Integrated terrestrial and satellite networks will allow providing ubiquitous access and global connectivity to remotely deployed battery-activated (machine-to-machine or IoT) sensors or handset devices with messaging/voice capacities via satellite, fostering a series of applications such as intelligent transportation, coastal monitoring, and smart agriculture. In addition, the agile measurement of sensors enables autonomous driving and fast disaster recovery. These applications could generate massive uplink connections in a short period that the network must handle to avoid failures and outages. This paper covers system aspects and refines the 3rd Generation Partnership Project (3GPP)-based solutions to support non-terrestrial networks and make their performance comparable to that of terrestrial networks for providing IoT communications. A general vertical handover framework is introduced for this integrated network to choose the most suitable access technology for a given service. Then, the whole system increases around 100% the successful transmission probability in heavy-loaded scenarios with a slight increment in the number of retransmissions and transmission delays compared to that of single connectivity solutions. Furthermore, we found that by fine-tuning network parameter configurations, the energy consumption and transmission delay can be decreased while providing reliable communications that will benefit small-sized IoT devices with limited resources.

In wake-up radio (WuR) enabled Internet of things (IoT) networks, a data communication occurs in a synchronous or asynchronous manner initiated either by a transmitter or receiver. A synchronous transmission is triggered when multiple devices report an event simultaneously, or by a common wake-up call. In this paper, we focus on synchronous transmissions and propose a multicast triggered${s}$ynchronous${t}$ransmission protocol, abbreviated as MURIST, which enables contention based and coordinated data transmissions among distributed devices in order to reduce transmissions latency and energy consumption. Furthermore, we develop a novel analytical model based on an absorbing Markov chain to evaluate the performance of MURIST in a network cluster. Unlike existing models that are merely targeted at the behavior of a single device, the novelty of our model resides in a generic framework to assess the behavior of a cluster of devices for synchronous data transmissions. Based on the analytical model, we obtain closed-form expressions for the distributions of successful and discarded transmissions, number of collisions, and delay, as well as for energy consumption. Extensive simulations are performed to validate the accuracy of the analytical model and evaluate the performance of our scheme versus that of two other schemes.

The new direction-finding feature incorporated in version 5.1 of the Bluetooth Core Specification accelerates the development of applications in the context of the Internet of Things (IoT) for indoor localization and asset tracking. We evaluate the position of the Bluetooth Low Energy (BLE) transmitter based on the angle of arrival (AoA) mechanism proposed by the Bluetooth standard using Software Defined Radio (SDR). The system is comprised of a phased array antenna, an analog section for the conversion of Radio Frequency (RF) signals to the baseband, followed by a digital section for AoA estimation. The experimental results validate the proposal with precise estimates and a high resolution for the AoA. The proposed approach can be used for digital beamforming to increase the operating range or decrease the level of power consumed by BLE devices in IoT applications.

This work analyses significant cases in which applications of molecular communication systems to nano/bio-hybrid medical field represent an ideal solution for medical therapies (e.g., for the treatment of diseases such as cancer). A review of the literature reveals that biocompatibility jointly with nanocommunication can be exploited to provide effective treatment of diseases and reduce side effects considerably when compared to conventional therapy. Biocompatibility avoids the immune response rejecting drugs and does not stimulate nerves, whereas nanocommunication is a promising technology that allows accessing small and delicate body sites non-invasively.

In massive machine-type communications (mMTC), an immense number of wireless devices communicate autonomously to provide users with ubiquitous access to information and services.The current 4G LTE-A cellular system and its Internet of Things (IoT) implementation, the narrowband IoT (NB-IoT), present appealing options for the interconnection of these wireless devices.However, severe congestion may arise whenever a massive number of highly-synchronized access requests occur.Consequently, access control schemes, such as the access class barring (ACB), have become a major research topic.In the latter, the precise selection of the barring parameters in a real-time fashion is needed to maximize performance, but is hindered by numerous characteristics and limitations of the current cellular systems.In this paper, we present a novel ACB configuration (ACBC) scheme that can be directly implemented at the cellular base stations.In our ACBC scheme, we calculate the ratio of idle to total available resources, which then serves as the input to an adaptive filtering algorithm.The main objective of the latter is to enhance the selection of the barring parameters by reducing the effect of the inherent randomness of the system.Results show that our ACBC scheme greatly enhances the performance of the system during periods of high congestion.In addition, the increase in the access delay during periods of light traffic load is minimal.

Wake-up Radios (WuRs) are becoming more popular in Internet of Things (IoT) networks owing to their overwhelming advantages such as low latency, high energy saving, and on-demand communication. In a multi-hop WuR-IoT network, route establishment between source and destination prior to actual data communications consumes a significant portion of energy. To reduce energy consumption and protocol overhead for route establishment, we propose a lightweight relay (LR) selection scheme, referred to as LR-WuR, where a lookup table and acknowledgment are used for next hop relay selection. We develop an analytical model to evaluate the performance of LR-WuR. Extensive simulations are performed to validate the accuracy of the analytical model. Furthermore, we present a framework for deriving optimal network parameters.

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 performance evaluation of the random access (RA) procedure in LTE-A has recently become a major research topic as these networks are expected to play a significant role in upcoming 5G networks. Up to now, the key performance indicators (KPIs) of the RA in LTE-A have been obtained either by performing a large number of simulations or by means of analytic models that sacrifice precision in exchange of simplicity. In this paper, we present an analytic model for the performance evaluation of the LTE-A RA procedure. Using this analytic model, each and every one of the KPIs suggested by the 3GPP can be obtained with a minimal error when compared to the results obtained by simulation. To the best of our knowledge, this is the most accurate analytic model of the LTE-A RA procedure.

The performance evaluation of the random access (RA) in LTE-A has recently become a major research topic as these networks are expected to play a major role in future 5G networks. Up to now, the key performance indicators (KPIs) of the RA in LTE-A have been obtained either by performing a large number of simulations or by means of analytic models that, oftentimes, sacrifice precision in exchange for simplicity. In this paper, we present an analytical model for the performance evaluation of the LTE-A RA procedure that incorporates the access class barring (ACB) scheme. By means of this model, each and every one of the KPIs suggested by the 3GPP can be obtained with minimal error when compared with results obtained by simulation. To the best of our knowledge, this paper presents the most accurate analytical model, which can be easily adapted to incorporate modifications of network parameters and/or extensions to the LTE-A system. In addition, our model of the ACB scheme can be easily incorporated to other analytic models of similar nature without further modifications.

The number of devices that communicate through the cellular system is expected to rise significantly over the coming years. But cellular systems, such as LTE-A, were designed to handle human-to-human traffic. Hence, they are not suitable for managing massive machine-to-machine communications. Therefore, additional congestion control methods must be developed and evaluated. Up to date, access class barring (ACB) and extended access barring (EAB) methods are the preferred solutions for reducing congestion in the access channels of the evolved NodeB. These methods are based on restricting the access of certain classes of UEs, so the system capacity is not exceeded. Due to the high complexity of the LTE-A system, evaluating its performance is not straightforward. Specifically, a large number of variables, coexistent mechanisms, and test scenarios make it difficult to identify the network parameters that enhance performance. In this paper, we analyze the ACB method in highly congested environments. For this, we evaluate the effect of ACB parameters (barring rates and barring times) by means of several key performance indicators (KPI) such as delay, energy consumption (preamble transmission attempts required) and success probability. We observed that ACB is appropriate for handling sporadic congestion intervals in LTE-A networks.