Member Nodes Research Articles

Group acknowledgement mechanism for beacon-enabled wireless sensor networks

Wireless sensor networks are network of a large number of sensor nodes used for remote applications. To the expressive growth in remote sensor networks, medium access control (MAC) works faster in wireless sensor networks. The MAC protocol has to be appropriately designed to ensure the proper operation of the sensor nodes in WSNs. This paper provides energy saving by proposing group acknowledgement MAC (GACK-MAC) for an aggregated data frame transmitted to a single destination, and reducing the overhead in IEEE 802.15.4 beacon enabled wireless sensor networks. In our proposed scheme, type of acknowledgement of each sensor node is decided by the coordinator based on traffic load of the network or number of packets present in the sensor node. We performed this by adopting the star and peer-to-peer tree topologies with a coordinator having coordination with all other member nodes. Simulations are conducted, and compared with the analytical models constructed to measure the throughput, and delay metrics. Moreover, our method enhances the battery lifetime by 35% on average, throughput of 42% at maximum, and decrease the delay by 33% for each packet in network of star topology and twice the above mentioned values for battery lifetime, throughput and reduces the delay by 49% in P2P topology of wireless sensor networks.

Data Collection Strategy Based on OSELM and Gray Wolf Optimization Algorithm for Wireless Sensor Networks.

In order to effectively reduce the energy consumption, improve the efficiency of data collection in HWSNs, and prolong the lifetime of the overall network, the clustering method is one of the most effective methods in the data collection methods for HWSNs. The data collection strategy of HWSNs based on the clustering method mainly includes three stages: (1) selecting the appropriate cluster head, (2) forming between clusters, and (3) transferring data between clusters. Among them, the selection of the cluster heads in the first stage. The optimal number of cluster heads in the formation of clusters in the second stage is the core and key to the clustering data collection of HWSNs. In the stage of cluster head selection, a data collection strategy for HWSNs based on the clustering method is proposed. Sink establishes an extreme learning machine neural network model. The cluster member nodes select cluster heads based on the remaining energy of the sensor node, the number of the neighbor node, and the distance to the sink. The best cluster head node is selected through the adaptive learning of the online sequence extreme learning machine. Through comprehensive consideration of various factors to complete the clustering process, the gray wolf algorithm is used to optimize the number of clusters, balance the effect of clustering, and improve the efficiency of data collection while reducing energy consumption. An energy efficient and reliable clustering data collection strategy for HWSNs based on the online sequence extreme learning machine and the gray wolf optimization algorithm is proposed in this paper. The simulation results show that the proposed algorithm not only significantly improves the efficiency of the data collection and reduces energy consumption but also comprehensively improves the reliability of the network and prolongs the network's lifetime.

A Lightweight Authentication Framework for Wireless Sensor Networks

Wireless Sensor Network (WSN) is emerging as a dominant technology with its applications in areas like agriculture, communication, environment monitoring, and surveillance. The inherited vulnerability and resource-constrained nature of sensor nodes led researchers to propose many lightweight cryptographic protocols for WSN. These sensors are low-cost, low energy, have low processing capability and have low storage restrictions. WSN suffers from many risks because of these unique constraints. This paper proposes a new lightweight security framework for WSNs and covers different lightweight cryptographic schemes for WSN applications. The aim is to provide cryptographic primitives for integrity, confidentiality, and protection from the man-in-the-middle and reply attacks. The work is based solely on symmetric cryptography and it has four phases; Network Initialization, Node Initialization, Nodes Communication, and Node Authentication. This work adopts the Low-Energy Adaptive Clustering Hierarchy (LEACH) framework, which deploys random rotation to distribute the energy among a group of nodes. The probability of attacking in LEACH is higher at cluster head and member nodes. Therefore, data transmission among communicated nodes is encrypted over multiple levels of protection by dynamic session keys to provide a high level of security. In addition, an authentication ticket is provided by a cluster head for each authenticated node to identify another node. The session keys are dynamically generated and updated during the communication to prevent compromising or capturing the keys. Through simulation and evaluation of the system, the results showed less energy consumption and efficient cryptographic primitive were compared with existing schemes

An Empirical Study of Soft Computing Approaches in Wireless Sensor Networks

The optimal CH selection for finding the shortest path among the CHs is improved by developing the hybrid K-means with Particle Swarm Optimization (PSO) based hybrid Ad-hoc On-demand Distance Vector (AODV) channelling algorithms. The alive nodes, total packet sending time, throughput, and NL are increased using this hybrid technique, whereas dead nodes and EC are minimized in the network. The proposed algorithm utilizes a rotational method of utilization of cluster head (CH) to ensure that all member nodes are utilized uniformly based on the incoming traffic. The proposed algorithm has been implemented, experimented with, and compared in performance with LEACH, DLBA and GLBA algorithms. The proposed hybrid approach outperforms the existing techniques regarding average energy consumption and load distribution.

Influential groups for seeding and sustaining nonlinear contagion in heterogeneous hypergraphs

Contagion phenomena are often the results of multibody interactions—such as superspreading events or social reinforcement—describable as hypergraphs. We develop an approximate master equation framework to study contagions on hypergraphs with a heterogeneous structure in terms of group size (hyperedge cardinality) and of node membership (hyperdegree). By mapping multibody interactions to nonlinear infection rates, we demonstrate the influence of large groups in two ways. First, we characterize the phase transition, which can be continuous or discontinuous with a bistable regime. Our analytical expressions for the critical and tricritical points highlight the influence of the first three moments of the membership distribution. We also show that heterogeneous group sizes and nonlinear contagion promote a mesoscopic localization regime where contagion is sustained by the largest groups, thereby inhibiting bistability. Second, we formulate an optimal seeding problem for hypergraph contagion and compare two strategies: allocating seeds according to node or group properties. We find that, when the contagion is sufficiently nonlinear, groups are more effective seeds than individual hubs.

Design of efficient location‐based multipath self‐adaptive balancer router using particle swarm optimization in wireless sensor network

SummaryThe power sensor terminals control in wireless sensor network (WSN) communicate within a small number of its radio range. As a branch operating based on the cluster routing protocol, it has proven to be effective in network topology management, energy reduction, and path connection link quality of the path. Location‐based clusters are categorized into member nodes and group/cluster heads (CHs). The CH election process increases the overhead of the network. The processing and energy limitations of the nodes are considered for the CH election process. The allocation of system resources is the major issue in the area of networks. In this work, the proposed multipath self‐adaptive balancer routing‐based particle swarm optimization (MLBAR‐PSO) achieves load balancing by means of the link quality of the route and the calculated average latency and reliability of the path. In this way, different trust values are set in each category. The forwarding node value is the value of each node based on the weight value, which is calculated based on the energy ratio of the node and the distance between the hiding node and the target node. Particle swarm optimization (PSO) selects the head cluster from a specific location within the cluster to enable other nodes to interact with the CH without spending energy. Also, the algorithm is used to find the optimal path for the base station and significantly increases the energy to reduce the distance and delay over the lifetime of the network.

An Energy-Efficient Data Aggregation Mechanism for IoT Secured by Blockchain

The Internet of Things (IoT) is getting important and interconnected technologies of the world, consisting of sensor devices. The internet is smoothly changing from an internet of people towards an Internet of Things, which permits various objects to connect to another wirelessly. The energy consumption of the IoT routing protocol can affect the network life span. In addition, the high volume of data produced by IoT will result in transmission collision, security issues, and energy dissipation due to increased data redundancy because tiny sensors are usually hard to recharge after they are deployed. Generally, to save energy, data aggregation reduces data redundancy at each node by turning some nodes into sleep mode and others into wake mode. Therefore, it is important to group the nodes with high data similarity using the fuzzy matrix. Then, the data received from the member nodes at the Cluster Head (CH) are analyzed using a fuzzy similarity matrix for clustering. In the next step, after clustering, some nodes are chosen from all groups as redundant nodes. The sleep scheduling mechanism is then applied to reduce data redundancy, network traffic jamming, and transmission costs. We have proposed an Energy-Efficient Data Aggregation Mechanism (EEDAM) secured by blockchain, which uses a data aggregation mechanism at the cluster level to save energy. As edge computing is used to provide on-demand trusted services to IoT with minimum delay, blockchain is integrated inside a cloud server, so the edge is validated by the blockchain to provide secure services to IoT. Finally, we performed simulations to calculate the performance of the proposed mechanism and compared it with the conventional energy-efficient algorithms. The simulation results show that the proposed structural design can successfully reduce the amount of data, provide proper security to the IoT, and extend the wireless sensor network (WSN).

Potential Energy Efficient Data Fusion Model for Wireless Sensor Networks

Wireless sensor networks have made a significant contribution to wireless sensor communication system based on resource constraints and limited computational sensors. Over the last decade, several focused research efforts have been made to investigate and provide solutions to problems relating to the energy efficiency data fusion aggregation in Wireless sensor networks. However, the problem of designing routes that are energy efficient has not been resolved. It is rather a tough effort to guarantee that the lifespan of a sensor is prolonged for a longer period because of the restricted computational capabilities of sensors, which are often coupled with energy constraints. The findings of this work present an enhanced energy-efficient technique for communication in sensor networks which consists of three distinct innovative frameworks. The suggested framework known as Data Fusion with Potential Energy Efficiency (DFWPEE) is responsible for the optimization of energy. The proposed work reduces energy consumption by using probabilistic methods and clustering. During the data fusion process, the Multiple Zone Data Fusion (MZDF) architecture uses a globular topology that helps with load balancing. The strategy presents an innovative routing approach that is used to aid in the performance of energy efficient routing in large-scale wireless sensor networks. By introducing the idea of routing agents, the framework for the Tree-Based Fusion Technique (TBFT), as suggested, comes up with an innovative method for dynamic reconfiguration. The plan enables the system to determine which sensor has a higher rate of energy dissipation and then immediately transfers the job of data fusion to a node that is more energy efficient. This threshold-based technique enables a sensor to perform both the role of a cluster head and the function of a member node. The node behaves as a cluster head until it achieves its threshold remnant energy and functions as a member node after it passes the threshold residual energy. Both of these roles may be played simultaneously. The mathematical modeling was done using the conventional radio energy model which improved the dependability of attained results. The proposed system delivers enhanced energy efficient communication performance when measured against existing implemented standards for energy efficient schemes. The enhanced technique uses nearly half as much energy as LEACH while focusing on reducing the overall time taken for the process to complete leading to enhanced performance.

Energy Aware Routing Protocol with Data Fusion and Machine Learning

The current wireless and communication system may be attributed to the contributions made by the Sensor Network in a significant measure. During the last decade, several efforts have been performed to examine and propose answers to challenges about the energy efficiency of wireless sensor network communications. Several different researchers has done these efforts. The challenge of constructing economical energy-use paths has not yet been overcome. Because sensors have limited computational capabilities, which are frequently coupled with energy limitations, it is rather difficult to guarantee that a sensor’s lifespan will be longer. This is because of the energy constraints often associated with these limitations. The results of this research have led to the development of a one-of-a-kind communication system for sensor networks that is not only environmentally friendly but also supported by three distinct revolutionary frameworks. The framework that has been recommended, which goes by the name Potential Energy Efficient Data Fusion (PEE-DF), is the one that is in charge of the optimization of energy. It achieves this with the aid of probabilistic approaches and clustering. The K-SOM (Korhonen self-organizing map) framework was designed using a globular topology, which aids load balancing during data fusion. K-SOM stands for Korhonen self-organizing map. This was done to ensure we got the most out of our resources. A novel method to routing is presented by the technique, which has the potential to be used to assist in the operation of energy-efficient routing in large-scale wireless sensor networks. The framework for the Tree-Based Fusion Technique (TBFT), which has been offered, comes up with a new way for dynamic reconfiguration. This is accomplished via the introduction of the concept of routing agents. The strategy enables the system to recognise which sensor has a greater energy dissipation rate and then instantly moves data fusion work to a more energy-efficient node. This allows the system to save energy. This approach, based on thresholds, enable a sensor to act as a cluster head up until it reaches its threshold remnant energy and then as a member node once it exceeds threshold residual energy. In other words, it may play both roles simultaneously. It is possible to fulfil both of these responsibilities at the same time. The findings have been mathematically modelled using a standard radio-energy model, which has enhanced the robustness of the findings, which is highly positive. The results were encouraging because of the increased robustness of the findings. Compared to the benchmark previously established for energy-efficient strategies, the proposed system demonstrates higher performance in terms of its ability to communicate while using less energy. In contrast to LEACH, the recommended system's findings reveal an almost fifty percent decrease in energy consumption, and at the same time, a reduction in the amount of time required to carry out the operation..

Echo Location Based Bat Algorithm for Energy Efficient WSN Routing

Due to the wide range of applications, Wireless Sensor Networks (WSN) are increased in day to day life and becomes popular. WSN has marked its importance in both practical and research domains. Energy is the most significant resource, the important challenge in WSN is to extend its lifetime. The energy reduction is a key to extend the network's lifetime. Clustering of sensor nodes is one of the well-known and proved methods for achieving scalable and energy conserving WSN. In this paper, an energy efficient protocol is proposed using metaheuristic Echo location-based BAT algorithm (ECHO-BAT). ECHO-BAT works in two stages. First Stage clusters the sensor nodes and identifies tentative Cluster Head (CH) along with the entropy value using BAT algorithm. The second stage aims to find the nodes if any, with high residual energy within each cluster. CHs will be replaced by the member node with high residual energy with an objective to choose the CH with high energy to prolong the network's lifetime. The performance of the proposed work is compared with Low-Energy Adaptive Clustering Hierarchy (LEACH), Power-Efficient Zoning Clustering Algorithm (PEZCA) and Chaotic Firefly Algorithm CH (CFACH) in terms of lifetime of network, death of first nodes, death of 125th node, death of the last node, network throughput and execution time. Simulation results show that ECHO-BAT outperforms the other methods in all the considered measures. The overall delivery ratio has also significantly optimized and improved by approximately 8%, proving the proposed approach to be an energy efficient WSN.

ETM-IoT: Energy-Aware Threshold Model for Heterogeneous Communication in the Internet of Things

The internet of things (IoT) has a wide variety of applications, which in turn raises many challenging issues. IoT technology enables devices to closely monitor their environment, providing context-aware intelligence based on the real-time data collected by their sensor nodes. The IoT not only controls these devices but also monitors their user's behaviour. One of the major issues related to IoT is the need for an energy-efficient communication protocol which uses the heterogeneity and diversity of the objects connected through the internet. Minimizing energy consumption is a requirement for energy-constrained nodes and outsourced nodes. The IoT nodes deployed in different geographical regions typically have different energy levels. This paper focuses on creating an energy-efficient protocol for IoT which can deal with the clustering of nodes and the cluster head selection process. An energy threshold model is developed to select the appropriate cluster heads and also to ensure uniform distribution of energy to those heads and member nodes. The proposed model envisages an IoT network with three different types of nodes, described here as advanced, intermediate and normal nodes. Normal nodes are first-level nodes, which have the lowest energy use; intermediate nodes are second-level nodes, which have a medium energy requirement; and the advanced class are third-level nodes with the highest energy use. The simulation results demonstrate that the proposed algorithm outperforms other existing algorithms. In tests, it shows a 26% improvement in network lifetime compared with existing algorithms.

Combination of improved Harris’s hawk optimization with fuzzy to improve clustering in wireless sensor network

A Wireless Sensor Network (WSN) is divided into groups of sensor nodes for efficient transmission of data from the point of measuring to sink. By performing clustering, the network remains energy-efficient and stable. An intelligent mechanism is needed to cluster the sensors and find an organizer node, the cluster head. The organizer node assembles data from its constituent nodes called member nodes, finds an optimal route to the sink of the network, and transfers the same. The nomination of cluster head is crucial since energy utilization is a major challenge of sensor nodes deployed over a hostile environment. In this paper, a fuzzy-based Improved Harris’s Hawk Optimization Algorithm (IHHO) is proposed to select an able cluster head for data communication. The fuzzy inference model ponders balance energy, distance from self to sink node, and vicinity of nodes from cluster head as input factors and decides if a candidate node is eligible for becoming a cluster head. The IHHO tunes the logic into an energy-efficient network with less complexity and more ease. The novelty of the paper lies in applying the hawk-pack technique based on fuzzy rules. Simulations show that the combination of Fuzzy based IHHO reduces the death of nodes through which network lifetime is enhanced.

SECURE AND ENERGY-AWARE ROUTING FOR RELIABLE DATA TRANSMISSION IN MOBILE AD HOC NETWORKS

Mobile Ad-Hoc Network (MANET) is a generic technology utilized in various real-time applications. The MANET is a self-configuring and self-directed system that quickly deploys in a network. Data transfer is a big issue in MANET due to poor wireless medium and lack of data security and reliability. The prior system established a mobile ad hoc content distribution strategy. The ideal Cluster Head (CH) is chosen using the Teaching Learning Based Optimization (TLBO) approach. However, due to these features, it is more vulnerable to attacks. In addition, it impacts packet delivery and throughput. The proposed system designed a Secure and Energy Aware Routing (SEAR) mechanism to improve MANET data transmission reliability and security. Initially, nodes are clustered by distance. Then the Weight-based Artificial Fish Swarm Algorithm (WAFSA) is used to pick the optimum Cluster Head (CH). Mobility, remaining energy, and connectivity are considered objective functions for CH selection. Securing communication between these nodes will require proper cryptography. The security mechanism generates secret keys using Advanced Encryption Standard (AES) to encrypt or decode messages delivered between member nodes. This security approach enables secure communication between member nodes and CHs, uses the NS-2 analytical platform, and outperforms the existing system regarding average energy drop, network lifetime, and encryption time.

Community detection on mixture multilayer networks via regularized tensor decomposition

We study the problem of community detection in multilayer networks, where pairs of nodes can be related in multiple modalities. We introduce a general framework, that is, mixture multilayer stochastic block model (MMSBM), which includes many earlier models as special cases. We propose a tensor-based algorithm (TWIST) to reveal both global/local memberships of nodes, and memberships of layers. We show that the TWIST procedure can accurately detect the communities with small misclassification error as the number of nodes and/or number of layers increases. Numerical studies confirm our theoretical findings. To our best knowledge, this is the first systematic study on the mixture multilayer networks using tensor decomposition. The method is applied to two real datasets: worldwide trading networks and malaria parasite genes networks, yielding new and interesting findings.

Lifetime Maximization of Heterogeneous WSN Using Fuzzy-based Clustering

Background: Wireless Sensor Network (WSN) is an arising field for research and development. It has various applications ranging from environmental monitoring to battlefield surveillance and more. WSN is a collection of multiple sensor nodes used for sensing the environment. But these sensing nodes are deployed in such areas where it is not that easy to reach, so battery used in these nodes becomes quite impossible to change, so we need to utilize this energy to get the maximum sensing for a long time. Objective: To use the Fuzzy approach in the clustering algorithm. Clustering is a key approach to prolong the network lifetime with minimum energy utilization. In this paper, our main concern is on the Cluster Head (CH) selection. So, we are proposing a clustering algorithm which is based on some of the attributes: Average Residual Energy of CHs, Average Distance from nodes to CHs, Standard Deviation of member nodes, and Average Distance from CH to Base Station(BS). Methods: Initially, some of the nodes are found having greater residual energy than the average network energy, and fifteen populations are made each having an optimum number of CHs. The final and best CHs set is chosen by determining the maximum fitness value using a fuzzy approach. Result: The result positively supports the energy-efficient utilization with lifetime maximization, which is compared with the Base algorithm [1] and LEACH [2] protocol based on residual energy and the number of nodes that die after performing some rounds. Conclusion: The proposed algorithm determines a fuzzy-based fitness value, provides load-balancing among all the networking nodes, and performs a selection of best Cluster Heads, resulting in prolonged network lifetime and maximized efficiency.

Community membership consistency applied to corporate board interlock networks

Community detection is a well-established method for studying the meso-scale structure of social networks. Applying a community detection algorithm results in a division of a network into communities that is often used to inspect and reason about community membership of specific nodes. This micro-level interpretation step of community structure is a crucial step in typical social science research. However, the methodological caveat in this step is that virtually all modern community detection methods are non-deterministic and based on randomization and approximated results. This needs to be explicitly taken into consideration when reasoning about community membership of individual nodes. To do so, we propose a metric of community membership consistency, that provides node-level insights in how reliable the placement of that node into a community really is. In addition, it enables us to distinguish the community core members of a community. The usefulness of the proposed metrics is demonstrated on corporate board interlock networks, in which weighted links represent shared senior level directors between firms. Results suggest that the community structure of global business groups is centered around persistent communities consisting of core countries tied by geographical and cultural proximity. In addition, we identify fringe countries that appear to associate with a number of different global business communities.

An Enhanced Clustering Method for Extending Sensing Lifetime of Wireless Sensor Network

Extending the lifetime of a wireless sensor network is vital in ensuring continuous monitoring functions in a target environment. Many techniques have appeared that seek to achieve such prolonged sensing gains. Clustering and improved selection of cluster heads play essential roles in the performance of sensor network functions. Cluster head in a hierarchical arrangement is responsible for transmitting aggregated data from member nodes to a base station for further user-specific data processing and analysis. Minimising the quick dissipation of cluster heads energy requires a careful choice of network factors when selecting a cluster head to prolong the lifetime of a wireless sensor network. In this work, we propose a multi-criteria cluster head selection technique to extend the sensing lifetime of a heterogeneous wireless sensor network. The proposed protocol incorporates residual energy, distance, and node density in selecting a cluster head. Each factor is assigned a weight using the Rank Order Centroid based on its relative importance. Several simulation tests using MATLAB 7.5.0 (R2007b) reveal improved network lifetime and other network performance indicators, including stability and throughput, compared with popular protocols such as LEACH and the SEP. The proposed scheme will be beneficial in applications requiring reliable and stable data sensing and transmission functions.

Global and local community memberships for estimating spreading capability of nodes in social networks

The analysis of spreading processes within complex networks can offer many important insights for the application in contexts such as epidemics, information dissemination or rumours. Particularly, structural factors of the network which either contribute or hinder the spreading are of interest, as they can be used to control or predict such processes. In social networks, the community structure is especially relevant, as actors usually participate in different densely connected social groups which emerge from various contexts, potentially allowing them to inject the spreading process into many different communities quickly. This paper extends our recent findings on the community membership of nodes and how it can be used to predict their individual spreading capability (Krukowski and Hecking, in: Benito, Cherifi, Cherifi, Moro, Rocha, Sales-Pardo (eds) Complex networks & their applications IX. Springer, Cham, pp 408–419, 2021) by further evaluating it on additional networks (both real-world networks and artificially generated networks), while additionally introducing a new local measure to identify influential spreaders that—in contrast to most other measures, does not rely on knowledge of the global network structure. The results confirm our recent findings, showing that the community membership of nodes can be used as a predictor for their spreading capability, while also showing that especially the local measure proves to be a good predictor, effectively outperforming the global measure in many cases. The results are discussed with regard to real-world use cases, where knowledge of the global structure is often not given, yet a prediction regarding the spreading capability highly desired (e.g., contact-tracing apps).

Energy Efficient Cluster Head Selection in Internet of Things Using Minimum Spanning Tree (EEMST)

ABSTRACT Internet of things network lifetime and energy issues are some of the most important challenges in today’s smart world. Clustering would be an effective solution to this, as all nodes would be arranged into virtual clusters, while one node will serve as the cluster head. The right selection of the cluster head will reduce energy consumption dramatically. This concept is more crucial for the internet of things, which is being widely distributed in environments such as forests or the smart agriculture sector. In this paper, an Energy Efficient Minimum Spanning Tree algorithm (EEMST) is presented to select the optimal cluster head and data routing based on graph theory for a multihop Internet of Things. This algorithm calculates the Euclidean distance-based minimum spanning tree based on a weighted graph. As a result, we use a weighted minimum spanning tree to choose the optimal cluster head and accordingly determine the shortest path for data transmission between member nodes and the cluster head. The proposed EEMST algorithm provides the possibility of intracluster multihop routing and also the possibility of intercluster single-hop routing. The simulated experimental results approve a significant improvement of the proposed algorithm in the IoT systems’ lifetime compared to the baselines.

Energy efficient data fusion algorithm base on clustered wireless sensor networks

The energy of wireless sensor network is limited and the network is vulnerable to the environment. To improve the energy efficiency and data reliability of wireless sensor networks, an energy efficient data fusion algorithm based on clustered wireless sensor networks (EEDFA) is proposed. EEDFA runs in the clustered network, in which the process of data collection and transmission is divided into several periods. Sensors collect data in time slots of the period and form a data vector whose dimension will be reduced to reduce the transmission load. At the same time, the interval type-2 fuzzy system is used to generate data reliability factors considering environmental interference to ensure the reliability of data. Each cluster head receives and distributed processing the data from their member nodes. After removing redundant data by the similarity function, the fused data is sent to the next hop cluster head or base station. Simulation results verify that EEDFA has lower bandwidth occupancy, lower energy consumption, and higher data accuracy than PFF, REDA, and SCDRE.