Optimal linear weighted cooperative spectrum sensing for clustered-based cognitive radio networks

The lack of spectrum resources restricts the development of wireless communication applications. In order to solve the problems of low spectrum utilization and channel congestion caused by the static division of spectrum resource, this paper proposes an optimal linear weighted cooperative spectrum sensing for clustered-based cognitive radio networks. In this scheme, different weight values will be assigned for cooperative nodes according to the SNR of cognitive users and the historical sensing accuracy. In addition, the cognitive users can be clustered, and the users with the better channel characteristics will be selected as cluster heads for gathering the local sensing information. Simulation results show that the proposed scheme can obtain better sensing performance, improve the detection probability and reduce the error probability.

Modified Dijsktra's Routing Algorithm for Security with Different Trust Degrees

A great number of efficient methods to improve the performance of the networks have been proposed in physical-layer security for wireless communications. So far, the security and privacy in wireless communications is optimized based on a fixed assumption about the trustworthiness or trust degrees (TD) of certain wireless nodes. The nodes are often classified into different types such as eavesdroppers, untrusted relays, and trusted cooperative nodes. Wireless nodes in different networks do not completely trust each other when cooperating or relaying information for each other. Optimizing the network based on trust degrees plays an important role in improving the security and privacy for the modern wireless network. We proposed a novel algorithm to find the route with the smallest total transmission time from the source to the destination and still guarantee that the accumulated TD is larger than a trust degree threshold. Simulation results are presented to analyze the affects of the transmit SNR, node density, and TD threshold on different network performance elements.

Research on Cooperative Communication Strategy and Intelligent Agent Directional Source Grouping Algorithms for Internet of Things

In order to improve the network layer of the Internet of things to improve transmission reliability, save time delay and energy consumption, the Internet of things cooperative communication and intelligent agent technology were studied in this paper. In cooperative communication, a new cooperative communication algorithm KCN (k-cooperative node), and a reliability prediction model are proposed. The k value is determined by the end-to-end reliability. After k cooperative nodes are selected, other nodes enter dormancy. In aggregate traffic allocation, game theory is used to model the traffic equilibrium and end-to-end delay optimization scenarios. In practice, the optimal duty cycle can be calculated, which makes some cooperative nodes enter an idle state to save energy. Under the premise of guaranteeing end-to-end delay, it is shown that the reliability of the proposed KCN algorithm is better than that of the other existing routing protocols. In the aspect of intelligent agent, a directional source grouping algorithm D-MIP is proposed. This algorithm studies the routing problem of multi-agent parallel access to multiple source nodes. A directed source packet multi-agent routing planning algorithm is proposed. The iterative algorithm of each source node is limited to a sector, and the optimal intelligent agent route is obtained by selecting an appropriate angle. Compared with other algorithms, it is shown through a lot of simulated results that energy consumption and time delay has been saved by the proposed D-MIP algorithm.

Energy-efficient collaborative transmission algorithm based on potential game theory for beamforming

A group of collaborative nodes can efficiently complete spatial long-distance transmission tasks using beamforming technology. However, a high sidelobe level interferes with communication quality, and uneven energy consumption of nodes affects network lifetime. This paper proposes an energy-efficient collaborative transmission algorithm based on potential game theory for beamforming. First, the minimum number of cooperative nodes is determined in accordance with the energy consumption and spacing limitation condition. A group of nodes satisfying the node spacing condition is selected as cooperative nodes based on the ring array to minimize communication interference among nodes. Second, a potential game model is proposed as a joint method for optimizing the collaborative parameters of the cooperative nodes and their energy consumption balancing features. Finally, the game process is continuously executed until the Nash equilibrium is reached. According to simulation results, the sidelobe level caused by the cooperative nodes is reduced and the transmission conflicts are lessened. Thus, the quality of communication links in between nodes in the network is improved. Energy efficiency is also promoted because a balancing of energy consumption is involved in the proposed potential game model, and network lifetime is effectively prolonged accordingly.

Multi-Hop Cooperative MIMO Transmission in Wireless Sensor Networks

This article presents a multihop spatial multiplexing cooperative MIMO (SM-CMIMO) transmission scheme. The SM-CMIMO scheme employs a novel configuration on MIMO system which works in a cooperative manner, to achieve a least energy consuming path, with reduced rate of bit error and higher data rate. In this approach, list of relay nodes located in equal distance at any route towards destination selected. The source selects a set of relay nodes and cooperative nodes to frame a cooperative MIMO System. The cooperative nodes are selected using NSA (Node Selection Algorithm) with no channel state, bit error rate and data rate constraints. Each hop is evaluated for suitable replacement with V-MIMO configurations. To reduce the depletion of energy at MIMO cooperative network, at the middle of these hops, relay nodes are selected and evaluated for possible replacement of long-hop with two smaller MIMO transmission hops. We show that proposed SM-CMIMO transmission offers significant energy efficiency improvement compared with SISO transmission. The performance of the system degrades indirectly with the channel correlation.

OCA-MAC: A Cooperative TDMA-Based MAC Protocol for Vehicular Ad Hoc Networks

Cooperative communication is an effective method of improving the transmission performance for vehicular ad hoc networks. However, the rapid movement of vehicles leads to frequent changes in network topology and reduces the probability of successful data transmission on the medium access control (MAC) layer. In this paper, we propose an Optimal Cooperative Ad hoc MAC protocol (OCA-MAC) based on time division multiple access (TDMA). OCA-MAC utilizes multiple cooperative nodes to forward data, so as to improve the probability of successful data transmission. It chooses cooperative nodes according to direct successful transmission probability, communication range between potential helper node and destination node, and available time slot. Meanwhile, in order to avoid excessive transmission redundancy caused by multiple cooperative forwarding, the optimal number of cooperative forwarding nodes is obtained through analysis of a probabilistic model. Simulation results show that OCA-MAC improves the successful data transmission rate and reduces the number of transmission times and transmission delay compared to the multichannel TDMA MAC protocol (VeMAC) and the cooperative ad hoc MAC protocol (CAH-MAC).

Factor Graph-Assisted Distributed Cooperative Positioning Algorithm in the GNSS System

The development of smart cities calls for improved accuracy in navigation and positioning services; due to the effects of satellite orbit error, ionospheric error, poor quality of navigation signals and so on, it is difficult for existing navigation technology to achieve further improvements in positioning accuracy. Distributed cooperative positioning technology can further improve the accuracy of navigation and positioning with existing GNSS (Global Navigation Satellite System) systems. However, the measured range error and the positioning error of the cooperative nodes exhibit larger reductions in positioning accuracy. In response to this question, this paper proposed a factor graph-aided distributed cooperative positioning algorithm. It establishes the confidence function of factor graphs theory with the ranging error and the positioning error of the coordinated nodes and then fuses the positioning information of the coordinated nodes by the confidence function. It can avoid the influence of positioning error and ranging error and improve the positioning accuracy of cooperative nodes. In the simulation part, the proposed algorithm is compared with a mainly coordinated positioning algorithm from four aspects: the measured range error, positioning error, convergence speed, and mutation error. The simulation results show that the proposed algorithm leads to a 30–60% improvement in positioning accuracy compared with other algorithms under the same measured range error and positioning error. The convergence rate and mutation error elimination times are only 1 / 5 to 1 / 3 of the other algorithms.

Resilient Multiscale Coordination Control against Adversarial Nodes

Multiscale consensus has been studied recently as a new concept in the field of multi-agent systems, which is able to accommodate many complicated coordination control tasks where values are measured in different scales due to, e.g., the constraints of physical environment. In this paper, we investigate the problem of resilient multiscale coordination control against a set of adversarial or non-cooperative nodes in directed networks. We design a multiscale filtering algorithm based upon local information which can withstand both faulty and Byzantine nodes. Building on the concept of network robustness, we establish necessary and sufficient conditions guaranteeing multiscale consensus with general time varying scales in the presence of globally bounded as well as locally bounded threats. In particular, for a network containing at most R faulty nodes, multiscale consensus is achieved if and only if the network is (R+1,R+1)-robust. The counterpart when having at most R Byzantine nodes instead is that the induced subnetwork of cooperative nodes is R+1-robust. Conditions guaranteeing resilient consensus for time-dependent networks are developed. Moreover, multiscale formation generation problems are introduced and solved as the generalizations. Finally, some numerical examples including applications in modular microgrids and power systems are worked out to demonstrate the availability of our theoretical results.