Security techniques for sensor systems and the Internet of Things

Sensor systems are becoming pervasive in many domains, and are recently being generalized by the Internet of Things (IoT). This wide deployment, however, presents significant security issues. We develop security techniques for sensor systems and IoT, addressing all security management phases. Prior to deployment, the nodes need to be hardened. We develop nesCheck, a novel approach that combines static analysis and dynamic checking to efficiently enforce memory safety on TinyOS applications. As security guarantees come at a cost, determining which resources to protect becomes important. Our solution, OptAll, leverages game-theoretic techniques to determine the optimal allocation of security resources in IoT networks, taking into account fixed and variable costs, criticality of different portions of the network, and risk metrics related to a specified security goal. Monitoring IoT devices and sensors during operation is necessary to detect incidents. We design Kalis, a knowledge-driven intrusion detection technique for IoT that does not target a single protocol or application, and adapts the detection strategy to the network features. As the scale of IoT makes the devices good targets for botnets, we design Heimdall, a whitelist-based anomaly detection technique for detecting and protecting against IoT-based denial of service attacks. Once our monitoring tools detect an attack, determining its actual cause is crucial to an effective reaction. We design a fine-grained analysis tool for sensor networks that leverages resident packet parameters to determine whether a packet loss attack is node- or link-related and, in the second case, locate the attack source. Moreover, we design a statistical model for determining optimal system thresholds by exploiting packet parameters variances. With our techniques\u27 diagnosis information, we develop Kinesis, a security incident response system for sensor networks designed to recover from attacks without significant interruption, dynamically selecting response actions while being lightweight in communication and energy overhead

Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs

Data and resource management in wireless networks via data compression, GPS-free dissemination, and learning

“This research proposes several innovative approaches to collect data efficiently from large scale WSNs. First, a Z-compression algorithm has been proposed which exploits the temporal locality of the multi-dimensional sensing data and adapts the Z-order encoding algorithm to map multi-dimensional data to a one-dimensional data stream. The extended version of Z-compression adapts itself to working in low power WSNs running under low power listening (LPL) mode, and comprehensively analyzes its performance compressing both real-world and synthetic datasets. Second, it proposed an efficient geospatial based data collection scheme for IoTs that reduces redundant rebroadcast of up to 95% by only collecting the data of interest. As most of the low-cost wireless sensors won’t be equipped with a GPS module, the virtual coordinates are used to estimate the locations. The proposed work utilizes the anchor-based virtual coordinate system and DV-Hop (Distance vector of hops to anchors) to estimate the relative location of nodes to anchors. Also, it uses circle and hyperbola constraints to encode the position of interest (POI) and any user-defined trajectory into a data request message which allows only the sensors in the POI and routing trajectory to collect and route. It also provides location anonymity by avoiding using and transmitting GPS location information. This has been extended also for heterogeneous WSNs and refined the encoding algorithm by replacing the circle constraints with the ellipse constraints. Last, it proposes a framework that predicts the trajectory of the moving object using a Sequence-to-Sequence learning (Seq2Seq) model and only wakes-up the sensors that fall within the predicted trajectory of the moving object with a specially designed control packet. It reduces the computation time of encoding geospatial trajectory by more than 90% and preserves the location anonymity for the local edge servers”--Abstract, page iv

IETF standardization in the field of the Internet of Things (IoT): a survey

Smart embedded objects will become an important part of what is called the Internet of Things. However, the integration of embedded devices into the Internet introduces several challenges, since many of the existing Internet technologies and protocols were not designed for this class of devices. In the past few years, there have been many efforts to enable the extension of Internet technologies to constrained devices. Initially, this resulted in proprietary protocols and architectures. Later, the integration of constrained devices into the Internet was embraced by IETF, moving towards standardized IP-based protocols. In this paper, we will briefly review the history of integrating constrained devices into the Internet, followed by an extensive overview of IETF standardization work in the 6LoWPAN, ROLL and CoRE working groups. This is complemented with a broad overview of related research results that illustrate how this work can be extended or used to tackle other problems and with a discussion on open issues and challenges. As such the aim of this paper is twofold: apart from giving readers solid insights in IETF standardization work on the Internet of Things, it also aims to encourage readers to further explore the world of Internet-connected objects, pointing to future research opportunities

Machine learning models for traffic classification in electromagnetic nano-networks

The number of nano-sensors connected to wireless electromagnetic nano-network generates different traffic volumes that have increased dramatically, enabling various applications of the Internet of nano-things. Nano-network traffic classification is more challenging nowadays to analyze different types of flows and study the overall performance of a nano-network that connects to the Internet through micro/nanogateways. There are traditional techniques to classify traffic, such as port-based technique and load-based technique, however the most promising technique used recently is machine learning. As machine learning models have a great impact on traffic classification and network performance evaluation in general, it is difficult to declare which is the best or the most suitable model to address the analysis of large volumes of traffic collected in operational nano-networks. In this paper, we study the classification problem of nano-network traffic captured by micro/nano-gateway, and then five supervised machine learning algorithms are used to analyze and classify the nano-network traffic from traditional traffic. Experimental analysis of the proposed models is evaluated and compared to show the most adequate classifier for nano-network traffic that gives very good accuracy and performance score to other classifiers.This work was supported in part by the ‘‘Agencia Estatal de Investigación’’ of ‘‘Ministerio de Ciencia e Innovación’’ of Spain under Project PID2019-108713RB-C51/MCIN/AEI/10.13039/501100011033, and in part by the ‘‘Agència de Gestió d’Ajuts Universitaris i de Recerca’’ (AGAUR) of the ‘‘Generalitat de Catalunya’’ under Grant 2021FI_B2 00091.Postprint (published version

Pervasive service discovery in low-power and lossy networks

Pervasive Service Discovery (SD) in Low-power and Lossy Networks (LLNs) is expected to play a major role in realising the Internet of Things (IoT) vision. Such a vision aims to expand the current Internet to interconnect billions of miniature smart objects that sense and act on our surroundings in a way that will revolutionise the future. The pervasiveness and heterogeneity of such low-power devices requires robust, automatic, interoperable and scalable deployment and operability solutions. At the same time, the limitations of such constrained devices impose strict challenges regarding complexity, energy consumption, time-efficiency and mobility. This research contributes new lightweight solutions to facilitate automatic deployment and operability of LLNs. It mainly tackles the aforementioned challenges through the proposition of novel component-based, automatic and efficient SD solutions that ensure extensibility and adaptability to various LLN environments. Building upon such architecture, a first fully-distributed, hybrid pushpull SD solution dubbed EADP (Extensible Adaptable Discovery Protocol) is proposed based on the well-known Trickle algorithm. Motivated by EADPs’ achievements, new methods to optimise Trickle are introduced. Such methods allow Trickle to encompass a wide range of algorithms and extend its usage to new application domains. One of the new applications is concretized in the TrickleSD protocol aiming to build automatic, reliable, scalable, and time-efficient SD. To optimise the energy efficiency of TrickleSD, two mechanisms improving broadcast communication in LLNs are proposed. Finally, interoperable standards-based SD in the IoT is demonstrated, and methods combining zero-configuration operations with infrastructure-based solutions are proposed. Experimental evaluations of the above contributions reveal that it is possible to achieve automatic, cost-effective, time-efficient, lightweight, and interoperable SD in LLNs. These achievements open novel perspectives for zero-configuration capabilities in the IoT and promise to bring the ‘things’ to all people everywhere

Efficient vertical handover in heterogeneous low-power wide-area networks

As the Internet of Things (IoT) continues to expand, the need to combine communication technologies to cope with the limitations of one another and to support more diverse requirements will proceed to increase. Consequently, we started to see IoT devices being equipped with multiple radio technologies to connect to different networks over time. However, the detection of the available radio technologies in an energy-efficient way for devices with limited battery capacity and processing power has not yet been investigated. As this is not a straightforward task, a novel approach in such heterogeneous networks is required. This article analyzes different low-power wide-area network technologies and how they can be integrated in such a heterogeneous system. Our contributions are threefold. First, an optimal protocol stack for a constrained device with access to multiple communication technologies is put forward to hide the underlying complexity for the application layer. Next, the architecture to hide the complexity of a heterogeneous network is presented. Finally, it is demonstrated how devices with limited processing power and battery capacity can have access to higher bandwidth networks combined with longer range networks and on top are able to save energy compared to their homogeneous counterparts, by measuring the impact of the novel vertical handover algorithm