Broadcast Strategies with Probabilistic Delivery Guarantee in Multi-Channel Multi-Interface Wireless Mesh Networks

Multi-channel multi-interface Wireless Mesh Networks permit to spread the load across orthogonal channels to improve network capacity. Although broadcast is vital for many layer-3 protocols, proposals for taking advantage of multiple channels mostly focus on unicast transmissions. In this paper, we propose broadcast algorithms that fit any channel and interface assignment strategy. They guarantee that a broadcast packet is delivered with a minimum probability to all neighbors. Our simulations show that the proposed algorithms efficiently limit the overhead

Effective Provisioning in Multi-Interface Multi-Channel Wireless Mesh Networks

Wireless Mesh Network (WMN) is a network communication technology that can provide high coverage and consistency using multihop communication features. Operating various applications in parallel on WMNs implies the need for improvement in the network’s performance, where capacity is one of the most significant factors. Multi-Interface Multi-Channel (MIMC) networks, a type of WMN, can increase overall network capacity by using several interfaces and channels simultaneously. However, employing many channels at once poses the problem of selecting suitable channels and interfaces for links while avoiding interference and efficiently utilizing the resources. The majority of MIMC WMN used the same type of wireless technology as their interfaces and a limited number of non-overlapping channels to reduce the likelihood of network interference. This thesis investigates the MIMC WMN provisioning problem by using three widely used wireless technologies: WiFi, Bluetooth, and Zigbee, with all their channels in the 2.4 GHz spectrum. To assess interference among links, we use a conflict graph for all channels of the three technologies. Furthermore, we formulate a joint interference-aware routing, Interface Assignment (IA), and Channel Assignment (CA) scheme using Integer Linear Programming (ILP) for both static and dynamic traffic, aiming to maximize the overall throughput considering bandwidth and latency requirements of requests. We use the Gurobi solver to implement the models and conduct a series of experiments in both cases. The numerical studies demonstrate that using various wireless technologies and properly managing channels leads to improved performance in terms of throughput while preventing interference and transmitting heavy real-time data

Enhanced multichannel routing protocols in MANET

Utilising multiple non-overlapping channels in MANET networking can improve performance and capacity. Most multichannel MAC and routing protocols rely on an extra radio interface, a common control channel or time synchronisation to support channel selection and routing, but only at the expense of hardware and power consumption costs. This thesis considers an alternative type of multichannel wireless network where each node has a single half-duplex radio interface and does not rely on a common control channel or time synchronisation. Multichannel MAC and routing protocols that adopt the Receiver Directed Transmission (RDT) communication scheme are investigated to assess their ability to implement a multichannel MANET. A novel multipath multichannel routing protocol called RMMMC is proposed to enhance reliability and fault-tolerance in the MANET. RMMMC introduces new route discovery and recovery processes. The former establishes multiple node and channel disjointed paths in different channels and accumulates them to acquire a full multi-hop path to each destination. The latter detects broken links and repairs them using pre-discovered backup routes. To enhance communication reliability, a novel cross-layer multichannel MAC mechanism called RIVC is proposed. It mitigates transmitting/rerouting data packets to a node that does not have an updated route information towards a destination and only allows data packets with valid routes to occupy the medium. The optional access mode in the MAC protocol is modified to early detect invalid routes at intermediate nodes and switchover to an alternative path. A new cross-layer multichannel MAC mechanism called MB is proposed to reduce contention in a busy channel and enhance load balancing. MB modifies the MAC back-off algorithm to let a transmitter node invoke an alternative path in the alternative channel when the retry count threshold is reached. The proposed multichannel protocols are implemented and evaluated by extensive NS2 simulation studies

Explainable AI and Interpretable Computer Vision:From Oversight to Insight

The increasing availability of big data and computational power has facilitated unprecedented progress in Artificial Intelligence (AI) and Machine Learning (ML). However, complex model architectures have resulted in high-performing yet uninterpretable ‘black boxes’. This prevents users from verifying that the reasoning process aligns with expectations and intentions. This thesis posits that the sole focus on predictive performance is an unsustainable trajectory, since a model can make right predictions for the wrong reasons. The research field of Explainable AI (XAI) addresses the black-box nature of AI by generating explanations that present (aspects of) a model's behaviour in human-understandable terms. This thesis supports the transition from oversight to insight, and shows that explainability can give users more insight into every part of the machine learning pipeline: from the training data to the prediction model and the resulting explanations. When relying on explanations for judging a model's reasoning process, it is important that the explanations are truthful, relevant and understandable. Part I of this thesis reflects upon explanation quality and identifies 12 desirable properties, including compactness, completeness and correctness. Additionally, it provides an extensive collection of quantitative XAI evaluation methods, and analyses their availabilities in open-source toolkits. As alternative to common post-model explainability that reverse-engineers an already trained prediction model, Part II of this thesis presents in-model explainability for interpretable computer vision. These image classifiers learn prototypical parts, which are used in an interpretable decision tree or scoring sheet. The models are explainable by design since their reasoning depends on the extent to which an image patch “looks like” a learned part-prototype. Part III of this thesis shows that ML can also explain characteristics of a dataset. Because of a model's ability to analyse large amounts of data in little time, extracting hidden patterns can contribute to the validation and potential discovery of domain knowledge, and allows to detect sources of bias and shortcuts early on. Concluding, neither the prediction model nor the data nor the explanation method should be handled as a black box. The way forward? AI with a human touch: developing powerful models that learn interpretable features, and using these meaningful features in a decision process that users can understand, validate and adapt. This in-model explainability, such as the part-prototype models from Part II, opens up the opportunity to ‘re-educate’ models with our desired norms, values and reasoning. Enabling human decision-makers to detect and correct undesired model behaviour will contribute towards an effective but also reliable and responsible usage of AI

2004 Graduate Bulletin

After 2003 the University of Dayton Bulletin went exclusively online. This copy was printed from the web and scanned by the Registrar’s Office. For general information about the university please see the Undergraduate Bulletin.https://ecommons.udayton.edu/bulletin_grad/1000/thumbnail.jp