A PUF-and biometric-based lightweight hardware solution to increase security at sensor nodes

Security is essential in sensor nodes which acquire and transmit sensitive data. However, the constraints of processing, memory and power consumption are very high in these nodes. Cryptographic algorithms based on symmetric key are very suitable for them. The drawback is that secure storage of secret keys is required. In this work, a low-cost solution is presented to obfuscate secret keys with Physically Unclonable Functions (PUFs), which exploit the hardware identity of the node. In addition, a lightweight fingerprint recognition solution is proposed, which can be implemented in low-cost sensor nodes. Since biometric data of individuals are sensitive, they are also obfuscated with PUFs. Both solutions allow authenticating the origin of the sensed data with a proposed dual-factor authentication protocol. One factor is the unique physical identity of the trusted sensor node that measures them. The other factor is the physical presence of the legitimate individual in charge of authorizing their transmission. Experimental results are included to prove how the proposed PUF-based solution can be implemented with the SRAMs of commercial Bluetooth Low Energy (BLE) chips which belong to the communication module of the sensor node. Implementation results show how the proposed fingerprint recognition based on the novel texture-based feature named QFingerMap16 (QFM) can be implemented fully inside a low-cost sensor node. Robustness, security and privacy issues at the proposed sensor nodes are discussed and analyzed with experimental results from PUFs and fingerprints taken from public and standard databases.Ministerio de Economía, Industria y Competitividad TEC2014-57971-R, TEC2017-83557-

An architecture framework for enhanced wireless sensor network security

This thesis develops an architectural framework to enhance the security of Wireless Sensor Networks (WSNs) and provides the implementation proof through different security countermeasures, which can be used to establish secure WSNs, in a distributed and self-healing manner. Wireless Sensors are used to monitor and control environmental properties such as sound, acceleration, vibration, air pollutants, and temperature. Due to their limited resources in computation capability, memory and energy, their security schemes are susceptible to many kinds of security vulnerabilities. This thesis investigated all possible network attacks on WSNs and at the time of writing, 19 different types of attacks were identified, all of which are discussed including exposures to the attacks, and the impact of those attacks. The author then utilises this work to examine the ZigBee series, which are the new generation of wireless sensor network products with built-in layered security achieved by secure messaging using symmetric cryptography. However, the author was able to uniquely identify several security weaknesses in ZigBee by examining its protocol and launching the possible attacks. It was found that ZigBee is vulnerable to the following attacks, namely: eavesdropping, replay attack, physical tampering and Denial of Services (DoS). The author then provides solutions to improve the ZigBee security through its security schema, including an end-to-end WSN security framework, architecture design and sensor configuration, that can withstand all types of attacks on the WSN and mitigate ZigBee’s WSN security vulnerabilities

Smart Wireless Sensor Networks

The recent development of communication and sensor technology results in the growth of a new attractive and challenging area - wireless sensor networks (WSNs). A wireless sensor network which consists of a large number of sensor nodes is deployed in environmental fields to serve various applications. Facilitated with the ability of wireless communication and intelligent computation, these nodes become smart sensors which do not only perceive ambient physical parameters but also be able to process information, cooperate with each other and self-organize into the network. These new features assist the sensor nodes as well as the network to operate more efficiently in terms of both data acquisition and energy consumption. Special purposes of the applications require design and operation of WSNs different from conventional networks such as the internet. The network design must take into account of the objectives of specific applications. The nature of deployed environment must be considered. The limited of sensor nodes� resources such as memory, computational ability, communication bandwidth and energy source are the challenges in network design. A smart wireless sensor network must be able to deal with these constraints as well as to guarantee the connectivity, coverage, reliability and security of network's operation for a maximized lifetime. This book discusses various aspects of designing such smart wireless sensor networks. Main topics includes: design methodologies, network protocols and algorithms, quality of service management, coverage optimization, time synchronization and security techniques for sensor networks

Microarchitectures pour la sauvegarde incrémentale, robuste et efficace dans les systèmes à alimentation intermittente

Embedded devices powered with environmental energy harvesting, have to sustain computation while experiencing unexpected power failures.To preserve the progress across the power interruptions, Non-Volatile Memories (NVMs) are used to quickly save the state. This dissertation first presents an overview and comparison of different NVM technologies, based on different surveys from the literature. The second contribution we propose is a dedicated backup controller, called Freezer, that implements an on-demand incremental backup scheme. This can make the size of the backup 87.7% smaller then a full-memory backup strategy from the state of the art (SoA). Our third contribution addresses the problem of corruption of the state, due to interruptions during the backup process. Two algorithms are presented, that improve on the Freezer incremental backup process, making it robust to errors, by always guaranteeing the existence of a correct state, that can be restored in case of backup errors. These two algorithms can consume 23% less energy than the usual double-buffering technique used in the SoA. The fourth contribution, addresses the scalability of our proposed approach. Combining Freezer with Bloom filters, we introduce a backup scheme that can cover much larger address spaces, while achieving a backup size which is half the size of the regular Freezer approach.Les appareils embarqués alimentés par la récupération d'énergie environnementale doivent maintenir le calcul tout en subissant des pannes de courant inattendues. Pour préserver la progression à travers les interruptions de courant, des mémoires non volatiles (NVM) sont utilisées pour enregistrer rapidement l'état. Cette thèse présente d'abord une vue d'ensemble et une comparaison des différentes technologies NVM, basées sur différentes enquêtes de la littérature. La deuxième contribution que nous proposons est un contrôleur de sauvegarde dédié, appelé Freezer, qui implémente un schéma de sauvegarde incrémentale à la demande. Cela peut réduire la taille de la sauvegarde de 87,7% à celle d'une stratégie de sauvegarde à mémoire complète de l'état de l'art. Notre troisième contribution aborde le problème de la corruption de l'état, due aux interruptions pendant le processus de sauvegarde. Deux algorithmes sont présentés, qui améliorent le processus de sauvegarde incrémentale de Freezer, le rendant robuste aux erreurs, en garantissant toujours l'existence d'un état correct, qui peut être restauré en cas d'erreurs de sauvegarde. Ces deux algorithmes peuvent consommer $23\%$ d'énergie en moins que la technique de ``double-buffering'' utilisée dans l'état de l'art. La quatrième contribution porte sur l'évolutivité de notre approche proposée. En combinant Freezer avec des filtres Bloom, nous introduisons un schéma de sauvegarde qui peut couvrir des espaces d'adressage beaucoup plus grands, tout en obtenant une taille de sauvegarde qui est la moitié de la taille de l'approche Freezer habituelle

Emerging Communications for Wireless Sensor Networks

Wireless sensor networks are deployed in a rapidly increasing number of arenas, with uses ranging from healthcare monitoring to industrial and environmental safety, as well as new ubiquitous computing devices that are becoming ever more pervasive in our interconnected society. This book presents a range of exciting developments in software communication technologies including some novel applications, such as in high altitude systems, ground heat exchangers and body sensor networks. Authors from leading institutions on four continents present their latest findings in the spirit of exchanging information and stimulating discussion in the WSN community worldwide

Evolutionary Approach to Efficient Provisioning and Self-organization in Wireless Sensor Networks (WSN)

Advances in low-power digital integration and microelectro-mechanical systems (MEMS) have paved the way for micro-sensors. These sensors are equipped with data processing capabilities along with sensory circuits. Sensor data are processed on these individual sensors and transmitted to the target (sink). Lowcost integration and small sizes of these sensors have generated special interest in the area of disposable-sensors and large scale platform management. Queries to these sensors are addressed to nodes which have data satisfying the same condition. However, these sensors may be constrained in energy, bandwidth, storage, and processing capabilities. Large number of such sensors along with these constraints creates a sensor-management problem. At the network layer it amounts to setting up the efficient route that transmits the non-redundant data from source to the sink in order to maximize one or more sensor objectives (e.g. battery (and sensor's) life, Sensor-Data yield). This is done while adapting to changing connectivity due to failure of some nodes and new nodes powering up. First part of the thesis propose a reduced-complexity genetic algorithm (GA) for optimization of multi-hop battery-constrained sensor networks. The goal of the system is to generate optimal number of sensor-clusters with cluster-heads. It results in minimization of the power consumption of the sensor system while maximizing the sensor objectives (coverage and exposure). The genetic algorithm is used to adaptively create various components such as cluster-members, cluster-heads, and next-cluster. These components are then used to evaluate the average fitness of the system based on the sequence of communication links towards the sink. We then enhance the genetic algorithm (GA) approach for secure deployment of resource constrained multi-hop sensor networks. The goal in this case is to achieve secure coverage and improve battery life by dynamically optimizing security attributes (Like authentication and encryption). Further, we augment the GA approach for intrusion detection of resource constrained multi-hop sensor networks. Traditional intrusion detection mechanisms have limited applicability to the sensor networks due to scarce battery and processing resources. Therefore, we propose an effective scheme that would offer a power efficient and lightweight approach to identify malicious attacks. We evaluate sensor node attributes by measuring the perceived threat and its suitability to host local monitoring node (LMN) that acts as trusted proxy agent for the sink and capable of securely monitoring its neighbors. Security attributes in conjunction with genetic algorithm jointly optimizes the selection of monitoring nodes (i.e., LMN) by dynamically evaluating node fitness by profiling workloads patterns, packet statistics, utilization data, battery status, and quality-of-service compliance. Second part of the thesis delves into application of Information Technology (and Industrial) Systems and devices where the use of sensor networks can deliver non-intrusive and effective telemetry for group-based server management. These systems (Like Data Centers or Shipment tracking) face major challenges in seamless integration of telemetry and control data that is essential to various autonomic management functions related to power, thermal, reliability, predictability, survivability, locality and adaptability. Such systems that are supported by a dense network of sense-points operating in noisy environment (Metals, Cables) are required to deliver reliable trends, measurements and analysis in a timely fashion. The traditional approaches to provide distributed observability and control using wired solutions are static, expensive, and nonscalable. We apply the proposed GA approach for this unique environment that replaces static wired sensors with dynamically reconfigurable battery-powered wireless sensors. The proposed technique employs machine learning approach to optimize sensor node function assignment, clustering decisions, route establishment and data collection trees for improved throughput that results in effective controls