Joint Channel Assignment and Opportunistic Routing for Maximizing Throughput in Cognitive Radio Networks

In this paper, we consider the joint opportunistic routing and channel assignment problem in multi-channel multi-radio (MCMR) cognitive radio networks (CRNs) for improving aggregate throughput of the secondary users. We first present the nonlinear programming optimization model for this joint problem, taking into account the feature of CRNs-channel uncertainty. Then considering the queue state of a node, we propose a new scheme to select proper forwarding candidates for opportunistic routing. Furthermore, a new algorithm for calculating the forwarding probability of any packet at a node is proposed, which is used to calculate how many packets a forwarder should send, so that the duplicate transmission can be reduced compared with MAC-independent opportunistic routing & encoding (MORE) [11]. Our numerical results show that the proposed scheme performs significantly better that traditional routing and opportunistic routing in which channel assignment strategy is employed.Comment: 5 pages, 4 figures, to appear in Proc. of IEEE GlobeCom 201

Cross-layer Balanced and Reliable Opportunistic Routing Algorithm for Mobile Ad Hoc Networks

For improving the efficiency and the reliability of the opportunistic routing algorithm, in this paper, we propose the cross-layer and reliable opportunistic routing algorithm (CBRT) for Mobile Ad Hoc Networks, which introduces the improved efficiency fuzzy logic and humoral regulation inspired topology control into the opportunistic routing algorithm. In CBRT, the inputs of the fuzzy logic system are the relative variance (rv) of the metrics rather than the values of the metrics, which reduces the number of fuzzy rules dramatically. Moreover, the number of fuzzy rules does not increase when the number of inputs increases. For reducing the control cost, in CBRT, the node degree in the candidate relays set is a range rather than a constant number. The nodes are divided into different categories based on their node degree in the candidate relays set. The nodes adjust their transmission range based on which categories that they belong to. Additionally, for investigating the effection of the node mobility on routing performance, we propose a link lifetime prediction algorithm which takes both the moving speed and moving direction into account. In CBRT, the source node determines the relaying priorities of the relaying nodes based on their utilities. The relaying node which the utility is large will have high priority to relay the data packet. By these innovations, the network performance in CBRT is much better than that in ExOR, however, the computation complexity is not increased in CBRT.Comment: 14 pages, 17 figures, 31 formulas, IEEE Sensors Journal, 201

Computational Intelligence Inspired Data Delivery for Vehicle-to-Roadside Communications

We propose a vehicle-to-roadside communication protocol based on distributed clustering where a coalitional game approach is used to stimulate the vehicles to join a cluster, and a fuzzy logic algorithm is employed to generate stable clusters by considering multiple metrics of vehicle velocity, moving pattern, and signal qualities between vehicles. A reinforcement learning algorithm with game theory based reward allocation is employed to guide each vehicle to select the route that can maximize the whole network performance. The protocol is integrated with a multi-hop data delivery virtualization scheme that works on the top of the transport layer and provides high performance for multi-hop end-to-end data transmissions. We conduct realistic computer simulations to show the performance advantage of the protocol over other approaches

EOAODV: Routing Protocol for Cognitive Radio Network

Cognitive Radio (CR) technology provides promising and a new solution to improve the spectrum utilization. In recent years, cognitive radio technology (CR) has been proposed to allow unlicensed secondary users (SUs) to opportunistically access the channels unused by primary users (PU). This paper focuses on designing Enhancement of Opportunistic Ad-hoc On Demand Distance Vector (EOAODV) routing protocol that uses shortest distance, Expected Transmissions Count (ETC) and residual energy as a parameter to select the most reliable link and the next forwarding node. The selection of route in the network by the traditional AODV is based on hop count. It is proposed to achieve a gain of Opportunistic Routing (OR) with AODV for cognitive radio wireless sensor networks (CRWSN) to improve its efficiency. In the OR work the nexthop node selection was based on only Expected Transmission Count (ETC). In this case if the same node is selected as nexthop for many times, energy of that node is drained and node may be dead. To overcome this problem, a technique is contributed that is energy based nexthop selection ie. EOAODV. The ETC is computed based on the quantized value of RSSI of the links with residue energy in the forwarding node. Using ETC the reliable link is computed and stored in routing table. The packets are tranmitted to the destination using channel details and the next hop, available in the routing table. The next hop selection is based on high energy in the nodes, shortest distance and least ETC

Performance analysis of Routing Protocol for Low power and Lossy Networks (RPL) in large scale networks

With growing needs to better understand our environments, the Internet-of-Things (IoT) is gaining importance among information and communication technologies. IoT will enable billions of intelligent devices and networks, such as wireless sensor networks (WSNs), to be connected and integrated with computer networks. In order to support large scale networks, IETF has defined the Routing Protocol for Low power and Lossy Networks (RPL) to facilitate the multi-hop connectivity. In this paper, we provide an in-depth review of current research activities. Specifically, the large scale simulation development and performance evaluation under various objective functions and routing metrics are pioneering works in RPL study. The results are expected to serve as a reference for evaluating the effectiveness of routing solutions in large scale IoT use cases