Many technical communities are vigorously pursuing research topics that contribute to the Internet of Things (IoT). Nowadays, as sensing, actuation, communication, and control become even more sophisticated and ubiquitous, there is a significant overlap in these communities, sometimes from slightly different perspectives. More cooperation between communities is encouraged. To provide a basis for discussing open research problems in IoT, a vision for how IoT could change the world in the distant future is first presented. Then, eight key research topics are enumerated and research problems within these topics are discussed.

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Research Directions for the Internet of Things

The Internet of Things vision: Key features, applications and open issues

https://pure.rug.nl/ws/files/2357318/2013EnergyBuildingsNguyen.pdf

Energy intelligent buildings based on user activity: A survey

Heating, ventilation and cooling (HVAC) is the largest source of residential energy consumption. In this paper, we demonstrate how to use cheap and simple sensing technology to automatically sense occupancy and sleep patterns in a home, and how to use these patterns to save energy by automatically turning off the home's HVAC system. We call this approach the smart thermostat. We evaluate this approach by deploying sensors in 8 homes and comparing the expected energy usage of our algorithm against existing approaches. We demonstrate that our approach will achieve a 28% energy saving on average, at a cost of approximately $25 in sensors. In comparison, a commercially-available baseline approach that uses similar sensors saves only 6.8% energy on average, and actually increases energy consumption in 4 of the 8 households.

/pdf/the-smart-thermostat-using-occupancy-sensors-to-save-energy-20hwucrj0i.pdf

The smart thermostat: using occupancy sensors to save energy in homes

In a world where resources are scarce and urban areas consume the vast majority of these resources, it is vital to make cities greener and more sustainable. Advanced systems to improve and automate processes within a city will play a leading role in smart cities. From smart design of buildings, which capture rain water for later use, to intelligent control systems, which can monitor infrastructures autonomously, the possible improvements enabled by sensing technologies are immense. Ubiquitous sensing poses numerous challenges, which are of a technological or social nature. This paper presents an overview of the state of the art with regards to sensing in smart cities. Topics include sensing applications in smart cities, sensing platforms and technical challenges associated with these technologies. In an effort to provide a holistic view of how sensing technologies play a role in smart cities, a range of applications and technical challenges associated with these applications are discussed. As some of these applications and technologies belong to different disciplines, the material presented in this paper attempts to bridge these to provide a broad overview, which can be of help to researchers and developers in understanding how advanced sensing can play a role in smart cities.

The Role of Advanced Sensing in Smart Cities

Programmable thermostats offer large potential energy savings without sacrificing comfort, but only if setback schedules are defined correctly. We present the concept of a self-programming thermostat that automatically creates an optimal setback schedule by sensing the occupancy statistics of a home. The system monitors occupancy using simple sensors in the home, similar to those already used in typical security systems, and the user defines the desired balance between energy and comfort using a single, intuitive knob. Our preliminary results show that this approach can reduce heating and cooling demand by up to 15% over the default setback schedule recommended by EnergyStar.

https://www.cs.virginia.edu/~whitehouse/research/buildingEnergy/gao09thermostat.pdf

Ge Gao

Papers

The smart thermostat: using occupancy sensors to save energy in homes

The self-programming thermostat: optimizing setback schedules based on home occupancy patterns