Adaptive Multicriteria Thresholding for Cooperative Spectrum Sensing in Cognitive Radio Ad Hoc Smart Grid Networks under Shadowing Effect

Cognitive radio is expected to be implemented in smart grids since it presents high reliability, high accuracy and low transmission time by utilizing licensed bands opportunistically. Shadowing environment affects the performance of channel availability detection of local spectrum sensing since it occurs occasionally. Therefore, the cooperative spectrum sensing is encouraged to be used for addressing shadowing issues. The principle cooperative spectrum sensing techniques suffer from unreliable local information from secondary users (SUs) who are encountered by the shadowing effect. Then, several alternative methods, adaptive majority rule and improved weight algorithm (IMA) is proposed by taking the SUs reliability into account. However, the unreliable SUs are still considered according to the algorithm. Therefore, in this paper, we propose an adaptive multi-criteria thresholding (AMT) to determine the channel availability according to the SUs reliability. The main contribution of AMT is three-fold. First, the new reliable weight calculation is proposed by utilizing analytic hierarchy process (AHP) under three major criteria. Second, AMT is flexible to the number of SUs since it adapts the decision weight on the optimal number of SUs according to the reliable SUs. Third, the shadowing issue is addressed by taking only reliable SUs into account.

Fast inactivation of Nav1.3 channels by FGF14 proteins: An unconventional way to regulate the slow firing of adrenal chromaffin cells

Using Nav1.3 and FGF14 KO mice, Martinez-Espinosa et al. provide new findings on how intracellular FGF14 proteins interfere with the endogenous fast inactivation gating and regulate the “long-term inactivation” of Nav1.3 channels that sets Nav channel availability and spike adaptation during sustained stimulation in adrenal chromaffin cells.

SPECTRUM AWARE CLUSTERING ALGORITHM BASED ON FUZZY LOGIC FOR COGNITIVE RADIO SENSOR NETWORK

In this paper, a clustering solution for periodic data gathering over WSNs using cognitive radio technology is proposed. The cluster heads (CHs) are selected according to the channel availability, residual energy, communication cost and node distribution parameters. Fuzzy logic and weight based techniques combines the four parameters for the CH selection. The cluster formation is based on the relative channel availability between the cluster member (CM) and CH to ensure stable cluster connectivity from link failure. To evaluate the proposed clustering algorithm, the performance of sensor networks is compared with CogLEACH, LEACH and CHEF routing protocols. The simulation results show that the proposed clustering algorithm effectively has a significant improvement with respect to the network stability without reducing the network instability and network lifetime. In addition, the proposed clustering solution also has a low and almost consistent CH energy consumption during the stability period indicating an efficient cluster formation.

An urban expressway forwarding scheme for cognitive Internet of vehicles

The Internet of vehicles is an essential component for building smart cities that can improve traffic safety and provide multimedia entertainment services. The cognitive radio–enabled Internet of vehicles was proposed to resolve the conflict between the increasing demand of Internet of vehicles applications and the limited spectrum resources. The multi-hop transmission is one of the most important issues in cognitive radio–enabled Internet of vehicles networks. Nevertheless, most existing forwarding solutions designed for the cognitive radio–enabled Internet of vehicles did not consider the urban expressway scenario, where primary base stations are densely installed with small coverage areas. In this case, it is difficult to ensure that the sender and the receiver of the same cognitive radio link have similar channel availability statistics, which makes cognitive radio links more likely to be interrupted. To address this challenge, we develop a multi-hop forwarding scheme to minimize the end-to-end delay for such networks. We first formulate the delay minimization problem as a non-linear integer optimization problem. Then, we propose an approach to select the relay candidates by jointly considering the high mobility of vehicles and the unique cognitive radio spectrum usage distributions in urban expressway scenarios. Finally, we propose the low-latency forwarding strategies by considering the channel availability and the delay cost of different situations of relay candidates. Simulations show the advantages of our proposed scheme, compared with state-of-art methods.

Game Theory based Channel Assignment and Load balancing for Cognitive Radio Ad-hoc Networks

Though various works have been done for handling end-to-end congestion control in traditional wireless adhoc networks, they lead to abnormal delay in Cognitive Radio Networks (CRN) due to the extra delaycaused by PU activities. While assigning channels along the route towards destination, channel availability, channel quality and channel switching delay should be considered. In this paper, we propose a Game theory based Channel Assignment and Load balancing(GTCALB) technique for multicast routing for CRAHN. In this technique, a channel matrix is constructed for each link with probability of channel availability, delay cost and channel quality. Then Game theory model is applied for each link in which a utility function is derived for each channel. Then the link with minimum overload is selected with a channel having maximum utility function. The proposed GTCALB technique is applied for each route, during the multicast route discovery. By NS2 simulation, it is shown that the GTCALB technique reduces the end-to-end delay and increases the throughput and packet delivery ratio for the constructed multicast routes.