The characteristics of traffic in core networks have changed in recent years, with the data flows among network nodes now showing large volumes but also large fluctuations during the day. In such a dynamic environment, the static allocation of network resources by making reservations at the peak rates is wasteful, both in terms of energy consumption and resource efficiency. Periodic reconfiguration of network resources is necessary for the full exploitation of an elastic optical network’s potential. In this paper, we present a resource reconfiguration scheme for an IP over elastic optical core network. The cornerstone of the proposed scheme is a novel traffic prediction mechanism based on reinforcement learning. Using the forecasting mechanism, a heuristic routing and spectrum assignment algorithm was designed to efficiently and fairly allocate network resources periodically. Simulation results show both a reduction of the bandwidth blocking probability and an increase of the fairness index regarding the network resources allocation over corresponding algorithms in the literature.

Communication networks for healthcare environments support various application types and should be capable of providing them with the required Quality of Service (QoS) in terms of reliable data delivery, considerable data rate and low latency. The introduction of Tactile Internet (TI) healthcare applications is expected to upgrade the level of provided services to patients. The requirements of these applications are very stringent and a major requirement is that the existing network infrastructure must support them. Passive Optical Networks (PONs) have been proposed as an ideal candidate to support such demanding environments. One of the major challenges of such networks, which significantly affects the provided QoS, is the effective resource allocation in both the time and the wavelength domains. The existing resource allocation algorithms for PONs were designed without considering the stringent requirements of TI applications thus making PON support for TI inefficient. In this paper, a new double per priority queue dynamic wavelength and bandwidth allocation algorithm is presented. This algorithm allocates the network resources efficiently and fairly using several techniques, in order to meet the QoS demands of Tactile and other types of healthcare applications. Extensive simulation results indicate the effectiveness of the proposed algorithm to provide the required QoS for medical applications under various simulation scenarios whereas other well-known schemes are shown to lack such support.

The rapid growth of the Internet of Things (IoT) ecosystem has revolutionized the traditional wireless communication arena questioning the established networking solutions in terms of battery life, device and deployment cost, coverage, availability, and support for a massive number of devices. Motivated by this, a novel medium access control (MAC) protocol utilizing Long Range (LoRa) technology is presented, based on the classical time-division multiple access (TDMA) scheme, amended properly to accommodate the Wake-Up Radio (WuR) technology capabilities. Aiming for low-power wide-area network (LPWAN) applications, the proposed protocol improves data latency and offers augmented performance due to the absence of collisions. Various simulation scenarios were devised in order to evaluate the performance. The results instate the proposed algorithm as a promising access scheme for the IoT Realm.

In recent years, the Internet of Things (IoT) is growing rapidly and gaining ground in a variety of fields. Such fields are environmental disasters, such as forest fires, that are becoming more common because of the environmental crisis and there is a need to properly manage them. Therefore, utilizing IoT for event detection and monitoring is an effective solution. A technique for monitoring such events over a large area is proposed in this research. This work makes use of the Long-Range Wide Area Network (LoRaWAN) protocol, which is capable to connect low-power devices distributed on large geographical areas. A learning-automata-based hybrid MAC model is suggested to reduce the transmission delay, when a small part of the network produces event packets stemming from an event occurrence that is related to environmental monitoring applications, such as events related to forest fires. The proposed hybrid MAC is evaluated via simulation, which indicates that it achieves significantly higher performance in terms of packet delay, when compared to traditional LoRaWAN schemes.

Low-power wide-area (LPWA) technologies have gained popularity in accordance with the explosive growth of the Internet of Things (IoT). Among others, LoRa is considered as the leading standard that can meet the needs of modern wireless networking, mainly offering energy efficiency and broad coverage as well as a massive amount of device support. In addition to the ALOHA protocol, which is the default channel access mechanism used by the standard, a number of alternatives have been proposed in the literature in an effort to ameliorate the overall network performance. Furthermore, with moving nodes gaining ground more and more in the IoT realm and the research being at a relatively premature stage, it is imperative to create innovative algorithms that support highly dense networks with fast moving nodes. Motivated by these reasons, this work proposes a novel medium access protocol that takes advantage of the increased capabilities of modern wake up radio (WuR) technology in order to achieve low latency and mitigate the risk of lost packets in IoT networks with moving nodes based on the LoRa technology. A number of simulation scenarios have been devised and the findings suggest that the proposed protocol achieves the set goals and improves existing solutions.