Showing papers on "Bluetooth published in 2021"

Unmanned Aerial Vehicles in Smart Agriculture: Applications, Requirements, and Challenges

Abstract: In the next few years, smart farming will reach each and every nook of the world. The prospects of using unmanned aerial vehicles (UAV) for smart farming are immense. However, the cost and the ease in controlling UAVs for smart farming might play an important role for motivating farmers to use UAVs in farming. Mostly, UAVs are controlled by remote controllers using radio waves. There are several technologies such as Wi-Fi or ZigBee that are also used for controlling UAVs. However, Smart Bluetooth (also referred to as Bluetooth Low Energy) is a wireless technology used to transfer data over short distances. Smart Bluetooth is cheaper than other technologies and has the advantage of being available on every smart phone. Farmers can use any smart phone to operate their respective UAVs along with Bluetooth Smart enabled agricultural sensors in the future. However, certain requirements and challenges need to be addressed before UAVs can be operated for smart agriculture-related applications. Hence, in this article, an attempt has been made to explore the types of sensors suitable for smart farming, potential requirements and challenges for operating UAVs in smart agriculture. We have also identified the future applications of using UAVs in smart farming.

Machine Learning in Wireless Sensor Networks for Smart Cities: A Survey

Abstract: Artificial intelligence (AI) and machine learning (ML) techniques have huge potential to efficiently manage the automated operation of the internet of things (IoT) nodes deployed in smart cities. In smart cities, the major IoT applications are smart traffic monitoring, smart waste management, smart buildings and patient healthcare monitoring. The small size IoT nodes based on low power Bluetooth (IEEE 802.15.1) standard and wireless sensor networks (WSN) (IEEE 802.15.4) standard are generally used for transmission of data to a remote location using gateways. The WSN based IoT (WSN-IoT) design problems include network coverage and connectivity issues, energy consumption, bandwidth requirement, network lifetime maximization, communication protocols and state of the art infrastructure. In this paper, the authors propose machine learning methods as an optimization tool for regular WSN-IoT nodes deployed in smart city applications. As per the author’s knowledge, this is the first in-depth literature survey of all ML techniques in the field of low power consumption WSN-IoT for smart cities. The results of this unique survey article show that the supervised learning algorithms have been most widely used (61%) as compared to reinforcement learning (27%) and unsupervised learning (12%) for smart city applications.

A Recent Survey on Internet of Things (IoT) Communication Protocols

Abstract: Internet of Things (IoT) consists of sensors embed with physical objects that are connected to the Internet and able to establish the communication between them without human intervene applications are industry, transportation, healthcare, robotics, smart agriculture, etc. The communication technology plays a crucial role in IoT to transfer the data from one place to another place through Internet. This paper presents various communication protocols, namely Zigbee, Bluetooth, Near Field Communication (NFC), LoRA, etc. Later, it provides the difference between different communication protocols. Finally, the overall discussion about the communication protocols in IoT.

Towards Large-Scale and Privacy-Preserving Contact Tracing in COVID-19 pandemic: A Blockchain Perspective

Abstract: Activity-tracking applications and location-based services using short-range communication (SRC) techniques have been abruptly demanded in the COVID-19 pandemic, especially for automated contact tracing. The attention from both public and policy keeps raising on related practical problems, including1) how to protect data security and location privacy' 2) how to efficiently and dynamically deploy SRC Internet of Thing (IoT) witnesses to monitor large areas' To answer these questions, in this paper, we propose a decentralized and permissionless blockchain protocol, named Bychain. Specifically, 1) a privacy-preserving SRC protocol for activity-tracking and corresponding generalized block structure is developed, by connecting an interactive zero-knowledge proof protocol and the key escrow mechanism. As a result, connections between personal identity and the ownership of on-chain location information are decoupled. Meanwhile, the owner of the on-chain location data can still claim its ownership without revealing the private key to anyone else. 2) An artificial potential field-based incentive allocation mechanism is proposed to incentivize IoT witnesses to pursue the maximum monitoring coverage deployment. We implemented and evaluated the proposed blockchain protocol in the real-world using the Bluetooth 5.0. The experiment and security analysis is shown to provide a real-world performance evaluation.

A Data-driven Inertial Navigation/Bluetooth Fusion Algorithm for Indoor Localization

Abstract: The introduction of data-driven inertial navigation provides new opportunities that the pedestrian dead reckoning could not well provide for constraining inertial system error drift on smartphones, and has been considered as another promising approach to meet the requirement of location-based services. However, indoor localization systems based on a single technology still have their limitations, such as the drift of inertial navigation and the received signal strength fluctuation of Bluetooth, making them unable to provide reliable positioning. To exploit the complementary strengths of each technology, this paper proposes a feasible fusion framework by utilizing a particle filter to integrate data-driven inertial navigation with localization based on Bluetooth Low Energy (BLE). For data-driven inertial navigation, under the premise of using the deep neural network with great potential in model-free generalization to regress pedestrian motion characteristics, we effectively combined the method of using gravity to stabilize inertial measurement units data to make the network more robust. Experimental results show that in the test of different smartphone usages, the proposed data-driven inertial navigation and BLE-based localization technology have good results in modeling user’s movement and positioning respectively. And due to this, the proposed fusion algorithm has almost unaffected by the usages of smartphones. Compared with BLE-based localization that achieved a good mean positional error (MPE) of 1.76m, for the four usages of texting, swinging, calling and pocket, the proposed fusion algorithm reduced the MPE by 32.35%, 20.51%, 20.74%, and 45.37%, respectively, and can further improve localization accuracy on the basis of existing fusion method.

A Novel Adaptive Battery-Aware Algorithm for Data Transmission in IoT-Based Healthcare Applications

Abstract: The internet of things (IoT) comprises various sensor nodes for monitoring physiological signals, for instance, electrocardiogram (ECG), electroencephalogram (EEG), blood pressure, and temperature, etc., with various emerging technologies such as Wi-Fi, Bluetooth and cellular networks. The IoT for medical healthcare applications forms the internet of medical things (IoMT), which comprises multiple resource-restricted wearable devices for health monitoring due to heterogeneous technological trends. The main challenge for IoMT is the energy drain and battery charge consumption in the tiny sensor devices. The non-linear behavior of the battery uses less charge; additionally, an idle time is introduced for optimizing the charge and battery lifetime, and hence the efficient recovery mechanism. The contribution of this paper is three-fold. First, a novel adaptive battery-aware algorithm (ABA) is proposed, which utilizes the charges up to its maximum limit and recovers those charges that remain unused. The proposed ABA adopts this recovery effect for enhancing energy efficiency, battery lifetime and throughput. Secondly, we propose a novel framework for IoMT based pervasive healthcare. Thirdly, we test and implement the proposed ABA and framework in a hardware platform for energy efficiency and longer battery lifetime in the IoMT. Furthermore, the transition of states is modeled by the deterministic mealy finite state machine. The Convex optimization tool in MATLAB is adopted and the proposed ABA is compared with other conventional methods such as battery recovery lifetime enhancement (BRLE). Finally, the proposed ABA enhances the energy efficiency, battery lifetime, and reliability for intelligent pervasive healthcare.

Securing ZigBee Communications Against Constant Jamming Attack Using Neural Network

Abstract: ZigBee is a wireless communication technology that has been widely used to provide low-bandwidth wireless services for Internet-of-Things applications, such as building automation, medical data collection, and industrial equipment control. As ZigBee operates in the industrial, scientific and medical radio frequency bands, it may suffer from unintentional interference from coexisting radio devices (e.g., WiFi and Bluetooth) and/or radio jamming attacks from malicious devices. Although many results have been produced to enhance ZigBee security, there is no technique that can secure ZigBee against jamming attack. In this article, we propose a new ZigBee receiver by leveraging MIMO technology, which is capable of decoding its desired signal in the presence of constant jamming attack. The enabler is a learning-based jamming mitigation method, which can mitigate the unknown interference using an optimized neural network. We have built a prototype of our proposed ZigBee receiver on a wireless testbed. Experimental results show that it is capable of decoding its packets in the face of 20-dB stronger jamming. The proposed ZigBee receiver offers an average of 26.7-dB jamming mitigation capability compared to off-the-shelf ZigBee receivers.

Bluetooth 5.1: An Analysis of Direction Finding Capability for High-Precision Location Services.

Abstract: This paper presents an in-depth overview of the Bluetooth 51 Direction Finding standard's potentials, thanks to enhancing the Bluetooth Low Energy (BLE) firmware This improvement allows producers to create location applications based on the Angle of Departure (AoD) and the Angle of Arrival (AoA) Accordingly, it is conceivable to design proper Indoor Positioning Systems (IPS), for instance, for the traceability of resources, assets, and people First of all, Radio Frequency (RF) radiogoniometry techniques, helpful in calculating AoA and AoD angles, are introduced in this paper Subsequently, the topic relating to signal direction estimation is deepened The Bluetooth Core Specification updates concerning version 51, both at the packet architecture and prototyping levels, are also reported Some suitable platforms and development kits for running the new features are then presented, and some basic applications are illustrated This paper's final part allows ascertaining the improvement made by this new definition of BLE and possible future developments, especially concerning applications related to devices, assets, or people's indoor localization Some preliminary results gathered in a real evaluation scenario are also presented

Internet-of-Things-Enabled Data Fusion Method for Sleep Healthcare Applications

Abstract: The Internet of Medical Things (IoMT) aims to exploit the Internet-of-Things (IoT) techniques to provide better medical treatment scheme for patients with smart, automatic, timely, and emotion-aware clinical services. One of the IoMT instances is applying IoT techniques to sleep-aware smartphones or wearable devices’ applications to provide better sleep healthcare services. As we all know, sleep is vital to our daily health. What is more, studies have shown a strong relationship between sleep difficulties and various diseases such as COVID-19. Therefore, leveraging IoT techniques to develop a longer lifetime sleep healthcare IoMT system, with a tradeoff between data transferring/processing speed and battery energy efficiency, to provide longer time services for bad sleep condition persons, especially the COVID-19 patients or survivors, is a meaningful research topic. In this study, we propose an IoT-enabled sleep data fusion networks (SDFN) module with a star topology Bluetooth network to fuse data of sleep-aware applications. A machine learning model is built to detect sleep events through an audio signal. We design two data reprocessing mechanisms running on our IoT devices to alleviate the data jam problem and save the IoT devices’ battery energy. The experiments manifest that the presented module and mechanisms can save the energy of the system and alleviate the data jam problem of the device.

Smart Helmet-Based Personnel Proximity Warning System for Improving Underground Mine Safety

Abstract: A smart helmet-based wearable personnel proximity warning system was developed to prevent collisions between equipment and pedestrians in mines. The smart helmet worn by pedestrians receives signals transmitted by Bluetooth beacons attached to heavy equipment, light vehicles, or dangerous zones, and provides visual LED warnings to the pedestrians and operators simultaneously. A performance test of the proposed system was conducted in an underground limestone mine. It was confirmed that as the transmission power of the Bluetooth beacon increased, the Bluetooth low energy (BLE) signal detection distance of the system also increased. The average BLE signal detection distance was at least 10 m, regardless of the facing angle between the smart helmet and Bluetooth beacon. The subjective workload for the smartphone-, smart glasses-, and smart helmet-based proximity warning system (PWS) was evaluated using the National Aeronautics and Space Administration task load index. All six workload parameters were the lowest when using the smart helmet-based PWS. The smart helmet-based PWS can provide visual proximity warning alerts to both the equipment operator and the pedestrian, and it can be expanded to provide worker health monitoring and hazard awareness functions by adding sensors to the Arduino board.

Indoor Navigation Support System for Patients with Neurodegenerative Diseases

Abstract: A handheld device (such as a smartphone/wearable) can be used for tracking and delivering navigation within a building using a wireless interface (such as WiFi or Bluetooth Low Energy), in situations when a traditional navigation system (such as a global positioning system) is unable to function effectively. In this paper, we present an indoor navigation system based on a combination of wall-mounted wireless sensors, a mobile health application (mHealth app), and WiFi/Bluetooth beacons. Such a system can be used to track and trace people with neurological disorders, such as Alzheimer’s disease (AD) patients, throughout the hospital complex. The Contact tracing is accomplished by using Bluetooth low-energy beacons to detect and monitor the possibilities of those who have been exposed to communicable diseases such as COVID-19. The communication flow between the mHealth app and the cloud-based framework is explained elaborately in the paper. The system provides a real-time remote monitoring system for primary medical care in cases where relatives of Alzheimer’s patients and doctors are having complications that may demand medical care or hospitalization. The proposed indoor navigation system has been found to be useful in assisting patients with Alzheimer’s disease (AD) while in the hospital building.

Energy-efficient modified DCC-MAC protocol for IoT in e-health applications

Abstract: Fifteen years ago, several works tried to establish the IEEE 802.15.3 version of ultra-wideband (UWB) like a wireless alternative to the USB, the HDMI, and the Bluetooth, but their efforts failed to gain traction. As UWB technology re-emerges as a suitable solution for the Internet of Things (IoT), we consider the use of this technology for e-health applications. The dynamic channel coding (DCC-MAC) protocol was designed for very low radiated power UWB ad-hoc networks based on dynamic channel coding with interference mitigation . This protocol combines the physical and the MAC layer where the main idea is to optimize the rate by allowing interfering sources to transmit if they are outside the exclusion region. In this paper, the existing DCC-MAC protocol is modified by introducing node energy consumption with simultaneously switching the power level between the nodes. We use a joint consideration of physical and multiple access layers to minimize the energy consumption of the network, and hence increasing its lifetime.

Method Confusion Attack on Bluetooth Pairing

Abstract: Bluetooth provides encryption, authentication, and integrity protection of its connections. These protection mechanisms require that Bluetooth devices initially establish trust on first use through a process called pairing. Throughout this process, multiple alternative pairing methods are supported.In this paper, we describe a design flaw in the pairing mechanism of Bluetooth. This flaw permits two devices to perform pairing using differing methods. While successfully interacting with each other, the devices are not aware of the Method Confusion. We explain how an attacker can cause and abuse this Method Confusion to mount a Method Confusion Attack. In contrast to other attacks targeting the pairing method, our attack applies even in Bluetooth’s highest security mode and cannot be mitigated in the protocol. Through the Method Confusion Attack, an adversary can infiltrate the secured connection between the victims and intercept all traffic.Our attack is successful in practically relevant scenarios. We implemented it as an end-to-end Proof of Concept for Bluetooth Low Energy and tested it with off-the-shelf smartphones, a smartwatch and a banking device. Furthermore, we performed a user study where none of the 40 participants noticed the ongoing attack, and 37 (92.5%) of the users completed the pairing process. Finally, we propose changes to the Bluetooth specification that immunize it against our attack.

Identification of Wearable Devices with Bluetooth

Abstract: With wearable devices such as smartwatches on the rise in the consumer electronics market, securing these wearables is vital. However, the current security mechanisms only focus on validating the user not the device itself. Indeed, wearables can be (1) unauthorized wearable devices with correct credentials accessing valuable systems and networks, (2) passive insiders or outsider wearable devices, or (3) information-leaking wearables devices. Fingerprinting via machine learning can provide necessary cyber threat intelligence to address all these cyber attacks. In this work, we introduce a wearable fingerprinting technique focusing on Bluetooth classic protocol, which is a common protocol used by the wearables and other IoT devices. Specifically, we propose a non-intrusive wearable device identification framework which utilizes 20 different Machine Learning (ML) algorithms in the training phase of the classification process and selects the best performing algorithm for the testing phase. Furthermore, we evaluate the performance of proposed wearable fingerprinting technique on real wearable devices, including various off-the-shelf smartwatches. Our evaluation demonstrates the feasibility of the proposed technique to provide reliable cyber threat intelligence. Specifically, our detailed accuracy results show on average 98.5 percent, 98.3 percent precision and recall for identifying wearables using the Bluetooth classic protocol.

Received Signal Strength Fingerprinting-Based Indoor Location Estimation Employing Machine Learning

Abstract: The fingerprinting technique is a popular approach to reveal location of persons, instruments or devices in an indoor environment. Typically based on signal strength measurement, a power level map is created first in the learning phase to align with measured values in the inference. Second, the location is determined by taking the point for which the recorded received power level is closest to the power level actually measured. The biggest limit of this technique is the reliability of power measurements, which may lack accuracy in many wireless systems. To this end, this work extends the power level measurement by using multiple anchors and multiple radio channels and, consequently, considers different approaches to aligning the actual measurements with the recorded values. The dataset is available online. This article focuses on the very popular radio technology Bluetooth Low Energy to explore the possible improvement of the system accuracy through different machine learning approaches. It shows how the accuracy–complexity trade-off influences the possible candidate algorithms on an example of three-channel Bluetooth received signal strength based fingerprinting in a one dimensional environment with four static anchors and in a two dimensional environment with the same set of anchors. We provide a literature survey to identify the machine learning algorithms applied in the literature to show that the studies available can not be compared directly. Then, we implement and analyze the performance of four most popular supervised learning techniques, namely k Nearest Neighbors, Support Vector Machines, Random Forest, and Artificial Neural Network. In our scenario, the most promising machine learning technique being the Random Forest with classification accuracy over 99%.

Experimental Demonstration for Indoor Localization Based on AoA of Bluetooth 5.1 Using Software Defined Radio

Abstract: The new direction-finding feature incorporated in version 5.1 of the Bluetooth Core Specification accelerates the development of applications in the context of the Internet of Things (IoT) for indoor localization and asset tracking. We evaluate the position of the Bluetooth Low Energy (BLE) transmitter based on the angle of arrival (AoA) mechanism proposed by the Bluetooth standard using Software Defined Radio (SDR). The system is comprised of a phased array antenna, an analog section for the conversion of Radio Frequency (RF) signals to the baseband, followed by a digital section for AoA estimation. The experimental results validate the proposal with precise estimates and a high resolution for the AoA. The proposed approach can be used for digital beamforming to increase the operating range or decrease the level of power consumed by BLE devices in IoT applications.

Networking Support for Bidirectional Cross-Technology Communication

Abstract: Recent research on physical layer cross technology communication (PHY-CTC) brings a timely answer for escalated wireless coexistence and open spectrum movement. PHY-CTC achieves direct communication among heterogeneous wireless technologies (e.g.,WiFi, Bluetooth, and ZigBee) in physical layer and thus brings communication support for coexistence service such as spectrum management and IoT device control. To put PHY-CTC into service, however, there still exists a gap due to its transmission failure and asymmetric link (i.e., one-way PHY-CTC) issues. In this paper, we propose NetCTC – the first networking support design for PHY-CTC to establish feedbacks (e.g., ACKs) and thus meet the upper layer networking requirements in heterogeneous unicast, multicast and broadcast. The core design of NetCTC is a real-time interaction mechanism which achieves reliable, transmission efficient and concurrent interactive communication among heterogeneous devices. We implement and evaluate NetCTC on commodity devices and the USRP-N210 platform. Our extensive evaluation demonstrates that NetCTC achieves reliable bidirectional cross technology communication under a full range of wireless configurations including stationary, mobile and duty-cycled settings.

Wireless Power Transmission Science Model

Abstract: India is a developing country, where the majority of population is striving for a better life. In recent times, people are interested in inventing new things; therefore wireless power transfer technology has been developed. This advanced technology helps people by allowing them to wirelessly operate household appliances and electrical equipment. Wi-Fi, Bluetooth, and other wireless power transfer technologies can be used. This study developed a WPT utilizing Bluetooth, which only allows a limited range. To overcome this drawback, Arduino boards are incorporated along with the Bluetooth to transfer the power. The proposed WPT can assist people in accessing the end application from any location. WPT is utilized in almost all the fields like industries, home, colleges, hospitals and so on. Nowadays, most of the things/house appliances are operated wirelessly to avoid voltage drops and other things. Furthermore, by increasing the frequency of the resistors present in the proposed model, the power can be transmitted to a longer distance. Due to increasing demand for automation in the present society, the proposed model has been designed by using Bluetooth. Although Wi-Fi may not be available in every home, smartphone is available in every home, so this research work has developed a project based on Bluetooth technology. The bluetooth devices assist in the transmission of electricity over longer distances by increasing bandwidth or transferring power up to the bandwidth energy. It is very simple and cost effective. With this model, the power can be easily transferred from one place to another with more bandwidth and reduced wired networks by utilizing the wireless medium.

TinyDL: Edge Computing and Deep Learning Based Real-time Hand Gesture Recognition Using Wearable Sensor

Abstract: Offloading data analysis to edge devices by decentralizing processing can be used to decrease bandwidth requirements, latency, and can decrease the total transmission time required in wireless devices. This can be especially useful for compact wearable devices used for health monitoring, human activity recognition, and gesture recognition, where sending raw data over wireless protocols such as Bluetooth can be both time and power consuming. By performing analysis on the wearable device, wireless radio usage can be greatly decreased, reducing a main power consumer on the device. Deep learning (DL) methods, specifically using Tensorflow (TF) and Keras were evaluated for their usage in such a case, in this example gesture recognition. A multilayer long short-term memory (LSTM) model was trained and evaluated off of data (10 gestures, 1000 trials total, balanced) from a finger-worn ring profile device that collected acceleration data, and was found to perform with accuracy from 75-95% per gesture. The attempted conversion of the model into a compressed TF Lite format, to allow for analysis on-device did not succeed, due to current incompatibilities between the different frameworks. Future work may improve the accuracy, and potentially expand the use of neural networks on wearables for health diagnostics or as input devices.

Bluetooth Low Energy and CNN-Based Angle of Arrival Localization in Presence of Rayleigh Fading

Abstract: Bluetooth Low Energy (BLE) is one of the key technologies empowering the Internet of Things (IoT) for indoor positioning. In this regard, Angle of Arrival (AoA) localization is one of the most reliable techniques because of its low estimation error. BLE-based AoA localization, however, is in its infancy as only recently direction-finding feature is introduced to the BLE specification. Furthermore, AoA-based approaches are prone to noise, multi-path, and path-loss effects. The paper proposes an efficient Convolutional Neural Network (CNN)-based indoor localization framework to tackle these issues specific to BLE-based settings. We consider indoor environments without presence of Line of Sight (LoS) links affected by Additive White Gaussian Noise (AWGN) with different Signal to Noise Ratios (SNRs) and Rayleigh fading channel. Moreover, by assuming a 3-D indoor environment, the destructive effect of the elevation angle of the incident signal is considered on the position estimation. The effectiveness of the proposed CNN-AoA framework is evaluated via an experimental testbed, where In-phase/Quadrature (I/Q) samples, modulated by Gaussian Frequency Shift Keying (GFSK), are collected by four BLE beacons. Simulation results corroborate effectiveness of the proposed CNN-based AoA technique to track mobile agents with high accuracy in the presence of noise and Rayleigh fading channel.

A Long-Distance Communication Architecture for Medical Devices Based on LoRaWAN Protocol

Abstract: Recent market studies show that the market for remote monitoring devices of different medical parameters will grow exponentially. Globally, more than 4 million individuals will be monitored remotely from the perspective of different health parameters by 2023. Of particular importance is the way of remote transmission of the information acquired from the medical sensors. At this time, there are several methods such as Bluetooth, WI-FI, or other wireless communication interfaces. Recently, the communication based on LoRa (Long Range) technology has had an explosive development that allows the transmission of information over long distances with low energy consumption. The implementation of the IoT (Internet of Things) applications using LoRa devices based on open Long Range Wide-Area Network (LoRaWAN) protocol for long distances with low energy consumption can also be used in the medical field. Therefore, in this paper, we proposed and developed a long-distance communication architecture for medical devices based on the LoRaWAN protocol that allows data communications over a distance of more than 10 km.

A Practice of BLE RSSI Measurement for Indoor Positioning.

Abstract: Bluetooth Low Energy (BLE) is one of the RF-based technologies that has been utilizing Received Signal Strength Indicators (RSSI) in indoor position location systems (IPS) for decades. Its recent signal stability and propagation distance improvement inspired us to conduct this project. Beacons and scanners used two Bluetooth specifications, BLE 5.0 and 4.2, for experimentations. The measurement paradigm consisted of three segments, RSSI–distance conversion, multi-beacon in-plane, and diverse directional measurement. The analysis methods applied to process the data for precise positioning included the Signal propagation model, Trilateration, Modification coefficient, and Kalman filter. As the experiment results showed, the positioning accuracy could reach 10 cm when the beacons and scanners were at the same horizontal plane in a less-noisy environment. Nevertheless, the positioning accuracy dropped to a meter-scale accuracy when the measurements were executed in a three-dimensional configuration and complex environment. According to the analysis results, the BLE wireless signal strength is susceptible to interference in the manufacturing environment but still workable on certain occasions. In addition, the Bluetooth 5.0 specifications seem more promising in bringing brightness to RTLS applications in the future, due to its higher signal stability and better performance in lower interference environments.

Financial Big Data Based on Internet of Things and Wireless Network Communication

Abstract: With the advent of the era of big data, Internet of things technology and wireless communication technology have been in a state of rapid development. Opportunities and challenges in all walks of life are being subverted. Financial management, as the foundation of corporate governance, is important for improving economic efficiency and achieving sustainable business development which plays an important role. In order to realize the management and classification of financial big data, better identify the financial data of different enterprises, strengthen the safe storage of financial information, and provide early warning for the security issues involved, this article is based on the Internet of things and wireless communication networks. In the method part, this article introduces the framework of the Internet of things, Bluetooth, and infrared data transmission in wireless network communication and the principles of financial big data. The algorithm introduces a single-user MIMO system, free space propagation, and spectrum and energy efficiency. The analysis part analyzes the spectrum efficiency of different algorithms, social utility, average number of retransmissions, comprehensive scores of competitiveness in various fields of the Internet of things, and the significance of financial indicators. By comparing the data, it can be seen that the algorithm in this paper is superior to the two algorithms of IAN-CoMP and IA-CoMP. When the number of users is 100, the social utility of the algorithm in this paper is 4.45, while IAN-CoMP is 3.43 and IA-CoMP is 3.67. When the number of users increases to 700, the social utility of the algorithm in this paper is 28.34. The other two algorithms are, respectively, 24.45 and 25.99, and we know that the social utility of the algorithm in this paper is the best. Through comprehensive analysis, it is concluded that the financial big data model based on the Internet of things and wireless network communication in this paper can better realize data management and collection, so as to meet the needs of information developers.