Decades of continuous scaling has reduced the energy of unit computing to virtually zero, while energy-efficient communication has remained the primary bottleneck in achieving fully energy-autonomous Internet-of-Things (IoT) nodes. This article presents and analyzes the tradeoffs between the energies required for communication and computation in a wireless sensor network, deployed in a mesh architecture over a 2400-acre university campus, and is targeted toward multisensor measurement of temperature, humidity and water nitrate concentration for smart agriculture. Several scenarios involving in-sensor analytics (ISA), collaborative intelligence (CI), and context-aware switching (CAS) of the cluster head during CI has been considered. A real-time co-optimization algorithm has been developed for minimizing the energy consumption in the network, hence maximizing the overall battery lifetime. Measurement results show that the proposed ISA consumes ≈ 467× lower energy as compared to traditional Bluetooth low energy (BLE) communication, and ≈ 69500× lower energy as compared with long-range (LoRa) communication. When the ISA is implemented in conjunction with LoRa, the lifetime of the node increases from a mere 4.3 h to 66.6 days with a 230-mAh coin cell battery, while preserving >99% of the total information. The CI and CAS algorithms help in extending the worst case node lifetime by an additional 50%, thereby exhibiting an overall network lifetime of ≈ 104 days, which is >90% of the theoretical limits as posed by the leakage current present in the system, while effectively transferring information sampled every second. A Web-based monitoring system was developed to continuously archive the measured data, and for reporting real-time anomalies.

The recent advancement of the Internet of Things (IoT) enables the possibility of data collection from diverse environments using IoT devices. However, despite the rapid advancement of low-power communication technologies, the deployment of IoT networks still faces many challenges. In this article, we propose a hybrid, low-power, wide-area network (LPWAN) structure that can achieve wide-area communication coverage and low-power consumption on IoT devices by utilizing both sub-GHz long-range radio and 2.4-GHz short-range radio. Specifically, we constructed a low-power mesh network with LoRa, a physical-layer standard that can provide long-range (kilometers) point-to-point communication using custom time-division multiple access (TDMA). Furthermore, we extended the capabilities of the mesh network by enabling ANT, an ultralow-power, short-range communication protocol to satisfy data collection in dense device deployments. Third, we demonstrate the performance of the hybrid network with two real-world deployments at the Purdue University campus and at the university-owned farm. The results suggest that both networks have superior advantages in terms of cost, coverage, and power consumption vis-à-vis other IoT solutions, like LoRaWAN.

This demo abstract introduces Vulcan, a fuzz testing tool for evaluating the robustness of wearable device by injecting intra-device and inter-device communication messages. Vulcan first builds a state-model of a wearable app by offline training then steers the app to a target state for injecting mutated messages. The target state of the app typically runs a high number of concurrent processes. By testing a set of 100 popular Wear OS apps, Vulcan was able to trigger 45 unique crashes and 18 system reboots. These system reboots are triggered by a fuzzing user-level app and we present a mitigation strategy to prevent it.

As we look to use Wear OS (formerly known as Android Wear) devices for fitness and health monitoring, it is important to evaluate the reliability of its ecosystem. The goal of this paper is to understand the reliability weak spots in Wear OS ecosystem. We develop a state-aware fuzzing tool, Vulcan, without any elevated privileges, to uncover these weak spots by fuzzing Wear OS apps. We evaluate the outcomes due to these weak spots by fuzzing 100 popular apps downloaded from Google Play Store. The outcomes include causing specific apps to crash, causing the running app to become unresponsive, and causing the device to reboot. We finally propose a proof-of-concept mitigation solution to address the system reboot issue.

Many activities in laboratories at Purdue require user movement that cannot be carefully orchestrated or planned out, e.g., in our hardware, manufacturing, or propulsion labs. In such environments, it is challenging for users to consciously maintain the required safe social distance. This project provides a technical approach to proactively monitor the distance between users utilizing the Bluetooth transmission-reception signal strength (RSSI). We use a lightweight machine learning model to map the signal strength to the distance and infer the direction of motion between any two users. The technology builds on a long line of research in the area of wireless signals, some of which has been carried out in our lab. It is lightweight (can be easily carried as a lanyard worn by users), low cost (less than $15 when produced in bulk), privacy preserving (no data need to be shared to any other organizations), proactive (provides warning messages prior to approaching unsafe distance). We have shown its effectiveness in our preliminary experiments.

The rapid adoption of smartphones with different types of advanced sensors has led to an increasing trend in the usage of mobile crowdsensing applications, e.g., to create hyper-local weather maps. However, the high energy consumption of crowdsensing, chiefly due to expensive network communication, has been found to be detrimental to the wide-spread adoption. We propose a framework, called Sense-Aid, that can provide energy-efficient mobile crowdsensing service, coexisting with the cellular network. There are two key innovations in Sense-Aid beyond prior work (Piggyback Crowdsensing-Sensys13)---the middleware running on the cellular network edge to orchestrate multiple devices present in geographical proximity to suppress redundant data collection and communication. It understands the state of each device (radio state, battery state, etc.) to decide which ones should be selected for crowdsensing activities at any point in time. It also provides a simple programming abstraction to help with the development of crowdsensing applications. We show the benefit of Sense-Aid by conducting a user study consisting of 60 students in our campus, compared to a baseline periodic data collection method and Piggyback Crowdsensing. We find that energy saving is 93.3% for Sense-Aid compared with Piggyback Crowdsensing in a representative case which requires 2 devices to provide barometric values within an area of a circle whose radius is 1 kilometer and requires periodic data collection every 5 minutes for a 90-minute test. The selection algorithm of Sense-Aid also ensures reasonable fairness in the use of the different devices.