Network Energy Consumption Research Articles

Sinkhole Attack Defense Strategy Integrating SPA and Jaya Algorithms in Wireless Sensor Networks.

A sinkhole attack is characterized by low difficulty to launch, high destructive power, and difficulty to detect and defend. It is a common attack mode for wireless sensor networks. This paper proposes a sinkhole attack detection and defense strategy integrating SPA and Jaya algorithms in wireless sensor networks (WSNs). Then, combined with the SPA trust model, the trust values of suspicious nodes were calculated, and the attack nodes were detected. The Jaya algorithm was adopted to avoid the attacked area so that nodes can find the route to communicate with the real Sink, and attack nodes are isolated in the network to improve the capabilities of network directional defense. The simulation results show that the improved detection algorithm can effectively detect malicious nodes in the network, and the defense strategy implemented in the attacked area can improve the packet delivery rate, reduce network delay and energy consumption, and improve the security and reliability of wireless sensor networks.

WLQRP: A Weighting Link-Quality-Based Routing Protocol for Underwater Sensor Networks

Due to challenges posed by long propagation distances, high mobility, limited bandwidth, multipath propagation, and Doppler effects in underwater acoustic sensor networks (UASNs), the design of routing protocols faces numerous complexities. To enhance reliability in sparse node regions and reduce network energy consumption, this paper proposes a dependable and energy-efficient routing protocol termed WLQRP for weighting link-quality-based routing protocol. WLQRP leverages link quality information between nodes for data forwarding. When computing the link quality between nodes, it considers not only the historical transmission success ratio between nodes and their immediate relay but also incorporates the link quality between the relay and the relay’s next-hop nodes. This approach effectively reduces the likelihood of encountering void regions. Moreover, the protocol judiciously selects the next-hop node to improve transmission success rates, thereby minimizing data packet retransmissions and reducing energy consumption. We conduct MATLAB simulations to evaluate the WLQRP protocol, comparing it against the VBF (vector-based forwarding) and the HH-VBF (hop-by-hop vector-based forwarding) underwater routing protocol. Experimental results demonstrate that WLQRP enhances network performance in terms of data transmission rate, energy consumption, and end-to-end delay. These findings validate the efficacy of the proposed protocol.

Optical switching will innovate intra data center networks [Invited Tutorial

Reflecting the recent slow-down in Moore’s law and the proliferation of artificial intelligence/machine learning workloads, the performance and energy consumption of networks are becoming barriers in high-performance computing (HPC) and data centers. Optical switches are expected to break these barriers, and indeed their introduction has recently commenced in data centers. This paper discusses how optical switching technologies can innovate future intra data center networks. Hyperscale data centers are much bigger in scale, and network requirements are slightly different from those of HPC. This paper focus on data center networks, since the impact of optical technologies will be more significant in data centers than in HPC. In addition to the scale issue, important metrics to be considered for network design are traffic characteristics and latency, both of which are highlighted in this paper. For hybrid (electrical packet and optical circuit) switching networks, the target latency for the optical circuit switch network (connection setup/teardown time) is shown to be around 10 µs, and the needed technologies are clarified and verified by experiments. The optical switch can simplify the present multi-tier switching network above tier-1 switches into a single tier configuration, which is possible with the development of efficient large port count optical switches. Among the different switching architectures, combining the different dimensions of space and wavelength is shown to be one of the best solutions. Fast switching needs fast device response time. Si photonics devices using Mach–Zehnder interferometers or ring-resonator-based switches and tunable filters are the most promising candidates; they offer cost-effective mass-production and fast operation and so are excellent candidates for the optical switches envisaged. Another critical technology to maximize the benefits of optical switches is a simple and low-latency control mechanism. Different approaches have been suggested as summarized in this work. Among them, harnessing optical switch parallelism is a unique technique that matches recent advances in electrical switch chips. A fast control network is realized by using a fully decentralized and asynchronous control mechanism. A hyperscale data center offers a wide variety of services, and no one system fits all needs. Optimization of parameters is an important task for maximizing the impact of optical switching in different kinds of data centers.

Compact optimized deep learning model for edge: a review

<p>Most real-time computer vision applications, such as pedestrian detection, augmented reality, and virtual reality, heavily rely on convolutional neural networks (CNN) for real-time decision support. In addition, edge intelligence is becoming necessary for low-latency real-time applications to process the data at the source device. Therefore, processing massive amounts of data impact memory footprint, prediction time, and energy consumption, essential performance metrics in machine learning based internet of things (IoT) edge clusters. However, deploying deeper, dense, and hefty weighted CNN models on resource-constraint embedded systems and limited edge computing resources, such as memory, and battery constraints, poses significant challenges in developing the compact optimized model. Reducing the energy consumption in edge IoT networks is possible by reducing the computation and data transmission between IoT devices and gateway devices. Hence there is a high demand for making energy-efficient deep learning models for deploying on edge devices. Furthermore, recent studies show that smaller compressed models achieve significant performance compared to larger deep-learning models. This review article focuses on state-of-the-art techniques of edge intelligence, and we propose a new research framework for designing a compact optimized deep learning (DL) model deployment on edge devices.</p>

A WSN clustering routing method based on an improved WOA-optimized K-means clustering algorithm

To address the problem of transmission energy consumption and premature node death in wireless sensor networks (WSN), a novel routing strategy by IWOA that optimizes the K-means (KM) is proposed. WOA is improved by using it to optimize the KM algorithm, and more evenly distributed and suitable cluster centers are selected as the initial cluster heads (CH). In the CH election process, a CH election function is established, and the optimal CH under the influence of multiple factors is obtained. The simulation results have verified the feasibility of the proposed scheme in this paper.

Energy-efficient algorithms for lossless data compression schemes in wireless sensor networks

Wireless sensor networks (WSNs) are reliant on limited power resources, primarily provided by small batteries within sensor nodes. Inefficient energy management within these networks can lead to premature battery depletion during data transmission between sensor nodes, significantly impacting network longevity. Data compression emerges as a viable strategy to mitigate energy consumption by reducing data size before transmission and employing various compression and decompression techniques. This work presents a comparative analysis of data compression algorithms tailored for WSNs. It studies and enhances two adaptive lossless data compression techniques, namely ‘Adaptive Lossless Data Compression’ (ALDC) and ‘Fast and Efficient, Lossless Adaptive Compression System’ (FELACS), as means to effectively manage energy consumption in wireless sensor networks. ALDC and FELACS algorithms encode differences between consecutive data readings, thereby reducing the number of bits required for encoding. ALDC employs Huffman coding, while FELACS leverages the Golomb-Rice coding method. Encoding data samples by using three Huffman tables interchangeably as an enhancement of the ALDC algorithm, resulted in an improvement in energy saving from 73 % to 77 %. Analysis of FELACS unveiled the impact of natural phenomena-induced anomalies on measured data, identified as outliers. The outliers disrupt data patterns and ranges, subsequently altering the optimal coding parameters for data samples, resulting in encoding and decoding errors. This study proposes a robust method for identifying and replacing outliers within sensor data, significantly enhancing compression performance. A reduction of variations in dataset patterns facilitated more accurate sampling and encoding of data. Consequently, fewer bits are required to encode data samples, rendering the algorithm energy-efficient and suitable for applications demanding error-free data recovery or meticulous error analysis. The proposed method was successfully applied to the modified ALDC algorithm, exhibiting efficient performance. An optimum block size of sampled data was discovered for Fishnet relative humidity deployment ensuring efficient transmission of environmental data real-world sensor network deployments like Fishnet, Lucerne, and Le Genepi. These findings underscore the potential for significant energy savings and improved data accuracy through adaptive lossless data compression techniques, making them valuable assets for applications with stringent energy constraints or demanding data integrity.

Using the IBM analog in-memory hardware acceleration kit for neural network training and inference

Analog In-Memory Computing (AIMC) is a promising approach to reduce the latency and energy consumption of Deep Neural Network (DNN) inference and training. However, the noisy and non-linear device characteristics and the non-ideal peripheral circuitry in AIMC chips require adapting DNNs to be deployed on such hardware to achieve equivalent accuracy to digital computing. In this Tutorial, we provide a deep dive into how such adaptations can be achieved and evaluated using the recently released IBM Analog Hardware Acceleration Kit (AIHWKit), freely available at https://github.com/IBM/aihwkit. AIHWKit is a Python library that simulates inference and training of DNNs using AIMC. We present an in-depth description of the AIHWKit design, functionality, and best practices to properly perform inference and training. We also present an overview of the Analog AI Cloud Composer, a platform that provides the benefits of using the AIHWKit simulation in a fully managed cloud setting along with physical AIMC hardware access, freely available at https://aihw-composer.draco.res.ibm.com. Finally, we show examples of how users can expand and customize AIHWKit for their own needs. This Tutorial is accompanied by comprehensive Jupyter Notebook code examples that can be run using AIHWKit, which can be downloaded from https://github.com/IBM/aihwkit/tree/master/notebooks/tutorial.

Optimizing Lifetime of Internet-of-Things Networks with Dynamic Scanning

With the development of Internet-of-Things (IoT) technology, industries such as smart agriculture, smart health, smart buildings, and smart cities are attracting attention. As a core wireless communication technology, Bluetooth Low Energy (BLE) is gaining a lot of interest as a highly reliable low-power communication technology. In particular, BLE enables a connectionless mesh network that propagates data in a flooding manner using advertising channels. In this paper, we aim to optimize the energy consumption of the network by minimizing the scanning time while preserving the reliability of the network. Maximizing network lifetime requires various optimizing algorithms, including exhaustive searching and gradient descent searching. However, they are involved with excessive computational complexity and high implementation costs. To reduce computational complexity of network optimization, we mathematically model the energy consumption of BLE networks and formulate maximizing network lifetime as an optimization problem. We first present an analytical approach to solve the optimization problem and show that finding the minima from the complicated objective function of the optimization problem does not guarantee a valid solution to the problem. As a low-complexity solution, we approximate the complicated objective function into a convex form and derive a closed-form expression of the suboptimal solution. Our simulation results show that the proposed suboptimal solution provides almost equivalent performance compared to the optimal solution in terms of network lifetime. With very low computational complexity, the proposed suboptimal solution can extensively reduce implementation costs.

Node depth Representation-based Evolutionary Multitasking Optimization for Maximizing the Network Lifetime of Wireless Sensor Networks

Wireless Sensor Networks (WSNs) face challenges related to limited energy resources in sensor nodes, making network lifetime and energy consumption critical considerations. In this paper, we introduce a novel approach to extend network lifetime and reduce energy consumption in WSNs by optimizing network architecture selection. We explore various architecture options, including single-layer and multi-layer networks, to determine the most suitable configuration. Several studies have applied knowledge transfer in evolutionary multitasking optimization to maximize network lifetime, achieving state-of-the-art results for the problem. However, they still have some drawbacks, such as redundant representations, i.e., many genotypes represent the same phenotype, and invalid solution generation. To address these limitations, we propose a new evolutionary multitasking algorithm incorporating efficient encoding to represent tree-structure solutions (network architectures) to the problem. Additionally, we tailor new genetic operators, such as recombination and mutation, to meet the specific requirements of our proposed evolutionary multitasking algorithm. The standout feature of our proposal lies in its ability to consistently generate valid solutions for the problem, which significantly reduces redundancy within the genotype search space and effectively satisfies the problem constraints. Additionally, our multitask evolutionary algorithm facilitates the exploration of various network architectures, harnessing knowledge transfer to enhance optimal performance and reduce computation time compared to single-task methods. We conduct comprehensive experiments on diverse datasets and use statistical tests, e.g., the Wilcoxon signed rank test, to verify the performance of our proposed algorithms. The empirical results demonstrate that our proposed algorithm outperforms existing methods and achieves state-of-the-art results in terms of solution quality, convergence rate, and running time across a wide range of data instances. Specifically, our algorithm provides 49.54% better solution quality on average across all data types compared to the previous state-of-the-art method.

Virtual Reality Opera Space: Energy-Efficient Clustering Protocol Based on Cluster Centers for Wireless Sensor Networks

Energy efficiency presents a notable challenge in wireless sensor networks (WSNs) as the sensor nodes, which are responsible for collecting and transmitting data, operate under limited energy capacity. The sensor nodes must generate as much power as possible to make the network last longer. Generally, every sensor node in a WSN has batteries with limited capacities. It can be challenging to replace this little battery at times due to its vast number and numerous environmental issues. Therefore, it is believed that energy-efficient communication is essential for prolonging the lifespan of a sensor node. This work suggests a brand-new routing protocol clustering method that is energy efficient. Initially, the bamboo growth optimizer novel metaheuristic method was built on differential equations of bamboo growth and the Gaussian mixture model (BFGO). Secondly, a Cluster Centered Cluster Head Selection Algorithm (C3HA) of a routing protocol (BFGO-C3HA) is presented, with the encoding method and fitness function being revised. This algorithm is based on the BFGO technique. It can reduce transmission distance and increase energy efficiency. The results of simulations show that applying BFGO-C3HA can efficiently lessen the sensor network's energy consumption while increasing the amount of information transmitted to extend the network's lifetime.

Optimization of Energy Consumption in 5G Networks Using Learning Algorithms in Reinforcement Learning

The 5G network is an evolution of the 4G LTE (Long Term Evolution) fast internet network that is widely adopted in smart phones or gadgets. 5G networks offer faster wireless internet for various purposes. This research is a literature review of several articles related to machine learning, specifically regarding energy consumption optimization with 5G networks and reinforcement learning algorithms.The results show that various techniques have evolved to overcome the complexity of large energy intake including integration with 5G networks and algorithms have been completed by many researchers. Related to electricity consumption, it was found that during 5G use cases, in a low site visitor load scenario and while reducing power intake takes precedence over QoS, power savings can be made by 80% with 50 ms latency, 75% with 20 ms and 10 ms latency, and 20% with 1 ms latency. If QoS is prioritized, then power savings reach a maximum of five percent with minimum impact in terms of latency. Moreover, with regards to power performance, it has been observed that DQN-assisted motion can offer improvements.

Deep Reinforcement Learning for Resource Demand Prediction and Virtual Function Network Migration in Digital Twin Network

Internet of Things (IoT) enables intelligent services varying with the complex and realtime environment to achieve network benefits, where Network Function Virtualization (NFV) can dynamically provide Virtualized Network Functions (VNFs) for IoT devices. In NFV-enabled IoT architecture, a service function chain (SFC) consists of an ordered set of virtual network functions (VNFs). However, the energy consumption of the VNF migration and SFC reconfiguration is one major issue owing to the dynamic characteristic of IoT network. In this paper, we propose a new paradigm digital twin (DT) to create the virtual twin of physical objects in IoT network, then, we formalize the problem as a mathematical model, which aims to minimize the energy consumption. To this end, we prove this problem is NP-hard and propose an algorithm Bidirectional Gated Recurrent Unit (Bi-GRU) based on federated learning to predict the resource requirement. Further more, according to the prediction result, which utilizing the deep reinforcement learning (DRL) algorithm for decision making of the VNF migration. Simulation results show that our proposed method can effectively reduce the number of VNFs to be migrated and economize the energy consumption of the digital twin IoT network.

Proposing a Algorithm for Optimizing Energy Consumption

The purpose of this paper is to use the social spider algorithm to improve energy consumption in wireless sensor networks. In this regard, finding a suitable place for routers was done with a social spider algorithm in the system by selecting and optimally placing elements in the network and revising the Objective function. To use it, the initial number was 12, and the maximum number of loop iterations in the algorithm was 100 replications. We achieved these values with several runs and errors. Besides, to achieve the best results, to solve the problem, they can be considered as the default algorithm. In this research, the MATLAB implementation language was used to implement the spider algorithm, and then NS 2.35 tool was used to simulate the network environment and use the algorithm. In this study, three standard gate placement algorithms were used for comparisons, including BRP, RDP, and RGP. The results showed that the proposed method could have the lowest energy consumption. It can cause fair and uniform energy consumption between all nodes. It will also increase the lifetime of the network. Optimization of routing algorithms for such networks by using the spider algorithm also reduced the number of errors (failures) in the system and increased the life of the network with the help of the mentioned method. The proposed method, compared to the techniques presented before this study, is acceptable and, while creating the desired capabilities in terms of security, does not impose much time overhead on the system

Spreading Factor and Coding Rate Allocation Method for LoRa Network

Aiming at the problems of frequent packet loss and high energy consumption in large-scale LoRa networks, this paper proposes a joint allocation method of Spreading Factor (SF) and Coding Rate (CR). Firstly, we establish the effect relationship of SF and CR on Frame Error Rate(FER), and pre-allocate SF and CR based on the node position to minimize the energy consumption of the node while meeting the FER requirement. Then, according to the collision model, the collision probability of each SF group is calculated, and the sequential water injection method is used to equalize the collision probability within each SF group, to improve the average packet arrival rate of the entire network. The simulation results show that compared to mainstream algorithms, the proposed algorithm obtains a 13% increment in terms of the network average packet success probability, and a 104% one in terms of the average energy efficiency. The proposed algorithm has high application value in many application scenarios such as smart agriculture and smart cities.

Cell-Free Networking for Integrated Data and Energy Transfer: Digital Twin Based Double Parameterized DQN for Energy Sustainability

Cell-free networking enables full cooperation among distributed access points (APs). This paper focuses on reducing the long-term energy consumption of a cell-free network in the downlink integrated data and energy transfer (IDET) for achieving energy sustainability. The resultant design includes both the AP classification on a large time-scale and the beamforming of the APs on a small time-scale in order to simultaneously satisfy the IDET requirements of data users and energy users. For dealing with binary integer actions (AP classification) and continuous actions (beamforming) together, we innovatively propose a stable double parameterized deep-Q-network (DP-DQN), which can be enhanced by a digital twin (DT) running in the intelligent core processor (ICP) so as to achieve faster and more stable convergence. Therefore, the cell-free network may avoid suffering from performance fluctuation during the training process. The simulation results demonstrate that our DP-DQN exceeds in convergence compared to other benchmarks while guaranteeing an optimal solution.

MGen: A Framework for Energy-Efficient In-ReRAM Acceleration of Multi-Task BERT

Recently, multiple transformer models, such as BERT, have been utilized together to support multiple natural language processing (NLP) tasks in a system, also known as multi-task BERT. Multi-task BERT with very high weight parameters increases the area requirement of a processing in resistive memory (ReRAM) architecture, and several works have attempted to address this model size issue. Despite the reduced parameters, the number of multi-task BERT computations remains the same, leading to massive energy consumption in ReRAM-based deep neural network (DNN) accelerators. Therefore, we suggest a framework for better energy efficiency during the ReRAM acceleration of multi-task BERT. First, we analyze the inherent redundancies of multi-task BERT and the computational properties of the ReRAM-based DNN accelerator, after which we propose what is termed the model generator, which produces optimal BERT models supporting multiple tasks. The model generator reduces multi-task BERT computations while maintaining the algorithmic performance. Furthermore, we present task scheduler, which adjusts the execution order of multiple tasks, to run the produced models efficiently. As a result, the proposed framework achieves maximally 4.4× higher energy efficiency over the baseline, and it can also be combined with the previous multi-task BERT works to achieve both a smaller area and higher energy efficiency.

Trading Off Delay and Energy Saving Through Advanced Sleep Modes in 5G RANs

While designed for being energy efficient, the deployment of 5G networks will further increase Radio Access Networks (RANs) energy consumption with the twofold effect to raise sustainability issues and increase operational costs for Mobile Network Operators (MNOs). However, the energy waste occurring during low traffic periods can be mitigated through Advanced Sleep Modes (ASMs) that make the BSs enter into progressively deeper and less consuming sleep modes. Deep sleep modes, unfortunately, have longer reactivation times, and may jeopardize service quality. In this paper, focusing on 5G latency requirements in low traffic periods, we propose a framework to dynamically adapt the ASM configuration settings to the actual traffic load so as to meet a desired constraint on the average BS reactivation delay.

Energy consumption analysis of power grid distribution transformers based on an improved genetic algorithm

With the promotion of energy transformation, the utilization ratio of electrical power is progressively rising. Since electrical power is challenging to store, real-time production and consumption become imperative, imposing significant demands on the dependability and operational efficiency of electrical power apparatus. Suppose the load distribution among multiple transformers within a transformer network exhibits inequality. In such instances, it will amplify the total energy consumption during the voltage conversion process, and local, long-term high-load transformer networks become more susceptible to failures. In this article, we scrutinize the matter of transformer energy utilization in the context of electricity transmission within grid systems. We propose a methodology grounded on genetic algorithms to optimize transformer energy usage by dynamically redistributing loads among diverse transformers based on their operational status monitoring. In our experimentation, we employed three distinct approaches to enhance energy efficiency. The experimental findings evince that this approach facilitates swifter attainment of the optimal power level and diminishes the overall energy consumption during transformer operation. Moreover, it exhibits a heightened responsiveness to fluctuations in power demand from the electrical grid. Experimental results manifest that this technique can truncate monitoring time by 27% and curtail the overall energy consumption of the distribution transformer network by 11.81%. Lastly, we deliberate upon the potential applications of genetic algorithms in the realm of power equipment management and energy optimization issues.

A Niche Adaptive Elite Evolutionary Algorithm for the Clustering Optimization of Intelligent Unmanned Agricultural Unmanned Aerial Vehicle Swarm Collaboration Networks

Nowadays, the intelligent unmanned agricultural unmanned aerial vehicle (UAV) swarm collaboration network (AUSCN) has fully demonstrated its advantages in agricultural monitoring and management. By using an AUSCN, multi-machine cooperation can be realized to expand the detection range, and more complex tasks can be completed without human participation, so as to improve work efficiency and reduce the consumption of manpower and material resources. In AUSCNs, clustering is a key method to lower energy consumption. However, there is a challenge to select cluster heads in AUSCNs because of the limitation of transmission distances and the complexity of network topological structures. In addition, this problem has been confirmed as NP-hard. In this paper, a new niche adaptive elite evolutionary algorithm (NAEEA) is proposed to solve this problem. NAEEAs can search within various complicated stochastic situations at high speeds with characterized high precision and fast convergence. This algorithm integrates the merits of elite selection and adaptive adjusting to achieve high performance. In NAEEAs, a new adaptive operator is designed to speed up the convergence rate, while a novel elite operator is proposed to avoid local optima and raise the exploration ability. Furthermore, a new niche operator is also proposed to increase the population diversity. The simulation results show that, compared with an evolutionary algorithm (EA), a simulated annealing algorithm (SA) and a leapfrog algorithm (SFLA), clustering energy consumption based on an NAEEA is significantly reduced, and the network energy consumption of the AUSCN is up to 21.43%, 25.00% and 25.76% lower than the other three algorithms, respectively.

Efficient Trajectory Planning for Optimizing Energy Consumption and Completion Time in UAV-Assisted IoT Networks

Quadrotor unmanned aerial vehicles (UAVs) have emerged as ubiquitous and agile robots and data carriers within the framework of the future Internet of Things (IoT) and mobile wireless networks. Yet, the insufficient onboard battery necessitates the optimization of energy consumption for both the UAV and IoT devices while ensuring that communication requirements are met. This paper therefore proposes a more accurate and mathematically tractable model for characterizing a UAV’s energy consumption concerning desired trajectories. This nonlinear model takes into account the UAV’s dynamics, brushless direct current (BLDC) motor dynamics, and aerodynamics. To optimize the communication time between IoT devices and the UAV, IoT devices are clustered using a modified GAK-means algorithm, with dynamically optimized communication coverage radii. Subsequently, a fly–circle–communicate (FCC) trajectory design algorithm is introduced and derived to conserve energy and save mission time. Under the FCC approach, the UAV sequentially visits the cluster centers and performs circular flight and communication. Transitions between cluster centers are smoothed via 3D Dubins curves, which provide physically achievable trajectories. Comprehensive numerical studies indicate that the proposed trajectory planning method reduces overall communication time and preserves UAV battery energy compared to other benchmark schemes.