Reliable data availability and transparent governance are fundamental requirements for distributed edge-to-cloud systems that must operate across multiple administrative domains. Conventional cloud-centric architectures centralize control and storage, creating bottlenecks and limiting autonomous collaboration at the network edge. This paper introduces a decentralized governance and service-management framework that leverages Decentralized Autonomous Organizations (DAOs) and Decentralized Applications (DApps) to to govern and orchestrate verifiable, tamper-resistant, and continuously accessible data exchange between heterogeneous edge and cloud components. By embedding blockchain-based smart contracts within swarm-enabled edge infrastructures, the approach enables automated decision-making, auditable coordination, and fault-tolerant data sharing without relying on trusted intermediaries. The proposed OASEES framework demonstrates how DAO-driven orchestration can enhance data availability and accountability in real-world scenarios, including energy grid balancing, structural safety monitoring, and predictive maintenance of wind turbines. Results highlight that decentralized governance mechanisms enhance transparency, resilience, and trust, offering a scalable foundation for next-generation edge-to-cloud data ecosystems.

This work presents a complete power side-channel evaluation of the MAYO secret linear map on a constrained embedded target, together with a first-order countermeasure evaluated on the same hardware. While MAYO ranks among the most embedded-friendly candidates in the National Institute of Standards and Technology (NIST) additional-signatures call, few countermeasures for it are quantified against an explicit leakage threshold. The unprotected map $\mathbf{O}·{\mathbf{x}}_i$ over $\mathrm{GF}\left(16\right)$ is evaluated against a masked and row-shuffled variant under an identical acquisition procedure. Test Vector Leakage Assessment (TVLA) of the unprotected map peaks at $\left|t\right|=52.88$ fixed-versus-random and at $\left|t\right|=195.7$ under a secret-dependent fixed-versus-fixed test, while a non-profiled Correlation Power Analysis (CPA) resolving the multiplicative-identity alias and temporal row-mixing collapses the first key row from 16⁸ to 24 candidates, with a median of 287 measured across 20 independent random keys. The proposed countermeasure pairs masking with per-pass row shuffling, in order to suppress the micro-architectural residue masking alone leaves; it realizes a leakage reduction approaching 54×, to $\left|t\right|=3.62$ with no failing point out of 24,000, at roughly 2.22× cycles. The evaluation is a laboratory assessment of the isolated map on an STM32F303 (ARM Cortex-M4) measured with a ChipWhisperer-Lite, and does not exercise complete MAYO signing.

This work presents an energy-efficient implementation of Unmanned Aerial Vehicle (UAV)-based systems over 5G networks with on-board accelerated processing capabilities and provides a preliminary evaluation of the integrated solution. The study is a two-fold comparative analysis focused on connectivity and edge processing for UAVs. Two discrete deployment scenarios are implemented, where standard 5G configuration with artificial neural network (ANN) processing is evaluated against 5G Reduced Capability (RedCap) connectivity, paired with Spiking Neural Networks (SNNs). Both proposed energy-efficient alternative solutions are designed to offer significant energy savings; this paper examines whether they are suitable candidates for energy-constrained environments, i.e., UAVs, and quantifies their impact on the overall energy consumption of the system. The integrated solution, with 5G RedCap/SNNs, achieves energy-use reductions approaching 60%, which translates to an approximate 35% increase in flight time. The experimental evaluations were performed in a real-world deployment using a 5G-equipped UAV with edge-processing capabilities based on NVIDIA’s Jetson Orin.

This work presents an energy efficient implementation for UAV-based systems over 5G networks with on-boarded accelerated processing capabilities and provides a preliminary evaluation analysis of the integrated solution. A two-fold comparative study focused on connectivity and edge processing for UAVs, realizes two discrete deployment scenarios, where standard 5G configuration with Artificial Neural Networks processing is evaluated against 5G RedCap connectivity paired with Spiking Neural Networks. Both proposed alternative energy efficient solutions, are designed to offer significant energy saving, and this paper examines if they are fit candidates for energy stringent environments, i.e., UAVs, and also quantify the impact on the overall energy consumption of the system. The integrated solution with 5G RedCap/SNN realizes energy-use reductions approaching 60%, which translated to approximately 35% of increased flight time. The experimental evaluations were performed in a real-world deployment using a 5G equipped UAV with edge processing capabilities based on NVIDIA’s Jetson Orin.

This work presents a complete power side-channel evaluation of the MAYO secret linear map on a constrained embedded target, together with a first-order countermeasure evaluated on the same hardware. While MAYO ranks among the most embedded-friendly candidates in the National Institute of Standards and Technology (NIST) additional signatures call, its defensive counterpart remains underdeveloped, as few countermeasures are quantified against an explicit leakage threshold. A two-fold study is realized, where the unprotected map O·xi over GF(16) is evaluated against a masked and row-shuffled variant under an identical acquisition procedure. Test Vector Leakage Assessment (TVLA) of the unprotected map peaks at |t|=53.7 fixed-versus-random and at |t|=195.7 under a secret-dependent fixed-versus-fixed test, while a non-profiled Correlation Power Analysis (CPA) resolving the multiplicative-identity alias and temporal row-mixing recovers 5 of 8 first-row nibbles, thus collapsing the row search space from 168 to 24 candidates. The proposed countermeasure combines masking with per-pass row shuffling, in order to suppress the micro-architectural residue that masking alone leaves. The proposed countermeasure realizes a leakage reduction approaching 54×, to |t|=3.62 with no failing point out of 24,000, at roughly 2.22× cycles. The experimental evaluations were performed in a real-world deployment on an STM32F303 (ARM Cortex-M4) measured with a ChipWhisperer-Lite.

The proliferation of Internet of Things (IoT) devices operating in swarm-like formations—from autonomous device fleets to federated service networks—demands a computing continuum that spans edge nodes, fog gateways, and cloud back-ends. Service meshes such as Istio provide transparent traffic management, observability, and policy enforcement for the microservice fabrics that underpin these continuums, yet their scalability behaviour under diverse coordination patterns and increasing load intensity remains poorly characterised. This paper introduces a two-axis taxonomy for benchmarking service-mesh scalability in IoT-to-cloud microservice continuums: Axis A captures four scaling tiers (Baseline, Moderate, High, Extreme) derived from offered load and concurrency, while Axis B is measured by the coordination-amplification factor $\phi$, the number of service-to-service calls that one request triggers, and groups the scenarios into federated patterns, in which services call only distinct downstream services, and peer-to-peer patterns, in which services also call peer instances of themselves. We instantiate the taxonomy across six representative IoT scenarios, each exercised through multi-phase Fortio load campaigns over an Istio-meshed Kubernetes cluster. Analysing 51 benchmark runs, we report percentile latency distributions (P50, P90, P99), load fidelity, and throughput–tail coupling. Because every scenario is driven with fixed-size payloads, per-request application processing is held constant, which lets us separate the effect of coordination structure from that of application weight. At matched application weight and offered load, peer-to-peer coordination adds only a modest tail premium over federated rollout (a factor of ≈1.2–1.8 at 100–200 RPS); the severe effects—up to an order-of-magnitude P99 growth from a scenario’s own baseline to saturation, and achieved throughput collapsing to roughly half the requested rate—emerge specifically for heavy-compute peer-exchange workloads driven toward saturation. This interaction of load, coordination structure, and application weight is what the two-axis grid is designed to expose, and negligible-compute acknowledgement endpoints additionally provide a direct per-hop mesh-overhead measurement. We deliberately scope the study to the cloud-side segment of the continuum—the layer on which the service mesh runs—and within it to the single-node data plane, where mesh overhead can be measured in isolation from cross-node network variability; device and edge tiers enter only as offered load, and no claim is made about inter-tier transport. The A×B grid exposes otherwise hidden interaction effects between coordination topology and scale, providing actionable design guidance for service-mesh capacity planning and auto-scaling in IoT microservice deployments.