An Energy-Saving LoRa Linear Network System With Adaptive Transmission Parameter

Robust Adaptive Control

The capacity and geographical coverage of the global communications network continue to expand. One consequence of this expansion is a steady growth in the overall energy consumption of the network. This is the first of two papers that explore the fundamental limits on energy consumption in optical communication systems and networks. The objective of these papers is to provide a framework for understanding how this growth in energy consumption can be managed. This paper (Part I) focuses on the energy consumption in optically amplified transport systems. The accompanying paper (Part II) focuses on energy consumption in networks. A key focus of both papers is an analysis of the lower bound on energy consumption. This lower bound gives an indication of the best possible energy efficiency that could ever be achieved. The lower bound on energy in transport systems is limited by the energy consumption in optical amplifiers, and in optical transmitters and receivers. The performance of an optical transport system is ultimately set by the Shannon bound on receiver sensitivity, and depends on factors such as the modulation format, fiber losses, system length, and the spontaneous noise in optical amplifiers. Collectively, these set a lower bound on the number of amplifiers required, and hence, the amplifier energy consumption. It is possible to minimize the total energy consumption of an optically amplified system by locating repeaters strategically. The lower bound on energy consumption in optical transmitters and receivers is limited by device and circuit factors. In commercial optical transport systems, the energy consumption is at least two orders of magnitude larger than the ideal lower bounds described here. The difference between the ideal lower bounds and the actual energy consumption in commercial systems is due to inefficiencies and energy overheads. A key strategy in reducing the energy consumption of optical transport systems will be to reduce these inefficiencies and overheads.

Wireless sensor “motes” is small or tiny embedded systems equipped with radios for wireless communication in the networks, which depend on batteries as a power source. The development of Medium Access Control (MAC) protocols which search is for to minimize the energy consumption in the wireless sensor network. Recent contention based MAC protocols reduce energy usage by placing the radio in a low power sleep state when not sending or receiving the message. In this paper the main emphasis is on the analysis of the Contention Based MAC Protocols and energy consumption in the networks. Based on the work done by various researchers conclude that S-MAC is the backbone of all the MAC protocols for wireless sensor networks. Our proposed work investigated the energy usage and compared the performance of IEEE 802.11 MAC protocol with S-MAC protocol on different modes like without periodic sleep and with periodic sleep on different performance metrics like Remaining Energy vs Time, Energy consumption vs global packet id and Average End to End Delay vs Time. The performance of S-MAC protocol improves on the basis of duty-cycle parameter, which determines the length of sleep period in a frame and this parameter is a variable. So changing the duty cycle will change the performance of S-MAC protocol. Finally, we have used the different routing protocol with S-MAC to evaluate the energy consumption. The experimented worked done on Network Simulator Ns2- 2.34.KeywordsMedium Access ProtocolWireless Sensor NetworkIdle listeningSleep StateS-MACEnergy ConsumptionDuty cycle

It is a very important topic to reduce network energy consumption for constructing green network in current computing area. Rate adaptation of network device provides an effective approach to optimize network energy consumption and improve network energy efficiency. In this paper, we propose a global optimization approach for network energy efficiency based on rate adaptation. According to the characteristics of service in network system, we establish an energy consumption model by abstracting network service components as a processing domain from a global point of view. In order to obtain the average rate of service and the switched number of running mode in processing domain, the service process of the processing domain is regarded as the queuing system with variable service rate. Then, we construct energy consumption optimization model to minimize network energy consumption. We give a genetic algorithm to solve energy consumption optimization model, and the comparison results of energy consumption and delay in simulation. Compare with the two GreenOSPF energy efficiency algorithms with rate adaptation in simulation, the energy consumption optimization model proposed by this paper is suitable for rate adaptation mechanism at link, and then obtain the goal of optimization and reducing of energy consumption.

The energy of sensor nodes is usually limited, and it is difficult to supplement energy frequently. Multi-hop communication, which can lead to unbalanced energy consumption in the whole network and seriously affect the life cycle of wireless sensor network, is generally used for infrastructure and testing linear network. Optimization of regionalization node deployment was used to balance energy consumption among all the nodes and establish the network optimization model. Combined with engineering application, hybrid particle swarm was used as model for the linear network transmission strategy. Finally, simulation was used for modeling verification. The results of simulation showed that the energy consumption of optimizing transmission strategy is lower with balanced energy consumption among all the nodes, which aims to reduce energy consumption so that the life cycle of the whole network can be improved.

This is Part II of a two-part paper that explores the fundamental limitations on energy consumption in optical communications. Part I covers energy consumption in optical transport. Part II explores the lower bound on energy consumption in optical switches and networks, analyzes the energy performance of a range of switching devices, and presents quantitative models of the lower bounds on energy consumption in these devices. These models are incorporated into a simple model of a global switched network and the lower bound on total network energy consumption is estimated. We compare the results of this bottom-up calculation of the lower bound on network energy with a previous top-down analysis of overall network energy consumption based on real-world data for state-of-the art equipment and “business-as-usual” forward projections. The present analysis confirms a previous finding in that in a global scale network, the energy consumption of the switching infrastructure is larger than the energy consumption of the transport infrastructure. We find that the theoretical lower bounds on transport energy identified in Part I and the switching energy in this paper are more than three orders of magnitude lower than predicted by a “business-as-usual” analysis. In this paper, we explore how the gap between the theoretical lower bounds on energy consumption and current trends in network energy efficiency can be closed. We argue that future research needs to focus on improving the energy efficiency of switching and on devising methods to reduce the quantity of switching infrastructure in the network. Further key strategies for reducing network energy consumption include developing of low-energy transport technologies, reducing the energy overheads associated with peripheral functions that are not central to the transport and switching of data, and reducing the energy consumption of the access network.

The LoRa technology is considered as one of the most potential solutions for Internet of Things (IoT) in the near future. The existing LoRa networks are based on the star topology. In this article, a tree network adopted for the LoRa technology and a communication protocol are proposed to mitigate the energy consumption constraints. In the proposed network, the nodes are self-configured based on the LoRa physical link behavior and can act as relays to propagate data from other nodes. The analysis, design, and evaluation of the proposed architecture for large scale LoRa networks are presented in detail. With analytical studies, the energy consumption of the star and tree LoRa networks are compared. The presented analytical results can provide developers with effective guidelines for scalable design and optimization of LoRa networks. Further, the results are verified using simulation and experimental tests. Both simulation and experimental results confirm that the presented method improves the energy consumption of the entire IoT network significantly. As a result, the presented tree network can be deployed in various applications.

Several studies and field tests have shown the potential of conservation voltage reduction (CVR) to decrease peak demand, energy consumption, and system losses. Nonetheless, the energy savings achievable by CVR is dependent on the load model, which could be constant power, current, impedance, or any combination of these models. Also, the use of distributed energy resources (DERs) at the distribution level presents a challenge to successful CVR implementation as these resources could cause voltage fluctuations in the system. This paper studies the impact of load models on CVR implementation in a distribution system feeder interfaced with renewable DERs. Load flow analyses are carried out using constant power (PQ) and constant impedance (Z) load models, respectively, and the resulting effects on feeder losses and energy consumption are observed as CVR is implemented. In addition, the impacts of DERs and voltage regulation devices on the feeder voltage profile are analyzed. The feeder losses showed opposite trends with constant PQ and Z loads as CVR is implemented. Overall, the results showed varied impacts of load models and DER output on feeder losses, energy consumption, and bus voltages in the distribution network.

This study addresses the challenges of real-time channel state estimation and adaptive parameter adjustment in dynamic LoRa networks, where the existing methods often fail to adapt efficiently to highly variable channel conditions. This study presents an innovative approach for real-time channel state estimation and adaptive parameter adjustment in long-range (LoRa) networks in dynamic Internet of Things (IoT) environments. When these types of networks are used in dynamic IoT environments, they are known to face challenges in the two above-mentioned areas. In our approach, a hybrid feature extraction method that integrates statistical analysis with domain-specific knowledge is utilized for real-time data labeling, focusing on the signal-to-noise (SNR) and received signal strength indicator (RSSI) metrics. This approach employs an adaptive sliding window technique for efficient processing of recent data. Subsequently, a multi-task long short-term memory (LSTM) neural network is introduced for the simultaneous prediction of multiple channel states. This multi-task model employs an online incremental learning approach to enhance the real-time performance and responsiveness of the model within dynamic environments. It also incorporates a confidence measure for estimated states to increase the prediction reliability. Finally, based on the confidence measure predictions and channel state estimation, the system dynamically adjusts the LoRa parameters, including the spreading factor, coding rate, transmission power, and bandwidth. Our results demonstrate that the confidence-based adaptive strategy coupled with adaptive sliding window processing and incremental learning effectively balances performance optimization with stability in challenging IoT scenarios. This study contributes a robust, data-driven approach for real-time channel state estimation and adaptive parameter control, addressing the unique challenges of IoT networks in dynamic environments. Our approach achieved a packet delivery ratio of 100%, reduced energy consumption to 0.07987 Joules per packet, and demonstrated a prediction accuracy between 97.70% and 97.9% for estimating the different channel states. This innovative framework provides significant improvements in channel state estimation, communication reliability, adaptive parameter control, and computational efficiency, thereby ensuring robust performance in IoT environments at the same time.

This paper presents a scalability analysis over a novel integer programming model devoted to optimize power consumption efficiency in heterogeneous wireless sensor networks. This model is based upon a schedule of sensor allocation plans in multiple time intervals subject to coverage and connectivity constraints. By turning off a specific set of redundant sensors in each time interval, it is possible to reduce the total energy consumption in the network and, at the same time, avoid partitioning the whole network by losing some strategic sensors too prematurely. Since the network is heterogeneous, sensors can sense different phenomena from different demand points, with different sample rates. As the problem instances grows the time spent to the execution turns impractcable.

Today main objective of wsn is to minimize the energy dissipation for the whole network. Clustering is one of the most knows methods widely used to face these challenges. Fix Cluster Head Selection (FCHS) routing protocol analyses how the system lifetime is improved by fixing the selection of clusters head. In this paper a cluster based communication protocol with considering the low energy consumption in wireless sensor networks, is introduced which balanced the energy load among the sensor node. Fix Cluster Head Selection (FCHS) analyses the cluster head selection to find the optimal probability of becoming a cluster head. Simulation result of FCHS corresponding to LEACH in mat lab shows that these designs increase the lifetime of the network 105 %. We found that FCHS yield longer stability region for higher values of extra energy brought by more powerful nodes. Finally, LEACH protocol and the improved algorithm simulate in MATLAB, and make Performance analysis and comparison in number of nodes alive, total energy consumption in network and a round of node death distribution. The results show that, FCHS compared with LEACH protocol, the improved algorithm prolongs the network life cycle, raises energy utilization and has good load balance.

The number of Machine-to-Machine (M2M) devices has increased massively in the last few years and will continue to increase in the years to come. Spectrum utilisation efficiency and energy efficiency are the main challenges and design goals for M2M networks. Cognitive radio (CR) is a promising technology that can address these challenges. In this paper, the authors have proposed and developed an energy efficient mechanism to reduce energy consumption in Cognitive M2M (CM2M) networks. Their solution guarantees the throughput and reliability constraints for CM2M Devices (CM2MDs). The proposed mechanism can reduce energy consumption in CM2M networks by exploiting efficient sensing and accessing schemes for CM2MDs. The authors further develop sleep-mode/switching and accessing techniques to work efficiently with the proposed mechanism. The simulation results show that the proposed mechanism guarantees a desirable throughput and reduces overall energy consumption in the network.

The robustness of wireless sensor networks (WSNs) has made it a preferred choice for many applications. WSNs should perform the intended sensing task with a limited amount of energy. Clustering methodology is used to reduce sensor nodes' energy consumption and increase the network lifetime by organizing nodes into independent groups. In this article, a hierarchical structured cluster routing (HSCR) protocol based on the principle of an m‐way balanced tree has been proposed for multi‐hop intra‐cluster communication and offload the cluster head. It enhances the network performance by providing an underlying architecture that reduces the intra‐cluster communication distance and the amount of data sent to the cluster head. In the proposed HSCR protocol, an m‐way tree‐based hierarchical structure has been proposed in which balanced structure is formed within each cluster so that data aggregation is performed at each intermediate parent node. The proposed approach can be implemented over any communication protocol used in WSNs. Simulation results prove that the proposed algorithms outperform traditional clustering algorithms, LEACH and CT‐RPL, in terms of network stability, energy consumption, and network lifetime. Simulation results affirm that the proposed algorithms outperform traditional clustering algorithms, LEACH and CT‐RPL, in terms of network stability, energy consumption, average intra‐cluster communication distance and network lifetime by 105.32%, 49.23%, 14.98%, 109.31%, and 35.75%, 15.32%, 17%, 24.31%, respectively.

A linear network is a type of wireless sensor network in which sparse nodes are deployed along a virtual line; for example, on streetlights or columns of a bridge, tunnel, and pipelines. The typical deployment of Linear Wireless Sensor Net-work (LWSN) creates an energy hole around the sink node since nodes near the sink nodes deplete their energy faster than others. Optimal network topology is one of the key factors that can help improve LWSN performance and lifetime. Finding optimal topology becomes tough in large network where total possible combinations is very high. We propose an Optimal Topology Generation (OpToGen) framework based on genetic algorithm for LWSN. Network deployment tools can use OpToGen to configure and deploy LWSNs. Through a discrete event simulator, we demonstrate that the use of genetic algorithm accomplishes fast convergence to optimal topologies as well as less computational overhead as compared to brute force search for optimal topology. We have evaluated OpToGen on the number of generations it took to achieve the best topology for various sized LWSNs. The trade-off between energy consumption and different network sizes is also reported.

This paper presents design considerations and implementation of linear wireless sensor networks (LWSNs) to be used in cathodic protection (CP) monitoring of oil and natural gas pipelines. Firstly, the paper overviews the CP monitoring system of pipelines. Then, it presents the proposed design of LWSN along with some implementation details by providing high-level design illustrations and system parameters. Moreover, some hardware and software design issues and challenges are addressed briefly. LWSN based CP monitoring system is designed for the use in high rise mountains along with deep valleys, and some other extreme or unattended locations where communication infrastructure is unavailable, or there exists limited or interrupted access to the infrastructure.