Densification of mmWave smallcells overlaid on the conventional macro cell is considered to be an essential technology for enhanced mobile broadband services and future IoT applications requiring high data rate e.g. automated driving in 5G communication networks. Taking into account actual measurement mobile traffic data which reveal dynamicity in both time and space, this paper proposes a joint optimization of user association and smallcell base station (BS)'s ON/OFF status. The target is to improve the system's energy efficiency while guaranteeing user's satisfaction measured through e.g. delay tolerance. Numerical analyses are conducted to show the effectiveness of the proposed algorithm against dynamic traffic variation.

This paper reports a novel 3D topology optimization method based on the finite difference time domain (FDTD) method for a dielectric lens antenna. To obtain an optimal lens with smooth boundary, we apply normalized Gaussian networks (NGnet) to 3D topology optimization. Using the proposed method, the dielectric lens with desired radiation characteristics can be designed. As an example of the optimization using the proposed method, the width of the main beam is minimized assuming spatial symmetry. In the optimization, the lens is assumed to be loaded on the aperture of a waveguide slot antenna and is smaller compared with the wavelength. It is shown that the optimized lens has narrower beamwidth of the main beam than that of the conventional lens.

This paper proposes a novel environmental monitoring strategy, incremental environmental monitoring, that enables scientists to reveal the ecology of wild animals in the field. We applied this strategy to the habitat of endangered freshwater fish. Specifically, we designed and implemented a network-based system using distributed sensors to continuously monitor and record the habitat of endangered fish. Moreover, we developed a set of analytical tools to exploit a variety of sensor data, including environmental time-series data such as amount of dissolved oxygen, as well as underwater video capturing the interaction of fish and their environment. We also describe the current state of monitoring the behavior and habitat of endangered fish and discuss solutions for making such environmental monitoring more efficient in the field.

For general multiple-input multiple-output (MIMO) interference networks, determining the feasibility conditions of interference alignment (IA) to achieve the maximum degree of freedom (DoF), is tantamount to accessing the maximum spatial resource of MIMO systems. In this paper, from the view of antenna configuration, we first explore the IA feasibility in the K-user MIMO interference channel (IC), G-cell MIMO interference broadcast channel (IBC) and interference multiple access channel (IMAC). We first give the concept of the equalized antenna, and all antenna configurations are divided into two categories, equalized antennas and non-equalized ones. The feasibility conditions of IA system with equalized antennas are derived, and the feasible and infeasible regions are provided. Furthermore, we study the correlations among IC, IBC and IMAC. Interestingly, the G-cell MIMO IBC and IMAC are two special ICs, and a systemic work on IA feasibility for these three interference channels is provided.

Review rating prediction is an important problem in machine learning and data mining areas and has attracted much attention in recent years. Most existing methods for review rating prediction on Location-Based Social Networks only capture the semantics of texts, but ignore user information (social links, geolocations, etc.), which makes them less personalized and brings down the prediction accuracy. For example, a user's visit to a venue may be influenced by their friends' suggestions or the travel distance to the venue. To address this problem, we develop a review rating prediction framework named TSG by utilizing users' review Text, Social links and the Geolocation information with machine learning techniques. Experimental results demonstrate the effectiveness of the framework.

Image quality assessment (IQA) is an inherent problem in the field of image processing. Recently, deep learning-based image quality assessment has attracted increased attention, owing to its high prediction accuracy. In this paper, we propose a fully-blind and fast image quality predictor (FFIQP) using convolutional neural networks including two strategies. First, we propose a distortion clustering strategy based on the distribution function of intermediate-layer results in the convolutional neural network (CNN) to make IQA fully blind. Second, by analyzing the relationship between image saliency information and CNN prediction error, we utilize a pre-saliency map to skip the non-salient patches for IQA acceleration. Experimental results verify that our method can achieve the high accuracy (0.978) with subjective quality scores, outperforming existing IQA methods. Moreover, the proposed method is highly computationally appealing, achieving flexible complexity performance by assigning different thresholds in the saliency map.

We have proposed a fish farm monitoring system for achieving efficient fish farming. In our system, sensor nodes are attached at fish to monitor its health status. In this letter, we propose a method for gathering sensor data from sensor nodes to sink nodes when the transmission range of sensor node is shorter than the size of fish cage. In our proposed method, a part of sensor nodes become leader nodes and they forward gathered sensor data to the sink nodes. Through simulation evaluations, we show that the data gathering performance of our proposed method is higher than that of traditional methods.

Software-defined networking (SDN) is recognized as the next-generation networking paradigm. The software-defined architecture for underwater acoustic sensor networks (SDUASNs) has become a hot topic. However, the current researches on SDUASNs is still in its infancy, which mainly focuses on network architecture, data transmission and routing. There exists some shortcomings that the scale of the SDUASNs is difficult to expand, and the security maintenance is seldom dabble. Therefore, a scalable software-definition architecture for underwater acoustic sensor networks (SSDUASNs) is introduced in this paper. It realizes an organic combination of the knowledge level, control level, and data level. The new nodes can easily access the network, which could be conducive to large-scale deployment. Then, the basic security authentication mechanism called BSAM is designed based on our architecture. In order to reflect the advantages of flexible and programmable in SSDUASNs, security authentication mechanism with pre-push (SAM-PP) is proposed in the further. In the current UASNs, nodes authentication protocol is inefficient as high consumption and long delay. In addition, it is difficult to adapt to the dynamic environment. The two mechanisms can effectively solve these problems. Compared to some existing schemes, BSAM and SAM-PP can effectively distinguish between legal nodes and malicious nodes, save the storage space of nodes greatly, and improve the efficiency of network operation. Moreover, SAM-PP has a further advantage in reducing the authentication delay.

This paper proposes Code-Modulated Synchronized (CMS) -OOK modulation scheme for normally-off wireless sensor networks, and demonstrates the operation of the transmitter for the CMS-OOK using 65nm SOTB (Silicon-On Thin Buried Oxide) CMOS technology. Based on investigating RF characteristics of SOTB CMOS, analog part of a CMS-OOK transmitter was designed, fabricated and evaluated in combination with based-FPGA digital part. With code modulation and controlling the carrier frequency by body bias of the SOTB devices, the spectrum of a CMS-OOK transmitter output is widen to achieve -62dBm/MHz peak power spectrum density at 15 MHz bandwidth. Chip of analog part on-board is supplied by 1V for power amplifier and 0.75V for the rest. It consumes average 83µW according to 83nJ/bit at 1kbps data transmission.

Applying Software Defined Network (SDN) technology to wireless networks are attracting much attention. Our previous study proposed a channel utilization method based on SDN/OpenFlow technology to improve the channel utilization efficiency of the multi-channel wireless backhaul network (WBN). However, since control messages are inherently transmitted with data traffic on a same channel in WBN, it inevitably degrades the network capacity. Specifically, the amount of control messages for collecting statistical information of each flow (FlowStats) linearly increases with the number of ongoing flows, thereby being the dominant overhead for backhaul networks. In this paper, we propose a new method that prevents the increase of control traffic while retaining the network performance of the previous method. Our proposed method uses statistical information of each interface (PortStats) instead of per-flow information (FlowStats), and handles multiple flows on the interface together if possible. Otherwise, to handle individual flow, we propose a way to estimate per-flow information without introducing extra control messages. Finally, we show that the proposed method offers the same performance with the previous method, while greatly reducing the amount of control traffic.

Throughput prediction is one of the promising techniques to improve the quality of service (QoS) and quality of experience (QoE) of mobile applications. To address the problem of predicting future throughput distribution accurately during the whole session, which can exhibit large throughput fluctuations in different scenarios (especially scenarios of moving user), we propose a history-based throughput prediction method that utilizes time series analysis and machine learning techniques for mobile network communication. This method is called the Hybrid Prediction with the Autoregressive Model and Hidden Markov Model (HOAH). Different from existing methods, HOAH uses Support Vector Machine (SVM) to classify the throughput transition into two classes, and predicts the transmission control protocol (TCP) throughput by switching between the Autoregressive Model (AR Model) and the Gaussian Mixture Model-Hidden Markov Model (GMM-HMM). We conduct field experiments to evaluate the proposed method in seven different scenarios. The results show that HOAH can predict future throughput effectively and decreases the prediction error by a maximum of 55.95% compared with other methods.

Cellular heterogeneous networks (HetNets) with densely deployed small cells can effectively boost network capacity. The co-channel interference and the prominent energy consumption are two crucial issues in HetNets which need to be addressed. Taking the traffic variations into account, this paper proposes a theoretical framework to analyze spectral efficiency (SE) and energy efficiency (EE) considering jointly further-enhanced inter-cell interference coordination (FeICIC) and spectrum allocation (SA) via a stochastic geometric approach for a two-tier downlink HetNet. SE and EE are respectively derived and validated by Monte Carlo simulations. To create spectrum and energy efficient HetNets that can adapt to traffic demands, a non-convex optimization problem with the power control factor, resource partitioning fraction and number of subchannels for the SE and EE tradeoff is formulated, based on which, an iterative algorithm with low complexity is proposed to achieve the sub-optimal solution. Numerical results confirm the effectiveness of the joint FeICIC and SA scheme in HetNets. Meanwhile, a system design insight on resource allocation for the SE and EE tradeoff is provided.

Efficiently locating nodes and allocating demand has been a significant problem for telecommunication network carriers. Most of location models focused on where to locate nodes and how to assign increasing demand with optical access networks. However, the population in industrialized countries will decline over the coming decades. Recent advance in the optical amplifier technology has enabled node integration; an excess telecommunication node is closed and integrated to another node. Node integration in low-demand areas will improve the efficiency of access networks in this approaching age of depopulation. A dynamic node integration problem (DNIP) has been developed to organize the optimal plan for node integration. The problem of the DNIP was that it cannot consider the requirements of network carriers. In actual situations, network carriers often want to specify the way each node is managed, regardless of the mathematical optimality of the solution. This paper proposes a requirement modeling language (RML) for the DNIP, with which the requirements of network carriers can be described. The described statements are used to solve the DNIP, and consequently the calculated optimal solution always satisfies the requirements. The validity of the proposed method was evaluated with computer simulations in a case study.

In this paper, the problem of lifetime extension of wireless sensor networks (WSNs) with redundant sensor nodes deployed in 3D vegetation-covered fields is modeled, which includes building communication models, network model and energy model. Generally, such a problem cannot be solved by a conventional method directly. Here we propose an Artificial Bee Colony (ABC) based optimal grouping algorithm (ABC-OG) to solve it. The main contribution of the algorithm is to find the optimal number of feasible subsets (FSs) of WSN and assign them to work in rotation. It is verified that reasonably grouping sensors into FSs can average the network energy consumption and prolong the lifetime of the network. In order to further verify the effectiveness of ABC-OG, two other algorithms are included for comparison. The experimental results show that the proposed ABC-OG algorithm provides better optimization performance.

Our previous work proposed a semi-blind single antenna interference cancellation scheme to cope with severe inter-cell interference in heterogeneous networks. This paper extends the scheme to allow multiple-receive-antenna implementation. It does not require knowledge of the training sequences of interfering signals and can cancel multiple interfering signals irrespective of the number of receive antennas. The proposed scheme applies an enhanced version of the quantized channel approach to suboptimal joint channel estimation and signal detection (JCESD) during the training period in order to blindly estimate channels of the interfering signals, while reducing the computational complexity of optimum JCESD drastically. Different from the previous work, the proposed scheme applies the quantized channel generation and local search at each individual receive antenna so as to estimate transmitted symbol matrices during the training period. Then, joint estimation is newly introduced in order to estimate a channel matrix from the estimated symbol matrices, which operates in the same manner as the expectation maximization (EM) algorithm and considers signals received at all receive antennas. Using the estimated channels, the proposed scheme performs multiuser detection (MUD) during the data period under the maximum likelihood (ML) criterion in order to cancel the interference. Computer simulations with two receive antennas under two-interfering-stream conditions show that the proposed scheme outperforms interference rejection combining (IRC) with perfect channel state information (CSI) and MUD with channels estimated by a conventional scheme based on the generalized Viterbi algorithm, and can achieve almost the same average bit error rate (BER) performance as MUD with channels estimated from sufficiently long training sequences of both the desired stream(s) and the interfering streams, while reducing the computational complexity significantly compared with full search involving all interfering signal candidates during the training period.

This paper aims to establish expressions for IEEE 802.11 string-topology multi-hop networks with transmission control protocol (TCP) traffic flow. The relationship between the throughput and transport-layer function in string-topology multi-hop network is investigated. From the investigations, we obtain an analysis policy that the TCP throughput under the TCP functions is obtained by deriving the throughput of the network with simplified into two asymmetric user datagram protocol flows. To express the asymmetry, analytical expressions in medium access control-, network-, and transport layers are obtained based on the airtime expression. The expressions of the network layer and those of transport layer are linked using the “delayed ACK constraint,” which is a new concept for TCP analysis. The analytical predictions agree well with the simulation results, which prove the validity of the obtained analytical expressions and the analysis policy in this paper.

In this paper, we define a wireless sensor network with multiple types of sensors as a wireless heterogeneous sensor network (WHSN), and propose an efficient query dissemination scheme (EDT) in the WHSN. The EDT based on total dominant pruning can forward queries to only the nodes with data requested by the user, thereby reducing unnecessary packet transmission. We show that the EDT is suitable for the WHSN environment through a variety of simulations.

In this paper we propose the Zone-based Energy Aware data coLlection (ZEAL) protocol. ZEAL is designed to be used in agricultural applications for wireless sensor networks. In these type of applications, all data is often routed to a single point (named “sink” in sensor networks). The overuse of the same routes quickly depletes the energy of the nodes closer to the sink. In order to minimize this problem, ZEAL automatically creates zones (groups of nodes) independent from each other based on the trajectory of one or more mobile sinks. In this approach the sinks collects data queued in sub-sinks in each zone. Unlike existing protocols, ZEAL accomplish its routing tasks without using GPS modules for location awareness or synchronization mechanisms. Additionally, ZEAL provides an energy saving mechanism on the network layer that puts zones to sleep when there are no mobile sinks nearby. To evaluate ZEAL, it is compared with the Maximum Amount Shortest Path (MASP) protocol. Our simulations using the ns-3 network simulator show that ZEAL is able to collect a larger number of packets with significantly less energy in the same amount of time.

Clustering is known to be an effective means of reducing energy dissipation and prolonging network lifetime in wireless sensor networks (WSNs). Recently, game theory has been used to search for optimal solutions to clustering problems. The residual energy of each node is vital to balance a WSN, but was not used in the previous game-theory-based studies when calculating the final probability of being a cluster head. Furthermore, the node payoffs have also not been expressed in terms of energy consumption. To address these issues, the final probability of being a cluster head is determined by both the equilibrium probability in a game and a node residual energy-dependent exponential function. In the process of computing the equilibrium probability, new payoff definitions related to energy consumption are adopted. In order to further reduce the energy consumption, an assistant method is proposed, in which the candidate nodes with the most residual energy in the close point pairs completely covered by other neighboring sensors are firstly selected and then transmit same sensing data to the corresponding cluster heads. In this paper, we propose an efficient energy-aware clustering protocol based on game theory for WSNs. Although only game-based method can perform well in this paper, the protocol of the cooperation with both two methods exceeds previous by a big margin in terms of network lifetime in a series of experiments.

Microblog data contains rich information of real-world events with great commercial values, so microblog-oriented natural language processing (NLP) tasks have grabbed considerable attention of researchers. However, the performance of microblog-oriented Chinese Word Segmentation (CWS) based on deep neural networks (DNNs) is still not satisfying. One critical reason is that the existing microblog-oriented training corpus is inadequate to train effective weight matrices for DNNs. In this paper, we propose a novel active learning method to extend the scale of the training corpus for DNNs. However, due to a large amount of partially overlapped sentences in the microblogs, it is difficult to select samples with high annotation values from raw microblogs during the active learning procedure. To select samples with higher annotation values, parameter λ is introduced to control the number of repeatedly selected samples. Meanwhile, various strategies are adopted to measure the overall annotation values of a sample during the active learning procedure. Experiments on the benchmark datasets of NLPCC 2015 show that our λ-active learning method outperforms the baseline system and the state-of-the-art method. Besides, the results also demonstrate that the performances of the DNNs trained on the extended corpus are significantly improved.