Joint delay and energy optimization in user-centric MEC networks with RIS and SWIPT

Spread application of wireless sensor networks (WSNs), has led many researchers aiming at energy optimization in these networks. The Proposed Energy Efficient Harmony Search Routing Algorithm (PEEHSRA) in accordance with a novel energy model is stated in this study to minimize the amount of energy consumption in WSNs. We first proposed an energy model on the basis of modulation scheme parameters for multi-hop transmission. Then, in order to develop a harmony search routing algorithm, pitch adjustment rate in the harmony memory was introduced based on both, the proposed energy model, and the node’s distance. Also, an energy-efficient fitness function was determined, which results in the most energy-efficient route for the network. At the end, four modulations including MQAM, MQPSK, OQPSK and MFSK were applied and the most energy-efficient one with corresponding parameters was specified. By comparing the results of the proposed method to the state-of-the-art studies, the improvements in extending the lifetime of the network was proved. These findings seem quite useful in designing an energy-efficient multi-hop WSN and pave the way for enhancement of the widespread applications in these networks.

Wireless Sensor Networks (WSNs) are consist of very tiny nodes structure, with very small batteries without having facility to recharge it. It works under any hard circumstances, hence this type of network is gaining high importance day by day. Maximum focus of researchers is to enhance or optimized its energy efficiency level. Routing protocol is the main energy expensive operation of sensor networks which utilize maximum energy resources of sensor network. Already a good number of routing protocols has been designed for energy efficiency or optimization with different approaches under controlled conditions. With this research we try to propose energy optimization of wireless sensor network through our proposed position response routing protocol (PRRP), under certain controlled parametric consideration. Research results shows a significant energy optimization in the life cycle of WSN. Index Terms—WSN, energy efficient, WSN routing protocol, GPS, PRRP, energy optimization

Today for continuous monitoring of patients, medical devices are to be monitored using the Wireless Body Area Network (WBAN). The devices are connected with the Internet of Things Enabled WBAN. Optical networks in recent years have dominated the communication field around the globe due to higher medical data transmission in real time. The bandwidth provided by this network are higher. But due to higher demands in spectrum, the available bandwidth should be used efficiently. Especially the unused bandwidth should be efficiently utilized. In this paper the bandwidth allocation strategy for the IoT enabled WBAN for optical network is presented which is based on Swarm intelligence. The dynamic bandwidth allocation (DBA) was done for the Ethernet passive optical network (NG-EPON). The existing pooling and machine learning approaches found in literature provide DBA management. But the swarm-based method enhances and optimizes the allocation process. This algorithm has the capacity to deal with the bandwidth demands of NGEPON. The novel DBA algorithm outperforms the existing algorithms in terms of average packet delay and average completion time. The method utilizes the ant colony optimization method along with dynamic bandwidth allocation. Implementation was done in MATLAB and NS2.

With the rapid development of the low-altitude economy, multi-UAV systems integrating semantic communication and mobile edge computing (MEC) have emerged as a promising solution for urban intelligence tasks. By extracting local semantic information and offloading the processed data to MEC servers, these systems enable low latency and low overhead communication. However, semantic processing introduces additional computational burdens, increasing the complexity of joint mobility control and resource scheduling. In such scenarios, the dynamic and computationally intensive environment places higher demands on UAV coordination and resource management. Performing effective orchestration of UAV trajectories, energy consumption, and latency has become a critical challenge. To address the issues of excessive delay and energy consumption, this paper proposes a federated deep reinforcement learning framework with a hybrid action space to jointly optimize UAV mobility control and resource allocation. Each UAV independently learns decision making strategies based on local state observations and periodically shares model parameters through federated learning to achieve global coordination. The simulation results demonstrate that the proposed method reduces total energy consumption by <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2 0 \% -2 5 \%$</tex> and cumulative communication latency by <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$10 \%-18 \%$</tex> under varying average semantic lengths, validating its effectiveness and robustness in MEC-enabled multiUAV semantic communication systems.

Joint energy and throughput optimization for MEC-enabled multi-UAV IoRT networks

Introduction Recent years have witnessed an increasing number of Internet of Things devices (IoTDs) deployed in power grids to monitor bidirectional information and power transfer, transforming them into smart grids. The densification of IoTDs in smart grids demands communication solutions that are simultaneously secure against eavesdropping and energy-efficient for sustainable operation. Methods This article proposes an unmanned aerial vehicle (UAV) and reconfigurable intelligent surface (RIS)-assisted framework in smart grids that maximizes worst-case secrecy energy efficiency via joint optimization of the UAV’s trajectory, beamforming, and phase shifts of RIS. A twin attention-driven deep reinforcement learning algorithm, TAMRRTD3, is developed, featuring attention-based state representation and regret-aware reward design to enhance learning accuracy and convergence. Results and Discussion Simulation results indicate that the proposed algorithm achieves a faster convergence rate and enhanced secrecy energy efficiency than the benchmark algorithms.

Developments in last decade in the field of electronics & communication, computer science and information technology field have resulted in the new technology paradigm, Wireless Sensor Networks. WSN is becoming increasingly popular due to their wide range of applications. For successful adaption of WSN in real field applications, there are certain issues which need to be addressed particularly the issue of energy consumption associated with sensor nodes. This limitation combined with node deployment has added many challenges to design and management of wireless sensor networks. The WSNs are involved in applications such as remote environment monitoring, event tracking in areas where providing electrical energy is very difficult. Moreover, the battery fed sensor nodes have very small energy associated with them, so preserving that energy and maximizing the usage of that energy is most critical issue in WSNs. Here, we present a survey of various energy optimization and lifetime enhancement techniques in wireless sensor networks and makes the reader aware about various energy saving techniques in WSN. They are analyzed from various points of view: Device hardware, Transmission, MAC protocols and Routing protocols.

WITHDRAWN: Energy optimization and reliable message communication in mobile adhoc networks using packet shifting

Low Earth Orbit (LEO) satellite constellations have gained considerable attention from both academia and industry due to their potential to provide reliable global coverage. However, the continuous movement of satellites causes periodic transitions between the sunlit and shadowed regions of orbit, leading to fluctuations in available energy. Satellites on the sunlit side can harvest solar energy to recharge their batteries, while those in the shadowprimarily rely on stored battery power. This paper presents a joint task and energy allocation framework that optimizes satellite energy consumption by considering three key factors: onboard processing of small tasks, relaying tasks to sunlit satellites for processing, and offloading larger tasks to ground stations when available. An Integer Linear Programming (ILP) approach is employed to determine the optimal energy distribution across these tasks, while a computationally efficient greedy algorithm is introduced as an alternative. Additionally, Dynamic Voltage and Frequency Scaling (DVFS) is incorporated to optimize the energy consumption during task processing by adjusting the processing frequency according to the available energy. The results indicate that ILP achieves optimal energy efficiency, while the greedy approach provides a near-optimal solution with only a 4.4% deviation.

Energy planning and optimization constitutes one of the most significant challenges for high-mobility networks. This paper proposes a novel framework to share, retain and refine end- to-end energy metrics in the joint memory of the nodes, over time scales over which this information can be spread to the network and utilized for energy planning decisions. We construct maps of end-to-end energy metrics that enable energy optimization in high-mobility networks. We show how to (1) compute the spatial derivatives of energy potentials in high-mobility networks, (2) construct energy maps on-demand via path integration methods, (3) distribute, share, fuse, and refine energy maps over time by information exchange during encounters, (4) allow the nodes to use energy maps for energy planning and optimization in delay- tolerant, high-mobility networks.

The Cell Zooming Method (CZM) is driven by network traffic, where the coverage of a Base Station (BS) adjusts according to traffic volume. Base stations experiencing low traffic are temporarily shut down, while the transmit power of neighboring BSs is increased to compensate for coverage loss by expanding their coverage areas. The algorithm determines which base stations to enable or disable based on traffic load. Subsequently, it ensures service quality by calculating the required transmit power for the active BSs to cover the areas of the disabled BSs. In this paper, an Unsupervised Cycle-Consistent Generative Adversarial Networks-based Energy Optimization in Green Cellular Networks (UCCGAN-EO-GCN) is proposed. Initially, data are collected from an LTE 4G network traffic dataset and processed during the pre-processing phase. The Robust Non-Smooth Kalman Filter (RNSKF) is employed to evaluate network load and adjust traffic volume from the collected data. A Dense Back-Projection Joint Attention Network (DBPJAN) is then used to manage the ON/OFF switching of base stations, calculate their capacity, and determine each cell's payload after forecasting the actual number of users connected to the base station. The proposed UCCGAN-EO-GCN approach achieves 28.92%, 24.15%, and 32.18% higher throughput and 25.02%, 26.41%, and 30.90% lower RMSE compared with existing techniques: Cellular Traffic Prediction using an Intelligent Method (CTP-IM), Energy Saving in 5G Mobile Communications through Traffic-Driven Cell Zooming Strategy (ESMC-TDCZS), and a Mathematical Model for the Cell Zooming Mechanism of Base Stations with a Categorization Approach (MM-CZM-BSC).

We consider the delay minimization problem in an energy harvesting communication network with energy cooperation. In this network, nodes harvest energy from nature for use in data transmission, and may transfer a portion of their harvested energies to neighboring nodes through energy cooperation. For fixed data and energy routing topologies, we determine the optimum data rates, transmit powers and energy transfers, subject to flow and energy conservation constraints, in order to minimize the network delay. We start with a simplified problem with fixed data flows and optimize energy management at each node for the case of a single energy harvest per node. This is tantamount to distributing each node's available energy over its outgoing data links and energy transfers to neighboring nodes. For this case, with no energy cooperation, we show that each node should allocate more power to links with more noise and/or more data flow. In addition, when there is energy cooperation, our numerical results indicate that, energy is routed from nodes with lower data loads to nodes with higher data loads. We extend this setting to the case of multiple energy harvests per node over time. In this case, we optimize each node's energy management over its outgoing data links and its energy transfers to neighboring nodes, over multiple time slots. For this case, with no energy cooperation, we show that, for any given node, the sum of powers on the outgoing links is equal to the single-link optimal power over time. Finally, we consider the problem of joint flow control and energy management for the entire network. We determine the necessary conditions for joint optimality of a power control, energy transfer and routing policy. We provide an iterative algorithm that updates the data and energy flows, and power distribution over outgoing data links. We show convergence to a Pareto-optimal operating point.

In real-time image and video processing boards, power, speed, and area are the most often used measures for determining the performance of motion imagery applications. Due to technological advancement, power consumption has gained major attention in real-time image processing ability compared to speed. The increase in on-chip temperature due to larger power consumption has resulted in reduced operating life of chip and battery-driven devices. In this work, a new logic family has been introduced i.e., dual-mode logic (DML), which provides flexibility between the optimization of energy and delay (E-D optimization). This gate can be switched between two modes of operation that is a static mode (CMOS-like mode), which provides low power consumption and dynamic mode, which provides high speed. Recently, power leakage has become a dominant problem due to continuous data transfer among a large number of connected devices. Thus, to reduce power leakage, a self-controllable voltage level (SVL) power reduction technique is used along with DML logic. In the SVL technique, a maximum dc voltage is provided to the active load circuit on-demand or decrease the dc supplied to the load circuit in the standby mode. Integrating DML with the SVL technique reduces power consumption as well as leakage power. A 4-bit RCA, 8-bit RCA, and 16-bit RCA are used for verifying the proposed method and comparison of performance parameters is done with a conventional circuit. Complete circuit implementation and simulation are carried out in TANNER EDA version 13 tools with operating voltage of 1 V. The proposed system is further applied to real-time image, and we obtain the finest resolution level with minimum power consumption.

Innovative research on mobile ad hoc networks (MANETs) and their wide applications have created a revolution of today's wireless communication. Efficient routing for dynamically changing network and unstable wireless medium is a challenging task. The conventional AODV routing protocol works on selecting the hop count-based shortest path while delivering the packet towards the destination. This often creates uneven load distribution among network nodes. As a result, any central node hopelessly carries heavier loads that significantly deplete node energy with higher packet loss and delay in processing. In this work, a protocol based on power and delay named as power and delay optimised AODV (PDO-AODV) has been presented which is based on load balanced routing strategy in AODV that improves the quality of service for MANETs. The proposed approach takes neighbouring node's power and delay into consideration for selecting load balanced path. Simulation results manifest that the proposed PDO-AODV achieves improved performance as compared to existing AODV routing protocol in terms of packet drop, network delay, throughput, and retransmission attempts.

Internet of Things (IoT) technology is one of the more advanced network communication technologies at present, and the IoT-based power dispatching system can improve the operation efficiency of power system. As the number of power users increases dramatically, higher requirements are put forward for the communication and management of IoT-based devices. The authentication method of traditional network structure can no longer adapt to the huge number of clients. For the problem of insufficient reliability of IPoE authentication method at the present stage, this paper proposes a model to enhance the reliability of IPoE authentication method. First, an intelligent scheduling optimization method based on SDH optical transmission technology is designed to complete the optimization of the scheduling network. The time delay of the communication network is reduced, and the reliability of the network is increased. Second, a data online collection module is established to complete the first optimization of the communication network. Finally, SDH optical transmission technology and design communication network terminal are integrated. The second optimization of the communication network is completed. After experimental testing, the proposed model can intelligently optimize the IoT-based power dispatching network. A standardized, manageable, and secure large-scale remote dispatching solution is realized.