Data Aggregation Nodes Research Articles

Topological information embedded convolutional neural network-based lotus effect optimization for path improvisation of the mobile anchors in wireless sensor networks

ABSTRACT Wireless sensor networks (WSNs) rely on mobile anchor nodes (MANs) for network connectivity, data aggregation, and location information. However, MANs’ mobility can disrupt energy consumption and network performance. Effective path improvisation algorithms are needed for MANs to optimize energy use, reduce data loss, and maintain network connectivity in dynamic WSN environments. To overcome these issues, Topological Information Embedded Convolutional Neural Network based Lotus Effect Optimization for Path Improvisation of the Mobile Anchors in Wireless Sensor Networks (TIECNN-PIMA-OAC-WSN) was proposed. The approach establishes a robust network setup and energy model, employing TIECNN for initial cluster formation and cluster head selection. The chosen cluster head, termed the Mobile Anchor, undergoes optimization using the Lotus effect optimization algorithm to determine the most efficient and shortest path. This work enhances both the topological information processing and energy efficiency of mobile anchor paths. The simulation outcomes prove the proposed technique attains 33.12%, 39.56%, and 42% higher network lifespan for sensor nodes density 40; 38.22%, 29.66%, and 41.33% higher network lifespan for sensor nodes density 60; 37.45%, 35.55%, and 43.67% higher network lifespan for sensor nodes density 80; 32.45%, 29.45%, and 46.66% higher network lifespan for sensor nodes density 100 analysed to the existing methods.

Hierarchical Energy Harvesting Aware Adaptive Fuzzy Routing with Data Compression for Energy Harvesting WSN

In present scenario of the world moving towards the smart systems. Smart systems and applications are developed using sensor nodes for data collection, data aggregation and decision making process. Wireless sensor nodes are configured in the physical environment for communications between the devices and the user. The number of limitations affecting the performance of wireless sensor based application. The various factors affecting the performance of the WSN based applications, one the major factor is energy efficiency of the sensors. Sensor nodes are battery powered devices and the sensors are dropping their energy during the transmissions. So we need a solution to overcome the energy efficiency issue of WSN based applications. In this paper, we proposed a methodology called Hierarchical energy harvesting adaptive fuzzy routing algorithm with uniform data quality compression to manage the energy efficiency of the sensor nodes. It uses a new energy management framework and allocating the energy budget to sensors and reduces the consumption of each node. The existing energy harvesting approaches only concentrate on find the best path and forward the data to base station. The performance evaluation shows the effective use of proposed method in WSN based applications.

Clustering, Routing, Scheduling, and Challenges in Bio-Inspired Parameter Tuning of Vehicular Ad Hoc Networks for Environmental Sustainability

Vehicular ad hoc networks (VANETs) are wireless networks of automotive nodes. Among the strategies used in VANETs to increase network connectivity are broadcast scheduling, data aggregation, and vehicular node clustering. In the context of extremely high node mobility and ambiguous vehicle distribution (on the road), VANETs degrade in flexibility and quick topology, facing significant issues such as network physical layout construction and unstable connections. These challenges make it difficult for vehicle communication to be robust, reliable, and scalable, especially in urban traffic networks. Numerous research investigations have revealed a nearly optimal solution to various VANET difficulties through the application of techniques derived from nature and evolution. On the other hand, as key productivity sectors continue to demand more energy, sustainable and efficient ways of using non-renewable resources continue to be developed. With the help of information and communication technologies (ICT), parameter tuning approaches can reduce accident rates, improve mobility, and mitigate environmental impacts. In this article, we explore evolutionary algorithms to mobile ad hoc networks (MANETs), as well as vehicular ad hoc networks (VANETs). A discussion of three major categories of optimization is provided throughout the paper. There are several significant research works presented regarding parameter tuning in cluster formation, routing, and scheduling of broadcasts. Toward the end of the review, key challenges in VANET and MANET research are identified.

Privacy-Preserving Data Aggregation Scheme Based on Federated Learning for IIoT

The extensive application of the Internet of Things in the industrial field has formed the industrial Internet of Things (IIoT). By analyzing and training data from the industrial Internet of Things, intelligent manufacturing can be realized. Due to privacy concerns, the industrial data of various institutions cannot be shared, which forms data islands. To address this challenge, we propose a privacy-preserving data aggregation federated learning (PPDAFL) scheme for the IIoT. In federated learning, data aggregation is adopted to protect model changes and provide data security for industrial devices. By utilizing a practical Byzantine fault tolerance (PBFT) algorithm, each round selects an IIoT device from each aggregation area as the data aggregation and initialization node, and uses data aggregation to protect the model changes of a single user while resisting reverse analysis attacks from the industrial management center. The Paillier cryptosystem and secret sharing are combined to realize data security, fault tolerance, and data sharing. A security analysis and performance evaluation show that the scheme reduces computation and communication overheads while guaranteeing data privacy, message authenticity, and integrity.

A Firebug Optimal Cluster based Data Aggregation for Healthcare Application

Majority of Wireless Sensor Networks (WSNs) are made up of huge sensor nodes with minimum cost, processing, communication capability and limited sensing. Because sensor nodes have limited resources, it’s critical to enhance bandwidth utilization, network lifetime and minimize the data transmission. The term data aggregation defines the method of integrating the sensor data to minimize the data transmission. However, WSN-based healthcare applications face privacy, and security concerns. In this paper design of a Fire Bug Swarm Optimal Cluster-Based Data Aggregation (FSOCDA) in WSN has been presented which is a useful method for WSN to save energy and extend network lifetime. The efficient clustering is carried out using FSO, which is utilized to choose the CH from among various nodes in the cluster that serve as data aggregation nodes. By altering the network scale, the computational competence of the FSOCDA technique is compared to that of the non-aggregate approach. Further, the data aggregation method is helpful in healthcare applications in terms of data transferring from host hospital to other.

Walking Position Data in Football Training Based on Embedded Action Recognition System

With the development of image recognition and pattern recognition, action recognition has become a hot research direction in the field of computer actions. Because the embedded platform has the advantages of small size and low power consumption, it is a good choice to use the embedded platform for action recognition. These sensor data are used to overcome the drift error of the gyroscope when the positioning data are collected, and the accuracy and reliability of the data are improved. The system uses two data forwarding nodes to aggregate the data of each sensor and then transmit it to the host computer. The data aggregation node can communicate and upload data by means of Bluetooth or wire. In order to deeply study the relevance of the embedded action recognition system in collecting football training positioning data, this paper uses simulation model establishment method, data collection method, and theory and practice combination method to collect samples, analyzes the embedded action recognition system, and streamlines the algorithm. Using computer technology as a support when building a simulation model, the first thing you need to have is a certain computer technology because when building a simulation model, not all objects can be modeled. At this time, only computer technology can be used as a support to build a simulation model of some objects and create an action recognition system that can record the position data in football training. After establishing the human body simulation recognition model, use MATLAB to extract the K bone information. When collecting, the human body is facing the K device, the number of acquisition frames is 25, 55, 75, 95, 105, and 205, and each frame number is collected 10 times. Take the average. The delay time is 4s. The result shows that the 20 key bone point outputs by K come from the RGB camera on the same side. Further study the actual utility of the compensated model in the presence of occlusion. They are worn on the middle of the thigh and calf, respectively. In a sensor, the measured value of thigh length L1 is 0.6 m, and the value of calf length L2 is 0.4 m. Take the right knee as an example. When the leg is raised, the b-axis coordinate increases by 2%, and the c-axis coordinate decreases by 1.8%. When the leg is lowered, the opposite is true. It can be seen that the compensated coordinate is consistent with the action. It is basically realized that starting from the embedded action recognition system, a model that can support the analysis of football positioning data is designed.

An Enhanced Routing and Lifetime Performance for Industrial Wireless Sensor Networks

Industrial Wireless Sensor Networks (IWSNs), especially energy resources, are scarce. Since sensor nodes are usually very dense, and the data sampled by the sensor nodes have high redundancy, data aggregation saves energy, reduces the number of transmissions, and eliminates redundancy. Many applications can be used in IIWSNs, and a new technique is introduced to detect multiple sensors embedded in different sensor nodes. Packets created by different applications have different properties. Sensors are resource-constrained devices because it is necessary to find effective reaction analysis methods and transfer sensed data to base stations. Since sensors are resource-constrained devices, efficient topologies require data distribution from base stations for management, aggregation, and cloud services in the business tier. To propose a Distributed time series Convergence Routing Protocol (DTSCR) is introduced to detect the correct relay node from the source sensors and transmit the data with the data aggregation node help of the eDCP (Edge Data Collection Protocol). The node will send data to the target source only when those in the process transmit the data, and the cutting edge of the terminal generates a trusted merger. This method can provide a reliable and efficient routing path scheme for information collection and dissemination with maximized packets exchanged between nodes and their Neighbor. Recognizes a specific set of sensor network applications to be flexible to this measurable range. We are also improving the DTSR roaming with both network size and node density.

Optimization of Water Microbial Concentration Monitoring System Based on Internet of Things

The Internet of Things is an emerging information industry. Applying the information collection, transmission, and processing technologies in the Internet of Things technology to environmental monitoring, environmental emergency, and other environmental protection supervision fields will greatly improve the speed and accuracy of environmental supervision and facilitate the scientific development of environmental protection. Through the Internet of Things, people can obtain a large amount of reliable real-time information, and it is not easy to be affected by time, place, and environment, while the wireless sensor network has the advantages of easy installation and low cost, so environmental monitoring through the Internet of Things is the future development trend. In this paper, in view of the current situation of water scarcity and serious water pollution in China, combined with the development trend and advantages of the Internet of Things (IoT), and based on the inadequacy of the existing microbial sensor data collection equipment, we propose a design scheme of microbial concentration monitoring system for waters based on IoT. The system is based on Zig Bee wireless sensor network to build a common data acquisition platform and design special hardware to carry out high-precision microbial sensor data acquisition in water and through the PC to complete the real-time measurement data storage, waveform display, and data processing. In this paper, the schematic diagram and PCB board design of the system hardware module NUC120 main control board, CC2530 RF board, Wi-Fi wireless communication module, and high-precision ADC acquisition module are completed and fabricated. Then, the four modules are combined to realize the development of the data aggregation node and data acquisition node of the dedicated Zig Bee wireless network hardware device.

Privacy Preserved Cyber-Physical Searching for Information-Centric Intelligent Agriculture

Technological evolution has brought great changes to traditional industries. Nowadays farmers obtain great benefits such as intelligent production and convenient management from intelligent agriculture. Nevertheless, there still exists many flaws in current solutions. Deficiencies in energy management, data transmission and privacy preservation are supposed to be solved. In this paper, the privacy preserved cyber-physical searching framework is proposed to tackle these problems. This four-layer framework can provide a convenient way for farmers to manage production by simply searching for information they interested in. The data source layer contains many sensors for data collection. The data aggregation layer consists of data aggregation nodes deployed in the farms. The data transfer layer employs drones for retrieving data from data aggregation nodes. The cloud control layer is responsible for searching requests processing and privacy preservation. To realize better data collection and searching. The information centric network is introduced into this framework. Moreover, some methods are employed for privacy preservation. The simulation results show the feasibility of this framework.

Wireless Pressure Sensor Assisted Orthopedic Nursing Effectiveness Evaluation

This paper combines flexible pressure sensing technology, wireless sensor network, and cloud platform technology to design and manufacture a medical miniature pressure sensor and its supporting system. The problem of noninvasive monitoring of the syndrome encountered in the clinic is used for real‐time monitoring and auxiliary diagnosis of the disease. Different from the current clinical use of “puncture” to measure intrafascial pressure, this system focuses on the noninvasive monitoring of compartment syndrome, using medical tape to paste a flexible microsensing unit on the injured area. The flexible sensor unit can measure the pressure here in real time and then can know the pressure in the fascia chamber. The flexible pressure sensor unit combines with the subsequent flexible circuit to send the measured data to the data in real time through wireless communication. The data aggregation node transmits the collected data to the upper computer through serial communication, and the upper computer software processes and stores the data and uploads it to the cloud server. In this experiment, it was observed that the concentrations of Ca and P showed the same fluctuating trend. With the gradual progress of the stretch, the concentrations of Ca and P increased with the increase in time, reaching approximately at the end of the extension. The peak value indicates that the osteoclast activity is enhanced at this time, the bone matrix is largely destroyed, and the Ca and P in the matrix are released into the serum in a large amount, thereby increasing the serum concentration. After the distraction ceases, it enters the healing period of the callus. At this time, the concentrations of Ca and P decrease with the increase in time and gradually reach a stable level, indicating that the osteoblast activity is enhanced at this time, the bone matrix begins to rebuild, and the Ca and P gradually increase. The deposited bone matrix gradually forms new bone and finally reaches a balance. Since the speed of extension in each experimental group is inconsistent, the time required to reach the same extension length is also inconsistent, so that the peak time is also inconsistent. After plotting the stress difference (△F) before and after stretching against time and speed, it is found that the relationship is linear. However, these two variables affect △F at the same time, so they cannot be isolated. Based on this, this subject uses multiple regression equations to fit the three relationships of stress difference (△F), time, and speed. In the process of distraction osteogenesis, with each distraction, the bone stress presents a trend from high to low. And as the stretch progresses, the measured stress value increases linearly at the same time point every day.

A new software-defined network architecture to solve energy balance problems in wireless sensor networks

Wireless sensor networks (WSNs) are an important part of the internet of things. In WSNs, the sensor battery is limited, and the entire network has a data aggregation node called Sink. When the sensor transmits data to the Sink in a multi-hop manner, the sensors around the Sink also help other sensors to forward the data to the Sink. This causes those sensors to die faster than other sensors. This problem is called energy hole problem. Previous solutions such as sensor uneven distribution schemes and clustering schemes are not only difficult to implement, but also increase the design cost of the system. In particular, traditional WSNs cannot be updated once deployed. In order to solve the above problem, we propose a method of combining SDN and WSNs. The simulation results show that the proposed method has good performance in terms of the number of surviving sensors, network capacity and residual energy.

A new software-defined network architecture to solve energy balance problems in wireless sensor networks

Wireless sensor networks (WSNs) are an important part of the internet of things. In WSNs, the sensor battery is limited, and the entire network has a data aggregation node called Sink. When the sensor transmits data to the Sink in a multi-hop manner, the sensors around the Sink also help other sensors to forward the data to the Sink. This causes those sensors to die faster than other sensors. This problem is called energy hole problem. Previous solutions such as sensor uneven distribution schemes and clustering schemes are not only difficult to implement, but also increase the design cost of the system. In particular, traditional WSNs cannot be updated once deployed. In order to solve the above problem, we propose a method of combining SDN and WSNs. The simulation results show that the proposed method has good performance in terms of the number of surviving sensors, network capacity and residual energy.

Simulation Based Topology Optimization in Wireless Sensor Network

Background: Wireless Sensor Network (WSN) is among the most promising technologies that can be used to monitor crucial ambient conditions. WSNs are capable of effectively monitoring the environmental parameters and any habitat necessary to be investigated. Sometimes, it is very important to periodically monitor the critical environmental parameters such as humidity, temperature, soil moisture, fire, volcanic eruptions, Tsunamis, seismic waves and many more to react proactively to save lives and assets. This research work is an endeavor to present the importance and to determine the precise inter- nodal distance required for distinct applications. The networks of the different terrain area and internodal distance are deployed to evaluate and analyze the performance metrics such as a number of messages received average end to end delay (secs), throughput (bps) and jitter (secs). The influence of varying inter-nodal distance on the performance of WSN is determined to select the most appropriate value of the distance between nodes in particular monitoring application. The patents related to the topology based analysis of wireless nodes are reconsidered. Methods: The placement of nodes and inter-nodal distance significantly influences the operation and performance of WSNs by diverging the ability of sensors to observe an event of interest and transmission of information to data aggregation nodes (sink nodes). Moreover, effective sensor placement also affects the resource management. The investigation of specific regions and habitats has peculiar constraints of node placement and inter-nodal distance making it highly application specific. In this research work, the intent is to monitor an entire area to attain optimum coverage to detect the occurrence of a significant event. The node placement and inter-nodal distance can be classified on the basis of the role played by the deployed nodes, like, placement of ordinary sensor nodes/Reduced Function Devices (RFDs) and relay nodes/Full Function Devices (FFDs), respectively. The sensors are compatible with IEEE 802.15.4/ZigBee protocol and application implemented is Constant Bit Rate (CBR) generator. This paper analyzed and evaluated the influence of placement and inter-nodal distance of RFDs to the data aggregation ability of sink node. The terrain area (m2) of different sensor networks deployed are 110×110, 200×200, 300×300, 400×400 and 500×500, respectively. The number of sensor nodes is constant equal to 100 to evaluate their ability to provide optimum performance. The parameter internodal distance is varied, keeping all other parameters constant to effectively evaluate its influence. The simulations are carried out on QualNet 6.1 simulator. Results: The variation in inter-nodal distance significantly influences the performance metrics of the network such as the number of messages received, average end to end delay, throughput and jitter. In this paper, the distance between sensor nodes and terrain areas of grid topology is varied accordingly to deduce that which value of the inter-nodal distance and network provides optimum performance. The thorough evaluation of the simulation results presented that the inter-nodal distance of 30 m and terrain area of 300×300 m2 has generated optimum performance by providing the highest number of messages received (208) and highest throughput (2544.34 bps). It is also capable of providing minimum end to end delay (14.45 secs) and lowest jitter (6.67 secs). Conclusion: The objective of this paper to determine the optimum inter-nodal distance and terrain area of a WSN of 100 nodes is successfully achieved. It is analyzed and evaluated that the inter-nodal distance of 30 m and terrain area of 300×300 m2 enhance and optimize the network performance significantly.

Semaphore Based Data Aggregation and Similarity Findings for Underwater Wireless Sensor Networks

A critical factor of underwater sensor networks (UWSN) is to maintain energy consumption at minimum, as immediate battery replacement is difficult. This is achieved by reducing duplication of data with similarity functions. The construction of optimal clustering is to avoid data loss. In this article, similarity function-based data aggregation with a Semaphore process is applied to UWSN to retain the energy level at an advantage. Sensor nodes (SNs) are clustered in a Date Palm Tree approach. The Minkowski Distance model is used in Data Aggregation Nodes (DANs) to check similar measures of readings collected from cluster members. The Semaphore concept is executed in all DANs and cluster heads (CHs) to enhance network life and regulate excessive exploitation of energy levels of the SN, DANs, and CHs. The message queue (MQ) can be used to allow the packets transferred from the DANs to the cluster heads (CHs). The proposed algorithm SBDA with similarity measures would result in better link quality, reduction in redundancy, data delay, and would control the consumption of energy.

Optimal Partial Aggregation Based Energy Delay Compromise Technique for Wireless Sensor Network

ABSTRACTWireless sensor networks mostly use aggregated node to collect data from sensor nodes in the network. The data aggregation process creates a major problem in normal data transmission such as energy consumption and delay. Many researchers have handled many time data aggregation problems and some aggregation functions used to aggregate multiple data into one or every data into one, which reduce energy consumption. In this paper, we propose optimal partial aggregation technique for compromise of both energy and delay. First, we compute data aggregation node in sensor networks based on the lifetime. The lifetime of sensor nodes depends on time-varying factors, such as consumed energy for sending data from leaves to aggregated node, queuing delay during aggregation, and tree depth delay. The time-varying lifetime is optimized by modified multi-fruit fly optimization algorithm and then higher lifetime nodes are selected as aggregated nodes. Second, the modified time on task (ToT) algorithm is used to minimize waiting time of aggregation delay. Third, a non-dominated sorting gravitational searching algorithm (NSGSA) is used to compute lifetime efficient routing path between source and destination via aggregated nodes. Simulations show that the proposed OPA technique improves energy and delay consumption with efficient lifetime compared with existing data aggregation techniques.

Energy efficient data aggregation in wireless sensor networks

To optimizing wireless sensor networks for secured data transmission both at cluster head and base station data aggregation is needed.Data aggregation is performed in every router while forwarding data. The life time of sensor network reduces because of employing energy inefficient nodes for data aggregation. Hence aggregation process in WSN should be optimized in energy efficient manner. There are some computational technics to implement aggrigation at cluster head and base station.When sensors are deployed at differet locations in wider area, it is possible to compromising attacks by adversaries. false data injected in compromised sensors during data aggregation process which results in false decision making at the Base Station (BS). Simple average data aggregation process is suitable only in attacker free environment.So to filter the false data during data aggregation, induced by the attacker. For every round of data agg.regation need to observe the behavior of nodes.So that it easy to minimize an impact of attacker contribution at the final result.For secure data aggregation process along with trustworthiness estimation using Trust wEighted Secure Data Aggregation algorithm (TESDA). Data aggregation process is optimized by performing aggregation in energy efficient manner through clustering.If the aggregator is compromised, then it affects entire aggregation accuracy. Hence it is necessary to propose a aggregation protocol that is resilient against compromised sensor and compromised aggregator in energy efficient and secure manner.

Review and Classification of Multichannel MAC Protocols for Low-Power and Lossy Networks

The so-called Internet of Things (IoT) aims at connecting every single object to the Internet with the purpose to automate every aspect of daily life. The IoT relies heavily on wireless low-power and lossy networks (LLNs) that collect information from the physical world and send the measurements to data aggregation and processing nodes. Most LLNs operate in the non-licensed industrial, scientific, and medical radio band, which is shared by a considerable number of systems. Coexisting wireless systems cause interference to each other, limiting their achievable performance. Multichannel communications enable frequency diversity, which in turn provides robustness against interference as well as increased network capacity. There is a considerable interest in multichannel medium access control (MAC) protocols for LLNs, including an evolving standard for the MAC layer of LLNs. In this paper, we review the latest advances in the topic and introduce a new classification framework for multichannel MAC protocols for LLNs. While our framework builds on previous review and classification studies, it adds aspects of a MAC protocol that reflect its interactions with the surrounding network stack. Seeing the resource constraints of the LLN devices, the study of such interactions—which is missing in prior classification efforts—can be the key for improving future designs. Relevant protocols published since 2006 are discussed and classified using the presented framework, including the recent multichannel MAC protocols for LLNs, such as the latest version of the IEEE 802.15.4 standard for time slotted channel hopping.

Data Aggregation and Principal Component Analysis in WSNs

Data aggregation plays an important role in wireless sensor networks (WSNs) as far as it reduces power consumption and boosts the scalability of the network, especially in topologies that are prone to bottlenecks (e.g. cluster-trees). Existing works in the literature use clustering approaches, principal component analysis (PCA) and/or compressed sensing (CS) strategies. Our contribution is aligned with PCA and explores whether a projection basis that is not the eigenvectors basis may be valid to sustain a normalized mean squared error (NMSE) threshold in signal reconstruction and reduce the energy consumption. We derivate first the NSME achieved with the new basis and elaborate then on the Jacobi eigenvalue decomposition ideas to propose a new subspace-based data aggregation method. The proposed solution reduces transmissions among the sink and one or more data aggregation nodes (DANs) in the network. In our simulations, we consider without loss of generality a single cluster network and results show that the new technique succeeds in satisfying the NMSE requirement and gets close in terms of energy consumption to the best possible solution employing subspace representations. Additionally, the proposed method alleviates the computational load with respect to an eigenvector-based strategy (by a factor of six in our simulations).

CACONET: Ant Colony Optimization (ACO) Based Clustering Algorithm for VANET.

A vehicular ad hoc network (VANET) is a wirelessly connected network of vehicular nodes. A number of techniques, such as message ferrying, data aggregation, and vehicular node clustering aim to improve communication efficiency in VANETs. Cluster heads (CHs), selected in the process of clustering, manage inter-cluster and intra-cluster communication. The lifetime of clusters and number of CHs determines the efficiency of network. In this paper a Clustering algorithm based on Ant Colony Optimization (ACO) for VANETs (CACONET) is proposed. CACONET forms optimized clusters for robust communication. CACONET is compared empirically with state-of-the-art baseline techniques like Multi-Objective Particle Swarm Optimization (MOPSO) and Comprehensive Learning Particle Swarm Optimization (CLPSO). Experiments varying the grid size of the network, the transmission range of nodes, and number of nodes in the network were performed to evaluate the comparative effectiveness of these algorithms. For optimized clustering, the parameters considered are the transmission range, direction and speed of the nodes. The results indicate that CACONET significantly outperforms MOPSO and CLPSO.

Quantum Boson Data Aggregation Scheduling in UOSN

Data Aggregation Scheduling in Ubiquitous sensor networks is a major research interest for many researchers with the objective of reducing energy consumption and maximizing the network lifetime. Also the capacity of a channel for transmitting quantum states called the quantum capacity should be maximized,The sensor nodes should periodically forward data to the base station. Since battery power and bandwidth are the two resources to be effectively utilized the sensor nodes forwards data to the nearest neighbor node which inturn forwards the data to its neighbors and this repeats until all the sensors data aggregates at the base station. In a large network scheduling the sensor nodes for forwarding the data is very much essential and since the topology of the USN changes dynamically , finding such a schedule is also very difficult. In [1] the authors proposed a bosonic network model for communication. In this paper we present a quantum bosonic data aggregation method for constructing the optimal schedule of nodes for data aggregation called QBDAS (Quantum Boson Data aggregation scheduling) protocol. The main idea is to search a optimal schedule through utilizing the observable or measurable physical properties the network topology.