Real-time and long lasting Internet of Things through semantic wake-up radios

The world is going towards the Internet of Things (IoT) where trillions of objects that are common in our lives will be enhanced and revolutionized by adding them computational and networking capabilities. Examples are cars, street lamps, industrial machinery, electrical appliances. The corner- stone of Internet of Things research is Wireless Sensor Networks (WSNs). These networks are made of hundreds of low-cost, low-complexity devices endowed with sensors to monitor the surrounding environment or objects. Typically these devices (also called sensors, nodes or motes) are battery-powered, therefore they can operate for a limited amount of time (i.e., days) before running out of energy. This is the main challenge that applications of Wireless Sensor Networks have to face. Since one of the major power consumers in a node is the radio transceiver, a lot of research effort has been put into finding solutions that keep the radio in a low-power state as much as possible, while not harming the communication capability. While this approach brings the network lifetime, i.e. the time before battery-operated nodes die having depleted their energy, to years or more, it introduces significant latency, as the energy reduction comes at the cost of not being able to reach nodes in deep sleep for long period of times. The most promising solution to this problem is the wake-up radio, an additional ultra-low power transceiver used for the sole purpose of triggering the activation of the high power, high bandwidth radio. Wake-up radio enabled IoT systems maintain always on their wake up radio, which has a negligible energy consumption, in this way optimizing both energy and latency performance metrics. Most of the research so far focused on the design of wake-up receivers, while a limited amount of communication protocols that take advantage of this radio has been proposed. Moreover, almost all of these protocols have been evaluated only through simulations. In this thesis we set to start filling this gap. We first evaluate the range performance of an ultra-low power wake-up receiver integrated into a state- of-the-art Wireless Sensor Network mote, the MagoNode++. Based on the results of this evaluation we deploy an outdoor testbed made of MagoNode++ motes. The testbed allows to validate in a real-world scenario our implementation of CTP-WUR, an extension of the widely used Collection Tree Protocol (CTP) for wake-up radio-enabled Wireless Sensor Networks. The comparison between CTP-WUR and CTP demonstrates that wake-up radios can effectively reduce the power consumption and obtain, at the same time, end-to-end latencies in the order of milliseconds, enabling new time critical applications. Based on the results and on the insights gained dur- ing the testbed evaluation a new version of CTP-WUR is presented that improves its performance across all the metrics taken into consideration: end-to-end packet latency, energy consumption and Packet Delivery Ratio

A novel wake-up receiver with addressing capability for wireless sensor nodes

Emerging low-power radio triggering techniques for wireless motes are a promising approach to prolong the lifetime of Wireless Sensor Networks (WSNs). By allowing nodes to activate their main transceiver only when data need to be transmitted or received, wake-up-enabled solutions virtually eliminate the need for idle listening, thus drastically reducing the energy toll of communication. In this paper we describe the design of a novel wake-up receiver architecture based on an innovative pass-band filter bank with high selectivity capability. The proposed concept, demonstrated by a prototype implementation, combines both frequency-domain and time-domain addressing space to allow selective addressing of nodes. To take advantage of the functionalities of the proposed receiver, as well as of energy-harvesting capabilities modern sensor nodes are equipped with, we present a novel wake-up-enabled harvesting-aware communication stack that supports both interest dissemination and converge casting primitives. This stack builds on the ability of the proposed WuR to support dynamic address assignment, which is exploited to optimize system performance. Comparison against traditional WSN protocols shows that the proposed concept allows to optimize performance tradeoffs with respect to existing low-power communication stacks. © 2014 IEEE

Building a green connected future: smart (Internet of) Things for smart networks

The vision of Internet of Things (IoT) promises to reshape society by creating a future where we will be surrounded by a smart environment that is constantly aware of the users and has the ability to adapt to any changes. In the IoT, a huge variety of smart devices is interconnected to form a network of distributed agents that continuously share and process information. This communication paradigm has been recognized as one of the key enablers of the rapidly emerging applications that make up the fabric of the IoT. These networks, often called wireless sensor networks (WSNs), are characterized by the low cost of their components, their pervasive connectivity, and their self-organization features, which allow them to cooperate with other IoT elements to create large-scale heterogeneous information systems. However, a number of considerable challenges is arising when considering the design of large-scale WSNs. In particular, these networks are made up by embedded devices that suffer from severe power constraints and limited resources. The advent of low-power sensor nodes coupled with intelligent software and hardware technologies has led to the era of green wireless networks. From the hardware perspective, green sensor nodes are endowed with energy scavenging capabilities to overcome energy-related limitations. They are also endowed with low-power triggering techniques, i.e., wake-up radios, to eliminate idle listening-induced communication costs. Green wireless networks are considered a fundamental vehicle for enabling all those critical IoT applications where devices, for different reasons, do not carry batteries, and that therefore only harvest energy and store it for future use. These networks are considered to have the potential of infinite lifetime since they do not depend on batteries, or on any other limited power sources. Wake-up radios, coupled with energy provisioning techniques, further assist on overcoming the physical constraints of traditional WSNs. In addition, they are particularly important in green WSNs scenarios in which it is difficult to achieve energy neutrality due to limited harvesting rates. In this PhD thesis we set to investigate how different data forwarding mechanisms can make the most of these green wireless networks-enabling technologies, namely, energy harvesting and wake-up radios. Specifically, we present a number of cross-layer routing approaches with different forwarding design choices and study their consequences on network performance. Among the most promising protocol design techniques, the past decade has shown the increasingly intensive adoption of techniques based on various forms of machine learning to increase and optimize the performance of WSNs. However, learning techniques can suffer from high computational costs as nodes drain a considerable percentage of their energy budget to run sophisticated software procedures, predict accurate information and determine optimal decision. This thesis addresses also the problem of local computational requirements of learning-based data forwarding strategies by investigating their impact on the performance of the network. Results indicate that local computation can be a major source of energy consumption; it’s impact on network performance should not be neglected

Development of Aerial-Ground Sensing Network: Architecture, Sensor Activation, and Spatial Path-Energy Optimization

Title from PDF of title page viewed May 13, 2019Dissertation advisor: ZhiQiang ChenVitaIncludes bibliographical references (pages 101-110)Thesis (Ph.D.)--School of Computing and Engineering. University of Missouri--Kansas City, 2019The advent of autonomous navigation, positioning, and in general robotics technologies has enabled the maturity of small to miniature-sized unmanned aerial vehicles (UAVs; or colloquially called drones) and their wide use in engineering practice as a low-cost and effective geospatial remote sensing solution. Meanwhile, wireless sensing network technology (WSN) has also matured in recent years with many applications found in engineering practice. In this dissertation, a novel aerial ground wireless sensing network (AG-WSN) is developed, which is expected to transform a number of critical geospatial sensing and monitoring practices, such as precision agriculture, civil infrastructure protection, and disaster response. Towards the maximal energy efficiency, three research problems are concerned in this dissertation. First, a radio-frequency (RF) wake-up mechanism is investigated for aerial activation of ground sensors using a UAV platform. Second, the data transmission under wireless interference between the UAV and ground WSN is experimentally investigated, which suggests practical relations and parameters for aerial-ground communication configuration. Last, this dissertation theoretically explores and develops an optimization framework for UAV's aerial path planning when collecting ground-sensor data. An improved mixed-integer non-linear programming approach is proposed for solving the optimal spatial path-energy using the framework of the traveling-salesman problem with neighborhoods.Introduction -- Development of radio-frequency sensor wake-up through UAV as an aerial gateway -- Experimental investigation of aerial-ground network communication towards geospatially large-scale structural health monitoring -- Spatial path-energy optimization for tactic unmanned aerial vehicles operation in aerial-ground networking -- Conclusion and future wor

The energy problem in resource constrained wireless networks

Today Wireless Sensor Networks are part of a wider scenario involving several wireless and wired communication technology: the Internet Of Things (IoT). The IoT envisions billions of tiny embedded devices, called Smart Objects, connected in a Internet-like structure. Even if the integration of WSNs into the IoT scenario is nowadays a reality, the main bottleneck of this technology is the energy consumption of sensor nodes, which quickly deplete the limited amount of energy of available in batteries. This drawback, referred to as the energy problem, was addressed in a number of research papers proposing various energy optimization approaches to extend sensor nodes lifetime. However, energy problem is still an open issue that prevents the full exploitation of WSN technology. This thesis investigates the energy problem in WSNs and introduces original solutions trying to mitigate drawbacks related to this phenomenon. Starting from solutions proposed by the research community in WSNs, we deeply investigate critical and challenging factors concerning the energy problem and we came out with cutting-edge low-power hardware platforms, original software energy-aware protocols and novel energy-neutral hardware/software solutions overcoming the state-of-art. Concerning low-power hardware, we introduce the MagoNode, a new WSN mote equipped with a radio frequency (RF) front-end which enhances radio performance. We show that in real applicative contexts, the advantages introduced by the RF front-end keep packet re-trasmissions and forwards low. Furthermore, we present the ultra low-power Wake-Up Radio (WUR) system we designed and the experimental activity to validate its performance. In particular, our Wake-up Radio Receiver (WRx) features a sensitivity of -50 dBm, has a current consumption of 579nA in idle-listening and features a maximum radio range of about 19 meters. What clearly resulted from the experimental activity is that performance of the WRx is strongly affected by noise. To mitigate the impact of noise on WUR communication we implemented a Forward Error Correction (FEC) mechanism based on Hamming code. We performed several test to determine the effectiveness of the proposed solution. The outcome show that our WUR system can be employed in environment where the Bit Error Rate (BER) induced by noise is up to 10^2, vice versa, when the BER induced by noise is in the order of 10´3 or below, it is not worth to use any Forward Error Correction (FEC) mechanism since it does not introduce any advantages compared to uncoded data. In the context of energy-aware solutions, we present two protocols: REACTIVE and ALBA-WUR. REACTIVE is a low-power over-the-air programming (OAP) protocol we implemented to improve the energy efficiency and lower the image dissemination time of Deluge T2, a well-known OAP protocol implemented in TinyOS. To prove the effectiveness of REACTIVE we compared it to Deluge exploiting a testbed made of MagoNode motes. Results of our experiments show that the image dissemination time is 7 times smaller than Deluge, while the energy consumption drops 2.6 times. ALBA-WUR redesigns ALBA-R protocol, extending it to exploit advantages of WUR technology. We compared ALBA-R and ALBA-WUR in terms of current consumption and latency via simulations. Results show that ALBA-WUR estimated network lifetime is decades longer than that achievable by ALBA-R. Furthermore, end-to-end packet latency features by ALBA-WUR is comparable to that of ALBA-R. While the main goal of energy optimization approaches is motes lifetime maximization, in recent years a new research branch in WSN emerged: Energy Neutrality. In contrast to lifetime maximization approach, energy neutrality foresees the perennial operation of the network. This can be achieve only making motes use the harvested energy at an appropriate rate that guarantees an everlasting lifetime. In this thesis we stress that maximizing energy efficiency of a hardware platform dedicated to WSNs is the key to reach energy neutral operation (ENO), still providing reasonable data rates and delays. To support this conjecture, we designed a new hardware platform equipped with our wake-up radio (WUR) system able to support ENO, the MagoNode++. The MagoNode++ features a energy harvester to gather energy from solar and thermoelectric sources, a ultra low power battery and power management module and our WUR system to improve the energy efficiency of wireless communications. To prove the goodness in terms of current consumption of the MagoNode++ we ran a series of experiments aimed to assess its performance. Results show that the MagoNode++ consumes only 2.8 µA in Low Power Mode with its WRx module in listening mode. While carrying on our research work on solutions trying to mitigate the energy problem, we also faced a challenging application context where the employment of WSNs is considered efficient and effective: structural health monitoring (SHM). SHM deals with the early detection of damages to civil and industrial structures and is emerging as a fundamental tool to improve the safety of these critical infrastructures. In this thesis we present two real world WSNs deployment dedicated to SHM. The first concerned the monitoring of the Rome B1 Underground construction site. The goal was to monitor the structural health of a tunnel connecting two stops. The second deployment concerned the monitoring of the structural health of buildings in earthquake-stricken areas. From the experience gained during these real world deployments, we designed the Modular Monitoring System (MMS). The MMS is a new low-power platform dedicated to SHM based on the MagoNode. We validated the effectiveness of the MMS low-power design performing energy measurements during data acquisition from actual transducers