Optical wireless sensor networks using tunable optical filters

A novel decision gathering scheme for optical wireless sensor networks (OWSNs) is proposed and thoroughly analyzed in this article. The proposed technique relies on a tunable optical filter (TOF) that is mounted on each sensor node. Specifically, the received laser signal from a central unit at any particular node is modulated through the TOF, where it is either rejected or passed in accordance to the sensed result. The output of the TOF is passed to a corner cube reflector (CCR) that reflects the optical signal either back to the central unit or to another node in the OWSN. Three different setups are considered to implement the proposed scheme and the pros and cons of each setup are thoroughly discussed. It is revealed that the energy requirement of each sensor node is minimized as energy is only consumed in the process of sensing and adjusting the TOF. The performance of the proposed scheme is analyzed through a mathematical framework to derive the average decision error rate (DER) for all setups. Moreover, simulation results analyze the impact of key system parameters including atmospheric turbulence, transmit power, link distance, filter efficiency and detection threshold.     

Multi-radius geographical spatial networks: Statistical characteristics and application to wireless sensor networks

In this paper, a new kind of complex network model named multi-radius geographical spatial networks is proposed. We investigate statistical characteristics of this model and then map wireless sensor networks (WSNs) to it based on an efficient mechanism of broadcasting radius adjustment. Analysis and simulation show that WSNs working under this mechanism obtain longer lifetime and faster data delivering speed than those in traditional uniform radius WSNs.     

Blind estimation of number of motion multi-human targets in wireless pyroelectric infrared sensor networks

Due to the features of low energy consumption and flexible networking, nowadays the pyroelectric sensor has been applied widely in areas such as network instruction detection or human body target tracking recognition. Moreover, accurate estimation and judgment about the number of human targets moving in the networks is the foundation of tracking and recognition. This paper, under the condition of being lack of relevant prior knowledge, presents a novel method which selects the maximum likelihood function of the Bayesian network models as the independent criterion. In addition, the objective function is optimally solved by the Laplace estimation. The results of numerous experiments on both simulation and hardware experimental platforms are shown that this method has capability to blindly estimate the number of motion multiple human targets in wireless pyroelectric infrared sensor networks.     

Adaptive multimode transmission in wireless sensor networks with energy harvesting

Limited energy has always been an important factor restricting the development of wireless sensor networks. The unbalanced energy consumption of nodes will accelerate the death of some nodes. To solve the above problems, an adaptive routing algorithm for energy collection sensor networks based on distributed energy saving clustering (DEEC) is proposed. In each hop of data transmission, the optimal mode is adaptively selected from four transmission modes: single-hop cooperative, multi-hop cooperative, single-hop non-cooperative and multi-hop non-cooperative, so as to reduce and balance the energy consumption of nodes. The performance of the proposed adaptive multi-mode transmission method and several benchmark schemes are evaluated and compared by computer simulation, where a few performance metrics such as the network lifetime and throughput are adopted. The results show that, the proposed method can effectively reduce the energy consumption of the network and prolong the network lifetime; it is superior to various benchmark schemes.     

Analysis of scalability for AODV routing protocol in wireless sensor networks

This paper presents preliminary work to address scalability concern over AODV protocol in wireless sensor network. Firstly, we discussed the scalability design issues with related work in context of wireless sensor networks (WSN). Following, we designed and illustrated wireless sensor network model. Finally, significance of scalability on the behaviour of application, MAC, transport and physical layer performance is described.     

Mobile sink-based data collection in event-driven wireless sensor networks using a modified ant colony optimization

The hotspot problem is one of the primary challenges in the wireless sensor networks (WSNs) because it isolates the sink node from the remaining part of the WSN. A mobile sink (MS)-based data acquisition strategy mitigates the hotspot problem, but the traditional MS-based data gathering approaches do not resolve the issue. However, the conventional techniques follow a fixed order of visits and static traversal of the MS. In this context, this paper uses a modified version of the ant colony optimization strategy for the data collected through a MS to mitigate the hotspot problem in the WSNs while improving the energy efficiency, network lifetime, throughput by reducing the packet loss and delay. In our work, we initially construct a forwarded load spanning tree to estimate the freight of each node in the WSN. Further, we choose RPs and their path simultaneously using the modified ACO algorithm by considering the forward loads, remaining energy, distance, etc. The proposed work also adopts the virtual RP selection strategy void unnecessary data exchanges between the nodes and RPs. Hence, it reduces the burden on relay nodes and optimize the energy usage among the nodes. We compare our approach with the recent ACO-based algorithms, and our approach outperforms them.     

Relay selection scheme based on deep reinforcement learning in wireless sensor networks

Cooperative communication technology has realized the enhancement in the wireless communication system’s spectrum utilization rate without resorting to any additional equipment; additionally, it ensures system reliability in transmission, increasingly becoming a research focus within the sphere of wireless sensor networks (WSNs). Since the selection of relay is crucial to cooperative communication technology, this paper proposes two different relay selection schemes subject to deep reinforcement learning (DRL), in response to the issues in WSNs with relay selection in cooperative communications, which can be summarized as the Deep-Q-Network Based Relay Selection Scheme (DQN-RSS), as well as the Proximal Policy Optimization Based Relay Selection Scheme (PPO-RSS); it further compared the commonly used Q-learning relay selection scheme (Q-RSS) with random relay selection scheme. First, the cooperative communication process in WSNs is modeled as a Markov decision process, and DRL algorithm is trained in accordance with the outage probability, as well as mutual information (MI). Under the condition of unknown instantaneous channel state information (CSI), the best relay is adaptively selected from multiple candidate relays. Thereafter, in view of the slow convergence speed of Q-RSS in high-dimensional state space, the DRL algorithm is used to accelerate the convergence. In particular, we employ DRL algorithm to deal with high-dimensional state space while speeding up learning. The experimental results reveal that under the same conditions, the random relay selection scheme always has the worst performance. And compared to Q-RSS, the two relay selection schemes designed in this paper greatly reduce the number of iterations and speed up the convergence speed, thereby reducing the computational complexity and overhead of the source node selecting the best relay strategy. In addition, the two relay selection schemes designed and raised in this paper are featured by lower-level outage probability with lower-level energy consumption and larger system capacity. In particular, PPO-RSS has higher reliability and practicability.     

Delay optimization and energy balancing algorithm for improving network lifetime in fixed wireless sensor networks

Since wireless in terms of energy-restricted processes, dispersion radii, processing power limitations, buffers, bandwidth-limited connections, active network topologies, and network stream of traffic outlines, sensor networks provide difficult design issues. The number of hops and latency are decreased if there is a relay mote because it interacts directly with relay motes that are closer to the destination mote. The tremendous intensive research in the area of Wireless Sensor Networks (WSN) has gained a lot of significance among the technical community and research. The job of WSN is to sense the data using sensor motes, pass on the data to the destination detection mote which is associated with a processing center and can be used in multiple spans of Internet of Things (IoT) applications. Wireless sensor network has a set of sensor motes. By making use of sensor mote placement strategy all the sensor motes are spread in an area with each mote having its own exceptional location. Internet of things applications are delay sensitive those applications have a challenge of forming the complete path at a lower delay constraint. The proposal is to modify the game theory energy balancing algorithm by making use of relay motes so that overall network lifetime is increased. It has been proved that modified GTEB is better with respect to existing algorithms in terms of delay, figure of hops, energy depletion, figure of alive motes, figure of dead motes, lifespan ratio, routing overhead and throughput.     

Fault-tolerant topology in the wireless sensor networks for energy depletion and random failure

Nodes in the wireless sensor networks(WSNs) are prone to failure due to energy depletion and poor environment,which could have a negative impact on the normal operation of the network. In order to solve this problem, in this paper, we build a fault-tolerant topology which can effectively tolerate energy depletion and random failure. Firstly, a comprehensive failure model about energy depletion and random failure is established. Then an improved evolution model is presented to generate a fault-tolerant topology, and the degree distribution of the topology can be adjusted. Finally, the relation between the degree distribution and the topological fault tolerance is analyzed, and the optimal value of evolution model parameter is obtained. Then the target fault-tolerant topology which can effectively tolerate energy depletion and random failure is obtained. The performances of the new fault tolerant topology are verified by simulation experiments. The results show that the new fault tolerant topology effectively prolongs the network lifetime and has strong fault tolerance.     

Scale-free networks via attaching to random neighbors

Preferential attachment is considered one of the key factors in the formation of scale-free networks. However, complete random attachment without a preferential mechanism can also generate scale-free networks in nature, such as protein interaction networks in cells. This article presents a new scale-free network model that applies the following general mechanisms: (i) networks expand continuously by the addition of new vertices, and (ii) new vertices attach to random neighbors of random vertices that are already well connected. The proposed model does not require global-based preferential strategies and utilizes only the random attachment method. Theoretical analysis and numerical simulation results denote that the proposed model has steady scale-free network characteristics, and random attachment without a preferential mechanism may generate scale-free networks.     

一种能耗均衡的无线传感器网络加权无标度拓扑研究

针对无线传感器网络节点能耗不均衡问题,通过对节点生命期建模,得出节点生命期受节点剩余能量和通信距离的影响,进而将两端节点生命期作为构建拓扑时边权重的影响因子,通过边权重控制节点权重,最终得出了一种能耗均衡的无线传感器网络加权无标度拓扑模型,并理论证明了该模型的点权、边权和节点度均服从幂律分布.实验结果表明,该模型具有无标度拓扑的强容错性,并有效的均衡了网络中的节点能耗,延长了网络的生命期.     

Enhancing network lifespan in wireless sensor networks using deep learning based Graph Neural Network

Inadequate energy of sensors is one of the most significant challenges in the development of a reliable wireless sensor network (WSN) that can withstand the demands of growing WSN applications. Implementing a sleep-wake scheduling scheme while assigning data collection and sensing chores to a dominant group of awake sensors while all other nodes are in a sleep state seems to be a potential way for preserving the energy of these sensor nodes. When the starting energy of the nodes changes from one node to another, this issue becomes more difficult to solve. The notion of a dominant set-in graph has been used in a variety of situations. The search for the smallest dominant set in a big graph might be time-consuming. Specifically, we address two issues: first, identifying the smallest possible dominant set, and second, extending the network lifespan by saving the energy of the sensors. To overcome the first problem, we design and develop a deep learning-based Graph Neural Network (DL-GNN). The GNN training method and back-propagation approach were used to train a GNN consisting of three networks such as transition network, bias network, and output network, to determine the minimal dominant set in the created graph. As a second step, we proposed a hybrid fixed-variant search (HFVS) method that considers minimal dominant sets as input and improves overall network lifespan by swapping nodes of minimal dominating sets. We prepared simulated networks with various network configurations and modeled different WSNs as undirected graphs. To get better convergence, the different values of state vector dimensions of the input vectors are investigated. When the state vector dimension is 3 or 4, minimum dominant set is recognized with high accuracy. The paper also presents comparative analyses between the proposed HFVS algorithm and other existing algorithms for extending network lifespan and discusses the trade-offs that exist between them. Lifespan of wireless sensor network, which is based on the dominant set method, is greatly increased by the techniques we have proposed.     

Local adaptive sampling for wireless sensor network powered by energy harvesting

For a sensor network, energy limitation is always a key factor to affect the continuous work of a sensor node. A good idea is harvesting energy from the environment to support the node to work continuously. However, energy from environment is varied with time, weather and season. So in order to use the varied environment energy, it is necessary to find a way to achieve real-time monitoring and adaptive working. In this paper, an algorithm called “Adaptive Sampling” was proposed to adapt the sample mode to the present energy condition. Simulation proves that the algorithm can make the CO₂ sensor flexibly achieve adaptive sampling under different energy condition with the least MSE 2.7767. This algorithm can be widely used in wireless sensor network power by energy harvesting for local adaptive sampling.     

一种基于势博弈的无线传感器网络拓扑控制算法

在实际的应用中,无线传感器网络常常由大量电池资源有限的传感器节点组成.如何降低网络功耗,最大化网络生存时间,是传感器网络拓扑控制技术的重要研究目标.随着传感节点的运行,节点的能量分布可能越来越不均衡,需要在考虑该因素的情况下,动态地调整节点的网络负载以均衡节点的能耗,达到延长网络生存时间的目的.该文引入博弈理论和势博弈的概念,综合考虑节点的剩余能量和节点发射功率等因素,设计了一种基于势博弈的拓扑控制模型,并证明了该模型纳什均衡的存在性.通过构造兼顾节点连通性和能耗均衡性的收益函数,以确保降低节点功耗的同时维持网络的连通性.通过提高邻居节点的平均剩余能量值以实现将剩余能量多的节点选择作为自身的邻居节点,提高节点能耗的均衡性.在此基础上,提出了一种分布式的能耗均衡拓扑控制算法.理论分析证明了该算法能保持网络的连通性.与现有基于博弈理论的DIA算法和MLPT算法相比,本算法形成的拓扑负载较重、剩余能量较小的瓶颈节点数量较少,节点剩余能量的方差较小,网络生存时间更长.     

A random walk evolution model of wireless sensor networks and virus spreading

In this paper, considering both cluster heads and sensor nodes, we propose a novel evolving a network model based on a random walk to study the fault tolerance decrease of wireless sensor networks (WSNs) due to node failure, and discuss the spreading dynamic behavior of viruses in the evolution model. A theoretical analysis shows that the WSN generated by such an evolution model not only has a strong fault tolerance, but also can dynamically balance the energy loss of the entire network. It is also found that although the increase of the density of cluster heads in the network reduces the network efficiency, it can effectively inhibit the spread of viruses. In addition, the heterogeneity of the network improves the network efficiency and enhances the virus prevalence. We confirm all the theoretical results with sufficient numerical simulations.     

无线传感网中基于质心的高效坐标压缩算法

鉴于无线传感网中有限的通信带宽和计算资源, 传感器节点传送压缩后的数据对于系统节能具有十分重要的意义. 本文提出一种利用反正切函数构建的非线性坐标压缩策略来降低系统的数据传输量. 得益于反正切函数的非线性特点, 在质心附近压缩率低, 离质心越远, 压缩率越高. 仿真结果显示, 所提算法特别适应于在质心附近具有高频分量的信号类型. 所提算法具有以下几个特点: 1)采样间隔是非均匀的; 2)可以在采样前实现压缩, 类似于压缩感知; 3)计算复杂度低, 算法简单易实现, 在实际应用中更具有优势. 关键词： 无线传感器网络 坐标压缩 非线性 质心     

Improved control of distributed parameter systems using wireless sensor and actuator networks:An observer-based method

In this paper,the control problem of distributed parameter systems is investigated by using wireless sensor and actuator networks with the observer-based method.Firstly,a centralized observer which makes use of the measurement information provided by the fixed sensors is designed to estimate the distributed parameter systems.The mobile agents,each of which is affixed with a controller and an actuator,can provide the observer-based control for the target systems.By using Lyapunov stability arguments,the stability for the estimation error system and distributed parameter control system is proved,meanwhile a guidance scheme for each mobile actuator is provided to improve the control performance.A numerical example is finally used to demonstrate the effectiveness and the advantages of the proposed approaches.     

分布式无线声传感网加权预测误差语声去混响方法

室内混响会严重降低语声质量,因此在室内语声通信中对混响的抑制显得尤为重要.针对无线声传感网,该文提出一种基于加权预测误差的分布式自适应去混响算法.通过调整传统递归最小二乘算法,所提出的分布式加权预测误差算法仅需利用一路相同的参考信号和其他节点的本地输出而非全部信号,便可实现最优输出,从而大幅度降低节点间传输的通道数与各...     

无线传感器网络中继节点布居算法的研究

本文表述的是在该应用背景下引入多约束条件, 并采用枚举法与贪婪寻优算法相结合的方法, 解决了在可以作为中继节点设置位置的预设中继节点位置集合内, 合理选择中继节点设置位置以及既存网络因添加新传感器节点所引起的中继节点追加的问题. 仿真实验表明, 本文提出的中继节点布居与追加优化算法能够保证多约束条件下网络的容错性. 同时提出的基于最小网络距离因子评价标准, 有效提高了中继节点布居算法的能效性.     

A single hop architecture exploiting cooperative beamforming for wireless sensor networks

In this paper, we propose a single hop architecture for a cooperative wireless sensor network and analyze the attained distributed beamforming gain performance using the theory of random arrays. All nodes in the system transmit a single carrier such that the signals add up constructively towards the direction of the fusion center. The potential directive beamforming gains are investigated for different sensor network densities which are expressed as the number of nodes per carrier wavelength squared. The multiple access capability of the sensor network is achieved by employing an on-off keying orthogonal signaling technique, which is usually employed in atmospheric optical systems. Finally, we investigate the average loss in directivity gain when the received signal from each sensor node follows a Ricean distribution. The results show that high directive gains can be achieved in practical wireless sensor networks using simple sensor nodes.