REALISTIC MOBILITY PATTERN GENERATOR DESIGN AND APPLICATION IN PATH PREDICTION ALGORITHM EV

REALISTIC MOBILITY PATTERN GENERATOR: DESIGN AND APPLICATION INPATH PREDICTION ALGORITHM EVALUATION

N. Frangiadakis, M. Kyriakakos, S. Hadjiefthymiades, L. MerakosCommunication Networks Laboratory, Dept. of Informatics and Telecommunications, University of Athens, Athens 15784, Greece{ ntg | miltos | shadj | merakos }@di.uoa.gr

Abstract: A platform for the simulation of mobility functions(e.g., protocols, and application architectures) is presented.This platform, named RMPG, is based on the assumption thatreal humans exhibit considerable spatial and temporal regular-ity in their moves. We base our simulation environment on therandom way-point algorithm incorporated in the ns tool. Usingthe Java-based RMPG we performed a series of simulations byconsidering a path prediction algorithm as a mobility function.We present our results.

Keywords: simulation, mobility traces, movement patternsI. INTRODUCTIONDuring the recent past, we witnessed an impressive growth inthe domains of wireless-mobile telecommunications andservices. The general framework that evolved from Distrib-uted Systems, and is currently described under the term Mo-bile Computing has attracted the interest of academia and theindustry. New protocols, schemes, and services are constantlystudied, developed and applied in real-life situations. Thisescalating development has also triggered discussions on moreenhanced paradigms like Ubiquitous Computing.

In this highly dynamic technological landscape, computersimulation environments and tools play a very important role.Their use allows the testing, debugging and optimisation ofnew solutions prior to prototype building, massive productionand large-scale deployment. On the Mobile Computing do-main, the application of simulation technology addressesproblems in areas like mobile networking (e.g., TCP, MIP),mobile information access (disconnected operation, middle-ware), adaptive applications (proxies, transcoding), locationsensitive systems, etc. To assess and evaluate schemes in-volving user mobility (to be referred to as mobility functions)the reproduction of movement traces quite similar to real hu-man mobility behaviour is viewed as very important.

Human movement patterns have certain important character-istics, like regularity, that contemporary simulation tools tendto ignore. Ignoring such attributes leads the wireless/mobilesystems engineer to non-optimally quantified solutions. Mod-ern tools regard user movement as highly stochastic in termsof velocity and direction thus, resulting to wrong estimations

and dimensioning. On the other hand one can easily name a lotof simulation tools that support mobile protocols but provideonly basic tools for the description of mobility. Indicative ex-amples are the popular OPNET and NS2 simulators. We re-view their support for mobility pattern generation.

In this paper, we present our proposal for dealing with theaforementioned problems. We discuss the development of aRealistic Mobility Pattern Generator (RMPG). RMPG’s de-sign is “based on the fact that everyone has some degree ofregularity in his/her movement, that is, the movement of peo-ple consists of random movement and regular movement andthe majority of mobile users has some regular daily (hourly,weekly, ....) movement patterns and follow these patterns moreor less every day ...” [3]. RMPG can be used as a platform fortesting mobility functions (e.g., protocols, applicationschemes) under realistic workload conditions. The paper alsodiscusses the use of RPMG for a mobility prediction algorithmthat can be used for the pro-active management of networkresources in a cellular environment. This prediction algorithmis an enhanced version of the algorithm in [2] with reducedstorage requirements and more robust learning processes.

The rest of the paper is structured as follows. In Section II, weprovide a brief overview of other mobility simulation plat-forms. In Section III, we briefly discuss the design of RMPGand the steps for recording traces and simulating mobilityfunctions. In Section IV, we discuss the assessment of mobil-ity prediction functions. Section V contains our conclusions.

II. PRIOR WORKIn this Section we briefly review other contemporary simula-tion platforms that support the generation of movement tracesfor the evaluation of mobility functions/protocols.

City Simulator by IBM alphaWorks [7], simulates a 3-D citywith up to 106 people moving along streets, buildings and