The widespread application of ultra-dense and multivariate Internet of Things (IoT) benefits from Network Function Virtualization (NFV) that provides flexible frameworks and effective management. NFV leverages the virtualization technologies to integrate the existing network functions of devices into standard servers, storages, and switches. Then, the network functions are achieved in software form to displace the private, dedicated and closed network devices. However, NFV also brings instability and challenges to the network management where the network dynamics, lack of visibility, and high frequency and abundant types of faults will increase the difficulty. Therefore, diagnosing the faults embedded in the generic NFV framework is crucial for the effective adoption of NFV to the IoT environment and thus ensuring the user services. This paper summarizes the differences and connections of fault diagnosis between the NFV framework and traditional networks, and introduces the challenges faced by NFV. Moreover, we provide a comprehensive survey of the state-of-the-art fault detection methods for the NFV framework. After an in-depth discussion of the fault propagation characteristics, we further present a detailed taxonomy of the fault localization approaches. Finally, we highlight the future research directions to provide ample space for improvement in applying NFV to the IoT environment.

Abstract In order to solve the problems of spectrum resource shortage, low spectrum utilization and shadow effect, a regional autonomous security cooperative spectrum sensing (CSS) method based on trust value is proposed to address the vulnerability of CSS technology in cognitive radio to malicious node attacks. Considering the difference in node position, the vulnerability of CSS to malicious node attacks, the large amount of centralized sensing data processing, and the large energy consumption of distributed sensing, based on the trust value update mechanism of ‘slow growth and fast recovery’, the perception area is divided and managed, and the final perception result is determined by majority voting based on the centralized sensing. The convergence value of the time difference of the perceptual process trust value is used to detect the anomaly of the node, and the cumulative difference in trust values between the nodes can detect and locate the malicious node. Experimental simulation results show that the proposed model is superior to traditional algorithms in spectral perception accuracy and malicious node detection.

This study considers the problem of localisation in mobile networks, a cooperative localisation algorithm based on fast clustering–multidimensional scaling (FC–MDS) is proposed. Firstly, a FC strategy suitable for mobile networks is given. In the inner‐cluster relative localisation stage, the authors combine the advantages of classical MDS with iterative MDS. In the inter‐cluster coordinate registration stage, the least squares based coordinate transformation method is used to reduce the registration error. Extensive simulation results show that the normalised root mean square error of the location estimates of FC–MDS close to Cramer–Rao lower bound, and the localisation accuracy of the irregular network is comparable to that of the regular network. The per‐time instant running time of FC–MDS is significantly lower than the iterative algorithm MDS–MAP (P, R).

The accuracy of cooperative localisation can be severely degraded in non‐line‐of‐sight (NLOS) environments. To mitigate the NLOS errors, the cooperative localisation problem based on time of arrival (TOA) under the mixed line‐of‐sight (LOS)/NLOS conditions is addressed. By studying the topological relationship between nodes, a TOA NLOS mitigation cooperative localisation algorithm based on the topological unit is proposed. This algorithm is implemented under the classical multidimensional scaling framework. The adjacent topological unit of NLOS measurements are successfully identified by using the LOS matrix, and the NLOS measurements are re‐estimated using topological units. The least‐square method is used to transform the relative coordinates into absolute coordinates depending on the location of the anchor nodes. Compared to the existing methods, by employing the topological unit, this algorithm only requires the number of LOS anchor nodes to be 2 in the two‐dimensional plane, and the better localisation performance can be achieved. Simulation results show that the proposed method works well for both the sparse and dense NLOS environments.