Limited fidelity of software-based wireless network simulations has prompted many researchers to build testbeds for developing and evaluating their wireless protocols and mobile applications. Since most testbeds are tailored to the needs of specific research projects, they cannot be easily reused for other research projects that may have different requirements on physical topology, radio channel characteristics or mobility pattern. In this paper, we describe the design, implementation and evaluation of MiNT-m, an experimentation platform devised specifically to support arbitrary experiments for mobile multi-hop wireless network protocols. In addition to inheriting the miniaturization feature from its predecessor MiNT [9], MiNT-m enables flexible testbed reconfiguration on an experiment-by-experiment basis by putting each testbed node on a centrally controlled untethered mobile robot. To support mobility and reconfiguration of testbed nodes, MiNT-m includes a scalable mobile robot navigation control subsystem, which in turn consists of a vision-based robot positioning module and a collision avoidance-based trajectory planning module. Further, MiNT-m provides a comprehensive network/experiment management subsystem that affords a user full interactive control over the testbed as well as real-time visualization of the testbed activities. Finally, because MiNT-m is designed to be a shared research infrastructure that supports 24x7 operation, it incorporates a novel automatic battery recharging capability that enables testbed robots to operate without human intervention for weeks.

As databases for real-world problems increase in size, there is a need in many situations to select and keep relevant training data for efficient storage and processing reasons.Support vector machines (SVMs) reportedly exhibit certain desirable properties in selecting and preserving useful training data as support vectors.This paper attempts to quantify the extent to which SVM training behaves like a model independent example selection procedure.Using several common machine-learning training databases, we compare the prediction results obtained by different classifiers, trained with data selected by SVMs and by two other example selection methods (IB2 and random sampling).Some interesting observations are made with explanations.

With the increase in the size of real-world databases, there is an ever-increasing need to scale up inductive learning algorithms.Incremental learning techniques are one possible solution to the scalability problem.In this paper, we propose three ctiteria to evaluate the robustness and reliability of incremental learning methods, and use them to study the robustness of an incremental training method for Support Vector Machines.We provide empirical results using benchmark machine learning datasets to show that support vectors form a svccdnct and suficient set for block-by-block incremental learning.