The database-driven spectrum access system recently attracted increasing amounts of attention. It has more benefits compared to the traditional sensing-based systems. To build a more practical and reliable system, a hybrid sensing-based and database-driven spectrum access system is a promising solution. In this paper, we consider the integration problem of the database information and sensing results, which is a very important factor in order in realizing the hybrid system. We propose the integration framework, which is implemented on the database engine. The framework is divided into two main components. The first one is to process the sensing results, which contains the predictions for locations without sensing results, and the fusion policy on the sensing samples. The second component is the dynamic integration process of the generated sensing results and the database information. We first model the evaluation of the integration results as a Partially Observable Markov Decision Process (POMDP), which enables the database engine to know its current status. Then, we propose an iterative algorithm for the database engine to dynamically adjust its integration policy. In this way, the balanced status of the generated spectrum map is maintained. Simulations are conducted to reveal the performance of our framework.

Opportunistic routing schemes have their advantages if applied in cognitive radio networks (CRNs), since the reliability is better ensured. However, it is impractical and inefficient to directly use opportunistic routing protocols in CRNs. In this paper, we propose a framework of applying opportunistic routing in CRNs. Our framework builds the multi-layer relay sets for each sender, instead of one general relay set, as seen in other wireless networks. A relay set on one layer refers to the set of nodes that are able to help relay on one channel. Moreover, to select a data transmission channel, each sender considers the channel quality as well as the potential reliability brought by the corresponding relay set. In order to better adjust to the situations when PUs suddenly become active during data transmission, we have each sender maintain one main relay set and one backup relay set. It effectively reduces the channel switching and relay set reselection cost during data transmission. We also give an effective adaptation scheme for each sender when all the relay sets fail. Finally, the simulation results indicate the superior performance of our scheme.

Inspired by the spectrum database in the white spaces, we propose a recommendation whose function is similar to that of the spectrum database. We aim at predicting the spectrum availabilities and recommending the part that is more likely to be available for the usage of SUs. The prediction on the spectrum availabilities is through running the spectrum mining algorithm on the collected spectrum data. Therefore, the two main components for our demo are the data collection and spectrum mining. After the mining process is finished, our system would give the predictions and recommendations on the spectrum that is more likely to be available.

Opportunistic routing is very promising when applied to cognitive radio networks (CRNs). However, this requires solving the forwarding node (FN) set selection problem for each node in CRNs. The FN set selection is different from that in traditional networks because the reliability under suddenly active primary users (PUs) and the co-channel interference have to be taken into account. Moreover, the selected FN set cannot stay static in CRNs due to the dynamic channel environment. It means that an adjustment scheme of the FN set is necessary. We construct a FN set selection model for CRNs, which prioritizes each FN candidate considering not only its ETX metrics, but also the channel dynamics and co-channel interference. Based on the defined priorities of each node, we propose three algorithms, a basic greedy algorithm, a greedy algorithm with one backtrack, and a maximum weighted independent set (MWIS) algorithm, for FN set selection aiming at different performance requirements. Extensive simulations are performed to evaluate our FN set selection algorithms.

Virtual backbones have brought about many benefits for routing and data transmission in traditional wireless networks. In cognitive radio networks (CRNs), virtual backbones can also play a critical role, and would increase the efficiency of routing and data transport. However, the virtual backbone construction for CRNs is more challenging than for traditional wireless networks due to opportunistic spectrum access. Moreover, when no common control channel is available to exchange the control information, this problem is even more difficult. In this paper, we propose a novel approach for constructing virtual backbones in CRNs, without relying on a common control channel. Our approach first utilizes the geographical information to let the nodes of a CRN self-organize into cells. Next, the nodes in each cell form into clusters, and a virtual backbone is established over the cluster heads. The virtual backbone is then applied to carry out the end-to-end data transmission. The proposed virtual backbone construction approach requires only limited exchange of control messages. It is efficient and highly adaptable under the opportunistic spectrum access. We analyze the capacity between an active node and a passive node in a single area. Our approach is testified through evaluation of the cost, and also through comparison with other models.

The advantages of virtual backbones have been proven in wireless networks. In cognitive radio networks (CRNs), virtual backbones can also play a critical role in both routing and data transport. However, the virtual backbone construction for CRNs is more challenging than for traditional wireless networks because of the opportunistic spectrum access. Moreover, when no common control channel is available to exchange the control information, this problem is even more difficult. In this paper, we propose a novel approach for constructing virtual backbones in CRNs, without relying on a common control channel. Our approach first utilizes the geographical information to let the nodes of a CRN self-organize into cells. Next, the nodes in each cell form into clusters, and a virtual backbone is established over the cluster heads. The virtual backbone is then applied to carry out the end-to-end data transmission. The proposed virtual backbone construction approach requires only limited exchange of control messages. It is efficient and highly adaptable under the opportunistic spectrum access. We evaluate our approach through extensive simulations.

The unpredictable activities of primary users (PUs) make the channel availabilities in cognitive radio networks (CRNs) very unstable. The dynamic channel availabilities cause routing in CRNs to be more difficult than in traditional wireless networks. Specifically, when a source node needs to select a route to reach the destination, the "optimal" route during the route selection phase may not be reliable because of the sudden appearance of a PU, and thus, may not be optimal during the data transmission phase. In this paper, we propose a novel routing protocol based on the source routing in traditional wireless networks. We consider the angle dimension of CRNs by assuming that the directional antennas are equipped on every node. The directional antennas can facilitate the marking of boundary areas of primary users. We use the USRP/Gnu radio to show the sensing result differences of different directions at the boundary area of a primary user. For every optional route between a source node and a destination node, we can evaluate its reliability and other performance by evaluating the PU areas it passes through, and estimating the possible transmission rate of each link on this route. Based on these parameters, we propose an algorithm for route selection, considering both the reliability and delay. Our routing protocol only requires very limited piggyback information, compared to other routing protocols in CRNs. It is efficient and highly adaptable under the dynamic channel availabilities. We evaluate our approach through extensive simulations.

This paper proposes a distributed rerouting scheme in cognitive radio networks (CRNs). Specifically, we consider how we can reroute for multiple sessions in a situation where switching channels cannot ensure end-to-end connectivity. We propose a ring searching-based framework for rerouting, which is distributed and considers both the channel availabilities and potential rerouting choices of other sessions. Our framework can be applied for the rerouting of multiple sessions in CRNs and does not require any communication among them. The objective in our model is to minimize the queueing delay for each session that needs rerouting. We propose a novel way to estimate the delay for each session. Each node, in our model, that will be receiving RREQ (Route Request) messages maintains two queues: one queue on each node contains a list of nodes whose RREQ is received, and the other one contains the probabilities of it being chosen. We apply an opportunistic overhear approach to predict the probabilities. Also, we take the channel availabilities into account when constructing the rerouting path. The simulation results prove the high performance of our scheme. We also show the influence of different parameter settings, including both network environment parameters and scheme parameters.

Cognitive radio networks (CRNs) promise to enable the next generation of communication networks. The channel assignment (CA) problem is one of the most important issues in CRNs. In this paper, our goal is to design highly efficient and localized protocols for CA. In addition, we want to maximize node connectivity after CA, which is important for packet delivery. To this end, we design two basic algorithms and an advanced algorithm framework. Within this framework, we can change the edge priority in CA to meet different requirements. Simulation results show that the proposed framework is fast (two rounds of communication among nodes, regardless of network size) and outperforms an existing method.

The channel assignment problem is one of the most important issues in cognitive radio networks (CRNs). Under a SINR-driven model, we consider channel assignments in a network using dynamic source routing (DSR). In this unicasting model, channel assignments are conducted in a relatively small scale of nodes, which are on the chosen route. In addition, we can make use of the route reply (RREP) message in DSR to estimate the SINR and the maximum data transmission rate of nodes on the chosen route. In this way, the source node can conduct the channel assignment in a more efficient way. We propose two algorithms for the single route and multi-route channel assignments, where the multi-route scheme uses alternative nodes to help transmitting. We give a complexity analysis of two algorithms and an extension of reducing complexity for the multi-route channel assignment algorithm. Finally, we conduct simulations of our two algorithms under networks with different densities and show that the performance of our algorithms is efficient.

There has been much research focusing on the routing problem in delay tolerant networks (DTNs). Much of the work has mainly focused on coding schemes for message distribution, while other work has been done on the probabilistic forwarding. Coding schemes achieve higher delivery rate via redundancy before forwarding, while probabilistic forwarding efficiently limits the abuse of the store and forward scheme, maintaining relatively high performance. Providing a reliable and efficient forwarding scheme proves to be challenging as coding and forwarding schemes should be jointly considered. In our paper, we present an optimal probabilistic forwarding scheme using fountain code, which we name as CFP (Coded Forwarding Protocol), where reliability and efficiency can be achieved at the same time. In CFP, We use fountain codes to encode messages and provide the forwarding rule to decide whether to forward messages to another node. The probabilistic forwarding problem is modeled as an optimal stopping problem, and our forwarding rule also considers the influence of fountain codes. We perform trace-driven simulations and compare CFP with other protocols. Simulation results show that, considering the delivery rate, delay, and number of forwardings, CFP performs better than other implemented protocols - Epidemic, which is the most original protocol, and OPF, which represents the optimal probabilistic forwarding protocol - in our simulation.

Cyber-Physical Systems require the integration of various heterogeneous networks. To evaluate proposed algorithms in network research, real-world test beds are an indispensable complement to simulations. In this paper, we present a flexible heterogeneous mesh networking platform (FHMESH) which interconnects various heterogeneous networks, i.e. wireless sensor network, wireless mesh network, FM radio network and the Internet. FHMESH builds agile and universal gateways using software defined radio technology, and the gateways form mesh backbone to interconnect those heterogeneous networks. Then, we present the platform architecture and the specific implementation details for FHMESH, and carry out some preliminary experiments. Even though the testing results of our system are not so perfect, we believe that the integration of various heterogeneous networks makes FHMESH work as a prototype for heterogeneous network research, and the flexible and universal gateway can facilitate the cross-layer research.