In this paper, we study the problem of resource allocation as well as pricing in the context of Internet of things (IoT) networks. We provide a novel pricing model for IoT services where all the parties involved in the communication scenario as well as their revenue and cost are determined. We formulate the resource allocation in the considered model as a multi-objective optimization problem where in addition to the resource allocation variables, the price values are also optimization variables. To solve the proposed multi-objective optimization problem, we use the scalarization method which gives different Pareto optimal solutions. We solve the resulting problems using the alternating approach based on the successive convex approximation (SCA) method which converges to a local solution with few iterations. We also consider a conventional approach where each entity tries to maximize its own revenue independently. Simulation results indicate that by applying the proposed joint framework, we can increase the total revenue compared to the conventional case while providing an almost complete fairness among the players. This is while the conventional approach fails to provide such a fairness.
Few papers in the literature have considered pricing schemes for resources other than spectrum. In D2D communications, power has been considered as a subject of trading in @cite_25 . In @cite_32 a D2D communication framework is considered in which the authors design a power-pricing framework based on the principle of the Stackelberg game. In @cite_48 , relay servers are a subject of pricing where sellers offer cooperative services at the cost of resources such as power by way of auction.
@cite_48 @cite_32 @cite_25
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On one hand, cooperative communication has been gaining more and more popularity since it has great potential to increase the capacity of wireless networks. On the other hand, the applications of cooperative communication technology are rarely seen in reality, even in some scenarios where the demands for bandwidth-hungry applications have pushed the system designers to develop innovative network solutions. A main obstacle lying between the potential capability of channel capacity improvement and the wide adoption of cooperative communication is the lack of incentives for the participating wireless nodes to serve as relay nodes. Hence, in this paper, we design TASC, an auction scheme for the cooperative communications, where wireless node can trade relay services. TASC makes an important contribution of maintaining truthfulness while fulfilling other design objectives. We show analytically that TASC is truthful and has polynomial time complexity. Extensive experiments show that TASC can achieve multiple economic properties without significant performance degradation compared with pure relay assignment algorithms. The Device-to-Device (D2D) communication is a promising technique to empower local wireless communications. However, without proper management it may generate interference to the existing network and degrade the overall performance. By treating each multipath as a virtual antenna, time-reversal (TR) signal transmission in a rich-scattering environment produces a spatial-temporal resonance which efficiently suppresses the inter-user interference (IUI) while boosting the signal power at the target receiver. In this work, we design a TR-based D2D hybrid network, where both primary users (PUs) and D2D pairs share the same time-frequency resources and use TR focusing effect to combat interference. With the purpose of enhancing D2D performance while providing a performance protection to PUs, an efficient optimal pricing algorithm is proposed to dynamically control interference through TR focusing strength control. Device-to-device (D2D) communication is offering smart phone users a choice to share files with each other without communicating with the cellular network. In this letter, we discuss the behaviors of two characters in the D2D data transaction model from an economic point of view: the data buyers who wish to buy a certain quantity of data, as well as the data sellers who wish to sell data through the D2D network. The optimal price and purchasing strategies are analyzed and deduced based on game theory.