延迟容忍传感器网络中基于移动协助的数据传输

提出了基于移动协助的动态数据传输算法(MAD, movement-assisted adaptive data delivery)。在缓冲区域内设置数据缓存节点,让基站在缓冲区内周期性地移动,数据动态地复制给更有可能到达缓冲区并且剩余能量较高的节点,然后基站在移动中将缓存节点中的数据进行收集。MAD是由数据传输和队列管理2部分组成。前者根据节点的运动趋势和剩余能量计算节点的转发概率,后者通过消息的生存时间和消息的最大复制数确定队列中消息发送的优先级及丢弃方法。仿真结果表明,与其他策略相比,MAD在传输成功率和网络寿命方面具有更好的性能。     

无线传感器网络中基于树的能量高效分布式精确数据收集算法

在大规模节点密集的多跳传感器网络中,精确数据收集存在着\"热区\"问题:越靠近Sink节点的传感器节点,其承担的数据转发量就越多,能量消耗也越快,从而成为瓶颈节点,缩短整个网络的生命周期.最大生命周期数据收集树的构建已被证明是NP完全问题.已有算法大多是集中式算法,不适用于大规模节点密集的传感器网络.本文提出一种分布式精确数据收集算法EEDAT,在大规模节点密集的传感器网络中,不仅能够保证每个节点到Sink的路径是最短路径(最少跳数),而且能有效延长网络生命周期.EEDAT分为两个基本步骤,首先随机生成一棵数据收集树,然后根据各个传感器节点的孩子数和剩余能量,对已生成的数据收集树进行调整,使得各个节点的负载尽量均衡,从而达到延长网络生命周期的目的.实验结果表明,与已有分布式算法LMST相比,EEDAT所构造的数据收集树能延长网络生命周期平均20%.     

Energy equalizing routing for fast data gathering in wireless sensor networks

Energy saving and fast responding of data gathering are two crucial factors for the performance of wireless sensor networks. A dynamic tree based energy equalizing routing scheme (DTEER) was proposed to make an effort to gather data along with low energy consumption and low time delay. DTEER introduces a dynamic multi-hop route selecting scheme based on weight-value and height-value to form a dynamic tree and a mechanism similar to token passing to elect the root of the tree. DTEER can simply and rapidly organize all the nodes with low overhead and is robust enough to the topology changes. When compared with power-efficient gathering in sensor information systems (PEGASIS) and the hybrid, energy- efficient, distributed clustering approach (HEED), the simulation results show that DTEER achieves its intention of consuming less energy, equalizing the energy consumption of all the nodes, alleviating the data gathering delay, as well as extending the network lifetime perfectly.     

A Genetic‐Algorithm‐Based Optimized Clustering for Energy‐Efficient Routing in MWSN

With the increasing demands for mobile wireless sensor networks in recent years, designing an energy‐efficient clustering and routing protocol has become very important. This paper provides an analytical model to evaluate the power consumption of a mobile sensor node. Based on this, a clustering algorithm is designed to optimize the energy efficiency during cluster head formation. A genetic algorithm technique is employed to find the near‐optimal threshold for residual energy below which a node has to give up its role of being the cluster head. This clustering algorithm along with a hybrid routing concept is applied as the near‐optimal energy‐efficient routing technique to increase the overall efficiency of the network. Compared to the mobile low energy adaptive clustering hierarchy protocol, the simulation studies reveal that the energy‐efficient routing technique produces a longer network lifetime and achieves better energy efficiency.     

用于无线传感网络的高能效移动数据收集方案

Energy conservation in Wireless Sensor Networks (WSNs) has always been a crucial issue and has received increased attention in the recent years. A transmission scheme for energy-constrained WSNs is proposed in this paper. The scheme, called MIHOP (MIMO and Multi-hop), combines cluster-based virtual MIMO and multi-hop technologies. The multi- hop mode is employed in transmitting data when the related sensors are located within a specific number of hops from the sink, and the virtual MIMO mode is used in transmitting data from the remaining sensor nodes. We compare the energy consumption of different transmission schemes and propose an algorithm for determining the optimal hop count in MIHOP. A controllable mobile sink that reduces the energy consumed in sensor transmission is also adopted for data collection. The theoretical analysis and the Monte Carlo simulation demonstrate that the proposed scheme significantly outperforms individual virtual MIMO, multi-hop technologies, and double-string networks in terms of energy conservation. The energy consumption levels under the MIHOP scheme are approximately 12.98%, 47.55% and 48.30% less than that under virtual MIMO schemes, multi-hop networks and double- string networks, respectively.     

面向能量受限传感网的最优数据收集策略

本文研究了能量受限条件下无线传感器网络(wireless sensor networks,WSNs)的最优数据收集策略问题.首先,传感器节点周期性采集数据并通过卡尔曼滤波器(Kalman filter,KF)对信息进行预处理以滤除噪声.其次,考虑到通信为主要耗能环节,设计最优数据发送策略令节点在特定轮内发送数据,使得满足网络生存周期前提下,基站获得的数据精度最高.具体来说,针对单跳网络,给出可使基站误差方差最小化的数据发送策略;在此基础上,进一步提出面向多跳网络的改进数据发送策略.最后,利用仿真和原型实验验证所提策略的有效性.     

无线传感器网络中一种能量均衡的基于连通支配集的数据收集算法

采用连通支配集来构建虚拟骨干可以减轻无线传感器网络的广播风暴问题.目前已有大量工作通过构造最小连通支配集形成网络虚拟骨干来进行高效数据收集.然而,最小连通支配集并不能有效均衡节点的能量耗费,导致网络生命周期较短.提出了一种能量均衡的基于连通支配集的分布式算法EBCDS来进行数据收集,通过选择能量水平和度均比较大的节点组成连通支配集,支配集中的节点组成一个规模不大但具有较高能量水平的网络骨干.网络中的所有数据沿骨干在较小的寻路空间中转发,能够节省节点能量,使骨干节点不会因为能量不足而过早死亡.理论分析表明,EBCDS能以O(nlogn)的消息复杂度构造连通支配集,仿真实验表明,EBCDS能有效节省节点能耗并延长网络生命周期.     

Battery-Aware Routing for Streaming Data Transmissions in Wireless Sensor Networks

Recent technological advances have made it possible to support long lifetime and large volume streaming data transmissions in sensor networks. A major challenge is to maximize the lifetime of battery-powered sensors to support such transmissions. Battery, as the power provider of the sensors, therefore emerges as the key factor for achieving high performance in such applications. Recent study in battery technology reveals that the behavior of battery discharging is more complex than we used to think. Battery powered sensors might waste a huge amount of energy if we do not carefully schedule and budget their discharging. In this paper we study the effect of battery behavior on routing for streaming data transmissions in wireless sensor networks. We first give an on-line computable energy model to mathematically model battery discharge behavior. We show that the model can capture and describe battery behavior accurately at low computational complexity and thus is suitable for on-line battery capacity computation. Based on this battery model we then present a battery-aware routing (BAR) protocol to schedule the routing in wireless sensor networks. The routing protocol is sensitive to the battery status of routing nodes and avoids energy loss. We use the battery data from actual sensors to evaluate the performance of our protocol. The results show that the battery-aware protocol proposed in this paper performs well and can save a significant amount of energy compared to existing routing protocols for streaming data transmissions. Network lifetime is also prolonged with maximum data throughput. As far as we know, this is the first work considering battery-awareness with an accurate analytical on-line computable battery model in sensor network routing. We believe the battery model can be used to explore other energy efficient schemes for wireless networks as well.     

WSN中一种基于延迟限定的数据收集算法

数据收集是无线传感器网络研究中的一个关键问题,目前基于树的数据收集方法经常会造成节点负载不均衡、树的高度无法控制等问题,从而使得数据收集延迟加大。针对该问题提出了一个新的算法——基于延迟限定的数据收集算法(DBDG),该算法从一棵最少跳数树(Fewest Hops Tree,FHT)出发,迭代地选择网络中的一条边加入树,通过限定树的高度来满足延迟限定,然后通过使树上"瓶颈节点"的度最小化来延长树的生命周期。仿真实验表明,与目前已有的协议相比,DBDG能在限定的高度内构造生命周期更长的生成树。     

Systematic energy-balanced cooperative transmission scheme in wireless sensor networks

Energy efficiency is a critical issue in wireless sensor networks(WSNs).In order to minimize energy consumption and balance energy dissipation throughout the whole network,a systematic energy-balanced cooperative transmission scheme in WSNs is proposed in this paper.This scheme studies energy efficiency in systematic view.For three main steps,namely nodes clustering,data aggregation and cooperative transmission,corresponding measures are put forward to save energy.These measures are well designed and tightly coupled to achieve optimal performance.A half-controlled dynamic clustering method is proposed to avoid concentrated distribution of cluster heads caused by selecting cluster heads randomly and to get high spatial correlation between cluster nodes.Based on clusters built,data aggregation,with the adoption of dynamic data compression,is performed by cluster heads to get better use of data correlation.Cooperative multiple input multiple output(CMIMO) with an energy-balanced cooperative cluster heads selection method is proposed to transmit data to sink node.System model of this scheme is also given in this paper.And simulation results show that,compared with other traditional schemes,the proposed scheme can efficiently distribute the energy dissipation evenly throughout the network and achieve higher energy efficiency,which leads to longer network lifetime span.By adopting orthogonal space time block code(STBC),the optimal number of the cooperative transmission nodes varying with the percentage of cluster heads is also concluded,which can help to improve energy efficiency by choosing the optimal number of cooperative nodes and making the most use of CMIMO.     

Distributed and Energy Efficient Self-Organization for On–Off Wireless Sensor Networks

In this paper, we utilize clustering to achieve energy efficiency for the on–off wireless sensor network, whose member nodes alternate between active and inactive states. In the proposed Distributed and Energy Efficient Self Organization (DEESO) scheme, the head election is adjusted adaptively to the remaining battery levels of local active nodes, which is a completely distributed approach compared to LEACH that relying on other routing schemes to access global information. Furthermore, we apply the Adaptive Channel Assignment (ACA) to address the on-off topology changes. Simulation results show that DEESO delivers 184% amount of data to the base station as LEACH for the same amount of energy consumption and the effective network lifetime is extended by around 50%.