IoA (Internet of Agents), combining MAS (Multi-Agent Systems) and IoT (Internet of Things), aims to provide an environment where diverse services can be performed and evaluated through heterogeneous agents’ cooperative participation. Given this, IoA requires a mechanism monitoring agents’ real-time behaviors to prevent undesired activities caused by compromised ones. This paper proposed a trust model to detect agents’ misbehavior by taking their roles into consideration, namely SP (Service Provider), SR (Service Rater), and SR+SP. Moreover, we conducted a real MAS implementation by using ROS 2 (Robot Operating System) to verify the preliminary results of SR and SP agents’ trust evaluation.
The IPv6 over Bluetooth Low Energy (BLE) standard defines the transfer of IP data via BLE connections. This connection-oriented approach provides high reliability but increases packet delays and requires substantial overhead to manage BLE connections. To overcome these drawbacks we present the design and implementation of IPv6 over BLE advertisements, a standard-compliant connection-less approach. We deploy our proposal on low-power IoT hardware and comparatively measure key network performance metrics in a public testbed. Our results show that IP over BLE advertisements offers network performance characteristics complementary to IP over connection-based BLE, trading lower reliability for shorter~latency.
ler for many new technologies, ranging from autonomous vehicles to shared mobile devices. In order to ensure high precision for those applications, GNSS augmentation systems are needed to provide correction data to reach a precision that is in the order of centimeters. Those systems can be provided as paid services where correction data are broadcast over a satellite link. In order to protect those systems and restrict their access only to paid users, encryption mechanisms must be adopted. SPARTN is an open industry standard for GNSS augmentation that has been specifically designed for supporting encryption, while saving bandwidth on the satellite link. In this paper, we propose APBE (Anti-Piracy Broadcast Encryption), a method to enhance the SPARTN security by providing protection against pirate customers that is specifically tailored to minimize bandwidth and storage. The proposed approach is demonstrated to be feasible via a real proof-of-concept implementation based on an embedded system. APBE is a candidate mechanism to be included in the future versions of SPARTN.
Body Coupled Communication (BCC) technology has the potential to revolutionise the wearable device field by providing substantial advantages in security, privacy and usability, yet understanding of the factors that influence signal path loss in BCC is crucial for developing accurate measurement techniques and refining the communication system. In this paper a new approach in proposed taking advantage of varying signal loss on touch to enable user input detection and interpretation, it demonstrates high accuracy and reliability. This technology can potentially provide new opportunities for visually impaired or disabled individuals by designating specific body locations as scenario activation triggers and enabling tactile and intuitive interaction with the technological environment. To study the impact of human touch on signal path loss multiple experiments were carried out and the findings enhance the understanding of the factors affecting BCC performance and provides an insight for further research.
The constant and continuous development of new technologies for the automatic processing of information has led to a massive growth of data and information in various areas that govern the development of social life. In recent years there has been a specific interest in the use of computer and telematic technologies in the justice sector to make the execution of processes and activities related to the management of law faster and more efficient (telematic civil process; collection, deposits and electronic archiving of provisions; minutes of hearings and documents; electronic notification of acts; etc.). Artificial Intelligence (AI) in the juridical-legal field represents a modern and advanced solution for managing the high quantity of valuable and necessary documents for carrying out justice activities. Given the particular criticality referred to the management of information and data, an architectural solution of the Edge Computing type is proposed for the localization of IT resources (of which a conceptual diagram is shown), which allows data processing in a decentralized way in opposition to the centralized one typical of Cloud Computing. Legal analytics technology, i.e., the use of various forms of AI ranging from Natural Language Processing (NLP) to Machine Learning and Data Science, can enhance the cognitive heritage represented by the jurisprudential and regulatory corpus (collection of material).
Distributed networks, such as wireless mesh networks, of- fer scalability, fault tolerance, and cost-effectiveness but face security challenges due to their decentralized nature. Tradi- tional security approaches are inadequate, necessitating the adoption of the Zero Trust security model. This paper in- troduces MeshShield, a security system for wireless mesh networks. It implements mutual authentication, continuous authentication, a decision engine, and node quarantine in a decentralized manner to enhance network resilience and mitigate risks. It ensures that only authenticated and au- thorized nodes join the network and enables periodic veri- fication of device behavior. Security information is shared among nodes, enabling a global view of network security. Malicious behavior detection triggers rapid response mea- sures, including broadcasting announcements and quaran- tining malicious nodes. Our evaluation demonstrates that MeshShield has minimal impacts on network performance (3.46% drop in throughput) and fairly low average CPU, and memory usage of 14.96%, and 1.38% respectively, making it suitable for resource-constrained devices. Compared to ex- isting approaches, MeshShield integrates security features in a distributed manner, providing an integrated system to safe- guard sensitive data in wireless mesh networks with nominal effects on network performance. 1
We introduce Fluidity, a framework enabling the flexible and adaptive deployment of modular applications in systems comprising cloud, edge, and mobile IoT nodes. Based on a declarative description of application requirements, Fluidity plans and executes an initial deployment of application components in the cloud-edge-mobile continuum. At runtime, Fluidity monitors resource availability and the position of mobile nodes, and adapts the deployment of the application accordingly, without any intervention from the application owner or system administrator. Notably, Fluidity allows applications to provide their own deployment and adaptation policies and to switch between different policies at runtime,while the application is running. We discuss the design and implementation of Fluidity in detail and provide an evaluation using a lab testbed, where the mobile node is a simulated drone. Our results show that the core mechanisms of Fluidity can adapt the application at reasonable overhead.
Establishing and maintaining secure communications in the Internet of Things (IoT) is vital to protect smart devices.Zero-interaction pairing (ZIP) and zero-interaction authentication(ZIA) enable IoT devices to establish and maintain secure communications without user interaction by utilizing devices’ ambient context, e.g., audio. For autonomous operation,ZIP and ZIA require the context to have enough entropy to resist attacks and complete in a timely manner. Despite the low-entropy context being the norm, like inside an unoccupiedroom, the research community has yet to come up with ZIP and ZIA schemes operating under such conditions. We propose HARDZIPA, a novel approach that turns commodity IoT actuators into injecting devices, generating high-entropy context. Here, we combine the capability of IoT actuators to impact the environment, e.g., emitting a sound, with a pseudorandom number generator (PRNG) featured by many actuators to craft hard-to-predict context stimuli. To demonstrate the feasibility of HARDZIPA, we implement it on off-theshelf IoT actuators, i.e., smart speakers, lights, and humidifiers. We comprehensively evaluate HARDZIPA, collectingover 80 hours of various context data in real-world scenarios. Our results show that HARDZIPA is able to thwart advancedactive attacks on ZIP and ZIA schemes, while doubling theamount of context entropy in many cases, which allows twotimes faster pairing and authentication.
Multi-agent systems (MASs) have gained considerable attention in the field of distributed computing due to their ability to provide technical interoperability, resource sharing and flexible coordination. Consequently, MAS are well-suited to address the challenges posed by the distributed and heterogeneous nodes within the device-edge-cloud continuum, including orchestration and standardization, optimal resource allocation,micro service placement policies, security and privacy.The objective of this study is to introduce the MLSysOps project, which aims at the autonomous management of the entire continuum tackling some of the challenges mentioned before. MLSysOps utilizes a hierarchical agent-based AI architecture to interface with the underlying resource management and application deployment/orchestration mechanisms. A comparative analysis is conducted between the existing related work and the proposed framework, highlighting the peculiarities and advantages of our approach.
Establishing and maintaining secure communications in the Internet of Things (IoT) is vital to protect smart devices. Zero-interaction pairing (ZIP) and zero-interaction authentication (ZIA) enable IoT devices to establish and maintain secure communications without user interaction by utilizing devices' ambient context, e.g., audio. For autonomous operation, ZIP and ZIA require the context to have enough entropy to resist attacks and complete in a timely manner. Despite the low-entropy context being the norm, like inside an unoccupied room, the research community has yet to come up with ZIP and ZIA schemes operating under such conditions. We propose HardZiPA, a novel approach that turns commodity IoT actuators into injecting devices, generating high-entropy context. Here, we combine the capability of IoT actuators to impact the environment, e.g., emitting a sound, with a pseudorandom number generator (PRNG) featured by many actuators to craft hard-to-predict context stimuli. To demonstrate the feasibility of HardZiPA, we implement it on off-the-shelf IoT actuators, i.e., smart speakers, lights, and humidifiers. We comprehensively evaluate HardZiPA, collecting over 80 hours of various context data in real-world scenarios. Our results show that HardZiPA is able to thwart advanced active attacks on ZIP and ZIA schemes, while doubling the amount of context entropy in many cases, which allows two times faster pairing and authentication.
Drones are an attractive platform for carrying sensor/actuator payloads for different civilian applications. Thanks to modern autopilots, it is now possible to run missions in a fully automated way, using suitable mission control programs. To minimize latency, such programs can run at the edge and have direct wireless connectivity with the drone. However, edge machines can have a limited wireless range, which may not suffice to support the mission at hand. To address this problem, we propose a protocol and algorithm for the transparent handover of mission execution between different edge-based mission controller stations that collectively cover the full mission area. We provide a detailed description of the proposed protocol. We also discuss a prototype implementation and evaluate its performance using a realistic testbed, showing that the protocol overhead is sufficiently small to support a wide range of applications.
Cloud-IoT deployments are ubiquitous and employed in various application domains, including smart buildings. Often employed in public spaces, IoT devices are exposed to various security threats. One such attack is “anomalous concept drift”. It occurs when an attacker tampers with a device causing it to report realistic sensor data that slowly deviates from the correct value. Evaluating concept drift detectors on real-world data is ideal. Though many indoor datasets exist, our real-world dataset provides a natural, long-term collection of indoor environmental sensor readings over six months. The dataset consists of environmental sensor samples collected via eight IoT devices in a real office setting. The dataset is particularly useful for evaluating concept drift detection algorithms as spatial aspects can be used along with the signals. The dataset has been made openly available, and in this paper we use it to inject malicious concept drifts and to evaluate the performance of several drift detection techniques. The injection tool’s source code is also publicly available.
Traditional wisdom for network management allocates network resources separately for the measurement and data transmission tasks. Heavy measurement tasks may take up resources for data transmission and significantly reduce network performance. It is therefore challenging for interference graphs, deemed as incurring heavy measurement overhead, to be used in practice in wireless networks. To address this challenge in wireless sensor networks, we propose to use power as a new dimension for interference graph estimation (IGE) and integrate IGE with concurrent flooding such that IGE can be done simultaneously with flooding using the same frequency-time resources. With controlled and real-world experiments, we show that it is feasible to efficiently achieve IGE via concurrent flooding on the commercial off-the-shelf (COTS) devices by controlling the transmit powers of nodes. We believe that efficient IGE would be a key enabler for the practical use of the existing scheduling algorithms assuming known interference graphs.
The prolonged lifetime of energy-harvesting (EH) LoRa networks requires that all EH LoRa sensors make the best use of available harvested energy in an energy-neutral manner to avoid power failures. This requirement is challenging to be fulfilled due to the unpredictability of ambient energy sources and the spatio-temporal heterogeneity of sensors’ harvesting abilities. We present EmbientLoRa, a novel predictive energy-management framework that enables energyneutral operation in EH LoRa networks via embedded intelligence. To achieve highly-accurate EH predictions, EmbientLoRa adopts a simple yet effective technique that allows each sensor to implement a machine learning pipeline locally at low cost. Coupled with adaptive EH management, it allows the sensors with higher harvested energy to transmit critical data more frequently in a probabilistic manner without sacrificing their lifetimes. Compared with the stateof-the-art, the results from testbed experiments reveal that EmbientLoRa improves EH prediction accuracy and transmission overhead, both by up to 1.5 times
The accuracy of ultra-wideband (UWB) ranging is severely affected when the direct path between devices is partly or fully occluded, i.e., in non-line-of-sight (NLOS) conditions. To detect and correct erroneous ranging measurements, many solutions based on machine learning models have been proposed, but they are usually deployed on edge devices rather than on the UWB device itself. In fact, existing works often focus on maximizing the NLOS classification accuracy and error correction performance, which results in large and computationally-complex models that cannot be run on UWB tags with limited processing power and memory. Whilst convenient, off-loading NLOS classification and error correction tasks to an edge device severely affects, among others, the scalability, privacy, and responsiveness of UWBbased localization systems, as tags need to actively exchange data with a third party and wait for its response, which may be delayed due to heavy load or unreliable communication. In this paper, we present InSight: a framework that enables the deployment of NLOS classification and error correction modelsdirectly on resource-constrained UWB devices. InSight allows to train and generate such models according to specific requirements (e.g., on memory usage and on runtime), and to shed light on how to reduce the model size and runtime without degrading the classification accuracy and error correction performance. The selected models are then seamlessly integrated into aNLOS engine running on the device alongside existing applications and supporting any localization service. With InSight, we can perform NLOS classification and error correction directly on an UWB tag in 0.6 ms, and with as little as 8 B of RAM and 19 kB of flash memory — while retaining a classification accuracy of up to 86% and reducing the 90th-percentile ranging error by more than 1 m. We further show how a localization service can leverage InSight to select only anchors in direct line-of-sight and to correct erroneous NLOS ranging measurements, which improves the 90th-percentile localization error by up to 1.6 m on our 120 m2 testbed.
Low-power wireless communication protocols based onsynchronous transmissions have recently gained popularity. In such protocols, packets can be demodulated correctly even though several devices transmit at the same time, which results in high reliability and energy efficiency. A by-product of synchronous transmissions is the beating effect: a sinusoidal pattern of constructive and destructive interference across the received signal. In this paper, we leverage this beating to propose a new localization approach. Specifically, we present BLoB, a system in which multiple anchors transmit packets synchronously using the constant tone extension, an optional bit sequence introduced by BLE 5.1, whose signal is sent with constant amplitude and frequency. We let mobile tags sample the superimposed signal resulting from the synchronous transmissions, and extract peaks in the beating and signal spectrum. These peaks provide key insights about the anchors’ location that complement received signal strength information and allow BLoB to derive a tag’s position with sub-meter accuracy. A key property of BLoB is that both anchors and tags employ a single antenna, in contrast to state-of-the-art localization schemes based on angle of arrival/departure information that require costly and bulky antenna arrays to achieve sub-meter accuracy. We implement BLoB on off-the-shelf BLE devices and evaluate its performance experimentally in both static and mobile settings, and in different environments: office rooms, library, meeting room, and sports hall. Our results show that BLoB can distinguish several anchors in a single synchronous transmission and that it retains a sub-meter localization accuracy even in challenging indoor environments.
We present APEX, a novel parameter exploration framework for low-power wireless protocols. APEX can autonomously derive an optimized set of parameters allowing a protocol to satisfy certain application requirements on the reliability, efficiency, or latency of communications. It does so without the need of expert knowledge, and by minimizing the number of testbed trials executed to gauge the protocol’s performance as a function of different parameter combinations. We have created a preliminary implementation of the framework coupled with the D-Cube testbed, and used it to parametrize the Baloo-Crystal protocol for different application requirements. Our results show that APEX can find an optimal parameter set with as few as 13 testbed trials in the median case, and reduce the average experimentation time by 65% compared to approaches based on exhaustive search.
This paper presents a comprehensive investigation into developing a fault detection and classification system for real-world IIoT applications. The study addresses challenges in data collection, annotation, algorithm development, and deployment. Using a real-world IIoT system, three phases of data collection simulate 11 predefined fault categories. We propose SMTCNN for fault detection and category classification in IIoT, evaluating its performance on real-world data. SMTCNN achieves superior specificity (3.5%) and shows significant improvements in precision, recall, and F1 measures compared to existing techniques.
The human body’s fat tissue can be used as a communication channel for radio frequency-based communication. As this channel supports high data rates, it enables many applications. Since these applications have very different requirements, there is a need for a flexible network API. We present some basic ideas for such an API.
Long range wireless transmission techniques such as LoRa are preferential candidates for a substantial class of IoT applications, as they avoid the complexity of multi-hop wireless forwarding. The existing network solutions for LoRa, however, are not suitable for peer-to-peer communication, which is a key requirement for many IoT applications. In this work, we propose a networking system - 6LoRa, that enables IPv6 communication over LoRa. We present a full stack system implementation on RIOT OS and evaluate the system on a real testbed using realistic application scenarios with CoAP. Our findings confirm that our approach outperforms existing solutions in terms of transmission delay and packet reception ratio at comparable energy consumption.
