An Energy Aware and Secure MAC Protocol for Tackling Denial of Sleep Attacks in Wireless Sensor Networks

Wireless sensor networks which form part of the core for the Internet of Things consist of resource constrained sensors that are usually powered by batteries. Therefore, careful energy awareness is essential when working with these devices. Indeed,the introduction of security techniques such as authentication and encryption, to ensure confidentiality and integrity of data, can place higher energy load on the sensors. However, the absence of security protection c ould give room for energy drain attacks such as denial of sleep attacks which have a higher negative impact on the life span ( of the sensors than the presence of security features. This thesis, therefore, focuses on tackling denial of sleep attacks from two perspectives A security perspective and an energy efficiency perspective. The security perspective involves evaluating and ranking a number of security based techniques to curbing denial of sleep attacks. The energy efficiency perspective, on the other hand, involves exploring duty cycling and simulating three Media Access Control ( protocols Sensor MAC, Timeout MAC andTunableMAC under different network sizes and measuring different parameters such as the Received Signal Strength RSSI) and Link Quality Indicator ( Transmit power, throughput and energy efficiency Duty cycling happens to be one of the major techniques for conserving energy in wireless sensor networks and this research aims to answer questions with regards to the effect of duty cycles on the energy efficiency as well as the throughput of three duty cycle protocols Sensor MAC ( Timeout MAC ( and TunableMAC in addition to creating a novel MAC protocol that is also more resilient to denial of sleep a ttacks than existing protocols. The main contributions to knowledge from this thesis are the developed framework used for evaluation of existing denial of sleep attack solutions and the algorithms which fuel the other contribution to knowledge a newly developed protocol tested on the Castalia Simulator on the OMNET++ platform. The new protocol has been compared with existing protocols and has been found to have significant improvement in energy efficiency and also better resilience to denial of sleep at tacks Part of this research has been published Two conference publications in IEEE Explore and one workshop paper

Energy Harvesting Techniques for Internet of Things (IoT)

The rapid growth of the Internet of Things (IoT) has accelerated strong interests in the development of low-power wireless sensors. Today, wireless sensors are integrated within IoT systems to gather information in a reliable and practical manner to monitor processes and control activities in areas such as transportation, energy, civil infrastructure, smart buildings, environment monitoring, healthcare, defense, manufacturing, and production. The long-term and self-sustainable operation of these IoT devices must be considered early on when they are designed and implemented. Traditionally, wireless sensors have often been powered by batteries, which, despite allowing low overall system costs, can negatively impact the lifespan and the performance of the entire network they are used in. Energy Harvesting (EH) technology is a promising environment-friendly solution that extends the lifetime of these sensors, and, in some cases completely replaces the use of battery power. In addition, energy harvesting offers economic and practical advantages through the optimal use of energy, and the provisioning of lower network maintenance costs. We review recent advances in energy harvesting techniques for IoT. We demonstrate two energy harvesting techniques using case studies. Finally, we discuss some future research challenges that must be addressed to enable the large-scale deployment of energy harvesting solutions for IoT environments

Wireless sensors and IoT platform for intelligent HVAC control

Energy consumption of buildings (residential and non-residential) represents approximately 40% of total world electricity consumption, with half of this energy consumed by HVAC systems. Model-Based Predictive Control (MBPC) is perhaps the technique most often proposed for HVAC control, since it offers an enormous potential for energy savings. Despite the large number of papers on this topic during the last few years, there are only a few reported applications of the use of MBPC for existing buildings, under normal occupancy conditions and, to the best of our knowledge, no commercial solution yet. A marketable solution has been recently presented by the authors, coined the IMBPC HVAC system. This paper describes the design, prototyping and validation of two components of this integrated system, the Self-Powered Wireless Sensors and the IOT platform developed. Results for the use of IMBPC in a real building under normal occupation demonstrate savings in the electricity bill while maintaining thermal comfort during the whole occupation schedule.QREN SIDT [38798]; Portuguese Foundation for Science & Technology, through IDMEC, under LAETA [ID/EMS/50022/2013

Performance Evaluation of Energy-Autonomous Sensors Using Power-Harvesting Beacons for Environmental Monitoring in Internet of Things (IoT)

Environmental conditions and air quality monitoring have become crucial today due to the undeniable changes of the climate and accelerated urbanization. To efficiently monitor environmental parameters such as temperature, humidity, and the levels of pollutants, such as fine particulate matter (PM2.5) and volatile organic compounds (VOCs) in the air, and to collect data covering vast geographical areas, the development of cheap energy-autonomous sensors for large scale deployment and fine-grained data acquisition is required. Rapid advances in electronics and communication technologies along with the emergence of paradigms such as Cyber-Physical Systems (CPSs) and the Internet of Things (IoT) have led to the development of low-cost sensor devices that can operate unattended for long periods of time and communicate using wired or wireless connections through the Internet. We investigate the energy efficiency of an environmental monitoring system based on Bluetooth Low Energy (BLE) beacons that operate in the IoT environment. The beacons developed measure the temperature, the relative humidity, the light intensity, and the CO2 and VOC levels in the air. Based on our analysis we have developed efficient sleep scheduling algorithms that allow the sensor nodes developed to operate autonomously without requiring the replacement of the power supply. The experimental results show that low-power sensors communicating using BLE technology can operate autonomously (from the energy perspective) in applications that monitor the environment or the air quality in indoor or outdoor settings

Pulse mode of operation : a new booster of TEG, improving power up to X2.7 : to better fit IoT requirements

Internet of Things (IoT) is becoming the new driver for semiconductor industry and the largest electronic market ever seen. The number of IoT nodes is already many times larger than the human population and is continuously growing. It is thus mandatory that IoT nodes become self-supplying with energy harvested from environment since periodic exchange of batteries in such a huge number of units (often located in inaccessible places e.g. industrial environment or elements of constructions) is impractical and soon will be simply impossible. Photovoltaic generators may easily harvest energy where light is available, but the IoT nodes often work in dark, hidden locations where the only available energy sources are heat losses. There, ThermoElectric Generators (TEGs) could be the best candidate, if not that if we speak of exploiting heat losses it often means very low temperature differences. This means conditions where TEGs power production drops down dramatically. In this paper we put forward a new idea of TEG's pulse operation that boosts the power production up to X2.7. This extends the domain of applicability of TEGs to lower temperature differences, where conventional TEGs are out of the game. Next, we show that the improvement X2.7 maintains also at larger temperature differences that presents obvious advantages

An Input Power-Aware Maximum Efficiency Tracking Technique for Energy Harvesting in IoT Applications

The Internet of Things (IoT) enables intelligent monitoring and management in many applications such as industrial and biomedical systems as well as environmental and infrastructure monitoring. As a result, IoT requires billions of wireless sensor network (WSN) nodes equipped with a microcontroller and transceiver. As many of these WSN nodes are off-grid and small-sized, their limited-capacity batteries need periodic replacement. To mitigate the high costs and challenges of these battery replacements, energy harvesting from ambient sources is vital to achieve energy-autonomous operation. Energy harvesting for WSNs is challenging because the available energy varies significantly with ambient conditions and in many applications, energy must be harvested from ultra-low power levels. To tackle these stringent power constraints, this dissertation proposes a discontinuous charging technique for switched-capacitor converters that improves the power conversion efficiency (PCE) at low input power levels and extends the input power harvesting range at which high PCE is achievable. Discontinuous charging delivers current to energy storage only during clock non-overlap time. This enables tuning of the output current to minimize converter losses based on the available input power. Based on this fundamental result, an input power-aware, two-dimensional efficiency tracking technique for WSNs is presented. In addition to conventional switching frequency control, clock nonoverlap time control is introduced to adaptively optimize the power conversion efficiency according to the sensed ambient power levels. The proposed technique is designed and simulated in 90nm CMOS with post-layout extraction. Under the same input and output conditions, the proposed system maintains at least 45% PCE at 4μW input power, as opposed to a conventional continuous system which requires at least 18.7μW to maintain the same PCE. In this technique, the input power harvesting range is extended by 1.5x. The technique is applied to a WSN implementation utilizing the IEEE 802.15.4- compatible GreenNet communications protocol for industrial and wearable applications. This allows the node to meet specifications and achieve energy autonomy when deployed in harsher environments where the input power is 49% lower than what is required for conventional operation

Autonomous Sensing Nodes for IoT Applications

The present doctoral thesis fits into the energy harvesting framework, presenting the development of low-power nodes compliant with the energy autonomy requirement, and sharing common technologies and architectures, but based on different energy sources and sensing mechanisms. The adopted approach is aimed at evaluating multiple aspects of the system in its entirety (i.e., the energy harvesting mechanism, the choice of the harvester, the study of the sensing process, the selection of the electronic devices for processing, acquisition and measurement, the electronic design, the microcontroller unit (MCU) programming techniques), accounting for very challenging constraints as the low amounts of harvested power (i.e., [μW, mW] range), the careful management of the available energy, the coexistence of sensing and radio transmitting features with ultra-low power requirements. Commercial sensors are mainly used to meet the cost-effectiveness and the large-scale reproducibility requirements, however also customized sensors for a specific application (soil moisture measurement), together with appropriate characterization and reading circuits, are also presented. Two different strategies have been pursued which led to the development of two types of sensor nodes, which are referred to as 'sensor tags' and 'self-sufficient sensor nodes'. The first term refers to completely passive sensor nodes without an on-board battery as storage element and which operate only in the presence of the energy source, provisioning energy from it. In this thesis, an RFID (Radio Frequency Identification) sensor tag for soil moisture monitoring powered by the impinging electromagnetic field is presented. The second term identifies sensor nodes equipped with a battery rechargeable through energy scavenging and working as a secondary reserve in case of absence of the primary energy source. In this thesis, quasi-real-time multi-purpose monitoring LoRaWAN nodes harvesting energy from thermoelectricity, diffused solar light, indoor white light, and artificial colored light are presented

Livestock Monitoring: Approaches, Challenges and Opportunities

This survey presents approaches and technologies for livestock identification, vital signs monitoring and location tracking. It first introduces the related concepts. Then, provides an analysis of existing solutions and highlights their strengths and limitations. Finally, it presents key challenges in the field, and discusses recent trends that must be factored in by researchers, implementers, and manufacturers towards future developments in the area.info:eu-repo/semantics/publishedVersio

Irrigation planning system for agricultural soils

Dissertação de mestrado integrado em Engenharia Eletrónica Industrial e ComputadoresO objetivo principal desta dissertação é o desenvolvimento de um sistema de planeamento de regas para a agricultura. Este projeto dá seguimento ao algoritmo desenvolvido em [1], capaz de criar um plano de rega de acordo com informações relativas ao solo, colheita, tipo de irrigação, humidade do solo e a previsão meteorológica de uma determinada localização. O sistema desenvolvido é composto por uma aplicação web e uma rede de dispositivos eletrónicos no campo. O sistema efetua todo o trabalho necessário, desde a aquisição de dados relativos à humidade do solo até à exibição dos planos de rega ao agricultor. A aplicação web utiliza a stack tecnológica MERN para fornecer uma interface ao utilizador, onde é possível gerir os pontos de rega e campos agrícolas, observar previsões meteorológicas e visualizar e obter atualizações relativas aos planos de rega, assim como alertar o agricultor através de noti ficações push quando condições alarmantes se verificam. Para além da interface com o utilizador, esta também obtém informações meteorológicas, executa o algoritmo de planeamento e agrega os dados de humidade do solo recolhidos pela rede de pontos de rega, através de um servidor CoAP. A rede de dispositivos eletrónicos no campo está encarregue de recolher informação relativa à humidade do solo e enviá-la para o servidor de hora a hora, recorrendo a diferentes tecnologias de forma a proporcionar uma solução flexível de baixo custo, com duas possibilidades de configuração, standalone e WSN, adequadas para diferentes cenários. A comunicação entre os dispositivos no campo e o servidor é baseada no protocolo CoAP. A configuração standalone é constituída por uma PCB, que combina um microntrolador low power com um circuito de energy harvesting. A esta, são conectados um painel solar, um conversor step-up, uma bateria Li-Po e um módulo de comunicações móveis (capaz de utilizar as tecnologias móveis GPRS/UMTS e NB-IoT), assim como até seis sensores de humidade do solo. A configuração WSN recorre à mesma PCB que a configuração standalone, utilizando um trans ceiver LoRa em vez do módulo de comunicações móveis. Esta comunica através da camada física LoRa com um edge device baseado na plataforma Raspberry Pi, que encaminha os pacotes rece bidos pela rede LoRa através do protocolo CoAP para o servidor. A rede LoRa desenvolvida é capaz de enviar mensagens downlink diárias e um data-rate adaptativo, que controla o link budget através do spreading factor e da potência de transmissão, recebendo pacotes recorrendo a um esquema adaptativo de seleção do spreading factor (ASFS) [2].The main objective of this dissertation is the development of an irrigation planning system for agri culture. This work builds upon the algorithm developed in [1], capable of creating an irrigation plan according to soil, crop, irrigation, soil moisture and weather forecast of a given location. The developed system is composed by a web application and a network of field electronic devices. The system does all the necessary work, from the retrieval of the soil moisture data to the display of irrigation prescription plans to the farmer. The web application resorts to the MERN technological stack to provide an interface to the farmer, where irrigation points and crop fields can be managed, forecasts observed and the irrigation plans can be retrieved and updated, while also alerting the farmer through push notifications when danger ous conditions are verified. Besides the interface with the farmer, it also gathers weather information, performs the irrigation planning and retrieves soil moisture data from the irrigation points through a CoAP server. The network of electronic devices is in charge of retrieving soil moisture information and sending it to the server on an hourly basis, using different technologies to provide a flexible low-cost solution with two different configurations, standalone and WSN, suitable to many different scenarios. The communication between field devices and the server is based on CoAP protocol. The standalone configuration consists of a PCB, where a low power microcontroller is paired with an energy harvesting circuit. To it, a solar panel, a step-up converter, Li-Po battery and a cellular communication module (capable of connectivity with both GPRS/UMTS and NB-IoT technologies) are connected, along with up to six soil moisture sensors. The WSN configuration makes use of the same PCB as the standalone configuration, using a LoRa transceiver instead. It communicates through the LoRa physical layer to an edge device based on the Raspberry Pi platform, which forwards the packets received from the LoRa network through CoAP to the web server. The LoRa network developed is capable of daily downlink messages and adaptive data-rate, where the link budget is controlled through the spreading factor and the transmission power, receiving packets through an adaptive spreading factor selection (ASFS) scheme [2].Firstly, I would like to show my gratitude to my advisors for their guidance and support during this project, and in particular to Professor Doctor Sérgio Lopes for the insightful discussions and dedication over the course of this work. I also would like to thank everyone involved in the research project 02/SAICT/2017-28247-FCT-TO-CHAIR, that supported the work developed in this dissertation