Efficient Routing and Scheduling in Wireless Networks

The temporal and spatial variation in wireless channel conditions, node mobility make it challenging to design protocols for wireless networks. In this thesis, we design efficient routing and scheduling algorithms which adapt to changing network conditions caused by varying link quality or node mobility to improve user-level performance. We design and analyze routing protocols for static, mobile and heterogeneous wireless networks. We analyze the performance of opportunistic and cooperative forwarding in static mesh networks showing that opportunism outperforms cooperation; we identify interference as the main cause for mitigating the potential gains achievable with cooperative forwarding. For mobile networks, we quantitatively analyze the tradeoff between state information collection (sampling frequency and number of bits per sample) and power consumption for a fixed source-to-destination goodput constraint. For heterogeneous networks comprising of both static and mobile nodes, we propose a greedy algorithm (adaptive-flood) which dynamically classifies individual nodes as routers/flooders depending on network conditions and demonstrate that it achieves performance equivalent to, and in some cases significantly better than, that of network-wide routing or flooding alone. Last, we consider an application-level wireless streaming scenario where multiple clients are streaming different videos from a cellular base station. We design a greedy algorithm for efficiently scheduling multiple video streams from a base station to mobile clients so as to minimize the total number of application-playout stalls. We develop models for coarse timescale wireless channel variation to aid network and application-layer protocol design

Optimal Caching and Routing in Hybrid Networks

Hybrid networks consisting of MANET nodes and cellular infrastructure have been recently proposed to improve the performance of military networks. Prior work has demonstrated the benefits of in-network content caching in a wired, Internet context. We investigate the problem of developing optimal routing and caching policies in a hybrid network supporting in-network caching with the goal of minimizing overall content-access delay. Here, needed content may always be accessed at a back-end server via the cellular infrastructure; alternatively, content may also be accessed via cache-equipped "cluster" nodes within the MANET. To access content, MANET nodes must thus decide whether to route to in-MANET cluster nodes or to back-end servers via the cellular infrastructure; the in-MANET cluster nodes must additionally decide which content to cache. We model the cellular path as either i) a congestion-insensitive fixed-delay path or ii) a congestion-sensitive path modeled as an M/M/1 queue. We demonstrate that under the assumption of stationary, independent requests, it is optimal to adopt static caching (i.e., to keep a cache's content fixed over time) based on content popularity. We also show that it is optimal to route to in-MANET caches for content cached there, but to route requests for remaining content via the cellular infrastructure for the congestion-insensitive case and to split traffic between the in-MANET caches and cellular infrastructure for the congestion-sensitive case. We develop a simple distributed algorithm for the joint routing/caching problem and demonstrate its efficacy via simulation.Comment: submitted to Milcom 201

Congestion-aware caching and search in information-centric Networks

ABSTRACT The performance of in-network caching in informationcentric networks, and of cache networks more generally, is typically characterized by network-centric performance metrics such as hit rate and hop count, with approaches to locating and caching content evaluated and optimized for these metrics. We believe that user-centric performance metrics, in particular the delay from when a content request is made by the user to the time at which the requested content has been completely downloaded, are also important. For such metrics, performance is often determined by link capacity constraints and network congestion. We investigate network cache management and search policies that account for path-level (content-server to content-requestor) congestion and file popularity in order to directly minimize user-centric, content-download delay. Through simulation, we find that our policies yield significantly better download delay performance than existing policies, even though these existing policies provide better performance according to traditional metrics such as cache hit rate and hop count

HER2-enriched subtype and novel molecular subgroups drive aromatase inhibitor resistance and an increased risk of relapse in early ER+/HER2+ breast cancer

BACKGROUND: Oestrogen receptor positive/ human epidermal growth factor receptor positive (ER+/HER2+) breast cancers (BCs) are less responsive to endocrine therapy than ER+/HER2- tumours. Mechanisms underpinning the differential behaviour of ER+HER2+ tumours are poorly characterised. Our aim was to identify biomarkers of response to 2 weeks’ presurgical AI treatment in ER+/HER2+ BCs. METHODS: All available ER+/HER2+ BC baseline tumours (n=342) in the POETIC trial were gene expression profiled using BC360™ (NanoString) covering intrinsic subtypes and 46 key biological signatures. Early response to AI was assessed by changes in Ki67 expression and residual Ki67 at 2 weeks (Ki672wk). Time-To-Recurrence (TTR) was estimated using Kaplan-Meier methods and Cox models adjusted for standard clinicopathological variables. New molecular subgroups (MS) were identified using consensus clustering. FINDINGS: HER2-enriched (HER2-E) subtype BCs (44.7% of the total) showed poorer Ki67 response and higher Ki672wk (p<0.0001) than non-HER2-E BCs. High expression of ERBB2 expression, homologous recombination deficiency (HRD) and TP53 mutational score were associated with poor response and immune-related signatures with High Ki672wk. Five new MS that were associated with differential response to AI were identified. HER2-E had significantly poorer TTR compared to Luminal BCs (HR 2.55, 95% CI 1.14–5.69; p=0.0222). The new MS were independent predictors of TTR, adding significant value beyond intrinsic subtypes. INTERPRETATION: Our results show HER2-E as a standardised biomarker associated with poor response to AI and worse outcome in ER+/HER2+. HRD, TP53 mutational score and immune-tumour tolerance are predictive biomarkers for poor response to AI. Lastly, novel MS identify additional non-HER2-E tumours not responding to AI with an increased risk of relapse

Climate change and living cities: Global problems with local solutions

10.1007/978-90-481-9867-2_2Climate Change and Sustainable Urban Development in Africa and Asia21-3