On the Accuracy of MANET Simulators David Cavin Yoav Sasson & André Schiper Presented by Michael W. Totaro Mobile Computing and Wireless Systems (MoCWiS)

Slides:



Advertisements
Similar presentations
Wireless Communication : LAB 3
Advertisements

Decentralized Reactive Clustering in Sensor Networks Yingyue Xu April 26, 2015.
Introduction to Wireless Sensor Networks
802.11a/b/g Networks Herbert Rubens Some slides taken from UIUC Wireless Networking Group.
CSLI 5350G - Pervasive and Mobile Computing Week 3 - Paper Presentation “RPB-MD: Providing robust message dissemination for vehicular ad hoc networks”
Improving TCP Performance over Mobile Ad Hoc Networks by Exploiting Cross- Layer Information Awareness Xin Yu Department Of Computer Science New York University,
Self-Organizing Hierarchical Routing for Scalable Ad Hoc Networking David B. Johnson Department of Computer Science Rice University Monarch.
Generated Waypoint Efficiency: The efficiency considered here is defined as follows: As can be seen from the graph, for the obstruction radius values (200,
MANETs Routing Dr. Raad S. Al-Qassas Department of Computer Science PSUT
Emulatore di Protocolli di Routing per reti Ad-hoc Alessandra Giovanardi DI – Università di Ferrara Pattern Project Area 3: Problematiche di instradamento.
An Analysis of the Optimum Node Density for Ad hoc Mobile Networks Elizabeth M. Royer, P. Michael Melliar-Smith and Louise E. Moser Presented by Aki Happonen.
Ad-Hoc Networking Course Instructor: Carlos Pomalaza-Ráez D. D. Perkins, H. D. Hughes, and C. B. Owen: ”Factors Affecting the Performance of Ad Hoc Networks”,
Performance Comparison of Routing Protocols for Ad Hoc Networks PATTERN ENDIF Ferrara.
NCKU CSIE CIAL1 Principles and Protocols for Power Control in Wireless Ad Hoc Networks Authors: Vikas Kawadia and P. R. Kumar Publisher: IEEE JOURNAL ON.
Networking Theory (Part 1). Introduction Overview of the basic concepts of networking Also discusses essential topics of networking theory.
CS541 Advanced Networking 1 Mobile Ad Hoc Networks (MANETs) Neil Tang 02/02/2009.
Matnet – Matlab Network Simulator for TinyOS Alec WooTerence Tong July 31 st, 2002.
WSN Simulation Template for OMNeT++
Adaptive Self-Configuring Sensor Network Topologies ns-2 simulation & performance analysis Zhenghua Fu Ben Greenstein Petros Zerfos.
Mobile and Wireless Computing Institute for Computer Science, University of Freiburg Western Australian Interactive Virtual Environments Centre (IVEC)
Component-Based Routing for Mobile Ad Hoc Networks Chunyue Liu, Tarek Saadawi & Myung Lee CUNY, City College.
A Cross Layer Approach for Power Heterogeneous Ad hoc Networks Vasudev Shah and Srikanth Krishnamurthy ICDCS 2005.
Peer-to-peer file-sharing over mobile ad hoc networks Gang Ding and Bharat Bhargava Department of Computer Sciences Purdue University Pervasive Computing.
CS401 presentation1 Effective Replica Allocation in Ad Hoc Networks for Improving Data Accessibility Takahiro Hara Presented by Mingsheng Peng (Proc. IEEE.
Network Topologies.
QualNet 2014/05/ 尉遲仲涵. Outline Directory Structure QualNet Basic Message & Event QualNet simulation architecture Protocol Model Programming.
Ad Hoc Networking via Named Data Michael Meisel, Vasileios Pappas, and Lixia Zhang UCLA, IBM Research MobiArch’10, September 24, Shinhaeng.
Hamida SEBA - ICPS06 June 26 th -29 th Lyon France 1 ARMP: an Adaptive Routing Protocol for MANETs Hamida SEBA PRISMa Lab. – G2Ap team
09/07/2004Peer-to-Peer Systems in Mobile Ad-hoc Networks 1 Lookup Service for Peer-to-Peer Systems in Mobile Ad-hoc Networks M. Tech Project Presentation.
NetworkProtocols. Objectives Identify characteristics of TCP/IP, IPX/SPX, NetBIOS, and AppleTalk Understand position of network protocols in OSI Model.
Lecture 2 TCP/IP Protocol Suite Reference: TCP/IP Protocol Suite, 4 th Edition (chapter 2) 1.
Redes Inalámbricas Máster Ingeniería de Computadores 2008/2009 Tema 7.- CASTADIVA PROJECT Performance Evaluation of a MANET architecture.
NetSim ZigBee Simulation Code Walkthrough in 10 steps
MOBILE AD-HOC NETWORK(MANET) SECURITY VAMSI KRISHNA KANURI NAGA SWETHA DASARI RESHMA ARAVAPALLI.
Qian Zhang Department of Computer Science HKUST Advanced Topics in Next- Generation Wireless Networks Transport Protocols in Ad hoc Networks.
A Distributed Scheduling Algorithm for Real-time (D-SAR) Industrial Wireless Sensor and Actuator Networks By Kiana Karimpour.
Institut für Betriebssysteme und Rechnerverbund Technische Universität Braunschweig Multi hop Connectivity in Mobile Ad hoc Networks (MANETs) Habib-ur.
Analysis of the Impact and Interactions of Protocol and Environmental Parameters on Overall MANET Performance Michael W. Totaro and Dmitri D. Perkins Center.
1 Core-PC: A Class of Correlative Power Control Algorithms for Single Channel Mobile Ad Hoc Networks Jun Zhang and Brahim Bensaou The Hong Kong University.
1 Heterogeneity in Multi-Hop Wireless Networks Nitin H. Vaidya University of Illinois at Urbana-Champaign © 2003 Vaidya.
BitTorrent enabled Ad Hoc Group 1  Garvit Singh( )  Nitin Sharma( )  Aashna Goyal( )  Radhika Medury( )
ENERGY-EFFICIENT FORWARDING STRATEGIES FOR GEOGRAPHIC ROUTING in LOSSY WIRELESS SENSOR NETWORKS Presented by Prasad D. Karnik.
Enrique Alba, Sebastián Luna, and Jamal ToutouhAccuracy and Efficiency in Simulating VANETs MCO’08 Metz, France September 8-10 th, 2008 MCO’08 Metz, France.
Designing Routing Protocol For Mobile Ad Hoc Networks Navid NIKAEIN Christian BONNET EURECOM Institute Sophia-Antipolis France.
ICOM 6115: Computer Systems Performance Measurement and Evaluation August 11, 2006.
Presentation of Wireless sensor network A New Energy Aware Routing Protocol for Wireless Multimedia Sensor Networks Supporting QoS 王 文 毅
An OLSR implementation, experience, and future design issues.
NGMAST 2008 A Proactive and Distributed QoS Negotiation Approach for Heterogeneous environments Anis Zouari, Lucian Suciu, Jean Marie Bonnin, and Karine.
TOPOLOGY MANAGEMENT IN COGMESH: A CLUSTER-BASED COGNITIVE RADIO MESH NETWORK Tao Chen; Honggang Zhang; Maggio, G.M.; Chlamtac, I.; Communications, 2007.
Chapter 9 Hardware Addressing and Frame Type Identification 1.Delivering and sending packets 2.Hardware addressing: specifying a destination 3. Broadcasting.
A Scalable Routing Protocol for Ad Hoc Networks Eric Arnaud Id:
1 Gossip-Based Ad Hoc Routing Zygmunt J. Haas, Joseph Halpern, LiLi Cornell University Presented By Charuka Silva.
An Energy Efficient MAC Protocol for Wireless LANs, E.-S. Jung and N.H. Vaidya, INFOCOM 2002, June 2002 吳豐州.
Tufts Wireless Laboratory School Of Engineering Tufts University Paper Review “An Energy Efficient Multipath Routing Protocol for Wireless Sensor Networks”,
a/b/g Networks Routing Herbert Rubens Slides taken from UIUC Wireless Networking Group.
Computer Simulation of Networks ECE/CSC 777: Telecommunications Network Design Fall, 2013, Rudra Dutta.
An Efficient Gigabit Ethernet Switch Model for Large-Scale Simulation Dong (Kevin) Jin.
Energy-Efficient Protocol for Cooperative Networks.
Using Ant Agents to Combine Reactive and Proactive strategies for Routing in Mobile Ad Hoc Networks Fredrick Ducatelle, Gianni di caro, and Luca Maria.
Efficient Geographic Routing in Multihop Wireless Networks Seungjoon Lee*, Bobby Bhattacharjee*, and Suman Banerjee** *Department of Computer Science University.
Performance Comparison of Ad Hoc Network Routing Protocols Presented by Venkata Suresh Tamminiedi Computer Science Department Georgia State University.
Wireless sensor and actor networks: research challenges Ian. F. Akyildiz, Ismail H. Kasimoglu
HoWL: An Efficient Route Discovery Scheme Using Routing History in Mobile Ad Hoc Networks Faculty of Environmental Information Mika Minematsu
Realistic Mobility Models for Vehicular Ad hoc Network (VANET) Simulations ITST 高弘毅 洪佳瑜 蔣克欽.
Analysis the performance of vehicles ad hoc network simulation based
A Location-Based Routing Method for Mobile Ad Hoc Networks
Simulators for Sensor Networks
Mobicom ‘99 Per Johansson, Tony Larsson, Nicklas Hedman
Introduction to Wireless Sensor Networks
Computer Simulation of Networks
Presentation transcript:

On the Accuracy of MANET Simulators David Cavin Yoav Sasson & André Schiper Presented by Michael W. Totaro Mobile Computing and Wireless Systems (MoCWiS) group UL Lafayette - CACS

Topics Overview Introduction Related Work Flooding Algorithm The Simulators Simulations Conclusions Q & A

Overview The simulation phase of MANET applications or protocol deployment requires meaningful simulation results The model on which the simulator is based should match as closely as possible to reality Simulation results of a straightforward algorithm using several popular simulators are presented, whereby significant divergences exist between the simulators

Introduction Context – Interest in MANETs (Mobile Ad-hoc Networks) requires adaptation of solutions from the traditional wired networks to the wireless environment – Simulation is a tool that can often help to improve or validate protocols – Generally speaking, all simulators provide a complete toolkit to developers that facilitates metrics collection and various wireless network diagnostics

Introduction (2) Accuracy of simulation results – Popular simulators such as NS-2, OPNET Modeler, and GloMoSim provide advanced simulation environments to test and debug networking protocols, including wireless applications – It is essential that the simulated behaviors match as closely as possible the reality – This latter requirement assumes that several issues are sufficiently addressed

Introduction (3) Accuracy of simulation results (cont’d) – First Issue Application is likely to rely on components such as a collision detection module, as well as radio propagation or MAC protocols Correct definitions of these components in the simulator is critical Typically, the algorithm being evaluated is modeled in detail; however, cross-layer interactions are very rarely taken into account

Introduction (4) Accuracy of simulation results (cont’d) – Second Issue Simulation parameters and the environment (e.g., mobility schemes, power ranges, connectivity) must be realistic Incorrect initial conditions may lead to unexpected results that are not realizable in a real network

Introduction (5) Accuracy of simulation results (cont’d) – Focus of research The research presented in this paper shows the results of a set of measures collected during the simulation of a flooding algorithm on three different simulators: OPNET, NS-2, and GloMoSim Special attention was given to setting the same parameters and considering the same scenarios in each simulator; nevertheless, very different results—barely compatible—were collected

Related Work The research literature offers an abundance of papers on the efficiency of wireless algorithms comparing relative performances of each by means of simulation Few of these papers, however, focus on possible divergences that may occur between simulators, probably because the researchers work with only a single simulator—one with which they are most familiar—and thus do not expect nor anticipate significant differences among simulators

Related Work (2) The physical layer and the important parameters that influence its behavior have been modeled in NS-2 and OPNET – Results suggest that the configuration affects seriously the absolute performance of a protocol, and can even change the relative ranking among protocols for the same scenario

Related Work (3) The effect of detail in MANET simulations has been studied – Appropriate levels of detail in simulation models for radio propagation and energy consumption remain questionable Simulations that are too detailed may not be easily adapted to expeditiously explore alternatives Conversely, simulations that lack detail can lead to misleading or incorrect results

Flooding Algorithm Introduction – A frequently used operation to spread information to the whole network is the broadcast of messages – The performance of the broadcast is likely to affect the global efficiency of any protocol using it; hence, the broadcast should be implemented in the most efficient way

Flooding Algorithm (2) Introduction – Simulations Peer-to-peer wireless network, roughly 50 nodes randomly placed on a 1km x 1km area Ad-hoc mode, without any central access point (infrastructureless) Every node (peer) has the same possibilities and functionalities

Flooding Algorithm (3) Flooding – Flooding a message over the network is a simple way to implement broadcast Node initiates a broadcast Message is transmitted to its neighborhood (i.e., all nodes within the sender’s transmission range) When the message is received by a recipient for the first time, the recipient re-broadcasts it

Flooding Algorithm (4) Flooding example

Flooding Algorithm (5) Drawback of Flooding → overhead of flooded messages in the network Under ideal conditions (i.e., all nodes received the broadcast) in a network of N nodes, a single broadcast will generate exactly N copies of itself – Likely to increase probability of collisions – Most nodes will receive the same message several times, thus keeping the shared medium unnecessarily busy

Flooding Algorithm (6) Architecture Algorithm protocol stack Assume that every message has unique ID

The Simulators Introduction – The way a new algorithm is integrated can be considerably different from one simulator to another – A summary of the different implementation approaches for each simulator is presented, along with particular requirements and challenges

The Simulators (2) OPNET Modeler – Can simulate many kinds of wired networks, and a compliant MAC layer implementation is also provided – Phases of OPNET deployment process 1. Choose and configure node models to use in simulations—for example, a wireless node, a workstation, a router, and so on 2. Build and organize network by connecting the different entities 3. Select the statistics you wish to collect during simulations

The Simulators (3) OPNET Modeler (cont’d) – In this experiment, the authors created a new node model which encapsulates MAC layer of OPNET, as well as an application process that implements the flooding algorithm Flooding algorithm process model is described as a state machine, whereby each state has code that is executed upon state activation A transition that links two states is followed whenever a certain condition carried by the transition is true Difficulty with OPNET is actually building the state machine for each level of the protocol stack

The Simulators (4) NS-2 – Discrete event network simulator that supports both wired and wireless networks, including most MANET routing protocols as well as an MAC layer implementation – Source code is split between C++ for its core engine, and OTcl, an object-oriented version of PCL for configuration and simulation scripts – Implementation and simulation steps 1. Implement the protocol by adding a combination of C++ and OTcl code to NS-2’s source base 2. Describe the simulation in an OTcl script 3. Run the simulation 4. Analyze the generated trace file

The Simulators (5) NS-2 (cont’d) – In this experiment, the authors adapted the implementation of flooding provided in NS-2 An Agent (which, in NS-2, represents an endpoint where packets are constructed, processed, or consumed) was implemented at the Application layer for the broadcast source, and the simulation trace was collected at the MAC layer Major challenges with NS-2 include: a substantial learning curve; difficult debugging; a large memory footprint; and, a lack of scalability

The Simulators (6) GloMoSim – Scalable simulation environment for wireless and wired networks, developed initially at UCLA Computing Laboratory – Provides various applications (CBR, ftp, telnet), transport protocols (tcp, udp), routing protocols (AODV, flooding), and mobility schemes (random waypoint, random drunken) – User must define specific scenarios in text configuration files app file—contains description of traffic to generate (e.g., app type, bit rate, and so on) Config file—contains description of other (remaining) parameters

The Simulators (3) GloMoSim (cont’d) – Statistics collected can be either textual or graphical – According to the authors, compared to OPNET, GloMoSim’s architecture is much less flexible

Simulations Static parametersVarying parameters Common constant parameters of the simulations Varying parameters that describe the behavior of an ad-hoc network and that can be set in a controlled way

Simulations (2) Metrics – First metric gives information about the time needed to flood a message Time delay: For a node n, this is the average time needed for one packet to reach n – Second metric measures the general efficiency of the algorithm Success rate: For a node n, this is the difference between the expected and the actual number of messages received at n – Third metric stores the overhead of messages that are unnecessarily flooded in the network Overhead: For a node n, this is the sum of duplicated packets received by n

Simulations (3) Results – Only the most striking graphs are provided in the paper – Several scenarios are defined by varying one or more parameters from the previous table labeled “Varying parameters” – For each scenario, the set of varied parameters is given in the table just above the graph

Simulations (4) This scenario depicts a critical factor that influences the success rate in MANETs: the effective transmission range Notice the apparent differences in trend between the simulators Success rate vs. Power range GloMoSim OPNET NS-2 Success rate vs. Power range

Simulations (5) This scenario evaluates the effects of node mobility on the flooding’s ability to deliver packets reliably Again, we see a significant difference in success rate Success rate vs. Mobility GloMoSim OPNET NS-2 Success rate vs. Mobility

Simulations (6) This scenario presents the average overhead of messages flooded in the network for a single simulation run This metric is related to the mean number of reachable neighbors (that is, within transmission range OPNET NS-2 GloMoSim Overhead vs. Mobility

Simulations (7) The final scenario compares average time delay needed to flood a message throughout the whole network This metric increases with the number of hops from source to destination and also whenever collisions occur Time delay vs. Mobility OPNET NS-2 GloMoSim Time delay vs. Mobility

Simulations (8) Analysis and interpretation – Simulation results of the flooding algorithm demonstrate that modeling of the MAC protocol and of the physical layer can lead to different results, depending upon the simulator – Possible reasons Differing physical layer implementations Implementation of a new protocol is itself difficult to transpose from one simulator to another Given that successive releases provide bug fixes, it is reasonable to assume that MANET simulators still contain errors or incompatibilities to IEEE standard

Conclusions (Authors) Instead of simulations, a more realistic scheme might entail a hybrid approach in which only the lowest layers—MAC and physical—and the mobility model are simulated and all the upper layers (from transport to application) are executed on a cluster of machines There is an important lack of real experiments the prove the feasibility of wireless protocols

Questions? ?