is needed to translate the user’s end-to-end goals into a form understandable by the cognitive process. The cognitive network also depends on a Software Adaptable Network that has both an external interface accessible to the cognitive network and network status sensors. These devices are used to provide

sensors

to call real-time a program or system that receives external interrupts or reads sensors connected to the physical world and outputs commands to it. Real-time programming is an essential industrial activ-

delivering them to a central portal, where the data is stored in a database for further analysis and visualization. In the automotive context, a variety of on-board and external sensors collect data as users drive. CarTel provides a simple query-oriented programming interface, handles large amounts

topological location. Rather, low-level communication is based on attributes that are external to the network topology and relevant to the application. When combined with dense deployment of nodes, this kind of named data enables in-network processing for data aggregation, collaborative signal processing

Abstract not found

. The model is implemented using particle filters and learned using Expectation-Maximization. The training data is drawn from a GPS sensor stream that was collected by the authors over a period of three months. We demonstrate that by adding more external knowledge about bus routes and bus stops, accuracy

. As part of our solution, we exploit the efficiency of public operations in the RSA cryptosystem and design protocols that place the computationally expensive operations on the parties external to the sensor network, when possible. Our protocols have been implemented on UC Berkeley MICA2 motes using

this paper, we first give a very brief overview of control engineering. The goal of control engineering is to improve, or in some cases ena- ble, the performance of a system by the addition of sensors, which measure various signals in the system and external command signals, control processors

, transmission range, and link quality, might not be effective in the physical world. To address this issue, online transmission power control that adapts to external changes is necessary. This paper presents ATPC, a lightweight algorithm of Adaptive Transmission Power Control for wireless sensor networks