Search for: All records

To cultivate healthy plants and high crop yields, growers must be able to measure soil moisture and irrigate accordingly. Errors in soil moisture measurements can lead to irrigation mismanagement with costly consequences. In this paper, we present a new approach to smart computing for irrigation management to address these challenges at a lower cost. We calibrate low cost, low precision soil moisture sensors to more accurately distinguish wet from dry soils using high cost, high precision Davis Instrument sensors. We investigate different modeling techniques including the natural log of the odds ratio (Log-odds), Monte Carlo simulation, and linear regression to distinguish between wet and moist soils and to establish a trustworthy threshold between these two moisture states. We have also developed a new smartphone application that simplifies the process of data collection and implements our analysis approach. The application is extensible by others and provides growers with low cost, data-driven decision support for irrigation. We implement our approach for UCSB’s Edible Campus student farm and empirically evaluate it using multiple test beds. Our results show an accuracy rate of 91% and lowers costs by 4x per deployment, making it useful for gardeners and farmers alike. 

Serverless computing has increased in popularity as a programming model for “Internet of Things” (IoT) applications that amalgamate IoT devices, edge-deployed computers and systems, and the cloud to interoperate. In this paper, we present Laminar – a dataflow pro- gram representation for distributed IoT application programming – and describe its implementation based on a network-transparent, event-driven, serverless computing infrastructure that uses append- only log storage to store all program state. We describe the initial implementation of Laminar, discuss some useful properties we obtained by leveraging log-based data structures and triggered com- putations of the underlying serverless runtime, and illustrate its performance and reliability characteristics using a set of benchmark applications. 

In this paper, we investigate extensions for Conflict-Free Replicated Data Types (CRDTs) that permit their use in failure-prone, heterogeneous, resource-constrained, distributed, multi-tier (cloud/edge/device) cloud deployments such as the Internet-of-Things (IoT), while addressing multiple CRDT limitations. Specifically, we employ distributed logging to implement robust, strong eventual consistency of replicas. Our approach also enables uniform reversal of operations and precludes the requirement of exactly-once delivery and idempotence imposed by operation-based CRDTs. Moreover, it exposes CRDT versions for use in debugging and history-based programming. We evaluate our approach for commonly used CRDTs and show that it enables higher operation throughput (up to 1.8x) versus conventional CRDTs for the workloads we consider. 

Data replication facilitates availability and recovery in a distributed environment. However, concurrent updates to multiple replicas result in divergence of data. Conflict-Free Replicated Data Types (CRDTs) are abstract data types that provide a principled approach to asynchronously reconcile this divergence. We propose a different perspective on the divergence of data, whereby we treat data divergences as versions of the data. That is, instead of treating it only as a problem that needs to be solved, we consider it also to be a feature that provides a way to track versioning and evolution of data. Versioning information is helpful in multiple scenarios, such as provenance tracking and system debugging. Doing so allows us to leverage concepts such as the version tree found in the literature for persistent (versioned) data structures. We show that many techniques used in CRDTs to order elements can be derived from version trees, which predates CRDTs by more than 20 years. Using version trees for maintaining order and append-only logs for storage, we propose a method to ensure convergence of arbitrary data types, while maintaining information related to the evolution of data. 

Data privacy has garnered significant attention recently due to diverse applications that store sensitive data in untrusted infrastructure. From a data management point of view, the focus has been on the privacy of stored data and the privacy of querying data at a large scale. However, databases are not solely query engines on static data, they must support updates on dynamically evolving datasets. In this paper, we lay out a vision for privacy-preserving dynamic data. In particular, we focus on dynamic data that might be stored remotely on untrusted providers. Updates arrive at a provider and are verified and incorporated into the database based on predefined constraints. Depending on the application, the content of the stored data, the content of the updates and the constraints may be private or public. We then propose PReVer, a universal framework for managing regulated dynamic data in a privacy-preserving manner. We explore a set of research challenges that PReVer needs to address in order to guarantee the privacy of data, updates, and/or constraints and address the consistent and verifiable execution of updates. This opens the space of privacy-preserving data management from the narrow perspective of private queries on static datasets to the larger space of private management of dynamic data. 

We present MSDBench – a set of benchmarks designed to illuminate the effects of deployment choices and operating system ab- stractions on microservices performance in IoT settings. The microser- vices architecture has emerged as a mainstay set of design principles for cloud-hosted, network-facing applications. Their utility as a design pattern for “The Internet of Things” (IoT) is less well understood. We use MSDBench to show the performance impacts of different deploy- ment choices and isolation domain assignments for Linux and Ambience, an experimental operating system specifically designed to support mi- croservices for IoT. These results indicate that deployment choices can have a dramatic impact on microservices performance, and thus, MSD- Bench is a useful tool for developers and researchers in this space. 

We introduce Canal, a programmable, topic-based, publish/subscribe system that is designed for multi-tier cloud deployments (e.g. edge-cloud, multi-cloud, IoT-cloud, etc.). Canal implements a triggered computational (i.e. “serverless”) programming model and provides developers with a uniform and portable programming interface. To achieve scalability and reliability, Canal combines the use of a distributed hash table (DHT) and replica consensus protocol to distribute and replicate functions, state, and data. Canal also decouples replica placement from the DHT topology to allow developers to optimize function placement for different objectives. We evaluate Canal using a real-world multi-tier IoT deployment and we use Canal to compare placement strategies, end-to-end performance, and failure recovery using both benchmarks and a real-world IoT-edge application. Our results show that Canal is able to achieve both low latency and reliability in this setting. 

Co-location of processing infrastructure and IoT devices at the edge is used to reduce response latency and long-haul network use for IoT applications. As a result, edge clouds for many applications (e.g. agriculture, ecology, and smart city deployments) must operate in remote, unattended, and environmentally harsh settings, introducing new challenges. One key challenge is heat exposure, which can degrade the performance, reliability, and longevity of electronics. For edge clouds, these problems are exacerbated because they increasingly perform complex workloads, such as machine learning, to affect data-driven actuation and control of devices and systems in the environment. The goal of our work is to protect edge clouds from overheating. To enable this, we develop a heat-budget-based scheduling system, called Sparta, which leverages dynamic voltage and frequency scaling (DVFS) to adaptively control CPU temperature. Sparta takes machine learning applications, datasets, and a temperature threshold as input. It sets the initial frequency of the CPU based on historical data and then dynamically updates it, according to the applications’ execution profile and ambient temperature, to safeguard edge devices. We find that for a suite of machine learning applications and deployment temperatures, Sparta is able to maintain CPU temperature below the threshold 94% of the time while facilitating improvements in execution time by 1.04x − 1.32x over competitive approaches. 

We present CAPLets, an authorization mechanism that extends capability based security to support fine grained access control for multi-scale (sensors, edge, cloud) IoT deployments. To enable this, CAPLets uses a strong cryptographic construction to provide integrity while preserving computational efficiency for resource constrained systems. Moreover, CAPLets augments capabilities with dynamic, user defined constraints to describe arbitrary access control policies. We introduce an application specific, turing complete virtual machine, CapVM, alongside with eBPF and Wasm, to describe constraints. We show that CAPLets is able to express permissions and requirements at a fine grain, facilitating construction of non-trivial access control policies. We empirically evaluate the efficiency and flexibility of CAPLets abstractions using resource constrained devices and end-to-end IoT deployments, and compare it against related mechanisms in wide use today. Our empirical results show that CAPLets is an order of magnitude faster and more energy efficient than current IoT authorization systems.