Powertrace: Network-level Power Profiling for Low-power Wireless Networks

Low-power wireless networks are quickly becoming a critical part of our everyday infrastructure. Power consumption is a critical concern, but power measurement and estimation is a challenge. We present Powertrace, which to the best of our knowledge is the first system for network-level power profiling of low-power wireless systems. Powertrace uses power state tracking to estimate system power consumption and a structure called energy capsules to attribute energy consumption to activities such as packet transmissions and receptions. With Powertrace, the power consumption of a system can be broken down into individual activities which allows us to answer questions such as “How much energy is spent forwarding packets for node X?”, “How much energy is spent on control traffic and how much on critical data?”, and “How much energy does application X account for?”. Experiments show that Powertrace is accurate to 94% of the energy consumption of a device. To demonstrate the usefulness of Powertrace, we use it to experimentally analyze the power behavior of the proposed IETF standard IPv6 RPL routing protocol and a sensor network data collection protocol. Through using Powertrace, we find the highest power consumers and are able to reduce the power consumption of data collection with 24%. It is our hope that Powertrace will help the community to make empirical energy evaluation a widely used tool in the low-power wireless research community toolbox

MobiCon: A mobile context-monitoring platform

User context is defined by data generated through everyday physical activity in sensor-rich, resource-limited mobile environments.</jats:p

Distributed Control for Cyber-Physical Systems

Networked Cyber-Physical Systems (CPS) are fundamentally constrained by the tight coupling and closed-loop control and actuation of physical processes. To address actuation in such closed-loop wireless control systems there is a strong need to re-think the communication architectures and protocols for maintaining stability and performance in the presence of disturbances to the network, environment and overall system objectives. We review the current state of network control efforts for CPS and present two complementary approaches for robust, optimal and composable control over networks. We first introduce a computer systems approach with Embedded Virtual Machines (EVM), a programming abstraction where controller tasks, with their control and timing properties, are maintained across physical node boundaries. Controller functionality is decoupled from the physical substrate and is capable of runtime migration to the most competent set of physical controllers to maintain stability in the presence of changes to nodes, links and network topology. We then view the problem from a control theoretic perspective to deliver fully distributed control over networks with Wireless Control Networks (WCN). As opposed to traditional networked control schemes where the nodes simply route information to and from a dedicated controller, our approach treats the network itself as the controller. In other words, the computation of the control law is done in a fully distributed way inside the network. In this approach, at each time-step, each node updates its internal state to be a linear combination of the states of the nodes in its neighborhood. This causes the entire network to behave as a linear dynamical system, with sparsity constraints imposed by the network topology. This eliminates the need for routing between “sensor → channel → dedicated controller/estimator → channel → actuator”, allows for simple transmission scheduling, is operational on resource constrained low-power nodes and allows for composition of additional control loops and plants. We demonstrate the potential of such distributed controllers to be robust to a high degree of link failures and to maintain stability even in cases of node failures

A formal, resource consumption-preserving translation from actors with cooperative scheduling to Haskell

We present a formal translation of a resource-Aware extension of the Abstract Behavioral Specification (ABS) language to the functional language Haskell. ABS is an actor-based language tailored to the modeling of distributed systems. It combines asynchronous method calls with a suspend and resume mode of execution of the method invocations. To cater for the resulting cooperative scheduling of the method invocations of an actor, the translation exploits for the compilation of ABS methods Haskell functions with continuations. The main result of this article is a correctness proof of the translation by means of a simulation relation between a formal semantics of the source language and a high-level operational semantics of the target language, i.e., a subset of Haskell. We further prove that the resource consumption of an ABS program extended with a cost model is preserved over this translation, as we establish an equivalence of the cost of executing the ABS program and its corresponding Haskell-Translation. Concretely, the resources consumed by the original ABS program and those consumed by the Haskell program are the same, considering a cost model. Consequently, the resource bounds automatically inferred for ABS programs extended with a cost model, using resource analysis tools, are sound resource bounds also for the translated Haskell programs. Our experimental evaluation confirms the resource preservation over a set of benchmarks featuring different asymptotic costs

A Map-algebra-inspired Approach for Interacting With Wireless Sensor Networks, Cyber-physical Systems or Internet of Things

The typical approach for consuming data from wireless sensor networks (WSN) and Internet of Things (IoT) has been to send data back to central servers for processing and analysis. This thesis develops an alternative strategy for processing and acting on data directly in the environment referred to as Active embedded Map Algebra (AeMA). Active refers to the near real time production of data, and embedded refers to the architecture of distributed embedded sensor nodes. Network macroprogramming, a style of programming adopted for wireless sensor networks and IoT, addresses the challenges of coordinating the behavior of multiple connected devices through a high-level programming model. Several macroprogramming models have been proposed, but none to date has adopted a comprehensive spatial model. This thesis takes the unique approach of adapting the well-known Map Algebra model from Geographic Information Science to extend the functionality of WSN/IoT and the opportunities for user interaction with WSN/IoT. As an inherently spatial model, the Map Algebra-inspired metaphor supports the types of computation desired from a network of geographically dispersed WSN nodes. The AeMA data model aligns with the conceptual model of GIS layers and specific layer operations from Map Algebra. A declarative query and network tasking language, based on Map Algebra operations, provides the basis for operations and interactions. The model adds functionality to calculate and store time series and specific temporal summary-type composite objects as an extension to traditional Map Algebra. The AeMA encodes Map Algebra-inspired operations into an extensible Virtual Machine Runtime system, called MARS (Map Algebra Runtime System) that supports Map Algebra in an efficient and extensible way. Map algebra-like operations are performed in a distributed manner. Data do not leave the network but are analyzed and consumed in place. As a consequence, collected information is available in-situ to drive local actions. The conceptual model and tasking language are designed to direct nodes as active entities, able to perform some actions on their environment. This Map Algebra inspired network macroprogramming model has many potential applications for spatially deployed WSN/IoT networks. In particular the thesis notes its utility for precision agriculture applications

A Learning-based Approach to Exploiting Sensing Diversity in Performance Critical Sensor Networks

Wireless sensor networks for human health monitoring, military surveillance, and disaster warning all have stringent accuracy requirements for detecting and classifying events while maximizing system lifetime. to meet high accuracy requirements and maximize system lifetime, we must address sensing diversity: sensing capability differences among both heterogeneous and homogeneous sensors in a specific deployment. Existing approaches either ignore sensing diversity entirely and assume all sensors have similar capabilities or attempt to overcome sensing diversity through calibration. Instead, we use machine learning to take advantage of sensing differences among heterogeneous sensors to provide high accuracy and energy savings for performance critical applications.;In this dissertation, we provide five major contributions that exploit the nuances of specific sensor deployments to increase application performance. First, we demonstrate that by using machine learning for event detection, we can explore the sensing capability of a specific deployment and use only the most capable sensors to meet user accuracy requirements. Second, we expand our diversity exploiting approach to detect multiple events using a distributed manner. Third, we address sensing diversity in body sensor networks, providing a practical, user friendly solution for activity recognition. Fourth, we further increase accuracy and energy savings in body sensor networks by sharing sensing resources among neighboring body sensor networks. Lastly, we provide a learning-based approach for forwarding event detection decisions to data sinks in an environment with mobile sensor nodes

Sophisticated Batteryless Sensing

Wireless embedded sensing systems have revolutionized scientific, industrial, and consumer applications. Sensors have become a fixture in our daily lives, as well as the scientific and industrial communities by allowing continuous monitoring of people, wildlife, plants, buildings, roads and highways, pipelines, and countless other objects. Recently a new vision for sensing has emerged---known as the Internet-of-Things (IoT)---where trillions of devices invisibly sense, coordinate, and communicate to support our life and well being. However, the sheer scale of the IoT has presented serious problems for current sensing technologies---mainly, the unsustainable maintenance, ecological, and economic costs of recycling or disposing of trillions of batteries. This energy storage bottleneck has prevented massive deployments of tiny sensing devices at the edge of the IoT. This dissertation explores an alternative---leave the batteries behind, and harvest the energy required for sensing tasks from the environment the device is embedded in. These sensors can be made cheaper, smaller, and will last decades longer than their battery powered counterparts, making them a perfect fit for the requirements of the IoT. These sensors can be deployed where battery powered sensors cannot---embedded in concrete, shot into space, or even implanted in animals and people. However, these batteryless sensors may lose power at any point, with no warning, for unpredictable lengths of time. Programming, profiling, debugging, and building applications with these devices pose significant challenges. First, batteryless devices operate in unpredictable environments, where voltages vary and power failures can occur at any time---often devices are in failure for hours. Second, a device\u27s behavior effects the amount of energy they can harvest---meaning small changes in tasks can drastically change harvester efficiency. Third, the programming interfaces of batteryless devices are ill-defined and non- intuitive; most developers have trouble anticipating the problems inherent with an intermittent power supply. Finally, the lack of community, and a standard usable hardware platform have reduced the resources and prototyping ability of the developer. In this dissertation we present solutions to these challenges in the form of a tool for repeatable and realistic experimentation called Ekho, a reconfigurable hardware platform named Flicker, and a language and runtime for timely execution of intermittent programs called Mayfly

Un système de composants distribué pour les réseaux de capteurs sans-fils

L'utilisation de réseaux de capteurs sans-fils (RCSF) se développe dans de nombreux domaines où l'informatique doit être intégrée au plus proche de l'environnement. Ce principe appelé informatique omniprésente se popularise par des applications dans de multiples domaines, de la domotique à l'étude d'environnements naturels en passant par la régulation des transports ou encore la surveillance de bâtiments à risques. Si les RCSF présentent de bonnes perspectives pour le domaine de l'informatique omniprésente, le matériel utilisé présente souvent des capacités très limitées et il est souvent compliqué de développer des applications ou de configurer de tels réseaux. Des travaux récemment réalisés au laboratoire DOMUS amènent la vision d'une informatique omniprésente autonome qui permettrait à plusieurs éléments d'un réseau de s'organiser entre eux pour limiter les interventions humaines. Dans cette vision, la reprogrammation dynamique des noeuds est utilisée pour simplifier et alléger le processus de reconfiguration du réseau. Le présent projet s'est donc intéressé à la problématique de la reprogrammation des noeuds du réseau dans une optique future d'informatique omniprésente autonome adaptée aux RCSF. Le présent projet de maîtrise a permis dans un premier temps de mettre en place un cadriciel de programmation par composants adapté aux ressources contraintes des RCSF. Ce système de programmation par composants (POC) appelé Nodecom se place comme une amélioration par rapport aux solutions de POC déjà existantes. En effet, Nodecom présente la première architecture hybride permettant à la fois de programmer en utilisant des composants statiques et à la fois de pouvoir charger de nouveaux composants de manière dynamique. Cette architecture hybride a permis d'alléger l'impact du système de programmation par composants tout en conservant la possibilité de reprogrammer dynamiquement certains composants. Dans un second temps, le projet a consisté à réaliser un dépôt distribué de composants qui permet à chaque noeud de charger dynamiquement n'importe quel composant publié à travers le réseau. Dans ce dépôt distribué, chaque noeud peut se voir attribuer le rôle de conserver une copie d'un fichier de composant dans sa mémoire locale. Pour ce faire, l'implémentation réalisée repose sur un algorithme de routage par clé inspiré des réseaux pair-à-pair traditionnels et adapté aux contraintes des plateformes utilisées. Les résultats de l'évaluation de ce système de composants distribué pour les réseaux de capteurs sans-fils sont encourageants puisqu'ils mettent en évidence les faibles besoins en mémoire du système. L'implémentation réalisée dans ce projet se place alors comme un bon support pour les travaux futurs qui chercheront à adapter la vision d'informatique omniprésente autonome au contexte des réseaux de capteurs sans-fils