The hitchhiker's guide to successful residential sensing deployments

Hnat, Timothy W.; Srinivasan, Vijay; Lu, Jiakang; Sookoor, Tamim; Dawson, Raymond H.; Stankovic, John A.; Whitehouse, Kamin

doi:10.1145/2070942.2070966

Cited by 130 publications

(95 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the main drawback of wearable sensing approaches is that, for long term home monitoring applications such as peace of mind monitoring [7] and energy conservation [5], users are uncomfortable wearing or carrying a sensor device all the time. Even if residents do agree to a wearable sensor, we observed in our own deployments [30] that users often forget to carry the tags, resulting in lost activity data.…”

Section: Wearable Sensingmentioning

confidence: 85%

“…For example, in many of our home deployments, an average of 20 man hours per home was spent just on installing direct sensors to monitor the use of light switches in the home. The sensors also required constant monitoring for failures, and were not aesthetically pleasing, since they were installed conspicuously on almost every object the user interacted with in the home [30].…”

Section: Non-invasive Direct Sensingmentioning

confidence: 99%

“…In particular, the advantage of not having to configure each sensor by using our AUTOLABEL algorithm may be lost, if the user has to physically install sophisticated sensors on each fixture. The contact reed switches used in the previous chapter to monitor water fixture usage are low cost and non-invasive; however, these magnetic reed switches are quite difficult to install for a novice, and are also inconvenient for everyday usage, since the aesthetics of water fixtures are affected by the presence of a conspicuous sensor on each faucet, flush or shower [55,30]. In addition to being difficult to install, magnetic reed switches only report when each water fixture is being used, and not how much water is consumed at each fixture, which is an important need for sustainability applications.…”

Section: Reducing Sensor Installation Effort To Monitor Water Fixturementioning

confidence: 99%

“…A key challenge in evaluating electrical load sensing systems, as observed in the literature [30,19], is accurately determining ground truth usage times. To measure ground truth, we replace the existing switches in each home with wireless ZWave smart switches [70], and connect each lamp to a wireless ZWave smart plug [70] that measures the power consumption of the appliance plugged into it.…”

Section: Real World Deploymentsmentioning

confidence: 99%

“…For example, many activity recognition systems currently in use require anywhere from 40-200 sensors depending on the size of the home [27,28], and may require on the order of 25-45 man hours in installing the sensors in the home [27,28,30].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Non-Invasive Sensor Solutions for Activity Recognition in SmartHomes

Srinivasan

Self Cite

View full text Add to dashboard Cite

Smart home sensor systems that infer residents' activities enable a number of exciting medical monitoring and energy conservation applications. Existing home activity recognition systems are invasive, since they require significant manual effort from the end user in installing or training the system, inconvenience the resident by requiring them to constantly wear tags, or require invasive cameras or expensive sensors. Our main hypothesis is that by effectively using data fusion techniques, leveraging the existing smart meter infrastructure in homes, and using only weak biometric sensing, we can build convenient, accurate home activity recognition solutions for the end user.The key challenges for home activity recognition systems addressed in this dissertation include reducing configuration effort from the end user, reducing sensor installation effort, and identifying residents in multi-person homes without using invasive sensors. To reduce user configuration effort, we develop an unsupervised activity recognition algorithm called AUTOLABEL that leverages data fusion and cross-home activity models to accurately recognize resident activities without user training. To eliminate many direct sensors in the home for activity recognition, we develop effective Bayesian data fusion techniques, which combine the existing smart meter infrastructure in homes, with one low cost, non-invasive sensor per room. In our WaterSense approach, we combine a single smart water meter per home, with an occupancy sensor per room to eliminate direct sensors on individual water fixtures. In our LightSense approach, we combine a single smart energy meter per home with a light sensor per room, to eliminate direct sensors on individual light fixtures. Finally, we propose the use of resident height, which is a weak biometric, in an effective data fusion approach, to identify residents for activity recognition in multi-person homes.We evaluate our proposed activity recognition solutions through short term prototype sensor deployments in home environments lasting from 7 to 10 days each. We show that our low cost, ii convenient solutions satisfy the activity recognition needs of numerous smart home applications, such as remote medical monitoring for elderly residents, and fine-grained resource consumption monitoring of light and water fixtures in the home. Finally, we observe that our unsupervised activity recognition algorithm can be used in a wireless snoop attack on smart homes, to infer the residents' daily activities with high accuracy in spite of encrypted wireless transmissions. We propose and evaluate a suite of privacy solutions to mitigate the inference accuracy of such an attack without affecting the performance or functionality of the home activity recognition system. iv

show abstract

Section: Wearable Sensingmentioning

confidence: 85%

Section: Non-invasive Direct Sensingmentioning

confidence: 99%

Section: Reducing Sensor Installation Effort To Monitor Water Fixturementioning

confidence: 99%

Section: Real World Deploymentsmentioning

confidence: 99%