Self-Adaptive resource allocation for event monitoring with uncertainty in Sensor Networks

Event monitoring is an important application of sensor networks. Multiple parties, with different surveillance targets, can share the same network, with limited sensing resources, to monitor their events of interest simultaneously. Such a system achieves profit by allocating sensing resources to missions to collect event related information (e.g., videos, photos, electromagnetic signals). We address the problem of dynamically assigning resources to missions so as to achieve maximum profit with uncertainty in event occurrence. We consider timevarying resource demands and profits, and multiple concurrent surveillance missions. We model each mission as a sequence of monitoring attempts, each being allocated with a certain amount of resources, on a specific set of events that occurs as a Markov process. We propose a Self-Adaptive Resource Allocation algorithm (SARA) to adaptively and efficiently allocate resources according to the results of previous observations. By means of simulations we compare SARA to previous solutions and show SARA’s potential in finding higher profit in both static and dynamic scenarios

Approximation Algorithms for Geometric Covering Problems for Disks and Squares

Geometric covering is a well-studied topic in computational geometry. We study three covering problems: Disjoint Unit-Disk Cover, Depth-(≤ K) Packing and Red-Blue Unit-Square Cover. In the Disjoint Unit-Disk Cover problem, we are given a point set and want to cover the maximum number of points using disjoint unit disks. We prove that the problem is NP-complete and give a polynomial-time approximation scheme (PTAS) for it. In Depth-(≤ K) Packing for Arbitrary-Size Disks/Squares, we are given a set of arbitrary-size disks/squares, and want to find a subset with depth at most K and maximizing the total area. We prove a depth reduction theorem and present a PTAS. In Red-Blue Unit-Square Cover, we are given a red point set, a blue point set and a set of unit squares, and want to find a subset of unit squares to cover all the blue points and the minimum number of red points. We prove that the problem is NP-hard, and give a PTAS for it. A "mod-one" trick we introduce can be applied to several other covering problems on unit squares

Impacts of different SNLS3 light-curve fitters on cosmological consequences of interacting dark energy models

We explore the cosmological consequences of interacting dark energy (IDE) models using the SNLS3 supernova samples. In particular, we focus on the impacts of different SNLS3 light-curve fitters (LCF) (corresponding to "SALT2", "SiFTO", and "Combined" sample). Firstly, making use of the three SNLS3 data sets, as well as the Planck distance priors data and the galaxy clustering data, we constrain the parameter spaces of three IDE models. Then, we study the cosmic evolutions of Hubble parameter $H(z)$, deceleration diagram $q(z)$, statefinder hierarchy $S^{(1)}_3(z)$ and $S^{(1)}_4(z)$, and check whether or not these dark energy diagnosis can distinguish the differences among the results of different SNLS3 LCF. At last, we perform high redshift cosmic age test using three old high redshift objects (OHRO), and explore the fate of the Universe. We find that, the impacts of different SNLS3 LCF are rather small, and can not be distinguished by using $H(z)$, $q(z)$, $S^{(1)}_3(z)$, $S^{(1)}_4(z)$, and the age data of OHRO. In addition, we infer, from the current observations, how far we are from a cosmic doomsday in the worst case, and find that the "Combined" sample always gives the largest 2$\sigma$ lower limit of the time interval between "big rip" and today, while the results given by the "SALT2" and the "SiFTO" sample are close to each other. These conclusions are insensitive to a specific form of dark sector interaction. Our method can be used to distinguish the differences among various cosmological observations.Comment: 12 pages, 7 figures, 2 tables, accepted for publication in Astronomy and Astrophysic

Climate change effects on marine species across trophic levels

Climate change and anthropogenic activities are producing a range of new selection pressures, both abiotic and biotic, on marine organisms. While there are numerous studies that have investigated the response of individual marine organisms to climate change, few studies have focused on differences in organismal responses across trophic levels. Such trophic differences in response to climate change may disrupt ecological interactions and thereby threaten marine ecosystem function. In addition, predation is known as a strong driver that impacts individuals and populations. Despite this, we still do not have a comprehensive understanding of how different trophic levels respond to climate change stressors, predation and their combined effects in marine ecosystems.The main focus of this thesis is to identify whether marine trophic levels respond differently to climatic stressors and predation. To explore these questions, I have used a combination of traditional mesocosm experiments, together with a statistical method called meta-analysis. I initiated the research by study the responses of marine gastropods at two trophic levels to ocean acidification and predation using long-term mesocosm experiments together with a gastropod-specific meta-analyses. I focused on the amount of phenotypic plasticity in morphological traits of snails when exposed to the two stressors. In order to generalise and test these assumptions among a greater number of marine taxa, I used the meta-analysis approach to investigate the effects of ocean acidification and warming, as well as their combined effects on four marine trophic levels. Finally, to study the individual and combined effects of ocean acidification and predation with respect to inducible defences, I again applied a mesocosm experiment and used blue mussels as a model species. By using long-term mesocosm experiments and the gastropod-specific meta-analysis on marine gastropods from two trophic levels, I showed that these trophic levels varied in their responses to both ocean acidification and predation. Gastropods at lower trophic levels exhibited greater phenotypic plasticity against predation, while those from higher trophic levels showed stronger tolerance to ocean acidification. Next, by using a meta-analysis, including a large number of species and taxa, examining the effects of ocean acidification and warming, I revealed that top-predators and primary producers were most tolerant to ocean acidification compared to other trophic levels. Herbivores on the other hand, were the most vulnerable trophic level against abiotic stress. Again, using the meta-analysis approach, but this time incorporating only factorial experimental data that included the interactive effects of ocean acidification and ocean warming, I showed that higher trophic levels again were the most tolerant trophic level, and herbivores being most sensitive, with respect to the combined effect of the two stressors. Contrary to previous discussions in the literature concerning multiple climate-related stressors, antagonistic and additive effects occurred most frequently, while synergistic effects were less common and which decreased with increasing trophic rank. Finally, by conducting a fully-factorial experiment using blue mussels, I found that mussels with previous experience contact with predator has developed greater inducible defences than ones without previous experience. However, levels of ocean acidification may mask predator cues, or obstruct shell material, and consequently disrupt blue mussels inducible defence from crab predation. In summary, marine trophic levels respond differently to both biotic and climatic stressors. Higher trophic levels, together with primary producers, were often more robust against abiotic stress and may therefore be better prepared for future oceans compare species from lower trophic levels. These results may provide vital information for: implementing effective climate change mitigation, to understand which stressors to act on, and when and where to intervene for prioritizing conservation actions