (ITS) used in different road scenarios of the world [3]. The Dynamic Rate and power allocation (DRPA) algorithm supports the mixture of elastic and inelastic traffic. Its aim is to increase the network performance by allocating power for each link and controlling the service rate of all flows in the network. Algorithm provides largest network utility, congestion control and fair allocation of resources. They suggest that scheduling problem is a common framework and algorithm efficiently use the network resources and provides better network utility [45]. The algorithm guarantees the solution to optimization problem. They investigate a problem of random access in Wireless Local Area Networks (WLANs). They formulate a Network Utility Maximization (NUM) problem. For certain utility functions, proves stability of concave utility maximization. By adjusting the utility function and solving the NUM problem, desired efficiency and fairness is achieved [6-8]. To increase the overall network utility proposes nodedisjoint multi-path routing. With simulations, we explore the mutual interferences on the behavior of node-disjoint paths. To get 50% reduction in average packet loss ratio, use different metrics [9-10]. The various issues of charging, rate control and routing for a communication network carrying elastic traffic. They demonstrated a model from which maximum-minimum fairness of rates emerges as a special case. The proposed model supports the charging schemes for broadband multiservice application. To make better fairness of resources choose right utility function. Equilibrium must meet between charges of user’s choice and allocated rates of network’s choice [11]. In [12], J. Mo and J. Warland demonstrated fair end-toend window based congestion control schemes. In several ways fairness characterizes. With the usage of optimization problem, relates window sizes and fair rates to each other. 348
Volume 2, Spl. Issue 2 (2015)
V. INTRODUCTION OF OPTIMAL FLOW CONTROL (OFC) AND ((FC) 2R) ALGORITHM As for VANETs nodes having high mobility, it becomes difficult to meet high throughput and better fairness among users. Introduction of OFC and ((FC) 2R) algorithm keeps up an acceptable level of performance degradation and throughput. ((FC)2R) approach get higher effective throughput and better fairness among effective flow rates. At source node (before transmission) and at destination node (after transmission), evaluates effective throughput and fairness among effective flows by arranging the network topology at different scenarios using OFC and ((FC)2R) approach. VI. PROPOSED WORK METHODOLOGY By using Dynamic Rate and Power Allocation Algorithm (DRPAA), solves the non convexity of elastic and inelastic traffic. DRPAA includes both stochastic dual theory and Particle Swarm Optimization (PSO). A network consists of number of nodes and links. Each link of network serves both elastic and inelastic traffic. The case when wireless ad hoc network serves different traffic, it may induce two menacing provocations: Because of time variations leads to the variations in capacity channel of the link. Also, elastic and inelastic traffic have different demand with respect to delay and throughput as elastic traffic is insensitive to delay and inelastic traffic is sensitive to delay. To increase the network performance, use Network Utility Maximization (NUM). To model the problem of fairly allocation of resources use NUM. We consider the problem of resource allocation for both kinds of traffic and it becomes difficult to solve the NUM problem. There are so many reasons: First is network states distribution. It requires to distribute the states because it is necessary to distribute the channel condition, thus effective methods must to decompose a NUM problem into several problems with each corresponding to one network state. Second is non convexity of optimization problem. To model elastic traffic use concave utility function but inelastic traffic is not modeled by non concave utility function. Thus, it makes a NUM problem becomes a non convex optimization problem. Thus to supporting both elastic traffic and inelastic traffic, requires an efficient algorithm for ad hoc network. Since for inelastic flow the utility functions are non concave. To decompose this non-convex problem, use Stochastic Dual Theory. By Particle Swarm Optimization (PSO) solves each problem. Thus DRPAA requires. DRPAA introduces some dual variables and stochastic problem decomposes into several problems. In this model for current time slot, the network state solve allocation rate and power in current time slot, t. Then the dual variables update for time slot as t+1. DRPAA repeats introduction and updating of dual variables until dual variables converge. Thus by using DRPAA, rate and power allocation at network level. The network performance depends on efficiency of the network so that how efficiently resources allocate during network congestion. In
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e-ISSN: 1694-2310 | p-ISSN: 1694-2426
order to keep the network connected, the transmission power is dominant as compared to the total power consumption. Here the power consumption is small. VII. CONCLUSION In this paper we focused on the problem of routing services and rate control for both the elastic and inelastic traffic. Here, we formulate the problem as non-convex optimization problem using DRPAA. To solve this optimization problem, proposes DRPAA [4]. In DRPAA, for each network state divide flow rates and power without having any knowledge about network state. Algorithm performs better in terms of utility of network and power consumption. We have derived here OFC problem with multiple paths. The OFC approach provides better fairness among different users and network utility. To improve the network utility performance and throughput and fairness of resources among users, use another algorithm called ((FC)2R) [1]. The algorithms improve the effective network utility and fairness of resources by considering elastic and inelastic traffic. These algorithms enhance the overall network performance and throughput even with misbehaving node REFERENCES [1]
Xiaomei Zhang, Xiaolei Dong, NaixueXiong, JieWu , Xiuqi Li, “Fault-aware flow control and multi-path routing in VANETs”, 2 September, 2014. [2] Jiong Jin, Marimuthu Palaniswami, Bhaskar Krishnamachari,“Rate control for heterogeneous wireless sensor networks”, Computer network, September 2012, vol. 56:3783-3794. [3] Georgios Karagiannis, Onur Altintas, EylemEkici, Geert Heijenk, Boangoat Jarupan, Kenneth Lin, and Timothy Weil, “Vehicular networking: a survey and tutorial on requirements, architectures, challenges, standards and solutions”, IEEE communications surveys and tutorials, 2011, vol. 13, No. 4:584–616. [4] Fei Wang, Xiaofeng Liao, SongtaoGuo, Hongyu Huang, Tingwen Huang, “Dynamic rate and power allocation in wireless ad hoc networks with elastic and inelastic traffic”. Wireless perscommunication.July 2012, vol. 70:435-457. [5] Juan José Jaramillo, R. Srikant, “Optimal scheduling for fair resource allocation in ad hoc networks with elastic and inelastic traffic”, IEEE/ACM transactions on networking, vol. 19, No. 4, August 2011. [6] Man Hon Cheung, “Random access for elastic and inelastic traffic in WLANs”, IEEE transactions on wireless communications, vol, 9, No. 6, June 2010. [7] Jang-Won Lee, Mung Chiang, A. Robert Calderbank, “Utilityoptimal random access control”, IEEE transaction on wireless communication, vol. 6, No. 7:2741–2751, July 2007. [8] PrashanthHande, Shengyu Zhang and Mung Chiang, “Distributed rate allocation for inelastic flows”, IEEE/ACM transactions on networking, vol. 15, No. 6, December 2007. [9] XiaoxiaHuang, Fang Y, “Performance study of node-disjoint multipath routing in vehicular ad hoc networks”, IEEE transaction on vehicular technology, vol. 58, No. 4, May 2009. [10] Can EmreKoksal, HariBalakrishnan, “Quality-aware routing metrics for time-varying wireless mesh networks”, IEEE journal on selected areas Communication, vol. 24, No. 11, November 2006. [11] Frank Kelly, “Charging and rate control for elastic traffic”, European transaction on telecommunication, 1997, vol. 8:33–37. [12] Jeonghoon Mo and Jean Walrand, “Fair end-to-end window-based congestion control”,IEEE/ACM transaction on networking, vol. 8, No.5:556–567, October 2000.
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