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To achieve security in wireless sensor networks, it is important to be able to encrypt messages sent among sensor nodes. Keys for encryption purposes must be agreed upon by communicating nodes. Due to resource constraints, achieving such key agreement in wireless sensor networks is non-trivial. Many key agreement schemes used in general networks, such as Diffie-Hellman and public-key based schemes, are not suitable for wireless sensor networks. Pre-distribution of secret keys for all pairs of nodes is not viable due to the large amount of memory used when the network size is large. Recently, a random key pre-distribution scheme and its improvements have been proposed.A common assumption made by these random key pre-distribution schemes is that no deployment knowledge is available. Noticing that in many practical scenarios, certain deployment knowledge may be available a priori, we propose a novel random key pre-distribution scheme that exploits deployment knowledge and avoids unnecessary key assignments. We show that the performance (including connectivity, memory usage, and network resilience against node capture) of sensor networks can be substantially improved with the use of our proposed scheme. The scheme and its detailed performance evaluation are presented in this paper.

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Abstract：Multi-Robot Task Allocation is a crucial issue before performing a certain task. This paper deals with a distributed task allocation method based on some special relation defined according to the performance of history cooperation between two robots. The algorithm we propose here is named TARARC-a Task Allocation algorithm based on Robot Ability and Relevance with group Collaboration, where robot ability is weighed by reliability, relevance represents a fresh concept of "history relevance" between every two robots to establish reasonable groups for better collaboration, and the group collaboration includes inter and inner group help strategy that are adopted when different nodes failures happen in unknown environment. TARARC emphasizes the role of "agent node" in each group that is responsible for task competition, group leadership, formation maintenance as well as task execution with changing agents. Simulation on Player/Stage shows that our mechanism is feasible and valid.