Design, analysis and implementation of a spatial-temporal, adaptive and multi-replication data centric storage framework for wireless sensor and actor networks

This PhD Thesis presents a novel framework for Data-Centric Storage(DCS) in a Wireless Sensor and Actor Network(WSAN) that enables the use of a multiple set of data replication nodes, which also change over the time. This allows reducing the average network traffic and energy consumption by adapting the number of replicas to applications’ traffic, while balancing energy burdens by varying their location. To that end we propose and validate a simple model to determine the optimal number of replicas, in terms of minimizing average traffic/energy consumption, from the measured applications’ production and consumption traffic. Simple mechanisms are proposed to decide when the current set of replication nodes should be changed, to enable new applications and sensor nodes to efficiently bootstrap into a working sensor network, to recover from failing nodes, and to adapt to changing conditions. Extensive simulations demonstrate that our approach can extend a sensor network’s lifetime by at least a 60%, and up to a factor of 10x depending on the lifetime criterion being considered. Furthermore, we have implemented our framework in a real testbed with 20 motes that validates in a small scenario those results obtained via simulation for large WSANs. Finally, we present a heuristic that adapts our framework to scenarios with spatially heterogeneous consumption and/or production traffic distributions providing an effective reduction in the overall traffic, as well as reducing the number of nodes that die over the time. --------------------------------------------------------------------------------------------------------------------------------------------Esta Tesis se enmarca en el campo de la redes de sensores y actuadores inalámbricas. Para este tipo de redes existe un sistema de almacenamiento y entrega de información totalmente distribuido denominado Data-Centric Storage (DCS). En dicho sistema se selecciona un nodo en la red para almacenar toda la información relativa a una aplicación o tipo de evento. Dicha elección se realiza mediante el uso de una función de hash que, usando como argumento el propio nombre de la aplicación (o tipo de evento), devuelve el identificador (e.g. coordenadas geográficas, identificador de nodo, etc) del nodo responsable de almacenar toda la información que deesa aplicación (o tipo de evento). El uso de un único nodo para almacenar todos los datos de un mismo tipo generados en la red tiende a generar un punto de saturación en la red (especialmente en términos energéticos) ya que una gran cantidad de tráfico es encaminada hacia un único punto. De hecho, no sólo el nodo seleccionado como nodo de almacenamiento, sino también todos aquellos que le rodean, experimentan un mayor gasto de recursos ya que son los encargados de rutar los mensajes hacia el nodo de almacenamiento. Este problema ha dado lugar a sistemas que utilizan multiples réplicas para aliviar la generacióon de un punto de congestión y elevado consumo energético en la red. Situando varios puntos de almacenamiento para un tipo de evento dado, es posible aliviar la congestión de un único punto. Sin embargo la generación de nuevas réplicas tiene un coste asociado, y por tanto existe un número de réplicas óptimo que minimiza el tráfico total en la red, que a su vez tiene un impacto directo en la reducción del consumo energético y la extensión del tiempo de vida de la red. En esta Tesis se proponen dos esquemas de replicación para redes de sensores que usan DCS como sistema de almacenamiento distribuido. Para ambos casos se han desarrollado modelos matemáticos que permiten conocer el número óptimo de réplicas que deben ser utilizadas (para minimizar el tráfico total en la red) en función de la intensidad de producción y consumo de un tipo de evento. El primer mecanismo, denominado Quadratic Adaptive Replication (QAR), propone el uso de una estructura mallada para la colocación de las réplicas. QAR mejora trabajos previos que ya proponían un esquema de replicación en grid, ya que es más adaptativo a las condiciones de tráfico en la red. El segundo mecanismo simplemente genera localizaciones aleatorias donde situar las replicas. Sorprendentemente, esta Tesis demuestra que es el mejor sistema de replicación, incluso por delante de QAR, ya que es el más adaptativo a las condiciones de tráfico. Además, tiene la gran ventaja de que es extremadamente simple y puede aplicarse en redes irregulares o que utlizan diferentes protocolos de enrutamiento. Los sistemas de replicación alivian el problema del punto único de congestión, pero no lo solucionan completamente, ya que siguen apareciendo puntos de congestión menores, tantos como réplicas sean usadas. Por tanto, la red sigue presentando una gran desigualdad en el consumo energético, ya que aquellos puntos seleccionados como réplicas (y sus vecinos) usan una mayor energía para desarrollar su actividad. Frente a este problema, se propone como solución el cambio de las réplicas a lo largo del tiempo. Esecialmente, se limita el tiempo que un nodo puede permanecer desempeñando el papel de réplica, de tal forma que, una vez pasado ese tiempo, otro nodo tomará esa responsabilidad. Aplicando esta propuesta se consigue un equilibrio en el consumo energético de los nodos de la red, lo que tiene un gran impacto en la extensión del tiempo de vida de la red. En los experimentos realizados, dicha extensión tiene un valor m´ınimo de un 60%, llegándose a extender el tiempo de la vida hasta 10 veces bajo ciertas definiciones de tiempo de vida de la red. La principal contribución de esta Tesis es la presentación de un marco de trabajo adaptativo tanto espacial como temporalmente que, basado en un modelo teórico, indica cuál es el número óptimo de replicas que deben ser usadas en un determinado periodo. En esta Tesis se propone un protocolo completo que cubre todas las funcionalidades para que dicho sistema pueda ser implementado y desplegado en el mundo real. Para demostrar que el sistema propuesto puede ser implementado en ndoos de sensores comerciales, esta Tesis presenta la implementación realizada en 20 motas del fabricante Jennic. Asimismo, se ha empleado un pequeño test de pruebas para confirmar la validez de los modelos matemáticos para la obtención del número óptimo de réplicas, así como para demostrar que el cambio de las réplicas a lo largo del tiempo genera una mejor distribución del consumo energético en la red

STARR-DCS: Spatio-temporal adaptation of random replication for data-centric storage

This article presents a novel framework for data-centric storage (DCS) in a wireless sensor and actor network (WSAN) that employs a randomly selected set of data replication nodes, which also change over time. This enables reductions in the average network traffic and energy consumption by adapting the number of replicas to applications' traffic, while balancing energy burdens by varying their locations. To that end, we propose and validate a simple model to determine the optimal number of replicas, in terms of minimizing average traffic/energy consumption, based on measurements of applications' production and consumption traffic. Simple mechanisms are proposed to decide when the current set of replication nodes should be changed, to enable new applications and nodes to efficiently bootstrap into a working WSAN, to recover from failing nodes, and to adapt to changing conditions. Extensive simulations demonstrate that our approach can extend a WSAN's lifetime by at least 60%, and up to a factor of 10× depending on the lifetime criterion being considered. The feasibility of the proposed framework has been validated in a prototype with 20 resource-constrained motes, and the results obtained via simulation for large WSANs have been also corroborated in that prototype.The research leading to these results has been partially funded by the Spanish MEC under the CRAMNET project (TEC2012-38362-C03-01) and the FIERRO project (TEC 2010- 12250-E), and by the General Directorate of Universities and Research of the Regional Government of Madrid under the MEDIANET Project (S2009/TIC-1468). G. de Veciana was supported by the National Science Foundation under Award CNS-0915928Publicad

Building efficient wireless infrastructures for pervasive computing environments

Pervasive computing is an emerging concept that thoroughly brings computing devices and the consequent technology into people\u27s daily life and activities. Most of these computing devices are very small, sometimes even invisible , and often embedded into the objects surrounding people. In addition, these devices usually are not isolated, but networked with each other through wireless channels so that people can easily control and access them. In the architecture of pervasive computing systems, these small and networked computing devices form a wireless infrastructure layer to support various functionalities in the upper application layer.;In practical applications, the wireless infrastructure often plays a role of data provider in a query/reply model, i.e., applications issue a query requesting certain data and the underlying wireless infrastructure is responsible for replying to the query. This dissertation has focused on the most critical issue of efficiency in designing such a wireless infrastructure. In particular, our problem resides in two domains depending on different definitions of efficiency. The first definition is time efficiency, i.e., how quickly a query can be replied. Many applications, especially real-time applications, require prompt response to a query as the consequent operations may be affected by the prior delay. The second definition is energy efficiency which is extremely important for the pervasive computing devices powered by batteries. Above all, our design goal is to reply to a query from applications quickly and with low energy cost.;This dissertation has investigated two representative wireless infrastructures, sensor networks and RFID systems, both of which can serve applications with useful information about the environments. We have comprehensively explored various important and representative problems from both algorithmic and experimental perspectives including efficient network architecture design and efficient protocols for basic queries and complicated data mining queries. The major design challenges of achieving efficiency are the massive amount of data involved in a query and the extremely limited resources and capability each small device possesses. We have proposed novel and efficient solutions with intensive evaluation. Compared to the prior work, this dissertation has identified a few important new problems and the proposed solutions significantly improve the performance in terms of time efficiency and energy efficiency. Our work also provides referrable insights and appropriate methodology to other similar problems in the research community

Secure Data Management and Transmission Infrastructure for the Future Smart Grid

Power grid has played a crucial role since its inception in the Industrial Age. It has evolved from a wide network supplying energy for incorporated multiple areas to the largest cyber-physical system. Its security and reliability are crucial to any country’s economy and stability [1]. With the emergence of the new technologies and the growing pressure of the global warming, the aging power grid can no longer meet the requirements of the modern industry, which leads to the proposal of ‘smart grid’. In smart grid, both electricity and control information communicate in a massively distributed power network. It is essential for smart grid to deliver real-time data by communication network. By using smart meter, AMI can measure energy consumption, monitor loads, collect data and forward information to collectors. Smart grid is an intelligent network consists of many technologies in not only power but also information, telecommunications and control. The most famous structure of smart grid is the three-layer structure. It divides smart grid into three different layers, each layer has its own duty. All these three layers work together, providing us a smart grid that monitor and optimize the operations of all functional units from power generation to all the end-customers [2]. To enhance the security level of future smart grid, deploying a high secure level data transmission scheme on critical nodes is an effective and practical approach. A critical node is a communication node in a cyber-physical network which can be developed to meet certain requirements. It also has firewalls and capability of intrusion detection, so it is useful for a time-critical network system, in other words, it is suitable for future smart grid. The deployment of such a scheme can be tricky regarding to different network topologies. A simple and general way is to install it on every node in the network, that is to say all nodes in this network are critical nodes, but this way takes time, energy and money. Obviously, it is not the best way to do so. Thus, we propose a multi-objective evolutionary algorithm for the searching of critical nodes. A new scheme should be proposed for smart grid. Also, an optimal planning in power grid for embedding large system can effectively ensure every power station and substation to operate safely and detect anomalies in time. Using such a new method is a reliable method to meet increasing security challenges. The evolutionary frame helps in getting optimum without calculating the gradient of the objective function. In the meanwhile, a means of decomposition is useful for exploring solutions evenly in decision space. Furthermore, constraints handling technologies can place critical nodes on optimal locations so as to enhance system security even with several constraints of limited resources and/or hardware. The high-quality experimental results have validated the efficiency and applicability of the proposed approach. It has good reason to believe that the new algorithm has a promising space over the real-world multi-objective optimization problems extracted from power grid security domain. In this thesis, a cloud-based information infrastructure is proposed to deal with the big data storage and computation problems for the future smart grid, some challenges and limitations are addressed, and a new secure data management and transmission strategy regarding increasing security challenges of future smart grid are given as well

Secure Data Management and Transmission Infrastructure for the Future Smart Grid

Power grid has played a crucial role since its inception in the Industrial Age. It has evolved from a wide network supplying energy for incorporated multiple areas to the largest cyber-physical system. Its security and reliability are crucial to any country’s economy and stability [1]. With the emergence of the new technologies and the growing pressure of the global warming, the aging power grid can no longer meet the requirements of the modern industry, which leads to the proposal of ‘smart grid’. In smart grid, both electricity and control information communicate in a massively distributed power network. It is essential for smart grid to deliver real-time data by communication network. By using smart meter, AMI can measure energy consumption, monitor loads, collect data and forward information to collectors. Smart grid is an intelligent network consists of many technologies in not only power but also information, telecommunications and control. The most famous structure of smart grid is the three-layer structure. It divides smart grid into three different layers, each layer has its own duty. All these three layers work together, providing us a smart grid that monitor and optimize the operations of all functional units from power generation to all the end-customers [2]. To enhance the security level of future smart grid, deploying a high secure level data transmission scheme on critical nodes is an effective and practical approach. A critical node is a communication node in a cyber-physical network which can be developed to meet certain requirements. It also has firewalls and capability of intrusion detection, so it is useful for a time-critical network system, in other words, it is suitable for future smart grid. The deployment of such a scheme can be tricky regarding to different network topologies. A simple and general way is to install it on every node in the network, that is to say all nodes in this network are critical nodes, but this way takes time, energy and money. Obviously, it is not the best way to do so. Thus, we propose a multi-objective evolutionary algorithm for the searching of critical nodes. A new scheme should be proposed for smart grid. Also, an optimal planning in power grid for embedding large system can effectively ensure every power station and substation to operate safely and detect anomalies in time. Using such a new method is a reliable method to meet increasing security challenges. The evolutionary frame helps in getting optimum without calculating the gradient of the objective function. In the meanwhile, a means of decomposition is useful for exploring solutions evenly in decision space. Furthermore, constraints handling technologies can place critical nodes on optimal locations so as to enhance system security even with several constraints of limited resources and/or hardware. The high-quality experimental results have validated the efficiency and applicability of the proposed approach. It has good reason to believe that the new algorithm has a promising space over the real-world multi-objective optimization problems extracted from power grid security domain. In this thesis, a cloud-based information infrastructure is proposed to deal with the big data storage and computation problems for the future smart grid, some challenges and limitations are addressed, and a new secure data management and transmission strategy regarding increasing security challenges of future smart grid are given as well