Search Results (139)

Edge Internet of Things (IoT) blockchain deployments increasingly rely on continuous transaction ingestion from resource-constrained IoT devices to nearby edge gateways over heterogeneous wireless links. In this setting, transient channel noise and packet corruption can inject invalid payloads into the edge processing pipeline and trigger unnecessary buffering, parsing, and, most critically, computationally expensive cryptographic operations such as digital signature verification. This leads to wasted computation, increased latency, and reduced energy efficiency at the edge, particularly under dense IoT traffic. This paper presents an energy-aware and fault-tolerant Quantum-Dot Cellular Automata (QCA)-based integrity pre-filter for IoT-to-edge blockchain transaction ingestion. At the circuit level, we adapt and modify a previously reported fault-tolerant five-input majority gate (MV5) structure and use it as a robust primitive for nanoscale integrity-screening circuits. Building on this modified MV5, we design a set of QCA integrity blocks, including a parity checker, a compact XNOR gate circuit, a parity-bit generation circuit, and a sender-to-channel/receiver nano-communication integrity workflow suitable for early screening of corrupted payloads. Compared with the best previously reported baseline considered in this study, the modified MV5 achieves 76.47% tolerance to single-cell omission defects, corresponding to a 17.47 percentage-point increase and an approximately 29.61% relative improvement over the prior 59% omission-tolerance result, while preserving 100% tolerance against extra-cell deposition defects. At the system level, the proposed circuit is discussed as a potential early screening stage for edge-IoT blockchain transaction ingestion. A bounded analytical model is used to estimate the possible reduction in unnecessary signature-verification workload under assumed corruption and detection conditions. This analysis is not intended as a deployment-level validation; full edge-node implementation, throughput measurement, queueing-delay evaluation, real traffic traces, retransmission behavior, and empirical signature-verification profiling remain future work. The proposed parity/chunk-parity pre-filter is designed for low-cost detection of random transmission-induced corruption and does not replace cryptographic authentication, hashing, digital signatures, CRC-based detection, or blockchain validation. All proposed designs are validated using QCADesigner tools. Full article

BCI biosensors enable continuous monitoring of neural activity, but existing systems face challenges in scalability, latency, and reliable integration with cloud infrastructure. This work presents a cloud-aware, real-time cognitive grid architecture for multimodal BCI biosensors, validated at the system level through a full physical prototype. The system integrates the BioAmp EXG Pill for signal acquisition with an RP2040 microcontroller for local preprocessing using edge-resident TinyML deployment for on-device feature/inference feasibility coupled with environmental context sensors to augment signal context for downstream analytics talking to the external world via Wi-Fi/4G connectivity. A tiered data pipeline was implemented: SD card buffering for raw signals, Redis for near-real-time streaming, PostgreSQL for structured analytics, and AWS S3 with Glacier for long-term archival. End-to-end validation demonstrated consistent edge-level inference with bounded latency, while cloud-assisted telemetry and analytics exhibited variable transmission and processing delays consistent with cellular connectivity and serverless execution characteristics; packet loss remained below 5%. Visualization was achieved through Python 3.10 using Matplotlib GUI, Grafana 10.2.3 dashboards, and on-device LCD displays. Hybrid deployment strategies—local development, simulated cloud testing, and limited cloud usage for benchmark capture—enabled cost-efficient validation while preserving architectural fidelity and latency observability. The results establish a scalable, modular, and energy-efficient biosensor framework, providing a foundation for advanced analytics and translational BCI applications to be explored in subsequent work, with explicit consideration of both edge-resident TinyML inference and cloud-based machine learning workflows. Full article

In classic communication systems, signals and data were mostly continuous in time, such as voice (fixed and mobile telephony, and radio systems) and video signals (Television services), Conversely, in modern communication systems, most signals are packet-based (text and images in messaging services and social media) and even continuous-time data has to be converted into a discrete-time nature data, such as video and voice services that are now discretized to be sent in packet-based communication systems. However, these classic communication systems were analyzed, studied, and designed using continuous-time analysis, such as the classic Erlang-B formula. This classic analysis can still be used in modern systems, but a discrete-based framework provides a seamless analysis and yields more accurate results. In this work, the effect of the system’s elementary time step is analyzed, and guidelines for its selection are provided to adequately analyze continuous-time systems within a discrete-time framework. To demonstrate the utility of the discretization and to consider these guidelines, we developed a mathematical analysis based on a discrete-time Markov chain to study a system with a buffer capacity under conventional and bursty traffic, which is commonly found in an Internet of Things application. The derived formulas allow us to quantify system performance under a discrete framework. This, in turn, allows us to provide some relevant guidelines for the elementary time step selection to adequately analyze continuous-time systems under a discrete-time framework. Full article

Broadcast-based flooding in wireless ad hoc networks is subject to the broadcast storm problem, characterized by excessive transmissions, collisions, and link losses. While randomized network coding (RNC) enhances resilience against packet losses, efficient buffer management and adaptive transmission strategies are essential. This paper proposes novel buffering mechanisms and adaptive coding strategies to improve data unit reception rates in RNC-based broadcast flooding. Our buffering mechanism combines Last-In-First-Out (LIFO) and Least Recently Used (LRU) discard policies. When buffers are full, it prioritizes the discarding of stale, incomplete buffers based on elapsed time since the last coded block arrival, thereby overcoming First-In-First-Out (FIFO) limitations that prematurely discard buffers before sufficient coded blocks have accumulated. Our adaptive coding dynamically adjusts transmitted coded packets based on data unit duplication rates without inter-node coordination, reducing blocks during high duplication and increasing them under difficult reception conditions. Simulation experiments using OMNeT++ and INET framework for Vehicular Ad Hoc Networks demonstrate that LIFO+LRU buffering significantly increases the received data units and prevents redundant reception, while adaptive coding further improves reception rates under challenging conditions. Full article

Edge artificial intelligence (AI) workloads generate bursty, heterogeneous traffic on Networks-on-Chip (NoCs) under tight energy and latency constraints. Hybrid NoCs that overlay electronic meshes with silicon photonic express links can reduce long-path latency via wavelength-division multiplexing, but thermal drift and intermittent optical availability complicate routing. This study introduces a decentralized, photonic-aware controller based on Deep Reinforcement Learning (DRL) with Proximal Policy Optimization (PPO). The policy uses router-local observables—per-port buffer occupancy with short histories, hop distance, a local injection estimate, and a per-cycle optical validity signal—and applies action masking so chosen outputs are always feasible; the controller is co-designed with the router pipeline to retain single-cycle decisions and a modest memory footprint. Cycle-accurate simulations with synthetic traffic and benchmark-derived traces evaluate mean packet latency, throughput, and energy per delivered bit against deterministic, adaptive, and recent DRL baselines; ablation studies isolate the roles of optical validity cues and locality. The results show consistent improvements in congestion-forming regimes and on long electronic paths bridged by photonic links, with robustness across mesh sizes and wavelength concurrency. Overall, the evidence indicates that photonic-aware PPO provides a practical, thermally robust control plane for hybrid NoCs and a scalable routing solution for AI-centric manycore and edge systems. Full article

Wireless Body Area Network (WBAN) is an emerging medical health monitoring technology. However, WBANs encounter critical challenges in balancing reliability, energy efficiency, and Quality of Service (QoS) requirements for life-critical medical data. The design of its Medium Access Control (MAC) protocol has challenges since dynamic body-shadowing effects and heterogeneous traffic patterns. In this paper, we propose the Nash Bargaining Rate-optimization MAC (NBR-MAC), a hybrid MAC protocol that integrates TDMA-based Guaranteed Time Slots (GTS) with CSMA/CA-based contention access. Unlike traditional schemes, we model the rate allocation as an Asymmetric Nash Bargaining Game, introducing a rigorous disagreement point to guarantee minimum service for critical nodes. The utility function is normalized to resolve dimensional inconsistencies, incorporating sensor priority, buffer status, and channel quality. The Nash Bargaining solution is derived after proving convexity and verifying the axioms. Superframe time slots are allocated based on sensor data priority. Simulation results demonstrate that the proposed protocol enhances transmission success ratio and throughput while reducing packet age and energy consumption under different load conditions. Full article

The fast-paced utilization of innovative Internet of Things (IoT) applications emphasizes the critical role that routing protocols play in designing an efficient communication system between network nodes. In this context, the lack of adaptive routing mechanisms in the standard Routing Protocol for Low-power and Lossy Networks (RPL), such as load balancing and congestion mechanisms, especially under heavy load scenarios, causes significant degradation of network performance. In this regard, integrating innovative and effective learning abilities, such as Reinforcement Learning, into an efficient routing policy has demonstrated promising solutions for future networks. Hence, this paper introduces Aris-RPL, an adaptive routing policy for the RPL protocol. Aris-RPL utilizes a multi-objective Q-learning algorithm to learn optimal paths. Each node translates neighboring node information into a Q-value representing a composite multi-objective metric, including Buffer Utilization, Energy Level, Received Signal Strength Indicator (RSSI), Overflow Ratio, and Child Count. Furthermore, Aris-RPL operates effectively during the exploitation and exploration phases and continuously monitors the network overflow ratio during exploitation to respond to sudden changes and maintain performance. The extensive Contiki OS 3.0/COOJA simulator experiments have verified Aris-RPL efficiency. It enhanced Control Overhead, Packet Delivery Ratio (PDR), End-to-End Delay (E2E Delay), and Energy Consumption results compared to other counterparts for all scenarios on average by 39%, 25%, 7%, and 38%, respectively. Full article

This article develops a hybrid neural network method for detecting UDP flooding in critical infrastructure microgrid protection systems. This method combines sequential statistics (CUSUM) and a multimodal convolutional 1D-CNN architecture with a composite scoring criterion. Input features are generated using packet-aggregated one-minute vectors with metrics for packet count, average size, source entropy, and HHI concentration index, as well as compact sketches of top sources. To ensure forensically relevant incident recording, a greedy artefact selection policy based on the knapsack problem with a limited forensic buffer is implemented. The developed method is theoretically justified using a likelihood ratio criterion and adaptive threshold tuning, which ensures control over the false alarm probability. Experimental validation on traffic datasets demonstrated high efficiency, with an overall accuracy of 98.7%, a sensitivity of 97.4%, an average model inference time of 5.3 ms (2.5 times faster than its LSTM counterpart), a controlled FPR of 0.96%, and a reduction in asymptotic detection latency with an increase in intensity from 35 to 12 s. Moreover, with a storage budget of 10 MB, 28 priority bins were selected (their total size was 7.39 MB), ensuring the approximate preservation of 85% of the most informative packets for subsequent examination. This research contribution involves the creation of a ready-to-deploy, resource-efficient detector with low latency, explainable statistical layers, and a built-in mechanism for generating a standardized evidence package to facilitate rapid law enforcement response. Full article

The Green Internet of Things (GIoTs) has emerged as a transformative paradigm for environmental conservation, enabling autonomous, self-sustaining sensor networks that operate without batteries and with minimal ecological footprint. This approach is especially critical for long-term mangrove monitoring in smart coastal cities, where conventional battery-powered systems are impractical due to frequent, costly, and environmentally disruptive replacements that hinder continuous data collection. This paper presents a self-sustaining GIoT sensing system for mangrove monitoring powered by sedimentary microbial fuel cells (SMFCs), enabling perpetual, battery-less, and zero-emission operation. A spatial dynamic energy management (DPM) strategy is implemented for the efficient integration of a microcontroller unit with a LoRa wireless communication transceiver and the SMFC harvested energy, ensuring a balanced self-sustained approach into a GIoT sensing network. Experimental results demonstrate an average power consumption of 190.45 μW per 14-byte data packet transmission, with each packet containing pH, electrical conductivity and ambient temperature measurements from the mangrove environment. Under the spatial DPM strategy, the network of four sensing nodes exhibited an energy consumption of 1.14 mWh. Given a harvested power density of 15.1 mW/m² from the SMFC, and utilizing a 0.1 F supercapacitor as an energy buffer, the system can support at least six consecutive data transmissions. These findings validate the feasibility of sustainable, low-power GIoT architectures for ecological monitoring. Full article

Across a diverse landscape of anonymity designs, the dominant paradigms—onion routing (e.g., Tor) and mix networks (e.g., Loopix)—carry intrinsic constraints: long-lived circuits invite traffic correlation, and mixnets often rely on a network-wide state, making it hard to reconcile anonymity and scalability. This paper presents Draughts, a fully decentralized system in which each packet follows an independent and dynamically determined transmission path. Built upon Jump routing, Draughts introduces three key innovations: (i) replacing global state $O\left(N\right)$ with local two-hop neighborhood knowledge $O\left(k^2\right)$; (ii) supporting anonymous replies to enable real-time bidirectional communication; and (iii) proposing a path-length control mechanism that balances anonymity and transmission efficiency. Evaluation results show that Draughts achieves strong sender anonymity, resists predecessor and traffic analysis attacks, and reduces receiver buffer maintenance overhead, achieving a favorable trade-off between anonymity and performance. Full article

With the arrival of sixth-generation (6G) wireless systems comes radical potential for the deployment of autonomous aerial vehicle (AAV) swarms in mission-critical applications, ranging from disaster rescue to intelligent transportation. However, 6G-supporting AAV environments present challenges such as dynamic three-dimensional topologies, highly restrictive energy constraints, and extremely low latency demands, which substantially degrade the efficiency of conventional routing protocols. To this end, this work presents a Q-learning-enhanced ad hoc on-demand distance vector (QL-AODV). This intelligent routing protocol uses reinforcement learning within the AODV protocol to support adaptive, data-driven route selection in highly dynamic aerial networks. QL-AODV offers four novelties, including a multipath route set collection methodology that retains up to ten candidate routes for each destination using an extended route reply (RREP) waiting mechanism, a more detailed RREP message format with cumulative node buffer usage, enabling informed decision-making, a normalized 3D state space model recording hop count, average buffer occupancy, and peak buffer saturation, optimized to adhere to aerial network dynamics, and a light-weighted distributed Q-learning approach at the source node that uses an ε-greedy policy to balance exploration and exploitation. Large-scale simulations conducted with NS-3.34 for various node densities and mobility conditions confirm the better performance of QL-AODV compared to conventional AODV. In high-mobility environments, QL-AODV offers up to 9.8% improvement in packet delivery ratio and up to 12.1% increase in throughput, while remaining persistently scalable for various network sizes. The results prove that QL-AODV is a reliable, scalable, and intelligent routing method for next-generation AAV networks that will operate in intensive environments that are expected for 6G. Full article

WSNs consist of numerous energy-constrained Sensor Nodes (SNs), making energy efficiency a critical challenge. This paper presents a novel multipath routing model designed to enhance network lifetime by simultaneously optimizing energy consumption, node connectivity, and transmission distance. The model employs an Improved Particle Swarm Optimization (IPSO) algorithm to dynamically determine the optimal weight coefficients of a cost function that integrates three parameters: residual energy, link reliability, and buffer capacity. A compressed Bloom filter is incorporated to improve packet transmission efficiency and reduce error rates. Simulation experiments conducted in the NS2 environment show that the proposed approach significantly outperforms existing protocols, including Reinforcement Learning Q-Routing Protocol (RL-QRP), Low Energy Adaptive Clustering Hierarchical (LEACH), On-Demand Distance Vector (AODV), Secure and Energy-Efficient Multipath (SEEM), and Energy Density On-demand Cluster Routing (EDOCR), achieving a 7.45% reduction in energy consumption and maintaining a higher number of active nodes over time. Notably, the model sustains 19 live nodes at round 800, whereas LEACH and APTEEN experience complete node depletion by that point. This adaptive, energy-aware routing strategy improves reliability, prolongs operational lifespan, and enhances load balancing, making it a promising solution for real-world WSN applications. Full article

We analyze a model of the packet buffer in which a new packet can be discarded with a probability connected to the buffer occupancy through an arbitrary dropping function. Crucially, it is assumed that packet lengths can be correlated in any way and that the interarrival time has a general distribution. From an engineering perspective, such a model constitutes a generalization of many active buffer management algorithms proposed for Internet routers. From a theoretical perspective, it generalizes a class of finite-buffer models with the tail-drop discarding policy. The contributions include formulae for the distribution of buffer occupancy and the average buffer occupancy, at arbitrary times and also in steady state. The formulae are illustrated with numerical calculations performed for various dropping functions. The formulae are also validated via discrete-event simulations. Full article

The growing use of unmanned aerial vehicles (UAVs) for real-time video surveillance in smart city and smart region infrastructures requires reliable and delay-aware data transmission models. In urban environments, UAV communication links are subject to stochastic variability, leading to jitter, packet loss, and unstable video delivery. This paper presents a novel approach based on the Graphical Evaluation and Review Technique (GERT) for modeling the transmission of video frames from UAVs over uncertain network paths with probabilistic feedback loops and lognormally distributed delays. The proposed model enables both analytical and numerical evaluation of key Quality-of-Service (QoS) metrics, including mean transmission time and jitter, under varying levels of channel variability. Additionally, the structure of the GERT-based framework allows integration with artificial intelligence mechanisms, particularly for adaptive routing and delay prediction in urban conditions. Spectral analysis of the system’s characteristic function is also performed to identify instability zones and guide buffer design. The results demonstrate that the approach supports flexible, parameterized modeling of UAV video transmission and can be extended to intelligent, learning-based control strategies in complex smart city environments. This makes it suitable for a wide range of applications, including traffic monitoring, infrastructure inspection, and emergency response. Beyond QoS optimization, the framework explicitly accommodates security and privacy preserving operations (e.g., encryption, authentication, on-board redaction), enabling secure UAV video transmission in urban networks. Full article

Digital substation technology adhering to the IEC 61850 standard has provided several opportunities and flexibility for the rapid growth and complexity of the present and future electrical grid. The communication infrastructure allows complete interoperability between legacy and modern devices. The emergence of 5G wireless communication and its utilization in substation operation presents significant advantages in terms of cost and scalability, while also introducing challenges. This paper identifies research gaps in the literature and offers valuable insights for future analysis by providing a simulation study using an empirical latency dataset of a 5G network to illustrate three aspects of substation operational challenges: coordination of protection schemes, sequential reception of packet data streams, and time synchronization processes. The findings show a mean latency of 8.5 ms for the 5G network, which is significantly higher than that of a wired Ethernet network. The results also indicate that the high latency and jitter compromise the selectivity of protection schemes. The variability in latency disrupts the sequence of arriving data packets such that the packet buffering and processing delay increases from around 1.5 ms to 11.0 ms and the buffer size would need to increase by 6 to 10 times to handle out-of-sequence packets. Additionally, a time synchronization success rate of 14.3% within a 0.1 ms accuracy range found in this study indicates that the IEEE 1588 protocol is severely affected by the latency fluctuations. Full article