Road conditions affect the safety of road users as potholes may cause traffic accidents. We design and implement a sensor-based application for road conditions detection. The sensor device includes a GPS NEO-6M module to get the pothole locations and an MPU-92/65 module as accelerometer and gyroscope sensor, which are attached to a NodeMCU ESP8266. We use REST API as the web service to connect the sensor device and users, who will access the information using the Android application on their smartphones. To cluster the acquired data, we use the k-means clustering algorithm. The algorithm clusters data into deep, medium and shallow potholes.

Harvesting energy from the environment makes it possible to deploy tiny sensors for long periods of time, with little or no required maintenance; however, this free energy makes testing and experimentation difficult. Environmental energy sources vary widely and are often difficult both to predict and to reproduce in the lab during testing. These variations are also behavior dependent—a factor that leaves application engineers unable to make even simple comparisons between algorithms or hardware configurations, using traditional testing approaches. In this article, we describe the design and evaluation of Ekho, an emulator capable of recording energy harvesting conditions and accurately recreating those conditions in the lab. This makes it possible to conduct realistic and repeatable experiments involving energy harvesting devices. Ekho is a general-purpose, mobile tool that supports a wide range of harvesting technologies. We demonstrate, using a working prototype, that Ekho is capable of reproducing solar, Radio Frequency (RF), and kinetic energy harvesting environments accurately and consistently. Our results show that Ekho can recreate harvesting-dependent program behaviors by emulating energy harvesting conditions accurately to within 77.4μA for solar and 15.0μA for kinetic environments, and can emulate RF energy harvesting conditions consistently.

Sensing platforms are becoming batteryless to enable the vision of the Internet of Things, where trillions of devices collect data, interact with each other, and interact with people. However, these batteryless sensing platforms—that rely purely on energy harvesting—are rarely able to maintain a sense of time after a power failure. This makes working with sensor data that is time sensitive especially difficult. We propose two novel, zero-power timekeepers that use remanence decay to measure the time elapsed between power failures. Our approaches compute the elapsed time from the amount of decay of a capacitive device, either on-chip Static Random-Access Memory (SRAM) or a dedicated capacitor. This enables hourglass-like timers that give intermittently powered sensing devices a persistent sense of time. Our evaluation shows that applications using either timekeeper can keep time accurately through power failures as long as 45s with low overhead.

Untethered sensing devices have, for decades, powered all system components (processors, sensors, actuators, etc) from a single shared energy store (battery or capacitor). When designing batteryless sensors that are powered by harvested energy, this traditional approach results in devices that charge slowly and that are more error prone, inflexible, and inefficient than they could be.

Energy harvesting is an indispensable mechanism of sensor devices that operate in perpetuity. While harvesting free energy from the environment has enabled many applications, it has also spawned new problems, and new paradigms. Notably, making decisions on when to use high power sensors and external components when energy is scarce, and future supply is unpredictable. Because sensor nodes generally share a single, centralized energy store, seemingly atomic, or unrelated sensing tasks can hamper each other by drawing the supply voltage too low, and draining the energy reservoir. This demonstration presents the United Federation of Peripherals (UFoP), a novel method of separating energy concerns in hardware by allocating dedicated energy storage (in the form of small capacitors) to specific sensor components, and charging them in a prioritized fashion with an analog front end. UFoP gives application designers a more deterministic view of energy and task scheduling, allowing them to make better informed decisions when developing sensor applications. Designers do not have to rely on crude estimates or simulations but can instead depend on in-situ analog measurements to opportunistically drive their applications.

Embedded systems that are wirelessly reprogrammed can be rendered useless by certain programming errors, excessive power consumption, or misconfigurations in the hardware. These types of situations can leave a device in a state that compromises its programmability, often rendering the device useless. Existing attempts to address the problem of robust wireless reprogramming have all been software-based solutions, that are vulnerable to certain errors, such as memory corruption, can corrupt the recovery programs. We propose a hardware-based solution to wireless reprogramming, physically separating the programmer and target device. This separation limits the propagation of errors, and ensures the device will always be recoverable. In this poster we will present the design and an early prototype of our approach -- an ultra-low-power, low-cost hardware solution to ensure recovery from fatal errors and reprogrammability in wireless systems. This poster discusses the current system design, initial results, and system analysis from our current prototype. We also present future and ongoing directions, as well as key research questions. This work was funded by National Science Foundation grants CNS-1314342 and CNS-1453607. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.

A Wireless Sensor Network (WSN) is composed of many sensor nodes which transmit their data wirelessly over a multi-hop network to data sinks. Since WSNs are subject to node failures, the network topology should be robust, so that when a failure does occur, data delivery can continue from all surviving nodes. A WSN is k-robust if an alternate length-constrained route to a sink is available for each surviving node after the failure of up to k-1 nodes. Determining whether a network is k-robust is an NP-complete problem. We develop a Constraint Programming (CP) approach for solving this problem which outperforms a Mixed-Integer Programming (MIP) model on larger problems. A network can be made robust by deploying extra relay nodes, and we extend our CP approach to an optimisation problem by using QuickXplain to search for a minimal set of relays, and compare it to a state-of-the-art local search approach.

Contiki's Cooja is a very popular Wireless Sensor Network (WSN) simulator, but it lacks support for modelling sensing coverage. We introduce WSN-Maintain, a Cooja-based tool for maintaining coverage requirements in an in-building WSN. To analyse the coverage of a building, WSN-Maintain takes as input the floorplan of the building, the coverage requirement of each region and the locations of sensor nodes. We take account of the heterogeneity of device specifications in terms of communication capability and sensing coverage. WSN-Maintain is run in parallel with the collect-view tool of Contiki, which was integrated into the Cooja simulator. We show that WSN-Maintain is able to automatically turn on redundant nodes to maintain the coverage requirement when active nodes fail and report failures that require physical maintenance. This tool allows us to evaluate different approaches to maintain coverage, including deferring physical maintenance to reduce operational costs.

We introduce ER-MAC, a hybrid MAC protocol for emergency response wireless sensor networks. ER-MAC is designed as a hybrid of the TDMA and CSMA approaches, giving it the flexibility to adapt to traffic and topology changes. It adopts a TDMA approach to schedule collision-free slots. Nodes wake up for their scheduled slots, but otherwise sleep to conserve energy. When an emergency occurs, nodes that participate in the emergency monitoring change their MAC behaviour by allowing contention in TDMA slots. Simulations in ns-2 show that ER-MAC outperforms Z-MAC with higher delivery ratio, lower latency, and lower energy consumption.