Recent developments in cyber-physical systems have increased the importance of maximizing the freshness of the information about the physical environment. However, optimizing the access policies of Internet of Things devices to maximize the data freshness, measured as a function of the Age-of-Information (AoI) metric, is a challenging task. This work introduces two algorithms to optimize the information update process in cyber-physical systems operating under thegenerate-at-will model, by finding an online policy without knowing the characteristics of the transmission delay or the age cost function. The optimization seeks to minimize the time-average cost, which integrates the AoI at the receiver and the data transmission cost, making the approach suitable for a broad range of scenarios. Both algorithms employ policy gradient methods within the framework of model-free reinforcement learning (RL) and are specifically designed to handle continuous state and action spaces. Each algorithm minimizes the cost using a distinct strategy for deciding when to send an information update. Moreover, we demonstrate that it is feasible to apply the two strategies simultaneously, leading to an additional reduction in cost. The results demonstrate that the proposed algorithms exhibit good convergence properties and achieve a time-average cost within 3% of the optimal value, when the latter is computable. A comparison with other state-of-the-art methods shows that the proposed algorithms outperform them in one or more of the following aspects: being applicable to a broader range of scenarios, achieving a lower time-average cost, and requiring a computational cost at least one order of magnitude lower.

Small data transmission (SDT) is a novel technique that allows IoT devices to transmit their payload data without a pre-allocated radio resource. However, unresolvable SDT collisions occur due to the unpredictability of IoT traffic. In this paper, we propose a semi-contention-free transmission scheme that combines dedicated time-slot allocation through hashing and random access with access control through a dueling deep Q-network. Focusing on uplink IoT traffic for SDT in a NOMA-enabled network with two clusters of devices, we implement the proposed scheme and demonstrate its effectiveness and performance with respect to throughput, latency, and energy consumption through extensive simulations.

The unprecedented surge of massive Internet of things (mIoT) traffic in beyond fifth generation (B5G) communication systems calls for transformative approaches for multiple access and data transmission. While classical model-based tools have been proven to be powerful and precise, an imminent trend for resource management in B5G networks is promoting solutions towards data-driven design. Considering an IoT network with devices spread in clusters covered by a base station, we present in this paper a novel model-free multiple access and data transmission framework empowered by reinforcement learning, designed for power-domain non-orthogonal multiple access networks to facilitate uplink traffic of small data packets. The framework supports two access modes referred to as contention-based and semi-contention-free, with its core component being a policy gradient algorithm executed at the base station. The base station performs access control and optimal radio resource allocation by periodically broadcasting two control parameters to each cluster of devices that considerably reduce data detection failures with a minimum computation requirement on devices. Numerical results, in terms of system and cluster throughput, throughput fairness, access delay, and energy consumption, demonstrate the efficiency and scalability of the framework as network size and traffic load vary.

In Beyond5G and 6G networks, a common theme is that sensing will play a more significant role than ever before. Over this trend, Integrated Sensing and Communications (ISAC) is focused on unifying the sensing functionalities and the communications ones and to pursue direct tradeoffs between them as well as mutual performance gains. We frame the resource tradeoff between the SAC functionalities within an economic setting. We model a service provision by one operator to the users, the utility of which is derived from both SAC functionalities. The tradeoff between the resources that the operator assigns to the SAC functionalities is analyzed from the point of view of the service prices, quantities and profits. We demonstrate that equilibrium quantities and prices exist. And we provide relevant recommendations for enforcing regulatory limits of both power and bandwidth.

Network slicing is an architectural enabling technology that logically decouples the current cellular networks into infrastructure providers (InPs) and Network Slice Tenants (NSTs). The network resources (e.g., radio access resources at each cell) are owned by the InP, and are shared by the NSTs to provide a service to their mobile users. In this context, we proposed a business model that includes resource allocation and user subscription to NSTs in a competitive setting, and provides, among other things, closed-form expressions for the subscription indicators in equilibrium of each NST at each cell. This model relies on the widely adopted logit model to characterize user subscriptions. However, as a consequence of user mobility and radio propagation, some of the underlying assumptions in the logit model do not hold. Therefore, further research is needed to assess the accuracy of the results provided by the logit model in a mobile radio scenario. We carry out a thorough evaluation of the validity of the model by comparing its results against those obtained through computer simulation. Our simulation model includes complete and realistic characterizations of user mobility and radio propagation. From the results, we conclude that in most cases the logit model provides valid results in a mobile radio scenario.

5G networks will make network sharing agreements between mobile operators technically possible. However, depending on the agreed and implemented quality‐of‐service isolation, the provision of services may lead to unsustainable business cases. In this paper, the economic feasibility of such arrangements is analyzed for the case of two operators. Concretely, while one network operator owns the spectrum, one virtual operator does not, and each one provides service to its subscriber base. Two sharing alternatives, namely, pooling and priority sharing, are studied regarding the profits that each operator gets. We conclude that the network operator is worse off under any circumstances under a pooling agreement, while a lump sum payment may leave the network operator better off under a priority sharing agreement.

A brokering platform distributing the data collected by a set of Wireless Sensor Networks (WSNs) to end customers is considered, where WSNs receive a payment from the broker proportional to the amount of data they provide and pay the network provider a flat fee, while end customers pay a flat fee to access those data. In the case where the broker can set both prices, the optimal pricing strategy, maximizing the broker's profit, is analysed as to its outcomes for all the stakeholders. The attractiveness for end customers is evaluated through the overall amount of data on offer and the service penetration rate (which is quite low for a typical context). The service penetration rate among WSNs (which is instead quite high), as well as their profit, are considered as the variables measuring the platform's success among WSNs. Additionally, the network fee is absorbed by the broker, with a negligible impact on the broker's profit and even a positive impact on the average WSN's profit. Finally, the overall sensing data rate and its impact on the platform's profit are also assessed.

This paper analyses the economic viability of serving Machine Type Communications (MTC) and Human Type Communications (HTC) on a common network infrastructure under monopolistic and duopolistic scenarios. The scenarios are compared against the case where one operator serves HTC. The service provision is modelled by a two-priority queue and the competition is analyzed as a two-stage game. In the first stage, each operator posts a price and in the second stage, users decide to subscribe or not to the service based on a utility function related with the packet delay and the price charged. The HTC-MTC coexistence is shown to be feasible in the duopolistic scenario. The entry of the second operator to serve MTC users is desirable not only from the point of view of resource usage efficiency and from the point of view of the users, but also from the point of view of the two operators. The HTC-MTC service provision is therefore feasible and economically viable, and more efficient than the baseline scenario.

A business model for the provision of wireless sensor network (WSN)-based services is proposed, where there exist three roles in the market: the WSNs, which provide sensing data; the service providers, which gather the data and compose useful services for the users; and end users. We assess an affine pricing scheme for the acquisition of the WSNs data and analyze the model in a duopoly scenario by means of game theory. The competition outcome is compared against a social optimum outcome. We show that the pricing scheme avoids that the providers use all available resources and that the prices are kept low, so that the consumer surplus improves at the expense of the providers profits and the overall social welfare.

In order that the Internet of Things (IoT) realize its full potential, it has been claimed for the IoT industry to follow the smartphone recipe of market-creating innovation. Indeed, the main mobile apps platforms derive a significant part of their brand value and pricing power from their reputation to have most innovative apps coming to them. Having witnessed that the same steady stream of developer-driven innovation is already emerging in IoT, demand for IoT-based services will be driven by this growing innovative community of IoT developers. In order to make it possible, main players should focus on building platforms that support third party applications instead of developing applications for their own devices. As a way to materialize the above idea, we propose a business model for IoT-based services where a platform creates a multisided market. The business model comprises a platform that serves as an intermediary between human users, app developers and Wireless Sensor Networks, so that the users use the apps, and the apps process the data supplied by the sensor networks. The platform, acting as a monopolist, posts a fee for each of the three sides so as to maximize its profit. We conduct an analysis of the profit maximization problem faced by the platform. We show that the relative strength of the value that advertisers attach to the users determines the platform price structure. All in all, a high valuation is beneficial for all agents (users, platform, developers, and WSNs).

The advent of the Internet of Things (IoT) is expected to bring major benefits to a wide range of areas. However, the successful deployment of the IoT calls for the existence of sustainable and well-understood business models. In this paper, we propose and analyze a business model for a likely scenario in the IoT, which is made up of WSNs, service providers and users. The service providers compete against each other in the intermediation between the virtualized WSNs and the users that benefit from enhanced services built on the sensed data. The service providers pay to the WSNs for the data and charge the users for the service. The model is analyzed by applying oligopoly theory and game theory, the conditions for the existence and uniqueness of the Nash equilibrium are established, and the equilibrium and the social optimum are obtained. Our results show that the business model is sustainable, provided that the users' sensitivity to the value-to-price ratio is not negligible and, in this situation, the number of active service providers is upper bounded by a value that depends on the sensitivity and the market size. Furthermore, the operation of such a market is shown to efficiently use the information provided by the WSNs, and, when compared to the social optimum, to produce an increase in users' and service providers' surpluses, but a reduction in WSNs' surplus.

A brokering platform is considered, acting as an intermediary to deliver the data collected by a set of wireless sensor networks as a bundle to end customers. The wireless sensor networks provide data at sensing rates following a Zipf law and are rewarded by a linear pricing scheme, while end customers pay a flat fee to access the whole bundle of data. The optimal pricing policy to be adopted by the broker is derived. Even sensor networks with a very small sensing rate find it profitable to join the platform, unlike the case of uniform distribution of sensing rates. The optimal price paid to WSNs grows as the sensing rate grows, acting as an incentive to deploy better technology. Instead, the penetration rate among end customers is equal to the case of uniform distribution, being quite low unless a pronounced sensitivity for prices forces the broker to lower prices so as to make the service attractive to end customers.

The paper deals specifically with spectrum leasing within cognitive radio networks and it covers the economic issues that arise when primary users lease spectrum to secondary users. It aims at assessing which benefit mobile users get from the entry of secondary operators in the market. A game theory-based model is built for the case of an incumbent primary operator and an entrant secondary operator.

Many studies on Internet Service Provider (ISP) interconnection make simplifying assumptions on the implementation of the service provision. Our work explicitly models the ISP service that is provided to users that run peer-to-peer applications and it analyses the behavior of competing ISPs. The ISPs have agreed to peer each other and each ISP has purchased transit service from one Internet Backbone Provider. The quality of service, the equilibrium prices and the market shares that the competition game yields are computed by means of our model. Our work assesses the strategy of an ISP which provisions its transit link against a competing ISP in terms of competitive advantage and social welfare. And it assesses the effect of the entrance of more competing ISPs.

The case for a competitive market operated by a Mobile Network Operator (MNO) and a Mobile Virtual Network Operator (MVNO) is analysed in the paper. The resource that is leased by the MNO to the MVNO is spectrum. The MNO and the MVNO compete "a la Bertrand" posting subscription prices and the mobile users may choose to subscribe to one operator. The scenario is modeled by a three-level game comprising a bargaining game, which models the spectrum leasing by the MNO; a competition game, which models the price competition between the MNO and the MVNO; and a subscription game, which models the subscription choice by the mobile users, and the outcome of which may be either not to subscribe, to subscribe to the MNO or to subscribe to the MVNO. The game is solved through backward induction, and each level has a specific solution concept: Shapley value, for the bargain; Nash equilibrium, for the competition; and Wardrop equilibrium, for the subscription. The paper assesses which conditions lead to an equilibrium where the competition does take place, which are expressed as restrictions for the spectrum leasing price agreed at the bargaining, and the spectrum efficiency improvement achieved by the MVNO. Furthermore, it argues that the amount of the leased spectrum should be fixed exogenously in order to achieve optimal user and social welfares.

Cognitive Radio (CR) networks are envisaged as the key technology to realize dynamic spectrum access and solving the scarcity of radio spectrum. Having a temporal characterization of the spectrum white spaces in the primary network is a key element for studying and designing radio resource management mechanisms in CR networks. In that sense, most of the studies in the literature rely on an ON-OFF model with exponentially distributed on and off times. The usage of that model, however, is principally based on its analytical tractability. In this paper we propose a versatile Markovian model for the duration of the spectrum white spaces. Our model builds on a simple model of the channel holding time (CHT) in the primary network and then matrix-analytic techniques are applied to derive and analyze the duration of the white spaces. Despite its simplicity, the proposed approach is proven to be able to model very accurately scenarios where the CHT distribution is of a more complex type not amenable to mathematical analysis. Our numerical results show that the duration of the white spaces exhibits a low sensitivity to the distribution of the channel holding time beyond the mean.