Rural roads are vital for the transportation of persons and goods, but are vulnerable to animal-vehicle collisions, which cause substantial environmental, economic and public health impacts every year. WiFi sensing has recently emerged as a cost-effective approach to detect hazardous situations on roads, overcoming some limitations of traditional monitoring systems. However, scaling WiFi sensing to cover long road sections introduces challenges, including interference between WiFi nodes and with other protocols operating in the same Industrial, Scientific, and Medical (ISM) bands, such as IEEE 802.15.4. In this work, we present a scalable, low-cost architecture for WiFi-based monitoring of rural roads. We develop and optimize machine learning models for real-time detection and classification of pedestrians, vehicles, and animals using low-cost IoT devices, achieving over 95% accuracy and a miss rate of only 0.7%. We evaluate the models under varied conditions, including different sensor distances, light rain, and previously unseen rural locations, demonstrating robustness across environmental variations. Additionally, we conduct network simulations to assess the coexistence of WiFi sensing with IEEE 802.15.4 communications and perform LoRaWAN experiments in rural environments to validate long-range data transmission. We also release an open dataset containing all evaluated scenarios to support reproducibility and further research. Our results confirm the feasibility of the proposed system in terms of sensing accuracy, network performance, and deployment cost.

In recent years, there has been an increase in research concerning Intrusion Detection Systems (IDS) for Internet of Things (IoT). Detecting network attacks is important to ensure network integrity and availability. Existing methods in the literature typically rely on monitoring network metrics and behavior for intrusion detection. On the other hand, the attack footprint affects not only the network metrics but also the operational metrics of individual devices. Operational metrics could be used to enable informed anomaly detection and enhance network-based intrusion detection systems approaches in the literature. Thus, this work evaluates the use of operational metrics from individual sensors for intrusion detection in the IoT paradigm. For that, we implemented and analyzed a centralized IDS that utilizes both network and operational metrics. Blackhole, Greyhole, and Flooding attacks were simulated on a network with emulated IoT devices. The IDS is implemented with an XGBoost classifier model that is validated by classifying a network with out-of-distribution attack cases. Despite the overhead caused in terms of processing and metrics transmission to the IDS, the operational metrics presented higher information gain and SHAP values in the collected metrics and increased IDS detection rate to 97% in the implemented attacks.

Software-defined networking (SDN) was devised to simplify network management and automate infrastructure sharing in wired networks. These benefits motivated the application of SDN in resource-constrained wireless networks to leverage solutions for complex applications. However, some of the core SDN traits expose the networks to Denial-of-Service (DoS) attacks. There are proposals in the literature to detect DoS in wireless SDN networks; however, not without shortcomings: there is little focus on resource constraints, high detection rates have been reported mostly for small networks and the detection is disengaged from the identification of the type of attack or the attacker. Our work targets these shortcomings by introducing a lightweight, online change point detector to monitor performance metrics that are impacted when the network is under attack. A key novelty is that the proposed detector is able to operate in either centralized or distributed mode. The centralized detector has very high detection rates and can further distinguish the type of attack from a list of known attacks. In turn, the distributed detector can be useful to identify the nodes launching the attack. Our proposal is tested over IEEE 802.15.4 networks. The results show detection rates exceeding 96% in networks of 36 and 100 nodes and identification of the type of attack with a probability exceeding 89% when using the centralized approach.

Low-power wireless networks are an integral part of the Internet of Things, composed of resourceconstrained devices harvesting ambient information.The appearance of unidirectional links is characteristic of low power wireless networking due to physical effects, device heterogeneity and manufacturing imperfections.Despite the prevalence of unidirectional links, most routing and radio duty cycling protocols designed for these networks do not account for such links.We provide unidirectional-link-capable protocols and study the impact of using such links on network performance indicators, such as the data delivery ratio, delay and energy consumption.Our protocols are flexible and flooding-free, leveraging centralized knowledge provided by the Software-Defined Networking paradigm.Our experiments reveal that, while unidirectional links must be detected, using them for routing enhances network performance only if the unidirectional links are long.

The Software Defined Networking (SDN) paradigm can provide flexible routing and potentially support the different communication patterns that exist in Wireless Sensor Networks (WSN). However applying this paradigm to resource-constrained networks is not straightforward, especially if security services are a requirement. Existing SDN-based approaches for WSN evolved over time, addressing resource-constrained requirements. However, they do not integrate security services into their design and implementation. This work’s main contribution is a secure-by-design SDN-based framework for Wireless Sensors Networks. Secure node admission and end-to-end key distribution to support secure communication are considered key services, which the framework must provide. We describe its specification, design, implementation, and experiments considering device and protocol constraints. The results indicate that our approach has achieved such goals with acceptable overheads up to medium sized networks.

Considering the flexibility of network-based control systems and the wide adoption of wireless IP networks, the relationship between Quality of Service (QoS) and Quality of Control (QoC) parameters is studied in a simple control loop implemented over a wireless ad hoc network. This relationship is investigated using ns-2 simulator in three scenarios: static without packet loss, static with packet loss and mobile. The first scenario was compared to a case study for wired networks presented in the literature. Similar results were obtained, according to the prediction that different packet-switched technologies would present similar results because the concepts of QoS parameters are general enough. Results related to the other two scenarios qualitatively show QoC problems that appear due to the high utilization rate of the network and route disruption due to mobility. These problems can be summarized by the "inertia" of the controlled variable. A proof-of-concept experiment was also implemented to complement and to evaluate simulations.

Malware analysis systems are essential to characterize malware behavior and to improve defense mechanisms. In dynamic malware analysis, the actions performed by malware in a sandbox are highly dependent on the interactions with other hosts and services. However, the current solutions superficially deal with the network environment that surrounds the sandbox, exposing limitations to traffic containment and network resources reconfiguration. We have already shown how Software-Defined Networking (SDN) could enable network access policies changes and thus exposing distinct malware actions. In this paper, we investigate the malware analysis process by considering the entire analysis environment, including a sandbox and other components that comprise it. We developed a fully-automated malware analysis solution that uses network layer as a tool to reconfigure the analysis environment. In that way, it is possible to implement per-flow containment rules, dynamic resources configuration, and to manipulate network traffic to impersonate services. Our experiments show that it is feasible to identify behavioral deviations in different analysis scenarios and reveal many more malware behaviors than those revealed by the state-of-the-art analysis systems.

The deployment of security services over Wireless Sensor Networks (WSN) and IoT devices brings significant processing and energy consumption overheads. These overheads are mainly determined by algorithmic efficiency, quality of implementation, and operating system. Benchmarks of symmetric primitives exist in the literature for WSN platforms but they are mostly focused on single platforms or single operating systems. Moreover, they are not up to date with respect to implementations and/or operating systems versions which had significant progress. Herein, we provide time and energy benchmarks of reference implementations for different platforms and operating systems and analyze their impact. Moreover, we not only give the first benchmark results of symmetric cryptography for the Intel Edison IoT platform but also describe a methodology of how to measure energy consumption on that platform.

Mechanisms to detect and analyze malicious software are essential to improve security systems. Current security mechanisms have limited success in detecting sophisticated malicious software. More than to evade analysis system, many malwares require specific conditions to activate their actions in the target system. The flexibility of Software-Defined Networking (SDN) provides an opportunity to develop a malware analysis architecture integrating different systems and networks profile configuration. In this paper we design an architecture specialized in malware analysis using SDN to dynamically reconfigure the network environment based on malware actions. As result, we demonstrate that our solution can trigger more malware's events than traditional solutions that do not consider sandbox surround environment as an important component in malware analysis.

Multiplexing data transmission in time and frequency is an alternative to medium contention in wireless networks. The IEEE 802.15.4e standard, and its Time Slotted Channel Hopping (TSCH) mode in particular, provides such possibility in the context of constrained devices, for instance wireless sensor networks. We analyze the performance of TSCH leveraging from a real implementation in emulated environment, assessing latency, delivery ratio and time to associate.

Wireless sensor networks (WSNs) and Internet of Things (IoT) have been used to build several eHealth systems, which enable a physician (or caregiver) to monitor a patient (or elderly) continuously and real time, either locally or remotely, at lower cost and being less intrusive in the routine than the traditional monitoring equipment. Physical therapy aiding systems are a particular case of such systems, with specific characteristics and requirements, concerning equipment, software, and system. This work presents a system aimed at assessing joint angle and vital signs to assist physical therapists. The system is also available to hydrotherapy, which imposes packaging, sensing, and communication challenges. We discuss our design and implementation, as well as common problems with this kind of application. We also present statistical analysis of the system results based on experiments conducted and compared to a goniometer, a standard physical therapy device.

Wireless Sensor Networks (WSN) are used for a variety of applications, including the target's trajectory estimation. Most proposed solutions are based on sequential estimation. However, in this paper we present a new solution to the trajectory estimation problem using the batch estimation approach. In our solution, we model the problem as a system of equations AX = b, with matrix A being sparse and vector X being the trajectory. Next, through multifrontal QR factorization, factorization A = QR is distributed between the sensors, which calculate it collaboratively and incrementally. Simulation results show that our solution has the same performance as the centralized estimator. Also, we demonstrate its implementation viability by showing that the processing and memory requirements are compatible to generic motes characteristics.

Motivated by the internets of the future, which will likely be considerably larger in size as well as highly heterogeneous and decentralized, we propose Decentralize-SDN, D-SDN, a framework that enables not only physical- but also logical distribution of the Software-Defined Networking (SDN) control plane. D-SDN accomplishes network control distribution by defining a hierarchy of controllers that can “match” an internet's organizational- and administrative structure. By delegating control between main controllers and secondary controllers, D-SDN is able to accommodate administrative decentralization and autonomy.It incorporates security as an integral part of the framework. This paper describes D-SDN and presents two use cases, namely network capacity sharing and public safety network services.

This paper proposes a novel approach to capacity sharing in hybrid networked environments, i.e., environments that consist of infrastructure-based as well as infrastructureless networks. The proposed framework is based on Software-Defined Networking (SDN) and provides flexible, efficient, and secure capacity sharing solutions in a variety of hybrid network scenarios. In this paper, we describe the challenges raised by capacity sharing in hybrid networks, describe our framework in detail and how it addresses these challenges, and discuss implementation issues. To the best of our knowledge, this is the first SDN-based capacity sharing solution that targets hybrid networks and that incorporates security as an integral part of the proposed approach.

Wireless Sensor Networks (WSNs) are typically energy constrained, hence, energy management is a fundamental factor determining their performance and lifetime. We present EM, an energy management tool for WSNs, which main idea is to extend the node lifetime, by adapting its duty cycle and the amount of tasks executed.

Since the discovery of Shor's algorithm, the anxiety about quantum computation has increased. A large amount of research has been conducted to discover new algorithms and to build a quantum computer. But it seems that a general purpose quantum computer is far from being achieved. Meanwhile, cryptographers around the world started to look for security algorithms that resist to quantum attacks, but these still need improvement to achieve practical execution time. This work proposes a quantum-classical hybrid architecture, focusing on photonic quantum computers. A small quantum coprocessor implementing the Grover search algorithm is used to perform the search for roots of polynomials in Fpq. This coprocessor is used to accelerate the decoding process of the McEliece cryptosystem.

In Wireless Sensor Networks, the base station is generally the only unconditionally trusted entity. If it is not connected for a period of time, the network is left unattended and sensor nodes cannot offload data in real time. Thus, until the base station becomes available, adversaries can compromise some sensor nodes and selectively destroy data. In order to prevent such attacks, providing the so-called “data survival”, some strategies can be employed. In this paper, we propose a new strategy based on the concept of secure multi-party protocols. The main advantages of the proposed strategy are not limited to the security, since it (1) preserves privacy, (2) enables the use o data aggregation, (3) enforces a level of trust among nodes, which collaborate to compute functions. Additionally, we implement and evaluate the proposed solution using a real sensor platform. Results show the low cost of the scheme in terms of energy consumption and processing time.

Security is an important concern in any modern network. This also applies to Wireless Sensor Networks (WSNs), especially those used in applications that monitor sensitive information (e.g., health care applications). However, the highly constrained nature of sensors impose a difficult challenge: their reduced availability of memory, processing power and energy hinders the deployment of many modern cryptographic algorithms considered secure. For this reason, the choice of the most memory-, processing- and energy-efficient security solutions is of vital importance in WSNs. To date, several authors have developed extensive analyses comparing different encryption algorithms and key management schemes, while very little attention has been given to message authentication mechanisms. In this paper, we address this issues by identifying Authenticated Encryption with Associated Data (AEAD) schemes suitable for WSNs and by evaluating their features and performance on TelosB sensor nodes. As a result of this analysis, we identify the recommended choices depending on the characteristics of the target network.

Public Safety Networks (PSNs) enable communication not only to warn the population about an imminent catastrophe, but also to coordinate teams during the crisis phase. As soon as authorities become aware of a crisis, they should organise a response just before the dissemination of warning messages to citizens in the crisis zone. Thus, it is important to prevent the leak of information before the communication to citizens. This paper explores the use of threshold cryptography for sharing valuable information (or secrets) in PSNs. The proposed protocol was designed to offer: (1) interoperability, (2) survivability, (3) security against adversary with specific goals (e.g., destroy the secrets). We implement the protocol in a wireless mesh network in order to demonstrate its efficiency.

Wireless Sensor Networks (WSNs) are a valuable technology to support countless applications in different areas. Given the WSN nodes resource constrained characteristics, designing energy-aware applications, communication protocols and security mechanisms are critical. The operating system (OS) running on the WSN node also interferes with the node overall behavior, and its energy consumption. In this paper, we develop a comparison between two different operating systems (Contiki and TinyOS) running on the same hardware platform (Crossbow TelosB). Using a set of tasks, which includes sensing, communication and security mechanisms, we evaluate their behavior in terms of energy consumption and execution time.

Security in Wireless Sensor Networks (WSNs) is highly dependent on the behavior of the base station. This happens because, if the network is left unattended, sensor nodes cannot offload data to the (secure) base station in real time and, thus, until the base station becomes available, adversaries can compromise some sensor nodes and selectively destroy data. In order to prevent such attacks, providing the so-called “data survival”, some strategies can be employed. In this paper, we discuss and analyze different data survival strategies using cryptography. To the best of our knowledge, we provide the first implementation of such techniques, using a real sensor platform for their evaluation. As a result, we show that the main costs for the data survival process are not as high as it could be expected, and that strategies based on private keys can be used even if one considers the highly resource constrained nature of sensors.

Abstract We present MARVIN, a new parallelizable message authentication code (MAC) based on the ALRED family. The new algorithm is designed with resource‐constrained platforms inmind and explores the structure of an underlying block cipher toprovide security at a small cost in terms of memory needs. Also,we show how MARVIN can be used as an authentication‐onlyfunction or else in an authenticated encryption with associateddata (AEAD) scheme. We define a new AEAD proposal called LETTERSOUP, which is based on the mode ofoperation. Finally, we LFSRC analyze the security and performance of theresulting schemes. Copyright © 2008 John Wiley & Sons, Ltd.

This paper presents an analytical model to predict energy consumption in saturated IEEE 802.11 single-hop ad hoc networks under ideal channel conditions. The model we introduce takes into account the different operational modes of the IEEE 802.11 DCF MAC, and is validated against packet-level simulations. In contrast to previous works that attempted to characterize the energy consumption of IEEE 802.11 cards in isolated, contention-free channels (i.e., single sender/receiver pair), this paper investigates the extreme opposite case, i.e., when nodes need to contend for channel access under saturation conditions. In such scenarios, our main findings include: (1) contrary to what most previous results indicate, the radio's transmit mode has marginal impact on overall energy consumption, while other modes (receive, idle, etc.) are responsible for most of the energy consumed; (2) the energy cost to transmit useful data increases almost linearly with the network size; and (3) transmitting large payloads is more energy efficient under saturation conditions