This research proposes a new dynamic energy management framework for a backbone Internet Protocol over Dense Wavelength Division Multiplexing (IP over DWDM) network. Maintaining the logical IP-layer topology is a key constraint of our architecture whilst saving energy by infrastructure sleeping and virtual router migration. The traffic demand in a Tier 2/3 network typically has a regular diurnal pattern based on people‟s activities, which is high in working hours and much lighter during hours associated with sleep. When the traffic demand is light, virtual router instances can be consolidated to a smaller set of physical platforms and the unneeded physical platforms can be put to sleep to save energy. As the traffic demand increases the sleeping physical platforms can be re-awoken in order to host virtual router instances and so maintain quality of service. Since the IP-layer topology remains unchanged throughout virtual router migration in our framework, there is no network disruption or discontinuities when the physical platforms enter or leave hibernation. However, this migration places extra demands on the optical layer as additional connections are needed to preserve the logical IP-layer topology whilst forwarding traffic to the new virtual router location. Consequently, dynamic optical connection management is needed for the new framework. Two important issues are considered in the framework, i.e. when to trigger the virtual router migration and where to move virtual router instances to? For the first issue, a reactive mechanism is used to trigger the virtual router migration by monitoring the network state. Then, a new evolutionary-based algorithm called VRM_MOEA is proposed for solving the destination physical platform selection problem, which chooses the appropriate location of virtual router instances as traffic demand varies. A novel hybrid simulation platform is developed to measure the performance of new framework, which is able to capture the functionality of the optical layer, the IP layer data-path and the IP/optical control plane. Simulation results show that the performance of network energy saving depends on many factors, such as network topology, quiet and busy thresholds, and traffic load; however, savings of around 30% are possible with typical medium-sized network topologies.

The Internet of Things (IoT) is widely adopted across various fields due to its flexibility and low cost. Energy-harvesting Wireless Sensor Networks (WSNs) are becoming a building block of many IoT applications and provide a perpetual source of energy to power energy-constrained IoT devices. However, the dynamic and stochastic nature of the available harvested energy drives the need for adaptive energy management solutions. Duty cycling is among the most prominent adaptive approaches that help consolidate the effort of energy management solutions at the routing and application layers to ensure energy sustainability and, hence, continuous network operation.  The IEEE 802.15.4 standard defines the physical layer and the Medium Access Control (MAC) sub-layer of low-data-rate wireless devices with limited energy consumption requirements. The MAC sub-layer’s functionalities include the scheduling of the duty cycle of individual devices. However, the scheduling of the duty cycle is left open to the industry. Various computational mechanisms are used to compute the duty cycle of IoT nodes to ensure optimal performance in energy sustainability and Quality of Service (QoS). Reinforcement Learning (RL) is the most employed mechanism in this context.  The literature depicts various RL-based solutions to adjust the duty cycle of IoT devices to adapt to changes in the IoT environment. However, these solutions are usually tailored to specific scenarios or focus mainly on one aspect of the problem, namely QoS performance or energy limitation. This work proposes a generic adaptive duty cycling solution and evaluates its performance under different energy generation and traffic conditions. Moreover, it emphasizes the energy sustainability aspect while taking the QoS performance into account.  While different approaches exist to achieve energy sustainability, Energy Neutral Operation (ENO)-based solutions provide the most prominent approach to ensure energy-sustainable performance. Nevertheless, these approaches do not necessarily guarantee optimal performance in QoS. This work adopts a Markov Decision Process (MDP) model from the literature that aims to minimize the distance from energy neutrality given the energy harvesting and ENO conditions. We introduce QoS penalties to the reward formulation to improve QoS performance.  We start by examining the performance in QoS against the benchmarking solution. Then, we analyze the performance using different energy harvesting and consumption profiles to further assess QoS performance and determine if energy sustainability is still maintained under different conditions. The results prove more efficient utilization of harvested energy when available in abundance. However, one limitation to our solution occurs when energy demand is high, or harvested energy is scarce. In such cases, we observe degradation in QoS due to IoT nodes adopting a low-duty cycle to avoid energy depletion. We further study the effect this limitation has on the solution's scalability. We also attempt to address this problem by assessing the performance using a routing solution that balances load distribution and, hence, energy demand across the network.