Many efforts have been devoted to maximizing network throughput in a multichannel multiradio wireless mesh network. Most current solutions are based on either purely static or purely dynamic channel allocation approaches. In this paper, we propose a hybrid multichannel multiradio wireless mesh networking architecture, where each mesh node has both static and dynamic interfaces. We first present an Adaptive Dynamic Channel Allocation protocol (ADCA), which considers optimization for both throughput and delay in the channel assignment. In addition, we also propose an Interference and Congestion Aware Routing protocol (ICAR) in the hybrid network with both static and dynamic links, which balances the channel usage in the network. Our simulation results show that compared to previous works, ADCA reduces the packet delay considerably without degrading the network throughput. The hybrid architecture shows much better adaptivity to changing traffic than purely static architecture without dramatic increase in overhead, and achieves lower delay than existing approaches for hybrid networks.

Cognitive radio (CR), which enables dynamic access of underutilized licensed spectrums, is a promising technology for more efficient spectrum utilization. Since cognitive radio enables the access of larger amount of spectrum, it can be used to build wireless mesh networks with higher network capacity, and thus provide better quality of services for high bit-rate applications. In this paper, we study the multisource video on-demand application in multi-interface cognitive wireless mesh networks. Given a video request, we find a joint multipath routing and spectrum allocation for the session to minimize its total bandwidth cost in the network, and therefore maximize the number of sessions the network can support. We propose both distributed and centralized routing and channel allocation algorithms to solve the problem. Simulation results show that our algorithms increase the maximum number of concurrent sessions that can be supported in the network, and also improve each session's performance with regard to spectrum mobility.

Wireless mesh networks have attracted great interest in the research community recently. Much effort has been devoted to maximizing the network performance using limited channel resources in a multichannel multiradio wireless mesh network. It is believed that the limited spectrum resource can be fully exploited by utilizing partially overlapping channels in addition to nonoverlapping channels in 802.11b/g networks. However, there are only few studies of channel assignment algorithms for partially overlapping channels. In this paper, we first formulate the optimal channel assignment problem with the goal of maximizing the overall network throughput or maximizing the throughput of a multicast application. For both cases, we present a greedy algorithm for partially overlapping channel assignment, and then propose a novel genetic algorithm, which has the potential to obtain better solutions. Through evaluation, we demonstrate that the overall network throughput can be dramatically improved by properly utilizing the partially overlapping channels. The genetic algorithm outperforms the greedy algorithm in mitigating the network interference, and therefore leads to higher network throughput. In addition, the multicast throughput can also be dramatically improved by using our algorithms compared to previous work.

We study the multisource video on-demand (VoD) application in multichannel multiradio wireless mesh networks. When a user initiates a new video request, the application can stream the video not only from the media servers, but also from the peers that have buffered the video. The multipath multisource video on-demand streaming has been applied in wired networks with great success. However, it remains a challenging task in wireless networks due to wireless interference. In this paper, we first focus on the problem of finding the maximum number of high-quality and independent paths from the user to the servers or peers for each VoD request by considering the effect of wireless interference. We formulate it as a constrained maximum independent paths problem and propose two efficient heuristic path discovery algorithms. Based on the multiple paths discovered, we further propose a joint routing and rate allocation algorithm, which minimizes the network congestion caused by the new VoD session. The algorithm is aware of the optimization for both existing and potential VoD sessions in the wireless mesh network. We evaluate our algorithms with real video traces. Simulation results demonstrate that our algorithm not only improves the average video streaming performance over all the coexisting VoD sessions in the network, but also increases the network's capacity of satisfying more subsequent VoD requests.

We study the multi-source video on-demand application in multi-channel multi-radio wireless mesh networks. When a user initiates a new video request, the application can stream the video not only from the media servers, but also from the peers that have buffered the video. The multi-path multi-source video on-demand streaming has been applied in wired networks with great success. However, it remains a challenging task in wireless networks due to wireless interference. In this paper, we first focus on the problem of finding the maximum number of high-quality and independent paths from the user to the servers or peers for each VoD request by considering the effect of wireless interference. We formulate it as a constrained maximum independent paths problem, and propose two efficient heuristic path discovery algorithms. Based on the multiple paths discovered, we further propose a joint routing and rate allocation algorithm, which minimizes the network congestion caused by the new VoD session. The algorithm is aware of the optimization for both existing and potential VoD sessions in the wireless mesh network. We evaluate our algorithms with real video traces. Simulation results demonstrate that our algorithm not only improves the average video streaming performance over all the coexisting VoD sessions in the network, but also increases the network's capacity of satisfying more subsequent VoD requests.

Cognitive radio, which enables dynamic access of under-utilized licensed spectrums, is a promising technology for more efficient spectrum utilization. Since cognitive radio enables the access of larger amount of spectrum, it can be used to build wireless mesh networks with higher network capacity, and thus provide better quality of services for high bit-rate applications. In this paper, we study the multi-source video on-demand application in multiinterface cognitive wireless mesh networks. Given a video request, we find a joint multi-path routing and spectrum allocation for the session to minimize its total bandwidth cost in the network, and therefore maximize the number of sessions the network can support. We propose both distributed and centralized routing and channel allocation algorithms to solve the problem. Our algorithms not only increase the maximum number of concurrent sessions that can be supported in the network, but also improve each session's adaptivity to spectrum mobility.

The wireless mesh network is an emerging technology that provides high quality service to end users as the "last milerdquo of the Internet. Furthermore, multicast communication is a key technology for wireless mesh networks. Multicast provides efficient data distribution among a group of nodes. However, unlike other wireless networks, such as sensor networks and MANETs, where multicast algorithms are designed to be energy efficient and to achieve optimal route discovery among mobile nodes, wireless mesh networks need to maximize throughput. This paper proposes two multicast algorithms: the level channel assignment (LCA) algorithm and the multichannel multicast (MCM) to improve the throughput for multichannel and multi-interface mesh networks. The algorithms build efficient multicast trees by minimizing the number of relay nodes and total hop count distances of the trees. The algorithms use dedicated channel assignment strategies to reduce the interference to improve the network capacity. We also demonstrate that using partially overlapping channels can further diminish the interference. Furthermore, additional interfaces help to increase the bandwidth, and multiple gateways can further shorten the total hop count distance. Simulations show that those algorithms greatly outperform the single-channel multicast algorithm. We also observe that MCM achieves better throughput and shorter delay while LCA can be realized in distributed manner.

Many efforts have been devoted to maximizing network throughput in a multi-channel multi-radio wireless mesh network. Current solutions are based on either pure static or pure dynamic channel allocation approaches. In this paper, we propose a hybrid multi-channel multi-radio wireless mesh networking architecture, where each mesh node has both static and dynamic interfaces. We first present an Adaptive Dynamic Channel Allocation protocol (ADCA), which considers optimization for both throughput and delay in the channel assignment. In addition, we also propose an Interference and Congestion Aware Routing protocol (ICAR) in the hybrid network with both static and dynamic links, which balances the channel usage in the network. Compared to previous work, our simulation results show that ADCA reduces the packet delay considerably without degrading the network throughput. Moreover, the hybrid architecture shows much better adaptivity to changing traffic than pure static architecture without dramatic increase in overhead.

This paper presents an adaptive partitioning scheme of sensor networks for node scheduling and topology control with the aim of reducing energy consumption. Our scheme partitions sensors into groups such that a connected backbone network can be maintained by keeping only one arbitrary node from each group in active status while putting others to sleep. Unlike previous approaches that partition nodes geographically, our scheme is based on the measured connectivity between pairwise nodes and does not depend on nodes' locations. In this paper, we formulate node scheduling with topology control as a constrained optimal graph partition problem, which is NP-hard, and propose a Connectivity-based Partition Approach (CPA), which is a distributed heuristic algorithm, to approximate a good solution. We also propose a probability-based CPA algorithm to further save energy. CPA can ensure K-vertex connectivity of the backbone network, which achieves the trade-off between saving energy and preserving network quality. Moreover, simulation results show that CPA outperforms other approaches in complex environments where the ideal radio propagation model does not hold.

Multicast is a key technology that provides efficient data communication among a set of nodes for wireless multi-hop networks. In sensor networks and MANETs, multicast algorithms are designed to be energy efficient and to achieve optimal route discovery among mobile nodes, respectively. However, in wireless mesh networks, which are required to provide high quality service to end users as the "last-mile" of the Internet, throughput maximization conflicting with scarce bandwidth has the paramount priority. We propose a Level Channel Assignment (LCA) algorithm and a Multi-Channel Multicast (MCM) algorithm to optimize throughput for multi-channel and multi-interface mesh networks. The algorithms first build a multicast structure by minimizing the number of relay nodes and hop count distances between the source and destinations, and use dedicated channel assignment strategies to improve the network capacity by reducing interference. We also illustrate that the use of partially overlapping channels can further improve the throughput. Simulations show that our algorithms greatly out-perform the single-channel multicast algorithm. We observe that MCM achieves better throughput and shorter delay while LCA can be realized in distributed manner.

In sensor localization, ultrasound based distance measurements will have large errors if line-of-sight paths are blocked between pairwise sensors. Because these outliers, once mixed together with other correct distance measurements, are difficult for localization algorithms to identify, we propose to exclude the outliers in the first step of distance measurement. Although the distance between pairwise sensors measured by ultrasound can have multiple values due to the multipath effect, our experiments find that a sensor's incorrect position, estimated from the distance measurement along a reflected path instead of a straight line, is always mirrored to the sensor's correct position. Based on this phenomena, we propose to use mobile beacons to measure the distance between pairwise sensors from multiple perspectives and filter incorrect values through a statistical approach. Our performance evaluation shows that the proposed algorithm can achieve better localization results than previous approaches in an indoor environment where multipath effects cannot be avoided