Internet-of-Things (IoTs) are becoming more and more popular in our life. IoT devices are generally designed for sensing or actuation purposes. However, the current sensing system on IoT devices lacks the understanding of sensing needs, which diminishes the sensing flexibility, isolation, and security when multiple sensing applications need to use sensor resources. In this work, we propose VirtSense, an ARM TrustZone based virtual sensing system, to provide each sensing application a virtual sensor instance, which further enables a safe, flexible and isolated sensing environment on the IoT devices. Our preliminary results show that VirtSense: 1) can provide virtual sensor instance for each sensing application so that the sensing needs of each application will be satisfied without affecting others; 2) is able to enforce access control policy even under an untrusted environment.
more »
« less
LegoSENSE: An Open and Modular Sensing Platform for Rapidly-Deployable IoT Applications
Domain-specific sensor deployments are critical to enabling various IoT applications. Existing solutions for quickly deploying sensing systems require significant amount of work and time, even for experienced engineers. We propose LegoSENSE, a low-cost open-source and modular platform, built on top of the widely popular Raspberry Pi single-board computer, that makes it simple for anyone to rapidly set up and deploy a customized sensing solution for application specific IoT deployments. In addition, the ‘plug and play’ and ‘mix and match’ functionality of LegoSENSE makes the sensor modules reusable, and allows them to be mixed and matched to serve a variety of needs. We show, through a series of user studies, that LegoSENSE enables users without engineering background to deploy a wide range of applications up to 9 × faster than experienced engineers without the use of LegoSENSE. We open-source the hardware and software designs to foster an ever-evolving community, enabling IoT applications for enthusiasts, students, scientists, and researchers across various application domains with or without prior experiences with embedded platforms or coding.
more »
« less
- Award ID(s):
- 1943396
- PAR ID:
- 10416023
- Date Published:
- Journal Name:
- IoTDI '23: Proceedings of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation
- Page Range / eLocation ID:
- 367 to 380
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Smart space administration and application development is challenging in part due to the semantic gap that exists between the high-level requirements of users and the low-level capabilities of IoT devices. The stakeholders in a smart space are required to deal with communicating with specific IoT devices, capturing data, processing it, and abstracting it out to generate useful inferences. Additionally, this makes reusability of smart space applications difficult, since they are developed for specific sensor deployments. In this article, we present a holistic approach to IoT smart spaces, the SemIoTic ecosystem, to facilitate application development, space management, and service provision to its inhabitants. The ecosystem is based on a centralized repository, where developers can advertise their space-agnostic applications, and a SemIoTic system deployed in each smart space that interacts with those applications to provide them with the required information. SemIoTic applications are developed using a metamodel that defines high-level concepts abstracted from the smart space about the space itself and the people within it. Application requirements can be expressed then in terms of user-friendly high-level concepts, which are automatically translated by SemIoTic into sensor/actuator commands adapted to the underlying device deployment in each space. We present a reference implementation of the ecosystem that has been deployed at the University of California, Irvine and is abstracting data from hundreds of sensors in the space and providing applications to campus members.more » « less
-
IoT devices influence many different spheres of society and are predicted to have a huge impact on our future. Extracting real-time insights from diverse sensor data and dealing with the underlying uncertainty of sensor data are two main challenges of the IoT ecosystem In this paper, we propose a data processing architecture, M-DB, to effectively integrate and continuously monitor uncertain and diverse IoT data. M-DB constitutes of three components:(1) model-based operators (MBO) as data management abstractions for IoT application developers to integrate data from diverse sensors. Model-based operators can support event-detection and statistical aggregation operators,(2) M-Stream, a dataflow pipeline that combines model-based operators to perform computations reflecting the uncertainty of underlying data, and (3) M-Store, a storage layer separating the computation of application logic from physical sensor data management, to effectively deal with missing or delayed sensor data. M-DB is designed and implemented over Apache Storm and Apache Kafka, two open-source distributed event processing systems. Our illustrated application examples throughout the paper and evaluation results illustrate that M-DB provides a realtime data-processing architecture that can cater to the diverse needs of IoT applications.more » « less
-
IoT messaging protocols are critical to connecting users and IoT devices. Among all the protocols, the Message Queuing and Telemetry Transport (MQTT) is arguably the most widely used. Mainstream IoT platforms leverage MQTT brokers, server side implementation of MQTT, to enable and mediate user-device communication (e.g., the transmission of control commands). There are over 70 open-source MQTT brokers, which have been widely adopted in production. Any security defects in those open-source MQTT brokers easily get into many endors' IoT deployments with amplified impacts, inevitably endangering the security of IoT applications and millions of users. We report the first systematic security analysis of open-source MQTT brokers in the wild. To enable the analysis, we designed and developed MQTTactic, a semiautomatic tool that can formally verify MQTT broker implementations based on generated security properties. MQTTactic is based on static code analysis, formal modeling, and automated model checking (with off-the-shelf model checker Spin). In designing MQTTactic, we characterize and address key technical challenges. MQTTactic currently focuses on authorization-related properties, and discovered 7 novel, zero-day flaws practically enabling serious, unauthorized access. We reported all flaws to related parties, who acknowledged the issues and have been taking actions to fix them. Our thorough evaluation shows that MQTTactic is effective and practical.more » « less
-
Multi-sensor IoT devices can gather different types of data by executing different sensing activities or tasks. Therefore, IoT applications are also becoming more complex in order to process multiple data types and provide a targeted response to the monitored phenomena. However, IoT devices which are usually resource-constrained still face energy challenges since using each of these sensors has an energy cost. Therefore, energy-efficient solutions are needed to extend the device lifetime while balancing the sensing data requirements of the IoT application. Cooperative monitoring is one approach for managing energy and involves reducing the duplication of sensing tasks between neighboring IoT devices. Setting up cooperative monitoring is a scheduling problem and is challenging in a distributed environment with resource-constrained IoT devices. In this work, we present our Distributed Token and Tier-based task Scheduler (DTTS) for a multi-sensor IoT network. Our algorithm divides the monitoring period (5 min epochs) into a set of non-overlapping intervals called tiers and determines the start deadlines for the task at each IoT device. Then to minimize temporal sensing overlap, DTTS distributes task executions throughout the epoch and uses tokens to share minimal information between IoT devices. Tasks with earlier start deadlines are scheduled in earlier tiers while tasks with later start deadlines are scheduled in later tiers. Evaluating our algorithm against a simple round-robin scheduler shows that the DTTS algorithm always schedules tasks before their start deadline expires.more » « less