WSNs中基于智能水滴的信宿路径规划算法

为了有效避免无线传感网络(WSNs)中的热点问题,常利用移动信宿收集数据。信宿依据预定路线遍历预定的驻留点(RPs)。而其他传感节点就将数据传输至离自己最近的驻留点。因此,构建最优的RPs非常重要。为此,提出基于智能水滴的信宿路径规划(IWD-SPP)算法。提出IWD-SPP算法的目的在于延长网络寿命,并最小化转发数据包的能量消耗。利用智能水滴算法构建最优的RPs,规划信宿的移动路径。仿真结果表明,提出的IWD-SPP算法在能量消耗和网络寿命方面的性能优于同类算法。     

WSNs中优化移动信宿路径的数据收集算法

收集数据是部署无线传感网络(WSNs)的根本目的。采用移动信宿策略可有效缓解WSNs的能耗问题,信宿的移动路径是该策略的关键。为此,提出基于伪驻留点的数据收集(VRDC)算法。VRDC算法先依据驻留点规划信宿路径,再依据路径选择伪驻留点(VRPs)。VRPs可通过一跳直接向移动信宿传输数据,而其他的节点则将数据传输至最近的VRPs,进而减少传输跳数,降低能耗。仿真结果表明,提出的VRDC算法能有效降低能耗,并平衡节点间的能耗。     

WSNs布局的粒子进化的多粒子群优化策略

在粒子进化的多粒子群算法基础上,提出了一种无线传感网络节点布局的优化策略.该策略通过多个粒子群彼此独立地搜索解空间,提高了算法的寻优能力,有效地避免了"早熟"问题,提高了算法的稳定性.仿真实验表明,与传统的粒子群算法相比,该算法有效覆盖率由75.36%提高到80.96%,收敛速度提高了19.4%.因此粒子进化的多粒子群优化策略具有比传统的粒子群算法更好的优化效果.     

混合无线传感网络覆盖优化的粒子群算法

为提高随机部署的传感网络覆盖性能,提出基于动态克隆粒子群的移动节点部署控制算法,用每个粒子表示所有移动节点的一种部署方案,在经典粒子群算法基础上,每次迭代结束后,粒子依据自身的覆盖性以及与群体中粒子的相似性决定其克隆数量和变异幅度,有效避免陷入早熟陷阱.通过与其他算法的对比仿真实验,表明该优化算法能更有效地提高网络覆盖性能.     

基于改进混合粒子群算法的无人机充电路径规划方法

无线可充电传感器网络(Wireless Rechargeable Sensor Networks, WRSNs)是由若干个可无线充电的传感器节点自组织形成的网络。利用无人机(Unmanned Aerial Vehicle, UAV)作为移动电源,对WRSNs网络中传感器节点进行充电,将无人机飞行能耗联合传感器节点充电量作为优化目标,提出一种惯性权重线性递减混合粒子群算法,对无人机的充电路径进行规划。仿真验证表明,相比传统的粒子群(Particle Swarm Optimization, PSO)算法,该算法在收敛速度、无人机飞行能耗以及传感器节点充电量等指标上都有明显的优势。     

自适应逃逸粒子群算法的网络节点覆盖优化仿真

研究无线传感器节点优化选择。传统LEACH分簇算法中,节点选择的随机性很大,没有很好地参考节点中的多个属性,通信的簇头分布也无规律,算法把能量消耗分摊到所有的节点上,一旦选择边沿节点作为簇头,一些节点必须经过长距离的路由转发才能到达簇头,造成通信效率较低。为了避免上述缺陷,提出了一种基于自适应逃逸粒子群算法的网络节点覆盖优化方法。建立自适应逃逸粒子群算法的数学模型,准确描述网络节点覆盖问题。利用自适应逃逸粒子群方法,计算无线传感网络节点最优位置,从而实现网络节点覆盖优化。实验结果表明,这种算法能够实现网络节点覆盖优化处理,从而提高无线传感网络数据传递的效率。     

基于量子粒子群算法的移动节点覆盖优化

提出了基于量子粒子群的无线传感器网络覆盖优化算法.由于在量子空间中粒子满足集聚态性质完全不同,使得该算法可以在整个可行区域内搜索.全局搜索能力远远优干基本粒子群,克服了粒子群算法容易陷入局部最优的缺点.仿真结果表明,该算法比基本粒子群算法拥有更好的覆盖优化效果。     

传感器网络的粒子群优化定位算法

无线传感器网络定位问题是一个基于不同距离或路径测量值的优化问题。由于传统的节点定位算法采用最小二乘法求解非线性方程组时很容易受到测距误差的影响,为了提高节点的定位精度,将粒子群优化算法引入到传感器网络定位中,提出了一种传感器网络的粒子群优化定位算法。该算法利用未知节点接收到的锚节点的距离信息,通过迭代方法搜索未知节点位置。仿真结果表明,该算法有效地抑制了测距误差累积对定位精度的影响,提高了节点的定位精度。     

水声传感器网络中粒子群与蒙特卡罗优化的移动定位算法

针对水声传感器网络中移动定位算法的误差和鲁棒性问题,提出两种蒙特卡罗移动定位算法:CRMCL(Circular Ring Monte Carlo Localization)和PRMCL(Particle Swarm Optimization for Circular Ring Monte Carlo Localiza-...     

无线传感网络布局的虚拟力导向微粒群优化策略

无线传感网络通常由固定传感节点和少量移动传感节点构成,动态无线传感网络布局优化有利于提高无线传感网络覆盖率和目标检测概率,是无线传感网络研究的关键问题之一.传统的虚拟力算法在优化过程中容易受固定传感节点的影响,无法实现全局优化.本文结合虚拟力算法和微粒群算法,提出一种面向无线传感网络布局的虚拟力导向微粒群优化策略.该策略通过无线传感节点间的虚拟力影响微粒群算法的速度更新过程,指导微粒进化,加快算法收敛.实验表明,虚拟力导向微粒群优化策略能快速有效地实现无线传感节点布局优化.与微粒群算法和虚拟力算法相比,虚拟力导向微粒群优化策略不仅网络覆盖率高,且收敛速度快,耗时少.     

Rendezvous Planning in Wireless Sensor Networks with Mobile Elements

Recent research shows that significant energy saving can be achieved in wireless sensor networks by using mobile elements (MEs) capable of carrying data mechanically. However, the low movement speed of MEs hinders their use in data-intensive sensing applications with temporal constraints. To address this issue, we propose a rendezvous-based approach in which a subset of nodes serve as the rendezvous points (RPs) that buffer data originated from sources and transfer to MEs when they arrive. RPs enable MEs to collect a large volume of data at a time without traveling long distances, which can achieve a desirable balance between network energy saving and data collection delay. We develop two rendezvous planning algorithms, RP-CP and RP-UG. RP-CP finds the optimal RPs when MEs move along the data routing tree while RP-UG greedily chooses the RPs with maximum energy saving to travel distance ratios. We design the Rendezvous-based Data Collection protocol that facilitates reliable data transfers from RPs to MEs in presence of significant unexpected delays in ME movement and network communication. Our approach is validated through extensive simulations.     

Energy-Efficient Routing Mechanism for Mobile Sink in Wireless Sensor Networks Using Particle Swarm Optimization Algorithm

Wireless Personal Communications - One of the most effective approaches to increase the lifetime of wireless sensor networks (WSNs), is the use of a mobile sink to collect data from sensor. In...     

Energy Efficient Clustering Algorithm Based on Particle Swarm Optimization Technique for Wireless Sensor Networks

Wireless Personal Communications - In this paper, we propose a Gated Recurrent Unit(GRU) neural network traffic prediction algorithm based on transfer learning. By introducing two gate structures,...     

Adaptive Optimization-based Routing in Wireless Mesh Networks

Routing is a critical component in wireless mesh networks. The inherent shared-medium nature of the wireless mesh networks, however, poses fundamental challenges to the design of effective routing policies that are optimal with respect to the resource utilization. Node churns and traffic fluctuations exacerbate such a problem. In this paper, we propose a novel adaptive routing algorithm for multiple subscribers in wireless mesh networks. We view a mesh network with multiple nodes as an entity that optimizes some global utility function constrained by the underlying MAC layer interference. By solving the optimization problem, the network is driven to an efficient operating point with a certain routing policies for each node. We then use this operating point information to adaptively find better paths, which is able to gear the network towards optimal routing. Further, we take the fluctuations of the network into consideration and thus render our algorithm more robust for a variety of network situations. Simulations demonstrate the efficiency and efficacy of our algorithm.     

无线传感器网络中簇首选择算法研究

传统的低功耗自适应集簇分层型协议（LEACH）算法在选择簇首时未能考虑到节点剩余能量对网络寿命的影响,使得簇首分布不够合理。为了克服该问题,在完全分布式成簇算法（HEED）协议的基础上,设计了一种根据节点剩余能量选择簇首的算法。在该算法中,剩余能量越大的节点越有可能成为簇首,进而承担更多数据传输责任,能量消耗更加平均,增强了算法的健壮性。仿真结果证实,提出的算法可以有效提高网络能量的使用效率,减少功耗,延长网络生存时间。     

Channel Selection in Virtual MIMO Wireless Sensor Networks

In this paper, we present two practical algorithms for selecting a subset of channels in virtual multiple-input–multiple-output (MIMO) wireless sensor networks (WSNs) to balance the MIMO advantage consumption of sensor cooperation. If intracluster node-to-node multihop needs be taken into account, the maximum spanning tree searching (MASTS) algorithm, with respect to the cross-layer design, always provides a path connecting all sensors. When the WSN is organized in a manner of cluster-to-cluster multihop, the singular-value decomposition-QR with threshold (SVD-QR-T) approach selects the best subset of transmitters while keeping all receivers active. The threshold is adaptive by means of fuzzy c-means (FCM). These two approaches are compared by simulation against the case without channel selection in terms of capacity, bit error rate (BER), and multiplexing gain with water filling or equal transmission power allocation. Despite less multiplexing gain, when water filling is applied, MASTS achieves higher capacity and lower BER than virtual MIMO without channel selection at moderate-to-high signal-to-noise ratio (SNR), whereas SVD-QR-T by FCM provides the lowest BER at high SNR; in the case of no water filling and equal transmission power allocation, MASTS still offers the highest capacity at moderate-to-high SNR, but SVD-QR-T by FCM achieves the lowest BER. Both algorithms provide satisfying performances with reduced resource consumption.      

A Qos-Aware,Hybrid Particle Swarm Optimization-Cuckoo Search Clustering Based Multipath Routing in Wireless Sensor Networks

Wireless Personal Communications - Wireless sensor networks applications have been used in many areas that are difficult to access by humans. However, Routing remains the key challenges in...