Isi Artikel Utama

Altaf Hussain
University of Agriculture Peshawar, Pakistan
Pakistan
Tariq Hussain
Univeristy of Agriculture Peshawar
Pakistan
Iqtidar Ali
Univeristy of Agriculture Peshawar
Pakistan
Muhammad Rafiq Khan
Univeristy of Agriculture Peshawar
Pakistan
Vol. 9 No. 1 (2020), Articles, pages 61-84
DOI: https://doi.org/10.14201/ADCAIJ2020916184
How to Cite

Abstract

Mobile Ad-hoc Network (MANET) is the most emerging and fast-expanding technology in the last two decades. One of the major issues and challenging areas in MANET is the process of routing due to dynamic topologies and high mobility of mobile nodes. The efficiency and accuracy of a protocol depend on many parameters in these networks. In addition to other parameters node velocity and propagation models are among them. Calculating signal strength at the receiver is the responsibility of a propagation model while the mobility of nodes is responsible for the topology of the network. A huge amount of loss in performance is occurred due to the variation of signal strength at the receiver and obstacles between transmissions. In this paper,it has been analyzed to check the impact of different propagation models on the performance of Optimized Link State Routing (OLSR) in Sparse and Dense scenarios in MANET. The simulation has been carried out in NS-2 by using performance metrics as average packet drop average latency and average Throughput. The results predicted that propagation models and mobility have a strong impact on the performance of OLSR in considered scenarios. 

Downloads

Download data is not yet available.

Rincian Artikel

References

Altimeter. (2013). The converged media imperative: How brands must combine. Paid, owned, and earned media. http://de.slideshare.net/Altimeter/the-converged-media-imperative

Altimeter. (2014). Data everywhere: Lessons from big data in the television industry (by Susan Etlinger). http://www.altimetergroup.com/2014/07/data-everywhere-lessons-from-big-data-in-the-television-industry/

Amit, R., & Zott, C. (2012). Creating Value through Business Model Innovation. Sloan Management Review, 53(3), 41-49.

Anderson, C. (2009). The longer Long Tail: How endless choice is creating unlimited demand (updated and ex-panded edition). London, UK: Random House Business Books.

Arsenault, A. H. (2017). The datafication of media: Big data and the media industries International Journal of Media & Cultural Politics, 13(1-2), 7-24. doi: https://doi.org/10.1386/macp.13.1-2.7_1

Askwith, I. D. (2007). Television 2.0: Reconceptualizing TV as an engagement medium. http://cmsw.mit.edu/television-2-0-tv-as-an-engagement-medium/

Bateson, G. (1951). Communication: The Social Matrix of Psychiatry. New York, W.W. Norton.

Baumann, S., Hasenpusch, T. C. (2016). Multi-Platform Television and Business Models: A Babylonian Clutter of Definitions and Concepts. Westminster Papers in Communication and Culture, 11(1), 85-102. http://dx.doi.org/10.16997/wpcc.219

Bobineau, J. (2014). SaveWalterWhite.Com: Audience Engagement als Erweiterung der Diegese in Breaking Bad. In J. Nesselhauf, & M. Schleich (Eds.), Quality-TV: Die narrative Spielwiese des 21. Jahrhunderts?! (pp. 227-240). Berlin: Lit-Verlag.

Boyd, D., & Crawford, K. (2011). Six provocations for big data: SSRN Scholarly Paper No. ID 1926431, Rochester, NY: Social Science Research Network, http://papers.ssrn.com/abstract=1926431

Brown, I. (2016). The economics of privacy, data protection and surveillance. In M. Latzer & J. M. Bauer (Eds.), Handbook on the economics of the Internet (pp. 247-262). Cheltenham and Northhampton, UK: Edward Elgar Publishing.

Bughin, J. (2016). Big data, Big bang? Journal of Big Data, 3(2). doi: https://doi.org/10.1186/s40537-015-0014-3

Bughin, J., Byers A. H., & Chui, M. (2016). How social technologies are extending the organization. http://www.mckinsey.com/industries/high-tech/our-insights/how-social-technologies-are-extending-the-organization

Buschow, C., Schneider, B. & Ueberheide, S. (2014). Tweeting television: Exploring communication activities on Twitter while watching TV. Communications - The European Journal of Communication Re-search (EJCR), 39(2), 129-149. doi: https://doi.org/10.1515/commun-2014-0009

Carr, D. (2013). Giving viewers what they want. New York Times. http://www.nytimes.com/2013/02/25/business/media/for-house-of-cards-using-big-data-to-guarantee-its-popularity.html?_r=0

Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS Quarterly, 36(4), 1165-1188.

Choudary, S. P. (2013). Why Business Models Fail. Pipes vs. Platforms. https://www.wired.com/insights/2013/10/why-business-models-fail-pipes-vs-platforms/

Couldry, N., Fotopoulou, A., & Dickens, L. (2016). Real social analytics: A contribution towards a phe-nomenology of a digital world. The British Journal of Sociology, 67(1), 118‐37.

Couldry, N., & Turow, J. (2014). Advertising, big data, and the clearance of the public realm: Marketers’ new approaches to the content subsidy. International Journal of Communication, 8, 1710-1726. http://ijoc.org/index.php/ijoc/article/view/2166/1161

Couldry, N., & Turow, J. (2014). Advertising, big data, and the clearance of the public realm: Marketers’ new approaches to the content subsidy. International Journal of Communication, 8, 1710-1726. http://ijoc.org/index.php/ijoc/article/view/2166/1161

Daidj, N. (2011). Media convergence and business ecosystems. Global Media Journal, 11(19), 1-13. http://www.globalmediajournal.com/open-access/media-convergence-and-business-ecosystems.pdf

DiZerega, G. (2004). Toward a Hayekian Theory of Commodification and Systemic Contradiction: Citi-zens, Consumers and the Media. The Review of Politics, 66(3), 445-468. doi: http://dx.doi.org/10.1017/S0034670500038869

Day, G. S. (2011). Closing the marketing capabilities gap. The Journal of Marketing, 75(4), 183-195.

Downes, L., & Nunes, P. (2014). Big Bang Disruption: Strategy in the Age of Devastating Innovation. https://hbr.org/2013/03/big-bang-disruption

Doyle, G. (2016). Resistance of channels: television distribution in the multiplatform era. Telematics and Informatics, 33(2), 693-702. doi: https://doi.org/10.1016/j.tele.2015.06.015

EBU Big Data Conference. (2018). https://www.ebu.ch/events/2018/02/big-data-conference-2018. Geneva, 28th of February to 1st of March, 2018.

Erevelles, S., Fukawa, N., & Swayne, L. (2016). Big Data Consumer Analytics and the Transformation of Marketing. Journal of Business Research, 6(2), 897-904. doi: https://doi.org/10.1016/j.jbusres.2015.07.001

Evens, T., & Van Damme, K. (2016). Consumers’ willingness to share personal data: Implications for newspapers’ business models. International Journal on Media Management, 18(1), 25-41. doi: https://doi.org/10.1080/14241277.2016.1166429

Felt, M. (2016). Social media and the social sciences: How researchers employ Big Data analytics. Big Data & Society, 3(1). doi: https://doi.org/10.1177/2053951716645828

Ferenstein, G. (2016, Jan., 20th). Netflic CEO explains why a «gut» feeling is still better than Big Data. Readwrite.com. http://readwrite.com/2016/01/20/netflix-big-data-intuition-reed-hastings/

Fleissner, P. (2006). Commodification, Information, Value and Profit. Poiesis & Praxis, 4(1), 39-53. http://dx.doi.org/10.1007/s10202-005-0007-y

Fuchs, C. (2012). Dallas Smythe Today - The Audience Commodity, the Digital Labour Debate, Marxist Political Economy and Critical Theory. Prolegomena to a Digital Labour Theory of Value. tripleC: Communication, Capitalism & Critique, 10(2), 692-740.

Fortune (2016). How Netflix Is Using Your Data. http://fortune.com/2016/09/19/netflix-streaming-tv-movies/ (Sept 19, 2016).

Freelon, D. (2014). On the interpretation of digital trace data in communication and social computing research. Journal of Broadcasting & Electronic Media, 58(1), 59-75. doi: https://doi.org/10.1080/08838151.2013.875018

Gandhi, B., Martinez-Smith, A., & Kuhlman, D. (2015). TV insights: Applications of big data to television. https://www.arris.com/globalassets/resources/white-papers/arris_applyingbigdatatotv_whitepaper_final.pdf

Gfk. (2015). Big Questions, Big Answers. Will harnessing smart data for audience analytics save the broadcast industry? https://www.gfk.com/fileadmin/user_upload/dyna_content/Global/documents/Whitepapers/GfK_WhitePaper_Big_Data_2015.pdf

Giglietto, F., & Selva, D. (2014). Second screen and participation: A content analysis on a full season dataset of tweets. Journal of Communication, 64, 260-277. doi: https://doi.org/10.1111/jcom.12085

Gillespie, T. (2014). The relevance of algorithms. In T. Gillespie, P. Boczkowski, & K. A. Foot (eds.), Media technologies. Essays on communication, materiality, and society (pp. 167-193). Cambridge, MA: MIT Press.

Gray, J. (2010). Show sold separately: Promos, spoilers and other media paratexts. New York, NY: New York University Press.

Green, A. (2016). Audience Measurement in the Data Age. IPSOS Connect. https://www.ipsos.com/sites/default/files/publication/1970-01/ipsos-audience-measurement-in-the-data-age.pdf

Guardian. (2014). Television must mine bigger data or risk being netflixed. https://www.theguardian.com/media-network/2014/aug/04/tv-big-data-mine-customer-netflix

Hasebrink, U., & Domeyer, H. (2012). Media repertoires as patterns of behavior and as meaningful prac-tices: A multimethod approach to media use in converging media environments. Participations. Jour-nal of Audience and Reception Studies, 9(2), 757-779.

Havens, T. (2014). Media programming in an era of big data. Media Industries Journal, 1(2). http://dx.doi.org/10.3998/mij.15031809.0001.202

Hepp, A. (2012). Mediatization and the ‘Moulding Force’ of the media. Communications, 37(1), 1-28. doi: http://dx.doi.org/10.1515/commun-2012-0001

Hepp, A., & Krotz, F. (2014). Mediatized worlds: Understanding everyday mediatization. In A. Hepp, & F. Krotz (eds.), Mediatized worlds: Culture and society in a media age (pp. 1-15). London: Palgrave.

Hermida, A., Fletcher, F., Korell, D., & Logan, D. (2012). Share, Like, Recommend. Decoding the Social Media News Consumer. Journalism Studies, 13(5-6), 815-824. doi: https://doi.org/10.1080/1461670X.2012.664430

Hill, S. (2014). TV audience measurement with big data. Big Data, 2(2), 76-86.

Jacobi, C., van Atteveldt, W. & Welbers, K. (2016). Quantitative analysis of large amounts of journalistic texts using topic modelling, Digital Journalism, 4(1), 89-106. doi: https://doi.org/10.1080/21670811.2015.1093271

Jenkins, H. (2008). Convergence culture: Where old and new media collide. New York, NY: New York University Press.

Jennes, I., Piersen, J., & Van den Broek, W. (2014). User Empowerment and Audience Commodification in a Commercial Television Context. The Journal of Media Innovations, 1(1), 71-87.

Kackman, M., Binfield, M., Payne, M. T., Perlman, A., & Sebok, B. (2011). Flow TV: Television in the age of media convergence. New York, NY: Routledge.

Kastrenakes, J. (2015, Sep., 23th). Netflix knows the exact episode of a TV show that gets you hooked. TheVerge.com. http://www.theverge.com/2015/9/23/9381509/netflix-hooked-tv-episode-analysis

Kastrenakes, J. (2015, Sep., 23). Netflix knows the exact episode of a TV show that gets you hooked. TheVerge.com. http://www.theverge.com/2015/9/23/9381509/netflix-hooked-tv-episode-analysis

Kelly, J. P. (2017). Television by the numbers. The challenges of audience measurement in the age of Big Data. Convergence. doi: https://doi.org/10.1177/1354856517700854

Kim, S. J. (2018). Audience Measurement and Analysis. In A. Albarran, B. Mierzejewska, & . J. Jung (Eds.), The Handbook of Media Management and Economics, 2nd ed. (pp. 379-393). Abingdon, Oxford: Routledge.

Kneale, D. (2016, Jan 21). Big Data Dream. Big data is everywhere-now what to do with it? New tools unlock the secrets of consumer desire. http://www.broadcastingcable.com/news/rights-insights/big-data-dream/147166

Kompare, D. (2011). More «moments of television»: Online cult television authorship. In M. Kackman, M. Binfield, M. T. Payne, A. Perlman, & B. Sebok (Eds.), Flow TV: Television in the age of media conver-gence (pp. 95-113). New York, NY: Routledge.

Kosterich, A., & Napoli, P. M. (2015). Reconfiguring the audience commodity: The institutionalization of social TV analytics as market information regime. Television & New Media, 17(3), 254-271. doi: https://doi.org/10.1177/1527476415597480

Krotz, F. (2009). Mediatization: A concept with which to grasp media and societal change. In K. Lundby (Ed.), Mediatization: Concept, changes, consequences (pp. 19-38). New York, NY: Peter Lang.

Lippell, H. (2016). Big Data in the Media and Entertainment Sectors. In J. M. Cavanillas, E. Curry, & W. Wahlster (Eds.), New Horizons for a Data-Driven Economy. A Roadmap for Usage and Exploitation of Big Data in Europe. doi: https://doi.org/10.1007/978-3-319-21569-3_1

Livingstone, S. (2015). Active audiences? the debate progresses but it is far from resolved. Communication Theory, 25(4), 439-446.

Lomborg, S., & Mortensen, M. (2017). Users across media. An introduction. Convergence, 23(4), 343-351.

Lotz, A. (2007). The television will be revolutionized. New York, NY: New York University Press.

Mackenzie, D., & Wajcman, J. (1985). The Social Shaping of Technology: How the Refrigerator got its hum. Milton Keynes, Open University Press.

Madrigal, A. C. (2014). How Netflix reverse-engineered Hollywood. The Atlantic. http://www.theatlantic.com/technology/archive/2014/01/how-netflix-reverse-engineered-hollywood/282679/

Mahrt, M., & Scharkow, M. (2013). The value of big data in digital media research. Journal of Broadcasting & Electronic Media, 57(1), 20-33. doi: https://doi.org/10.1080/08838151.2012.761700

Manovich, L. (2012). Trending: The promises and the challenges of big social data. In M. K. Gold (Ed.), Debates in the Digital Humanities (pp. 460-75). Minneapolis: University of Minnesota Press.

Manyika, J., Chui, M, Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., Byers, A. H. (2011). Big data: The next frontier for innovation, competition, and productivity. http://www.mckinsey.com/business-functions/business-technology/ourinsights/big-data-the-next-frontier-for-innovation

Mathieu, D., Vicente-Mariño, M., José Brites, M., Amaral, I., Chimirri, N. A., Finger, J., Romic, B., Saa-riketo, M., Tammi, R., Torres da Silva, M., & Pacheco, L. (2016). Methodological challenges in the transition towards online audience research. Participations: Journal of Audience & Reception Studies, 13 (1), 289-320. http://www.participations.org/Volume%2013/Issue%201/S2/2.pdf

McGrath, R. G. (2013). Broadcast TV needs a new business model. http://blogs.hbr.org/2013/04/watching-broadcast-tv-for-a-ne/

McKinsey Global Institute. (2016). The age of analytics: Competing in a data-driven world (by N. Henke, J. Bughin, M. Chui, J. Manyika, T. Saleh, B. Wiseman, & G. Sethupathy). http://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/the-age-of-analytics-competing-in-a-data-driven-world

Meehan, E. R. (1984). Ratings and the institutional approach: A third answer to the commodity ques-tion. Critical Studies in Mass Communication, 1(2), 216-225. doi: https://doi.org/10.1080/15295038409360032

Mittell, J. (2011). TiVoing childhood: Time-shifting a generation’s concept of television. In M. Kack-man, M. Binfield, M. T., Payne, A. Perlman, & B. Sebok (Eds.), Flow TV: Television in the age of media convergence (pp. 46-54). New York, NY: Routledge.

Murschetz, P. C. (2016). Connected television: Media convergence, industry structure and corporate strat-egies. In E. L. Cohen (Ed.), Communication Yearbook 40 (pp. 69-93). New York, NY: Routledge. http://dx.doi.org/10.1080/23808985.2015.11735256

Napoli, P. M. (2011). Audience evolution: New technologies and the transformation of media audiences. New York, NY: Columbia University Press.

Napoli, P. M. (2014). Automated media: An institutional theory perspective on algorithmic media pro-duction and consumption. Communication Theory, 24(3), 340-360. doi: https://doi.org/10.1111/comt.12039

Napoli, P. M. (2016a). Special Issue Introduction. Bid data and media management. International Journal of Media Management, 18(1), 1-7.

Napoli, P. M. (2016b). The audience as product, consumer, and producer in the contemporary media marketplace. In G. F. Lowe, & C. Brown (Eds.), Managing Media Firms and Industries: What’s So Spe-cial About Media Management? (pp. 261-275). Berlin: Springer International Publishing.

Nelson, J. L., & Webster, J. G. (2016). Audience currencies in the age of big data. International Journal on Media Management, 18(1), 9-24. doi: https://doi.org/10.1080/14241277.2016.1166430

O’Ferrell, P. (2015). Big data will impact the television industry? http://www.kiteknology.com/en/news/big-data-will-impact-television-industry

Parks, M. R. (2014). Big data in communication research: Its contents and discontents. Journal of Commu-nication, 64, 355-360. doi: https://doi.org/10.1111/jcom.12090

Perez, C. (2010). Technological revolutions and techno-economic paradigms. Cambridge Journal of Econom-ics, 34(1), 185-202. doi: https://doi.org/10.1093/cje/bep051

Rogers, M. C., Epstein, M. & Reeves, J. L. (2002). The Sopranos as HBO brand equity: The art of com-merce in the age of digital reproduction. In D. Lavery (Ed.), This thing of ours: Investigating the Sopranos (pp. 42-57). New York, NY: Columbia University Press.

Schäfer, M. T., & van Es, K. (2017). The Datafied Society. Studying Culture through Data. Amsterdam: Am-sterdam University Press.

Scharkow, M. (2013). Thematic content analysis using supervised machine learning: An empirical evalua-tion using German online news. Quality & Quantity, 47(2), 761-773. doi: https://doi.org/10.1007/s11135-011-9545-7

Schlütz, D. (2016). Contemporary quality TV: The entertainment experience of complex serial narratives. In E. L. Cohen (Ed.), Communication Yearbook 40 (pp. 95-124). New York, NY: Routledge. doi: https://doi.org/10.1080/23808985.2015.11735257

Smith, M. D., & Telang, Rahul (2016). Streaming, Sharing, Stealing. Big Data and the Future of Entertainment. Cambridge, MA: MIT Press.

Smythe, D. W. (1977). Communications: Blindspot of Western Marxism. Canadian Journal of Political and Social Theory, 1(3), 1-27.

Stone, M. L. (2014). Big data for media. Oxford, UK: Reuters Institute for the Study of Journalism.

Trottier, D. (2014). Big Data ambivalence: Visions and risks in practice. In M. Hand, & S. Hillyard (Ed.), Big Data? Qualitative approaches to digital research (pp. 51-72). Bingley/UK: Emerald Group Publishing. doi: https://doi.org/10.1108/S1042-31922014000001300

van Dijck, J. (2014). Datafication, dataism and dataveillance: Big Data between scientific paradigm and ideology. Surveillance & Society, 12(2), 197-208.

van Es, K. (2017). An Impending Crisis of Imagination Data-Driven Personalization in Public Service Broadcasters. Ed. by B. Cammaerts, N. Anstead & R. Stupart. Media@LSE Working Paper Series. http://www.lse.ac.uk/media@lse/research/mediaWorkingPapers/pdf/Working-Paper-43.pdf

Vidgen, R. (2014). Creating business value from Big Data and business analytics: organizational, managerial and human resource implications. http://www.nemode.ac.uk/?page_id=1062

Wagner-Pacifici, R., Mohr, J. W., & Breiger, R. L. (2015). Ontologies, methodologies, and new uses of Big Data in the social and cultural sciences. Big Data & Society, 2(2). doi: https://doi.org/10.1177/2053951715613810

Williams, R. (2003[1974]). Television: Technology and cultural form. London, UK: Routledge.

Wirth, W., Von Pape, T., & Karnowski, V. (2008). An integrative model of mobile phone appropriation. Journal of Computer-Mediated Communication, 13(3), 593-617. doi: https://doi.org/10.1111/j.1083-6101.2008.00412

Wywy. (2016). Programmatic TV: How it works, the players & the right strategies. http://wywy.com/market-view/programmatic-tv/