To mesh or not to mesh: flexible wireless indoor communication among mobile robots in industrial environments

Mobile robots such as automated guided vehicles become increasingly important in industry as they can greatly increase efficiency. For their operation such robots must rely on wireless communication, typically realized by connecting them to an existing enterprise network. In this paper we motivate that such an approach is not always economically viable or might result in performance issues. Therefore we propose a flexible and configurable mixed architecture that leverages on mesh capabilities whenever appropriate. Through experiments on a wireless testbed for a variety of scenarios, we analyse the impact of roaming, mobility and traffic separation and demonstrate the potential of our approach

A two‐step authentication framework for Mobile ad hoc networks

The lack of fixed infrastructure in ad hoc networks causes nodes to rely more heavily on peer nodes for communication. Nevertheless, establishing trust in such a distributed environment is very difficult, since it is not straightforward for a node to determine if its peer nodes can be trusted. An additional concern in such an environment is with whether a peer node is merely relaying a message or if it is the originator of the message. In this paper, we propose an authentication approach for protecting nodes in mobile ad hoc networks. The security requirements for protecting data link and network layers are identified and the design criteria for creating secure ad hoc networks using several authentication protocols are analyzed. Protocols based on zero knowledge and challenge response techniques are presented and their performance is evaluated through analysis and simulation

Intrusion Detection in Mobile Ad Hoc Networks Using Classification Algorithms

In this paper we present the design and evaluation of intrusion detection models for MANETs using supervised classification algorithms. Specifically, we evaluate the performance of the MultiLayer Perceptron (MLP), the Linear classifier, the Gaussian Mixture Model (GMM), the Naive Bayes classifier and the Support Vector Machine (SVM). The performance of the classification algorithms is evaluated under different traffic conditions and mobility patterns for the Black Hole, Forging, Packet Dropping, and Flooding attacks. The results indicate that Support Vector Machines exhibit high accuracy for almost all simulated attacks and that Packet Dropping is the hardest attack to detect.Comment: 12 pages, 7 figures, presented at MedHocNet 200