Cooperative sensing systems that exchange state estimates are exposed to two types of adversaries, namely, insiders who transmit correctly authenticated falsified content and outsiders who modify messages in transit after compromising a symmetric link key, each requiring a distinct mitigation strategy. These attacks are observationally identical to a detector that examines only message content, although an insider must be revoked and an outsider must be countered through key rotation and link hardening. To distinguish between insider falsification and outsider in-transit message modification, a cross-layer intrusion detection and attack-attribution framework named QSec-DAI is proposed. Per-message anomaly scores are supplied by a recurrent detector, and a hybrid-symmetric, post-quantum and quantum authentication stack is arbitrated by belief-desire-intention agents under a finite-key budget. Authentication is used as an active probe because a suspicious link is hardened, and the persistence or disappearance of the anomaly is then observed. On real cooperative-localization data, an area under the receiver operating characteristic curve of 0.981 is achieved. Benign, insider and outsider classes are attributed with a macro-averaged accuracy of 0.794 and a man-in-the-middle recall of 0.719. Under the explicitly defined attribution mapping, outsider recall is zero for the evaluated baselines that remain in fixed-symmetric mode after key exposure. In the real-data evaluation, malicious influence on fusion is limited to 0.10 percent. The no-cooperation control indicates that several classical defenses suppress attacks mainly by discarding cooperative information rather than by preserving useful cooperation. Protocol-level fault injection shows that replay is rejected while monotonic freshness state is intact, whereas compromise of the verifier or of all independent strong credentials removes defensible outsider identifiability. The quantum component is therefore presented as one resource-constrained strong-authentication option rather than as a source of quantum-enhanced anomaly detection.
Iacovos I. Ioannou, M. Georgiades· Electronics· 0 citations
Antenna selection is investigated as a means of reducing radio-frequency (RF) chain power in massive multiple-input multiple-output (MIMO) base stations under realistic channel state information (CSI) impairments. The study is motivated by the mismatch between conventional selection objectives and multi-cell operation with estimation error, pilot contamination, spatial correlation and inter-cell interference. APCS-Boost-R is introduced as the primary contribution. An interference-whitened D-optimal seed is combined with projected rank-one exchanges and a calibrated surrogate that incorporates a user-side interference-plus-noise report and a closed-form estimation-error correction. APCS-Boost-RG is retained as an optional graph neural network (GNN) refinement in which residual exchanges are ranked after the algebraic solution has been formed, while feasibility and non-degradation of the calibrated surrogate are verified deterministically. In a three-cell urban macro configuration derived from Third Generation Partnership Project (3GPP) TR 38.901 with 64 antennas, 16 active RF chains and eight users per cell, APCS-Boost-R achieves 19.364 bit/s/Hz over 200 paired realizations. Improvements of 2.58 percent over APCS-Boost, 6.76 percent over greedy search and 10.16 percent over a genetic algorithm are obtained. APCS-Boost-RG adds 0.019 bit/s/Hz but is treated as an optional refinement because it requires a second-stage neighborhood evaluation and offline model maintenance. In the archived common timing record, APCS-Boost-R requires 20.376 ms per three-cell realization, compared with 12.728 ms for APCS-Boost, 57.775 ms for norm-initialized greedy search and 41.302 ms for the genetic algorithm, while APCS-Boost-RG requires 24.0 ms versus 20.4 ms for APCS-Boost-R in the separate archived learned-stage record. Separate reconstructions on the documented reproducibility host require 55.3±14.5 ms for APCS-Boost-R and 592.2±181.9 ms for a complete APCS-Boost-RG rebuild. Additional paired examinations confirm robustness across stronger search budgets, report imperfections, regularized precoding, coordination, near-field sensitivity, hardware perturbations, and configurations ranging from 32 to 128 antennas and one to seven cells.
Iacovos I. Ioannou, V. Vassiliou· Network· 0 citations
This paper presents an energy-management and state-of-charge (SoC) balancing scheme, denoted OEMSS, for a DC microgrid comprising photovoltaic generation, a fuel-cell source, two energy storage systems (ESSs), and six household loads. A demand-driven power-allocation layer first determines whether generation is sufficient, ESS support is required, or priority-based load scheduling must be activated. A supervisory balancing layer then allocates the fleet charging or discharging request by using a capacity-weighted average SoC and separate mode-dependent correction laws. The balancing command is dimensionally expressed as an energy-capacity deviation divided by the control interval and is projected onto the SoC and power limits. A Python simulation driven by recorded generation profiles is used to evaluate four seasonal operating conditions. In the tested equal-capacity case, the maximum inter-ESS SoC deviation is reduced from 18% to 4.8%, synchronization is reached within approximately 2 to 4 h, and simulated over-discharge events are avoided. The reported increase from 45% to approximately 70% is interpreted as a 25-percentage-point increase in the ESS storage contribution rate, rather than an increase in conversion efficiency. During shortage intervals, the retained priority demand is supplied, whereas satisfaction of the original uncurtailed demand is not claimed. A discrete-time Lyapunov analysis gives the nominal convergence condition 0<γb<2, and the online implementation has O(J+K+H) time complexity. The study provides simulation evidence for a simple coordinated allocation rule; hardware performance, battery-life extension, converter-level stability, and global optimality remain to be established.
M. Sadiq, Saher Javaid, Iacovos I. Ioannou et al.· Energies· 0 citations
Wireless Mesh Networks (WMNs) are a key enabling technology for dynamic, infrastructure-limited IoT environments. The routing protocol is the central design choice in any WMN deployment because throughput, end-to-end delay, energy consumption and delivery reliability are directly affected by it. A systematic, simulation-based evaluation of two widely studied WMN routing protocols is presented: the reactive Ad hoc On-Demand Distance Vector (AODV, RFC 3561) protocol and the proactive Destination-Sequenced Distance-Vector (DSDV) protocol. Simulations were conducted in OMNeT++ 6.3 with the INET 4.5 framework across five network densities $(N \in\{10,20,30,40,50\}$ nodes) in a $1000 ~\mathrm{m} \times 1000 ~\mathrm{m}$ IEEE 802.11g area with a many-to-one UDP traffic pattern representative of IoT data collection. A density-dependent crossover was revealed at approximately $N=20$: lower delay was achieved by DSDV in sparse networks, whereas higher throughput, higher delivery reliability and lower energy consumption were achieved by AODV at higher densities. At $N=50, \approx 35 \%$ higher throughput, zero routing failures and $\approx 8 \%$ lower energy consumption are delivered by AODV. It is indicated by the MAC-layer contention behavior that DSDV's high-density degradation is mainly driven by IEEE 802.11 channel saturation rather than by routing-algorithm deficiencies. Deployment guidelines derived from these findings are provided.
Alá F. Khalifeh, Abdulla Ababneh, Iacovos I. Ioannou· IEEE Jordan Conference on Ap...· 0 citations
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