KRATOS: An Open Source Hardware-Software Platform for Rapid Research in LPWANs

Long-range (LoRa) radio technologies have recently gained momentum in the IoT landscape, allowing low-power communications over distances up to several kilometers. As a result, more and more LoRa networks are being deployed. However, commercially available LoRa devices are expensive and propriety, creating a barrier to entry and possibly slowing down developments and deployments of novel applications. Using open-source hardware and software platforms would allow more developers to test and build intelligent devices resulting in a better overall development ecosystem, lower barriers to entry, and rapid growth in the number of IoT applications. Toward this goal, this paper presents the design, implementation, and evaluation of KRATOS, a low-cost LoRa platform running ContikiOS. Both, our hardware and software designs are released as an open- source to the research community.Comment: Accepted at WiMob 201

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

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

Wake-up radio-based data forwarding for green wireless networks

This paper presents G-WHARP, for Green Wake-up and HARvesting-based energy-Predictive forwarding, a wake-up radio-based forwarding strategy for wireless networks equipped with energy harvesting capabilities (green wireless networks). Following a learning-based approach, G-WHARP blends energy harvesting and wake-up radio technology to maximize energy efficiency and obtain superior network performance. Nodes autonomously decide on their forwarding availability based on a Markov Decision Process (MDP) that takes into account a variety of energy-related aspects, including the currently available energy and that harvestable in the foreseeable future. Solution of the MDP is provided by a computationally light heuristic based on a simple threshold policy, thus obtaining further computational energy savings. The performance of G-WHARP is evaluated via GreenCastalia simulations, where we accurately model wake-up radios, harvestable energy, and the computational power needed to solve the MDP. Key network and system parameters are varied, including the source of harvestable energy, the network density, wake-up radio data rate and data traffic. We also compare the performance of G-WHARP to that of two state-of-the-art data forwarding strategies, namely GreenRoutes and CTP-WUR. Results show that G-WHARP limits energy expenditures while achieving low end-to-end latency and high packet delivery ratio. Particularly, it consumes up to 34% and 59% less energy than CTP-WUR and GreenRoutes, respectively

Optimal power control in green wireless sensor networks with wireless energy harvesting, wake-up radio and transmission control

Wireless sensor networks (WSNs) are autonomous networks of spatially distributed sensor nodes which are capable of wirelessly communicating with each other in a multi-hop fashion. Among different metrics, network lifetime and utility and energy consumption in terms of carbon footprint are key parameters that determine the performance of such a network and entail a sophisticated design at different abstraction levels. In this paper, wireless energy harvesting (WEH), wake-up radio (WUR) scheme and error control coding (ECC) are investigated as enabling solutions to enhance the performance of WSNs while reducing its carbon footprint. Specifically, a utility-lifetime maximization problem incorporating WEH, WUR and ECC, is formulated and solved using distributed dual subgradient algorithm based on Lagrange multiplier method. It is discussed and verified through simulation results to show how the proposed solutions improve network utility, prolong the lifetime and pave the way for a greener WSN by reducing its carbon footprint

Wireless Sensor Networks

The aim of this book is to present few important issues of WSNs, from the application, design and technology points of view. The book highlights power efficient design issues related to wireless sensor networks, the existing WSN applications, and discusses the research efforts being undertaken in this field which put the reader in good pace to be able to understand more advanced research and make a contribution in this field for themselves. It is believed that this book serves as a comprehensive reference for graduate and undergraduate senior students who seek to learn latest development in wireless sensor networks

Implementation of a wireless sensor network with eZ430-RF2500 development tools and MSP430FG4618/F2013 experimenter boards from Texas Instruments

Wireless sensor networks have found a great deal of applications in diverse areas. Recent interest has been focused on low-power feature of the sensor nodes because the power consumption is always an issue for wireless sensor nodes which are supplied from the batteries. The eZ430RF2500 Development Tool and MSP430FG4618/F2013 Experimenter Board from Texas Instruments have integrated MSP430 family of ultralow-power microcontrollers and CC2500 low-power wireless RF transceivers which are suitable for low-power, low-cost wireless applications. In this thesis, the features of these TI devices are explored and a wireless sensor network is implemented with these devices. To implement the routing algorithms we have assumed a hierarchical architecture, where one (slave) experimenter board serves as the access point for a number of sensor nodes. A master board controls the slave boards. Multiple access control protocols are developed using the features of these devices, using channelization and polling. Energy efficiency of the wireless sensor network is also addressed by using the wake on radio feature of the devices. An application of this wireless sensor network is described in this thesis which is to measure temperature of several rooms in a building and display all the temperature data on a PC

A critical analysis of research potential, challenges and future directives in industrial wireless sensor networks

In recent years, Industrial Wireless Sensor Networks (IWSNs) have emerged as an important research theme with applications spanning a wide range of industries including automation, monitoring, process control, feedback systems and automotive. Wide scope of IWSNs applications ranging from small production units, large oil and gas industries to nuclear fission control, enables a fast-paced research in this field. Though IWSNs offer advantages of low cost, flexibility, scalability, self-healing, easy deployment and reformation, yet they pose certain limitations on available potential and introduce challenges on multiple fronts due to their susceptibility to highly complex and uncertain industrial environments. In this paper a detailed discussion on design objectives, challenges and solutions, for IWSNs, are presented. A careful evaluation of industrial systems, deadlines and possible hazards in industrial atmosphere are discussed. The paper also presents a thorough review of the existing standards and industrial protocols and gives a critical evaluation of potential of these standards and protocols along with a detailed discussion on available hardware platforms, specific industrial energy harvesting techniques and their capabilities. The paper lists main service providers for IWSNs solutions and gives insight of future trends and research gaps in the field of IWSNs

Enabling Hardware Green Internet of Things: A review of Substantial Issues

Between now and the near future, the Internet of Things (IoT) will redesign the socio-ecological morphology of the human terrain. The IoT ecosystem deploys diverse sensor platforms connecting millions of heterogeneous objects through the Internet. Irrespective of sensor functionality, most sensors are low energy consumption devices and are designed to transmit sporadically or continuously. However, when we consider the millions of connected sensors powering various user applications, their energy efficiency (EE) becomes a critical issue. Therefore, the importance of EE in IoT technology, as well as the development of EE solutions for sustainable IoT technology, cannot be overemphasised. Propelled by this need, EE proposals are expected to address the EE issues in the IoT context. Consequently, many developments continue to emerge, and the need to highlight them to provide clear insights to researchers on eco-sustainable and green IoT technologies becomes a crucial task. To pursue a clear vision of green IoT, this study aims to present the current state-of-the art insights into energy saving practices and strategies on green IoT. The major contribution of this study includes reviews and discussions of substantial issues in the enabling of hardware green IoT, such as green machine to machine, green wireless sensor networks, green radio frequency identification, green microcontroller units, integrated circuits and processors. This review will contribute significantly towards the future implementation of green and eco-sustainable IoT