Efficient decentralized communications in sensor networks

This thesis is concerned with problems in decentralized communication in large networks. Namely, we address the problems of joint rate allocation and transmission of data sources measured at nodes, and of controlling the multiple access of sources to a shared medium. In our study, we consider in particular the important case of a sensor network measuring correlated data. In the first part of this thesis, we consider the problem of correlated data gathering by a network with a sink node and a tree communication structure, where the goal is to minimize the total transmission cost of transporting the information collected by the nodes, to the sink node. Two coding strategies are analyzed: a Slepian-Wolf model where optimal coding is complex and transmission optimization is simple, and a joint entropy coding model with explicit communication where coding is simple and transmission optimization is difficult. This problem requires a joint optimization of the rate allocation at the nodes and of the transmission structure. For the Slepian-Wolf setting, we derive a closed form solution and an efficient distributed approximation algorithm with a good performance. We generalize our results to the case of multiple sinks. For the explicit communication case, we prove that building an optimal data gathering tree is NP-complete and we propose various distributed approximation algorithms. We compare asymptotically, for dense networks, the total costs associated with Slepian-Wolf coding and explicit communication, by finding their corresponding scaling laws and analyzing the ratio of their respective costs. We argue that, for large networks and under certain conditions on the correlation structure, "intelligent", but more complex Slepian-Wolf coding provides unbounded gains over the widely used straightforward approach of opportunistic aggregation and compression by explicit communication. In the second part of this thesis, we consider a queuing problem in which the service rate of a queue is a function of a partially observed Markov chain, and in which the arrivals are controlled based on those partial observations so as to keep the system in a desirable mildly unstable regime. The optimal controller for this problem satisfies a separation property: we first compute a probability measure on the state space of the chain, namely the information state, then use this measure as the new state based on which to make control decisions. We give a formal description of the system considered and of its dynamics, we formalize and solve an optimal control problem, and we show numerical simulations to illustrate with concrete examples properties of the optimal control law. We show how the ergodic behavior of our queuing model is characterized by an invariant measure over all possible information states, and we construct that measure. Our results may be applied for designing efficient and stable algorithms for medium access control in multiple accessed systems, in particular for sensor networks

Somatic mutations render human exome and pathogen DNA more similar

Immunotherapy has recently shown important clinical successes in a substantial number of oncology indications. Additionally, the tumor somatic mutation load has been shown to associate with response to these therapeutic agents, and specific mutational signatures are hypothesized to improve this association, including signatures related to pathogen insults. We sought to study in silico the validity of these observations and how they relate to each other. We first addressed whether somatic mutations typically involved in cancer may increase, in a statistically meaningful manner, the similarity between common pathogens and the human exome. Our study shows that common mutagenic processes increase, in the upper range of biologically plausible frequencies, the similarity between cancer exomes and pathogen DNA at a scale of 12-16 nucleotide sequences and established that this increased similarity is due to the specific mutation distribution of the considered mutagenic processes. Next, we studied the impact of mutation rate and showed that increasing mutation rate generally results in an increased similarity between the cancer exome and pathogen DNA, at a scale of 4-5 amino acids. Finally, we investigated whether the considered mutational processes result in amino-acid changes with functional relevance that are more likely to be immunogenic. We showed that functional tolerance to mutagenic processes across species generally suggests more resilience to mutagenic processes that are due to exposure to elements of nature than to mutagenic processes that are due to exposure to cancer-causing artificial substances. These results support the idea that recognition of pathogen sequences as well as differential functional tolerance to mutagenic processes may play an important role in the immune recognition process involved in tumor infiltration by lymphocytes

A convergence theorem for controlled queues with partial observations

We consider a queuing problem in which both the service rate of a finite-buffer queue and its rate of arrivals are functions of the same partially observed Markov chain. Basic performance indices of this device, such as long term throughput and loss rates, are expressed in terms of an invariant measure over a suitable finite-dimensional simplex. In this paper we prove the existence of that invariant measure

Flow control for multiple access queues

We study the problem of finding a characterization for the channel that results when a queue is operated under multiple access conditions. In such systems, the mechanism by which different sources gain access to the channel plays a fundamental role in defining what is the channel available to each source. In this paper therefore we study the structure and properties of these control devices in some detail. Under some (mild) technical conditions, and under modeling assumptions inspired by TCP/IP's flow control (the standard control algorithm in the current Internet), we are able to characterize the optimal controller for this problem. We also present some numerical simulations, to help develop an intuition on what exactly this control box does

Networked Slepian-Wolf: Theory and Algorithms

In this paper, we consider the minimization of a relevant energy consumption related cost function in the context of sensor networks where correlated sources are generated at various sets of source nodes and have to be transmitted to some set of sink nodes. The cost function we consider is given by the product [rate] × [link weight]. The minimization is achieved by jointly optimizing the transmission structure, which we show consists of a superposition of trees from each of the source nodes to its corresponding sink nodes, and the rate allocation across the source nodes. We show that the overall minimization can be achieved in two concatenated steps. First, the optimal transmission structure has to be found, which in general amounts to finding a Steiner tree and second, the optimal rate allocation has to be obtained by solving a linear programming problem with linear cost weights determined by the given optimal transmission structure. We also prove that, if any arbitrary traffic matrix is allowed, then the problem of finding the optimal transmission structure is NP-complete. For some particular traffic matrix cases, we fully characterize the optimal transmission structures and we also provide a closed-form solution for the optimal rate-allocation. Finally, we analyze the design of decentralized algorithms in order to obtain exactly or approximately the optimal rate allocation, depending on the traffic matrix case. For the particular case of data gathering, we provide experimental results showing a good performance in terms of approximation ratios

Adaptive Distributed Algorithms for Power-Efficient Data Gathering in Sensor Networks

In this work, we consider the problem of designing adaptive distributed processing algorithms in large sensor networks that are efficient in terms of minimizing the total power spent for gathering the spatially correlated data from the sensor nodes to a sink node. We take into account both the power spent for purposes of communication as well as the power spent for local computation. Our distributed algorithms are also matched to the nature of the correlated field, namely, for piecewise smooth signals, we provide two distributed multiresolution wavelet-based algorithms, while for correlated Gaussian fields, we use distributed prediction based processing. In both cases, we provide distributed algorithms that perform network division into groups of different sizes. The distribution of the group sizes within the network is the result of an optimal trade-off between the local communication inside each group needed to perform decorrelation, the communication needed to bring the processed data (coefficients) to the sink and the local computation cost, which grows as the network becomes larger. Our experimental results show clearly that important gains in power consumption can be obtained with respect to the case of not performing any distributed decorrelating processing

On Network Correlated Data Gathering

We consider the problem of correlated data gathering by a network with a sink node and a tree communication structure, where the goal is to minimize the total transmission cost of transporting the information collected by the nodes, to the sink node. Two coding strategies are analyzed: a Slepian-Wolf model where optimal coding is complex and transmission optimization is simple, and a joint entropy coding model with explicit communication where coding is simple and transmission optimization is difficult. This problem requires a joint optimization of the rate allocation at the nodes and of the transmission structure. For the Slepian-Wolf setting, we derive a closed form solution and an efficient distributed approximation algorithm with a good performance. For the explicit communication case, we prove that building an optimal data gathering tree is NP-complete and we propose various distributed approximation algorithms

Transcriptional profiling of vaccine-induced immune responses in humans and non-human primates

There is an urgent need for pre-clinical and clinical biomarkers predictive of vaccine immunogenicity, efficacy and safety to reduce the risks and costs associated with vaccine development. Results emerging from immunoprofiling studies in non-human primates and humans demonstrate clearly that (i) type and duration of immune memory are largely determined by the magnitude and complexity of the innate immune signals and (ii) genetic signatures highly predictive of B-cell and T-cell responses can be identified for specific vaccines. For vaccines with similar composition, e.g. live attenuated viral vaccines, these signatures share common patterns. Signatures predictive of vaccine efficacy have been identified in a few experimental challenge studies. This review aims to give an overview of the current literature on immunoprofiling studies in humans and also presents some of our own data on profiling of licensed and experimental vaccines in non-human primates

Associations of Tissue Tumor Mutational Burden and Mutational Status With Clinical Outcomes With Pembrolizumab Plus Chemotherapy Versus Chemotherapy For Metastatic NSCLC

INTRODUCTION: We evaluated tissue tumor mutational burden (tTMB) and mutations in STK11, KEAP1, and KRAS as biomarkers for outcomes with pembrolizumab plus platinum-based chemotherapy (pembrolizumab-combination) for NSCLC among patients in the phase 3 KEYNOTE-189 (ClinicalTrials.gov, NCT02578680; nonsquamous) and KEYNOTE-407 (ClinicalTrials.gov, NCT02775435; squamous) trials. METHODS: This retrospective exploratory analysis evaluated prevalence of high tTMB and STK11, KEAP1, and KRAS mutations in patients enrolled in KEYNOTE-189 and KEYNOTE-407 and the relationship between these potential biomarkers and clinical outcomes. tTMB and STK11, KEAP1, and KRAS mutation status was assessed using whole-exome sequencing in patients with available tumor and matched normal DNA. The clinical utility of tTMB was assessed using a prespecified cutpoint of 175 mutations/exome. RESULTS: Among patients with evaluable data from whole-exome sequencing for evaluation of tTMB (KEYNOTE-189, n = 293; KEYNOTE-407, n = 312) and matched normal DNA, no association was found between continuous tTMB score and overall survival (OS) or progression-free survival for pembrolizumab-combination (Wald test, one-sided p \u3e 0.05) or placebo-combination (Wald test, two-sided p \u3e 0.05) in patients with squamous or nonsquamous histology. Pembrolizumab-combination improved outcomes for patients with tTMB greater than or equal to 175 compared with tTMB less than 175 mutations/exome in KEYNOTE-189 (OS, hazard ratio = 0.64 [95% confidence interval (CI): 0.38‒1.07] and 0.64 [95% CI: 0.42‒0.97], respectively) and KEYNOTE-407 (OS, hazard ratio = 0.74 [95% CI: 0.50‒1.08 and 0.86 [95% CI: 0.57‒1.28], respectively) versus placebo-combination. Treatment outcomes were similar regardless of KEAP1, STK11, or KRAS mutation status. CONCLUSIONS: These findings support pembrolizumab-combination as first-line treatment in patients with metastatic NSCLC and do not suggest the utility of tTMB, STK11, KEAP1, or KRAS mutation status as a biomarker for this regimen