Search engine and content providers: neutrality, competition and integration

In recent years, there has been a rising concern about the policy of major search engines. The concern comes from search bias, which refers to the ranking of the results of a keyword search on the basis of some other principle than the sheer relevance. This search bias is also named as search non-neutrality. In this paper, we analyse one non-neutral behaviour, that is, a behaviour that results in a search bias: the payment by content providers to the search engine (a.k.a. side payment) in order to improve the chances to be located and accessed by a user. A game theory-based model is presented where a search engine and two content providers interact strategically, while the aggregated behaviour of users is modelled by a demand function. The utility of each stakeholder (i.e. the users, the search engine and each content provider) when the search engine is engaged in such a non-neutral behaviour is compared with that of the neutral case, when no such side payment is present. Additionally, the paper analyses the organisation of such an industry, specifically, the search engine and content providers incentives for a partial and full merger with the content providers, and the effects of each organisation on the users. This paper concludes by identifying the circumstances under which the search bias, on the one hand, and the integration, on the other hand, will effectively result in the users being harmed. This eventual harmful situation will provide a rationale for regulatory measures to be adopted.This work has been supported by the Spanish Ministry of Economy and Competitiveness through project TIN2010-21378-C02-02.Guijarro Coloma, LA.; Pla, V.; Vidal Catalá, JR.; Martínez Bauset, J. (2015). Search engine and content providers: neutrality, competition and integration. Transactions on Emerging Telecommunications Technologies. 26(2):164-178. https://doi.org/10.1002/ett.2827S164178262European Commission Antitrust: commission probes allegations of antitrust violations by google 2010 http://europa.eu/rapid/pressReleasesAction.do?reference=IP/10/1624Manne, G. A., & Wright, J. D. (2011). If Search Neutrality is the Answer, What’s the Question? SSRN Electronic Journal. doi:10.2139/ssrn.1807951Lenard, T. M., & May, R. J. (Eds.). (2006). Net Neutrality or Net Neutering: Should Broadband Internet Services be Regulated. doi:10.1007/0-387-33928-0Altman E Legout A Xu Y Proceedings of IFIP Networking 2011 68 81Ozel, O., & Uysal-Biyikoglu, E. (2012). Network-wide energy efficiency in wireless networks with multiple access points. Transactions on Emerging Telecommunications Technologies, 24(6), 568-581. doi:10.1002/ett.2543Alptekin, G. I., & Bener, A. B. (2011). Spectrum trading in cognitive radio networks with strict transmission power control. European Transactions on Telecommunications, 22(6), 282-295. doi:10.1002/ett.1477Coucheney, P., Maille, P., & Tuffin, B. (2013). Impact of Competition Between ISPs on the Net Neutrality Debate. IEEE Transactions on Network and Service Management, 10(4), 425-433. doi:10.1109/tnsm.2013.090313.120326Coucheney P Maillé P Tuffin B Comparison of search engines non-neutral and neutral behaviors First Workshop on Pricing and Incentives in Networks (W-PIN 2012), Co-located with ACM Sigmetrics/Performance, ACM 2012 1 8Palme E Dellarocas C Calin M Sutanto J Attention allocation in information-rich environments: the case of news aggregators Proceedings of the 14th Annual International Conference on Electronic Commerce, ACM 2012 25 26Guijarro L Pla V Tuffin B Maillé P Coucheney P A game theory-based analysis of search engine non-neutral behavior 2012 8th EURO-NGI Conference on, Next Generation Internet (NGI), IEEE 2012 119 124Singh, N., & Vives, X. (1984). Price and Quantity Competition in a Differentiated Duopoly. The RAND Journal of Economics, 15(4), 546. doi:10.2307/2555525Niyato, D., & Hossain, E. (2008). Competitive Pricing for Spectrum Sharing in Cognitive Radio Networks: Dynamic Game, Inefficiency of Nash Equilibrium, and Collusion. IEEE Journal on Selected Areas in Communications, 26(1), 192-202. doi:10.1109/jsac.2008.080117Jia J Zhang Q Competitions and dynamics of duopoly wireless service providers in dynamic spectrum market Proceedings of the 9th ACM International Symposium on Mobile Ad Hoc Networking and Computing, ACM 2008 313 322Kluberg, J., & Perakis, G. (2012). Generalized Quantity Competition for Multiple Products and Loss of Efficiency. Operations Research, 60(2), 335-350. doi:10.1287/opre.1110.101

QoS based Route Management in Cognitive Radio Networks

Cognitive radio networks are smart networks that automatically sense the channel and adjust the network parameters accordingly. Cognitive radio is an emerging technology that enables the dynamic deployment of highly adaptive radios that are built upon software defined radio technology. The radio technology allows the unlicensed operation to be in the licensed band. The cognitive radio network paradigm therefore raises many technical challenges such as the power efficiency, spectrum management, spectrum detection, environment awareness, the path selection as well as the path robustness, and security issues. Traditionally, in the routing approaches in the wired network, each node allows a maximum load through the selected route while traditionally in the routing approaches in wireless network, each node broadcasts its request with the identification of the required destination. However, the existing routing approaches in cognitive radio networks (CRN) follow the traditional approaches in wireless network especially those applied for ad hoc networks. In addition, these traditional approaches do not take into account spectrum trading as well as spectrum competition among licensed users (PUs). In this thesis, a novel QoS based route management approach is proposed by introducing two different models; the first model is without game theory and the second model is with game theory. The proposed QoS routing algorithm contains the following elements: (i) a profile for each user, which contains different parameters such as the unlicensed user (secondary user, SU) identification, number of neighbors, channel identification, neighbor identification, probabilities of idle slots and the licensed user (primary user, PU) presence. In addition, the radio functionality feature for CRN nodes gives the capability to sense the channels and therefore each node shares its profile with the sensed PU, which then exchanges its profile with other PUs, (ii) spectrum trading, a PU calculates its price based on the SU requirements, (iii) spectrum competition, a new coefficient α is defined that controls the price because of competition among PUs and depends on many factors such as the number of primary users, available channels, and duration of the usage, (iv) a new function called QoS function is defined to provide different levels of quality of service to SUs, and (v) the game theory concept adds many features such as the flexibility, the dynamicity in finding solutions to the model and the dynamic behaviors of users. Based on the previous elements, all possible paths are managed and categorized based on the level of QoS requested by SUs and the price offered by the PU. The simulation results show that the aggregate throughput and the average delay of the routes determined by the proposed QoS routing algorithm are superior to existing wireless routing algorithms. Moreover, network dynamics is examined under different levels of QoS

Secondary user pricing strategies in a cognitive radio environment

There has been a growing demand for spectrum availability due to inefficient management of the radio frequency spectrum and underutilization of all spectrum bands. Spectrum has been managed with the same approach for over the last decade and only recently due to the phenomenal growth in mobile and broadband communications has attention been given to it. Intelligent communication systems such as cognitive radio have been identified in assisting the need for the limited resource, wireless spectrum. If spectrum trading becomes commercially successful, it can provide great economic and social benefits for the service provider, primary and secondary users. In order to maintain viability of spectrum trading, a pricing strategy is necessary for secondary users, it is also imperative to find a game theory model that minimally impacts the primary users in terms of their service, however it should aid in decreasing the cost to the primary users. Game theory along with economic theory is used to analyse the relationships/cooperation between the users and service provider. This work contributes to the field of dynamic spectrum access and aims to compare pricing strategies of secondary users in terms of the revenue earned by the primary service providers as well as investigate the impact of regulations on said pricing strategies. The pricing strategies modelled and simulated in MATLAB include the market-equilibrium pricing strategy and the competitive pricing strategy. These two strategies are chosen as they are the most relevant in South Africa. The two pricing strategies are compared in terms of advantages and disadvantages as well the revenue earned by each of the primary services. The framework for testing is provided along with the test cases. The influence of telecommunication regulations and policy on the frameworks and results are discussed in detail as well as the impact of the telecommunication regulation and policy in South Africa