多移动基站无线传感器网络生命期最大化算法

无线传感器网络（WSN）的传感器节点一般由电池提供能源,故能量管理在WSN中是一个基础问题．针对受限多移动基站网络生命期最大化问题,提出了一个MMBEC算法．鉴于移动基站受到实际道路和自身能量的双重限制,算法首先通过规划平衡子回路达到基站间负载的平衡,然后控制基站周期性逗留达到邻近道路节点数据通信量的平衡．由于平衡子回路是NPC问题,采用近似算法找到近似解,控制基站周期性逗留保证邻近道路节点能量几乎同时耗尽,从而延长网络生命期．模拟实验结果证明该算法与现有算法相比能提高网络生命期和吞吐量．     

基于马尔可夫链的无线传感器网络分布式调度方法

能量效率是无线传感器网络(Wireless sensor network, WSN)研究中的核心问题之一. 当节点采用电池供电时, 有限的能量限制了网络的生存周期, 从而对无线传感器网络的大规模应用提出了挑战. 本文基于马尔可夫链, 提出了一种实用的、协作分布式的调度方法, 并从理论上证明了该方法的收敛性. 该方法不仅可对节点的休眠/唤醒进行调度, 还可以对节点数据发送进行调度以减少数据冲突的发生. 仿真实验结果表明, 该方法能够有效地减少节点能量的消耗, 且对其他网络性能的影响较小.     

Termite-hill: Performance optimized swarm intelligence based routing algorithm for wireless sensor networks

A wireless sensor network (WSN) is a large collection of sensor nodes with limited power supply, constrained memory capacity, processing capability, and available bandwidth. The main problem in event gathering in wireless sensor networks is the formation of energy-holes or hot spots near the sink. Due to the restricted communication range and high network density, events forwarding in sensor networks is very challenging, and require multi-hop data forwarding. Improving network lifetime and network reliability are the main factors to consider in the research associated with WSN. In static wireless sensor networks, sensors nodes close to the sink node run out of energy much faster than nodes in other parts of the monitored area. The nodes near the sink are more likely to use up their energy because they have to forward all the traffic generated by the nodes farther away to the sink. The uneven energy consumption results in network partitioning and limit the network lifetime. To this end, we propose an on-demand and multipath routing algorithm that utilizes the behavior of real termites on hill building termed Termite-hill which support sink mobility. The main objective of our proposed algorithm is to efficiently relay all the traffic destined for the sink, and also balance the network energy. The performance of our proposed algorithm was tested on static, dynamic and mobile sink scenarios with varying speed, and compared with other state-of-the-art routing algorithms in WSN. The results of our extensive experiments on Routing Modeling Application Simulation Environment (RMASE) demonstrated that our proposed routing algorithm was able to balance the network traffic load, and prolong the network lifetime.     

An enhanced real-time routing protocol with load distribution for mobile wireless sensor networks

Mobile wireless sensor network (MWSN) is a wireless ad hoc network that consists of a very large number of tiny sensor nodes communicating with each other in which sensor nodes are either equipped with motors for active mobility or attached to mobile objects for passive mobility. A real-time routing protocol for MWSN is an exciting area of research because messages in the network are delivered according to their end-to-end deadlines (packet lifetime) while sensor nodes are mobile. This paper proposes an enhanced real-time with load distribution (ERTLD) routing protocol for MWSN which is based on our previous routing protocol RTLD. ERTLD utilized corona mechanism and optimal forwarding metrics to forward the data packet in MWSN. It computes the optimal forwarding node based on RSSI, remaining battery level of sensor nodes and packet delay over one-hop. ERTLD ensures high packet delivery ratio and experiences minimum end-to-end delay in WSN and MWSN compared to baseline routing protocol. In this paper we consider a highly dynamic wireless sensor network system in which the sensor nodes and the base station (sink) are mobile. ERTLD has been successfully studied and verified through simulation experiment.     

Improvement of battery lifetime in software-defined network using particle swarm optimization based cluster-head gateway switch routing protocol with fuzzy rules

The software-defined network (SDN) is one of the network architectures, in which the data plane and control plane is divided from each other, and the network can be handled using a sensibly centralized controller and this method is adopted to reconfigure the wireless sensor network automatically. In this article, to implement the SDN in MANET, in which control nodes can be chosen in SDN dynamically for the activation of MANET function to allocate the works to other mobile nodes to the base station. However, in the field of mobile ad hoc networks, the network lifetime, and battery lifetime is one of the major problems and the energy consumption can play a significant rule for the transmission of data in the SDN. Therefore, in this article, particle swarm optimization (PSO) based CGSR (cluster-head gateway switch routing protocol) algorithm with fuzzy rules is proposed to increase the network lifetime of battery powered mobile nodes by reducing the energy consumptions of each node in software-defined MANET. In this proposed method, a routing method that can permit various mobile nodes with low battery power to transmits the data from source node to base station. We design a PSO based CGSR routing protocol by selecting the routing mobile nodes using fuzzy rules for packet transmission. In CGSR process, the formation of cluster and selection of cluster head is executed depending on the particle swarm optimization method. This proposed routing protocol can be used to enhance the battery lifetime by extension of the network lifetime with numerical analysis for efficient route node selection.     

无线传感网中一种智能数据融合算法的实现及仿真分析

在无线传感网中,传感器节点一般都由自身装配的电池供电,难以进行电量补充,因此节约电量对于无线传感网来说至关重要.为了提高无线传感网能量使用效率,延长网络生存时间,提出了一种结合遗传算法和粒子群算法优化BP神经网络的智能数据融合算法 GAPSOBP(BP Neural Network Data Fusion algorithm optimized by Genetic algorithm and Particle swarm).GAPSOBP算法将无线传感网的节点类比为BP神经网络中的神经元,通过神经网络提取无线传感网采集的感知数据并结合分簇路由对收集的传感数据进行融合处理,从而大幅减少发往汇聚节点的网络数据量.仿真结果表明,与经典LEACH算法和PSOBP算法相比,GAPSOBP算法能有效减少网络通信量,节约节点能量,显著延长网络生存时间.     

无线传感器网络极小连通支配集算法的改进

无线传感器网络中,基于极小连通支配集的虚拟骨干网的构建使得路由搜索空间集中在支配节点之间,能够有效节省网络资源,减少冗余转发节点。首先提出连通支配集的数学模型。基于WL算法,提出改进的极小连通支配集分布式算法。仿真结果表明,改进算法求得的连通支配集较小,可为无线传感器网络中的路由协议提供通讯基础。     

WSN中面向数据源搜索的MA任播路由策略

为了减少传感器节点的能耗,延长无线传感器网络的生命周期,将任播运用到WSN的MA路由之中,提出了一种面向数据源搜索的移动代理路由策略。首先利用人工免疫系统的多样性和自适应的特点,找出MA访问数据源的最佳顺序,然后利用基于能量限制的任播算法,在一个数据源的多个感知节点中,选择满足能量条件的节点进行迁移。仿真实验表明,该策略能够以最小代价访问各个数据源,且均衡地使用网络各节点的能量,从而延长了WSN的生存周期,具有较好的通用性。     

An adaptive in-network aggregation operator for query processing in wireless sensor networks

A wireless sensor network (WSN) is composed of tens or hundreds of spatially distributed autonomous nodes, called sensors. Sensors are devices used to collect data from the environment related to the detection or measurement of physical phenomena. In fact, a WSN consists of groups of sensors where each group is responsible for providing information about one or more physical phenomena (e.g., group for collecting temperature data). Sensors are limited in power, computational capacity, and memory. Therefore, a query engine and query operators for processing queries in WSNs should be able to handle resource limitations such as memory and battery life. Adaptability has been explored as an alternative approach when dealing with these conditions. Adaptive query operators (algorithms) can adjust their behavior in response to specific events that take place during data processing. In this paper, we propose an adaptive in-network aggregation operator for query processing in sensor nodes of a WSN, called ADAGA (ADaptive AGgregation Algorithm for sensor networks). The ADAGA adapts its behavior according to memory and energy usage by dynamically adjusting data-collection and data-sending time intervals. ADAGA can correctly aggregate data in WSNs with packet replication. Moreover, ADAGA is able to predict non-performed detection values by analyzing collected values. Thus, ADAGA is able to produce results as close as possible to real results (obtained when no resource constraint is faced). The results obtained through experiments prove the efficiency of ADAGA.     

一种基于分簇蚁群策略的无线传感器网络路由算法

如何最大化地延长网络的生存时间是无线传感器（WSN）网络研究的核心问题.基于分簇策略,提出一种能量有效的路由算法（EEA）.该算法利用分簇原理减少了参与寻找最优路径的节点数,从而降低了系统的能耗.同时设计一种改进的最优路径评价标准,该标准兼顾了传输路径上各节点的剩余能量和最优路径上总的能量消耗.仿真结果表明,与其他蚁群策略的路由算法（如：基于蚁群算法的路由算法（ARA）和EEAWSN）相比,该算法能在寻找最优路径时避开剩余能量少的节点,使最优路径上各节点的能量呈整体性衰落,从而沿长了网络的寿命.     

Satisfying the target network lifetime in wireless sensor networks

Generally, the lifetime of a wireless sensor network (WSN) is defined as the duration until any sensor node dies due to battery exhaustion. If the traffic load is not properly balanced, the batteries of some sensor nodes may be depleted quickly, and the lifetime of the WSN will be shortened. While many energy-efficient routing schemes have been proposed for WSNs, they focus on maximizing the WSN lifetime. In this paper, we propose a scheme that satisfies a given ‘target’ lifetime. Because energy consumption depends on traffic volume, the target lifetime cannot be guaranteed through energy-efficient routing alone. We take an approach that jointly optimizes the sensing rate (i.e., controlling the sensor-traffic generation or duty cycle) and route selection. Satisfying the target lifetime while maximizing the sensing rate is a NP-hard problem. Our scheme is based on a simple Linear Programming (LP) model and clever heuristics are applied to compute a near-optimal result from the LP solution. We prove that the proposed scheme guarantees a 1/2-approximation to the optimal solution in the worst case. The simulation results indicate that the proposed scheme achieves near-optimality in various network configurations.     

Hierarchical role-based data dissemination in wireless sensor networks

Data dissemination from multiple sources to mobile sinks is fundamental and challenging in WSN applications due to limited energy supply of sensor nodes and sink mobility. Previous data dissemination protocols either rely on an energy-consuming coordinate system or build an inefficient backbone. In this paper, we propose a hierarchical role-based data dissemination (HRDD) protocol in wireless sensor networks. In HRDD, a small number of sensor nodes are assigned to serve as cluster heads and agents to form the data dissemination backbone and mitigate unnecessary query forwarding. In addition, HRDD designs an efficient data delivery mechanism that provides shorter paths to accelerate data delivery as well as reduce the number of data transmissions. An adaptive backbone maintenance mechanism is also introduced for low-energy cluster heads and agents to reduce their load, thereby prolonging the network lifetime. The experimental results show that HRDD achieves the longer network lifetime, the shorter delay, and the high success ratio compared to the prior work.     

Energy-Efficient Routing Using Novel Optimization with Tabu Techniques for Wireless Sensor Network

Wireless Sensor Network (WSN) consists of a group of limited energy source sensors that are installed in a particular region to collect data from the environment. Designing the energy-efficient data collection methods in large-scale wireless sensor networks is considered to be a difficult area in the research. Sensor node clustering is a popular approach for WSN. Moreover, the sensor nodes are grouped to form clusters in a cluster-based WSN environment. The battery performance of the sensor nodes is likewise constrained. As a result, the energy efficiency of WSNs is critical. In specific, the energy usage is influenced by the loads on the sensor node as well as it ranges from the Base Station (BS). Therefore, energy efficiency and load balancing are very essential in WSN. In the proposed method, a novel Grey Wolf Improved Particle Swarm Optimization with Tabu Search Techniques (GW-IPSO-TS) was used. The selection of Cluster Heads (CHs) and routing path of every CH from the base station is enhanced by the proposed method. It provides the best routing path and increases the lifetime and energy efficiency of the network. End-to-end delay and packet loss rate have also been improved. The proposed GW-IPSO-TS method enhances the evaluation of alive nodes, dead nodes, network survival index, convergence rate, and standard deviation of sensor nodes. Compared to the existing algorithms, the proposed method outperforms better and improves the lifetime of the network.     

无线传感器网络中基于数据融合的移动代理曲线动态路由算法研究

和传统的C/S模型相比,移动代理模型在数据融合方面更适合无线传感器网络.在基于移动代理的数据融合算法中,移动代理访问传感节点的顺序以及总数对算法的效率、网络寿命等有着重大影响.为此提出了一种基于数据融合的移动代理曲线动态路由算法设计方案.通过构造特定数据结构的数据报文和数据表,给出了目标节点基本信息收集算法获取目标节点到处理节点的最优路径;将移动代理路由归结为一个优化问题,由静态路由算法求出移动代理迁移的静态最优路由节点序列,进而获得了移动代理基于曲线的动态路由算法.理论分析和模拟实验表明,随着传感器网络规模的增大和传感数据量的增加,和其它算法相比,该算法有更小的网络耗能和延时.     

移动社会网络中社会性路由算法研究

在无法部署Sink的无线传感器网络中, 数据采集者(即:能够收集数据的人或移动设备)在网络的任意位置收集数据, 即泛在数据收集。网络区域中的节点数量庞大, 能量有限, 如何能有效地采集到全部节点的数据是一个难点。提出一个网络生命周期最大化的泛在数据收集协议MULAC。MULAC以用户所在当前位置为圆心, 半径为r的区域内选择一个节点v。以v为根构造一棵最大化生命周期树T。网络中的节点可以通过T传送数据给v, 数据采集者可以通过v接收到网络中的全部数据。当数据采集者移动到其他位置, T将根据用户新的位置改变根节点, 并且以最小的能量耗费调整树结构, 从而延长全网的寿命。在收集数据过程中保证无线传感器网络生命周期最大化是一个NP完全问题, MULAC能够近似最优的解决此问题。仿真实验和理论分析表明, MULAC能有效延长网络生命周期。     

基于贪婪算法无线传感器网络中继节点布局的研究

为实现远距离的无线通信, 在网络中添加中继节点, 采用多跳路由传输数据。对于中继节点的布局问题, 依据线性结构使网络整体能量消耗最小的特征, 提出一种中继节点贪婪布局算法。该算法通过最近贪婪策略、中继节点通信容量、传感器节点数据转发跳数等约束方法限制中继节点的布局位置。理论分析和实验验证了该算法能够有效减少能量消耗, 延长网络寿命。     

可充电无线传感网络能量均衡路由算法

针对可充电无线传感网络中的能量均衡路由问题,提出在稳定功率无线充电和监测数据收集网络场景下的多路径路由算法和机会路由算法,以实现网络的能量均衡。首先,通过电磁传播理论构建了无线传感节点的充电和接收功率关系模型;然后,考虑网络中无线传感节点的发送能耗和接收能耗,基于上述充电模型将网络能量均衡的路由问题转化为网络节点运行时间的最大最小化问题,通过线性规划得到的各链路流量用以指导路由中数据流量分配;最后,考虑一种更加现实的低功耗的场景,并提出了一种基于机会路由的能量均衡路由算法。实验结果表明,与最短路径路由（SPR）和期望周期最短路由（EDC）算法相比较,所提出的两种路由算法均能有效提高采集能量的利用率和工作周期内的网络生命周期。     

基于邻居节点预状态的无线传感器网络故障诊断算法

针对无线传感器网络（WSN）故障节点率高于50%时故障检测率降低的问题,提出一种基于邻居节点预状态及邻居节点数据的无线传感器节点故障诊断算法。首先利用节点自身历史数据对节点状态进行初步预判断;然后结合节点间相似性和邻居节点的预状态对节点状态进行最终的判断;最后利用移动传感器节点将故障节点信息通过最优路径发送给基站,有效地减少了通信次数。仿真实验在100 m×100 m的方形区域内模拟WSN。实验结果表明,与传统的分布式故障诊断（DFD）算法相比,诊断精度提升了9.84个百分点,并且当节点故障率高达50%时,该算法仍能达到95%的诊断精度。在实际应用中,所提算法在提高故障诊断精度的同时,能有效地减少能量消耗、延长网络寿命。     

无线传感器网络基于权值的极小支配集路由算法

在无线传感器网络设计中,为节约系统能量、延长网络寿命,提出了基于权值极小支配集路由算法(Minimal dominating set with weight,WMDS).该算法的路由搜索主要集中在生成的支配集及网关节点内.当网络中少数节点发生变化时,只需个别相关节点更新它们的状态,不需要网络中所有节点重新计算支配集.考虑到网络内传感器节点能量分布均衡,各节点可以轮换充当支配点,支配点的数据融合可以减少传输信息包的数量.仿真实验表明,WMDS算法能得到较小的支配集,从而有效减少网络广播过程中的转发节点数,节省了网络资源.路由算法明显减少了信息包传输的数量,均衡了各节点的能量消耗,有效地延长了网络的寿命.     

Broadcast routing in wireless sensor networks with dynamic power management and multi-coverage backbones

In a wireless sensor network (WSN), nodes are power constrained. As a consequence, protocols must be energy efficient to prolong the network lifetime while keeping some quality-of-service (QoS) requirements. In WSNs, most protocols resort to the broadcast of control messages like, for example, for the topology control (TC) of the network. On its turn, TC itself can be applied to improve the broadcast of data packets in the network, and because only a subset of nodes need to be active at any time, it is possible to extend the network lifetime. We investigate some alternatives to improve broadcasting in WSN for an extended network lifetime. This is accomplished in two ways. First, we adapt the dynamic power management with scheduled switching modes (DPM-SSM) technique to a blind flooding protocol (i.e., FLOOD). To capture the battery capacity recovery effect as a result of applying DPM, we consider a more realistic battery model (i.e., Rakhmatov-Vrudhula battery model). Second, we implement a multi-coverage TC solution for computing an energy efficient broadcast backbone. Extensive simulation results using the NS2 network simulator show that it is possible to extend the network lifetime while keeping good broadcasting performance.