Transmission Energy Consumption Research Articles

Energy harvesting for mobile agents supporting wireless sensor networks

Abstract Wireless sensor networks (WSNs) have been deployed widely in many different application including in civil and military fields. The sensing data from the networks is very diverse and may cost huge energy consumption for transmission. Hence, mobile sensors with high capacity are deployed to support the static sensors dealing with longer range communicating distances and also supplying power wirelessly the sensors if possible. This paper focuses on an energy harvesting (EH) design for either mobile agents or sensor nodes in WSNs. A hybrid EH system can harvest energy from ambient environment around to power the mobile sensors and static sensors. In addition, these mobile sensors and transfer power wirelessly to the sensors if required. We provide different scenarios for harvesting energy from both RF and solar energy to support the devices. All the designed circuits based on mathematical equations are provided, specifically. Simulation and experimental results are addressed to clarify all the scenarios for the networks. The results show promise and practical.

Optimized Data Sampling and Energy Consumption in IIoT: A Federated Learning Approach

Real-time environment monitoring is a key application in Industrial Internet of Things, where sensors proactively collect and transmit environmental data to the controller. However, due to limited wireless resources, keeping sensors’ sampled data fresh at the controller is critical. This work aims to investigate the trade-off between the sensor’s data-sampling frequency and long-term data transmission energy consumption while maintaining information freshness. Leveraging the entropic risk measure (ERM), we jointly minimize the global transmission energy’s mean and variance subject to probabilistic constraints on information freshness. Furthermore, while jointly saving the model training energy, we adopt the federated learning (FL) paradigm and propose an FL-based two-stage iterative optimization framework to optimize the aforementioned objective. Specifically, we iteratively learn the sampling frequency via Bayesian optimization and minimize the long-term ERM of the global energy consumption via Lyapunov optimization. Numerical results show that the proposed FL-based scheme saves substantial executing energy with less performance loss. Quantitatively, compared with the centralized learning baseline, the proposed FL-based framework saves up to 69% model training energy at the expense of a mere increased objective outcome, i.e., 6.3% in the global data transmission energy consumption ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$9.936\times 10^{-5}$ </tex-math></inline-formula> in ERM) under 0.4% bias from the global optimal data-sampling frequency.

Joint Optimization of Energy Consumption and Data Transmission in Smart Body Area Networks.

In Wireless Body Area Networks (BAN), energy consumption, energy harvesting, and data communication are the three most important issues. In this paper, we develop an optimal allocation algorithm (OAA) for sensor devices, which are carried by or implanted in human body, harvest energy from their surroundings, and are powered by batteries. Based on the optimal allocation algorithm that uses a two-timescale Lyapunov optimization approach, we design a framework for joint optimization of network service cost and network utility to study energy, communication, and allocation management at the network edge. Then, we formulate the utility maximization problem of network service cost management based on the framework. Specifically, we use OAA, which does not require prior knowledge of energy harvesting to decompose the problem into three subproblems: battery management, data collection amount control and transmission energy consumption control. We solve these through OAA to achieve three main goals: (1) balancing the cost of energy consumption and the cost of data transmission on the premise of minimizing the service cost of the devices; (2) keeping the balance of energy consumption and energy collection under the condition of stable queue; and (3) maximizing network utility of the device. The simulation results show that the proposed algorithm can actually optimize the network performance.

A Novel Deep Reinforcement Learning Approach for Task Offloading in MEC Systems

With the internet developing rapidly, mobile edge computing (MEC) has been proposed to offer computational capabilities to tackle the high latency caused by innumerable data and applications. Due to limited computing resources, the innovation of computation offloading technology for an MEC system remains challenging, and can lead to transmission delays and energy consumption. This paper focuses on a task-offloading scheme for an MEC-based system where each mobile device is an independent agent and responsible for making a schedule based on delay-sensitive tasks. Nevertheless, the time-varying network dynamics and the heterogeneous features of real-time data tasks make it difficult to find an optimal solution for task offloading. Existing centralized-based or distributed-based algorithms require huge computational resources for complex problems. To address the above problem, we design a novel deep reinforcement learning (DRL)-based approach by using a parameterized indexed value function for value estimation. Additionally, the task-offloading problem is simulated as a Markov decision process (MDP) and our aim is to reduce the total delay of data processing. Experimental results have shown that our algorithm significantly promotes the users’ offloading performance over traditional methods.

An Automatic and Intelligent Internet of Things for Future Agriculture

Great advances in artificial intelligence and the Internet of Things have been made in recent years. This work develops an intelligent agricultural cultivation system to realize automated and intelligent farm cultivation management and operation. Experimental results demonstrate that the proposed system reduces water consumption by 50% and 30%, respectively, below those of fixed and threshold irrigation methods and predicts more accurately the amount of irrigation water required. The proposed dynamic detection transmission mechanism performs 97.8% fewer communications than are performed in timing transmission mode, effectively reducing overall energy consumption for transmission. Experimental results show that the proposed system reduces cultivation time by 40.94% below that required by the traditional cultivation method, and supports more effective management. Based on all of the experimental results, the proposed method supports convenient cultivation for growers.

An Energy Balance-Oriented Clustering Routing Protocol for Cognitive Radio Sensor Networks

Uneven clustering is an effective solution to solve the energy hole problem in multihop cognitive radio sensor networks (CRSNs). However, current literature leaves control overhead and multihop intercluster routing out of consideration and adopts constant weight coefficients for cluster radius calculation instead of reasonably setting their values according to specific network configurations. To solve the above problems, a distributed energy balance-oriented uneven clustering routing protocol (EBUCRP) is proposed in this article. First, by fully considering the energy consumption of control information exchange and data transmission, EBUCRP theoretically derives the optimal cluster radius of each unit within which distributed clusters are formed. Second, communication capability-based selection criteria are defined from joint energy and spectral efficiency perspectives to help select high-quality cluster heads and next-hop relays. In this case, the monitoring information can be transferred to the sink through efficient intracluster data aggregation and intercluster multihop forwarding. Third, EBUCRP strictly restricts the energy consumption of control information exchange during network operation. Simulation results show that EBUCRP gains obvious advantages over competing protocols in enhancing the lifetime and supervision capability of both single-hop and multihop CRSNs.

Catenarian-Trim Medley Routing System for Energy Balancing in Dispensed Computing Networks

Balancing energy consumption to prolong the life of a dispensed computing network is a challenging issue for researchers. In multi-hop transmission, most of the sensor node energy is depleted for the data transmission from the sensor node to the sink and receiving data from other sensor nodes. The energy consumption in the wireless sensor networks (WSN) is directly proportional to the square of the distance between source and destination. While receiving the data, energy consumption is fixed but can play a considerable role. By reducing or minimizing the transmission distance and utilizing the optimal distribution of nodes can control the energy consumption rate between one node to another. This approach leads to performance improvement in the dispensed computing networks. Chain-based routing is a novel idea where each sensor node receives data from one sensor node and transmits it to the nearest neighbor node in the same chain. This strategy results in the formation of a single chain of all nodes and balances the energy consumption. In this paper, a hybrid energy-efficient scheme combining the chain and tree-based routings has been proposed. The proposed Catenarian-Trim Medley (CTM) routing scheme has been investigated to optimize the transmission distance and energy consumption balancing for the dispensed computing networks.

Intelligent Resource Optimization for Blockchain-Enabled IoT in 6G via Collective Reinforcement Learning

Artificial intelligence (AI)-enabled Internet of Things (IoT) has attracted great interests. The accuracy of data training model in AI is vital for further development of IoT. In addition, with the increasing number of intelligent IoT devices, the amounts of data available for transmission, learning and training can lead to serious communication burdens and data reliability issues. In order to address these issues, we study novel network architectures in future 6G networks to support the intelligent IoT. Moreover, inspired by the collective learning of humans, we introduce and adopt a novel method named as collective reinforcement learning (CRL) in the intelligent IoT to realize the sharing of learning and training results. To ensure security and privacy, as well as improve computing efficiency, blockchain, mobile edge computing (MEC) and cloud computing are applied to protect data security and enrich computing resources. On this basis, we formulate an optimization problem in the intelligent IoT based on the proposed framework to optimize transmission latency and energy consumption. Simulation results demonstrate that the system performance has improved significantly. At last, some research challenges and open issues are pointed out to the intelligent IoT in future networks.

Expected Area-Based Real-Time Routing Protocol for Supporting Mobile Sinks in Wireless Sensor Networks

To deliver real-time data within a desired time deadline, a spatiotemporal approach has been proposed in wireless sensor networks. In the approach, real-time data can be delivered with a delivery speed calculated by both the distance from a source to a static sink and the desired time deadline. In the case of a mobile sink, because the distance from a source to the sink would be dynamically changed due to its movement, real-time data cannot be delivered due to the inability to calculate a delivery speed. Thus, we propose a novel real-time routing protocol, called EAR2 (expected area-based real-time routing) for mobile sinks. Instead of the immediate distance, EAR2 considers the static distance to the expected area of a mobile sink’s location using its movement speed. To satisfy the desired time deadline, EAR2 guarantees that the total summation of the unicasting time to the expected area and the flooding time within the expected area can be smaller than the deadline. To do this, EAR2 calculates a data delivery speed by using the static distance and the unicasting time and exploits the flooding time observed from various network environments. Because EAR2 has a high flooding time and data loss problems due to a single flooding point, we propose a protocol called EAR2M with multiple flooding points, which reduces the flooding time and enhances the reliability of data transmission. We also propose two extensions of EAR2M, called EAR2M_R and EAR2M_E for efficient data transmission from a source to multiple flooding points. They aim to reduce the transmission delay and the energy consumption for efficient data transmission among multiple flooding points, respectively. Simulation results show that EAR2 improves the deadline miss ratio and the energy consumption, with averages improvements of 47.17% and 29.99% over the existing schemes, respectively. Furthermore, EAR2M with four flooding points enhances the deadline miss ratio and the energy consumption by an average of 12.69% and 131.86% over EAR2.

Link-Aware Frame Selection for Efficient License Plate Recognition in Dynamic Edge Networks

With the booming development of Internet of Things (IoT) and computer vision technology, running vision-based applications on IoT devices becomes an overwhelming tide. In vision-based applications, the Automatic License Plate Recognition (ALPR) is one of the fundamental services for smart-city applications such as traffic control, auto-drive and safety monitoring. However, existing works about ALPR usually assume that IoT devices have sufficient power to transmit the whole captured stream to edge servers via stable network links. Considering the limited resources of IoT devices and high-dynamic wireless links, this assumption is not suitable for realizing an efficient ALPR service on low-power IoT devices in real wireless edge networks. In this paper, we propose a link-aware frame selection scheme for ALPR service in dynamic edge networks aiming to reduce the transmission energy consumption of IoT devices. Specifically, we tend to select a few key frames instead of the whole stream and transmit them under good links. We propose a two-layer recognition frame selection algorithm to optimize the frame selection by exploiting both the video content variation and real-time link quality. The extensive results show that, by carefully selecting the offloaded frames to edge servers, our algorithm can significantly reduce the energy consumption of devices by 46.71% and achieve 97.95% recognition accuracy in the high-dynamic wireless link of the edge network.

A Collision-Avoided MAC Protocol With Time Synchronization and Power Control for Underwater Sensor Networks

In recent years, there has been a great deal of interest in underwater sensor networks (UWSNs) because they can effectively monitor the submerged environment. However, due to the long delay, high bit error rate, and limited propagation bandwidth of underwater acoustic channels, the media access control (MAC) protocol cannot function effectively in underwater acoustic sensor networks, particularly in offshore waters. To solve these issues, an MAC protocol with time synchronization and power control (TSPC) is proposed for a particular scenario. In addition, the proposed protocol is a joint channel access scheme, and the central nodes are deployed on an offshore seabed, which can be used as a synchronization reference standard time. A power control strategy is also implemented to limit the communication range of nodes. In brief, integrating TSPC with layered concepts and distributed clustering algorithms can effectively prevent communication collisions and reduce the energy consumption of UWSNs. The simulation results indicate that the TSPC MAC protocol performs well with regard to packet transmission rate, synchronization error, collision-free operation, and energy consumption.

A deep learning scheme for efficient multimedia IoT data compression

Multimedia Internet of Things (MIoT) devices and networks will face many power and communication overhead constraints given the volume of multimedia sensed data. One classic approach to overcoming the difficulty of large-scale data is to use lossy compression. However, current lossy compression algorithms require a limited compression rate to maintain acceptable perceived image quality. This is commonly referred to as the image quality-compression ratio trade-off. Motivated by current breakthroughs in computer vision, this article proposes recovering high-quality decompressed images at the application server level using a deep learning-based super-resolution model. As a result, this paper proposes ignoring the trade-off between image quality and size and increasing the reduction size further by using a lossy compressor with downscaling to conserve energy. The experimental study demonstrates that the proposed technique effectively improves the visual quality of compressed and downscaled images. The proposed solution was evaluated on resource-constrained microcontrollers. The obtained results show that the transmission latency and energy consumption can be decreased by up to 10% compared to conventional lossy compression techniques.

Traffic-Aware Transmission Strategy of Fog Cell in Green Industrial Internet

In Industrial Internet application scenarios, due to the ubiquitous connection requirements of the massive Internet of Things (IoT) devices in the edge layer. The data transmission rate is reduced, and the transmission delay increases, increasing the transmission energy consumption per bit. So a low-energy transmission strategy based on real-time edge layer traffic sensing is proposed. First, a mixed-integer modeling method for low-energy transmission of the IoT is proposed. This method aims to optimize the overall energy consumption of the system. The low-energy transmission task of the IoT is modeled as a mixed-integer linear programming problem. Second, a traffic prediction method for the estimation of the number of access packets is designed. Solve the problem of fog access point (F-AP) state change caused by the real-time change of network load. Finally, an energy-driven mapping strategy is designed. The transmission task can be dynamically mapped to the appropriate F-AP. The simulation results show that the strategy proposed in this article can effectively reduce the transmission energy consumption of IoT devices and the overall energy consumption of the system in the massive device access scenario.

A slotted random request scheme for connectionless data transmission in bluetooth low energy 5.0

Bluetooth low energy (BLE) is a personal area network (PAN) technology that has been widely implemented in various Internet of Things (IoT) services. BLE defines a connectionless communication method based on request–response to more effectively adapt to services with high flexibility requirements. This communication method can be divided into three steps: the neighbor discovery process (NDP), scan request, and scan response. First, the NDP enables a device known as the scanner to discover another device known as the advertiser nearby. In the second step, the scanner sends a scan request to the advertiser which, after receiving the scan request, sends a data packet in response. Unfortunately, this method is flawed in that the simultaneous discovery of an advertiser by multiple scanners would cause the request packets they send to collide with high probability. In fact, these collisions may always occur when scanners continue to discover common advertisers. To solve this problem, various schemes have been proposed to reduce the probability of a collision. However, these schemes are only suitable for application scenarios with fewer scanners; when more scanners are present, their latency and energy consumption increase rapidly. This prompted us to propose a scheme to improve the performance of the connectionless communication method. In addition, convex optimization was used in the proposed model to minimize the transmission delay and energy consumption. The final simulation results prove that the proposed scheme effectively reduces the delay and the energy consumption when many scanners are involved.

Minimum weight clustered dominating tree problem

We discuss minimum weight clustered dominating trees that find applications in the wireless sensor network design based on a clustered independent set structure. A cluster consists of a master sensor and the sensors belonging to its sensing radius. Masters collect, filter, and transmit the sensed data to a central sensor responsible for processing all sensed information. The data sent from a cluster to the central sensor follow a unique path. It alternates between a master and a bridge node, in this order. A bridge allows data communication between two neighboring clusters. The larger the distance between masters and bridges, the higher the energy consumption for data transmission. To reduce energy consumption and increase the network lifetime, we investigate a clustered tree structure of minimum total link distances. We propose hop- and flow-based models, introduce valid inequalities for them, and discuss five exponential families of cuts when embedded into the branch-and-cut framework of the CPLEX solver. Our models benefit properly of the CPLEX Benders’ decomposition. We also highlight the differences between the topology of the clustered tree of minimum cost and the one of the (non-clustered) minimum dominating tree of the corresponding instances in terms of cost and number of solution nodes.

A Comprehensive Performance Evaluation of Chinese Energy Supply Chain under “Double-Carbon” Goals Based on AHP and Three-Stage DEA

In 2020, China put forward the goals of “peak carbon dioxide emissions” and “carbon neutrality” (“double-carbon”) and it is urgent for the energy industry to achieve green transformation. Aiming at the rigid requirements of the carbon-peaking and carbon-neutrality goals (“double-carbon”), this study established a performance evaluation index system for an energy supply chain of a four-tier structure based on the “double-carbon” goals, calculating its weight by the analytic hierarchy process (AHP). On this basis, a three-stage data envelopment analysis (DEA) evaluation model was established to evaluate the performance of the energy supply chain in 2010–2019. According to the three-stage DEA evaluation mode, the initial input–output efficiency value of the energy supply chain was calculated by the DEA-BCC (extended by Banker, Charnes and Cooper) model and DEA-CCR (proposed by Charnes, Cooper and Rhodes) model and the influence of environmental noise was eliminated by stochastic frontier analysis (SFA) regression; we then obtained the adjusted efficiency value for the energy supply chain. At the same time, taking 2015 as the dividing point, the advantages and disadvantages between the traditional energy supply chain and new energy supply chain were analyzed and summarized. Further analysis and suggestions are provided to consumers, enterprises and countries from four aspects: energy supply, energy production and processing, energy transmission and distribution and energy consumption.

Dictionary Learning-Based Multichannel ECG Reconstruction Using Compressive Sensing

Multi-channel electrocardiogram (MECG) compression on lightweight wireless body area network (WBAN) is highly challenging for long-term eHealthcare monitoring. Energy consumption involved in wireless transmission poses an obstacle in the implementation of WBAN. MECG is widely used to diagnose cardiovascular diseases (CVDs), which require a considerable time to extract sufficient clinically relevant data from subjects. Since wireless MECG data transmission dominates energy cost and memory consumption of wireless sensor nodes, it is favorable to reduce the data size without any loss of diagnostic information. Compressed sensing (CS) provides efficient encoding schemes for data reduction and energy consumption in wireless transmission. We propose an adaptive multiple dictionary learning-based joint CS model for MECG compression, which exploits spatial correlation and adaptive features existing in MECG signals. We demonstrate performance of the proposed algorithm with the Physikalisch-Technische Bundesanstalt database (PTB) for MECG compression. Results from the proposed scheme can achieve excellent reconstruction quality with fewer measurements than other existing CS-based approaches. It is the most suitable technique for long-term MECG compression using lightweight WBAN devices.

Construction and Realization Path Analysis of Cross-Border E-Commerce Logistics Collaboration Model

In order to improve the synergy effect of cross-border e-commerce slogistics, this paper combines intelligent logistics communication technology to construct a cross-border e-commerce collaborative logistics model. Moreover, this paper proposes a parallel energy-efficient routing algorithm based on 3D cell space to improve the system operation effect. In addition, this paper deeply analyzes the data fusion function performed by MA when accessing nodes and proposes a data fusion criterion to judge the necessity of data fusion on nodes. Finally, this paper balances the energy consumption of e-commerce logistics data fusion and data transmission so that the sum of the two is minimized. The data simulation analysis shows that the cross-border e-commerce logistics collaboration model proposed in this paper has certain effects, and it can be applied to the actual operation of cross-border e-commerce.

Routing Protocol for Wireless Sensor Networks Based on Archimedes Optimization Algorithm

One of the key design problems of wireless sensor networks is to reduce energy consumption and improve node survival. Because the battery in sensor nodes is difficult to replace and the energy is limited, the design of energy-saving routing protocol becomes very important. Aiming at the problems of limiting node energy and shortening network lifetime in wireless sensor networks, a WSNs routing protocol based on improved archimedes optimization algorithm (IAOAR) is proposed in this paper. Firstly, in the cluster establishment phase, the protocol defines different fitness functions according to the energy, number and distance of sensor nodes to select the appropriate initial cluster. Then the fitness value of the node is calculated, and the weight is dynamically updated according to the number of iterations to improve the optimization ability of AOA algorithm. Virtual force is introduced to adjust the selected cluster head position to ensure the selection of optimal cluster heads, reduce the transmission energy consumption of common sensor nodes. In the data transmission stage, combined with the improved ant colony algorithm, the shortest transmission path to the sink node is constructed to reduce the energy consumption of long-distance data transmission at the cluster head. The simulation results showed that compared with LEACH, E-LEACH, LEACH-ANT and MAXLEACH protocols, the network life cycle of the protocol proposed in this paper was improved by 140.55%, 107.09%, 99.24% and 96.76% respectively. It effectively reduced the speed of node death, balances the network energy consumption and prolonged the network lifetime.

A Reinforcement Learning Based Transmission Parameter Selection and Energy Management for Long Range Internet of Things

Internet of Things (IoT) landscape to cover long-range applications. The LoRa-enabled IoT devices adopt an Adaptive Data Rate-based (ADR) mechanism to assign transmission parameters such as spreading factors, transmission energy, and coding rates. Nevertheless, the energy assessment of these combinations should be considered carefully to select an accurate combination. Accordingly, the computational and transmission energy consumption trade-off should be assessed to guarantee the effectiveness of the physical parameter tuning. This paper provides comprehensive details of LoRa transceiver functioning mechanisms and provides a mathematical model for energy consumption estimation of the end devices EDs. Indeed, in order to select the optimal transmission parameters. We have modeled the LoRa energy optimization and transmission parameter selection problem as a Markov Decision Process (MDP). The dynamic system surveys the environment stats (the residual energy and channel state) and searches for the optimal actions to minimize the long-term average cost at each time slot. The proposed method has been evaluated under different scenarios and then compared to LoRaWAN default ADR in terms of energy efficiency and reliability. The numerical results have shown that our method outperforms the LoRa standard ADR mechanism since it permits the EDs to gain more energy. Besides, it enables the EDs to stand more, consequently performing more transmissions.