This paper investigates equilibrium strategies in networked control systems subject to false data injection (FDI) attacks, employing a game-theoretic approach. Our framework characterizes the dynamics that revolves around the freshness of the data from multiple sensors using the age of incorrect information (AoII) metric. The interaction between legitimate transmitters, aiming to minimize the system AoII and their transmission costs, and a malicious adversary, aiming to maximize AoII while managing FDI costs, is modeled as a non-cooperative game. We analytically demonstrate the existence and uniqueness of a Nash equilibrium (NE) and derive explicit conditions characterizing equilibrium resource allocation strategies. We also present examples of applications related to secure healthcare and automotive control. The numerical results validate our theoretical findings, highlighting the strategic impact of the system parameters, including drift rates, FDI and transmission costs, and resource constraints. Our analysis yields actionable guidelines for enhancing sensor security through parameter tuning and resource allocation.

We study a control system where sensor measurements are transmitted to a remote station. Information may become outdated due to system drift or compromised by malicious false data injection. To quantify the impact of staleness and inaccuracy in the information at the receiver’s side, we use Age of Incorrect Information (AoII). In particular, we consider the Excess AoII above a certain threshold as our key objective to minimize, which we argue to be a sensible goal for many real-time control systems. We adopt a game-theoretic framework to model the strategic interaction between a transmitter, which aims to minimize both Excess AoII and transmission costs, and a malicious agent, which seeks to maximize the same Excess AoII metric while minimizing its own costs. Our analysis reveals the existence of a Nash equilibrium for this game, and we investigate how the system parameters influence the adversary’s decision to attack, identifying the conditions under which an attack becomes advantageous or not.

In the era of Industry 5.0, securing Industrial Control Systems (ICS) is increasingly vital, especially when relying on legacy communication protocols like Modbus TCP that may lack built-in protection mechanisms. This paper addresses the challenge of preserving the confidentiality of internal system states from potential cyber adversaries through a security-by-design framework. We propose a novel approach that leverages Discrete Event Systems (DES) theory to model communication flows and applies probabilistic opacity to quantify the risk of state disclosure. Central to our method is the concept of selective encryption: instead of encrypting all messages, we strategically encrypt only those events that could reveal sensitive information. This gives rise to a budget-constrained optimization problem, where the goal is to enforce opacity under resource limitations. To solve this efficiently, we develop a greedy algorithm that maximizes security by allocating encryption effort to the most critical events. The proposed method is validated using a representative example featuring two distinct query types, demonstrating its capability to limit information leakage while keeping low the computational overhead.

Cyber-Physical-Human Systems (CPHS) are complex systems centered on human beings. Currently, both industries and societies are even more dependent on CPHS, which have application in various fields and can have interactions of humans and CPS at various levels. In this context cyber-physical attacks become increasingly dangerous both for the physical plant and the human being. Thus, safety and security of CPHS must be simultaneously guaranteed, and the investigation of vulnerabilities of CPHS applications is of paramount importance to avoid any harm to human lives. In order to achieve this target, the current work presents a thorough analysis of the most recent literature in this field. The selected papers are classified by level of interaction between CPS and human (i.e., physiological, cognitive, and population-wide) and then by type of specific application, to investigate challenges, opportunities, and open questions. A detailed discussion concludes the paper reporting on the current advancements and promising future research paths.

Modern logistic operations in warehouses consist of human and robots working in the same large indoor or outdoor areas. To design safe and efficient area and avoid collisions or accidents, localization and tracking of humans need to be deployed. The development of location based service, such as human activity monitoring, has been one of the drivers for the last decade interest in indoor or outdoor positioning and localization. In this work, a novel tracking system integrating inertial measurements and an ultra-wide band infrastructure is proposed to follow and localize humans in a warehouse scenario. The system has been designed to be self-configurable, able to learn online most of the needed parameters. Being the computational load low, it can be implemented on wearable devices. We tested the tracking system in a real outdoor scenario where the adaptive online algorithms have shown their effectiveness and improvements with respect to existing approaches.

The Control Area Network (CAN) represents the standard bus for intra-vehicular networks communication. Unfortunately, CAN was not designed to be a secure protocol. Communications over CAN do not take advantage of any security feature (e.g., cryptography and authentication), raising different vulnerabilities in critical applications. This lack of security is even more emphasized in recent CAN networks, which integrate remote connection capabilities (e.g., Bluetooth and WiFi). This insecurity-by-design led to the development of specific mechanisms to patch CAN vulnerabilities. Many proposed solutions rely on implementing optimized cryptographic primitives and assume that the cryptographic keys were previously shared among the different nodes during the production phase, omitting the issue related to keys distribution and update. We propose SENECAN, a solution that combines watermarking and wired jamming to secure the CAN bus's key distribution. Our solution leverages intentional interference and spread spectrum watermarking to achieve security properties such as confidentiality, integrity, authentication, and anti-replay. Compared to other works, SENECAN does not require any modification of the CAN protocol and system architecture. Instead, it requires an additional CAN transceiver and an initial transmission overhead. Finally, we tested the effectiveness and functioning of the SENECAN distribution schema in a real CAN environment.

This article addresses the issue of assigning electric vehicles to charging stations, minimizing the maximum completion time. It envisions the interaction between electric vehicles and the charging infrastructure to match supply and demand in a decentralized and collaborative fashion. For this reason, the assignment issue is regarded as a linear integer programming problem and a Lagrangian relaxation heuristic is proposed to solve it. Thus, each electric vehicle selects the charging station and the most convenient path to minimize its own completion time. The completion time of each electric vehicle is composed of the travel time (TT), the waiting time (WT) at the station, and the charging time. The Lagrangian relaxation heuristic results are more effective compared to other local heuristic procedures performances and demonstrate a fair allocation of the electric vehicles to the charging stations. The analysis of the time components of the solution on a real urban network highlights that the TT is negligible with respect to the WT and charging time, that are comparable. Therefore, a reservation policy is also considered.

Critical Infrastructures (CIs) such as power grid, water and gas distribution are controlled by Industrial Control Systems (ICS). Sensors and actuators of a physical plant are managed by the ICS. Data and commands transmitted over the network from the Programmable Logic Controllers (PLCs) are saved and parsed within the Historian. Generally, this architecture guarantees to check for any process anomalies that may occur due to component failures and cyber attacks. The other use of this data allows activities such as forensic analysis. To secure the network is also crucial to protect the communication between devices. A cyber attack on the log devices could jeopardize any forensic analysis be it for maintenance, or discovering an attack trail. In this paper is proposed a strategy to secure plant operational data recorded in the Historian and data exchange in the network. An integrity checking mechanism, in combination with blockchain, is used to ensure data integrity. Data redundancy is achieved by applying an efficient replication mechanism and enables data recovery after an attack.

In the last decade, many efforts have been devoted to indoor localization and positioning. In this paper, a hybrid indoor localization system has been developed within the European project REFIRE for emergency situations. The REFIRE solution estimates the user's pose according to a prediction-correction scheme. The user is equipped with a waist-mounted inertial measurement unit and a radio frequency identification (RFID) reader. In the correction phase, the estimation is updated by means of geo-referenced information fetched from passive RFID tags predeployed into the environment. Accurate position correction is obtained through a deep analysis of the RFID system radiation patterns. To this end, extensive experimental trials have been performed to assess the RFID system performance, both in static and dynamic operating conditions. Experimental validation in realistic environments shows the effectiveness of the proposed indoor localization system, even during long-last missions and/or using a limited number of tags.

Complex and heterogeneous systems characterize the Industry 4.0. Due to the Information Technology (IT) convergence towards the Operational Technology (OT), the development of innovative cyber-physical security tools represents a milestone for the Industrial Control Systems (ICSs) protection. In this context, honeypots are systems used as decoys to detect and analyze malicious actions. However, industrial networks require specific honeypot development capabilities. In this work, we present MimePot, a cyber-physical honeypot conceived for industrial control networks. Compared to classic honeypots, MimePot offers a model-based approach: it is able to simulate physical processes to lure skilled attackers targeting industrial plants. Moreover, MimePot uses the Software Defined Networking (SDN) technology to provide a consistent future proof security approach. We demonstrate the usefulness of MimePot performing data integrity attacks against a water distribution system in a simulated environment.

Over the last year, the Internet of Things (IoT) drove the development of cyber-physical systems, leading the convergence between information and operational technologies. This coupling improves performances and saves costs but increases the number of vulnerabilities and the attack surface to malicious actors. Consequently, it is mandatory to understand how IoT devices can be properly integrated into a more secure environment. This issue is even more crucial in the field of Critical Infrastructures, such as energy, water, transportation, and telecommunications. To this aim, this paper analyzes the concept of opacity for Discrete Event Systems (DES) and applies it to a real system. The opacity describes the ability of the system to keep some states secret even if an attacker knows the happening of some events. The proposed approach is validated by applying the concept of opacity on the analysis over a simple system. It is demonstrated that opacity can be guaranteed and exploited when systems having different levels of security are integrated.

In this paper, we provide a novel framework to assess the vulnerability/robustness of a network with respect to pair-wise nodes' connectivity. In particular, we consider attackers that aim, at the same time, at dealing the maximum possible damage to the network in terms of the residual connectivity after the attack and at keeping the cost of the attack (e.g., the number of attacked nodes) at a minimum. Differently from the previous literature, we consider the attacker perspective using a multiobjective formulation and, rather than making hypotheses on the mindset of the attacker in terms of a particular tradeoff between the objectives, we consider the entire Pareto front of nondominated solutions. Based on that, we define novel global and local robustness/vulnerability indicators and we show that such indices can be the base for the implementation of effective protection strategies. Specifically, we propose two different problem formulations and we assess their performances. We conclude this paper by analyzing, as case studies, the IEEE118 power network and the U.S. Airline Network as it was in 1997, comparing the proposed approach against centrality measures.

Industrial Control systems traditionally achieved security by using isolation from the outside and proprietary protocols to communicate inside. This paradigm is changed with the advent of the Industrial Internet of Things that foresees flexible and interconnected systems. In this contribution, the threats coming from this new approach are analyzed and a framework for identify them is proposed. It is based on the common signature based intrusion detection system developed in the information technology domain, however, to cope with the constraints of the operation technology domain, it exploits anomaly based features. Specifically, it is able to analyze the traffic on the network at application layer by mean of deep packet inspection, parsing the information carried by the proprietary protocols. Two different topologies are adopted to cope also with legacy systems. A simple set up is considered to prove the effectiveness of the approach.

We present an innovative smartphone-centric tracking system for indoor and outdoor environments, based on the joint utilization of dead-reckoning and computer vision (CV) techniques. The system is explicitly designed for visually impaired people (although it could be easily generalized to other users) and it is built under the assumption that special reference signals, such as painted lines, colored tapes or tactile pavings are deployed in the environment for guiding visually impaired users along pre-defined paths. Thanks to highly optimized software, we are able to execute the CV and sensor-fusion algorithms in run-time on low power hardware such as a normal smartphone, precisely tracking the users movements.

The security of Cyber Physical Systems is a key factor since these architectures are being applied in many critical scenarios, such as power or water plants, hospitals, etc. In the state of the art, several approaches have been proposed to deal with the possible attacks that could be inferred to the Cyber Physical Systems. This contribution proposes a method for counter fighting the injection of tampered data in the communication channel. If this attack is not timely detected, it may result in severe disruption of the system or even in its complete damage. The proposed approach is based on coding the physical output of the system through permutation matrices whose scheme varies based on a randomly generated sequence. The strength of this method is in reducing the possibility of a successful injection attack while limiting the computational complexity. The experimental tests prove the effectiveness of the proposed scheme in detecting the performed attack while granting the real time constraints of the Cyber Physical Systems.

The protection of Critical Infrastructures represents a fundamental topic of interest for the modern societies. The testing in real scenarios is hard to achieve due to the severe interdependence between these critical systems and the human well-being. The paper is devoted to introduce a low-cost testbed developed to emulate a Critical Infrastructure as complex cyber-physical system. Moreover, it proposes an approach to identify cyber threats exploiting both cyber and physical behavior of the system. Specifically, common tools from model based fault diagnosis and intrusion detection are applied to determine incipient threats and support for the operator in decision making. All the hardware and software developed are implemented in an open-source fashion in order to share investigation resources with the scientific community.

Due to their relevance, systems such as water distribution, power grids, and gas pipelines are studied in order to guarantee robustness and security. Control systems, fault detection algorithms, and intrusion detection systems represent the crucial systems for critical infrastructure protection development. Behind each of these tools, a human operator is called to be the last decision maker in case of emergency. In this paper, a decision support system, based on the Analytic Hierarchy Process, is proposed and tested in order to suggest to the security operator the best way to recover the safe and secure state of a critical infrastructure under attack. Supported by a laboratory testbed, the behavior of a water distribution system has been emulated. A specific cyber threat has been exploited and identified to highlight the methodology designed for the decision support.

In this paper a low cost solution for implementing an indoor localization system is proposed. The Android app is based on a background service able to log data retrieved from the 9-Degree of Freedom IMU embedded in a smartphone and to compute the current user position. In particular, an Android application is developed: it is able to track the position of a user in indoor environment using smartphones embedded inertial sensors. The estimated position is shown on a map in a graphic user interface. This system has been developed for rescuers and building maintenance workers and it is able to track user in a planar environment. The key idea is to obtain a cheap solution still able to guarantee the room level accuracy. Experimental tests show the effectiveness of the proposed solution.

Localization and tracking are fundamental features in emergency response operations, where the mission leader needs to be aware of the team location. This paper addresses the localization for rescuers by exploiting wireless sensor networks embedded in the environment. Specifically, a pre-deployed network is considered and a localization algorithm is designed to find the location of the node and to track the rescuers cooperatively. Nodes estimate their own positions, while rescuers improve and augment their location awareness during mission by navigating across via points suggested by the network, thus improving the overall localizability. Experimental results show the effectiveness of the approach.

In this paper, an algorithm to estimate the position of a pedestrian in a 3-dimensional space is introduced. The proposed algorithm exploits the data provided by a waist-worn inertial platform and does not rely on the presence of any external infrastructure. Relevant features are extracted from the accelerometer data and are used to detect pedestrian activities such as standing, walking, going upstairs, or going downstairs. The estimate of the position is updated through a step detection procedure, which combines the signals provided by the inertial platform with the information about the pedestrian activity class.

Distance-based network localization is a very effective way to obtain the position of nodes in a network via range measuring; this, however, requires nodes with nontrivial sensing capabilities. Range-free techniques, conversely, use much simpler nodes but often provide just a rough estimate on the node's position. In this paper we develop a hybrid localization algorithm that combines range-free and distance-based sensors. A detailed simulation campaign aimed at corroborating the findings and at showing the potentialities of the approach concludes the paper.

Localization and tracking support is useful in many contexts and becomes crucial in emergency response scenarios: being aware of team location is one of the most important knowledge for incident commander. In this work both localization and tracking for rescuers are addressed in the framework of REFIRE project. The designed positioning system is based on the well-known prediction-correction schema adopted in field robotics. Proprioceptive sensors, i.e., inertial sensors and magnetometer, mounted on the waist of the rescuers, are used to form a coarse estimation of the locations. Due to the drift of inertial sensors, the position estimate needs to be updated by exteroceptive sensors, i.e., RFID system composed by tags embedded in the emergency signs as exteroceptive sensors and a wearable tag-reader. In long-lasting mission RFID tags reset the drift by providing a positioning having room-level accuracy.

Wireless Sensor Networks (WSNs) are at the forefront of emerging technologies due to the recent advances in Microelectromechanical Systems (MEMSs). The inherent multidisciplinary nature of WSN attracted scientists coming from different areas stemming from networking to robotics. WSNs are considered to be unattended systems with applications ranging from environmental sensing, structural monitoring, and industrial process control to emergency response and mobile target tracking. Most of these applications require basic services such as self-localization or time synchronization. The distributed nature and the limited hardware capabilities of WSN challenge the development of effective applications. In this paper, the self-localization problem for sensor networks is addressed. A distributed formulation based on the Information version of the Kalman Filter is provided. Distribution is achieved by neglecting any coupling factor in the system and assuming an independent reduced-order filter running onboard each node. The formulation is extended by an interlacement technique. It aims to alleviate the error introduced by neglecting the cross-correlation terms by "suitably” increasing the noise covariance matrices. Real experiments involving MICAz Mote platforms produced by Crossbows along with simulations have been carried out to validate the effectiveness of the proposed self-localization technique.

In this paper the self-localization problem for sensor networks is addressed. Given a set of nodes deployed in an environment, self-localization consists of finding out the location of all nodes in regard to any topology or metric of interest. Nodes are assumed to be equipped with a sensor board able to provide these inter-node distances. In addition, a few nodes are assumed to be equipped with some absolute position devices. According to this scenario, a new distributed algorithm based on an extended information filter is proposed. This algorithm provides an accurate estimation of node positions with a reasonable computational complexity, even when in presence of noisy measurements. Real experiments, carried out by exploiting Micaz Motes platforms, have been performed to show the effectiveness of the proposed technique.

Localisation is one of the most important tasks to be accomplished in order to realize the complete autonomy of a mobile robot. In this paper, a new strategy for global localisation is proposed. Applying this method a robot is able to safely initialise its position or relocalise itself in case of recovery of pose tracking failure. The algorithm presented adopts a hybrid approach. First a particle filter is used to generate hypotheses on the possible pose supposing that no movements are allowed to avoid collisions. Thereafter safe trajectories are planned and executed to reduce the remaining ambiguities while the hypotheses are monitored and validated by a set of parallel extended Kalman filters. The novelty of this approach stands on the ability to generate the pose hypotheses without any feature-based knowledge. As a consequence, a landmark-based description of the environment is no longer required for the algorithm execution.

One of the most important problems in mobile robotics is to realize the complete robot's autonomy. In order to achieve this goal several tasks have to be accomplished. Among them, the robot's ability to localise itself turns out to be critical. The research community has provided, through the years, different methodologies to face the localisation problem, such as the Kalman filter or the Monte Carlo Integrations methods. In this paper a different approach relying on a specialisation of the genetic algorithms is proposed. The novelty of this approach is to take advantage of the complex networks theory for the spatial deployment of the population to more quickly find out the optimal solutions. In fact, modelling the search space with complex networks and exploiting their typical connectivity properties, results in a more effective exploration of such space.

This paper deals with a new approach to multi robot localization. An Interlaced extended Kalman filter is shown to be a good solution to the problem of estimating the pose of a team of robots with a fully decentralized algorithm. Moreover, it is feasible to dynamically "correct" the estimation autonomously evaluated by each single robot, updating this quantity anytime two robots randomly come across. The algorithm combines the robustness of a full state EKF with the simplicity of its interlaced implementation. It does not need global supervision, and allows a large flexibility in using exteroceptive sensors. The paper presents some simulations to show the feasibility of the approach considering a set of robots equipped with different combinations of sensors and with wireless communication devices able to support data exchange when they are sufficiently close

Real-time applications ask for reduced computational cost algorithms. In robotic exploration of unstructured environments the problem is more challenging: several tasks, at the same time, must be carried on ranging from reactive behaviours to the building of a structured representation of the environment itself. Many sensor signals have to be processed at each step to estimate both landmarks and robot positions. This mapping aptitude can be implemented through an extended Kalman filter recently proposed in a previous paper. Due to the large number of estimated variables, and real-time constraints, the filter is better implemented in its interlaced version. The novelty of this paper consists in extending the IEKF filter, removing some hypothesis on the linearity of both state transition and observation mapping, in order to further reduce computational burden and then achieve a better tradeoff among computational load and accuracy.