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

Recently, Wireless Sensor Networks (WSNs) have attracted lot of attention due to their pervasive nature and their wide deployment in Internet of Things, Cyber Physical Systems, and other emerging areas. The limited energy associated with WSNs is a major bottleneck of WSN technologies. To overcome this major limitation, the design and development of efficient and high performance energy harvesting systems for WSN environments are being explored. We present a comprehensive taxonomy of the various energy harvesting sources that can be used by WSNs. We also discuss various recently proposed energy prediction models that have the potential to maximize the energy harvested in WSNs. Finally, we identify some of the challenges that still need to be addressed to develop cost-effective, efficient, and reliable energy harvesting systems for the WSN environment.

Energy harvesting in wireless sensor networks: A comprehensive review

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

Energy intelligent buildings based on user activity: A survey

This paper presents Glossy, a novel flooding architecture for wireless sensor networks. Glossy exploits constructive interference of IEEE 802.15.4 symbols for fast network flooding and implicit time synchronization. We derive a timing requirement to make concurrent transmissions of the same packet interfere constructively, allowing a receiver to decode the packet even in the absence of capture effects. To satisfy this requirement, our design temporally decouples flooding from other network activities. We analyze Glossy using a mixture of statistical and worst-case models, and evaluate it through experiments under controlled settings and on three wireless sensor testbeds. Our results show that Glossy floods packets within a few milliseconds and achieves an average time synchronization error below one microsecond. In most cases, a node receives the flooding packet with a probability higher than 99.99 %, while having its radio turned on for only a few milliseconds during a flood. Moreover, unlike existing flooding schemes, Glossy's performance exhibits no noticeable dependency on node density, which facilitates its application in diverse real-world settings.

https://homepages.dcc.ufmg.br/~mmvieira/cc/papers/ferrari11glossy.pdf

Efficient network flooding and time synchronization with Glossy

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.

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

Homes are rich with information about people's energy consumption, medical health, and personal or family functions. In this paper, we present our experiences deploying large-scale residential sensing systems in over 20 homes. Deploying small-scale systems in homes can be deceptively easy, but in our deployments we encountered a phase transition in which deployment effort increases dramatically as residential deployments scale up in terms of 1) the number of nodes, 2) the length of time, and 3) the number of houses. In this paper, we distill our experiences down to a set of guidelines and design principles to help future deployments avoid the potential pitfalls of large-scale sensing in homes.

https://www.cs.virginia.edu/~stankovic/psfiles/hnat11hitchhikers.pdf

The hitchhiker's guide to successful residential sensing deployments

We present the Flash flooding protocol for rapid network flooding in wireless sensor networks. Traditional flooding protocols can be very slow because of neighborhood contention: nodes cannot propagate the flood until neighboring nodes have finished their transmissions. The Flash flooding protocol avoids this problem by allowing concurrent transmissions among neighboring nodes. It relies on the capture effect to ensure that each node receives the flood from at least one of its neighbors, and introduces new techniques to either recover from or prevent too many concurrent transmissions. We evaluate the Flash flooding protocol on both a 48-node wireless sensor network testbed and in a trace-based simulator. Our results indicate that the Flash flooding protocol can reduce latency by as much as 80%, achieving flooding latencies near the theoretical lower bound without sacrificing coverage, reliability or power consumption.

https://www.cs.virginia.edu/~whitehouse/research/flash/lu09flash.pdf

Flash Flooding: Exploiting the Capture Effect for Rapid Flooding in Wireless Sensor Networks

Lighting is the largest single energy consumer in commercial buildings. In this paper, we demonstrate how to improve the effectiveness of daylight harvesting with a single light sensor on each window. Our system automatically infers the window orientation and the cloudiness levels of the current sky to predict the incoming daylight and set window transparency accordingly. We evaluate our system with ten weeks of empirical data traces collected from windows around an office building and compare our approach with non-predictive feedback control. Experimental results show that our scheme can infer the orientation of a window to within ±7° of the actual orientation and improve energy savings by 10% over existing approaches without sacrificing user comfort.

https://www.cs.virginia.edu/~whitehouse/research/buildingEnergy/lu-buildsys10.pdf

Using simple light sensors to achieve smart daylight harvesting

Daylight harvesting is the use of natural sunlight to reduce the need for artificial lighting in buildings. The key challenge of daylight harvesting is to provide stable indoor lighting levels even though natural sunlight is not a stable light source. In this paper, we present a new technique called SunCast that improves lighting stability by predicting changes in future sunlight levels. The system has two parts: 1) it learns predictable sunlight patterns due to trees, nearby buildings, or other environmental factors, and 2) it controls the window transparency based on a quadratic optimization over predicted sunlight levels. To evaluate the system, we record daylight levels at 39 different windows for up to 12 weeks at a time, and apply our control algorithm on the data traces. Our results indicate that SunCast can reduce glare by 59% over a baseline approach with only a marginal increase in artificial lighting energy.

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Jiakang Lu

Papers

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

The hitchhiker's guide to successful residential sensing deployments

Flash Flooding: Exploiting the Capture Effect for Rapid Flooding in Wireless Sensor Networks

Using simple light sensors to achieve smart daylight harvesting

SunCast: fine-grained prediction of natural sunlight levels for improved daylight harvesting