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

Supporting Cyber-Physical Systems with Wireless Sensor Networks: An Outlook of Software and Services

Sensing, communication, computation and control technologies are the essential building blocks of a cyber-physical system (CPS). Wireless sensor networks (WSNs) are a way to support CPS as they provide fine-grained spatial-temporal sensing, communication and computation at a low premium of cost and power. In this article, we explore the fundamental concepts guiding the design and implementation of WSNs. We report the latest developments in WSN software and services for meeting existing requirements and newer demands; particularly in the areas of: operating system, simulator and emulator, programming abstraction, virtualization, IP-based communication and security, time and location, and network monitoring and management. We also reflect on the ongoing efforts in providing dependable assurances for WSN-driven CPS. Finally, we report on its applicability with a case-study on smart buildings

Energy-aware strategy for data forwarding in IoT ecosystem

The Internet of Things (IoT) is looming technology rapidly attracting many industries and drawing research attention. Although the scale of IoT-applications is very large, the capabilities of the IoT-devices are limited, especially in terms of energy. However, various research works have been done to alleviate these shortcomings, but the schemes introduced in the literature are complex and difficult to implement in practical scenarios. Therefore, considering the energy consumption of heterogeneous nodes in IoT eco-system, a simple energy-efficient routing technique is proposed. The proposed system has also employed an SDN controller that acts as a centralized manager to control and monitor network services, there by restricting the access of selfish nodes to the network. The proposed system constructs an analytical algorithm that provides reliable data transmission operations and controls energy consumption using a strategic mechanism where the path selection process is performed based on the remaining energy of adjacent nodes located in the direction of the destination node. The proposed energy-efficient data forwarding mechanism is compared with the existing AODV routing technique. The simulation result demonstrates that the protocol is superior to AODV in terms of packet delivery rate, throughput, and end-to-end delay

Driving Big Data – Integration and Synchronization of Data Sources for Artificial Intelligence Applications with the Example of Truck Driver Work Stress and Strain Analysis

This paper contributes to the issue of big data analysis and data quality with the specific field of time synchronization. As a highly relevant use case, big data analysis of work stress and strain factors for driving professions is outlined. Drivers experience work stress and strain due to trends like traffic congestion, time pressure or worsening work conditions. Although a large professional group with 2.5 million (US) and 3.5 million (EU) truck drivers, scientific analysis of work stress and strain factors is scarce. Driver shortage is growing into a large-scale economic and societal challenge, especially for small businesses. Empirical investigations require big data approaches with sources like physiological and truck, traffic, weather, planning or accident data. For such challenges, accurate data is required, especially regarding time synchronization. Awareness among researchers and practitioners is key and first solution approaches are provided, connecting to many further Machine Learning and big data applications

Choosing IoT-connectivity? A guiding methodology based on functional characteristics and economic considerations

Along with the growing market of Internet of Things (IoT), the set of IoT-connectivity networks is also continuously expanding. Large-scale deployments of the new low-power wide-area networks and announcements of new technologies prove this trend. Although these new technologies take care of some key IoT-challenges, such as communication cost and power consumption, careful consideration of the IoT-connectivity is still required since there is no one-size-fits-all solution. Typical comparisons of IoT-connectivity networks are based on technical characteristics but remain unpractical when it comes down to comparing functional characteristics. Questions on public network accessibility, ability for private network deployments, and cost considerations related to IoT-connectivity networks often remain unanswered. In this work, a 2-step methodology is proposed to guide IoT-developers in choosing an appropriate connectivity network. First, a questionnaire walkthrough eliminates IoT-connectivity networks based on mismatches between their functional characteristics and the functional requirements of the IoT-applications. In a second step, an evaluation of the main cost components related to IoT-connectivity indicates the most economical solution. As an illustration, we present 2 case studies: (1) deploying smart shipping containers in the port of Antwerp and (2)installing shop'n go parking spaces, which detect vehicle presence via asensor

Towards large-scale and collaborative spectrum monitoring systems using IoT devices

Mención Internacional en el título de doctorThe Electromagnetic (EM) spectrum is well regulated by frequency assignment authorities, national regulatory agencies and the International Communication Union (ITU). Nowadays more and more devices such as mobile phones and Internet-of-Things (IoT) sensors make use of wireless communication. Additionally we need a more efficient use and a better understanding of the EM space to allocate and manage efficiently all communications. Governments and telecommunication operators perform spectrum measurements using expensive and bulky equipments scheduling very specific and limited spectrum campaigns. However, this approach does not scale as it can not allow to widely scan and analyze the spectrum 24/7 in real time due to the high cost of the large deployment. A pervasive deployment of spectrum sensors is required to solve this problem, allowing to monitor and analyze the EM radio waves in real time, across all possible frequencies, and physical locations. This thesis presents ElectroSense, a crowdsourcing and collaborative system that enables large scale deployments using Internet-of-Things (IoT) spectrum sensors to collect EM spectrum data which is analyzed in a big data infrastructure. The ElectroSense platform seeks a more efficient, safe and reliable real-time monitoring of the EM space by improving the accessibility and the democratization of spectrum data for the scientific community, stakeholders and the general public. In this work, we first present the ElectroSense architecture, and the design challenges that must be faced to attract a large community of users and all potential stakeholders. It is envisioned that a large number of sensors deployed in ElectroSense will be at affordable cost, supported by more powerful spectrum sensors when possible. Although low-cost Radio Frequency (RF) sensors have an acceptable performance for measuring the EM spectrum, they present several drawbacks (e.g. frequency range, Analog-to-Digital Converter (ADC), maximum sampling rate, etc.) that can negatively affect the quality of collected spectrum data as well as the applications of interest for the community. In order to counteract the above-mentioned limitations, we propose to exploit the fact that a dense network of spectrum sensors will be in range of the same transmitter(s). We envision to exploit this idea to enable smart collaborative algorithms among IoT RF sensors. In this thesis we identify the main research challenges to enable smart collaborative algorithms among low-cost RF sensors. We explore different crowdsourcing and collaborative scenarios where low-cost spectrum sensors work together in a distributed manner. First, we propose a fast and precise frequency offset estimation method for lowcost spectrum receivers that makes use of Long Term Evolution (LTE) signals broadcasted by the base stations. Second, we propose a novel, fast and precise Time-of-Arrival (ToA) estimation method for aircraft signals using low-cost IoT spectrum sensors that can achieve sub-nanosecond precision. Third, we propose a collaborative time division approach among sensors for sensing the spectrum in order to reduce the network uplink bandwidth for each spectrum sensor. By last, we present a methodology to enable the signal reconstruction in the backend. By multiplexing in frequency a certain number of non-coherent low-cost spectrum sensors, we are able to cover a signal bandwidth that would not otherwise be possible using a single receiver. At the time of writing we are the first looking at the problem of collaborative signal reconstruction and decoding using In-phase & Quadrature (I/Q) data received from low-cost RF sensors. Our results reported in this thesis and obtained from the experiments made in real scenarios, suggest that it is feasible to enable collaborative spectrum monitoring strategies and signal decoding using commodity hardware as RF sensing sensors.Programa de Doctorado en Ingeniería Telemática por la Universidad Carlos III de MadridPresidente: Bozidar Radunovic.- Secretario: Paolo Casari.- Vocal: Fco. Javier Escribano Aparici

Energy aware optimization for low power radio technologies

The explosive growth of IoT is pushing the market towards cheap, very low power devices with a strong focus on miniaturization, for applications such as in-body sensors, personal health monitoring and microrobots. Proposing procedures for energy efficiency in IoT is a difficult task, as it is a rapidly growing market comprised of many and very diverse product categories using technologies that are not stable, evolving at a high pace. The research in this field proposes solutions that go from physical layer optimization up to the network layer, and the sensor network designer has to select the techniques that are best for its application specific architecture and radio technology used. This work is focused on exploring new techniques for enhancing the energy efficiency and user experience of IoT networks. We divide the proposed techniques in frame and chip level optimization techniques, respectively. While the frame level techniques are meant to improve the performance of existing radio technologies, the chip level techniques aim at replacing them with crystal-free architectures. The identified frame level techniques are the use of preamble authentication and packet fragmentation, advisable for Low Power Wide Area Networks (LPWANs), a technology that offers the lowest energy consumption per provided service, but is vulnerable in front of energy exhaustion attacks and does not perform well in dense networks. The use of authenticated preambles between the sensors and gateways becomes a defence mechanism against the battery draining intended by attackers. We show experimentally that this approach is able to reduce with 91% the effect of an exhaustion attack, increasing the device's lifetime from less than 0.24 years to 2.6 years. The experiments were conducted using Loadsensing sensor nodes, commercially used for critical infrastructure control and monitoring. Even if exemplified on LoRaWAN, the use of preamble authentication is extensible to any wireless protocol. The use of packet fragmentation despite the packet fits the frame, is shown to reduce the probability of collisions while the number of users in the duty-cycle restricted network increases. Using custom-made Matlab simulations, important goodput improvement was obtained with fragmentation, with higher impact in slower and denser networks. Using NS3 simulations, we showed that combining packet fragmentation with group NACK can increase the network reliability, while reducing the energy consumed for retransmissions, at the cost of adding small headers to each fragment. It is a strategy that proves to be effective in dense duty-cycle restricted networks only, where the headers overhead is negligible compared to the network traffic. As a chip level technique, we consider using radios for communication that do not use external frequency references such as crystal oscillators. This would enable having all sensor's elements on a single piece of silicon, rendering it even ten times more energy efficient due to the compactness of the chip. The immediate consequence is the loss of communication accuracy and ability to easily switch communication channels. In this sense, we propose a sequence of frequency synchronization algorithms and phases that have to be respected by a crystal-free device so that it can be able to join a network by finding the beacon channel, synthesize all communication channels and then maintain their accuracy against temperature change. The proposed algorithms need no additional network overhead, as they are using the existing network signaling. The evaluation is made in simulations and experimentally on a prototype implementation of an IEEE802.15.4 crystal-free radio. While in simulations we are able to change to another communication channel with very good frequency accuracy, the results obtained experimentally show an initial accuracy slightly above 40ppm, which will be later corrected by the chip to be below 40 ppm.El crecimiento significativo de la IoT está empujando al mercado hacia el desarrollo de dispositivos de bajo coste, de muy bajo consumo energético y con un fuerte enfoque en la miniaturización, para aplicaciones que requieran sensores corporales, monitoreo de salud personal y micro-robots. La investigación en el campo de la eficiencia energética en la IoT propone soluciones que van desde la optimización de la capa física hasta la capa de red. Este trabajo se centra en explorar nuevas técnicas para mejorar la eficiencia energética y la experiencia del usuario de las redes IoT. Dividimos las técnicas propuestas en técnicas de optimización de nivel de trama de red y chip, respectivamente. Si bien las técnicas de nivel de trama están destinadas a mejorar el rendimiento de las tecnologías de radio existentes, las técnicas de nivel de chip tienen como objetivo reemplazarlas por arquitecturas que no requieren de cristales. Las técnicas de nivel de trama desarrolladas en este trabajo son el uso de autenticación de preámbulos y fragmentación de paquetes, aconsejables para redes LPWAN, una tecnología que ofrece un menor consumo de energía por servicio prestado, pero es vulnerable frente a los ataques de agotamiento de energía y no escalan frente la densificación. El uso de preámbulos autenticados entre los sensores y las pasarelas de enlace se convierte en un mecanismo de defensa contra el agotamiento del batería previsto por los atacantes. Demostramos experimentalmente que este enfoque puede reducir con un 91% el efecto de un ataque de agotamiento, aumentando la vida útil del dispositivo de menos de 0.24 años a 2.6 años. Los experimentos se llevaron a cabo utilizando nodos sensores de detección de carga, utilizados comercialmente para el control y monitoreo de infrastructura crítica. Aunque la técnica se ejemplifica en el estándar LoRaWAN, el uso de autenticación de preámbulo es extensible a cualquier protocolo inalámbrico. En esta tesis se muestra también que el uso de la fragmentación de paquetes a pesar de que el paquete se ajuste a la trama, reduce la probabilidad de colisiones mientras aumenta el número de usuarios en una red con restricciones de ciclos de transmisión. Mediante el uso de simulaciones en Matlab, se obtiene una mejora importante en el rendimiento de la red con la fragmentación, con un mayor impacto en redes más lentas y densas. Usando simulaciones NS3, demostramos que combinar la fragmentación de paquetes con el NACK en grupo se puede aumentar la confiabilidad de la red, al tiempo que se reduce la energía consumida para las retransmisiones, a costa de agregar pequeños encabezados a cada fragmento. Como técnica de nivel de chip, consideramos el uso de radios para la comunicación que no usan referencias de frecuencia externas como los osciladores basados en un cristal. Esto permitiría tener todos los elementos del sensor en una sola pieza de silicio, lo que lo hace incluso diez veces más eficiente energéticamente debido a la integración del chip. La consecuencia inmediata, en el uso de osciladores digitales en vez de cristales, es la pérdida de precisión de la comunicación y la capacidad de cambiar fácilmente los canales de comunicación. En este sentido, proponemos una secuencia de algoritmos y fases de sincronización de frecuencia que deben ser respetados por un dispositivo sin cristales para que pueda unirse a una red al encontrar el canal de baliza, sintetizar todos los canales de comunicación y luego mantener su precisión contra el cambio de temperatura. Los algoritmos propuestos no necesitan una sobrecarga de red adicional, ya que están utilizando la señalización de red existente. La evaluación se realiza en simulaciones y experimentalmente en una implementación prototipo de una radio sin cristal IEEE802.15.4. Los resultados obtenidos experimentalmente muestran una precisión inicial ligeramente superior a 40 ppm, que luego será corregida por el chip para que sea inferior a 40 ppm.Postprint (published version