Movement-Efficient Sensor Deployment in Wireless Sensor Networks With Limited Communication Range.

We study a mobile wireless sensor network (MWSN) consisting of multiple mobile sensors or robots. Three key factors in MWSNs, sensing quality, energy consumption, and connectivity, have attracted plenty of attention, but the interaction of these factors is not well studied. To take all the three factors into consideration, we model the sensor deployment problem as a constrained source coding problem. %, which can be applied to different coverage tasks, such as area coverage, target coverage, and barrier coverage. Our goal is to find an optimal sensor deployment (or relocation) to optimize the sensing quality with a limited communication range and a specific network lifetime constraint. We derive necessary conditions for the optimal sensor deployment in both homogeneous and heterogeneous MWSNs. According to our derivation, some sensors are idle in the optimal deployment of heterogeneous MWSNs. Using these necessary conditions, we design both centralized and distributed algorithms to provide a flexible and explicit trade-off between sensing uncertainty and network lifetime. The proposed algorithms are successfully extended to more applications, such as area coverage and target coverage, via properly selected density functions. Simulation results show that our algorithms outperform the existing relocation algorithms

Fast Biconnectivity Restoration in Multi-Robot Systems for Robust Communication Maintenance

Maintaining a robust communication network plays an important role in the success of a multi-robot team jointly performing an optimization task. A key characteristic of a robust multi-robot system is the ability to repair the communication topology itself in the case of robot failure. In this paper, we focus on the Fast Biconnectivity Restoration (FBR) problem, which aims to repair a connected network to make it biconnected as fast as possible, where a biconnected network is a communication topology that cannot be disconnected by removing one node. We develop a Quadratically Constrained Program (QCP) formulation of the FBR problem, which provides a way to optimally solve the problem. We also propose an approximation algorithm for the FBR problem based on graph theory. By conducting empirical studies, we demonstrate that our proposed approximation algorithm performs close to the optimal while significantly outperforming the existing solutions

LOCALIZED MOVEMENT CONTROL CONNECTIVITY RESTORATION ALGORITHMS FOR WIRELESS SENSOR AND ACTOR NETWORKS

Wireless Sensor and Actor Networks (WSANs) are gaining an increased interest because of their suitability for mission-critical applications that require autonomous and intelligent interaction with the environment. Hazardous application environments such as forest fire monitoring, disaster management, search and rescue, homeland security, battlefield reconnaissance, etc. make actors susceptible to physical damage. Failure of a critical (i.e. cut-vertex) actor partitions the inter-actor network into disjointed segments while leaving a coverage hole. Maintaining inter-actor connectivity is extremely important in mission-critical applications of WSANs where actors have to quickly plan an optimal coordinated response to detected events. Some proactive approaches pursued in the literature deploy redundant nodes to provide fault tolerance; however, this necessitates a large actor count that leads to higher cost and becomes impractical. On the other hand, the harsh environment strictly prohibits an external intervention to replace a failed node. Meanwhile, reactive approaches might not be suitable for time-sensitive applications. The autonomous and unattended nature of WSANs necessitates a self-healing and agile recovery process that involves existing actors to mend the severed inter-actor connectivity by reconfiguring the topology. Moreover, though the possibility of simultaneous multiple actor failure is rare, it may be precipitated by a hostile environment and disastrous events. With only localized information, recovery from such failures is extremely challenging. Furthermore, some applications may impose application-level constraints while recovering from a node failure. In this dissertation, we address the challenging connectivity restoration problem while maintaining minimal network state information. We have exploited the controlled movement of existing (internal) actors to restore the lost connectivity while minimizing the impact on coverage. We have pursued distributed greedy heuristics. This dissertation presents four novel approaches for recovering from node failure. In the first approach, volunteer actors exploit their partially utilized transmission power and reposition themselves in such a way that the connectivity is restored. The second approach identifies critical actors in advance, designates them preferably as noncritical backup nodes that replace the failed primary if such contingency arises in the future. In the third approach, we design a distributed algorithm that recovers from a special case of multiple simultaneous failures. The fourth approach factors in application-level constraints on the mobility of actors while recovering from node failure and strives to minimize the impact of critical node failure on coverage and connectivity. The performance of proposed approaches is analyzed and validated through extensive simulations. Simulation results confirm the effectiveness of proposed approaches that outperform the best contemporary schemes found in literature

Multi-robot deployment planning in communication-constrained environments

A lo largo de los últimos años se ha podido observar el aumento del uso de equipos de robots en tareas en las cuales es imposible o poco eficiente la intervención de los humanos, e incluso que implica un cierto grado de riesgo para una persona. Por ejemplo, monitorización de entornos de difícil acceso, como podrían ser túneles, minas, etc. Éste es el tema en el que se ha enfocado el trabajo realizado durante esta tesis: la planificación del despliegue de un equipo de agentes para la monitorización de entornos.La misión de los agentes es alcanzar unas localizaciones de interés y transmitirle la información observada a una estación base estática. Ante la ausencia de una infraestructura de comunicaciones, una transmisión directa a la base es imposible. Por tanto, los agentes se deben coordinar de manera autónoma, de modo que algunos de ellos alcancen los objetivos y otros realicen la función de repetidor para retransmitir la información.Nos hemos centrado en dos líneas de investigación principales, relacionadas con dos maneras del envío de la información a la estación base. En el primer enfoque, los agentes deben mantener un enlace de comunicación con la base en el momento de alcanzar los objetivos. Con el fin de, por ejemplo, poder interactuar desde la base con un robot que ha alcanzado el objetivo. Para ello hemos desarrollado un método que obtiene las posiciones óptimas para los agentes utilizados a modo de repetidor. A continuación, hemos implementado un método de planificación de caminos de modo que los agentes pudiesen navegar el máximo tiempo posible dentro de zonas con señal. Empleando conjuntamente ambos métodos, los agentes extienden el área de cobertura de la estación base, estableciendo un enlace de comunicación desde la misma hasta los objetivos marcados.Utilizando este método, el equipo es capaz de lidiar con variaciones del entorno si la comunicación entre los agentes no se pierde. Sin embargo, los eventos tan comunes e irrelevantes para los seres humanos, como el simple cierre de una puerta, pueden llegar a ser críticos para el equipo de robots. Ya que esto podría interrumpir la comunicación entre el equipo. Por ello, hemos propuesto un método distribuido para que el equipo sea capaz de reconectarse, formando una cadena hacia un objetivo, en escenarios donde haya variaciones con respecto al mapa inicial que poseían los robots.La segunda parte de la presente tesis se ha centrado en misiones de recopilación de datos de un entorno. Aquí la comunicación con la estación base, en el instante de alcanzar un objetivo, no es necesaria y a menudo imposible. Por tanto, en este tipo de escenarios, es más eficiente que algunos agentes, llamados trabajadores, recopilen datos del entorno, y otros, denominados colectores, reúnan la información de los que trabajan para periódicamente retransmitirla a la base. De este modo tan solo los colectores realizan largos viajes a la estación base, mientras que los trabajadores emplean la mayor parte de su tiempo exclusivamente a la recopilación de datos.Primero, hemos desarrollado dos métodos para la planificación de caminos para la sincronización entre los trabajadores y colectores. El primero, muestrea el espacio de manera aleatoria, para obtener una solución lo más rápido posible. El segundo, usando FMM, es más lento, pero obtiene soluciones óptimas.Finalmente, hemos propuesto una técnica global para la misión de recopilación de datos. Este método consiste en: encontrar el mejor balance entre la cantidad de trabajadores y colectores, la mejor división del escenario en áreas de trabajo para los trabajadores, la asociación de los trabajadores para transmitir los datos recopilados a los colectores o directamente a la estación base, así como los caminos de los colectores. El método propuesto trata de encontrar la mejor solución con el fin de entregar la mayor cantidad de datos y que el tiempo de "refresco" de los mismos sea el menor posible.<br /

Foundations of coverage algorithms in autonomic mobile sensor networks

Drones are poised to become a prominent focus of advances in the near future as hardware platforms manufactured via mass production become accessible to consumers in higher quantities at lower costs than ever before. As more ways to utilize such devices become more popular, algorithms for directing the activities of mobile sensors must expand in order to automate their work. This work explores algorithms used to direct the behavior of networks of autonomous mobile sensors, and in particular how such networks can operate to achieve coverage of a field using mobility. We focus special attention to the way limited mobility affects the performance (and other factors) of algorithms traditionally applied to area coverage and event detection problems. Strategies for maximizing event detection and minimizing detection delay as mobile sensors with limited mobility are explored in the first part of this work. Next we examine exploratory coverage, a new way of analyzing sensor coverage, concerned more with covering each part of the coverage field once, while minimizing mobility required to achieve this level of 1-coverage. This analysis is contained in the second part of this work. Extending the analysis of mobility, we next strive to explore the novel topic of disabled mobility in mobile sensors, and how algorithms might react to increase effectiveness given that some sensors have lost mobility while retaining other senses. This work analyzes algorithm effectiveness in light of disabled mobility, demonstrates how this particular failure mode impacts common coverage algorithms, and presents ways to adjust algorithms to mitigate performance losses. --Abstract, page iv