Over the coming years, it is expected that the number of machine-to-machine (M2M) devices that communicate through LTE-A networks will rise significantly for providing ubiquitous information and services. However, LTE-A was devised to handle human-to-human traffic, and its current design is not capable of handling massive M2M communications. Access Class Barring (ACB) is a congestion control scheme included in the LTE-A standard that aims to spread the accesses of user equipments (UEs) through time so that the signaling capabilities of the eNB are not exceeded. Notwithstanding its relevance, the potential benefits of the implementation of ACB are rarely analyzed accurately. In this paper, we conduct a thorough performance analysis of the LTE-A random access channel (RACH) and ACB as defined in 3GPP specifications. Specifically, we seek to enhance the performance of LTE-A in massive M2M scenarios by modifying certain configuration parameters and by the implementation of ACB.We observed that ACB is appropriate for handling sporadic periods of congestion. Concretely, our results reflect that the access success probability of M2M UEs in the most extreme test scenario suggested by the 3GPP improves from approximately 30%, without any congestion control scheme, to 100% by implementing ACB and setting its configuration parameters properly.

With the implementation of channel assembling (CA) techniques, higher data rate can be achieved for secondary users in multi-channel cognitive radio networks. Recent studies which are based on loss systems show that maximal capacity can be achieved using dynamic CA strategies. However the channel allocation schemes suffer from high blocking and forced termination when primary users become active. In this paper, we propose to introduce queues for secondary users so that those flows that would otherwise be blocked or forcibly terminated could be buffered and possibly served later. More specifically, in a multi-channel network with heterogeneous traffic, two queues are separately allocated to real-time and elastic users and channel access opportunities are distributed between these two queues in a way that real-time services receive higher priority. Two queuing schemes are introduced based on the delay tolerance of interrupted elastic services. Furthermore, continuous time Markov chain models are developed to evaluate the performance of the proposed CA strategy with queues, and the correctness as well as the preciseness of the derived theoretical models are verified through extensive simulations. Numerical results demonstrate that the integration of queues can further increase the capacity of the secondary network and spectrum utilization while decreasing blocking probability and forced termination probability.

In wake-up radio (WuR) enabled wireless sensor networks (WSNs), a node triggers a data communication at any time instant by sending a wake-up call (WuC) in an on-demand manner. Such wake-up operations eliminate idle listening and overhearing burden for energy consumption in duty-cycled WSNs. Although WuR exhibits its superiority for light traffic, it is inefficient to handle high traffic load in a network. This paper makes an effort towards improving the performance of WuR under diverse load conditions with a twofold contribution. We first propose three protocols that support variable traffic loads by enabling respectively clear channel assessment (CCA), backoff plus CCA, and adaptive WuC transmissions. These protocols provide various options for achieving reliable data transmission, low latency, and energy efficiency for ultralow power consumption applications. Then, we develop an analytical framework based on an M/G/1/2 queue to evaluate the performance of these WuR protocols. Discrete-event simulations validate the accuracy of the analytical models.

Using cellular networks for providing machine-to- machine (M2M) connectivity offers numerous advantages regarding coverage, deployment costs, security and management, among others. Nevertheless, having a large number of M2M devices activated simultaneously is difficult to tackle at the evolved Node B, and it causes complications in the connection establishment. The random access channel (RACH) in LTE-A is adequate for handling human-to-human (H2H) communications. However, for the efficient provision of simultaneous H2H/M2M communications, it is necessary to optimize the available access control mechanisms so that network overload is avoided and a better QoS can be offered. Access Class Barring (ACB) has shown to be effective in reducing the number of simultaneous users contending for access. However, it is still not clear how to dynamically adapt its parameters, especially in highly changing scenarios with bursty traffic as it can occur when M2M communications are involved. We propose a dynamic algorithm based on reinforcement learning to adapt the barring rate parameter of ACB. This algorithm can adapt it to different traffic conditions, reducing congestion and hence the number of collisions in the RACH. The results show that our proposed mechanism increases the access success probability for all the users while barely impacting H2H users and other key performance indicators.

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.

To accommodate spectrum access in multichannel cognitive radio networks (CRNs), the channel-assembling technique, which combines several channels together as one channel, has been proposed in many medium access control (MAC) protocols. However, analytical models for CRNs enabled with this technique have not been thoroughly investigated. In this paper, two representative channel-assembling strategies that consider spectrum adaptation and heterogeneous traffic are proposed, and the performance of these strategies is evaluated based on the proposed continuous-time Markov chain (CTMC) models. Moreover, approximations of these models in the quasistationary regime are analyzed, and closed-form capacity expressions are derived in different conditions. The performance of different strategies, including the strategy without assembling, is compared with one another based on the numerical results obtained from these models and validated by extensive simulations. Furthermore, simulation studies are also performed for other types of traffic distributions to evaluate the validity and the preciseness of the mathematical models. Through both analyses and simulations, we demonstrate that channel assembling represented by the investigated strategies can improve the system performance if a proper strategy is selected with appropriate system parameter configurations.