An ant colony optimization approach for maximizing the lifetime of heterogeneous wireless sensor networks

Maximizing the lifetime of wireless sensor networks (WSNs) is a challenging problem. Although some methods exist to address the problem in homogeneous WSNs, research on this problem in heterogeneous WSNs have progressed at a slow pace. Inspired by the promising performance of ant colony optimization (ACO) to solve combinatorial problems, this paper proposes an ACO-based approach that can maximize the lifetime of heterogeneous WSNs. The methodology is based on finding the maximum number of disjoint connected covers that satisfy both sensing coverage and network connectivity. A construction graph is designed with each vertex denoting the assignment of a device in a subset. Based on pheromone and heuristic information, the ants seek an optimal path on the construction graph to maximize the number of connected covers. The pheromone serves as a metaphor for the search experiences in building connected covers. The heuristic information is used to reflect the desirability of device assignments. A local search procedure is designed to further improve the search efficiency. The proposed approach has been applied to a variety of heterogeneous WSNs. The results show that the approach is effective and efficient in finding high-quality solutions for maximizing the lifetime of heterogeneous WSNs

Robust Energy Management for Green and Survivable IP Networks

Despite the growing necessity to make Internet greener, it is worth pointing out that energy-aware strategies to minimize network energy consumption must not undermine the normal network operation. In particular, two very important issues that may limit the application of green networking techniques concern, respectively, network survivability, i.e. the network capability to react to device failures, and robustness to traffic variations. We propose novel modelling techniques to minimize the daily energy consumption of IP networks, while explicitly guaranteeing, in addition to typical QoS requirements, both network survivability and robustness to traffic variations. The impact of such limitations on final network consumption is exhaustively investigated. Daily traffic variations are modelled by dividing a single day into multiple time intervals (multi-period problem), and network consumption is reduced by putting to sleep idle line cards and chassis. To preserve network resiliency we consider two different protection schemes, i.e. dedicated and shared protection, according to which a backup path is assigned to each demand and a certain amount of spare capacity has to be available on each link. Robustness to traffic variations is provided by means of a specific modelling framework that allows to tune the conservatism degree of the solutions and to take into account load variations of different magnitude. Furthermore, we impose some inter-period constraints necessary to guarantee network stability and preserve the device lifetime. Both exact and heuristic methods are proposed. Experimentations carried out with realistic networks operated with flow-based routing protocols (i.e. MPLS) show that significant savings, up to 30%, can be achieved also when both survivability and robustness are fully guaranteed

End-to-End Kernel Learning with Supervised Convolutional Kernel Networks

In this paper, we introduce a new image representation based on a multilayer kernel machine. Unlike traditional kernel methods where data representation is decoupled from the prediction task, we learn how to shape the kernel with supervision. We proceed by first proposing improvements of the recently-introduced convolutional kernel networks (CKNs) in the context of unsupervised learning; then, we derive backpropagation rules to take advantage of labeled training data. The resulting model is a new type of convolutional neural network, where optimizing the filters at each layer is equivalent to learning a linear subspace in a reproducing kernel Hilbert space (RKHS). We show that our method achieves reasonably competitive performance for image classification on some standard "deep learning" datasets such as CIFAR-10 and SVHN, and also for image super-resolution, demonstrating the applicability of our approach to a large variety of image-related tasks.Comment: to appear in Advances in Neural Information Processing Systems (NIPS