Cognitive Radio (CR) is an emerging technology to solve the issue of scarce spectrum resource utilization. The routing plays a significant role in CR Ad-hoc network and it has two major problems, those are spectrum load balance and energy efficiency. Hence, maintaining the spectrum load balance and energy efficiency becomes a difficult task in CR Ad-hoc Network. In this research, a new Multicast Ad-hoc On-Demand Distance Vector (MAODV) with Particle Swarm Optimization (PSO) algorithm was introduced for selecting best route of less energy consumption in the CR networks. The routing algorithm is implemented with an efficient particle encoding scheme and multi-objective fitness function. Moreover, the data packets are secured by using the RSA algorithm. The optimized lifetime attained from the desired CR ad-hoc networks by using this RSA technique. The multiple scenarios were simulated to compute and analyse the performances in terms of energy consumption, Packet Delivery Ratio (PDR), end to end delay, and throughput. The proposed method named as MAODV-PSO-RSA, which implemented in Network Simulator-2 (NS2). The main objective of the MAODV-PSO-RSA method is to improve the energy consumption during the routing process. The simulation results showed that MAODV-PSO-RSA method had improved 1-2.2 % of network performance compared to the existing methods such as Bio-Inspired Routing Protocol (BIRP) and Improved Frog Leap Inspired Protocol (IFLIP).

Abstract:As there is rapid development of wireless and mobile communications, limited opening band is unable to meet the growing usage of mobile communication. In order to make better use of scarce spectrum resources, Cognitive Radio (CR) has been proposed to exploit underutilization portions of the spectrum, which is capable of interference sensing, environmental learning, and dynamic spectrum access. In this paper, a new routing algorithm for Cognitive Radio networks has been proposed. The algorithm is based on atomic time, known as DDCR (DillyDally Cognitive Routing). The DillyDally path is found by calculating the transmission delay of given packets on every link (weighted link). The transmission delays are found by using classic Dijkstra algorithm. Simulation results show the End-End delay of DDCR is less than that of the traditional routing algorithm. Thereby the network performance is improved.

For the past few years, centralized decision-making is being used for malicious node identification in wireless sensor networks (WSNs). Generally, WSN is the primary technology used to support operations, and security issues are becoming progressively worse. In order to detect malicious nodes in WSN, a blockchain-routing- and trust-model-based jellyfish search optimizer (BCR-TM-JSO) is created. Additionally, it provides the complete trust-model architecture before creating the blockchain data structure that is used to identify malicious nodes. For further analysis, sensor nodes in a WSN collect environmental data and communicate them to the cluster heads (CHs). JSO is created to address this issue by replacing CHs with regular nodes based on the maximum remaining energy, degree, and closeness to base station. Moreover, the Rivest–Shamir–Adleman (RSA) mechanism provides an asymmetric key, which is exploited for securing data transmission. The simulation outcomes show that the proposed BCR-TM-JSO model is capable of identifying malicious nodes in WSNs. Furthermore, the proposed BCR-TM-JSO method outperformed the conventional blockchain-based secure routing and trust management (BSRTM) and distance degree residual-energy-based low-energy adaptive clustering hierarchy (DDR-LEACH), in terms of throughput (5.89 Mbps), residual energy (0.079 J), and packet-delivery ratio (89.29%).

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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.