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With the widespread application of Internet of things (IoT), the interference problem becomes more and more serious, which results in not only the poor network performance but also the increased energy consumption of IoT nodes. Therefore, in this paper, we investigate the problem of how to locate the interference sources and infer their communication relationships in the cognitive radio IoT (CR-IoT) deployment scenario. First, we utilize MDS-MAP(P) algorithm with dynamic power control to realize cooperative self-localization of the CR-IoT nodes, which is more energy-efficient than all the IoT nodes equipped with global positioning system (GPS) receivers. Then, we propose a non-cooperative localization method to determine the inference sources with the angle of arrivals (AoAs) measured by the CR-IoT nodes. Finally, the communication relationship between interference sources can be inferred based on the association rule of signals. The network simulation results validate that the proposed methods can locate the interference sources accurately with low energy consumption and correctly infer their communication relationships, which is helpful for the interrupted CR-IoT nodes to take a specific opportunistic transmission policy to reduce their energy consumption.

Important location inference is one of the main content in location big data intelligence mining, and it has important application value in tracking target users and LBA. This paper takes regional ID type location data as input. Aiming at the problem that the current inference methods based on the frequency statistics of the work and rest periods do not take into account the difference in user time activity rules, the accuracy of the inference is poor, and a personal important location inference method based on the time state distribution is proposed. The method first divides the mobile user's work and rest time period, and then estimates the parameters of the state distribution of the user registration time in each base station area. Finally, match the estimated result with the work and rest time period and output the home or work label to complete the inference of personal important locations. The experimental results show that compared with the frequency statistics method, this method has higher accuracy rate and lower parameter estimation error, and it has certain reference significance for the inference of personal important locations with regional ID type location as input.

Security risk assessment is an important part of network security operation. The existing risk assessment methods are mainly divided into qualitative assessment, quantitative assessment and comprehensive assessment. This paper summarizes the advantages and disadvantages of related methods through analyzing, and provides certain assistance for the assessors to choose methods when implementing assessment.

Identifying critical nodes from the point of view that destructiveness equals to criticality, the traditional identification methods usually consider the degree of damage but does not analyze the type of damage. The critical nodes identified have a certain universality, but the pertinence is not strong. In this paper, a critical node identification method based on the analysis of network partition for Ad Hoc network is proposed. Firstly, the Kernighan-Lin algorithm is applied to group the nodes in the network, and then the boundary nodes between different groups are selected from the grouping results. Finally, the boundary nodes are re-screened by combining the node’s degree value to select the nodes that can lead to the partition of the network. The experimental results show that the failure of identified critical nodes can directly lead to network partitioned into multiple subnets, destroy the connectivity of the network seriously.

Topology is the basis of connectivity analysis and critical nodes identification for Ad Hoc network. The existing methods of topology inference are divided into two types: cooperative and non-cooperative. The cooperative inference methods are usually based on the premise of network access. It usually needs to send a large number of detection packets, and the operation cost is large. The non-cooperative inference methods are usually based on the analysis of a large number of detection data, but the data acquisition is difficult and takes a long time. From the view of coverage, this paper inferred the physical topology of Ad Hoc network with critical transmission distance under the condition that the location information and the number of nodes, the size of the deployment area are known. The simulation results on Exata demonstrate the feasibility of the inference method.

Exata has excellent performance in wireless communication, but in the process of Ad Hoc network simulation, it can not realize the inference and visualization of network topology. In this paper, we analyze the AODV protocol, and use the information contained in the RREP message to realize the inference of the network topology. The experimental results prove the feasibility of the method.

Multi-class classification algorithm of support vector machine (SVM) has always been a research hotspot. A new multi-class SVM algorithm, naming recall reordering adaptive directed acyclic graphs (RRADAG), is proposed from the perspective of error detection to solve the error accumulation existed in multi-class SVM algorithm of which is based on Directed Acyclic Graphs (DAG). By detecting the output of the classifier and recalling the possible error decision, RADAR has solved the problem of error accumulation. The experimental results generated on UCI standard data sets show that RRADAG outperforms the previously existing algorithms on accuracy.