Biblio

Although function model has been widely applied to develop various operator decision support systems, the modeling process is essentially a manual work, which takes significant efforts on knowledge acquisition. It would greatly improve the efficiency of modeling if relevant information can be automatically retrieved from engineering documents. This paper investigates the possibility of automated transformation from P&IDs to a function model called MFM via AutomationML. Semantics and modeling patterns of MFM are established in AutomationML, which can be utilized to convert plant topology models into MFM models. The proposed approach is demonstrated with a small use case. Further topics for extending the study are also discussed.

Cognitive radio network (CRN) is regarded as an emerging technology for better spectrum efficiency where unlicensed secondary users (SUs) sense RF spectrum to find idle channels and access them opportunistically without causing any harmful interference to licensed primary users (PUs). However, RF spectrum sensing and sharing along with reconfigurable capabilities of SUs bring severe security vulnerabilities in the network. In this paper, we analyze physical-layer security (secrecy rates) of SUs in CRN in the presence of eavesdroppers, jammers and PU emulators (PUEs) where SUs compete not only with jammers and eavesdroppers who are trying to reduce SU's secrecy rates but also against PUEs who are trying to compel the SUs from their current channel by imitating the behavior of PUs. In addition, a legitimate SU competes with other SUs with a sharing attitude for dynamic spectrum access to gain a high secrecy rate, however, the malicious users (i.e., attackers) attempt to abuse the channels egotistically. The main contribution of this work is the design of a game theoretic approach to maximize utilities (that is proportional to secrecy rates) of SUs in the presence of eavesdroppers, jammers and PUEs. Furthermore, SUs use signal energy and cyclostationary feature detection along with location verification technique to detect PUEs. As the proposed approach is generic and considers different attackers, it can be particularized to a situation with eavesdroppers only, jammers only or PUEs only while evaluating physical-layer security of SUs in CRN. We evaluate the performance of the proposed approach using results obtained from simulations. The results show that the proposed approach outperforms other existing methods.

The paper presents a joint optimization algorithm for coverage and capacity in heterogeneous cellular networks. A joint optimization objective related to capacity loss considering both coverage hole and overlap area based on power density distribution is proposed. The optimization object is a NP problem due to that the adjusting parameters are mixed with discrete and continuous, so the bacterial foraging (BF) algorithm is improved based on network performance analysis result to find a more effective direction than randomly selected. The results of simulation show that the optimization object is feasible gains a better effect than traditional method.