Search Results (7)

The Internet of Things (IoT) has rapidly evolved into a central component of today’s technological landscape, enabling seamless connectivity and communication among a vast array of devices. It underpins automation, real-time monitoring, and smart infrastructure, serving as a foundation for Industry 4.0 and paving the way toward Industry 5.0. Despite the potential of IoT systems to transform industries, these systems face a number of challenges, most notably the lack of processing power, storage space, and battery life. Whereas cloud and fog computing help to relieve computational and storage constraints, energy limitations remain a severe impediment to long-term autonomous operation. Among the threats that exploit this weakness, the Denial-of-Sleep (DoSl) attack is particularly problematic because it prevents nodes from entering low-power states, leading to battery depletion and degraded network performance. This research investigates machine-learning (ML) and deep-learning (DL) methods for identifying such energy-wasting behaviors to protect IoT energy resources. A dataset was generated in a simulated IoT environment under multiple DoSl attack conditions to validate the proposed approach. Several ML and DL models were trained and tested on this data to discover distinctive power-consumption patterns related to the attacks. The experimental results confirm that the proposed models can effectively detect anomalous behaviors associated with DoSl activity, demonstrating their potential for energy-aware threat detection in IoT networks. Specifically, the Random Forest and Decision Tree classifiers achieved accuracies of 98.57% and 97.86%, respectively, on the held-out 25% test set, while the Long Short-Term Memory (LSTM) model reached 97.92% accuracy under a chronological split, confirming effective temporal generalization. All evaluations were conducted in a simulated environment, and the paper also outlines potential pathways for future physical testbed deployment. Full article

C-based XC binary materials and their (XC)_m/(YC)_n (X, Y ≡ Si, Ge and Sn) superlattices (SLs) have recently gained considerable interest as valuable alternatives to Si for designing and/or exploiting nanostructured electronic devices (NEDs) in the growing high-power application needs. In commercial NEDs, heat dissipation and thermal management have been and still are crucial issues. The concept of phonon engineering is important for manipulating thermal transport in low-dimensional heterostructures to study their lattice dynamical features. By adopting a realistic rigid-ion-model, we reported results of phonon dispersions ${\mathsf{\omega }}_{\mathrm{j}}^{\mathrm{S}\mathrm{L}}\left(\overrightarrow{\mathrm{k}}\right) \mathrm{o}\mathrm{f} \mathrm{n}\mathrm{o}\mathrm{v}\mathrm{e}\mathrm{l} \mathrm{s}\mathrm{h}\mathrm{o}\mathrm{r}\mathrm{t}-\mathrm{p}\mathrm{e}\mathrm{r}\mathrm{i}\mathrm{o}\mathrm{d} {\left(\mathrm{X}\mathrm{C}\right)}_{\mathrm{m}}/{(\mathrm{Y}\mathrm{C})}_{\mathrm{n}}\left[001\right] \mathrm{S}\mathrm{L}\mathrm{s}$, for m, n = 2, 3, 4 by varying phonon wavevectors $\left|{\overrightarrow{\mathrm{k}}}_{\mathrm{S}\mathrm{L}}\right|$ along the growth ${\mathrm{k}}_{||}$ ([001]), and in-plane ${\mathrm{k}}_{\perp }$ ([100], [010]) directions. The SL phonon dispersions displayed flattening of modes, especially at high-symmetry critical points Γ, Z and M. Miniband formation and anti-crossings in ${\mathsf{\omega }}_{\mathrm{j}}^{\mathrm{S}\mathrm{L}}\left(\overrightarrow{\mathrm{k}}\right)$ lead to the reduction in phonon conductivity ${\mathsf{\kappa }}_{\mathrm{z}}$ along the growth direction by an order of magnitude relative to the bulk materials. Due to zone-folding effects, the in-plane phonons in SLs exhibited a strong mixture of XC-like and YC-like low-energy ${\mathsf{\omega }}_{\mathrm{T}\mathrm{A}}$, ${\mathsf{\omega }}_{\mathrm{L}\mathrm{A}}$ modes with the emergence of stop bands at certain $\left|{\overrightarrow{\mathrm{k}}}_{\mathrm{S}\mathrm{L}}\right|$. For thermal transport applications, the results demonstrate modifications in thermal conductivities via changes in group velocities, specific heat, and density of states. Full article

We report a novel dual-band barrier infrared detector (DBIRD) design using InAs/GaSb type-II superlattices (T2SLs). The DBIRD structure consists of back-to-back barrier diodes: a “blue channel” (BC) diode which has an nBp architecture, an n-type layer of a larger bandgap for absorbing the blue band infrared/barrier/p-type layer, and a “red channel” (RC) diode which has a pBn architecture, a p-type layer of a smaller bandgap for absorbing the red band infrared/barrier/n-type layer. Each has a unipolar barrier using a T2SL lattice matched to a GaSb substrate to impede the flow of majority carriers from the absorbing layer. Each channel in the DBIRD can be independently accessed with a low bias voltage as is preferable for high-speed thermal imaging. The device modeling of DBIRDs and simulation results of the current–voltage characteristics under dark and illuminated conditions are also presented. They predict that the dual-band operation of the DBIRD will produce low dark currents and 45–56% quantum efficiencies for the in-band photons in the BC with ${\lambda }_c$ = 5.58 μm, and a nearly constant 32% in the RC with ${\lambda }_c$ = 8.05 μm. The spectral quantum efficiency of the BC for 500 K blackbody radiation is approximately 50% over the range of $\lambda$ = 3–4.7 μm, while that of the RC has a peak of 42% at 5.9 μm. The DBIRD may provide improved high-speed dual-band imaging in comparison with NBn dual-band detectors. Full article

In the context of smart cities, the integration of artificial intelligence (AI) and the Internet of Things (IoT) has led to the proliferation of AIoT systems, which handle vast amounts of data to enhance urban infrastructure and services. However, the collaborative training of deep learning models within these systems encounters significant challenges, chiefly due to data privacy concerns and dealing with communication latency from large-scale IoT devices. To address these issues, multi-level split federated learning (multi-level SFL) has been proposed, merging the benefits of split learning (SL) and federated learning (FL). This framework introduces a novel multi-level aggregation architecture that reduces communication delays, enhances scalability, and addresses system and statistical heterogeneity inherent in large AIoT systems with non-IID data distributions. The architecture leverages the Message Queuing Telemetry Transport (MQTT) protocol to cluster IoT devices geographically and employs edge and fog computing layers for initial model parameter aggregation. Simulation experiments validate that the multi-level SFL outperforms traditional SFL by improving model accuracy and convergence speed in large-scale, non-IID environments. This paper delineates the proposed architecture, its workflow, and its advantages in enhancing the robustness and scalability of AIoT systems in smart cities while preserving data privacy. Full article

A³B⁵ materials used for the superlattice (SL) fabrication have properties that enable the design of devices optimized for infrared (IR) detection. These devices are used in the military, industry, medicine and in other areas of science and technology. The paper presents the theoretical assessment and analysis of the InAs/InAs_1−xSb_x type-II superlattice (T2SL) (grown on GaSb buffer layer) strain impact on the bandgap energy and on the effective masses of electrons and holes at 150 K. The theoretical research was carried out with the use of the commercial program SimuApsys (Crosslight). The k·p method was adopted in T2SL modeling. Luttinger coefficients (γ₁, γ₂ and γ₃) were assessed assuming the Kane coefficient F = 0. The bandgap energy of ternary materials (InAs_xSb_1−x) was determined assuming that the bowing parameter (b_g) for the above-mentioned temperature is b_g = 750 meV. The cutoff wavelength values were estimated based on the theoretically determined absorption coefficients (from approximation the quadratic absorption coefficient). The bandgap energy was calculated according to the following formula: E_g = 1.24/λ_cutoff. The theoretical simulations allowed us to conclude that the strain in T2SL causes the E_g shift, which also has an impact on the effective masses m_e and m_h, playing an important role for the device’s optical and electrical performance. The T2SLs-simulated results at 150 K are comparable to those measured experimentally. Full article

A³B⁵ materials used in the construction of a superlattice have properties that enable the design of devices (to include avalanche photodiodes) optimized for use in infrared detection. These devices are used in the military and medicine industries, and in other areas of science and technology. This paper presents a theoretical assessment and analysis of the impact of stresses on an InAs/InAsSb type-II superlattice (T2SL) grown on a GaSb buffer layer, considering band gap energy and effective masses at a temperature of 150 K. The theoretical research was carried out with the use of the commercial platform “SimuApsys” (Crosslight). The method kp 8·8 (k = 0.06) was adopted in T2SL modeling. Luttinger coefficients (γ₁, γ₂ and γ₃) were assessed assuming the Kane coefficient F = 0. The band gap energy of InAsSb ternary materials was determined assuming that the bowing parameter for the above-mentioned temperature was b_g = 0.75 eV. The cut-off wavelength values were estimated on the basis of theoretically determined absorption coefficients (α). The energy gap was calculated according to the following formula: E_g = 1.24/λ_cut-off. From the analysis of theoretical results, it can be concluded that the stresses in T2SL cause the E_g shift, which also has an impact on the influence on the change of the effective masses m_e and m_h, which play an important role in the optical and electrical parameters of the detection structure. The simulated theoretical parameters T2SL at 150 K are comparable to those measured experimentally. Full article

Group awareness is playing a major role in the efficiency of mission planning and decision-making processes, particularly those involving spatially distributed collaborative entities. The performance of this concept has remarkably increased with the advent of the Internet of Things (IoT). Indeed, a myriad of innovative devices are being extensively deployed to collaboratively recognize and track events, objects, and activities of interest. A wide range of IoT-based approaches have focused on representing and managing shared information through formal operators for group awareness. However, despite their proven results, these approaches are still refrained by the inaccuracy of information being shared between the collaborating distributed entities. In order to address this issue, we propose in this paper a new belief-management-based model for a collaborative Internet of Drones (IoD). The proposed model allows drones to decide the most appropriate operators to apply in order to manage the uncertainty of perceived or received information in different situations. This model uses Hierarchical Analysis Process (AHP) with Subjective Logic (SL) to represent and combine opinions of different sources. We focus on purely collaborative drone networks where the group awareness will also be provided as service to collaborating entities. Full article