Adaptive topology learning of camera network across non-overlapping views

An adaptive topology learning approach is proposed to learn the topology of a practical camera network in an unsupervised way. The nodes are modeled by the Gaussian mixture model. The connectivity between nodes is judged by their cross-correlation function, which is also used to calculate their transition time distribution. The mutual information of the connected node pair is employed for transition probability calculation. A false link eliminating approach is proposed, along with a topology updating strategy to improve the learned topology. A real monitoring system with five disjoint cameras is built for experiments. Comparative results with traditional methods show that the proposed method is more accurate in topology learning and is more robust to environmental changes.

Topology Adaptation for Robust Ad Hoc Cyberphysical Networks under Puncture-Style Attacks

Many cyber physical networks will involve ad hoc deployments utilizing peer-to-peer communications. Examples include transportation systems where a group of moving cars communicate in order to avoid collisions, teams of robotic agents that work together in support of disaster recovery, and sensor networks deployed for health-care monitoring, monitoring the operation of a factory plant or to coordinate and actuate mechanisms for energy conservation in a building. These networks may face a variety of threats that puncture their connectivity and, should their performance degrade, the result could be catastrophic. Consider, for example, a vehicular ad hoc network where communication assists collision avoidance. In such a case, degradation could lead to vehicle accidents. Therefore, in order to overcome network performance degradations and the puncture of a network （such as blackhole or jamming） which is under attack, we propose an algorithm called the Fiedler Value Power Adjustment Topology Adaption （FVPATA）. FVPATA aims to dynamically adapt an ad hoc network＇s topology, even if the attacker varies its location and in the case of an interference-style attack by increasing the interference power. The algorithm utilizes the formulation from the graph theory which works with the Fiedler value to guide each node in wireless ad hoc network utilizing power adjustments to enhance the network＇s overall robustness. The advantage of the proposed mechanism is that it is a light-weight approach which is totally distributed, based on topology updates inherent in the Optimized Link State Routing （OLSR） protocol and, hence, it is unnecessary to introduce additional messages. Additionally, an algorithm was developed to resolve problems involving asymmetric links that arise in ad hoc networks by eliminating unnecessary energy consumption of Fiedler nodes. Simulation results using NS3 show that the proposed mechanism successfully decreases the average amount of hops used by 50% and the delay of flows when nodes are migrating at a modest rate below 60 m/min.

Finite-time adaptive consensus of a class of multi-agent systems

Multi-agent systems(MASs) are ubiquitous in natural and artificial systems. This paper aims to establish the finite-time adaptive consensus criterion for a class of MASs with nonlinear dynamics. Traditionally, the finite-time consensus criterion is often established based on the prior information on Lipschitz constants and the eigenvalues of Laplacian matrix. However, it is difficult to acquire the above prior information for most real-world engineering systems. To overcome the above difficulty, this paper develops the finite-time consensus criteria for a class of MASs with nonlinear dynamics via adaptive technique. In detail, we design the finite-time distributed node-based and edge-based adaptive consensus protocols for a class of MASs with fixed and switching topologies. Numerical simulations are also given to validate the proposed finite-time adaptive consensus criterion.

A Semantic Searching Scheme in Heterogeneous Unstructured P2P Networks

Semantic-based searching in peer-to-peer （P2P） networks has drawn significant attention recently. A number of semantic searching schemes, such as GES proposed by Zhu Y et al., employ search models in Information Retrieval （IR）. All these IR-based schemes use one vector to summarize semantic contents of all documents on a single node. For example, GES derives a node vector based on the IR model： VSM （Vector Space Model）. A topology adaptation algorithm and a search protocol are then designed according to the similarity between node vectors of different nodes. Although the single semantic vector is suitable when the distribution of documents in each node is uniform, it may not be efficient when the distribution is diverse. When there are many categories of documents at each node, the node vector representation may be inaccurate. We extend the idea of GES and present a new class-based semantic searching scheme （CSS） specifically designed for unstructured P2P networks with heterogeneous single-node document collection. It makes use of a state-of-the-art data clustering algorithm, online spherical k-means clustering （OSKM）, to cluster all documents on a node into several classes. Each class can be viewed as a virtual node. Virtual nodes are connected through virtual links. As a result, the class vector replaces the node vector and plays an important role in the class-based topology adaptation and search process. This makes CSS very efficient. Our simulation using the IR benchmark TREC collection demonstrates that CSS outperforms GES in terms of higher recall, higher precision, and lower search cost.