The flexible nature of elastic optical networks (EONs) effectively uses spectral resources for optical communication by allocating the minimum required bandwidth to network connections. Since the energy consumption of such networks scales with the magnitude of bandwidth demand, addressing the issue of energy wastage is important. This fact has a profound impact on the design of efficient schemes for energy aware optical networks, and adaptivity arises as one of the most important properties of these networks. Learning Automata are Artificial Intelligence tools that have been used in networking algorithms, when adaptivity to the characteristics of the network environment can result in significantly improved network performance. In this work, a new adaptive power-aware algorithm is introduced, which selectively switches off bandwidth-variable optical transponders (BVTs) under low utilization conditions, to achieve energy efficiency. A novel adaptive scheme, which makes use of Learning Automata to significantly reduce the total energy consumption, while at the same time avoiding the onset of congestion, is proposed. The proposed scheme monitors network congestion, in terms of Bandwidth Blocking Probability (BBP), and the learning mechanism finds the optimal amount of energy-saving so that congestion is avoided, while at the same time significant energy savings are achieved. The proposed Learning Energy-Saving Algorithm (LESA) is evaluated via extensive simulation results, which indicate that it achieves an energy saving of up to 50%, compared to other energy efficient solutions.INDEX TERMS Adaptivity, elastic optical networks, energy-efficiency, learning automata, metropolitan networks.

Summary Elastic optical networks (EON) have emerged as a solution to the growing needs of the future internet, by allowing for greater flexibility, spectrum efficiency, and scalability, when compared to WDM solutions. EONs achieve those improvements through finer spectrum allocation granularity. However, due to the continuity and contiguity constrains, distant connections that are routed through multiple hops suffer from increased bandwidth blocking probability (BBP), while more direct connections are easier to form. This paper proposes HopWindows, a novel method that strategically allocates bandwidth to connections based on their hop distance. This new algorithm applies masks that control the range of frequency slots (FSs) allocated to each n‐hop connection. Furthermore, a new network metric is introduced, the normalized bandwidth blocking probability (normalized BBP). Utilization of this metric ensures increased fairness to distant connections. Extended simulation results are presented which indicate that the proposed HopWindows method achieves a superior performance over the well‐known FirstFit algorithm. The proposed algorithm may achieve a decrease in bandwidth blocking probability of up to 50%.

In ad hoc wireless networks, devices that normally cannot directly communicate route their messages through intermediate nodes. The number of those nodes is called hop count, a useful metric in estimating the distance between 2 nodes. Current methods usually depend on special nodes, called anchors, that need accurate localization information, in order to calculate an estimate for the average distance traversed per hop. The drawback of this approach is that anchor nodes increase the overall cost and complexity of the system. To address this problem, this letter proposes a novel, anchor node-free algorithm that can achieve a useful estimate for actual distance between 2 nodes, by analytically finding an estimate for the average maximum distance traveled per hop and multiplying with the hop count. The only requirement is the a priori knowledge of the networks' node density and the node range. The performance of our method is compared with a recent anchor node-based method and is shown to yield similar location estimation accuracy, despite the fact that it does not use anchor nodes.

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