Public Testbeds Research Articles

Efficient and Stateless P2P Routing Mechanisms for the Internet of Things

Arbitrary point-to-point (P2P) routing is becoming a necessity in a multitude of Internet-of-Things (IoT) applications, including building automation, smart cities, and smart manufacturing. For this reason, new P2P routing protocols such as lightweight on-demand ad hoc distance-vector routing protocol next generation (LOADng) and ad hoc on-demand distance vector routing-based RPL protocol (AODV-RPL) are being standardized. Both protocols are inspired by AODV, and hence, they might suffer from broadcast storms caused by flooding route requests (RREQs) and related issues. Thus, while AODV-RPL takes advantage of RPL mechanisms, LOADng uses blind flooding to forward RREQs. However, both lack effective techniques for avoiding unnecessary RREQ transmissions when routes are found. In this article, we first deploy stopping Trickle timers to ensure scalable, efficient, and reliable dissemination of RREQs. Second, we devise and present new techniques to suppress unnecessary RREQs with optimizations for radio duty-cycled networks. Finally, the two mechanisms are combined in a third proposal for better efficiency. These mechanisms provide individually and collectively great enhancements to P2P routing protocols, such as AODV-RPL and LOADng, while staying backward compatible with their base specifications. The performance of the proposed mechanisms when applied to LOADng has been validated using both extensive time-accurate simulations and large-scale public testbeds. The obtained results have shown the effectiveness of the proposed mechanisms with around 50% less overhead and savings in energy consumption, along with a 20% gain in route discovery ratio at the expense of an increase in discovery delays.

Creating Repeatable Computer Science and Networking Experiments on Shared, Public Testbeds

There are many compelling reasons to use a shared, public testbed such as GENI, Emulab, or PlanetLab to conduct experiments in computer science and networking. These testbeds support creating experiments with a large and diverse set of resources. Moreover these testbeds are constructed to inherently support the repeatability of experiments as required for scientifically sound research. Finally, the artifacts needed for a researcher to repeat their own experiment can be shared so that others can readily repeat the experiment in the same environment. However using a shared, public testbed is different from conducting experiments on resources either owned by the experimenter or someone the experimenter knows. Experiments on shared, public testbeds are more likely to use large topologies, use scarce resources, and need to be tolerant to outages and maintenances in the testbed. In addition, experimenters may not have access to low-level debugging information. This paper describes a methodology for new experimenters to write and deploy repeatable and sharable experiments which deal with these challenges by: having a clear plan; automating the execution and analysis of an experiment by following best practices from software engineering and system administration; and building scalable experiments. In addition, the paper describes a case study run on the GENI testbed which illustrates the methodology described.

Semantics-Preserving Bag-of-Words Models and Applications

The Bag-of-Words (BoW) model is a promising image representation technique for image categorization and annotation tasks. One critical limitation of existing BoW models is that much semantic information is lost during the codebook generation process, an important step of BoW. This is because the codebook generated by BoW is often obtained via building the codebook simply by clustering visual features in Euclidian space. However, visual features related to the same semantics may not distribute in clusters in the Euclidian space, which is primarily due to the semantic gap between low-level features and high-level semantics. In this paper, we propose a novel scheme to learn optimized BoW models, which aims to map semantically related features to the same visual words. In particular, we consider the distance between semantically identical features as a measurement of the semantic gap, and attempt to learn an optimized codebook by minimizing this gap, aiming to achieve the minimal loss of the semantics. We refer to such kind of novel codebook as semantics-preserving codebook (SPC) and the corresponding model as the Semantics-Preserving Bag-of-Words (SPBoW) model. Extensive experiments on image annotation and object detection tasks with public testbeds from MIT's Labelme and PASCAL VOC challenge databases show that the proposed SPC learning scheme is effective for optimizing the codebook generation process, and the SPBoW model is able to greatly enhance the performance of the existing BoW model.