MH-REACH-Mote: supporting multi-hop passive radio wake-up for wireless sensor network

A passive wake-up radio in a wireless sensor network (WSN) has the advantage of increasing network lifetime by using a wake-up radio receiver (WuRx) to eliminate unnecessary idle listening. A sensor node equipped with a WuRx can operate in an ultra-low-power sleep mode, waiting for a trigger signal sent by the wake-up radio transmitter (WuTx). The passive WuRx is entirely powered by the energy harvested from radio transmissions sent by the WuTx. Therefore, it has the advantage of not consuming any energy locally, which would drain the sensor node's battery. Even so, the high amount of energy required to wake up a passive WuRx by a WuTx makes it difficult to build a multi-hop passive wake-up sensor network. In this paper, we describe and discuss our implementation of a battery-powered sensor node with multi-hop wake-up capability using passive WuRxs, called MH-REACH-Mote (Multi-hop-Range EnhAnCing energy Harvester-Mote). The MH-REACH-Mote is kept in an ultra-low-power sleep mode until it receives a wake-up trigger signal. Upon receipt, it wakes up and transmits a new trigger signal to power other passive WuRxs. We evaluate the wake-up range and power consumption of an MH-REACH-Mote through a series of field tests. Results show that the MH-REACH-Mote enables multi-hop wake-up capabilities for passive WuRxs with a wake-up range of 9.4m while requiring a reasonable power consumption for WuTx functionality. We also simulate WSN data collection scenarios with MH-REACH-Motes and compare the results with those of active wake-up sensor nodes as well as a low power listening approach. The results show that the MH-REACH-Mote enables a longer overall lifetime than the other two approaches when data is collected infrequently.Peer ReviewedPostprint (author's final draft

Wireless distance estimation with low-power standard components in wireless sensor nodes

In the context of increasing use of moving wireless sensor nodes the interest in localizing these nodes in their application environment is strongly rising. For many applications, it is necessary to know the exact position of the nodes in two- or three-dimensional space. Commonly used nodes use state-of-the-art transceivers like the CC430 from Texas Instruments with integrated signal strength measurement for this purpose. This has the disadvantage, that the signal strength measurement is strongly dependent on the orientation of the node through the antennas inhomogeneous radiation pattern as well as it has a small accuracy on long ranges. Also, the nodes overall attenuation and output power has to be calibrated and interference and multipath effects appear in closed environments. Another possibility to trilaterate the position of a sensor node is the time of flight measurement. This has the advantage, that the position can also be estimated on long ranges, where signal strength methods give only poor accuracy. In this paper we present an investigation of the suitability of the state-of-the-art transceiver CC430 for a system based on time of flight methods and give an overview of the optimal settings under various circumstances for the in-field application. For this investigation, the systematic and statistical errors in the time of flight measurements with the CC430 have been investigated under a multitude of parameters. Our basic system does not use any additional components but only the given standard hardware, which can be found on the Texas Instruments evaluation board for a CC430. Thus, it can be implemented on already existent sensor node networks by a simple software upgrade.Comment: 8 pages, Proceedings of the 14th Mechatronics Forum International Conference, Mechatronics 201

Wireless wake-up sensor network for structural health monitoring of large-scale highway bridges

To realize in-situ structural health monitoring of critical infrastructure such as bridges, we present a powerful, but also low power and flexible, wireless sensor node utilizing a wake-up transceiver. The sensor node is equipped with several kinds of sensors, such as temperature, pressure and acceleration for in-situ monitoring of critical infrastructure. In addition to these commonly used sensors in wireless sensor networks, some nodes are equipped with global navigation satellite system receivers (GNSS) and others with tilt and acceleration sensors of very high accuracy that were developed by Nothrop Grumman LITEF GmbH. We present a low power wakeup multi-hop routing protocol that is able to transmit data with little overhead by supporting the use of wake-up receivers in combination with long-range communication radios. The wireless sensor nodes and the routing protocol are tested at a large-scale highway bridge in south-west Germany, where a prototype network was installed in June 2015 following a first test installation earlier in June at the same bridge. A gateway node equipped with a Global System for Mobile Communications (GSM) modem transfers the network data to a remote server located at the University of Freiburg

Extensible FlexRay communication controller for FPGA-based automotive systems

Modern vehicles incorporate an increasing number of distributed compute nodes, resulting in the need for faster and more reliable in-vehicle networks. Time-triggered protocols such as FlexRay have been gaining ground as the standard for high-speed reliable communications in the automotive industry, marking a shift away from the event-triggered medium access used in controller area networks (CANs). These new standards enable the higher levels of determinism and reliability demanded from next-generation safety-critical applications. Advanced applications can benefit from tight coupling of the embedded computing units with the communication interface, thereby providing functionality beyond the FlexRay standard. Such an approach is highly suited to implementation on reconfigurable architectures. This paper describes a field-programmable gate array (FPGA)-based communication controller (CC) that features configurable extensions to provide functionality that is unavailable with standard implementations or off-the-shelf devices. It is implemented and verified on a Xilinx Spartan 6 FPGA, integrated with both a logic-based hardware ECU and a fully fledged processor-based electronic control unit (ECU). Results show that the platform-centric implementation generates a highly efficient core in terms of power, performance, and resource utilization. We demonstrate that the flexible extensions help enable advanced applications that integrate features such as fault tolerance, timeliness, and security, with practical case studies. This tight integration between the controller, computational functions, and flexible extensions on the controller enables enhancements that open the door for exciting applications in future vehicles

WSN and RFID Integration in the IoT scenario: an Advanced Safety System for Industrial Plants

The paper proposes and discusses the integration of WSN and RFID technologies in the IoT scenario. The proposed approach is based on the REST paradigm, thanks to which the two technologies can be seamless integrated by representing sensors, actuators and RFID related data as network resources globally addressable through state-of-the-art IoT protocols. The integration approach is detailed for the Smart Factory use case by proposing and developing an advanced IoT-based WSN and RFID integrated solution aiming at improving safety in industrial plants. The developed system can guarantee a safe access to factory dangerous areas in which safety equipments are required. In the paper, the system design is first presented, then, all the developed hardware and software solutions are described before presenting system performance results in a real test bed. System performance are reported in terms of response time and accuracy for authorization control and location tracking applications

Design, Implementation, and Performance Evaluation of a Flexible Low-Latency Nanowatt Wake-Up Radio Receiver

Wireless sensor networks (WSNs) have received significant attention in recent years and have found a wide range of applications, including structural and environmental monitoring, mobile health, home automation, Internet of Things, and others. As these systems are generally battery operated, major research efforts focus on reducing power consumption, especially for communication, as the radio transceiver is one of the most power-hungry components of a WSN. Moreover, with the advent of energy-neutral systems, the emphasis has shifted toward research in microwatt (or even nanowatt) communication protocols or systems. A significant number of wake-up radio receiver (WUR) architectures have been proposed to reduce the communication power of WSN nodes. In this work, we present an optimized ultra-low power (nanowatt) wake-up receiver for use in WSNs, designed with low-cost off-the-shelf components. The wake-up receiver achieves power consumption of 152 nW (with-32 dBm sensitivity), sensitivity up to-55 dBm (with maximum power of 1,2 μW), latency from 8 μs, tunable frequency, and short commands communication. In addition, a low power solution, which includes addressing capability directly in the wake-up receiver, is proposed. Experimental results and simulations demonstrate low power consumption, functionality, and benefits of the design optimization compared with other solutions, as well as the benefits of addressing false positive (FP) outcomes reduction

Internet of Things-aided Smart Grid: Technologies, Architectures, Applications, Prototypes, and Future Research Directions

Traditional power grids are being transformed into Smart Grids (SGs) to address the issues in existing power system due to uni-directional information flow, energy wastage, growing energy demand, reliability and security. SGs offer bi-directional energy flow between service providers and consumers, involving power generation, transmission, distribution and utilization systems. SGs employ various devices for the monitoring, analysis and control of the grid, deployed at power plants, distribution centers and in consumers' premises in a very large number. Hence, an SG requires connectivity, automation and the tracking of such devices. This is achieved with the help of Internet of Things (IoT). IoT helps SG systems to support various network functions throughout the generation, transmission, distribution and consumption of energy by incorporating IoT devices (such as sensors, actuators and smart meters), as well as by providing the connectivity, automation and tracking for such devices. In this paper, we provide a comprehensive survey on IoT-aided SG systems, which includes the existing architectures, applications and prototypes of IoT-aided SG systems. This survey also highlights the open issues, challenges and future research directions for IoT-aided SG systems

Energy-Efficient Boarder Node Medium Access Control Protocol for Wireless Sensor Networks

This paper introduces the design, implementation, and performance analysis of the scalable and mobility-aware hybrid protocol named boarder node medium access control (BN-MAC) for wireless sensor networks (WSNs), which leverages the characteristics of scheduled and contention-based MAC protocols. Like contention-based MAC protocols, BN-MAC achieves high channel utilization, network adaptability under heavy traffic and mobility, and low latency and overhead. Like schedule-based MAC protocols, BN-MAC reduces idle listening time, emissions, and collision handling at low cost at one-hop neighbor nodes and achieves high channel utilization under heavy network loads. BN-MAC is particularly designed for region-wise WSNs. Each region is controlled by a boarder node (BN), which is of paramount importance. The BN coordinates with the remaining nodes within and beyond the region. Unlike other hybrid MAC protocols, BN-MAC incorporates three promising models that further reduce the energy consumption, idle listening time, overhearing, and congestion to improve the throughput and reduce the latency. One of the models used with BN-MAC is automatic active and sleep (AAS), which reduces the ideal listening time. When nodes finish their monitoring process, AAS lets them automatically go into the sleep state to avoid the idle listening state. Another model used in BN-MAC is the intelligent decision-making (IDM) model, which helps the nodes sense the nature of the environment. Based on the nature of the environment, the nodes decide whether to use the active or passive mode. This decision power of the nodes further reduces energy consumption because the nodes turn off the radio of the transceiver in the passive mode. The third model is the least-distance smart neighboring search (LDSNS), which determines the shortest efficient path to the one-hop neighbor and also provides cross-layering support to handle the mobility of the nodes. The BN-MAC also incorporates a semi-synchronous feature with a low duty cycle, which is advantageous for reducing the latency and energy consumption for several WSN application areas to improve the throughput. BN-MAC uses a unique window slot size to enhance the contention resolution issue for improved throughput. BN-MAC also prefers to communicate within a one-hop destination using Anycast, which maintains load balancing to maintain network reliability. BN-MAC is introduced with the goal of supporting four major application areas: monitoring and behavioral areas, controlling natural disasters, human-centric applications, and tracking mobility and static home automation devices from remote places. These application areas require a congestion-free mobility-supported MAC protocol to guarantee reliable data delivery. BN-MAC was evaluated using network simulator-2 (ns2) and compared with other hybrid MAC protocols, such as Zebra medium access control (Z-MAC), advertisement-based MAC (A-MAC), Speck-MAC, adaptive duty cycle SMAC (ADC-SMAC), and low-power real-time medium access control (LPR-MAC). The simulation results indicate that BN-MAC is a robust and energy-efficient protocol that outperforms other hybrid MAC protocols in the context of quality of service (QoS) parameters, such as energy consumption, latency, throughput, channel access time, successful delivery rate, coverage efficiency, and average duty cycle.https://doi.org/10.3390/s14030507

Development of portable air quality sensor network based on IoT devices

Air pollution has been one of the major agendas around the globe in recent years. With rising awareness among all citizens, it's of extraordinary importance to measure data related to air pollution in order to have interested parties making informative decisions. Network composed of IoT devices has been one of the tools researchers relied on. Recent years saw rapid progress in sensor technology, which in turn flourished the market for low-cost sensors, giving citizens opportunities to measure various physical properties with affordable and portable sensors. Countless organizations have deployed wireless sensor networks (WSN) involving the usage of IoT devices and budget-friendly sensing hardware. Statistical Analysis of Networks and Systems (SANS) is a research group of the Computer Architecture Department at the Polytechnic University of Catalonia, which has launched several campaigns using WSN composed of Captor devices. Researchers then use relevant machine learning techniques to provide more meaningful information out of otherwise flawed data. A platform using a technology stack composed of Captor devices and machine learning techniques has gone through several stages and is still in the progress of improving itself. This thesis discusses the latest iteration of such a platform, by means of introducing characteristics of hardware, software, as well as machine learning methodologies used. By overviewing and comparing the older iterations of Captor and similar platforms used by other researchers, this thesis hopes to serve as a reference outlook into the current and future development of WSN (with focus on air-quality), where innovations are constantly needed to improve its capabilities. The result of the thesis is an autonomous IoT device, Captor4b, that is self-sufficient for at least 1 month and half where the autonomy can be further tweaked by adjusting duty cycle of Raspberry Pi and Arduino Nano separately from a software perspective