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In today’s computing environment, where Artificial Intelligence (AI) and data processing are moving toward the Internet of Things (IoT) and Edge computing paradigms, benchmarking resource-constrained devices is a critical task to evaluate their suitability and performance. Between the employed devices, Single-Board Computers arise as multi-purpose and affordable systems. The literature has explored Single-Board Computers performance when running high-level benchmarks specialized in particular application scenarios, such as AI or medical applications. However, lower-level benchmarking applications and datasets are needed to enable new Edge-based AI solutions for network, system and service management based on device and component performance, such as individual device identification. Thus, this paper presents LwHBench, a low-level hardware benchmarking application for Single-Board Computers that measures the performance of CPU, GPU, Memory and Storage taking into account the component constraints in these types of devices. LwHBench has been implemented for Raspberry Pi devices and run for 100 days on a set of 45 devices to generate an extensive dataset that allows the usage of AI techniques in scenarios where performance data can help in the device management process. Besides, to demonstrate the inter-scenario capability of the dataset, a series of AI-enabled use cases about device identification and context impact on performance are presented as exploration of the published data. Finally, the benchmark application has been adapted and applied to an agriculture-focused scenario where three RockPro64 devices are present.

Trust, security, and privacy are three of the major pillars to assemble the fifth-generation network and beyond. Despite such pillars are principally interconnected, a multitude of challenges arise that need to be addressed separately. 5G networks ought to offer flexible and pervasive computing capabilities across multiple domains according to user demands and assure trustworthy network providers. To this end, distributed marketplaces expect to boost the trading of heterogeneous resources so as to enable the establishment of pervasive service chains between cross-domains. Yet, the need for selecting reliable parties as “marketplace operators” plays a pivotal role in achieving a trustworthy ecosystem. Two of the principal blockages in managing foreseeable networks are the need to consider trust as a property in the resource provisioning process and adapt previous trust models to accomplish the new network and business requirements. In this regard, this article is centered on the trust management of 5G multi-party network resource provisioning. As a result, a reputation-based trust framework is proposed as a Trust-as-a-Service (TaaS) solution for a distributed multi-stakeholder environment where requirements such as zero trust and zero-touch principles should be met. Besides, a literature review is also conducted to recognize the network and business requirements currently envisaged. Finally, the validation of the proposed trust framework was performed in a real research environment, the 5GBarcelona testbed, leveraging 12% of a 2.1 GHz CPU with 20 cores and 2% of the 30 GiB memory. These outcomes reveal the TaaS solution’s feasibility and conservative approach in the context of determining reliable network operators.

The connectivity and resource-constrained nature of single-board devices open the door to cybersecurity concerns affecting Internet of Things (IoT) scenarios. One of the most important issues is the presence of unauthorized IoT devices that want to impersonate legitimate ones by using identical hardware and software specifications. This situation can provoke sensitive information leakages, data poisoning, or privilege escalation in IoT scenarios. Combining behavioral fingerprinting and Machine/Deep Learning (ML/DL) techniques is a promising approach to identify these malicious spoofing devices by detecting minor performance differences generated by imperfections in manufacturing. However, existing solutions are not suitable for single-board devices since they do not consider their hardware and software limitations, underestimate critical aspects such as fingerprint stability or context changes, and do not explore the potential of ML/DL techniques. To improve it, this work first identifies the essential properties for single-board device identification: uniqueness, stability, diversity, scalability, efficiency, robustness, and security. Then, a novel methodology relies on behavioral fingerprinting to identify identical single-board devices and meet the previous properties. The methodology leverages the different built-in components of the system and ML/DL techniques, comparing the device internal behavior with each other to detect variations that occurred in manufacturing processes. The methodology validation has been performed in a real environment composed of 15 identical Raspberry Pi 4 Model B and 10 Raspberry Pi 3 Model B＋ devices, obtaining a 91.9% average TPR with an XGBoost model and achieving the identification for all devices by setting a 50% threshold in the evaluation process. Finally, a discussion compares the proposed solution with related work, highlighting the fingerprint properties not met, and provides important lessons learned and limitations.

In the last years, the number of connections in mobile telecommunication networks has increased rampantly, and in consequence, the number and type of relationships among entities. Should such interactions are to be profitable, entities will need to rely on each other. Hence, mobile telecommunication networks demand trust and reputation models that allow developing feasible communications in 5G and beyond networks, through which a group of entities can establish chains of services between cross-operators/domains, with security and trustworthiness. One of the key obstacles to achieving generalized connectivity beyond 5G networks is the lack of automatized, efficient, and scalable models for establishing security and trust. In this vein, this article proposes a pre-standardization approach for reputation-based trust models beyond 5G. To this end, we have realized a thorough review of the literature to match trust standardization approaches. An abstract set of requirements and key performance indicators has been extracted, and some pre-standardization recommendations proposed to fulfill essential conditions of future networks and to cover the lack of common trust and reputation models beyond 5G.