Transmission Energy Consumption Research Articles

Machine learning‐based data fusion method for wireless sensor networks

AbstractFor wireless sensor networks (WSNs), sensor nodes lose a certain amount of energy during the information collection and transmission process, and sensor nodes powered by non‐replaceable batteries have limited energy and need to be controlled for energy consumption. In the face of the energy consumption issue in WSN data transmission, research has been conducted to analyze data fusion methods in order to reduce energy consumption. Based on machine learning techniques, a Deep Stacked Auto‐Encoder (DSAE) model is constructed and trained using a layer‐wise greedy approach. By combining this model with WSN, an algorithm based on the DSAE model, called Deep Stacked Auto‐Encoder Data Fusion Algorithm (DSAEDFA), is obtained to do data fusion. The results show that compared to other algorithms, the proposed fusion algorithm has better fusion performance. When the number of iterations is set to 500, the DSAEDFA has 281 surviving nodes, which is 10 more than the Back‐Propagation Data Fusion Algorithm (BPDFA) and 144 more than the Low Energy Adaptive Clustering Hierarchy (LEACH) algorithm. When the number of failed nodes is 40, the DSAEDFA has a network survival time of 2562 rounds, which is 746 rounds longer than the LEACH algorithm. The research method effectively extends the lifespan of wireless sensor networks and reduces data transmission energy consumption. Compared to previous methods, the proposed method consider the factors of node residual energy and distance on the basis of traditional routing protocols, making the selection of cluster heads more reasonable. The proposed method can organically combine the DSAE model with the clustering model, optimize the data fusion method, and improve the performance of the algorithm. In addition, by combining the DSAE model, a machine learning technique with clustering models has been expanded in terms of the application scope.

IoT driven joint compressed sensing and shallow learning approach for ECG signal-reconstruction

Because biological signal transmission in real time might be very demanding, cloud and internet of things (IoT) infrastructure are required. To do this, the main component of the signal serves as the focal point of a reconstruction strategy that has been developed. The input is transferred to the intended destination once it has been encoded. Security is an important consideration that must not be disregarded. For long-term healthcare monitoring via lightweight wireless networks, electrocardiogram (ECG) compression is a major difficulty. Reducing energy consumption in wireless data transmission and precisely calculating error rates for data reconstruction are two essential components of compressed sensing. The application of effective encoding methods is crucial for these considerations. We present multi-task compressed sensing (MT-CS), a unique method for compressing ECG data. When used to wireless network systems with several embedded sensors, this technique is quite effective. From the ECG data, the model learns the fundamental adaptive properties needed for correlation. We use the multiparameter intelligent monitoring in intensive care (MIMIC-II) dataset to investigate the performance of the suggested MT-CS reconstruction technique in order to assess its strength and application. In comparison to current compressed sensing methods, the simulation results demonstrate that the suggested reconstruction methodology utilizing MT-CS generates high-quality reconstruction signals with fewer observations.

Economic optimization and thermodynamic analysis of a novel nuclear district cooling system

“Yanlong” pool-type low-temperature heating reactor (DHR-400) stands at a highly promising nuclear heating technology. However, its annual utilization duration faces constraints when exclusively employed for winter heating. To cope with this challenge, a novel nuclear district cooling system driven by DHR-400 is proposed for the first time in this paper. As the core component of the proposed nuclear district cooling system, single-effect double-lift LiBr-H2O absorption refrigeration system (SDARS) can significantly increase the temperature difference between the supply and return of the primary heating network (PHN), thereby reducing the transmission energy consumption. Despite the significant potential, the optimization design of SDARS has received limited attention. An optimization model for SDARS with the annual cost (AC) as the objective function is developed. Focusing on a real district cooling scenario in Xi'an High-tech Zone, the heat transfer areas of all process units and the operating parameters of all streams in SDARS are optimized. Results demonstrate that the optimal annual average coefficient of performance (AACOP) is improved by 10.63 % and AC is reduced by 5.44 % compared with the Benchmark case. The dynamic payback period for the combined heating and cooling system driven by DHR-400 is reduced by 5.53 years compared to the heating-only system.

Green building adaptation in hot-humid climates: assessment of coconut and corn husk fiber composite bricks as energy-efficient building envelopes

PurposeThis study has assessed the thermal performance of locally fabricated bio-based building envelopes made of coconut and corn husk composite bricks to reduce building wall heat transmission load and energy consumption towards green building adaptation.Design/methodology/approachSamples of coconut fiber (coir) and corn husk fiber bricks were fabricated and tested for their thermophysical properties using the Transient Plane Source (TPS) 2500s instrument. A simulation was conducted using Dynamic Energy Response of Building - Lunds Tekniska Hogskola (DEROB-LTH) to determine indoor temperature variation over 24 h. The time lag and decrement factor, two important parameters in evaluating building envelopes, were also determined.FindingsThe time lag of the bio-based composite building envelope was found to be in the range of 4.2–4.6 h for 100 mm thickness block and 10.64–11.5 h for 200 mm thickness block. The decrement factor was also determined to be in the range of 0.87–0.88. The bio-based composite building envelopes were able to maintain the indoor temperature of the model from 25.4 to 27.4 °C, providing a closely stable indoor thermal comfort despite varying outdoor temperatures. The temperature variation in 24 h, was very stable for about 8 h before a degree increment, providing a comfortable indoor temperature for occupants and the need not to rely on air conditions and other mechanical forms of cooling. Potential energy savings also peaked at 529.14 kWh per year.Practical implicationsThe findings of this study present opportunities to building developers and engineers in terms of selecting vernacular materials for building envelopes towards green building adaptation, energy savings, reduced construction costs and job creation.Originality/valueThis study presents for the first time, time lag and decrement factor for bio-based composite building envelopes for green building adaptation in hot climates, as found in Ghana.

Edge Computing Security of Mobile Communication System Based on Computer Algorithms

With the progress of information technology recently, mobile communication system edge computing (EC) has been widely used in all walks of life, but the traditional mobile communication system EC mode has security problems such as privacy disclosure, malicious tampering, and virus attacks. Computer algorithms has brought new vitality to EC in mobile communication systems. This paper analyzed the application of computer algorithm in EC mode of mobile communication system, and selected 20 users as the research object. This paper adopted traditional computing mode (such as cloud computing) and computer algorithm-based mobile communication system EC security research. This text compared the effects of two modes on security performance, data transmission efficiency, energy consumption, cost savings, and user satisfaction. The experimental results in this paper showed that the average security of EC mode of mobile communication system based on computer algorithm was 84%, and the average data transmission time was 4.8[Formula: see text]s. The energy consumption was 40%, and the cost savings and user satisfaction were 432,000 yuan and 13 points, respectively. Both were superior to the traditional edge counting mode. The EC mode of mobile communication system using computer algorithms can significantly improve the security of mobile communication, data transmission speed, cost savings, user satisfaction, and reduce energy consumption. This model has important significance and value for social development.

Joint trajectory, transmission time and power optimization for multi-UAV data collecting system

Unmanned aerial vehicles (UAVs) have been generally applied in the field of communication due to their small size, flexible mobility, and convenient deployment. As a mobile base station, the UAV node can quickly establish a line-of-sight link with the ground node, thereby improving communication performance. In this paper, we study a multi-UAV assisted data collecting system. Specifically, in the case of limited system energy consumption, UAV flight energy consumption and ground node data transmission energy consumption are considered as an general limitation, and considering the channel interference between nodes, a multi-UAV assisted data collection model is studied. An non-convex problem that maximizes the minimum amount of data collected from ground nodes is further formulated. Since the original optimization problem is non-convex that difficult to solve directly, the problem is first decomposed into four sub-problems, and then the solution of each sub-problem is obtained by using successive convex approximation and block coordinate descent method. Finally, based on the solution of the four subproblems, an iterative algorithm for joint optimization of data transmission planning, transmission power, UAV trajectory and mission time is proposed. Simulation experiments show that the proposed algorithm can obtain more transmission data than the baseline algorithms.

A low-resistance local component design method based on biomimicry and a random forest model: A pipe elbow case study

The energy consumption of building transmission and distribution systems accounts for more than 30% of building energy consumption, and the prevalence of localized components in building transmission and distribution systems leads to severe energy losses and resistance effects during system operation. To solve this problem, our research team has developed a series of resistance reduction components. This paper proposes a low-resistance local component shape optimization method based on biomimicry and machine learning, taking pipe elbows as an example, and the normalized shape optimization parameters of elbows with different pipe diameters are given. Through numerical simulation, full-size experiments, energy dissipation analysis and other methods, the optimized elbow resistance reduction effect and resistance reduction mechanism are verified and analyzed. The results showed that the resistance reduction rate of the elbow was 19%–26% for different pipe diameters and Reynolds numbers, and the optimized elbow significantly reduced energy dissipation during the flow process. Previously, the shape optimization problem of local components focused mainly on rectangular ducts that can be simplified in two dimensions; this study extends the optimization objective to circular pipes, which provides an effective method for guiding the optimal design of low-resistance local components in building transmission and distribution systems.

WHOOPH: whale optimization-based optimal placement of hub node within a WBAN

Biosensor nodes of a wireless body area network (WBAN) transmit physiological parameter data to a central hub node, spending a substantial portion of their energy. Therefore, it is crucial to determine an optimal location for hub placement to minimize node energy consumption in data transmission. Existing methods determine the optimal hub location by sequentially placing the hub at multiple random locations within the WBAN. Performance measures like link reliability or overall node energy consumption in data transmission are estimated for each hub location. The best-performing location is finally selected for hub placement. Such methods are time-consuming. Moreover, the involvement of other nodes in the process of hub placement results in an undesirable loss of network energy. This paper shows the whale optimization algorithm (WOA)-based hub placement scheme. This scheme directly gives the best location for the hub in the least amount of time and with the least amount of help from other nodes. The presented scheme incorporates a population of candidate solutions called "whale search agents". These agents carry out the iterative steps of encircling the prey (identifying the best candidate solution), bubble-net feeding (exploitation phase), and random prey search (exploration phase). The WOA-based model eventually converges into an optimized solution that determines the optimal location for hub placement. The resultant hub location minimizes the overall amount of energy consumed by the WBAN nodes for data transmission, which ultimately results in an elongated lifespan of WBAN operation. The results show that the proposed WOA-based hub placement scheme outperforms various state-of-the-art related WBAN protocols by achieving a network lifetime of 8937 data transmission rounds with 93.8% network throughput and 9.74 ms network latency.

Biomedical signal compression using deep learning based multi-task compressed sensing

<span>Real-time transmission of biomedical signals is immensely challenging and requires cloud and internet of things (IoT) infrastructure. Security is also an important factor; however, to accomplish this, a reconstruction method is developed in which the entire signal is supplied as an input, the primary portion is considered here, and the signal is further encoded and transmitted to the destination. Electrocardiogram (ECG) compression for the lightweight wireless network is quite challenging for long-term healthcare monitoring. Compressed sensing (CS) involves efficient encoding mechanisms for error rate estimation for reconstruction and energy consumption for wireless transmission of data. We propose a multi-task compressed sensing (MT-CS) reconstruction mechanism in this study for ECG compression of data is most chosen for a wireless network system that has various sensors embedded in it. This model further extracts the essential adaptive features for correlation existing in the ECG signals. The performance of the proposed MT-CS reconstruction mechanism is evaluated on the multiparameter intelligent monitoring in intensive care (MIMIC-II) dataset, which ensures its robustness and generalization. The results obtained upon simulation ensure that the proposed MT-CS based reconstruction approach ensures excellent reconstruction signal with fewer measurements in comparison with the existing state-of-art CS techniques.</span>

Event-triggered fixed/preassigned time stabilization of state-dependent switching neural networks with mixed time delays

<abstract><p>This study employed an event-triggered control (ETC) strategy to investigate the problems of fixed-time stabilization (FTS) and preassigned-time stabilization (PTS) for state-dependent switching neural networks (SDSNNs) that involved mixed time delays. To enhance the network's generalization capability and accelerate convergence stabilization, a more intricate weight-switching mechanism was introduced, then to mitigate transmission energy consumption, this paper proposed a tailored event-triggering rule that triggered the ETC solely at predetermined time points. This rule ensured the stability of the system while effectively reducing energy consumption. Using the Lyapunov stability theory and various inequality techniques, this paper presented new results for FTS and PTS of SDSNNs. The validity of these findings was supported by conducting data simulations in two illustrative examples.</p></abstract>

On data-induced CO[formula omitted] emissions of vehicle automation: An overlooked emission source

CO2 emission of vehicles and its influence on climate change is a widely discussed topic already for many years. New CO2 emission norms for vehicles have been introduced based on the propulsion of the vehicle, to reduce future CO2 emissions. Automated vehicles (AVs) have potential in reducing emissions by optimizing routes and speed profiles. However, they also generate extra emissions due to large data involved. Whether the norms can be met with these extra data-induced emissions of AVs remains an open question. This paper provides an approach to determine the CO2 emissions of these data related aspects. The approach dissects data-induced emissions stemming from energy consumption of the sensing components, the computing platform, disks inside the vehicle, wireless communication networks and data centers. We apply the approach to estimate CO2 emissions for varying scenarios of technology composition and energy mix. Sensitivity analysis shows that the energy intensity of wireless communication networks and the data transmission rate from vehicle to data center have the strongest influence on the resulting CO2 emissions. The energy mix also significantly affects whether the norms can be met. For high amounts of data transmission, compliance with the norms seem to be difficult in most scenarios. We recommend that the energy consumption of wireless communication networks and data transmission from vehicle to data center should be further optimized. Future work should focus on empirical evidence to validate/falsify the key assumptions in this paper, which will lead to a more accurate estimate of automation-induced emissions.

Importance-aware data selection and resource allocation for hierarchical federated edge learning

Compared to traditional machine learning approaches, federated learning (FL) is effective in dealing with mobile device data privacy issues. Apart from utilizing the cloud computing server as the model aggregation center, edge computing servers can also be advocated as intermediaries to perform model aggregation near the devices, which can reduce transmission latency and energy consumption. In this paper, we consider a multi-layer federated edge learning framework where both cloud and edge servers are used for FL and design a Data Importance-aware Hierarchical Federated Edge Learning (DHFL) scheme. We develop a joint data selection and resource allocation algorithm based on data importance to maximize learning efficiency in DHFL. To solve this problem, we decompose it into three sub-problems including edge-device association, resource allocation and data selection. By presenting the optimal strategy for each edge-device group, the optimal association between devices and edge servers is achieved through an iterative global cost reduction adjustment process, and data selection is performed by using convex optimization scheme. Extensive simulations are carried out to verify the proposed scheme and show that our proposal can achieve smaller training loss using less than 1/6 of the data and reduce latency by 80% compared to FedAvg.

Low-cost and high-performance channel access strategies for Internet of Nano-Things applications

Nanodevices, which are only a few nanometers (nm) in size, are interconnected to form the Internet of Nano-Things (IoNT) that performs complex operations. One of the key challenges is ensuring efficient channel access control for nanodevices, especially when dealing with large network sizes. Medium access control (MAC) protocols serve this purpose, but traditional approaches are not practical due to the inherent constraints of nanodevices. In this paper, we propose two novel MAC protocols for IoNT applications. The first protocol, Slot Assignment-Based (SAB) MAC, is a contention-free method relying on scheduling. In contrast to its counterparts, it enables simultaneous packet transmission through Time Spread On-Off Keying (TS-OOK), effectively minimizing the collision probability and end-to-end delay. The second protocol, Receiver-Initiated and Directed (RID) MAC, adopts a contention-based approach to reduce unnecessary transmissions caused by flooding. It achieves this by limiting the number of active nanodevices within a time interval using directional antennas without incurring scheduling overhead. We evaluated the performance of these protocols through comprehensive simulations, comparing them with counterparts in terms of packet transmission success, energy consumption, end-to-end delay and setup overhead. In dense topologies, SAB-MAC outperforms Transparent (TRN) MAC by approximately twice the packet transmission success reaching up to 95.73%. It accomplishes this with 1000 times lower end-to-end delay and reduced setup overhead than Time Division Multiple Access (TDMA). Conversely, RID-MAC achieves twice the packet transmission success of TRN-MAC and ten times that of unicast-based methods, all with lower end-to-end delay and nearly equivalent energy consumption. Consequently, due to its superior performance SAB-MAC is the optimal choice for communication between nanorouters (NRs). However, RID-MAC is more suitable for communication between nanosensors (NSs), as it incurs no setup overhead.

Shrimp Pond Monitoring System using Cooperative Wireless Sensor Network Multi-Hop Technique based on Internet of Things

Water quality is a crucial factor in maintaining the survival and growth of shrimp. Manual water quality monitoring in shrimp ponds is no longer effective due to the need for periodic monitoring to maintain stable water quality. Therefore, online monitoring using various sensors installed in each pond is necessary. However, there are several challenges to overcome, such as the large expanse of the shrimp ponds, which may lead to data loss due to signal disruptions, and limited energy to power the sensors. To address these issues, this paper proposes the cooperative Wireless Sensor Network (WSN) technique with a multi-hop method for communication in the monitoring process. The system consists of five sensor nodes: temperature sensor, pH sensor, water level sensor, intake water flow sensor, and drain water flow sensor. The cooperative WSN multi-hop technique helps reduce energy consumption in the sensor nodes during measurement and data transmission, while also preventing data packet loss. This is achieved through the use of relay nodes that strengthen signals and forward data to the sink node. As a result, the battery life is extended, and energy usage in the monitoring process can be optimized. The system enables real-time online monitoring and can be accessed through a smartphone application. The results of this study show that the total energy consumption for data transmission in the sensor nodes is 9.64 J, while the total energy consumption for data forwarding in the relay nodes is 9.15 J. The total energy consumption in the transmit and receive processes is 18.79 J or 5.2 mWh. Therefore, it can be concluded that the energy savings of the proposed system is 4.3 mWh or approximately 45%, and is more efficient than the previous system.

A categorized information fusion model for reliable services delivery in smart cities

Wireless Sensor Network serves as the core source of data in the Internet of Things (IoT) environment. The WSN nodes sense, aggregate, and relay sensed data for the different services in the IoT. The reliability of the aggregated and transmitted information is crucial for the effectiveness of IoT services. The data from multiple sensors at different timestamps need to be fused for efficient data transmission, reducing complexity and improving the reliability of the data being transmitted, improving the quality of IoT services. Therefore, data fusion is an essential aspect of WSN, where multiple sensors collect and combine data to produce a single, more comprehensive data for transmission. This data fusion reduces the amount of data transmission and network energy consumption and increases the accuracy of the result. This paper introduces a Categorised Information Fusion Model (CIFM) to minimise the replication in handling the aggregation of multiple instances of sensor data by categorising the information. The proposed CIFM employs federated learning for distributed verification of different aggregation time frames. This learning identifies the replication of sensor data from different sensors based on time frames and aggregation instances. In the multi-timed aggregation level, the sensed information's interfering distribution is diminished by controlling the relaying instance. The recurrent learning instances diminish the multi-timed information based on the occurrence factor. This recurrent learning improves delivery precision by controlling computation complexity and fusion time. The CIFM model also reduces the communication overhead by reducing the amount of replicated information that needs to be transmitted and processed and providing a consistent delivery ratio. The use of federated learning also allows for privacy-preserving data processing as the raw data is only processed locally, and aggregated results are shared. The performance of the proposed CIFM model is evaluated through simulations and compared with other existing fusion methods. The results show that the proposed model outperforms existing accuracy and communication efficiency methods. This approach helps to simplify the process of aggregating and relaying the data in the WSN to improve the performance and efficiency of IoT services. The goal of CIFM is to provide a more effective way to process and utilise the information from the sensors to deliver accurate and reliable data to the IoT platform. The CIFM improves the fusion rate by 7.77 % and minimize the fusion time up to 13.03 % compared to other methods.

Communication Energy Optimization of UAV-assisted WSN Data Transmission

Unmanned aerial vehicle (UAV)-assisted wireless sensor network (WSN) data transmission is a hot topic of research in recent years. This article mainly focuses on the energy consumption optimization of WSN data transmission with the assistance of UAVs. First, we established a communication coverage model for UAV-WSN and derived the energy consumption of data transmission for UAV-WSN, determining the optimal number of cluster heads (CHs) under different UAV flight heights. Second, we built a model for the energy consumption of WSN data transmission and the UAV flight height, and determined the optimal flight height of the UAV, while also dividing the UAV task area into segments. Then, we proposed a model that dynamically optimized the UAV data collection position according to the CH location, which can further reduce the energy consumption of CHs during data transmission. Finally, we used simulation experiments to verify the feasibility and effectiveness of our proposed scheme.

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 conserving cost selection for fine-grained computational offloading in mobile edge computing networks

In mobile edge computing networks, total or partial computational offloading of delay sensitive and compute intensive applications to the nearby edge servers is a promising solution to reduce transmission delay and energy consumption of mobile nodes (MNs). However, in computational offloading of an application to edge server, considerations of energy consumption and time delay are not sufficient for the decision of minimum cost of application. In this article, a novel battery dependent cost selection strategy for computational offloading of an application capable for sequential execution of dependent sub tasks is proposed. The strategy considers current battery level of MN in the cost function for optimal selection of offloading policy based on the amount of data, computational resources, and radio resources of the server. Our comprehensive cost function takes into account all the said parameters, calculates all the costs for possible offloading policies, and chooses the policy with minimum cost from a huge dataset. The cost selection is energy efficient for low battery level and prioritizes faster execution for full battery level of MNs. A deep neural network is trained on the generated dataset to minimize the overhead of computations. Simulation results reveal that our proposed strategy outperforms the benchmark strategies in terms of deep neural network accuracy for different sizes of dataset, energy consumption, time delay, and cost for different sizes of applications.

Computing Offloading Based on TD3 Algorithm in Cache-Assisted Vehicular NOMA-MEC Networks.

In this paper, in order to reduce the energy consumption and time of data transmission, the non-orthogonal multiple access (NOMA) and mobile edge caching technologies are jointly considered in mobile edge computing (MEC) networks. As for the cache-assisted vehicular NOMA-MEC networks, a problem of minimizing the energy consumed by vehicles (mobile devices, MDs) is formulated under time and resource constraints, which jointly optimize the computing resource allocation, subchannel selection, device association, offloading and caching decisions. To solve the formulated problem, we develop an effective joint computation offloading and task-caching algorithm based on the twin-delayed deep deterministic policy gradient (TD3) algorithm. Such a TD3-based offloading (TD3O) algorithm includes a designed action transformation (AT) algorithm used for transforming continuous action space into a discrete one. In addition, to solve the formulated problem in a non-iterative manner, an effective heuristic algorithm (HA) is also designed. As for the designed algorithms, we provide some detailed analyses of computation complexity and convergence, and give some meaningful insights through simulation. Simulation results show that the TD3O algorithm could achieve lower local energy consumption than several benchmark algorithms, and HA could achieve lower consumption than the completely offloading algorithm and local execution algorithm.

An energy-saving joint resource allocation approach for mobile edge computing based on NOMA

Mobile edge computing can use the wireless access network to provide the services required by telecom users and cloud computing functions nearby, thereby creating a service environment with high performance, low latency and high bandwidth. Computational task offloading is a core issue in mobile edge computing. In order to improve the security when computing tasks are partially offloaded in non-orthogonal multiple access-based mobile edge computing systems, this paper studies the physical layer security of MEC networks considering the presence of eavesdroppers. We employ the secrecy outage probability to measure the secrecy performance of computation offloading. Under the premise of comprehensively considering the joint constraints of transmit power, local task calculation and confidentiality outage probability, we introduce the energy consumption weight factor to balance the transmission energy consumption and calculation energy consumption, and finally achieve the minimum weighted sum of system energy consumption. On the premise of satisfying two user priorities, in order to reduce system overhead, we design a joint task offloading and resource allocation mechanism. The mechanism uses binary search-based iterative optimization algorithm to seek the optimal solution after problem transformation, and obtain optimal task offloading and power allocation. A large number of simulation results show that the algorithm proposed in this paper can effectively reduce system energy consumption.