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The lifetime of a wireless sensor network mainly depends on battery capacity and energy consumption at each node for operations such as, sensing, processing and communication. Popular approaches to save energy have been to intelligently duty cycle and restrict the frequency of these operations, rendering lower quality data at the sink. In this article, we propose Virtual Sensing Framework (VSF), which reduces the frequency of the above mentioned operations at each node while not compromising on the sensing interval, and hence resulting in higher quality data at the sink. VSF creates virtual sensors at the sink to exploit the spatio-temporal correlations among sensed data. Using an adaptive model at every sensing iteration, the virtual sensors can predict multiple consecutive sensor data for dormant physical sensors with the help of only a few active physical sensors. We show that even when the sensed data represents different parameters (e.g., light, temperature), our proposed technique works well. Applying our technique on the real-world data sets, we attain substantial reduction in energy consumption per node while maintaining high accuracy of the sensed data. To achieve higher energy reduction, VSF has to be used in conjunction with various layers and protocols of the communication stack. Thus, it has the potential to open up new research insights to make the best use of statistical properties of collected sensor data in a network.

A robot as a coworker or a cohabitant is becoming mainstream day-by-day with the development of low-cost sophisticated hardware. However, an accompanying software stack that can aid the usability of the robotic hardware remains the bottleneck of the process, especially if the robot is not dedicated to a single job. Programming a multipurpose robot requires an on the fly mission scheduling capability that involves task identification and plan generation. The problem dimension increases if the robot accepts tasks from a human in natural language. Though recent advances in NLP and planner development can solve a variety of complex problems, their amalgamation for a dynamic robotic task handler is used in a limited scope. Specifically, the problem of formulating a planning problem from natural language instructions is not studied in details. In this work, we provide a non-trivial method to combine an NLP engine and a planner such that a robot can successfully identify tasks and all the relevant parameters and generate an accurate plan for the task. Additionally, some mechanism is required to resolve the ambiguity or missing pieces of information in natural language instruction. Thus, we also develop a dialogue strategy that aims to gather additional information with minimal question-answer iterations and only when it is necessary. This work makes a significant stride towards enabling a human-like task understanding capability in a robot.

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