Cross-layer optimizations in multi-hop ad hoc networks

Unlike traditional wireless networks, characterized by the presence of last-mile, static and reliable infrastructures, Mobile ad Hoc Networks (MANETs) are dynamically formed by collections of mobile and static terminals that exchange data by enabling each other's communication. Supporting multi-hop communication in a MANET is a challenging research area because it requires cooperation between different protocol layers (MAC, routing, transport). In particular, MAC and routing protocols could be considered mutually cooperative protocol layers. When a route is established, the exposed and hidden terminal problems at MAC layer may decrease the end-to-end performance proportionally with the length of each route. Conversely, the contention at MAC layer may cause a routing protocol to respond by initiating new routes queries and routing table updates. Multi-hop communication may also benefit the presence of pseudo-centralized virtual infrastructures obtained by grouping nodes into clusters. Clustering structures may facilitate the spatial reuse of resources by increasing the system capacity: at the same time, the clustering hierarchy may be used to coordinate transmissions events inside the network and to support intra-cluster routing schemes. Again, MAC and clustering protocols could be considered mutually cooperative protocol layers: the clustering scheme could support MAC layer coordination among nodes, by shifting the distributed MAC paradigm towards a pseudo-centralized MAC paradigm. On the other hand, the system benefits of the clustering scheme could be emphasized by the pseudo-centralized MAC layer with the support for differentiated access priorities and controlled contention. In this thesis, we propose cross-layer solutions involving joint design of MAC, clustering and routing protocols in MANETs. As main contribution, we study and analyze the integration of MAC and clustering schemes to support multi-hop communication in large-scale ad hoc networks. A novel clustering protocol, named Availability Clustering (AC), is defined under general nodes' heterogeneity assumptions in terms of connectivity, available energy and relative mobility. On this basis, we design and analyze a distributed and adaptive MAC protocol, named Differentiated Distributed Coordination Function (DDCF), whose focus is to implement adaptive access differentiation based on the node roles, which have been assigned by the upper-layer's clustering scheme. We extensively simulate the proposed clustering scheme by showing its effectiveness in dominating the network dynamics, under some stressing mobility models and different mobility rates. Based on these results, we propose a possible application of the cross-layer MAC+Clustering scheme to support the fast propagation of alert messages in a vehicular environment. At the same time, we investigate the integration of MAC and routing protocols in large scale multi-hop ad-hoc networks. A novel multipath routing scheme is proposed, by extending the AOMDV protocol with a novel load-balancing approach to concurrently distribute the traffic among the multiple paths. We also study the composition effect of a IEEE 802.11-based enhanced MAC forwarding mechanism called Fast Forward (FF), used to reduce the effects of self-contention among frames at the MAC layer. The protocol framework is modelled and extensively simulated for a large set of metrics and scenarios. For both the schemes, the simulation results reveal the benefits of the cross-layer MAC+routing and MAC+clustering approaches over single-layer solutions.

Indicatori patologici di rilevanza diagnostica nel carcinoma squamocellulare orale felino

Il carcinoma squamocellulare è il tumore maligno orale più frequente nel gatto e si caratterizza per diagnosi spesso tardiva e prognosi infausta. Il progetto riguarda la ricerca di marker di rilevanza dia-gnostica nel carcinoma squamocellulare orale felino (FOSCC), al fine di sviluppare un test di scree-ning non invasivo. È stata condotta un’analisi retrospettiva delle disregolazioni del gene oncosoppres-sore TP53 in campioni istologici di FOSCC e di una popolazione di controllo (lesioni infiammatorie croniche orali e mucose orali normali feline). Tramite next-generation sequencing (NGS) sono state rilevate mutazioni di TP53 nel 69% dei FOSCC, ed anche l’espressione immunoistochimica della pro-teina p53 era presente nel 69% dei tumori, con una concordanza discreta (77%) fra le due alterazioni. Nella popolazione di controllo erano presenti disregolazioni di p53 solo in due lesioni infiammatorie (3%). Successivamente è stata effettuata un’analisi prospettica con NGS della metilazione del DNA di 17 geni, noti per essere disregolati nel carcinoma squamocellulare orale umano o felino, insieme all’analisi mutazionale di TP53, in campioni istologici di FOSCC e in un gruppo di controllo. Le stesse indagini molecolari sono state svolte in parallelo su campioni di cellule prelevate mediante brushing orale. Utilizzando 6 dei geni indagati differenzialmente metilati nei FOSCC (FLI1, MiR124-1, KIF1A, MAGEC2, ZAP70, MiR363) e lo stato mutazionale diTP53, è stato impostato un algoritmo diagnostico per differenziare i FOSCC dalla mucosa orale non neoplastica. Applicato ai brushing, l’algoritmo è risultato positivo (indicativo di carcinoma) in 24/35 (69%) gatti con FOSCC, contro 2/60 (3%) controlli (sensibilità: 69%; specifici-tà: 97%). La quota di FOSCC identificati era significativamente maggiore nei gatti sottoposti a prelievo in anestesia generale rispetto ai gatti svegli. Questi risultati sono incoraggianti per il riconoscimento precoce del FOSCC tramite brushing orale. Saranno necessari ulteriori studi su casistiche più ampie per validare questa metodica e migliorarne la sensibilità.

Real-time anomaly detection of gamma-ray bursts for the Cherenkov Telescope Array using deep learning

The Cherenkov Telescope Array (CTA) will be the next-generation ground-based observatory to study the universe in the very-high-energy domain. The observatory will rely on a Science Alert Generation (SAG) system to analyze the real-time data from the telescopes and generate science alerts. The SAG system will play a crucial role in the search and follow-up of transients from external alerts, enabling multi-wavelength and multi-messenger collaborations. It will maximize the potential for the detection of the rarest phenomena, such as gamma-ray bursts (GRBs), which are the science case for this study. This study presents an anomaly detection method based on deep learning for detecting gamma-ray burst events in real-time. The performance of the proposed method is evaluated and compared against the Li&Ma standard technique in two use cases of serendipitous discoveries and follow-up observations, using short exposure times. The method shows promising results in detecting GRBs and is flexible enough to allow real-time search for transient events on multiple time scales. The method does not assume background nor source models and doe not require a minimum number of photon counts to perform analysis, making it well-suited for real-time analysis. Future improvements involve further tests, relaxing some of the assumptions made in this study as well as post-trials correction of the detection significance. Moreover, the ability to detect other transient classes in different scenarios must be investigated for completeness. The system can be integrated within the SAG system of CTA and deployed on the onsite computing clusters. This would provide valuable insights into the method's performance in a real-world setting and be another valuable tool for discovering new transient events in real-time. Overall, this study makes a significant contribution to the field of astrophysics by demonstrating the effectiveness of deep learning-based anomaly detection techniques for real-time source detection in gamma-ray astronomy.