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.

To date, the main limitation to the development of telecom networks mainly depended on two factors, namely the cost of the network equipment and transmission. However, nowadays, the enormous spread and growth of the Internet has resulted in a significant increase in the energy consumption of the network equipment. Particularly, the rise in Internet traffic has led to both the increase of the amount of required equipment and the augmentation of energy consumption of such network devices. Consequently, CapEx and OpEx of modern telecommunication networks render the market penetration of such deployment laborious. This surge in power consumption may ultimately lead to an impediment to this continued growth. Since climate change and environmental protection are also of rising concern, many research efforts are being made to raise awareness of environmental issues and reduce the energy footprint of telecommunication networks. In this context, this study targets the reduction of the energy consumption of an IP over WDM backbone network. Already known lightpath bypass and traffic grooming strategies have been used. Although previously proposed schemes based on lightpath bypass approaches and traffic grooming capabilities have been implemented and consequently are able to reduce the overall power consumption of an optical core network, they do not bear in mind, first, the number of the established lightpaths in the virtual topology and second, the exploitation of the residual capacity of the existing lightpaths. This study overcomes the above drawbacks by introducing three novel heuristic schemes which manage to significantly lower the number of the established lightpaths in the virtual topology and take advantage of the remaining capacity of the optical paths by exploiting the lightpaths’ resources in a more efficient way. These power-aware heuristics will focus on energy efficiency issues related to the planning and the operation phase of a WDM optical network. Extensive simulation results indicate a promising performance improvement in terms of power consumption when these proposed heuristics are compared to similar, already developed, techniques for energy saving.

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.

Long range (LoRa) is one of the most widespread members of the low-power wide area network technologies family. Its ability to cover long distances combined with the robustness to radio frequency propagation effects make it an ideal solution in the deployment of low-cost sensor networks. However, two of the most critical issues that LoRa networks have to deal with are their scalability and reliability. The default medium access mechanism is the Pure-Aloha, which while ensuring low-energy consumption in end devices (EDs), due to its distributed and simple operation, does not offer any protection to packet collisions. As a result, the contention-based Pure-Aloha offers satisfactory operation in situations of low- and medium-offered load, while when the interconnected devices and the offered load increase, the number of collisions increases as well, resulting in a decrease of transmissions reliability. In this article we present a novel reinforcement learning (RL) assisted time division duplex protocol for LoRa networks. The operation of the proposed protocol is based on a combination of mechanisms and techniques that ensure the reliability required based on a predetermined value, combined with low-energy consumption for the EDs. The more important employed operating elements of the proposed protocol are the time division separation between uplink and downlink transmissions, the use of the channel activity detection mechanism, as well as the implementation of an RL mechanism to adjust basic parameters of the network operation. The effectiveness of the proposed protocol was evaluated through a number of simulation scenarios, where the results showed a significant improvement both in the reliability and fairness, as well as in the energy consumption of the EDs, compared to the existing and other relevant protocols from the literature.