English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The rapid increase in the adoption of Internet-of-Things (IoT) devices raisescritical privacy concerns as these devices can access a variety of sensitivedata. The current status quo of relying on manufacturers' cloud services toprocess this data is especially problematic since users cede control once theirdata leaves their home. Multiple recent incidents further call into question ifvendors can indeed be trusted with users' data. At the same time, users desirecompelling features supported by IoT devices and ML-based cloud inferenceswhich compels them to subscribe to manufacturer-managed cloud services. Analternative to use a local in-home hub requires substantial hardwareinvestment, management, and scalability limitations. This paper proposesSelf-Serviced IoT (SSIoT), a clean-slate approach of using a hybrid hub-cloudsetup to enable privacy-aware computation offload for IoT applications.Uniquely, SSIoT enables opportunistic computation offload to public cloudproviders while still ensuring that the end-user retains complete end-to-endcontrol of their private data reducing the trust required from public cloudproviders. We show that SSIoT can leverage emerging function-as-a-servicecomputation (e.g. AWS Lambda) to make these offloads cost-efficient, scalableand high performance as long as key limitations of being stateless, limitedresources, and security isolation can be addressed. We build an end-to-endprototype of SSIoT and evaluate it using several micro-benchmarks and exampleapplications representing real-world IoT use cases. Our results show that SSIoTis highly scalable, as compared to local-only approaches which struggle with aslittle as 2-4 apps in parallel. We also show that SSIoT is cost-efficient(operating a smart doorbell for $10 a year) at the cost of minimal additionallatency as compared to a local-only hub, even with a hardware ML accelerator.

Self-Serviced IoT: Practical and Private IoT Computation Offloading with Full User Control