We consider convergecast in a multi-hop sensor network, in which sensors sample physical processes of interest and transmit packets containing these samples to a single sink node over a routing tree. The sink node remotely estimates these processes based on received packets. Our objective is to design centralized multi-hop sampling and scheduling policies that minimize the average age of information (AAoI) across sensors for networks operating under a complete interference model as well as random losses in the wireless network. We propose a policy that samples sources and schedules links according to an analytically characterized priority index. Through simulations, we compare the AAoI of the proposed policy with policies from prior work and show improved performance. The improvement in performance is observed to be larger as the number of sources increases or when the reliability of links decreases demonstrating the utility of the proposed policy. An extension to $K$ -hop interference model is also discussed.
This paper investigates the Maximum Link Scheduling and Shortest Link Scheduling problems with power assignment, considering two widely adopted interference models: the Signal-to-Interference-plus-Noise Ratio (SINR) model and the Rayleigh fading model, and proposes two approximation algorithms.
A. Amirkhani, Neda Mohammadi· IEEE Access· 0 citations
This work theoretically proves that TSDM achieves the desired mean and temporal variance for each flow, and conducts extensive simulations on two open joint throughput-AoI optimization problems, finding that TSDM significantly outperforms existing scheduling policies.
Deploying Autonomous Underwater Vehicle (AUV) swarms is pivotal for oceanographic exploration. However, swarm formation control is constrained by harsh Underwater Acoustic Communication Networks (UACNs), especially in dynamic, non-fully connected topologies. To address high packet collision rates and excessive signaling overhead in existing Medium Access Control (MAC) protocols, this paper proposes a novel Cluster-based Mobile MAC (CM-MAC) protocol for hierarchical clustered AUV networks. Operating under a decentralized three-tier architecture, the protocol coordinates transmissions using local state information. We establish transmission constraints to prevent packet collisions among mobile nodes. Building upon the transmission constraints, genetic algorithms are applied to the transmission scheduling across all layers to optimize the sending sequence and timing, reducing overall latency. Simulation results show that the CM-MAC protocol significantly improves network throughput and reduces information-sharing update intervals when compared to traditional TDMA, pure Aloha, and random-access CM-MAC. This study presents a robust communication framework for coordinating large-scale AUV swarms in complex underwater environments.
Wireless Sensor Networks are constrained by limited node energy, making energy-efficient routing protocols essential for extending network lifetime. This paper presents an Adaptive Dual-Transmit Enhanced LEACH protocol that dynamically adjusts intra-cluster transmission amplification factors based on per-round cluster geometry. The approach addresses the fixed amplification factor used in Enhanced LEACH by computing an adaptive intra-cluster distance threshold from the mean member-to-cluster-head distance and deriving a dynamic amplification reduction factor that increases as clusters become more compact. This enables greater energy savings when network conditions permit. Performance was evaluated through thirty-trial Monte Carlo simulations on a 100-node, 200 × 200 m network. The adaptive mechanism reduced the effective free-space amplification coefficient by 62.97% and achieved cumulative intra-cluster energy savings of 5.589 J. Statistical analysis showed significant improvements over the base protocol, including increases of 8.8% in First Node Death, 2.9% in Last Node Death, 1.5% in throughput, and 1.5% in energy efficiency (212.47 versus 209.28 packets/J). Scalability analysis for network sizes ranging from 50 to 200 nodes further confirmed that the proposed approach introduced no scalability penalties while providing progressively greater benefits at higher node densities. The results indicate that adaptive transmission amplification can enhance network lifetime and energy utilisation without materially increasing protocol complexity, since the adaptive computation adds only a constant-time, per-round arithmetic step to the existing clustering procedure.
D. Nyitamen, M. Abdullahi· Journal of Engineering Resea...· 0 citations
Reliable low-latency communication is a critical requirement in enterprise wireless networks such as hospitals, offices, and campuses. This paper proposes an earliest deadline first (EDF)-Lyapunov-Robbins-Monro (ELR), a stochastic scheduling algorithm for IEEE 802.11bn (Wi-Fi 8) Multi-Access Point Coordination Coordinated-Spatial Reuse (MAPC C-SR) networks that jointly accounts for queue stability and deadline-aware latency regulation under bursty traffic. A Lyapunov drift-based criterion for a group is adopted to ensure queues remain stable under varying traffic loads. Since the optimal balance between queue backlog and deadline urgency cannot be determined a priori under bursty traffic, EDF term is incorporated into the selection metric with a tunable balance parameter $\alpha$, governed by Robbins-Monro stochastic approximation scheme. The proposed algorithm addresses the inability of existing schedulers to track sudden congestion under bursty traffic, by dynamically adjusting $\alpha$ to suppress sharp delay spikes. Simulations over a four-access point (AP) enterprise deployment under bursty Markov-Modulated Poisson Process (MMPP) traffic demonstrate that ELR achieves 14.23%, 13.26%, and 7.97% reduction in 99th percentile delay over maximum number of packets (MNP), oldest packet (OP), and traffic alignment tracker (TAT) respectively under high load with 16 stations (STAs).
Hiya Shah· International Conference on...· 0 citations
This study provides an efficient joint optimization scheme for building high-capacity, wide-coverage, and long-lifespan complex Internet of Things (IoT) networks.
Shengli Pang, Yuanyuan Ma, Xianjin Cheng et al.· Italian National Conference...· 1 citation
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