An intelligent active probing and trace-back scheme for IoT anomaly detection

Due to their simple hardware,sensor nodes in IoT are vulnerable to attack,leading to data routing blockages or malicious tampering,which significantly disrupts secure data collection.An Intelligent Active Probing and Trace-back Scheme for IoT Anomaly Detection(APTAD)is proposed to collect integrated IoT data by recruiting Mobile Edge Users(MEUs).(a)An intelligent unsupervised learning approach is used to identify anomalous data from the collected data by MEUs and help to identify anomalous nodes.(b)Recruit MEUs to trace back and propose a series of trust calculation methods to determine the trust of nodes.(c)The last,the number of active detection packets and detection paths are designed,so as to accurately identify the trust of nodes in IoT at the minimum cost of the network.A large number of experimental results show that the recruiting cost and average anomaly detection time are reduced by 6.5 times and 34.33%respectively,while the accuracy of trust identification is improved by 20%.

Using UAV to Detect Truth for Clean Data Collection in Sensor⁃Cloud Systems

Mobile edge users(MEUs)collect data from sensor devices and report to cloud systems,which can facilitate numerous applications in sensor‑cloud systems(SCS).However,because there is no effective way to access the ground truth to verify the quality of sensing devices’data or MEUs’reports,malicious sensing devices or MEUs may report false data and cause damage to the platform.It is critical for selecting sensing devices and MEUs to report truthful data.To tackle this challenge,a novel scheme that uses unmanned aerial vehicles(UAV)to detect the truth of sensing devices and MEUs(UAV‑DT)is proposed to construct a clean data collection platform for SCS.In the UAV‑DT scheme,the UAV delivers check codes to sensor devices and requires them to provide routes to the specified destination node.Then,the UAV flies along the path that enables maximal truth detection and collects the information of the sensing devices forwarding data packets to the cloud during this period.The information collected by the UAV will be checked in two aspects to verify the credibility of the sensor devices.The first is to check whether there is an abnormality in the received and sent data packets of the sensing devices and an evaluation of the degree of trust is given;the second is to compare the data packets submitted by the sensing devices to MEUs with the data packets submitted by the MEUs to the platform to verify the credibility of MEUs.Then,based on the verified trust value,an incentive mechanism is proposed to select credible MEUs for data collection,so as to create a clean data collection sensor‑cloud network.The simulation results show that the proposed UAV‑DT scheme can identify the trust of sensing devices and MEUs well.As a result,the proportion of clean data collected is greatly improved.

Subcarrier and power allocation algorithm based on inter-cell interference mitigation for OFDMA system

This article proposes a dynamic subcarrier and power allocation algorithm for multicell orthogonal frequency division multiple access （OFDMA） downlink system, based on inter-cell interference （ICI） mitigation. Different from other ICI mitigation schemes, which pay little attention to power allocation in the system, the proposed algorithm assigns channels to each user, based on proportional-fair （PF） scheduling and ICI coordination, whereas allocating power is based on link gain distribution and the loading bit based on adaptive modulation and coding （AMC） in base transceiver station （BTS）. Simulation results show that the algorithm yields better performance for data services under fast fading.