TL;DR
The proposed algorithm can achieve the tradeoff between the energy efficiency and QoS provisioning by sacrificing about 10% network lifetime but improving about 15% outage performance.
A novel method for the Search and Optimization of IoT Service Composition towards QoS and Energy balance (SOISC-QE) is proposed and an enhanced Dung Beetle Optimization (DBO) is designed by integrating two complementary mechanisms.
The practical architecture known as software‐defined networking (SDN) enables the Internet of Things (IoT) to function in various applications. Also, SDN has been adopted for effective routing in wireless networks. The controller intends to work using an algorithm to offer secure routing. However, some existing algorithms must provide optimized and secured routing paths. This study presents a new method for selecting the most suitable route by combining the Markov Chain Model (MCM) with reinforcement learning techniques (MCM‐RLA). The aim is to ensure that the chain and reward functions align with the Quality of Service (QoS). The reward regarding the following successive routing path is analyzed where SDN‐enabled IoT enhances the routing based on the prior routing ideas. Moreover, the entire network is managed via the network remotely. The performance of the anticipated is compared with various prevailing approaches. Multiple metrics like packet delivery rate (PDR), network lifetime, routing overhead, energy efficiency, and delay are compared to attain suitable WSN performance via efficient routing.
In this paper, we propose an Energy-Aware Cross-Layer Communication and Computation Optimization (EACCO) framework to maximize energy efficiency and operational lifetime of indoor Internet of Things (IoT) swarm robotic networks. The method takes into account energy harvesting, adaptive scheduling, communication and computation altogether in a single cross-layer optimization framework. Results of simulation show that EACCO reduced the energy consumed per day to 2.0-2.3 Wh/day and obtained the energy neutral ratio (ENR) of 1.05-1.18, increased the packet delivery ratio (PDR) to 95-97% and increased mission lifetime to 30-36 hours compared to other energy-only, EDF and FIFO schedulers under the same operating conditions. The results show the effectiveness of EACCO to design reliable, energy neutral and sustainable indoor IoT swarm robotic systems.
Wireless Sensor Networks (WSNs) play an im-portant role in Internet of Things (IoT) appli-cations because they enable continuous envi-ronmental monitoring and data collection. Nu-merous previous studies introduced clustering and routing algorithms for efficient data trans-fer in WSN. However, they still face issues such as high latency, minimum network life-time, high energy consumption, and commu-nication delay. To address these issues, this study proposes a novel Energy-Aware Cluster Optimization Routing (EACO-R) protocol. Ini-tially, Fuzzy Enhanced Hierarchical Clustering (FEHC) is utilized to generate stable and bal-anced clusters by accounting for uncertainty in sensor node distribution. The best cluster heads are then selected by an Adaptive Levy Mutation-based Dynamic Enzyme Action Op-timizer (ALM-DEAO) employing residual en-ergy, local node density, and Euclidean dis-tance to the base station. Finally, the Multi-Head Attention-assisted Dynamic Priority Ad-justment Deep Q-Network (MHA-DPA-DQN) creates adaptive energy-aware routing paths de-pending on residual energy, connection stability, transmission distance, and network congestion. The simulation results demonstrate that the pro-posed model obtained 42.4J in energy consumption analysis, which is lower than the existing approach
This paper proposes a QoS-aware and energy-efficient metaheuristic optimization-based service placement strategy for an integrated IoT and fog computing environment to improve QoS and demonstrates that the developed hybrid algorithm reduces energy consumption by 3.09% and minimize network usage significantly compared with baselines.
Findings validate the efficacy of incorporating swarm intelligence into the 5G architectures as a viable and self-optimizing solution for the promotion of connectivity and signal power performance in the next-generation high-density wireless networks.
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