High-speed wireless access technologies have evolved over the last years setting new challenges for TCP. That is, to effectively utilize the available network resources and to minimize the effects of wireless channel errors on TCP performance. This paper introduces a new TCP variant, called TCP-BIAD aiming at enhancing TCP performance in broadband wireless access networks. We provide analytical expressions for evaluating the stability, throughput, fairness and friendliness properties of our proposal, and we validate our results by means of computer simulations. Initial results presented in this paper show that this approach achieves high network utilization levels in a wide range of network conditions, while maintaining an adequately fair and friendly behavior with respect to coexisting TCP flows.

Open-access femtocell networks assure the cellular user of getting a better and stronger signal. However, due to the small range of femto base stations (FBSs), any motion of the user may trigger handover. In a dense environment, the possibility of such handover is very frequent. To avoid frequent communication disruptions due to phenomena such as the ping-pong effect, it is necessary to ensure the effectiveness of the cell selection method. Existing selection methods commonly uses a measured channel/cell quality metric such as the channel capacity (between the user and the target cell). However, the throughput experienced by the user is time-varying because of the channel condition, i.e., owing to the propagation effects or receiver location. In this context, the conventional approach does not reflect the future performance. To ensure the efficiency of cell selection, user's decision needs to depend not only on the current state of the network, but also on the future possible states (horizon). To this end, we implement a learning algorithm that can predict, based on the past experience, the best performing cell in the future. We present in this paper a reinforcement learning (RL) framework as a generic solution for the cell selection problem in a non-stationary femtocell network that selects, without prior knowledge about the environment, a target cell by exploring past cells' behavior and predicting their potential future states based on Q-learning algorithm. Then, we extend this proposal by referring to a fuzzy inference system (FIS) to tune Q-learning parameters during the learning process to adapt to environment changes. Our solution aims at minimizing the frequency of handovers without affecting the user experience in terms of channel capacity. Simulation results demonstrate that· our solution comes very close to the performance of the opportunistic method in terms of capacity, while fewer handovers are required on average.· the use of fuzzy rules achieves better performance in terms of received reward (capacity) and number of handovers than fixing the values of Q-learning parameters.

The past decade has witnessed a growing interest in vehicular networking. Initially motivated by traffic safety, vehicles equipped with computing, communication and sensing capabilities will be organized into ubiquitous and pervasive networks with a significant Internet presence while on the move. Large amount of data can be generated, collected, and processed on the vehicular networks. Big data on vehicular networks include useful and sensitive information which could be exploited by malicious intruders. But intrusion detection in vehicular networks is challenging because of its unique features of vehicular networks: short range wireless communication, large amount of nodes, and high mobility of nodes. Traditional methods are hard to detect intrusion in such sophisticated environment, especially when the attack pattern is unknown, therefore, it can result unacceptable false negative error rates. As a novel attempt, the main goal of this research is to apply data mining methodology to recognize known attacks and uncover unknown attacks in vehicular networks. We are the first to attempt to adapt data mining method for intrusion detection in vehicular networks. The main contributions include: 1) specially design a decentralized vehicle networks that provide scalable communication and data availability about network status; 2) applying two data mining models to show feasibility of automated intrusion detection system in vehicular networks; 3) find the detection patterns of unknown intrusions.

Landslides during heavy rainfall cause a great amount of damage in terms of both property and human life. To predict landslide disasters, we designed and implemented a wireless sensor network using our existing highly fault tolerant ad-hoc network. Since many sensors must be used, we propose a new MAC protocol that allows the network to support more sensor terminals. Our protocol is a hybrid CSMA/Psuedo-TDMA scheme which allows the terminals to decide their transmission timing independently in a random fashion. A timing beacon is not required, so power consumption can be reduced. Simulation results show that the number of terminals supported by the network can be greatly increased.

We are developing an optical layer-2 switch network that uses both wavelength-division multiplexing and time-division multiplexing technologies for efficient traffic aggregation in metro networks. For efficient traffic aggregation, path bandwidth control is key because it strongly affects bandwidth utilization efficiency. We propose a fast time-slot allocation method that uses hierarchical calculation, which divides the network-wide bandwidth-allocation problem into small-scale local bandwidth-allocation problems and solves them independently. This method has a much shorter computation complexity and enables dynamic path bandwidth control in large-scale networks. Our network will be able to efficiently accommodate dynamic traffic with limited resources by using the proposed method, leading to cost-effective metro networks.

The traditional multicast switch fabrics, which were mainly developed from the unicast switch fabrics, currently are not able to achieve high efficiency and flexible large-scale scalability. In the light of lattice theory and multicast concentrator, a novel multistage interconnection multicast switch fabric is proposed in this paper. Comparing to traditional multicast switch fabrics, this multicast switch fabric has the advantages of superior scalability, wire-speed, jitter-free multicast with low delay, and no queuing buffer. This paper thoroughly analyzes the performance of the proposed multicast switch fabric with supporting priority-based multicast. Simulations on packet loss rate and delay are discussed and presented at normalized load. Moreover, a detailed FPGA implementation is given. Practical network traffic tests provide evidence supporting the feasibility and stability of the proposed fabric.

Wireless body area networks (BANs) are attracting great attention as a future technology of wireless networks for healthcare and medical applications. Wireless BANs can generally be divided into two categories, i.e., wearable BANs and implant BANs. However, the performance requirements and channel propagation characteristics of these two kinds of BANs are quite different from each other, that is, wireless signals are approximately transmitted along the human body as a surface wave in wearable BANs, on the other hand, the signals are transmitted through the human tissues in implant BANs. As an effective solution for this problem, this paper first introduces a dual-mode communication system, which is composed of transmitters for in-body and on-body communications and a receiver for both communications. Then, we evaluate the bit error rate (BER) performance of the dual-mode communication system via computer simulations based on realistic channel models, which can reasonably represent the propagation characteristics of on-body and in-body communications. Finally, we conduct a link budget analysis based on the derived BER performances and discuss the link parameters including system margin, maximum link distance, data rate and required transmit power. Our computer simulation results and analysis results demonstrate the feasibility of the dual-mode communication system in wireless BANs.

An n-dimensional folded hypercube, denoted by FQn, is an enhanced n-dimensional hypercube with one extra link between nodes that have the furthest Hamming distance. Let FFv (respectively, FFe) denote the set of faulty nodes (respectively, faulty links) in FQn. Under the assumption that every fault-free node in FQn is incident to at least two fault-free links, Hsieh et al. (Inform. Process. Lett. 110 (2009) pp.41-53) showed that if |FFv|+|FFe| ≤ 2n-4 for n ≥ 3, then FQn-FFv-FFe contains a fault-free cycle of length at least 2n-2|FFv|. In this paper, we show that, under the same conditional fault model, FQn with n ≥ 5 can tolerate more faulty elements and provides the same lower bound of the length of a longest fault-free cycle, i.e., FQn-FFv-FFe contains a fault-free cycle of length at least 2n-2|FFv| if |FFv|+|FFe| ≤ 2n-3 for n ≥ 5.

We consider the problem of optimizing the quantizer design for distributed estimation systems where all nodes located at different sites collect measurements and transmit quantized data to a fusion node, which then produces an estimate of the parameter of interest. For this problem, the goal is to minimize the amount of information that the nodes have to transmit in order to attain a certain application accuracy. We propose an iterative quantizer design algorithm that seeks to find a non-regular mapping between quantization partitions and their codewords so as to minimize global distortion such as the estimation error. We apply the proposed algorithm to a system where an acoustic amplitude sensor model is employed at each node for source localization. Our experiments demonstrate that a significant performance gain can be achieved by our technique as compared with standard typical designs and even with distributed novel designs recently published.

In vehicular ad hoc networks, the efficient and reliable dissemination of emergency messages in a highly mobile environment under dense or sparse network is a significant challenge. This paper proposes a new vehicular broadcast protocol, called ACK-CAST, that can operate effectively in both dense and sparse network scenarios. ACK-CAST relies on acknowledgment messages from one-hop neighbors to select the next rebroadcasting vehicle. Simulation results show that ACK-CAST outperforms the SERVUS protocol in terms of the end-to-end delay, message delivery ratio and network overhead.

Available bandwidth, along with latency and packet loss rate, is an essential metric for the efficient operation of overlay network applications. However, the measurement of available bandwidth creates a larger traffic overhead than other metrics. Measurement conflicts on route-overlapping paths can also seriously degrade measurement accuracy and cause a non-negligible increase in the network load. In this paper, we propose a distributed method for measuring the available bandwidth in overlay networks that can reduce measurement conflicts while maintaining high measurement accuracy at low cost. Our main idea is that neighboring overlay nodes exchange route information to detect overlapping paths and share the measurement results of overlapping paths to configure parameter settings for available bandwidth measurements. Our simulation results show that the relative errors in the measurement results of our method are approximately only 65% of those of the existing method. The measurement accuracy of our method remains better than that of the existing method when the total measurement traffic loads of both methods are equal.

Dynamic spectrum access is the key approach in cognitive wireless regional area networks, and it is adopted by secondary users to access the licensed radio spectrum opportunistically. In order to realize real-time secondary spectrum usage, a dynamic spectrum access model based on stochastic differential games is proposed to realize dynamic spectrum allocation; a Nash equilibrium solution to the model is given and analyzed in this paper. From an overall perspective, the relationships between available spectrum percentage and the spectrum access rate are studied. Changes in the available spectrum percentage of the cognitive wireless regional area networks involve a deterministic component and a stochastic component which depends upon an r-dimensional Wiener process. The Wiener process represents an accumulation of random influences over the interval, and it reflects stochastic and time-varying properties of the available spectrum percentage. Simulation results show that the dynamic spectrum access model is efficient, and it reflects the time-varying radio frequency environment. Differential games are useful tools for the spectrum access and management in the time-varying radio environment.

Mobility load balancing (MLB) is a key technology for self-organization networks (SONs). In this paper, we explore the mobility load balancing problem and propose a unified cell specific offset adjusting algorithm (UCSOA) which more accurately adjusts the largely uneven load between neighboring cells and is easily implemented in practice with low computing complexity and signal overhead. Moreover, we evaluate the UCSOA algorithm in two different traffic conditions and prove that the UCSOA algorithm can get the lower call blocking rates and handover failure rates. Furthermore, the interdependency of the proposed UCSOA algorithm's performance and that of the inter-cell interference coordination (ICIC) algorithm is explored. A self-organization soft frequency reuse scheme is proposed. It demonstrates UCSOA algorithm and ICIC algorithm can obtain a positive effect for each other and improve the network performance in LTE system.

In this paper, we focus on the multi-way relaying channel where K users wish to exchange information with each other within two phases. Precoding at each user and the relay is carefully constructed to ensure that the signals from the same user pair are grouped together and cross-pair interference can be cancelled. Reliable detection is challenging at the relay since the observation constellation is no longer one of the regular ones, due to the fact that a relay observation is the superposition of the messages from one of the $rac{1}{2}K(K-1)$ user pairs. When the trellis coded modulation is used at each node, a simple constellation mapping function and a reduced-states decoding scheme can be applied at the relay, which result in much lower complexity. Furthermore, a modified version of the decoding method is also developed which is called the re-encoding-avoidance scheme at the relay. Monte-Carlo simulation results are provided to demonstrate the performance of the proposed scheme.

Many kinds of data can be represented as a network or graph. It is crucial to infer the latent structure underlying such a network and to predict unobserved links in the network. Mixed Membership Stochastic Blockmodel (MMSB) is a promising model for network data. Latent variables and unknown parameters in MMSB have been estimated through Bayesian inference with the entire network; however, it is important to estimate them online for evolving networks. In this paper, we first develop online inference methods for MMSB through sequential Monte Carlo methods, also known as particle filters. We then extend them for time-evolving networks, taking into account the temporal dependency of the network structure. We demonstrate through experiments that the time-dependent particle filter outperformed several baselines in terms of prediction performance in an online condition.

The rapid variation of wireless channels and feedback delay make the available channel state information (CSI) outdated in dynamic wireless multi-hop networks, which significantly degrades the accuracy of cross-layer resource allocation. To deal with this problem, a cross-layer resource allocation scheme is proposed for wireless multi-hop networks by taking the outdated CSI into account and basing compensation on the results of channel prediction. The cross-layer resource allocation is formulated as a network utility maximization problem, which jointly considers congestion control, channel allocation, power control, scheduling and routing with the compensated CSI. Based on a dual decomposition approach, the problem is solved in a distributed manner. Simulation results show that the proposed algorithm can reasonably allocate the resources, and significantly improve the throughput and energy efficiency in the network.

Online Social Networks (OSNs) have recently been playing an important role in communication. From the audience aspect, they enable audiences to get unlimited information via the information feeding mechanism (IFM), which is an important part of the OSNs. The audience relies on the quantity and quality of the information served by it. We found that existing IFMs can result in two problems: information overload and cultural ignorance. In this paper, we propose a new type of IFM that solves these problems. The advantage of our proposed IFM is that it can filter irrelevant information with consideration of audiences' culture by using the Naïve Bayes (NB) algorithm together with features and factors. It then dynamically serves interesting and important information based on the current situation and preference of the audience. This mechanism helps the audience to reduce the time spent in finding interesting information. It can be applied to other cultures, societies and businesses. In the near future, the audience will be provided with excellent, and less annoying, communication. Through our studies, we have found that our proposed IFM is most appropriate for Thai and some groups of Japanese audiences under the consideration of audiences' culture.

Packet loss and energy dissipation are two major challenges of designing large-scale wireless sensor networks. Since sensing data is spatially correlated, compressed sensing (CS) is a promising reconstruction scheme to provide low-cost packet error correction and load balancing. In this letter, assuming a multi-hop network topology, we present a CS-oriented data aggregation scheme with a new measurement matrix which balances energy consumption of the nodes and allows for recovery of lost packets at fusion center without additional transmissions. Comparisons with existing methods show that the proposed scheme offers higher recovery precision and less energy consumption on TinyOS.

In this paper, we focus on a centralized spectrum access strategy in a cognitive radio network, in which a single licensed spectrum with one primary user (PU) and several secondary users (SUs) (multiple input streams) are considered. We assume the spectrum can be divided into multiple channels and the packets from variable SUs can arrive at the system simultaneously. Taking into account the priority of the PU, we suppose that one PU packet can occupy the whole licensed spectrum, while one SU packet will occupy only one of the channels split from the licensed spectrum when that channel is not used. Moreover, in order to reduce the blocking ratio of the SUs, a buffer with finite capacity for the SUs is set. Regarding the packet arrivals from different SUs as multiple input streams, we build a two-dimensional Markov chain model based on the phase of the licensed spectrum and the number of SU packets in the buffer. Then we give the transition probability matrix for the Markov chain. Additionally, we analyze the system model in steady state and derive some important performance measures for the SUs, such as the average queue length in the buffer, the throughput and the blocking ratio. With the trade-off between different performance measures, we construct a net benefit function. At last, we provide numerical results to show the change trends of the performance measures with respect to the capacity of the SU buffer under different network conditions, and optimize the capacity of the SU buffer accordingly.

Much work in cooperative communication has been done from the perspective of the physical and network layers. However, the exact impact of signal error rate performance on cooperative routing discovery still remains unclear in multihop ad hoc networks. In this paper, we show the symbol error rate (SER) performance improvement obtained from cooperative commutation, and examine how to incorporate the factor of SER into the distributed routing discovery scheme called DGCR (Dynamic Geographic Cooperative Routing). For a single cooperative communication hop, we present two types of metric to specify the degree that one node is suitable for becoming the relay node. One metric is the potential of a node to relay with optimal SER performance. The other metric is the distance of a node to the straight line that passes through the last forwarding node and the destination. Based on location knowledge and contention scheme, we combine the two metrics into a composite metric to choose the relay node. The forwarding node is chosen dynamically according to the positions of the actual relay node and the destination. Simulation results show that our approach outperforms non-cooperative geographic routing significantly in terms of symbol error rate, and that DGCR's SER performance is better than traditional geographic cooperative routing with slight path length increase.