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Key establishment and management is the core of security protocols for wireless sensor networks deployed in the hostile environment. Due to the strict resource constraints, traditional asymmetric key cryptosystems, such as public/private key based schemes, are infeasible for large-scale wireless sensor networks. Research shows that pre-distributing pairwise keys into wireless sensor nodes before deployment is a practical way to deal with the key establishment problem. Existing random key based key pre-distribution schemes only provide probabilistic connectivity of the network and some level of network resilience. In this paper, we propose an efficient pairwise key establishment and management scheme to achieve both network connectivity and resilience for static wireless sensor networks. Compared with current key pre-distribution schemes, our scheme supports large network size, and has lower communication and computational overhead.

This is a repository copy of Smart frequency control in low inertia energy systems based on frequency response techniques: A review.

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We give two fundamental methods for evaluation of classical free energies of all the integrable models admitting soliton solutions; the sine-Gordon equation is one example. Periodic boundary conditions impose integral equations for allowed phonon and soliton momenta. From these, generalized Bethe-A nsatz and functional-integration methods using action-angle variables follow. Results for free energies coincide, and coincide with those that we find by transfer-integral methods. Extension to the quantum case, and quantum Bethe A nsatz, on the lines to be reported elsewhere for the sinh-Gordon equation, is indicated.

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An adaptive ant colony algorithm is proposed to overcome the premature convergence problem in the conventional ant colony algorithm. The adaptive ant colony is composed of three groups of ants: ordinary ants, abnormal ants and random ants. Each ordinary ant searches the path with the high concentration pheromone at the high probability, each abnormal ant searches the path with the high concentration pheromone at the low probability, and each random ant randomly searches the path regardless of the pheromone concentration. Three groups of ants provide a good initial state of pheromone trails together. As the optimization calculation goes on, the number of the abnormal ants and the random ants decreases gradually. In the late optimization stage, all of ants transform to the ordinary ants, which can rapidly concentrate to the optimal paths. Simulation results show that the algorithm has a good optimization performance, and can resolve traveling salesman problem effectively.