Data Transmission Time Research Articles

Efficient CityCam-to-Edge Cooperative Learning for Vehicle Counting in ITS

Vehicle counting is a fundamental component in Intelligent Transportation System (ITS) for city traffic management. Although a number of vehicle counting approaches have been proposed, their essential drawbacks limit the efficacy of vehicle counting in real applications. In this paper, we propose a CityCam-to-Edge cooperative learning framework by cooperating multiple city cameras with an edge server to count vehicles more efficiently. Our learning framework consists of a lightweight feature extraction scheme deployed on the city cameras and a vehicle counting model implemented on the edge server. We devise the lightweight feature extraction scheme by leveraging multiple convolutional layers with few kernels in the design of deep learning architecture to reduce the utilization of parameters for feature extraction, so that the city cameras’ memory consumption and the data transmission time can be greatly reduced. Moreover, we design two novel vehicle counting models, F2F-M and O2O-M, to improve the counting performance by exploiting the temporal correlation among videos captured from multiple city cameras in a frame-to-frame manner and a video-to-video manner, respectively. By combining the lightweight feature extraction scheme and the proposed vehicle counting models, we obtain two end-to-end vehicle counting models, Lite-F2F-M and Lite-O2O-M. Finally, via conducting extensive experiments, we demonstrate that Lite-F2F-M and Lite-O2O-M models outperform the state-of-the-art in terms of vehicle counting accuracy and time efficiency.

Packet Routing in Dynamic Multi-Hop UAV Relay Network: A Multi-Agent Learning Approach

The multi-hop unmanned aerial vehicle (UAV) network can serve as data relays where ground users (GUs) do not have reliable direct connections to the base station (BS). Existing works mainly focus on simple dual-hop system. In this paper, we investigate the packet routing problem in a multi-hop UAV relay network to minimize the data transmission time and enhance the network throughput. However, the dynamic network topology due to UAV mobility makes the packet routing challenging since the limited communication range of each UAV leads to volatile wireless connection. Moreover, the line-of-sight communication links may cause strong interference among UAVs. Towards this end, we propose a novel multi-agent deep reinforcement learning based algorithm, named as multi-agent QMIX (MAQMIX) to: 1) design proper UAVs’ trajectories to serve the moving GUs while maintaining the network connection; 2) allocate frequency resource properly among UAVs to alleviate the impact of interference; and 3) choose a proper next hop UAV for each data packet to reduce the transmission time and probability of network congestion. The proposed MAQMIX has two novel training mechanisms, i.e., intra-UAV and inter-UAV training mechanisms, which can tackle the large action space issue and coordinate the training among UAVs in the multi-hop UAV relay network. Simulation results demonstrate that the MAQMIX outperforms baseline schemes in terms of the network congestion avoidance, throughput, and transmission time.

Time allocation improvement method for UAV-based wireless energy transfer cooperative mobile edge

Wireless power transfer (WPT) and mobile edge computing (MEC) are two advanced technologies that could improve the computing power and range of mobile devices. However, by integrating the unmanned aerial vehicle (UAV) into wireless powered MEC systems, wireless energy transfer will be susceptible to the “double near–far” effect. Therefore, in order to further overcome the influence of the “double near–far” effect, this paper considers the optimization of time slot allocation for UAV-assisted wireless powered cooperative MEC system, which includes an access point (UAV) and two mobile devices. The purpose of the study is to minimize the total transmission energy of the UAV while satisfying the delay and size of the computational tasks, so this paper proposed a 2:1:1 time-slot optimization allocation method. The method exploits the synergy of users so that the mobile device which is closer to the UAV acts as an offloading relay, by combining power and time slot optimization to minimize the total energy consumption of the UAV. Compared with the equal time slot scheme before the improvement, this method can not only utilize the wireless transmission energy to charge the mobile device for more time in the first period, but also can save the time of data transmission of the closer device in the third period, and it can enhance the rate of data transmission of the mobile devices at the same time. The results show that the task capacity of the system computed will be increased compared to the original scheme; the total transmission rate of the whole system is also improved by the same order of magnitude. The simulation results verify the effectiveness and reliability of the algorithm of the paper, and the comprehensive performance of the system can be maximized by the flexible offloading algorithm.

Scientific Workflows in IoT Environments: A Data Placement Strategy Based on Heterogeneous Edge-Cloud Computing

In Industry 4.0 and Internet of Things (IoT) environments, the heterogeneous edge-cloud computing paradigm can provide a more proper solution to deploy scientific workflows compared to cloud computing or other traditional distributed computing. Owing to the different sizes of scientific datasets and the privacy issue concerning some of these datasets, it is essential to find a data placement strategy that can minimize data transmission time. Some state-of-the-art data placement strategies combine edge computing and cloud computing to distribute scientific datasets. However, the dynamic distribution of newly generated datasets to appropriate datacenters and exiting the spent datasets are still a challenge during workflows execution. To address this challenge, this study not only constructs a data placement model that includes shared datasets within the individual and among multiple workflows across various geographical regions, but also proposes a data placement strategy (DYM-RL-DPS) based on algorithms of two stages. First, during the build-time stage of workflows, we use the discrete particle swarm optimization algorithm with differential evolution to pre-allocate initial datasets to proper datacenters. Then, we reformulate the dynamic datasets distribution problem as a Markov decision process and provide a reinforcement learning–based approach to learn the data placement strategy in the runtime stage of scientific workflows. Through using the heterogeneous edge-cloud computing architecture to simulate IoT environments, we designed comprehensive experiments to demonstrate the superiority of DYM-RL-DPS. The results of our strategy can effectively reduce the data transmission time as compared to other strategies.

PbCP: A profit-based cache placement scheme for next-generation IoT-based ICN networks

The large number of connected devices generating a massive amount of data in the emerging technologies such as the Internet of Things (IoT), will surpass the capabilities of the current Internet infrastructure. Therefore, it is essential to design a new communication paradigm to meet the demands and requirements of new applications. Information-Centric Networking (ICN) is a new candidate for future Internet architecture that aims at addressing different challenges in the currently used host-centric Internet. In ICN, the data/content is searched, routed, located, and retrieved through the name of the content rather than the address of data source. ICN also enables in-network caching where the intermediate nodes can cache the content. In-network caching helps in improving the network scalability and reduces the content retrieval delay. To date, multiple caching schemes have been designed for IoT data management in ICN; however, most of them suffer from scalability or ignore constraints on the IoT devices. Moreover, the placement of content in a large-scale environment needs further improvement and optimization since it impacts overall network performance. To fill the gaps, in this paper, we combine various metrics that affect caching performance. We formulate the content placement as an optimization problem and then propose a profit-based caching scheme that harnesses the benefits of the Tabu local search algorithm. The simulation results and comparative analysis show that the proposed strategy outperforms the existing state-of-art schemes, notably in terms of memory resources utilization, data transmission time, content diversity ratio, and cache replacement operations.

Semantic Depth Data Transmission Reduction Techniques Based on Interpolated 3D Plane Reconstruction for Light-Weighted LiDAR Signal Processing Platform

In vehicles for autonomous driving, light detection and ranging (LiDAR) is one of the most used sensors, along with cameras. LiDAR sensors that produce a large amount of data in one scan make it difficult to transmit and calculate data in real-time in the vehicle’s embedded system. In this paper, we propose a platform based on semantic depth data-based data reduction and reconstruction algorithms that reduce the amount of data transmission and minimize the errors between original and restored data in a vehicle system using four LiDAR sensors. The proposed platform consists of four LiDAR sensors, an integrated processing unit (IPU) that reduces the data of the LiDAR sensors, and the main processor that reconstructs the reduced data and processes the image. In the proposed platform, the 58,000 bytes of data constituting one frame detected by the VL-AS16 LiDAR sensor were reduced by an average of 87.4% to 7295 bytes by the data reduction algorithm. In the IPU placed near the LiDAR sensor, the memory usage increased by the data reduction algorithm, but the data transmission time decreased by an average of 40.3%. The transmission time where the vehicle’s processor received one frame of data decreased from an average of 1.79 to 0.28 ms. Compared with the original LiDAR sensor data, the reconstructed data showed an average error of 4.39% in the region of interest (ROI). The proposed platform increased the time required for image processing in the vehicle’s main processor by an average of 6.73% but reduced the amount of data by 87.4% with a decrease in data accuracy of 4.39%.

Learning Based Massive Data Offloading in the IoV: Routing Based on Pre-RLGA

With the increasing demand for bulk data transmission, offloading techniques to transmit data via mobile physical media is becoming common increasingly. The development of the Internet of Vehicles (IoV) provides new solutions for data offloading, as the IoV brings massive storage and transmission capabilities. However, the impact caused by the mobility of physical media brings a challenge to delay optimization and route selection of data offloading. In this paper, we consider a data transmission network architecture based on the Manhattan mobility model. The vehicle carries data on a fixed route in this scenario, enabling data transmission between geographically distant data centers. In order to reduce the total transmission time and reduce the impact of retransmission, we consider the temporal convolutional network (TCN) model to predict the allocation of the weight of delay. Next, we solve the optimal routing problem using a genetic algorithm based on a reinforcement learning mechanism (RLGA) to pre-allocate resources for offloading requests. The experimental results show that the proposed data offloading method can reduce the load on the cellular network and decrease the data transmission time, average transmission hops, and retransmission times compared with existing methods.

A forest fire monitoring and detection system based on wireless sensor networks

Global climate change is affecting Earth in various ways, including glacial detachment, sea-level rise, and more intense heat waves, which in turn contribute to fires in areas rich in flora and fauna, causing erosion and changes in plant and animal ranges. Despite international efforts to mitigate climate change, the problem remains uncontrolled in many areas. In this study, a forest fire monitoring and early warning system is proposed. It is based on a wireless sensor network (WSN) that measures the environment to detect and prevent fires in rural areas. The WSN uses open-access technologies and the Zigbee standard for radiofrequency communication and sends collected data to a web server via GPRS and TCP/IP protocol. The WSN's performance is evaluated using two metrics: data transmission rate and time delay. The WSN achieved an average success rate of data transmission greater than 86% with an average time delay of less than 500 milliseconds in all tests, demonstrating the potential of WSNs as near real-time forest fire detection systems.

Time-Critical Data Transmission Scheme in Wireless Sensor Networks Using Machine Learning Approach

Wireless sensor network has been extensively used in many real time wireless sensor networks applications. Due to limitations of hardware resources and restricted communication capabilities of sensor nodes, it is very challenging to use wireless sensor networks in real time data transmission. Data collection and routing is the main issue in such applications. To enhance the performance under such real time transmission scenario, it is essential to make the protocol intelligent to choose the appropriate path with change in network scenario. In this paper, we propose a machine learning based Medium Access Control (MAC) protocol to handle real time traffic in wireless sensor networks. To deal with the limitations of WSN in real time application, the proposed scheme can help to increase the performance of time-critical wireless sensor network applications. Simulation results authorize our work, and confirm the accuracy of the proposed MAC protocol strategy is higher than the existing work.

Secure Token–Key Implications in an Enterprise Multi-Tenancy Environment Using BGV–EHC Hybrid Homomorphic Encryption

Authentication, authorization, and data access control are playing major roles in data security and privacy. The proposed model integrated the multi-factor authentication–authorization process with dependable and non-dependable factors and parameters based on providing security for tenants through a hybrid approach of fully homomorphic encryption methodology: the enhanced homomorphic cryptosystem (EHC) and the Brakersky–Gentry–Vaikuntanathan (BGV) scheme. This research was composed of four major elements: the fully homomorphic encryption blended schemes, EHC and BGV; secure token and key implications based on dependable and don-dependable factors; an algorithm for generating the tokens and the suitable keys, depending on the user’s role; and the execution of experimental test cases by using the EHC algorithm for key and token generation, based on dependable and non-dependable parameters and time periods. The proposed approach was tested with 152 end-users by integrating six multi-tenants, five head tenants, and two enterprise levels; and achieved a 92 percent success rate. The research integrated 32-bit plain text in the proposed hybrid approach by taking into consideration the encryption time, decryption time, and key generation time of data transmission via cloud servers. The proposed blended model was efficient in preventing data from ciphertext attacks and achieved a high success rate for transmitting data between the multi-tenants, based on the user-role-user type of enterprise cloud servers.

UV3D: Underwater Video Stream 3D Reconstruction Based on Efficient Global SFM

With the increasing demand for underwater resource exploration, three-dimensional (3D) reconstruction technology is important for searching for lost underwater civilizations, underwater shipwrecks, or seabed structures. However, faced with the limitations of underwater unmanned systems in terms of energy, bandwidth, and transmission delay, 3D reconstruction technology based on video streams as direct data will not work well. We propose a terminal image processing strategy to save data transmission time and cost and to obtain 3D scene information as soon as possible. Firstly, we propose an adaptive threshold key frame extraction algorithm based on clustering, which extracts key frames from the video stream as structure from motion (SFM) image sequences. On this basis, we enhance the underwater images with sufficient and insufficient illumination to improve the image quality and obtain a better visual effect in the 3D reconstruction step. Additionally, we choose global SFM to construct the scene and propose a faster rotation averaging method, the least trimmed square rotation averaging (LTS-RA) method, based on the least trimmed squares (LTS) and L1RA methods. It is proven to reduce 19.97% of the time through experiments. Finally, our experiments demonstrate that the dense point cloud saves about 70% of the transmission cost compared to video streaming.

TDOPS: Time series based deduplication and optimal data placement strategy for IIoT in cloud environment

As a result of the advancements in Industry 4.0, the amount of data collected within industries are continuously expanding to achieve an innovative environment within the industry by maximizing asset usage. Meanwhile, the redundancy rate is increasing in cloud storage, which has an impact on data storage and analysis. To lower the rate of redundancy, the proposed system comprises a Time series-based deduplication technique. In the Time series-based deduplication technique, the Adaptive Multi-Pattern Boyer Moore Horspool (AM-BMH) algorithm, and Merkle tree were used to produce time-series data. Another significant challenge is that the geographically distributed cloud system has encountered that the data placement methodology with high-priced transportation costs for data transmission. To overcome this issue, an optimal data placement strategy using Modified Distribution is proposed. Thus the proposed Time Series-based Deduplication and Optimal Data Placement Strategy (TDOPS) is found to be effective when compared with the existing system. The various parameters like space reduction, efficient retrieval, data transportation costs, and data transmission time are taken into the account in the cloud environment for an evaluation. The proposed scheme saves 98 percent of storage space, 55 percent computation overhead, and improves 60% of cloud storage efficacy.

Smart hydroponic based on nutrient film technique and multistep fuzzy logic

<span>Automation in hydroponics is have been a great change. Research with fuzzy logic control it’s designed to add to each parameter one by one. In a way microcontroller will activate one by one relay to regulate the parameters with fuzzy logic. While parameter calibration is done, calibration is needed for the next checking if the parameter were not optimal, until its parameter optimal. Multistep fuzzy is used to counter measure the same activation of the relay. With adding real time data monitoring to the system. From test result evaluating multistep fuzzy logic method were 100% works as expected. with another testing approach for best module for sending real time data monitoring for hydroponics. From the real time data transmission method, the success of sending data is 30% from the ESP82166 and 75% of the NRF24L01 with a shortage of the NRF24L01 data loss. For the relay activation can be accommodate with dynamic programming. As for multistep fuzzy logic for hydroponic tested to reach optimal water condition for kale crops resulting in average 12.8 iterations calibration from condition where researches add water only from the start.</span>

MANET Proactive and Reactive Routing Protocols

A group of wireless nodes forms a configuration that is linked to a remote medium forming dynamic topology. A routing protocol enables packets to travel from the sender to the destination via intermediate nodes. When a device or intermediate node delivers information to other nodes in an Ad hoc network, the node consumes some energy, and data transmission may be interrupted as all of the power is consumed. MANET is often power-driven devices, the difficult component in MANET is to lower the power consumption of energy by the intermediate devices in the network so that the network remains active at the time of data transmission. The characteristics, uses, and problems of MANET are described in this research. In addition, we look at the MANET routing protocols. The performance of alternative routing protocols, such as DSDV, AODV, AOMDV, and DSR, is then compared using performance measures. The NS2.35 simulator is used to conduct the performance assessment.

Temporal trend of QRS voltage amplitude on surface sensing in patients with arrhythmogenic cardiomyopathy implanted with a subcutaneous ICD

Abstract Funding Acknowledgements Type of funding sources: None. Introduction Previous studies have reported a significant decrease in ventricular sensing amplitude during follow-up in patients with arrhythmogenic cardiomyopathy (ACM) implanted with trans-venous implantable cardioverter defibrillator (ICD). No data are yet available on sensing amplitudes values over time for subcutaneous ICD (S-ICD). Low QRS voltage on surface ECG is one of the diagnostic clues in ACM. This, together with the progressive nature of the disease and the not-negligible incidence of inappropriate shocks due to oversensing, raise safety concerns on the use of S-ICD in this population. Aim The aim of this study was to evaluate the trend of QRS amplitude on subcutaneous ECG (S-ECG) over time in patients with ACM implanted with S-ICD. Methods We conducted a retrospective analysis on a cohort of consecutive patients with ACM implanted with S-ICD in two tertiary centers, prospectively enrolled in the home-monitoring program. The S-ECGs recorded by the S-ICD at the time of first and last data transmission were analyzed. The voltage amplitude was estimated for each QRS complex available, and the values obtained were compared between the two time points (i.e. baseline and last follow-up available). The primary endpoint of our study was the proportion of patients in which the decrease of QRS amplitude during follow-up was statistically significant. Results Eleven patients (1 female, 9%) were enrolled in this study. The mean age was 43±10 years. An exclusive right ventricular involvement was reported in 5 patients (46%), 2 patients (18%) had an exclusive left ventricular, and 4 (36%) a biventricular involvement. Three patients (27%) were implanted in secondary prevention. During a median follow-up of 9 months (IQR 6-17), 6 patients (55%, 95%CI 24-84%) experienced a statistically significant reduction in the S-ECG QRS amplitude (Figure A). Considering these 6 patients, the mean absolute reduction was -0.46±0.32 mV corresponding to a relative reduction of -19%±11%. Moreover, we found a statistically significant correlation between follow-up duration and QRS amplitude relative reduction (r=-0.89, p=0.018, Figure B). No patients experienced inappropriate shocks. Conclusions In a cohort of ACM patients implanted with S-ICD the majority of patients experienced a significant reduction in the amplitude of QRS voltage during follow-up, confirming a potential safety concern on the use of S-ICD in this population. Further studies are needed to investigate this issue.

P9 TEMPORAL TREND OF QRS VOLTAGE AMPLITUDE ON SURFACE SENSING IN PATIENTS WITH ARRHYTHMOGENIC CARDIOMYOPATHY IMPLANTED WITH A SUBCUTANEOUS ICD

Abstract Introduction Previous studies have reported a significant decrease in ventricular sensing amplitude during follow–up in patients with arrhythmogenic cardiomyopathy (ACM) implanted with trans–venous implantable cardioverter defibrillator (ICD). No data are yet available on sensing amplitude values over time for subcutaneous ICD (S–ICD). Low QRS voltage on surface ECG is one of the diagnostic clues in ACM. This, together with the progressive nature of the disease and the not–negligible incidence of inappropriate shocks due to oversensing, raise safety concerns on the use of S–ICD in this population. The aim of this study was to evaluate the trend of QRS amplitude on subcutaneous ECG (S–ECG) over time in patients with ACM implanted with S–ICD. Methods We conducted a retrospective analysis on a cohort of consecutive patients with ACM implanted with S–ICD in two Italian tertiary centers, prospectively enrolled in the home–monitoring program. The S–ECGs recorded at the time of first and last data transmission were analyzed. The voltage amplitude was estimated for each QRS complex available, and the values obtained were compared between the two time points (i.e. baseline and last follow–up available). The primary endpoint of our study was the proportion of patients in which the decrease of QRS amplitude during follow–up was statistically significant. Results Eleven patients (1 female, 9%) were enrolled in this study. The mean age was 43±10 years. An exclusive right ventricular involvement was reported in 5 patients (46%), 2 patients (18%) had an exclusive left ventricular, and 4 (36%) a biventricular involvement. Three patients (27%) were implanted in secondary prevention. During a median follow–up of 9 months (IQR 6–17), 6 patients (55%, 95%CI 24–84%) experienced a statistically significant reduction in the S–ECG QRS amplitude (Fig. A). Considering these 6 patients, the mean absolute reduction was –0.46±0.32 mV corresponding to a relative reduction of –19%±11%. Moreover, we found a statistically significant correlation between follow–up duration and QRS amplitude relative reduction (r=–0.89, p = 0.018, Fig. B). No patients experienced inappropriate shocks. Conclusions In a cohort of ACM patients implanted with S–ICD the majority of patients experienced a significant reduction in the amplitude of QRS voltage during follow–up, confirming a potential safety concern on the use of S–ICD in this population. Further studies are needed to investigate this issue.

IoT for Residential Monitoring Using ESP8266 and ESP-NOW Protocol

This research aims to create a cluster housing monitoring system. This system combines IoT with the ESP-NOW protocol. In this system, the head of security and homeowners can unify the state of the house through a web application and an android application. The system will send notifications via the web and the LINE messaging application if there is an intrusion or gas leak or no fire is detected. The contribution of this research is to shorten the path and time of data transmission and make it easier for homeowners and heads of security to monitor and take action as soon as possible when something goes wrong in the residential area. The method used in this research is experimental. There are 4 main parts to this system, namely ESP-NOW sender, ESP-NOW receiver, gateway module, and server. All microcontrollers in this system use the NodeMCU ESP8266 because the NodeMCU, apart from being a controller, also functions as a WiFi module. By using the ESP-NOW protocol, it is possible for every microcontroller, both the sender and the receiver, to communicate without using WiFi WAP. In this study, the ESP-NOW sender collected 10 installed in each house to unify the conditions of the house. The ESP-NOW receiver worked to receive data from all senders. Any data received by the receiver will be sent to the gateway serially. The gateway module functions to send all messages to the server online. Application on the server-side built using PHP and MySQL. The display of the system can be opened through the android application or web application. The results of this test indicate that the system is running well with a 100% success proportion where every message from the sender is stored on the server-side, and the application can provide notifications according to the scenario of the system.

Long short‐term memory‐based power‐aware algorithm for prompt heterogenous activity

SummaryHuman–computer interaction plays a vital role in wireless body area networks, internet of things, and big data. Wearables are low‐power devices with minimal battery capacity. In general, wearables suffer from energy losses due to changes in the user's body posture, diffraction, reflection, and shadowing of the human body. As a result, many control packets are needed to ensure proper communication in wireless body area network. Hence, this research proposes a long short‐term memory‐based power‐aware (LSTM‐PA) algorithm to ensure burst data transmission during prompt heterogeneous activities in the presence and absence of inter‐WBAN interference. The proposed algorithm predicts the best quality time (BQT) for data transmission by activity classification and robust R2 similarity (RRS) metric. The minimum transmission power is estimated by the critical point classification technique. The activity classification accuracy is 92% in the LSTM‐PA algorithm. The energy consumption in the node is reduced by up to 46.34% compared to benchmark algorithms.

A Study of an IT-Assisted Higher Education Model Based on Distributed Hardware-Assisted Tracking Intervention.

This paper presents an in-depth study and analysis of the model of higher education using distributed hardware tracking intervention of information technology. The MEC-based dynamic adaptive video stream caching technology model is proposed. The model dynamically adjusts the bit rate by referring to the broadband estimation and cache occupancy data to ensure users have a smooth experience effect. Simulation results show that the model has fewer transcoding times and generates lower latency than the traditional model, which is suitable for dual-teacher classroom scenarios and further improves the quality of the user's video viewing experience. The model uses an edge cloud collaborative architecture to migrate the rendering technology to an edge server closer to the user side, enabling real-time interaction, computation, and rendering, reducing the time of data transmission as well as computation time. According to the blended learning-based adaptive intervention model, three rounds of teaching practice are conducted to validate the effectiveness of the intervention model in terms of both student process performance and outcome performance, thereby improving learning adaptability and improving learning effect. Teachers' teaching has a significant impact on learning motivation (β = 0.311, p < 0.01), which in turn affects learning adaptability. Teachers use scientific teaching methods to stimulate students' learning motivation, mobilize enthusiasm, and improve learning adaptability. Under the communication topology of the system as a directed graph, a multi-intelligent system dynamic model with grouping is established; i.e., the intragroup intelligence has the same dynamics but is different between groups, and all system dynamics are unknown. The proposed novel policy iterative algorithm is used to learn the optimal control protocol and achieve optimal consistency control. The effectiveness of the algorithm is demonstrated by the simulation experimental results. The simulation results show that the model has lower latency and energy consumption compared to the cloud rendering model, which is suitable for the safety education classroom scenario and solves the outstanding problems of network connection rate and cloud service latency.

A Safe Cognitive Radio Spectrum Handoff Method Using Coordinating Cognitive User

A new cognitive user emulation attack (CUEA) in a cognitive radio network (CRN), which could be utilized by intruders during spectrum handoff is introduced. The need for more efficient spectrum utilization in our increasingly digitalized society is becoming more important. This has given raise to the variety of new security threats. A safe handoff mechanism that could successfully counter such an attack by introducing a coordinating cognitive user that computes the level of trust of each cognitive user based on its behavioral characteristics is proposed. Malicious users could be effectively identified by the coordinating cognitive user by looking up the trust values. The activity of the proposed mechanism is validated using MATLAB simulation. The simulation executed describes the utilization of the proposed mechanism by correctly identifying the probability of false detection, detection rate, incorrect detection shown as it decreases the data transmission time or increases the transmission rates of primary user's signals.