Given the potential benefits offered by wireless sensor networks(WSNs), they are becoming an appealing technology for process,manufacturing, and industrial control applications. In thisthesis, we propose a novel approach to WSN protocol design forcontrol applications. The protocols are designed to minimize theenergy consumption of the network, while meeting reliability andpacket delay requirements. The parameters of the protocol areselected by solving a constrained optimization problem, where theobjective is to minimize the energy consumption and theconstraints are the probability of successful packet reception andthe communication delay. The proposed design methodology allowsone to perform a systematic tradeoff between the controlrequirements of the application and the network energyconsumption. An important step in the design process is thedevelopment of analytical expressions of the performanceindicators. We apply the proposed approach to optimize the networkfor various communication protocols. In Paper A, we present an adaptive IEEE 802.15.4 for energyefficient, reliable, and low latency packet transmission. Thebackoff mechanisms and retry limits of the standard are adapted tothe estimated channel conditions. Numerical results show that theproposed protocol enhancement is efficient and ensures a longerlifetime of the network under different conditions. Furthermore,we investigate the robustness and sensitivity of the protocol topossible errors during the estimation process.   In Paper B, we investigate the design and optimization ofduty-cycled WSNs with preamble sampling over IEEE 802.15.4. Theanalytical expressions of performance indicators are developed andused to optimize the duty-cycle of the nodes to minimize energyconsumption while ensuring low latency and reliable packettransmissions. The optimization results in a significant reductionof the energy consumption compared to existing solutions. The cross-layer protocol called Breath is proposed in Paper C. Theprotocol is suitable for control applications by using theconstrained optimization framework proposed in the thesis. It isbased on randomized routing, CSMA/CA MAC, and duty-cycling. Theprotocol is implemented and experimentally evaluated on a testbed,and it is compared with a standard IEEE 802.15.4 solution. Breathexhibits a good distribution of the work load among the networknodes, and ensures a long network lifetime. / <p>Korr av felaktigt ISBN; 978-91-7415-441-5</p>

The solar wind interaction with the magnetosphere-ionosphere system continuously drives plasma convection in the polar regions of the ionosphere. The flow velocity and the shape of the convection pattern are closely dependent on the interplanetary conditions, in particular the direction of the interplanetary magnetic field (IMF). The main driver of the system is considered to be magnetic reconnection between the IMF and the terrestrial field, a process that is most efficient during southward IMF when the magnetic fields at the dayside magnetopause are anti-parallell, and less efficient but still present when the IMF is northward. Additional driving may be caused by waves at the magnetopause flanks, where viscous effects can lead to an energy, momentum and plasma exchange across the boundary. In this work, we make use of the characteristics of the ionospheric convection and particle precipitation to investigate the nature of the driving dynamos, and large statistical data sets for steady solar wind conditions are used to derive the general behavior of the driving processes and their dependence on interplanetary conditions. The results show that the primary dynamo responsible for the convection in the boundary layer is closely dependent on the sign of the IMF Bz component, the average potential over the boundary layer region increases from &lt;1 kV for steady southward IMF up to the order of 10kV for strictly northward conditions with reconnection poleward of the cusps, whereas the magnitude of magnetic field only has a minor influence at most. This could for example indicate that the magnetopause is more unstable to Kelvin-Helmholtz waves for parallel rather than anti-parallel magnetic fields, or that magnetic reconnection on the dayside suppresses other processes. It is well known that the ionospheric potential drop saturates during strong driving conditions and southward IMF. The results presented here also show that the same phenomenon occurs when the IMF is northward. This gives additional information on the physics governing the solar wind-magnetosphere-ionosphere interaction, and may impose new restrictions on the theories explaining the saturation.

The importance of electricity in everyday life and demands to improve the reliability of distribution systems force utilities to operate and plan their networks in a more secure and economical manner. With higher demands on reliability from both customers and regulators, a big pressure has been put on the security of electricity supply which is considered as a fundamental requirement for modern societies. Thus, efficient solutions for reliability and security of supply improvements are not just of increasing interest, but also have significant socio-economic relevance. Distribution system planning (DSP) is one of the major activities of distribution utilities to deal with reliability enhancement. This thesis deals with developing optimization algorithms, which aim is to min- imize customer interruption costs, and thus maximize the reliability of the system. This is implemented either by decreasing customer interruption duration, frequency of customer interruptions or both. The algorithms are applied on a single or multi- ple DSP problems. Mixed-integer programming has been used as an optimization approach. It has been shown that solving and optimizing each one of the DSP problems contributes greatly to the reliability improvement, but brings certain challenges. Moreover, applying algorithms on multiple and integrated DSP problems together leads to even bigger complexity and burdensome. However, going toward this inte- grated approach results in a more appropriate and realistic DSP model. The idea behind the optimization is to achieve balance between reliability and the means to achieve this reliability. It is a decision making process, i.e. a trade-off between physical and pricing dimension of security of supply. / <p>QC 20200925</p>

Wireless Sensor Networks have become popular during the last years. The introduction ofIPv6 which broadened the address space available, IEEE802.15.4 and adaption layers such as6loWPAN have allowed the intercommunication of small devices. These networks are usefulin many scenarios such as civil monitoring, mining, battle eld operations, as well as consumerproducts. Hence, practical security solutions for the intercommunication must be provided,ensuring privacy, authenticity, integrity and data freshness. In most cases, WSN nodes arenot tamper-proof and have very limited available resources and capabilities which makes PKIcurrently not attractive for this environment. At the same time, key pre-distribution providetoo low security for most applications. Therefore, the communication bootstrapping or thekey generation and distribution problem is an important concern to be addressed with theadditional di culty of the constrained capabilities of WSN nodes. In this thesis, a solution tothis problem is described. It makes use of ECDH and the curve K-163 for key exchange, AESCCM-128 for symmetric encryption to lower the processing overhead and a partial challengesolving chain as well as a TAS to provide strong authentication. Several hash functions havebeen analysed as well as several random number generating approaches. At the same time, inorder to t the key generation and distribution algorithms together with the regular sensoroperation, code optimizations were carried out on the cryptographic library Relic-Toolkit,reducing the memory footprint in 4KB; code reductions on Contiki OS allowed it to run usingonly 18KB of ash; and the peripheral drivers developed for the CC430 reduced as well thecomputation time. The solution allows to generate and distribute the keys in situ and isproved to be resilient to most adversaries while taking into account scalability, portability,energy consumption and making it suitable for consumer applications.

In this thesis, the problem of finding an optimal training sequence for estimating a MIMO flat fading channel with spatially and temporally correlated Gaussian noise is considered. The methods analyzed tailor the training sequence not only according to the known statistical CSI but also to the specific purpose the channel estimate will fulfill. The task of obtaining the optimal training sequence is formulated in two different ways, either guaranteeing a specific performance or setting a maximum training power budget. Two different applications are considered, the ZF precoder and the MMSE equalizer. The performance of the training sequence obtained by minimizing a metric that is representative for this applications is compared to using the training sequence that minimizes the channel estimate mean square error. Additionally, since some approximations are required to solve the optimization problem when using the application-oriented metrics, the impact of these is analyzed. Two different approximations that allow convexification and lead to SDP formulations are considered for each problem. The two approximations and problem formulations are analyzed in terms of performance, training power and outage probability. The SDP formulations are then compared to the solutions provided by builtin functions in MATLAB that converge to a minimum in order to obtain information about how far from optimal the solutions obtained from the SDP are.

Reliability of power system depends on the up to date knowledge of the system state for operation and control. Shifting from large conventional production units to small and/or renewable DG connected in the distribution network means more control and monitoring system require for the Distributed System operator caused by active generation and reactive power consumption by DG. Therefore it is interesting to explore concepts in fast and scalable topology processors for monitoring and controlling applications such as state estimation, OPF and static and dynamic stability assessment in electrical distribution network the need is evident to validate with meshed network to analyze the overall performance of the proposed methodology\Decentralized Topology Inference of Electrical Distribution Networks". The topology inference processor is require minimal prior knowledge of electrical network structure by taking a series of time-stamped process measurements from each bays of each substation in the network and distinguished between connected and unconnected bays. This master thesis project has implemented an IEEE reference electric power distribution network in Simulink platform , integrating the reference electrical network with the Java-based multi agent topology inference application as well as having investigated. This project has included work in the real time simulation of a standard IEEE reference distribution network, OPC server interfacing between reference model and the topology inference application, testing and analysis of the application. The reference model is selected to provide a sucient case to analyses and validate the methodology.

This master thesis report studies the possibility to modify a fast video encoder specialized for video conferencing applications (c65), in order to make it more suitable for encoding of general video content. The modications includes coding with hierarchical B pictures and various other operations like rate distortion optimizations and improved motion estimation. Results show that the hierarchical coding and the other modifications contribute positively to the coding efficiency of the encoder. Considerations about the work results are given, regarding the new encoding scheme (in terms of coding delay and memory requirements) and a comparison of coding efficiency for different coding structures. Finally, ideas for future works are presented.

For the time being, Remote Sensing automatized techniques are conventionally designed to be used exclusively on data captured by aparticular sensor system. This convention was only adopted after evidence suggested that, in the field, algorithms that yield great resultson data from one specific satellite or sensor, tend to underachieve on data from similar sensors. With this effect in mind, we will refer to remote sensing imagery as heterogeneous.There have been attempts to compensate every effect on the data and obtain the underlying physical property that carries the information, the ground reflectance. Because of their improvement of the informative value of each image, some of them have even been standardized as common preprocessing methods. However, these techniques generally require further knowledge on certain atmospheric properties at the time the data was captured. This information is generally not available and has to be estimated or guessed by experts, avery time consuming, inaccurate and expensive task. Moreover, even if the results do improve in each of the treated images, a significant decrease of their heterogeneity is not achieved. There have been more automatized proposals to treat the data in the literature, which have been broadly named RRN (Relative Radiometric Normalization) algorithms. These consider the problem of heterogeneity itself and use properties strictly related to the statistics of remote sensing imagery to solve it. In this master thesis, an automatic algorithm to reduce heterogeneity in generic imagery is designed, characterized and evaluated through crossed classification results on remote sensing imagery.

Buildings are among the largest consumers of energy in the world. A significant part of this energy can be attributed to Heating, Ventilation and Air Conditioning (HVAC) systems, which play an important role in maintaining acceptable thermal and air quality conditions in common building. For this reason, improving energy eciency in buildings is today a primary objective for the building industry, as well as for the society in general. However, in order to successfully control buildings, control systems must continuously adapt the operation of the building to various uncertainties (external air temperature, occupants' activities, etc.) while making sure that energy eciency does not compromise occupant's comfort and well-being. Several promising approaches have been proposed; among them, Model Predictive Control has received particular attention, since it can naturally achieve systematic integration of several factors, such as weather forecasts, occupancy predictions, comfort ranges and actuation constraints. This advanced technique has been shown to bring signicant improvements in energy savings. Model Predictive Control employs a model of the system and solves an on-line optimization problem to obtain optimal control inputs. The on-line computation, as well as the modelling eort, can lead to diculties in the practical integration into a building management system. To cope with this problem, another possibility is to obtain o-line the optimal control prole as a piecewise ane and continuous function of the initial state. By doing so, the computation associated with Model Predictive Control becomes a simple function evaluation, which can be performed eciently on a simple and cheap hardware. In this thesis, an implicit and an explicit formulation of Model Predictive Control for HVAC systems are developed and compared, showing the practical advantages of the explicit formulation.

Performance of many P2P systems depends on the ability  to construct a ran- dom overlay network among the nodes. Current state-of-the-art techniques for constructing random overlays have an implicit  requirement that any two nodes in the system should always be able to communicate and establish a link be- tween them.  However, this is not the case in some of the environments where distributed systems are required to be deployed,  e.g, Decentralized Online So- cial Networks, Wireless networks, or networks with limited connectivity because of NATs/firewalls,  etc. In such restricted networks, every node is able to com- municate with only a predefined set of nodes and thus, the existing solutions for constructing random overlays are not applicable.In this thesis we propose a gossip based peer sampling service capable of running on top of such restricted networks and producing an on-the-fly random overlay.  The service provides ev- ery participating node with a set of uniform random nodes from the network, as well as efficient routing paths for reaching those nodes via the restricted net- work. We perform extensive experiments on four real-world networks and show that  the resulting overlays rapidly converge to random overlays. The results also exhibit that the constructed random overlays have self healing behaviour under churn and catastrophic failures.