An Energy Driven Architecture for Wireless Sensor Networks

Most wireless sensor networks operate with very limited energy sources-their batteries, and hence their usefulness in real life applications is severely constrained. The challenging issues are how to optimize the use of their energy or to harvest their own energy in order to lengthen their lives for wider classes of application. Tackling these important issues requires a robust architecture that takes into account the energy consumption level of functional constituents and their interdependency. Without such architecture, it would be difficult to formulate and optimize the overall energy consumption of a wireless sensor network. Unlike most current researches that focus on a single energy constituent of WSNs independent from and regardless of other constituents, this paper presents an Energy Driven Architecture (EDA) as a new architecture and indicates a novel approach for minimising the total energy consumption of a WS

Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs

Accurate supercapacitor modeling for energy-harvesting wireless sensor nodes

Supercapacitors are often used in energy-harvesting wireless sensor nodes (EH-WSNs) to store harvested energy. Until now, research into the use of supercapacitors in EH-WSNs has considered them to be ideal or over-simplified, with non-ideal behavior attributed to substantial leakage currents. In this brief, we show that observations previously attributed to leakage are predominantly due to redistribution of charge inside the supercapacitor. We confirm this hypothesis through the development of a circuit-based model which accurately represents non-ideal behavior. The model correlates well with practical validations representing the operation of an EH-WSN, and allows behavior to be simulated over long periods

A Structured Hardware/Software Architecture for Embedded Sensor Nodes

Owing to the limited requirement for sensor processing in early networked sensor nodes, embedded software was generally built around the communication stack. Modern sensor nodes have evolved to contain significant on-board functionality in addition to communications, including sensor processing, energy management, actuation and locationing. The embedded software for this functionality, however, is often implemented in the application layer of the communications stack, resulting in an unstructured, top-heavy and complex stack. In this paper, we propose an embedded system architecture to formally specify multiple interfaces on a sensor node. This architecture differs from existing solutions by providing a sensor node with multiple stacks (each stack implements a separate node function), all linked by a shared application layer. This establishes a structured platform for the formal design, specification and implementation of modern sensor and wireless sensor nodes. We describe a practical prototype of an intelligent sensing, energy-aware, sensor node that has been developed using this architecture, implementing stacks for communications, sensing and energy management. The structure and operation of the intelligent sensing and energy management stacks are described in detail. The proposed architecture promotes structured and modular design, allowing for efficient code reuse and being suitable for future generations of sensor nodes featuring interchangeable components

In-Network Distributed Solar Current Prediction

Long-term sensor network deployments demand careful power management. While managing power requires understanding the amount of energy harvestable from the local environment, current solar prediction methods rely only on recent local history, which makes them susceptible to high variability. In this paper, we present a model and algorithms for distributed solar current prediction, based on multiple linear regression to predict future solar current based on local, in-situ climatic and solar measurements. These algorithms leverage spatial information from neighbors and adapt to the changing local conditions not captured by global climatic information. We implement these algorithms on our Fleck platform and run a 7-week-long experiment validating our work. In analyzing our results from this experiment, we determined that computing our model requires an increased energy expenditure of 4.5mJ over simpler models (on the order of 10^{-7}% of the harvested energy) to gain a prediction improvement of 39.7%.Comment: 28 pages, accepted at TOSN and awaiting publicatio

Extending the Energy Framework for Network Simulator 3 (ns-3)

The problem of designing and simulating optimal transmission protocols for energy harvesting wireless networks has recently received considerable attention, thus requiring for an accurate modeling of the energy harvesting process and a consequent redesign of the simulation framework to include it. While the current ns-3 energy framework allows the definition of new energy sources that incorporate the contribution of an energy harvester, the integration of an energy harvester component into an existing energy source is not straightforward using the existing energy framework. In this poster, we propose an extension of the energy framework currently released with ns-3 in order to explicitly introduce the concept of an energy harvester. Starting from the definition of the general interface, we then provide the implementation of two simple models for the energy harvester. In addition, we extend the set of implementations of the current energy framework to include a model for a supercapacitor energy source and a device energy model for the energy consumption of a sensor. Finally, we introduce the concept of an energy predictor, that gathers information from the energy source and harvester and use this information to predict the amount of energy that will be available in the future, and we provide an example implementation. As a result of these efforts, we believe that our contributions to the ns-3 energy framework will provide a useful tool to enhance the quality of simulations of energy-aware wireless networks.Comment: 2 pages, 4 figures. Poster presented at WNS3 2014, Atlanta, G