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De senaste åren har drivkraften för forskning inom trådlösa sensornätverk ökat markant. Detta är framförallt på grund av de potentiella fördelar som den nya tekniken tillhandahåller.Vi har efter förfrågan av Ivan Kruzela tagit på oss uppgiften att skapa en laboration som är tänkt att ingå i den andra årskursen för högskoleingenjörerna i Data- och Telekommunikation på Centrum för Teknikstudier (CTS), Malmö Högskola.Laborationen handlar om routing och är den tredje i raden av laborationer med trådlösa sensorer. Protokollet som används heter XMesh och är tillverkat av Crossbow Technology. Ambitionen är att laborationen ska leda till förståelse för hur routing uppstår i trådlösa sensornätverk, med avseende på parametrar och andra faktorer som påverkar nodernas val av rutt.Examensarbetet resulterade i en laborationshandledning med tre tillhörande bilagor. / Over the recent years, research in Wireless Sensor Networks (WSNs) has increased significantly. This is mainly because of the potential advantages that this new technology provides.We have upon request from Ivan Kruzela taken on the task of creating a lab, which is supposed to be included in the second year of the Bachelors’ of Computer and Telecom Engineering at the Centre for Technology Studies (CTS), Malmoe University. The lab deals with routing and the protocol used is named XMesh and is created by Crossbow Technology. The ambition is that the lab will lead to an understanding of how routing occurs in wireless sensor networks, regarding parameters and other factors that affects the routing choices of the nodes.The work resulted in the lab and three associated appendices.

In this thesis, we study the role of interdiction in the Vehicle Routing Problem (VRP), which naturally arises in humanitarian logistics and military applications. We assume that in a general network, each arc has a chance to be interdicted. When interdiction happens, the vehicle traveling on this arc is lost or blocked and thus unable to continue the trip. We model the occurrence of interdiction as a given probability and consider the multi-period expected delivery. Our objective is to minimize the total travel cost or to maximize the demand fulfillment, depending on the supply quantity. This problem is called the Vehicle Routing Problem with Interdiction (VRPI). We first prove that the proposed VRPI problems are NP-hard. Then we show some key analytical properties pertaining to the optimal solutions of these problems. Most importantly, we examine Dror and Trudeau's property applied to our problem setting. Finally, we present efficient heuristic algorithms to solve these problems and show the effectiveness through numerical studies. / Thesis / Master of Science (MSc)

<p> Global routing in VLSI (very large scale integration) design is one of the most challenging discrete optimization problems in computational theory and practice. In this thesis, we present a polynomial time approximation algorithm for the global routing problem based on an integer programming formulation. The algorithm features a theoretical approximation bound, while ensuring all the routing demands are concurrently satisfied.</p> <p> We provide both a serial and a parallel implementation, as well as develop several heuristics to improve the quality of the solution and reduce running time. Our computational tests on a well-known benchmark set show that, combined with certain heuristics, our new algorithms perform very well compared with other integer programming approaches.</p> / Thesis / Master of Science (MSc)

This thesis studies multicasting high bandwidth media traffic in wireless mesh networks (WMNs). Traditional multicast methods use a single multicast tree to reach all destinations, and adapt the multicast rate to the destination with the worst path quality. This approach does not fully utilize the network resources nor distinguish the quality of service (QoS) requirements of different users. It also penalizes the users having better path quality and requiring higher QoS. In multi-hop transmissions, the end-to-end transmission rate is limited by the link with the worst transmission conditions. This makes it difficult to multicast high-bandwidth media traffic with good quality. Using multiple description coding (MDC), the source traffic can be split into multiple sub-streams, referred to as descriptions, each of which requires a much lower bandwidth and can be transmitted along separate paths. In this thesis, we study routing and QoS provisioning jointly for multicasting multiple description (MD) encoded media traffic in WMNs. Routing for the multiple descriptions is jointly studied, while considering the channel quality of different links in the network and QoS at individual destinations. The work in this thesis is divided into two parts. The first part (Chapters 3 and 4) considers balanced descriptions, each of which contributes equally to the quality of the recovered media at a destination, and we study the problem of power efficient multicasting for the MD-encoded media traffic in WMNs. In Chapter 3, single-hop transmissions are considered. That is, the access points (APs) that store the source traffic communicate with the destination nodes directly. We study two problems jointly, description assignments and power allocations. The former is to assign a description for each AP to transmit, and the latter is to allocate the transmission power for the APs. Different power efficiency objectives are considered, subject to satisfying the QoS requirements of the destination nodes. For each objective, an optimization problem is formulated and heuristic solutions are proposed. Chapter 4 extends the work to multi-hop transmissions, where relay stations (RSs) are available to forward the traffic from the APs to the destinations. We consider two different routing structures based on whether an RS is allowed to forward more than one description. The objective is to minimize the total transmission power of the APs and the RSs in the network, subject to the QoS requirements of the destinations. An optimum problem is formulated and then translated to an integer and linear programming problem, and a centralized scheme with much lower complexity is proposed. Following that, a distributed scheme, referred to as minimum weight k-path scheme, is proposed, which builds one multicast tree for each description. By permitting only neighboring nodes to exchange related information, the scheme allows each node to find its best parent node based on the additional transmission power needed to establish the link. The second part (Chapter 5) of the thesis considers unbalanced descriptions. Routing for the multiple descriptions is jointly considered with application layer performance, so that the maximum distortion of recovered media at the destinations is minimized. An optimization problem is first formulated, and a centralized scheme with lower complexity is proposed. The centralized scheme first finds a set of candidate paths for each destination based on a predefined set of criteria, then it iteratively expands the multicast trees by only merging the paths that minimize the maximum distortion for all destinations. A distributed scheme is also proposed by modifying the minimum weight k-path scheme. In the modified scheme, each RS makes a local decision to join different multicast trees based on the expected distortion among its connected downstream nodes. The proposed multicasting schemes require much lower implementation complexity, compared to the optimum solutions. The centralized scheme is more suitable for small size networks, and achieves close-to-optimum performance for a wide range of parameter settings. The distributed scheme only requires neighboring nodes to exchange information, and can be implemented to networks with a relatively large number of APs, RSs, and destination nodes. / Thesis / Doctor of Philosophy (PhD)

We propose solution strategies for hard Mixed Integer Programming (MIP) problems, with a focus on distributed parallel MIP optimization. Although our proposals are inspired by the Less-than-truckload (LTL) freight routing problem, they are more generally applicable to hard MIPs from other domains. We start by developing an Integer Programming model for the Less-than-truckload (LTL) freight routing problem, and present a novel heuristic for solving the model in a reasonable amount of time on large LTL networks. Next, we identify some adaptations to MIP branching strategies that are useful for achieving improved scaling upon distribution when the LTL routing problem (or other hard MIPs) are solved using parallel MIP optimization. Recognizing that our model represents a pseudo-Boolean optimization problem (PBO), we leverage solution techniques used by PBO solvers to develop a CPLEX based look-ahead solver for LTL routing and other PBO problems. Our focus once again is on achieving improved scaling upon distribution. We also analyze a technique for implementing subtree parallelism during distributed MIP optimization. We believe that our proposals represent a significant step towards solving big-data driven optimization problems (such as the LTL routing problem) in a more efficient manner. / Thesis / Doctor of Philosophy (PhD) / Less-than-truckload (LTL) freight transportation is a vital part of Canada's economy, with revenues running into billions of dollars and a cascading impact on many other industries. LTL operators often have to deal with large volumes of shipments, unexpected changes in traffic conditions, and uncertainty in demand patterns. In an industry that already has low profit margins, it is therefore vitally important to make good routing decisions without expending a lot of time. The optimization of such LTL freight networks often results in complex big-data driven optimization problems. In addition to the challenge of finding optimal solutions for these problems, analysts often have to deal with the complexities of big-data driven inputs. In this thesis we develop several solution strategies for solving the LTL freight routing problem including an exact model, novel heuristics, and techniques for solving the problem efficiently on a cluster of computers. Although the techniques we develop are inspired by LTL routing, they are more generally applicable for solving big-data driven optimization problems from other domains. Experiments conducted over the years in consultation with industry experts indicate that our proposals can significantly improve solution quality and reduce time to solution. Furthermore, our proposals open up interesting avenues for future research.

Indiana University-Purdue University Indianapolis (IUPUI) / During recent years, the field of computer vision has evolved rapidly. Convolutional Neural Networks (CNNs) have become the chosen default for implementing computer vision tasks. The popularity is based on how the CNNs have successfully performed the well-known computer vision tasks such as image annotation, instance segmentation, and others with promising outcomes. However, CNNs have their caveats and need further research to turn them into reliable machine learning algorithms. The disadvantages of CNNs become more evident as the approach to breaking down an input image becomes apparent. Convolutional neural networks group blobs of pixels to identify objects in a given image. Such a technique makes CNNs incapable of breaking down the input images into sub-parts, which could distinguish the orientation and transformation of objects and their parts. The functions in a CNN are competent at learning only the shift-invariant features of the object in an image. The discussed limitations provides researchers and developers a purpose for further enhancing an effective algorithm for computer vision. The opportunity to improve is explored by several distinct approaches, each tackling a unique set of issues in the convolutional neural network’s architecture. The Capsule Network (CapsNet) which brings an innovative approach to resolve issues pertaining to affine transformations by sharing transformation matrices between the different levels of capsules. While, the Residual Network (ResNet) introduced skip connections which allows deeper networks to be more powerful and solves vanishing gradient problem. The motivation of these fusion of these advantageous ideas of CapsNet and ResNet with Squeeze and Excite (SE) Block from Squeeze and Excite Network, this research work presents SE-Residual Capsule Network (SE-RCN), an efficient neural network model. The proposed model, replaces the traditional convolutional layer of CapsNet with skip connections and SE Block to lower the complexity of the CapsNet. The performance of the model is demonstrated on the well known datasets like MNIST and CIFAR-10 and a substantial reduction in the number of training parameters is observed in comparison to similar neural networks. The proposed SE-RCN produces 6.37 Million parameters with an accuracy of 99.71% on the MNIST dataset and on CIFAR-10 dataset it produces 10.55 Million parameters with 83.86% accuracy.