Modified Dwarf Mongoose Optimization Enabled Energy Aware Clustering Scheme for Cognitive Radio Wireless Sensor Networks

Cognitive radio wireless sensor networks(CRWSN)can be defined as a promising technology for developing bandwidth-limited applications.CRWSN is widely utilized by future Internet of Things(IoT)applications.Since a promising technology,Cognitive Radio(CR)can be modelled to alleviate the spectrum scarcity issue.Generally,CRWSN has cognitive radioenabled sensor nodes(SNs),which are energy limited.Hierarchical clusterrelated techniques for overall network management can be suitable for the scalability and stability of the network.This paper focuses on designing the Modified Dwarf Mongoose Optimization Enabled Energy Aware Clustering(MDMO-EAC)Scheme for CRWSN.The MDMO-EAC technique mainly intends to group the nodes into clusters in the CRWSN.Besides,theMDMOEAC algorithm is based on the dwarf mongoose optimization(DMO)algorithm design with oppositional-based learning(OBL)concept for the clustering process,showing the novelty of the work.In addition,the presented MDMO-EAC algorithm computed a multi-objective function for improved network efficiency.The presented model is validated using a comprehensive range of experiments,and the outcomes were scrutinized in varying measures.The comparison study stated the improvements of the MDMO-EAC method over other recent approaches.

Bidirectional secondary transmissions with energy harvesting in cognitive wireless sensor networks

To the existing spectrum sharing schemes in wireless-powered cognitive wireless sensor networks,the protocols are limited to either separate the primary and the secondary transmission or allow the secondary user to transmit signals in a time slot when it forwards the primary signal.In order to address this limitation,a novel cooperative spectrum sharing scheme is proposed,where the secondary transmission is multiplexed with both the primary transmission and the relay transmission.Specifically,the process of transmission is on a three-phase time-switching relaying basis.In the first phase,a cognitive sensor node SU1 scavenges energy from the primary transmission.In the second phase,another sensor node SU2 and primary transmitter simultaneously transmit signals to the SU1.In the third phase,the node SU1 can assist the primary transmission to acquire the opportunity of spectrum sharing.Joint decoding and interference cancellation technique is adopted at the receivers to retrieve the desired signals.We further derive the closed-form expressions for the outage probabilities of both the primary and secondary systems.Moreover,we address optimization of energy harvesting duration and power allocation coefficient strategy under performance criteria.An effective algorithm is then presented to solve the optimization problem.Simulation results demonstrate that with the optimized solutions,the sensor nodes with the proposed cooperative spectrum sharing scheme can utilize the spectrum in a more efficient manner without deteriorating the performance of the primary transmission,as compared with the existing one-directional scheme in the literature.

Cooperative Spectrum Sensing for Cognitive Radio-Wireless Sensors Network Based on OR Rule Decision to Enhance Energy Consumption in Greenhouses

Wireless sensors networks (WSNs) combined with cognitive radio have developed and solved the limited space of the frequency spectrum. In this paper, we propose different types of spectrums sensing and their own decisions depend on the probabilities that applied into fusion center, and how these probabilities’ techniques help to enhance the energy consumption of WSNs. In the same way, the importance of designing balanced distribution between the wireless sensors networks and their own sinks. This research also provides an overview of security issues in CR-WSN, especially in Spectrum Sensing Data Falsification (SSDF) attacks that enforces harmful effects on spectrum sensing and spectrum sharing. We adopt OR rule as four types of CRSN sensing protocolin greenhouses application by using Matlab and Netsim simulators. Our results show that the designing balanced wireless sensors and their sinks in greenhouses are very significant to decrease the energy, which is due to the traffic congestion in the sink range area. Furthermore, by applying OR rule has enhanced the energy consumption, and improved the sensors network lifetime compared to cognitive radio network.

A Fault-Tolerant Cooperative Spectrum Sensing Algorithm over Cognitive Radio Network Based on Wireless Sensor Network

A serious threat to cognitive radio networks that sense the spectrum in a cooperative manner is the transmission of false spectrum sensing data by malicious sensor nodes. SNR fluctuations due to wireless channel effects complicate handling such attackers even further. This enforces the system to acquire authentication. Actually, the decision maker needs to determine the reliability or trustworthiness of the shared data. In this paper, the evaluation process is considered as an estimation dilemma on a set of evidences obtained through sensor nodes that are coordinated in an underlying wireless sensor network. Then, a likelihood-based computational trust evaluation algorithm is proposed to determine the trustworthiness of each sensor node's data. The proposed procedure just uses the information which is obtained from the sensor nodes without any presumptions about node’s reliability. Numerical results confirm the effectiveness of the algorithm in eliminating malicious nodes or faulty nodes which are not necessarily conscious attackers.

Managing Energy Consumption of Wireless Sensors Networks in Multiple Greenhouses

An extensive area implementation of fully observed greenhouses motivates on research, especially in remote greenhouses. However, implementation of wireless sensor networks (WSNs) is still needed for investigation. Cognitive radio sensor networks (CRSNs) took advantage of using the cognitive radio (CR) concept to which allowed wireless sensor networks to dynamically access into white space channels which is unused channels. In this paper, we adopted the Generalized Implicit-OR as CRSN sensing protocol to reduce the energy consumption and increase the network lifetime in multiple numbers of greenhouses. Our results showed that enhanced energy consumption and improved network lifetime compared to ordinary WSN.

AN ADAPTIVE MEASUREMENT SCHEME BASED ON COMPRESSED SENSING FOR WIDEBAND SPECTRUM DETECTION IN COGNITIVE WSN

An Adaptive Measurement Scheme (AMS) is investigated with Compressed Sensing (CS) theory in Cognitive Wireless Sensor Network (C-WSN). Local sensing information is collected via energy detection with Analog-to-Information Converter (AIC) at massive cognitive sensors, and sparse representation is considered with the exploration of spatial temporal correlation structure of detected signals. Adaptive measurement matrix is designed in AMS, which is based on maximum energy subset selection. Energy subset is calculated with sparse transformation of sensing information, and maximum energy subset is selected as the row vector of adaptive measurement matrix. In addition, the measurement matrix is constructed by orthogonalization of those selected row vectors, which also satisfies the Restricted Isometry Property (RIP) in CS theory. Orthogonal Matching Pursuit (OMP) reconstruction algorithm is implemented at sink node to recover original information. Simulation results are performed with the comparison of Random Measurement Scheme (RMS). It is revealed that, signal reconstruction effect based on AMS is superior to conventional RMS Gaussian measurement. Moreover, AMS has better detection performance than RMS at lower compression rate region, and it is suitable for large-scale C-WSN wideband spectrum sensing.

Cognitive Power Management in Wireless Sensor Networks

Dynamic power management （DPM） in wireless sensor nodes is a well-known technique for reducing idle energy consumption. DPM controls a node＇s operating mode by dynamically toggling the on/off status of its units based on predictions of event occurrences. However, since each mode change induces some overhead in its own right, guaranteeing DPM＇s eificiency is no mean feat in environments exhibiting non-determinism and uncertainty with unknown statistics. Our solution suite in this paper, collectively referred to as cognitive power management （CPM）, is a principled attempt toward enabling DPM in statistically unknown settings and gives two different analytical guarantees. Our first design is based on learning automata and guarantees better-than-pure-chance DPM in the face of non-stationary event processes. Our second solution caters tor an even more general setting in which event occurrences may take on an adversarial character. In this case, we formulate the interaction of an individual mote with its environment in terms of a repeated zero-sum game in which the node relies on a no-external-regret procedure to learn its mini-max strategies in an online fashion. We conduct numerical experiments to measure the performance of our schemes in terms of network lifetime and event loss percentage.

Performance Analysis of Multi-Channel CR Enabled IoT Network with Better Energy Harvesting

Wireless Sensor Networks(WSNs)can be termed as an autoconfigured and infrastructure-less wireless networks to monitor physical or environmental conditions,such as temperature,sound,vibration,pressure and motion etc.WSNs may comprise thousands of Internet of Things(IoT)devices to sense and collect data from its surrounding,process the data and take an automated and mechanized decision.On the other side the proliferation of these devices will soon cause radio spectrum shortage.So,to facilitate these networks,we integrate Cognitive Radio(CR)functionality in these networks.CR can sense the unutilized spectrum of licensed users and then use these empty bands when required.In order to keep the IoT nodes functional all time,continuous energy is required.For this reason the energy harvested techniques are preferred in IoT networks.Mainly it is preferred to harvest Radio Frequency(RF)energy in the network.In this paper a region based multi-channel architecture is proposed.In which the coverage area of primary node is divided as Energy Harvesting Region and Communication Region.The Secondary User(SU)that are the licensed user is IoT enabled with Cognitive Radio(CR)techniques so we call it CR-enabled IoT node/device and is encouraged to harvest energy by utilizing radio frequency energy.To harvest energy efficiently and to reduce the energy consumption during sensing,the concept of overlapping region is given that supports to sense multiple channels simultaneously and help the SU to find best channel for transmitting data or to harvest energy from the ideal channel.From the experimental analysis,it is proved that SU can harvest more energy in overlapping region and this architecture proves to consume less energy during data transmission as compared to single channel.We also show that channel load can be highly reduced and channel utilization is proved to be more proficient.Thus,this proves the proposed architecture cost-effective and energy-efficient.

无线认知传感器网络的研究

在无线传感器网络中引入认知无线电技术,构成无线认知传感器网络,可以减少ISM频段干扰,提高数据传输率。对无线认知传感器网络的研究目前已成为一个新的研究领域。介绍了无线认知传感器网络的概念,从无线认知传感器网络的节点、频谱感知、频谱决策、频谱共享、频谱迁移、网络层、传输层、应用层等方面综述了现有的研究成果,讨论了目前存在的问题和需要进一步研究的方向。

认知视角下能量感知的ZigBee网络树型路由优化算法

为解决ZigBee Cluster-Tree路由算法路径选择不优的问题,提出了一种能量感知的ZigBee树型路由EZTR(Energy-Aware ZigBee tree routing)算法.该算法利用每个节点感知的地址信息,按照ZigBee网络树型结构计算下一跳邻居节点到目的节点之间的跳数可避免网络的环路效应,通过引入认知概念,在跳数集合中选出最短路径以降低跳数.在ZigBee网络节点能量的感知过程中,当所选路径存在低能量节点时,及时启用备用节点,从而避免节点因能量过度消耗成为失效节点.NS2(Network simulator version 2)仿真实验表明,EZTR算法可提高网络分组递交率,有效减少节点转发跳数和平均网络延时,减小网络整体能耗,为提高网络的实时性和延长网络生命周期提供理论支持.

智能电网WCSN安全体系架构研究

作为下一代电力系统,智能电网应具备向电网控制中心实时可靠传输信息的能力。为了解决智能电网无线传感器网络面临的异构无线网络共存、频谱资源紧张和海量数据处理等问题,我们在智能电网引入了无线认知传感器网络的理念。文章分析了在智能电网中引入认知无线传感器网络(WCSN)的必要性,列举了智能电网、认知无线电网络和无线传感器网络的相关研究成果,描述了智能电网WCSN分层结构包括家域网、邻域网和广域网,提出了智能电网WCSN安全架构。

微电网数据通信无线传感器网络性能的跨层控制方法研究

针对无线传感器网络满足微电网数据通信规范性能指标要求问题,探索了一种微电网数据传输无线传感器网络协议栈跨层协作服务质量控制方法。通过分析影响微电网无线传感器网络数据传输的性能指标的主要因素,构造了包含10类概念顶点集合的模糊认知图,以数学模型方式表达无线通信网络中的主要参量之间的因果关系,建立了微电网数据传输无线传感器网络实时性和可靠性服务质量对应的状态空间模型。依据模糊认知图的概念顶点属性模糊推理机制,探讨了一种微电网数据传输无线传感器网络跨层协作服务质量的模糊控制方法,给出了以满足微电网数据通信规范性能指标要求的3种不同状况下的网络参数调节方法和路由路径动态调整策略。通过对微电网数据传输中不同数据产生率情况下的网络性能分析仿真实验分析,结果表明,本方法可为基于无线传感器网络的微电网数据通信提供更有效的QoS保障。

智能配电网无线传感器通信网络的跨层协作控制

为提高智能配电网无线传感器网络通信的实时性和可靠性,提出一种堆栈协议层之间跨层协作的控制模型。以无线传感器网络节点内各个影响因素为概念节点,建立起节点内模糊认知图结构模型,利用隶属关系确定该模型内概念节点之间的影响程度,进而建立整个通信系统的跨层控制模型。控制模型根据网络电力数据的通信状态,通过自学习和训练方法获得各个协议层参数之间的影响权重,实时调整物理层、MAC层、传输层参数以及路由策略相互协作,来保证数据通信的实时性和可靠性。最后对系统的网络接收的数据量、节点队列传输数据的平均延时以及网络的吞吐量等指标进行测试。结果表明,所提出的基于模糊认知图的无线传感器网络跨层协作控制方案符合智能配电网无线传感器网络通信特征,并能够满足其通信实时性与可靠性的需求。

认知无线电在矿井无线传感器网络系统中的应用

为了有效地实现矿井环境监测,将认知无线电(CR)技术和无线传感器网络(WSNs)技术相结合,提出了CR-WSNs新概念。分析了煤矿井下特殊的工作环境,介绍了适用于矿井的CR-WSNs监测系统结构、节点的硬件组成和CR-WSNs模块间的通信,给出了简单的性能测试方案。实际测试结果表明:CR-WSNs比普通WSNs具有更强的抗干扰性能。

一种宽带频谱检测的空域频域压缩感知方法

压缩感知(CS)技术使快速宽频检测成为可能,采用CS技术的WSN可为认知无线电用户提供频谱信息。针对WSN检测到的频谱数据,该文建立了一种基于空域和频域2维压缩的空频压缩感知(SFCS)模型,提出了相应的重构算法,并详细分析了SFCS的各种性能。仿真结果证实了在同一检测概率下,SFCS模型下比传统模型所需传输数据更少;在相同总压缩率下,该文算法下的ROC性能优于传统算法的性能。

认知无线传感器网络新型SVM频谱感知策略

阐述了基于认知无线传感器网络背景运用支持向量机的可行性。针对低信噪比噪声复杂性高的无线环境,单一的识别方法难以获得相对准确的结果。基于隐马尔可夫模型(Hidden Markov Model,HMM)对传统SVM频谱感知算法进行了优化,采用多个分类器集成降低识别错误和增强识别鲁棒性。采用最小二乘法将线性不等式约束转化为线性约束得到最优超平面来分割主信号和噪声干扰,对主用户状态进行决策,最后与传统能量检测算法比较性能。仿真结果表明,基于SVM频谱感知性能更接近理论值,比能量检测更为可靠与准确,错误率为1.6%,在低SNR下检测概率比能量检测高出18%,具有更优的检测性能与鲁棒性。

无线多媒体传感器网络中的动态频谱分配技术研究

无线多媒体传感器网络(WMSNs)具有实时监控,收集和处理多媒体信息的功能,有广泛的应用前景。较之传统无线传感器网络,WMSNs无线传输多媒体信息需要更大带宽。然而,随着无线通信设备的广泛应用,有限的可用频谱资源日益匮乏。利用动态频谱分配技术,可以扩展WMSNs的通信频段,增强抗干扰能力。考虑到WMSNs节点的物理限制,如计算能力和能量供应,该文提出了适合WMSNs的频谱感知方法和频谱管理方法。频谱感知采用各节点的轮换机制感知整个频段;频谱管理可以确保对授权用户影响最小的信道被首先使用。WMSNs使用上述方法可以感知周围无线电环境,利用空闲私有频段进行无线通信。最后,通过实验证明了该文提出的动态频谱分配技术对WMSNs的有效性。

基于SIR模型的智能电网WCSN数据伪造攻击研究

为了解决智能电网无线传感器网络面临的异构无线网络共存、频谱资源紧张和海量数据处理等问题,在智能电网引入了认知无线传感器网络(WCSN)。文章对智能电网WCSN中的数据伪造攻击进行了研究。在此类攻击中,恶意认知无线传感器节点(传染节点)通过向其他认知无线传感器节点(易感节点)发送伪造的频谱感知数据和设备能耗信息,导致控制中心做出错误的频谱分配和电力调度决策。采用流行病理论中的SIR模型,对智能电网WCSN中的数据伪造攻击信息传播过程进行了建模,研究了流行病爆发的潜在决定因素。最后,通过仿真验证了智能电网WCSN数据伪造攻击SIR模型,并对系统动态特性进行了分析。

认知WSN中基于能量有效性自适应观测的梯度投影稀疏重构方法

针对认知无线传感器网络中传感器节点侧的模拟信息转换器对本地感知数据进行稀疏表示与压缩测量,该文提出一种基于能量有效性观测的梯度投影稀疏重构(GPSR)方法。该方法根据事件区域内认知节点对实际感知到的非平稳信号空时相关性结构,映射到小波正交基级联字典进行稀疏变换,通过加权能量子集函数进行自适应观测,以能量有效的方式获取合适的观测值,同时对所选观测向量进行正交化构造测量矩阵。汇聚节点采用GPSR算法进行自适应压缩重构。仿真比较了GPSR自适应重构与正交匹配追踪(OMP)重构算法。仿真结果表明,在压缩比小于0.2的区域内,基于能量有效性观测的GPSR自适应重构效果优于传统随机高斯测量信号重构。在相同节点数情况下,GPSR自适应压缩重构方法在低信噪比区域内具有较小的重构均方误差,且该方法所需观测数明显低于随机高斯观测,同时有效保障了感知节点的能耗均衡。

认知无线传感器网络中能耗有效的协作频谱感测算法

提出了一种新的认知无线传感器网络中能耗有效的协作频谱感测算法。首先,为了降低分布式传感节点的能耗,假定传感节点的瞬时信噪比和平均信噪比已知,分析频谱感测节点的能耗与最优检测门限值之间的数学模型。然后,结合感测节点选择和判决门限设定理论,研究基于判决节点选择的有效协作频谱感测方案。理论分析和仿真结果表明,算法有效地降低了认知传感器网络的节点总能耗,提高了能耗效率。