When communication network planning-design is performed, especially in a short-term case, it is important to utilize existing facilities in the construction of the new network. In this paper, link capacity assignment problem (CA problem) for packet-switched networks is investigated with the consideration of the existing network. To deal with this, per-unit cost of existing link capacity is thought to be less than that of newly installed capacity and a link cost function is modeled by a non-linear, non-differentiable one which is composed of two portions of capacity cost. After formulating the CA problem, two optimum algorithms derived from Lagrange multiplier method are presented and a modified algorithm is used for solving the CA problem in order to reduce the computation time. Some numerical results show that according to the values of link traffic flows, there will be links whose capacities must be set equally to the existing values. Moreover, when link cost difference is introduced in the CA problem, the number of links that the capacities of which have to be changed from existing values is less than that of linear cost function case, i.e., the case without consideration of the cost difference in link capacity.

With the increase of the number of wireless sensing or metering devices, the collection of sensing data using wireless communication becomes an important part of a smart grid system. Cognitive radio technology can be used to facilitate the deployment of smart grid systems. In this paper, we propose a data collection and dissemination framework for cognitive radio smart grid systems to fully utilize wireless resources while maintaining a reliably connected and efficient topology for each channel. In the proposed framework, each sensor node selects a channel considering the primary user (PU) channel utilization and network connectivity. In this way, the data collection and dissemination can be performed with a high reliability and short delay while avoiding a harmful effect on primary users. We use computer simulations to evaluate the proposed framework.

Orthogonal Frequency Division Multiple Access (OFDMA) is the technique for the next generation wireless networks, whose enhanced capacity is to serve a combination of traffic with diverse QoS requirements. To realize this, the resource allocation scheme has to be carefully designed so that the instantaneous channel condition, QoS provision, and the network utilization are integrated. In this paper, we propose the resource allocation scheme for downlink traffic of 2 classes; guaranteed and non-guaranteed, having different traffic contracts. We provide guaranteed throughput for the guaranteed class by considering the cost incurred from serving this class. Then, we formulate the assignment problem with the objective of minimizing this cost. For the non-guaranteed class, we aim to maximize network utilization and to maintain throughput fairness, by employing Proportional Fairness (PF) utility function and emphasizing on the portion of network resource that the user received and the individual user's queue length. We use a heuristic approach to schedule users' data into the downlink subframe by exploiting multi-user multi-channel diversity to utilize system's bandwidth efficiently. Intensive simulation shows that our scheme differentiates classes of traffic and provides satisfied throughput, lower packet drop rate, and lower queuing delay to the guaranteed class, comparing with those of the non-guaranteed class. Furthermore, the results also show that the scheme is fair to users in the same class in both throughput and service time.

We have developed a novel downlink packet scheduling scheme for a multiuser OFDMA system in which a subchannel can be time-multiplexed among multiple users. This scheme which is called Matrixed-based Proportional Fairness can provide a high system throughput while ensuring fairness. The scheme is based on a Proportional Fairness (PF) utility function and can be applied to any of the PF-based schedulers. Our scheduler explores multichannel multiuser diversity by using a two-dimensional matrix combining user selection, subchannel assignment, and time slot allocation. Furthermore, unlike other PF-based schemes, our scheme considers finitely backlogged queues during the time slot allocation. By doing so, it can exploit multichannel multiuser diversity to utilize bandwidth efficiently and with throughput fairness. Additionally, fairness in the time domain is enhanced by limiting the number of allocated time slots. Intensive simulations considering finitely backlogged queues and user mobility prove the scheme's effectiveness.

Scalable video coding with different modulation and coding schemes (MCSs) applied to different video layers is very appropriate for wireless multicast services because it can provide different video quality to different users according to their channel conditions, and a promising solution to handle packet losses induced by fading wireless channels is the use of layered hybrid FEC/ARQ scheme according to light-weight feedback messages from users about how many packets they have received. It is important to choose an appropriate MCS for each layer, decide how many parity packets in one layer should be transmitted, and determine the resources allocated to multiple video sessions to apply scalable video coding to wireless multicast streaming. We prove that such resource allocation problem is NP-hard and propose an approximate optimal algorithm with a polynomial run time. The algorithm can get the optimal transmission configuration to maximize the expected utility for all users where the utility can be a generic non-negative, non-decreasing function of the received rate. The results from simulations revealed that our algorithm offer significant improvements to video quality over a nave algorithm, an optimal algorithm without feedback from users, and an algorithm with feedback from designated users, especially in scenarios with multiple video sessions and limited radio resources.

Traffic volume anomalies refer to apparently abrupt changes in the time series of traffic volume, which can propagate through the network. Detecting and tracing these anomalies is a critical and difficult task for network operators. In this paper, we first propose a traffic decomposition method, which decomposes the traffic into three components: the trend component, the autoregressive (AR) component, and the noise component. A traffic volume anomaly is detected when the AR component is outside the prediction band for multiple links simultaneously. Then, the anomaly is traced using the projection of the detection result matrices for the observed links which are selected by a shortest-path-first algorithm. Finally, we validate our detection and tracing method by using the real traffic data from the third-generation Science Information Network (SINET3) and show the detected and traced results.

To accurately evaluate and manage future distributed wireless networks, it is indispensable to fully understand cooperative propagation channels. In this contribution, we propose cascaded multi-keyhole channel models for analyzing cooperative diversity wireless communications. The cascaded Wishart distribution is adopted to investigate the eigenvalue distribution of the multi-keyhole MIMO (multiple input multiple output) channel matrix, and the capacity performance is also presented for the wireless systems over such channels. A diversity order approximation method is proposed for better evaluating the eigenvalue and capacity distributions. The good match of analytical derivations and numerical simulations validates the proposed models and analysis methods. The proposed models can provide an important reference for the optimization and management of cooperative diversity wireless networks.

The biggest challenge in multi-cell MIMO multiplexing systems is how to effectively suppress the other-cell interference (OCI) since the OCI severely decrease the system performance. Cooperation among cells is one of the most promising solutions to OCI problems. However, this solution suffers greatly from delay and overhead issues, which make it impractical. A coordinated MIMO system with a simplified cooperation between the base stations is a compromise between the theory and practice. We aim to devise an effective resource allocation algorithm based on a coordinated MIMO system that largely alleviates the OCI. In this paper, we propose a joint resource allocation algorithm incorporating intra-cell beamforming multiplexing and inter-cell interference suppression, which adaptively allocates the transmitting power and schedules users while achieving close to an optimal system throughput under proportional fairness consideration. We formulate this problem as a nonlinear combinational optimization problem, which is hard to solve. Then, we decouple the variables and transform it into a problem with convex sub-problems that can be solve but still need heavy computational complexity. In order to implement the algorithm in real-time scenarios, we reduce the computational complexity by assuming an equal power allocation utility to do user scheduling before the power allocation. Extensive simulation results show that the joint resource allocation algorithm can achieve a higher throughput and better fairness than the traditional method while maintains the proportional fairness. Moreover, the low-complexity algorithm obtains a better fairness and less computational complexity with only a slight loss in throughput.

To improve the data rate in OFDMA-based wireless networks, Carrier Aggregation (CA) technology has been included in the LTE-Advanced standard. Different Carrier Component (CC) capacities of users under the same eNodeB (eNB, i.e. Base Station) make it challenging to allocate resources with CA. In this paper, we jointly consider CC and Resource Block (RB) assignments, and power allocation to achieve proportional fairness in the long term. The goal of the problem is to maximize the overall throughput with fairness consideration. We consider a more general CC assignment framework that each User Equipment (UE) (i.e. Mobile Station) can support any number of CCs. Furthermore, we have proved the problem is NP-hard, even if power is equally allocated to RBs. Thus, first an optimal RB assignment and power allocation algorithm is proposed and then a carrier aggregation enabled joint resource allocation algorithm called CARA is proposed. By jointly considering CC and RB assignments, and power allocation, the proposed approach can achieve better performance. Simulation results show the proposed algorithm can significantly improve performance, e.g., total throughput compared with the existing algorithm.

We propose a distributed data management approach in this paper for a large-scale position-tracking system composed of multiple small systems based on wireless tag technologies such as RFID and Wi-Fi tags. Each of these small systems is called a domain, and a domain server manages the position data of the users belonging to its managing domain and also to the other domains but temporarily residing in its domain. The domain servers collaborate with each other to globally manage the position data, realizing the global position tracking. Several domains can be further grouped to form a larger domain, called a higher-domain, so that the whole system is constructed in a hierarchical structure. We implemented the proposed approach in an experimental environment, and conducted a performance evaluation on the proposed approach and compared it with an existing approach wherein a central server is used to manage the position data of all the users. The results showed that the position data processing load is distributed among the domain servers and the traffic for position data transmission over the backbone network can be significantly restrained.

Numerous studies have shown that scaling exponents of internet traffic change over time or scaling ranges. In order to analyze long-range dependent traffic with changing scaling exponents over time scales, we propose a multi-scale traffic model that incorporates the notion of a piecewise self-similar process, a process with spectral changes on its scaling behavior. We can obtain a performance curve smoothened over the range of queue length corresponding to time scales with different scaling exponents by adopting multiple self-similar processes piecewise into different spectra of time scale. The analytical method for the multiscale fractional Brownian motion is discussed as a model for this approach. A comparison of the analytical and simulation results, using traffic data obtained from backbone networks, shows that our model provides a good approximation for Gaussian traffic.

With the increasing popularity of multicast and real-time streaming service applications, efficient channel assignment algorithms that handle both multicast and unicast traffic in wireless mesh networks are needed. One of the most effective approaches to enhance the capacity of wireless networks is to use systems with multiple channels and multiple radio interfaces. However, most of the past works focus on vertex coloring of a general contention graph, which is NP-Complete, and use the greedy algorithm to achieve a suboptimal result. In this paper, we combine unicast and multicast with a transmission set, and propose a framework named Chordal Graph Based Channel Assignment (CGCA) that performs channel assignment for multicast and unicast traffic in multi-channel multi-radio wireless mesh networks. The proposed framework based on chordal graph coloring minimizes the interference of the network and prevents unicast traffic from starvation. Simulation results show that our framework provides high throughput and low end-to-end delay for both multicast and unicast traffic. Furthermore, our framework significantly outperforms other well-known schemes that have a similar objective in various scenarios.

The integration of multihop relays with orthogonal frequency-division multiple access (OFDMA) cellular infrastructures can meet the growing demands for better coverage and higher throughput. Resource allocation in the OFDMA two-hop relay system is more complex than that in the conventional single-hop OFDMA system. With time division between transmissions from the base station (BS) and those from relay stations (RSs), fixed partitioning of the BS subframe and RS subframes can not adapt to various traffic demands. Moreover, single-hop scheduling algorithms can not be used directly in the two-hop system. Therefore, we propose a semi-distributed algorithm called ASP to adjust the length of every subframe adaptively, and suggest two ways to extend single-hop scheduling algorithms into multihop scenarios: link-based and end-to-end approaches. Simulation results indicate that the ASP algorithm increases system utilization and fairness. The max carrier-to-interference ratio (Max C/I) and proportional fairness (PF) scheduling algorithms extended using the end-to-end approach obtain higher throughput than those using the link-based approach, but at the expense of more overhead for information exchange between the BS and RSs. The resource allocation scheme using ASP and end-to-end PF scheduling achieves a tradeoff between system throughput maximization and fairness.

This paper proposes an advanced hybrid network architecture and a comprehensive network design of the next-generation science information network, called SINET3. Effectively combining layer-1 switches and IP/MPLS routers, the network provides layer-1 end-to-end circuit services as well as IP and Ethernet services and enables flexible resource allocation in response to service demands. The detailed network design focuses on the tangible achievement of providing a wide range of network services, such as multiple layer services, multiple virtual private network services, advanced qualities of service, and layer-1 bandwidth on demand services. It also covers high-availability capabilities and effective resource assignment in the hybrid network. The cost reduction effect of our network architecture is also shown in this paper.

Detecting a variety of anomalies caused by attacks or accidents in computer networks has been one of the real challenges for both researchers and network operators. An effective technique that could quickly and accurately detect a wide range of anomalies would be able to prevent serious consequences for system security or reliability. In this article, we characterize detection techniques on the basis of learning models and propose an unsupervised learning model for real-time anomaly detection in computer networks. We also conducted a series of experiments to examine capabilities of the proposed model by employing three well-known machine learning algorithms, namely multivariate normal distribution, k-nearest neighbor, and one-class support vector machine. The results of these experiments on real network traffic suggest that the proposed model is a promising solution and has a number of flexible capabilities to detect several types of anomalies in real time.

Networking technologies have recently been evolving and network applications are now expected to support flexible composition of upper-layer network services, such as security, QoS, or personal firewall. We propose a multi-platform framework called FreeNA* that extends existing applications by incorporating the services based on user definitions. This extension does not require users to modify their systems at all. Therefore, FreeNA is valuable for experimental system usage. We implemented FreeNA on both Linux and Microsoft Windows operating systems, and evaluated their functionality and performance. In this paper, we describe the design and implementation of FreeNA including details on how to insert network services into existing applications and how to create services in a multi-platform environment. We also give an example implementation of a service with SSL, a functionality comparison with relevant systems, and our performance evaluation results. The results show that FreeNA offers finer configurability, composability, and usability than other similar systems. We also show that the throughput degradation of transparent service insertion is 2% at most compared with a method of directly inserting such services into applications.

Cognitive radio ad hoc networks can be used to solve the problems of limited available spectrum and inefficient spectrum usage by adaptively changing their transmission parameters. Routing protocol design has a significant impact on the network performance. However, an efficient protocol that takes account of primary user flows and the long-term channel assignment issue in route selection is still missing. In this paper, we propose AODV-cog, a cognitive routing protocol for CSMA/CA ad hoc networks based on AODV. AODV-cog chooses a route by considering the effect on the primary users, available channel bandwidth and link reliability. AODV-cog also takes account of future channel utilization which is an important but underexplored issue. AODV-cog switches channels for secondary user flows when network congestion occurs. We use theoretical analysis and computer simulations to show the advantage of AODV-cog over existing alternatives.

Multiple-antenna wireless systems, also known as multiple-input multiple-output (MIMO) cellular networks, can improve the capacity and reliability of communications. To realize these advantages, a packet scheduler should effectively allocate radio resources to users in a fair way. The previously proposed MIMO schedulers have problems such as ignoring traffic arrival process or complexity. We propose a load adaptive multi-output fair queueing (LA-MO-FQ) scheduler, which is based on a fair queueing algorithm with mechanisms for rate selection, compensation of lagging users, and virtual time system. Since some of the scheduler's system parameters are sensitive to the traffic load, it dynamically adjusts them in a way with low complexity so the system performs better. Intensive simulation studies considering the mobility of users and the traffic arrival demonstrate the good performance of LA-MO-FQ. Furthermore, we also propose in this paper some formulae for the time and service fairness comparisons of MIMO schedulers and we use them for comparison with some famous existing schedulers.

Authentication issue has been mostly ignored to ensure fast handoff in 802.11 Wireless Mesh Network (WMN). With the proliferation of WMNs in recent years for practical deployment, secured fast handoff has drawn much attention to enforce authenticated access while reduce the extra delay caused by enabling authentication operations. In this paper, we present an overview on the state-of-the-art advance in this field and tackle the problem from a practical perspective based on experiments and analysis on our real-world testbed HAWK. We propose a novel fast handoff scheme Network-assisted Radio Signature to eliminate probing delay by taking advantage of the characteristic of the actual dynamic topology about mesh routers in WMN. Moreover, we apply an optimistic authentication mechanism Dual Re-authentication to counteract the authentication delay while providing the secured wireless access. In this manner, we have reduced the end-to-end handoff delay of WMN back again to a level below 50 ms to achieve secured handoff and support time-sensitive applications. We describe detailed mechanisms, simulation, implementation and experimental results. To our best knowledge, we are the first to achieve such an optimal performance of secured fast handoff.