This paper describes challenges within IoT ecosystems from the perspective of cybersecurity testing along with a proposed approach to address them that will be investigated in a recently started Horizon Europe project named TELEMETRY. The key observations regarding the design of the framework are summarised as follows. There is a need to consider the full lifecycle of IoT components – at their design time, their integration into systems, and operation of those systems. Threats and risks can propagate when components are connected together in systems - vulnerabilities in one component can affect other components in a system. IoT devices present limitations to current testing and management due to geographical distribution, opacity and limited processing power. Risk assessment fulfils an important requirement because it enables assessment of what elements are important to the system’s stakeholders, how these elements may be compromised, and how the compromises may be controlled. Feedback from operational monitoring of IoT devices can inform firmware updates / patches to the devices but there is a significant challenge in rolling out these patches to multiple low-power devices geographically distributed

Network slicing enables operators to virtually partition network resources and instantiate different virtual networks, supporting flexible quality of service, in the form of service level agreements (SLAs). Optimizing resource allocation for network slicing is a complex task, given the dynamicity and randomness of network conditions. Leveraging principles of reinforcement learning, we propose an algorithmic solution to optimize the SLA success rate across a radio access network. Tailoring the algorithm to the problem at hand, and leveraging some prior knowledge on the system to be optimized, we ensure fast convergence, data efficiency, and safe exploration. The solution is scalable both in the number of cells and in the number of slices. Extensive numerical evaluations on a simulated environment show the effectiveness of the proposed solution and its advantages versus both simple baselines and sophisticated solutions based on deep reinforcement learning, in terms of speed of convergence and SLA success rate.

An edge data center can host applications that require low-latency access to nearby end devices. If the resource requirements of the applications exceed the capacity of the edge data center, some non-latency-critical application components may be offloaded to the cloud. Such offloading may incur financial costs both for the use of cloud resources and for data transfer between the edge data center and the cloud. Moreover, such offloading may violate data protection requirements if components process sensitive data. The operator of the edge data center has to decide which components to keep in the edge data center and which ones to offload to the cloud. In this paper, we formalize this problem and prove that it is strongly NP-hard. We introduce an optimization algorithm that is fast enough to be run online for dynamic and automatic offloading decisions, guarantees that the solution satisfies hard constraints regarding latency, data protection, and capacity, and achieves near-optimal costs. We also show how the algorithm can be extended to handle multiple edge data centers. Experiments show that the cost of the solution found by our algorithm is on average only 2.7% higher than the optimum.

Data produced by end devices like smartphones, sensors or IoT devices can be stored and processed across a continuum of compute resources, from end devices via fog nodes to the cloud, enabling reduced latency, increased processing speed and energy savings. However, the data may be sensitive (e.g., personal data or confidential commercially sensitive information), with regulatory or other requirements for its protection. Protecting sensitive data in the dynamic, heterogeneous, and decentralized cloud-to-edge continuum is very challenging. This paper describes a solution: FogProtect, an integrated set of four technologies to protect data in the cloud-to-edge continuum. Fog-Protect addresses four concerns: (i) control and enforcement of distributed data access and usage; (ii) management of distributed data protection policies; (iii) risk assessment for data assets in the cloud-to-edge continuum; (iv) automated optimisation and adaptation to address identified risks. FogProtect operates dynamically, reacting to system changes or detected vulnerabilities to keep the data secure across the cloud- to-edge continuum. This paper describes an overview of the FogProtect concept, discusses each of the four approaches, and illustrates their usage for the protection of data in three real-world use cases.

Wireless networks are becoming more and more complex. New radio access technologies and cell densification have provided increased efficiency and met the needs of an increasing traffic demand. On the other hand, such increment in complexity poses new challenges to legacy network management and optimization solutions. Traditionally, network optimization has been performed by applying static, rule-based schemes. Both in academia and industry, there is a consensus about the need to transit from static optimization approaches to more automated, dynamic, cognitive techniques. In this paper we embrace this perspective and we propose a set of cognitive algorithms for network performance optimization. We leverage the electric antenna tilt to achieve improved network performance. The proposed algorithms rely on reinforcement learning principles and are validated in a simulated environment.

Today's wireless and mobile service platforms are typically monolithic and centralized in nature, and they do not support heterogeneous service access and the sharing of service usage experience. New sources of revenue for providers are expected to include tailored, personalized, and dynamically composed services that are fast to market, cost efficient, and provide compelling user experience. To meet the current market needs, the SPICE service delivery platform extends the conventional IMS by supporting advanced added-value services that are composed of more primitive services. We describe the IMS role and functions in SPICE, and the use of ontology and Semantic Web technologies for achieving a better knowledge management in mobile service platforms.