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We consider the problem of identifying solutions to a stochastic system under multiple constraints. The objective function and the constraints are expressed in terms of performance measures of the system that are observable only via a simulation model parameterized by a finite number of decision variables. In solving for such a system, one faces the much harder challenge of verifying the feasibility of a potential solution. Toward this, we present cgR-SPLINE, a multistart simulation optimization (SO) algorithm on integer spaces. cgR-SPLINE sequentially solves random restarts of a gradient-based local search routine with increasing precision. The local search routine in turn solves progressively stricter outer approximations of the underlying problem. The local solution estimator from a recently ended restart is probabilistically compared against an incumbent solution, thus generating a sequence of global solution estimators. The optimal convergence rate of the solution iterates is observed to be sub-exponential, slower than the exponential rate observed for SO problems on unconstrained discrete spaces. Additionally, efficiency for cgR-SPLINE dictates that the number of multistarts and the total simulation budget be sublinearly related, implying an increased emphasis on exploration than is prescribed in the continuous context. Heuristics for choosing constraint relaxations and solution reporting demonstrate good finite-time performance on three SO problems, of which two are nontrivial. The extension of cgR-SPLINE's framework to mixed spaces seems a natural next step. The presence of infeasible points arbitrarily close to the stochastic boundary, however pose challenges for consistency. We present a general framework for mixed spaces that is very much along the lines of cgR-SPLINE and propose ideas for specific algorithmic refinements and solution reporting. Strategically locating the restarts of a multistart SO algorithm appears to be a largely unexplored research topic. Toward achieving efficiency during the exploration phase, we present ideas for ``antithetically" generating the restarts from probability measures constructed from the SO algorithm's performance trajectory. Asymptotic behavior of the proposed sampling strategy and policies for optimal parameter selection are presently conjectural, but appear promising based on the outcomes of preliminary experiments. / Ph. D.

The Internet-of-Things is an open source architecture for enabling information sharing between globally connected devices, which existing system do not offer. However, the Internet-of-Things induces the single points of failure and long communication delay. Thus, the Sensible Things platform is proposed, which is a fully distributed system. So far, it has produced components to share sensor and actuator information on the Internet. In the past, manual work was problematic since physical access could be difficult on remote locations. There were also difficulties to detect if the devices were actually working properly. Therefore, the thesis mainly focuses on the functionality which is able to check status, update software automatically and restart the devices with new software. The thesis first analyzes the mechanism of the automatic updating and describes the methods for it. The automatic updates of demonstrator is implemented in My-Eclipse. Finally, this paper describes the evaluation of the automatic updating in Sensible Things platform.

The information era has led the manufacturer of trucks and logistics solution providers are inclined towards software as a service (SAAS) based solutions. With advancements in software technologies like artificial intelligence and deep learning, the domain of computer vision has achieved significant performance boosts that it competes with hardware based solutions. Firstly, data is collected from a large number of sensors which can increase production costs and carbon footprint in the environment. Secondly certain useful physical quantities/variables are impossible to measure or turns out to be very expensive solution. So in this dissertation, we are investigating the feasibility of providing the similar solution using a single sensor (dashboard- camera) to measure multiple variables. This provides a sustainable solution even when scaled up in huge fleets. The video frames that can be collected from the visual perception of the truck (i.e. the on-board camera of the truck) is processed by the deep learning techniques and operational data can be extracted. Certain techniques like the image classification and semantic segmentation outputs were experimented and shows potential to replace costly hardware counterparts like Lidar or radar based solutions. / Informationstiden har lett till att tillverkare av lastbilar och logistiklösningsleve -rantörer är benägna mot mjukvara som en tjänst (SAAS) baserade lösningar. Med framsteg inom mjukvaruteknik som artificiell intelligens och djupinlärnin har domänen för datorsyn uppnått betydande prestationsförstärkningar att konkurrera med hårdvarubaserade lösningar. För det första samlas data in från ett stort antal sensorer som kan öka produktionskostnaderna och koldioxidavtry -cket i miljön. För det andra är vissa användbara fysiska kvantiteter / variabler omöjliga att mäta eller visar sig vara en mycket dyr lösning. Så i denna avhandling undersöker vi möjligheten att tillhandahålla liknande lösning med hjälp av en enda sensor (instrumentbrädkamera) för att mäta flera variabler. Detta ger en hållbar lösning även när den skalas upp i stora flottor. Videoramar som kan samlas in från truckens visuella uppfattning (dvs. lastbilens inbyggda kamera) bearbetas av djupinlärningsteknikerna och operativa data kan extraher -as. Vissa tekniker som bildklassificering och semantiska segmenteringsutgång -ar experimenterades och visar potential att ersätta dyra hårdvaruprojekt som Lidar eller radarbaserade lösningar.