VLC Turbulence Effects on the Performance of the Fish School Behavior Modeling Mobile Diffusion Adaptive Networks in Underwater Environments

Implementation of different telecommunication systems based on wireless optical technologies requires careful awareness of the link conditions in order to predict the performance of each system and its expectations. Wireless optical communication channels, like telecommunication channels, have a fading phenomenon, which is called optical turbulence. A particular class of adaptive networks has the ability to move nodes and can move and converge to moving or static targets. The applications of these networks include dynamic and regional observation and pursuit of underwater military objects. The best type of communication technologies proposed for such networks is Visible Light Communication, or VLC, through which sensors, like the fish schools, with the optical communication between each other, move toward the targets. Investigating the impact of channel conditions and optical noise on these networks are other innovations of this research. In this paper, we model the behavior of a fish school in underwater VLC conditions using a mobile diffusion network. Our simulation results show the effects of water properties on the convergence of the mobile network nodes to a certain target. It is shown that as the water temperature, salinity level and the distance between the nodes increase, the convergence error rises and the nodes become departed from the target position.

     

Computer-Assisted Collaborative Learning for Enhancing Students Intellectual Ability Using Machine Learning Techniques

This paper proposes low complexity detection for internet of underwater things communication. The signal is transmitted from the source to the destination using several sensors. To simplify the computational operations at the transmitter and the sensory nodes, a single carrier frequency domain equalizer is proposed and amplify-and-forward protocols are employed. Fast Fourier transform and use of cyclic prefix are also proposed to simplify these algorithms when compared to time-domain equalization. As precise channel data is difficult to capture in underwater communications, the adaptive implementation of FDE is proposed as a solution that can be employed when the channel experiences a fast doppler shift. The two adaptive detectors are based on the least mean-square and recursive least square principles. Numerical simulations show that the performance of the bit error rate performance of the proposed detectors is close to that of the ideal minimum mean square error.

     

Estimation of Nonlinear Wireless Channel Based on Incremental Spline Adaptive Filtering

Channel estimation in a wireless sensor network is imperative to error-free information dissemination and data collection. The estimation procedure is challenging if there exists a nonlinear distortion to the communication signal due to the radio-frequency components in the transmitting or receiving entity. It has drawn attention to nonlinear system modeling for channel estimation, where lately, one of the most important methods has been spline adaptive filter (SAF). The necessity of updating both linear filter coefficients and nonlinear control points makes the adaptation process slow. Hence, we propose an incremental spline adaptive filter using the least mean square algorithm (ISAF-LMS), which acquires faster convergence while estimating non-linearity along with linear filter coefficients. The steady-state performance of the proposed method is carried out by following the energy conservation approach. The simulation result shows faster convergence in the distributed case than in non-cooperative estimation. Further, the performance is compared with diffusion SAF and incremental version of conventional Volterra adaptive filter-based nonlinear channel estimation (IVLMS). The proposed algorithm performance is better than IVLMS.

     

An optimum step-size assignment for incremental LMS adaptive networks based on average convergence rate constraint

This paper presents an optimum step-size assignment for incremental least-mean square adaptive networks in order to improve its robustness against the spatial variation of observation noise statistics over the network. We show that when the quality of measurement information (in terms of observation noise variances) is available, we can exploit it to adjust the step-size parameter in an adaptive network to obtain better performance. We formulate the optimum step-size assignment as a constrained optimization problem and then solve it via the Lagrange multipliers approach. The derived optimum step-size for each node requires information from other nodes, thus with some justifiable assumptions, near-optimum solutions are derived that depend only on local information. We show that the incremental LMS adaptive network with near-optimal step sizes has improved robustness and steady-state performance. Simulation results are also presented to support the theoretical results.     

分布式子带自适应滤波算法

为了解决分布式最小均方算法在输入信号相关性较高时收敛速度较慢、分布式仿射投影算法计算复杂度较高等问题,本文提出了两种分布式子带自适应滤波算法,即递增式和扩散式子带自适应滤波算法.分布式子带自适应滤波算法将节点信号进行子带分割来降低信号的相关性,从而加快收敛速度.由于用于子带分割的滤波器组中包含了抽取单元,所以分布式子带自适应滤波算法和对应的分布式最小均方算法的计算复杂度相近.仿真结果表明,与分布式最小均方算法相比,分布式子带自适应滤波算法具有更好的收敛性能.     

基于P-NOMA的上行水声通信网络容量分析

在通信距离不尽相同节点的最优适用频段可能会部分重叠的背景下,针对水声系统频率资源利用率低的问题,提出了一种基于部分非正交多址接入(Partial Non-orthogonal Multiple Access, P-NOMA)技术的上行链路水声通信方法。该方法中各P-NOMA节点在非重叠频段内采用正交多址接入技术传输信息,在重叠频段内利用最大或受限的发送功率同时同频发送信息给目的节点,目的节点采用串行干扰消除方法提取重叠频段内的各P-NOMA节点信息。根据功率谱密度与容量之间的关系,分析了P-NOMA节点在注水算法和功率均匀分布下的可达容量,以及信干噪比限制对最小发射功率的影响。仿真结果验证了基于P-NOMA技术的水声上行通信网络在系统容量方面具有的优势。     

The Impact of Deployment Pattern and Routing Scheme on the Lifetime in Multi-Sink Wireless Sensor Network

Extensive use of sensor and actuator networks in many real-life applications introduced several new performance metrics at the node and network level. Since wireless sensor nodes have significant battery constraints, therefore, energy efficiency, as well as network lifetime, are among the most significant performance metrics to measure the effectiveness of given network architecture. This work investigates the performance of an event-based data delivery model using a multipath routing scheme for a wireless sensor network with multiple sink nodes. This routing algorithm follows a sink initiated route discovery process with the location information of the source nodes already known to the sink nodes. It also considers communication link costs before making decisions for packet forwarding. Carried out simulation compares the network performance of a wireless sensor network with a single sink, dual sink, and multi sink networking approaches. Based on a series of simulation experiments, the lifetime aware multipath routing approach is found appropriate for increasing the lifetime of sensor nodes significantly when compared to other similar routing schemes. However, energy-efficient packet forwarding is a major concern of this work; other network performance metrics like delay, average packet latency, and packet delivery ratio are also taken into the account.

     

水声网络通信性能分析

该文根据水声信道传播特性及水声网络结构特征,建立了基于水声网络的通信性能分析模型,并仿真分析了对等网络和汇聚网络两种水声网络结构的通信性能与节点间距、网络规模、工作繁忙度以及发射功率等因素的联系,结果表明通过适当的网络设置和协议设计能够优化网络的通信性能,并且存在使通信带宽效率最优的水声网络节点间距。该文的研究成果对水声网络应用具有指导意义。     

一种水声信道自适应均衡算法研究

由声波传播的多途效应所引起的码间干扰及信号衰落,使得水下数据传输的速率和可靠性都大大降低。针对多途干扰严重的水声信道,将一种基于最小均方误差算法的信道自适应均衡器应用于水声信号处理中。对该均衡器的基本理论及其结构进行了归纳,结合水声多途信道的基本特点,建立了仿真模型,重点分析了步长参数、信噪比等对算法收敛速度和稳态误差等方面性能的影响,为实现高质量的水下信息传递提供技术支持。     

Performance of Smart Antenna Under Different Fading Conditions

Optimizing the current distribution of an evenly spaced antenna array has shown to be an efficient approach for reducing side lobe levels. In this article, the Tchebyscheff distribution-based antenna array synthesis approach is combined with an adaptive signal processing algorithm for beamforming and side lobe level reduction in smart antennas in various fading situations. The performance of smart antennas in uniformly spaced linear, planar, circular, and semi-circular arrays are evaluated. The presence of Rayleigh and Rician channels is examined in the network. The least mean square (LMS) and normalised least mean square (NLMS) algorithms are applied as adaptive algorithms. In fading environments, the NLMS algorithm with Tchebyscheff distribution outperforms than the LMS algorithm with Tchebyscheff distribution, with a side lobe level decrease of 11.23 dB. The lowest side lobe achieved with the NLMS algorithm with Tchebyscheff distribution is???45.59 dB for uniform planar array.

     

Non-stationary Channel Estimation with Diffusion Adaptation Strategies Over Distributed Networks

In this paper we address non-stationary channel estimation problem with diffusion least mean square algorithm in distributed adaptive wireless sensor networks. Here we estimate channel coefficients or taps that are produced with Rayleigh fading models. All detailed explanations regarding to this fading channel type are presented and it is explained that how we can extend channel estimation with sensor networks to other newly presented channel types. We use the tracking performance analysis of diffusion cooperation over adaptive sensor networks to investigate the reliability of used algorithms and show the link between channel estimation problem and tracking a time varying entity. Theoretical analyzes are performed and the results are compared with simulation performance diagrams. It is proven that there is a reasonable match between these two outcomes. We present our results with the mean square deviation criteria.     

Energy efficient watchman based flooding algorithm for IoT-enabled underwater wireless sensor and actor networks

In the task of data routing in Internet of Things enabled volatile underwater environments, providing better transmission and maximizing network communication performance are always challenging. Many network issues such as void holes and network isolation occur because of long routing distances between nodes. Void holes usually occur around the sink because nodes die early due to the high energy consumed to forward packets sent and received from other nodes. These void holes are a major challenge for I-UWSANs and cause high end-to-end delay, data packet loss, and energy consumption. They also affect the data delivery ratio. Hence, this paper presents an energy efficient watchman based flooding algorithm to address void holes. First, the proposed technique is formally verified by the Z-Eves toolbox to ensure its validity and correctness. Second, simulation is used to evaluate the energy consumption, packet loss, packet delivery ratio, and throughput of the network. The results are compared with well-known algorithms like energy-aware scalable reliable and void-hole mitigation routing and angle based flooding. The extensive results show that the proposed algorithm performs better than the benchmark techniques.     

网络分层的水下传感器网络覆盖保持路由算法

在以监测为目的的水下传感器网络中,较好的网络覆盖率和连通率是完成监测任务的重要保证.以改善覆盖效果为目标的水下覆盖保持路由算法NCPR算法相对比LEACH-Coverage-U算法有效的延长了网络覆盖时间,但是该算法连通性表现较差,同时存在靠近SINK节点的簇首由于需要转发大量数据而过早死亡的问题.本文提出一种分布式的网络不均匀分层的覆盖保持路由(Network Unevenly Layered Coverage Preserving Routing,NULCPR)算法,由SINK节点开始逐层向下建立网络,同时每层网络节点通信半径也随层号增加而逐渐增大.每层网络独立运行NCPR算法以使该层节点成簇,并通过簇首向上建立连通链路以保证网络连通.仿真结果表明,与NCPR算法相比,NULCPR算法提高了网络连通率以及覆盖率,并且降低了网络能耗,证明了该算法的有效性.     

Power-efficient and interference-free link scheduling algorithms for connected wireless sensor networks

A fundamental aspect in performance engineering of wireless sensor networks (WSN) is optimizing the set of links that can be concurrently activated to meet a given signal-to-interference-plus-noise ratio (SINR) threshold. The solution of this combinatorial problem is a key element in wireless link scheduling. Another key architectural goal in WSN is connectivity. The connectivity of sensor nodes is critical for WSN, as connected graphs can be used for both data collection and data dissemination. In this paper, we investigate the joint scheduling and connectivity problem in WSN assuming the SINR model. We propose algorithms to build connected communication graphs with power-efficient links to be scheduled simultaneously in one time slot. The algorithms aiming at minimizing the number of time slots needed to successfully schedule all the given links such that the nodes can communicate without interference in the SINR model. While power-efficient and interference-free schedules reduce energy consumption, minimization of the schedule length (shortest link scheduling) has the effect of maximizing network throughput. We propose one greedy randomized constructive heuristic, two local search procedures, and three greedy randomized adaptive search procedures metaheuristics. We report computational experiments comparing the effectiveness of the proposed algorithms. Our simulation also shows the trade-off between power consumption and schedule length and the results indicate that not only the overall performance of our algorithms, but also show that the total power and schedule length value of its solutions are better than the existing work.

     

Distributed estimation of sensors position in underwater wireless sensor network

In this paper, a localisation method for determining the position of fixed sensor nodes in an underwater wireless sensor network (UWSN) is introduced. In this simple and range-free scheme, the node localisation is achieved by utilising an autonomous underwater vehicle (AUV) that transverses through the network deployment area, and that periodically emits a message block via four directional acoustic beams. A message block contains the actual known AUV position as well as a directional dependent marker that allows a node to identify the respective transmit beam. The beams form a fixed angle with the AUV body. If a node passively receives message blocks, it could calculate the arithmetic mean of the coordinates existing in each messages sequence, to find coordinates at two different time instants via two different successive beams. The node position can be derived from the two computed positions of the AUV. The major advantage of the proposed localisation algorithm is that it is silent, which leads to energy efficiency for sensor nodes. The proposed method does not require any synchronisation among the nodes owing to being silent. Simulation results, using MATLAB, demonstrated that the proposed method had better performance than other similar AUV-based localisation methods in terms of the rates of well-localised sensor nodes and positional root mean square error.     

Time-variant balanced routing strategy for underwater wireless sensor networks

In the past decade, researchers’ interest in Underwater Wireless Sensors Networks has rapidly increased. There are several challenges facing the lifetime of UWSNs due to the harsh characteristics of the underwater environment. Energy efficiency is one of the major challenges in UWSNs due to the limited battery budget of the sensor nodes. In this paper, we aim at tackling the energy sink-hole problem that especially hits nodes close to the sink when they run out of battery power before other sensors in the network. We prove that we can evenly distribute the transmission load among mobile sensor nodes by letting sensor nodes adjust their transmission ranges. In this paper, we suppose that sensor nodes may adjust their transmission power up to three levels. Consequently, we strive for deriving the optimal load weight for each possible transmission power level that leads to fair energy consumption among all underwater sensors while taking into account the underwater sensors mobility. Performance results show that energy sink-hole problem is overcame and hence the network lifetime is maximized.

     

基于NS-3的声电协同网络实现及路由性能分析

水下无线通信主要依靠水声通信的方式进行信息传输。但水声链路本身具有高时延和高误码率等不足,为水下应用提供低时延的通信服务是一项具有挑战性的工作。声电协同网(CRAN)旨在充分利用水面无线电链路弥补水声网络(UAN)的性能局限,提升网络的整体性能。其中,CRAN中的路由协议需要构建声、电混合路径,是声电协同网络研究中的关键问题。该文首先在网络模拟器3(NS-3)中设计并实现了声电浮标节点与CRAN协议栈,搭建了CRAN的仿真平台。随后探讨了以无线自组网按需平面距离向量路由协议(AODV)为代表的被动式路由在CRAN中的应用。该文发现,AODV协议使用的距离向量准则在CRAN中能够更多地选择高速的无线电链路进行数据转发,有效地降低了网络传输时延。最后,通过仿真对AODV与其他协议的性能进行了对比、分析。结果表明,CRAN在投递率、传输时延、网络吞吐量、能效和路由响应速度方面对比水声通信网有较大提升。同时,以AODV为代表的被动路由协议,相比于以优化链路状态路由协议(OLSR)为代表的主动路由协议更适用于CRAN。     

无线传感器网络的分区异构分簇协议研究

针对无线传感器网络能量受限和路由协议中节点能量消耗不均衡的问题,提出一种新的无线传感器网络的分区异构分簇协议(PHC协议).该协议的核心是将3种不同能量等级的节点根据能量的不同分别部署在不同区域,能量较高的高级节点和中间节点使用聚类技术通过簇头直接传输数据到汇聚点,能量较低的普通节点则直接传输数据到汇聚点.仿真结果表明,该协议通过对节点合理的分配部署,使簇头分布均匀,更好地均衡了节点的能量消耗,延长了网络的稳定期,提高了网络的吞吐量,增强了网络的整体性能.     

An analytical model for adaptive routing networks

Real-time network routing (RTNR) is a new adaptive routing method. With RTNR, switches have a simple way of exchanging link status bit map information, thereby determining the availability and load conditions of the direct and all two-link paths to the destination. Link busy-idle status is exchanged between the network nodes using a bit map data exchange through the common channel signaling (CCS) network, and calls are set up where there is the most available capacity in the network. To date the analysis of RTNR networks has been limited to simulation models. The present authors develop an analytical model for the AT&T network under RTNR, which is shown to provide good agreement with simulation models. The analytical model for RTNR networks uses an Erlang fixed point method to solve the nonlinear equations describing dynamical network behavior. The equations include the link state probability, network flows, link arrival rates, adaptive trunk reservation level, and adaptive path selection depth. The link state model provides the aggregate link state probabilities through solution of the birth-death equations, and models the adaptive nature of trunk reservation. The network flow model provides a method to calculate the traffic flow using the least busy concept employed in RTNR, and also models the adaptive nature of the path selection depth. The analytical model addresses asymmetrical networks, and computational examples show the differences from the simulation model to be small. The authors also use the analytical model to examine key RTNR parameters over a range of values     

Optimized deep learning driven signal detection and adaptive channel estimation in underwater acoustic IoT networks

Nowadays, multiple input multiple outputs with orthogonal frequency division multiplexing (MIMO-OFDM) provide better communication performance that can be applied to the fast-growing Internet of Things (IoTs). In underwater IoT, information fades away rapidly due to varying water conditions. Therefore, the MIMO model can be applied to the OFDM acoustic system, enabling high-speed data transmission without affecting the channel effectively. However, detecting the underwater signal and estimating the channel is highly necessary for enhancing underwater acoustic communication (UWAC). Recently, many techniques have been introduced for effectively performing signal detection and channel estimation. However, those techniques face high time complexity due to increased channel interferences and noises during data transmission. Hence, this article brings a novel technique for SD and CE for the UWAC-IoT-enabled MIMO-OFDM system. An adaptive recursive least square (ARLS) technique is proposed in this study for CE that aids in evaluating the acoustic channel parameters effectively. For performing SD, a bi-directional deep pelican convolutional neural network (BDPCNN) technique is introduced to ensure the presence and absence of signals at the receiver end. The proposed method is analyzed via the MATLAB platform, and the performances are analyzed under different water types like turbid water, coastal water, clear ocean water, and pure seawater. Different performance metrics like bit error rate (BER), mean square error (MSE), energy efficiency (EE), and time complexity are analyzed with different existing techniques. The experimental section obtains the BER of 0.0086, 0.013, 0.017, and 0.021 for turbid, coastal, clear, and pure seawater, respectively.