![]() IoT has proved its crucial role in supporting the elderly using the latest technological advancements which make eldercare hassle-free. Most of them are suffering from aging-associated diseases like dementia, Parkinson's disease, Alzheimer’s disease, etc. Assistance is very helpful for them during this period. Since the aging global population is increasing day by day, the need for proper and better elder care has become necessary to promote active and healthy aging. We also show that SSIoT is cost-efficient (operating a smart doorbell for $10 a year) at the cost of minimal additional latency as compared to a local-only hub, even with a hardware ML accelerator. Our results show that SSIoT is highly scalable, as compared to local-only approaches which struggle with as little as 2-4 apps in parallel. We build an end-to-end prototype of SSIoT and evaluate it using several micro-benchmarks and example applications representing real-world IoT use cases. AWS Lambda) to make these offloads cost-efficient, scalable and high performance as long as key limitations of being stateless, limited resources, and security isolation can be addressed. We show that SSIoT can leverage emerging function-as-a-service computation (e.g. Uniquely, SSIoT enables opportunistic computation offload to public cloud providers while still ensuring that the end-user retains complete end-to-end control of their private data reducing the trust required from public cloud providers. This paper proposes Self-Serviced IoT (SSIoT), a clean-slate approach of using a hybrid hub-cloud setup to enable privacy-aware computation offload for IoT applications. An alternative to use a local in-home hub requires substantial hardware investment, management, and scalability limitations. At the same time, users desire compelling features supported by IoT devices and ML-based cloud inferences which compels them to subscribe to manufacturer-managed cloud services. Multiple recent incidents further call into question if vendors can indeed be trusted with users' data. The current status quo of relying on manufacturers' cloud services to process this data is especially problematic since users cede control once their data leaves their home. The rapid increase in the adoption of Internet-of-Things (IoT) devices raises critical privacy concerns as these devices can access a variety of sensitive data.
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