Wireless sensor networks (WSNs) have been used in many applications by deploying tiny and stationary sensors. In recent years, a lot of studies proposed to introduce mobility capability to sensor nodes in order to exploit the advantages of mobility, particularly to restore connectivity in disjoint WSNs. While the studies demonstrated various capabilities of the proposed connectivity algorithms via simulation, real node and testbed implementations were mostly lacking due to unavailability of proper mobile nodes. Since this may hinder the direct applicability of the algorithms in realistic settings, testbeds which can be constructed with low-cost and commercial-off-the-shelf (COTS) hardware are required for realistic evaluations of the connectivity restoration algorithms. In this thesis, we design a low-cost mobile sensor node called iRobotSense, by integrating iRobot Create platform with IRIS sensor. Then, a mobile sensor network (MSN) testbed of iRobotSense nodes is used to implement and evaluate a widely used connectivity restoration algorithms, namely PADRA. Furthermore, all of the previous works exploiting mobility of the nodes to achieve recovery in a partitioned network have assumed reachability of the nodes to the selected destinations via a direct path movement. However, in real-world applications, such assumption makes the schemes impractical in case of encountering obstacles or intolerable terrains. Besides, even if direct path movement is successful, optimal energy efficiency cannot be attained by neglecting the elevation or friction of the terrain. Thus, in the recovery efforts, terrain type, elevation as well as the obstacles should be taken into account. In this thesis, we re-design an existing connectivity restoration approach in disjoint MSNs to fit these requirements and evaluate the performance issues when realistic terrains are assumed. Rather than following a direct path, movement trajectory is determined based on a path planning algorithm which considers the risk and elevation of terrain sections to be visited while avoiding obstacles and highly elevated terrain sections.

This thesis presents contributions to node localization in mobile sensor networks. Considering the importance of localization algorithms in identifying the location of an event in order to better determine the action to be taken by the user, a new localization algorithm to operate in mobile networks was designed, assuming an uncontrollable movement of the nodes. This algorithm, which we call CentroidM, has the Centroid method as a stand. Positive features of the Centroid algorithm were kept while their limitations due to the dynamic characteristcs of the network movement were mitigated. Besides the concern regarding the accuracy of the method, the power consumption of the algorithm was addressed too. The design of an dedicated integrated circuit to deal with the localization task in a mobile network is justified given the high activity of such a subsystem in the given scenario. This way, the focus of this Thesis relies on a low power localization algorithm for mobile networks, with characteristics to allow raising calculated coordinates accuracy in both static and mobile scenarios. The results show that the CentroidM is 30% more accurate than the Centroid on a static scenario, and 40% when we consider a node movement without actuators. These benefits have some computational overhead, increasing 2.76 times the time spent by the CentroidM to run a localization process. However, simulation results showed it is possible to remove such overhead and still keep the achieved estimation gains near 10%. The implementation of the localization method was accomplished through an integrated circuit, which reduced the energy expended during the computation of the coordinates by an order of magnitude when comparing with the execution of the algorithm in a low power commercial microcontroller. / Esta Tese apresenta contribuições ao processo de localização de nodos em redes de sensores móveis. Considerando a importância de algoritmos de localização em uma rede para identificar o local do evento sob estudo e determinar a ação a ser tomada pelo usuário, é desenvolvido um novo algoritmo de localização para operar em redes de sensores móveis, focando o aspecto da mobilidade não controlada dos nodos. Este algoritmo, ao qual denominamos CentroidM, teve como base de desenvolvimento o método Centroid. Foram mantidas as características positivas do Centroid e exploradas as limitações do método para a sua execução em uma rede que considera o movimento. Além do objetivo em reduzir o erro das estimativas de posicionamento calculadas pelo algoritmo, o consumo de potência do método apresentado neste trabalho é enfatizado. O projeto de um circuito integrado dedicado que desempenhe o processo de localização em uma rede móvel é justificado pela intensa utilização de tal subsistema neste tipo de rede, além dos benefícios que um hardware dedicado traz face à redução da energia gasta nesta operação. Desta forma, o foco deste trabalho recai sobre um algoritmo de localização para redes móveis e as características desta abordagem que permitiram aumentar a precisão das estimativas de posicionamento tanto em um cenário com ausência de movimento dos nodos, quanto em outro onde o aspecto da mobilidade sem atuadores foi considerada. Os resultados obtidos nesta Tese demostraram que o CentroidM é 30% mais preciso que o Centroid para um cenário estático e 40% considerando a mobilidade da rede. O ganho alcançado em precisão teve um custo computacional que elevou em 2, 76 vezes o tempo gasto pelo CentroidM para realizar um procedimento de localização. Contudo, os resultados de simulação mostraram que é possível eliminar a sobrecarga computacional e ainda assim atingir ganhos em precisão próximos a 10%. O desenvolvimento do método de localização é complementado pela sua implementação em um circuito integrado dedicado, reduzindo a energia gasta no processo de estimativa da posição em uma ordem da magnitude face à execução do algoritmo em um microcontrolador comercial de baixo consumo.

Mobile platforms for archaeological purposes have increased in use over the last 20 years with many of the developments coming from Continental Europe. Mobile platform developments have mainly focused on one type of instrumentation, offering multiple sensors, depths of detection or frequencies. This development of mobile platforms has focused on data acquisition rates but has not considered the physical impact on the soil. The Geoscan Research Mobile Sensor Platform (MSP40) was intended to improve survey efficiency and remain a lightweight system. The platform can collect two earth resistance configurations that show directional variation of the current flow through soil. Additional sensors were integrated on to the square frame of the hand-pulled cart to record simultaneous fluxgate gradiometer data and a microtopographic surveys. Ground based geophysical investigation will always have a physical impact on a site. The MSP40 is no exception but careful selection of wheel types and the lightweight frame limit the damage compared to many mobile arrays. The MSP40 has been tested on a number of different soils at various times of the year with encouraging results; however issues with overcoming the contact resistance of electrodes remain. The continuous collection rate and combination of techniques means a slight drop in data quality is inevitable. However the increased data density, multiple-sensors and improved rate of collection offset reductions in data quality. The research has shown that the MSP40 can perform low impact rapid site assessments on ¿vulnerable¿ sites, whilst maximising the information gained from a single traverse. / AHRC, Geoscan Research

Human activity recognition (HAR) is a growing area within machine learning as the possible applications are vast, especially with the growing amount of collectable sensor data as Internet of Things-devices are becoming more accessible. This project aims to contribute to HAR by developing two supervised machine learning algorithms that are able to distinguish between four different human activities. We collected data from the tri-axial accelerometer in two different smartphones while doing these activities, and put together a dataset. The algorithms that were used was a convolutional neural network (CNN) and a support vector machine (SVM), and they were applied to the dataset separately. The results show that it is possible to accurately classify the activities using the algorithms and that a short time window of 3 seconds is enough to classify the activities with an accuracy of over 99% with both algorithms. The SVM outperformed the CNN slightly. We also discuss the result and continuations of this study. / Mlinsklig aktivitetsigenkanning (HAR) lir ett vlixande omrade inom maskininllirning da de mojliga applikationerna lir stora, speciellt med den vlixande mangd insamlingsbar sensordata da Internet of Things-enheter blir mer atkomliga. Detta projekt siktar pa att bidra till HAR genom att utveckla tva algoritmer som kan urskilja mellan fyra olika mlinskliga aktiviteter. Vi samlade in data fran den treaxlade accelerometern i tva olika smarta telefoner medans dessa aktiviteter utfordes, och satte ihop ett dataset. Algoritmerna som anvlindes var ett faltande neuralt nlitverk och en stodvektormaskin, och de applicerades separat pa datasetet. Resultaten visar att det lir mojligt att med slikerhet klassificera aktiviteterna genom att anvlinda dessa algoritmer och att ett kort tidsfonster med 3 sekunder av data lir tillrlickligt for att klassificera med en slikerhet pa over 99% med bada algoritmerna. Stodvektormaskinen presterade nagot blittre an det neurala nlitverket. Vi diskuterar liven resultatet och fortsatta studier. / Kandidatexjobb i elektroteknik 2021, KTH, Stockholm

Mobile platforms for archaeological purposes have increased in use over the last 20 years with many of the developments coming from Continental Europe. Mobile platform developments have mainly focused on one type of instrumentation, offering multiple sensors, depths of detection or frequencies. This development of mobile platforms has focused on data acquisition rates but has not considered the physical impact on the soil. The Geoscan Research Mobile Sensor Platform (MSP40) was intended to improve survey efficiency and remain a lightweight system. The platform can collect two earth resistance configurations that show directional variation of the current flow through soil. Additional sensors were integrated on to the square frame of the hand-pulled cart to record simultaneous fluxgate gradiometer data and a microtopographic surveys. Ground based geophysical investigation will always have a physical impact on a site. The MSP40 is no exception but careful selection of wheel types and the lightweight frame limit the damage compared to many mobile arrays. The MSP40 has been tested on a number of different soils at various times of the year with encouraging results; however issues with overcoming the contact resistance of electrodes remain. The continuous collection rate and combination of techniques means a slight drop in data quality is inevitable. However the increased data density, multiple-sensors and improved rate of collection offset reductions in data quality. The research has shown that the MSP40 can perform low impact rapid site assessments on 'vulnerable' sites, whilst maximising the information gained from a single traverse.

In this work, connectivity graphs have been studied as models of local interactions in multi-agent robotic systems. A systematic study of the space of connectivity graphs has been done from a geometric and topological point of view. Some results on the realization of connectivity graphs in their respective configuration spaces have been given. A complexity analysis of networks, from the point of view of intrinsic structural complexity, has been given. Various topological spaces in networks, as induced from their connectivity graphs, have been recognized and put into applications, such as those concerning coverage problems in sensor networks. A framework for studying dynamic connectivity graphs has been proposed. This framework has been used for several applications that include the generation of low-complexity formations as well as collaborative beamforming in sensor networks. The theory has been verified by generating extensive simulations, with the help of software tools of computational homology and semi-definite programming. Finally, several open problems and areas of further research have been identified.

Mobile sinks are vital in many wireless sensor applications for efficient data collection, data querying, and localized sensor reprogramming. Mobile sinks prolong the lifetime of a sensor network. However, when sensor networks with mobile sinks are deployed in a hostile environment, security became a critical issue. They become exposed to varieties of malicious attacks. Thus, anti threats schemes and security services, such as mobile sink?s authentication and pairwise key establishment, are essential components for the secure operation of such networks. Due to the sensors, limited resources designing efficient security schemes with low communication overhead to secure communication links between sensors and MS (Mobile Sink) is not a trivial task. In addition to the sensors limited resources, sink mobility required frequent exchange of cryptography information between the sensors and MS each time the MS updates its location which imposes extra communication overhead on the sensors. In this dissertation, we consider a number of security schemes for WSN (wireless sensor network) with MS. The schemes offer high network?s resiliency and low communication overhead against nodes capture, MS replication and wormhole attacks. We propose two schemes based on the polynomial pool scheme for tolerating nodes capture: the probabilistic generation key pre-distribution scheme combined with polynomial pool scheme, and the Q-composite generation key scheme combined with polynomial pool scheme. The schemes ensure low communication overhead and high resiliency. For anti MS replication attack scheme, we propose the multiple polynomial pools scheme that provide much higher resiliency to MS replication attack as compared to the single polynomial pool approach. Furthermore, to improve the network resiliency against wormhole attack, two defensive mechanisms were developed according to the MS mobility type. In the first technique, MS uses controlled mobility. We investigate the problem of using a single authentication code by sensors network to verify the source of MS beacons, and then we develop a defensive approach that divide the sensor network into different authentication code?s grids. In the second technique, random mobility is used by MS. We explore the use of different communication channels available in the sensor hardware combined with polynomial pool scheme.

This thesis proposes a framework for controlling mobile robot networks based on a fluid dynamics paradigm. The approach is inspired by natural behaviors of fluids demonstrating desirable characteristics for collective robots. The underlying mathematical formalism is developed through establishing analogies between fluid bodies and multi-robot systems such that robots are modeled as fluid elements that constitute a fluid body. The governing equations of fluid dynamics are adapted to multi-robot systems and applied on control of robots. The model governs flow of a robot based on its local interactions with neighboring robots and surrounding environment. Therefore, it provides a layer of decentralized reactive control on low level behaviors, such as obstacle avoidance, deployment, and flow. These behaviors are inherent to the nature of fluids and provide emergent coordination among robots. The framework also introduces a high-level control layer that can be designed according to requirements of the particular task. Emergence of cooperation and collective behavior can be controlled in this layer via a set of parameters obtained from the mathematical description of the system in the lower layer. Validity and potential of the approach have been experimented through simulations primarily on two common collective robotic tasks / deployment and navigation. It is shown that gas-like mobile sensor networks can provide effective coverage in unknown, unstructured, and dynamically changing environments through self-spreading. On the other hand, robots can also demonstrate directional flow in navigation or path following tasks, showing that a wide range of multi-robot applications can potentially be developed using the framework.

A new class of wireless sensor networks has recently appeared due to the pervasiness of cellular phones with embedded sensors, mobile Internet connectivity, and location technologies. This mobile wireless sensor network has the potential to address large-scale societal problems and improve the people's quality of life in a better, faster and less expensive fashion than current solutions based on static wireless sensor networks. Ubiquitous Sensing is the umbrella term used in this dissertation that encompasses location-based services, human-centric, and participatory sensing applications. At the same time, ubiquitous sensing applications are bringing a new series of challenging problems. This dissertation proposes and evaluates G-Sense, for Global-Sense, an architecture that integrates mobile and static wireless sensor networks, and addresses several new problems related to location-based services, participatory sensing, and human-centric sensing applications. G-Sense features the critical point algorithms, which are specific mechanisms to reduce the power consumption by continous sensing applications in cellular phones, and reduce the amount of data generated by these applications. As ubiquitous sensing applications have the potential to gather data from many users around the globe, G-Sense introduces a peer-to-peer system to interconnect sensing servers based on the locality of the data. Finally, this dissertation proposes and evaluates a multiobjective model and a hybrid evolutionary algorithm to address the efficient deployment of static wireless sensor nodes when monitoring critical areas of interest.

<p>This dissertation presents a novel distributed optimal control (DOC) problem formulation that is applicable to multiscale dynamical systems comprised of numerous interacting systems, or agents, that together give rise to coherent macroscopic behaviors, or coarse dynamics, that can be modeled by partial differential equations (PDEs) on larger spatial and time scales. The DOC methodology seeks to obtain optimal agent state and control trajectories by representing the system's performance as an integral cost function of the macroscopic state, which is optimized subject to the agents' dynamics. The macroscopic state is identified as a time-varying probability density function to which the states of the individual agents can be mapped via a restriction operator. Optimality conditions for the DOC problem are derived analytically, and the optimal trajectories of the macroscopic state and control are computed using direct and indirect optimization algorithms. Feedback microscopic control laws are then derived from the optimal macroscopic description using a potential function approach.</p><p>The DOC approach is demonstrated numerically through benchmark multi-agent trajectory optimization problems, where large systems of agents were given the objectives of traveling to goal state distributions, avoiding obstacles, maintaining formations, and minimizing energy consumption through control. Comparisons are provided between the direct and indirect optimization techniques, as well as existing methods from the literature, and a computational complexity analysis is presented. The methodology is also applied to a track coverage optimization problem for the control of distributed networks of mobile omnidirectional sensors, where the sensors move to maximize the probability of track detection of a known distribution of mobile targets traversing a region of interest (ROI). Through extensive simulations, DOC is shown to outperform several existing sensor deployment and control strategies. Furthermore, the computation required by the DOC algorithm is proven to be far reduced compared to that of classical, direct optimal control algorithms.</p> / Dissertation