Approximation algorithm for constructing data aggregation trees for wireless sensor networks

This paper considers the problem of constructing data aggregation trees in wireless sensor networks (WSNs) for a group of sensor nodes to send collected information to a single sink node. The data aggregation tree contains the sink node, all the source nodes, and some other non-source nodes. Our goal of constructing such a data aggregation tree is to minimize the number of non-source nodes to be included in the tree so as to save energies. We prove that the data aggregation tree problem is NP-hard and then propose an approximation algorithm with a performance ratio of four and a greedy algorithm. We also give a distributed version of the approximation algorithm. Extensive simulations are performed to study the performance of the proposed algorithms. The results show that the proposed algorithms can find a tree of a good approximation to the optimal tree and has a high degree of scalability.     

Data aggregation in wireless sensor networks using ant colony algorithm

Data aggregation is important in energy constraint wireless sensor networks which exploits correlated sensing data and aggregates at the intermediate nodes to reduce the number of messages exchanged network. This paper considers the problem of constructing data aggregation tree in a wireless sensor network for a group of source nodes to send sensory data to a single sink node. The ant colony system provides a natural and intrinsic way of exploring search space in determining data aggregation. Moreover, we propose an ant colony algorithm for data aggregation in wireless sensor networks. Every ant will explore all possible paths from the source node to the sink node. The data aggregation tree is constructed by the accumulated pheromone. Simulations have shown that our algorithm can reduce significant energy costs.     

无线传感器网络中基于最小延时的数据汇集树构建与传输调度算法

无线传感器网络的数据通信模式问题是目前的研究热点,针对现有的无线传感器网络数据汇集算法延时较大这一不足,对最小延时数据汇集树和传输调度问题进行了研究。提出一种基于度约束的汇集树构建算法(DCAT)。该算法按照 BFS 方式遍历图,当遍历到每个节点时,通过确定哪些节点与汇点更近来确定潜在母节点集合。然后,选择图中度数最小的潜在母节点作为当前被遍历节点的母节点。此外,为了在给定的汇集树上进行高效地数据汇集,还提出两种新的基于贪婪的TDMA传输调度算法：WIRES-G 和 DCAT-Greedy。利用随机生成的不同规模的传感器网络,参照当前最新算法,对文中方法的性能进行了全面评估。结果表明,与当前最优算法相比,文中调度算法与文中汇集树构建算法结合起来,可显著降低数据汇集的延时。     

WSN中基于最小延时的数据汇集树构建与传输调度算法

针对现有的无线传感器网络数据汇集算法延时较大的不足,对最小延时数据汇集树和传输调度问题进行了研究。提出一种基于度约束的汇集树构建算法(DCAT)。该算法按照BFS方式遍历图,当遍历到每个节点时,通过确定哪些节点与汇点更近来确定潜在母节点集合。然后,选择图中度数最小的潜在母节点作为当前被遍历节点的母节点。此外,为了在给定的汇集树上进行高效的数据汇集,还提出两种新的基于贪婪的TDMA传输调度算法:WIRES-G和DCAT-Greedy。利用随机生成的不同规模的传感器网络,参照当前最新算法,对所提方法的性能进行了全面评估。结果表明,与当前最优算法相比,将所提调度算法与所提汇集树构建算法结合起来,可显著降低数据汇集的延时。     

Fault-tolerant design of wireless sensor networks with directional antennas

A tree structure is often used in wireless sensor networks to deliver sensor data to a sink node. Such a tree can be built using directional antennas as they offer considerable advantage over the omni-directional ones. A tree is adequate for data gathering from all sensor nodes if no node fails. We study the problem of enhancing the fault tolerance of a data gathering tree by adding additional links so that failure of a sensor or a pair of adjacent sensors would not disconnect the tree. We prove that the least-cost tree augmentation problem is NP-complete and provide approximation algorithms one for single node failure and the other for a pair of adjacent node failure, with performance bounds of two and four respectively.     

基于节点度-限制的数据融合树构建算法

在无线传感网络部分应用中,数据传输必须在指定时延约束下完成.因此,最小化网络时延和最大化网络寿命成为无线传感网络的研究热点.为此,提出基于节点度-限制的数据融合树构建DC-DATC(Degree-Constrained-Based Data Aggregation Tree Constructing)算法.DC-DATC算法减少了高节点度对数据融合时延的影响.依据预设的节点度阈值,只有节点度小于阈值的节点才能加入数据融合树.利用数据融合树传输数据,降低了数据传输量,提高了能量利用率.实验结果表明,DC-DATC算法降低了融合时延,且平衡了网络能耗,最终实现了网络寿命的扩延.     

无线传感器网络中一种全局节能的聚合树构建算法

针对聚合树构建过程中存在大量冗余广播消息,导致全局能耗过大的问题,分析了聚合树构建算法性能,提出了一种全局节能聚合树构建算法,只需在已知树内部交换信息即可得到离树最近的源节点,避免了网络中广播消息的产生,其构建能耗受节点密度影响很小且保持在较低水平。由于构建时间不受探测消息周期制约,可有效提高聚合树构建速度。实验表明该聚合树构建方法能有效降低消息交换数量及构建时间,聚合效果等同于采用贪婪增长树(GIT)算法的聚合树。     

Multi-objective node deployment in WSNs: In search of an optimal trade-off among coverage,lifetime, energy consumption,and connectivity

The increased demand of Wireless Sensor Networks (WSNs) in different areas of application have intensified studies dedicated to the deployment of sensor nodes in recent past. For deployment of sensor nodes some of the key objectives that need to be satisfied are coverage of the area to be monitored, net energy consumed by the WSN, lifetime of the network, and connectivity and number of deployed sensors. In this article the sensor node deployment task has been formulated as a constrained multi-objective optimization (MO) problem where the aim is to find a deployed sensor node arrangement to maximize the area of coverage, minimize the net energy consumption, maximize the network lifetime, and minimize the number of deployed sensor nodes while maintaining connectivity between each sensor node and the sink node for proper data transmission. We assume a tree structure between the deployed nodes and the sink node for data transmission. Our method employs a recently developed and very competitive multi-objective evolutionary algorithm (MOEA) known as MOEA/D-DE that uses a decomposition approach for converting the problem of approximation of the Pareto fronts (PF) into a number of single-objective optimization problems. This algorithm employs differential evolution (DE), one of the most powerful real parameter optimizers in current use, as its search method. The original MOEA/D has been modified by introducing a new fuzzy dominance based decomposition technique. The algorithm introduces a fuzzy Pareto dominance concept to compare two solutions and uses the scalar decomposition method only when one of the solutions fails to dominate the other in terms of a fuzzy dominance level. We have compared the performance of the resulting algorithm, called MOEA/DFD, with the original MOEA/D-DE and another very popular MOEA called Non-dominated Sorting Genetic Algorithm (NSGA-II). The best trade-off solutions from MOEA/DFD based node deployment scheme have also been compared with a few single-objective node deployment schemes based on the original DE, an adaptive DE-variant (JADE), original particle swarm optimization (PSO), and a state-of-the art variant of PSO (Comprehensive Learning PSO). In all the test instances, MOEA/DFD performs better than all other algorithms. Also the proposed multi-objective formulation of the problem adds more flexibility to the decision maker for choosing the necessary threshold of the objectives to be satisfied.     

基于动态数据压缩的能量采集无线传感网络数据收集优化

针对能量采集无线传感网络（WSN）中的数据收集优化问题,考虑传感器节点能量采集的时空变化特性,提出一种基于节点动态采样速率和数据压缩的策略,以实现网络中采样数据总量的最大化。首先,提出一种根据节点的邻居信息决定其最优压缩策略的本地压缩算法,基于节点在数据汇聚树中的拓扑位置考虑其数据接收和转发能耗,逐渐增加其采样速率直到其总能耗到达采集能耗阈值。接着构造网络性能的全局优化问题并提出一种启发式的算法,通过迭代求解线性规划问题计算最优的采样速率和压缩策略。实验结果表明,与现有的自适应传感和压缩率选择方案相比,所提出的两种数据收集优化算法能够维持更加稳定的传感器节点电量水平并实现更高的网络性能。     

无线传感器网络中的改进数据聚集调度算法

针对现有聚集数据调度近似算法具有较高延时上界的问题,提出一种改进的聚集数据调度近似算法。建立一棵根在中心结点的广度优先搜索树,分层构造一个最大独立集(MIS),使MIS中相邻的2个结点相距两跳。将MIS中的结点连接起来,形成一棵根在中心结点的数据聚集调度树,使结点按数据聚集调度树进行分层数据调度。在数据聚集调度树的构造过程中,对于任意支配点,以最小的结点连接其相距两跳的支配点。对于2个相邻支配点的公共邻居支配点,通过在距中心点最近的支配点加入数据聚集树,使其在数据调度过程中将数据发送给距中心点最近的支配点,从而降低数据的聚集延时。实验结果表明,与SAS算法、Guo’s算法和IAS算法相比,该算法的数据聚集延时更低,其延时上界为14R+△?10。     

Data Gathering with Tunable Compression in Sensor Networks

We study the problem of constructing a data gathering tree over a wireless sensor network in order to minimize the total energy for compressing and transporting information from a set of source nodes to the sink. This problem is crucial for advanced computationally intensive applications, where traditional "maximum" in-network compression may result in significant computation energy. We investigate a tunable data compression technique that enables effective trade-offs between the computation and communication costs. We derive the optimal compression strategy for a given data gathering tree and then investigate the performance of different tree structures for networks deployed on a grid topology, as well as general graphs. Our analytical results pertaining to the grid topology and simulation results pertaining to the general graphs indicate that the performance of a simple greedy approximation to the Minimal Steiner Tree (MST) provides a constant-factor approximation for the grid topology and good average performance on the general graphs. Although, theoretically, a more complicated randomized algorithm offers a polylogarithmic performance bound, the simple greedy approximation of MST is attractive for practical implementation.     

Particle swarm optimization based clustering algorithm with mobile sink for WSNs

Wireless sensor networks with fixed sink node often suffer from hot spots problem since sensor nodes close to the sink usually have more traffic burden to forward during transmission process. Utilizing mobile sink has been shown as an effective technique to enhance the network performance such as energy efficiency, network lifetime, and latency, etc. In this paper, we propose a particle swarm optimization based clustering algorithm with mobile sink for wireless sensor network. In this algorithm, the virtual clustering technique is performed during routing process which makes use of the particle swarm optimization algorithm. The residual energy and position of the nodes are the primary parameters to select cluster head. The control strategy for mobile sink to collect data from cluster head is well designed. Extensive simulation results show that the energy consumption is much reduced, the network lifetime is prolonged, and the transmission delay is reduced in our proposed routing algorithm than some other popular routing algorithms.     

无线传感器网络最小覆盖集的贪婪近似算法

网络生命期是限制无线传感器网络发展的一个瓶颈.在保证网络监控性能的前提下,仅调度部分节点工作而让其余节点处于低功耗的休眠状态,可以有效节省能耗,延长网络生命期.节点调度的目标是寻找一个能够覆盖监控区域的最小节点集合,这是一个NP难问题,目前,其近似算法的性能较低.提出了一种基于贪婪法的最小覆盖集近似算法,在构造覆盖集的过程中,优先选择扩展面积最大的有效节点加入覆盖集.理论分析表明,该算法能够构造出较好的覆盖集,时间复杂度为O(n),其中,n为初始节点总数.实验数据表明,该算法的性能要优于现有算法,得到的覆盖集的平均大小比现有算法减小了14.2%左右,且执行时间要短于现有算法.当初始节点分布较密时,该算法得到的平均覆盖度小于1.75,近似比小于1.45.     

无线传感器网络基于数据汇聚的路由

提出了一种针对无线传感器网络的路由协议,该路由采用最小传输成本生成树的数据汇聚机制。具体方法是首先将传感区域内的传感器节点采集的数据传送给传感区域内离汇聚点最近的节点,将这些数据进行汇聚操作后,将汇聚的结果通过最短路径传递给网络汇聚点。仿真结果显示,采用最小传输代价生成树的路由协议能减少数据传输量50%-80%,并具有较小的传输时延。     

能量高效的传感器网络数据查询路由*

提出一种能量高效的传感器网络数据查询路由EEDQ(energy-efficient data query),EEDQ以sink节点为根节点,构造最小路由生成树,由sink节点发出查询任务,查询结果由叶子节点向sink节点传输,传输过程中进行数据汇聚.实验表明,EEDQ相比direct transmission,大大提高了传感器网络的生命周期.     

An energy-efficient data gathering method based on compressive sensing for pervasive sensor networks

This paper proposes an energy-efficient data gathering method called CN-MSTP (Combining Minimum Spanning Tree with Interest Nodes) for pervasive wireless sensor networks, basing on Compressive sensing (CS) and data aggregation. The proposed CN-MSTP protocol selects different nodes at random as projection nodes, and sets each projection node as a root to construct a minimum spanning tree by combining with interest nodes. Projection node aggregates sensor reading from sensor nodes using compressive sensing. We extend our method by letting the sink node participate in the process of building a minimum tree and introduce eCN-MSTP. We compare our methods with the other methods. Simulation results indicate that our two methods outperform the other methods in overall energy consumption saving and load balance and hence prolong the lifetime of the network.     

双层无线传感网络的3连通近似算法

主要研究双层无线传感网络模型,即数据信息流只能在传感器和中继器或中继器和中继器之间传输,而不能在传感器之间传输。近似算法基于两个子问题：k圆盘覆盖问题和单层传感网络的k连通问题,而后在部分中继器周围设置“等六边形”结构的中继器点,最终达到整个网络的3-连通水平。该算法的最终性能比为8α+β,其中α为k圆盘覆盖近似算法的性能比,β为单层传感网络的k连通近似算法的性能比。     

一种基于能耗度量的融合树构建算法

基于传感器节点能耗情况对数据压缩以及数据融合进行了分析,针对在非完全融合情况下,贪婪增长树(GIT)算法构建融合树时并不能很好选择最优路由的问题,提出了一种基于能耗度量的融合树构建算法,通过融合节点反馈能耗以及到达Sink节点的跳数信息,对多个路由的能耗进行评估,进而选择低能耗路由.同时提出了一种由信息源节点进行路径加强的策略,减小了路径加强信息量以及多路径记录带来的负担.模拟实验数据表明,该算法在数据融合压缩比较小的情况下节能效果优于贪婪增长树GIT算法,并且随着信息源与Sink节点距离的增大,路径加强信息的数量也有很大降低.     

An improved ant colony optimization-based approach with mobile sink for wireless sensor networks

Traditional wireless sensor networks (WSNs) with one static sink node suffer from the well-known hot spot problem, that of sensor nodes near the static sink bear more traffic load than outlying nodes. Thus, the overall network lifetime is reduced due to the fact some nodes deplete their energy reserves much faster compared to the rest. Recently, adopting sink mobility has been considered as a good strategy to overcome the hot spot problem. Mobile sink(s) physically move within the network and communicate with selected nodes, such as cluster heads (CHs), to perform direct data collection through short-range communications that requires no routing. Finding an optimal mobility trajectory for the mobile sink is critical in order to achieve energy efficiency. Taking hints from nature, the ant colony optimization (ACO) algorithm has been seen as a good solution to finding an optimal traversal path. Whereas the traditional ACO algorithm will guide ants to take a small step to the next node using current information, over time they will deviate from the target. Likewise, a mobile sink may communicate with selected node for a relatively long time making the traditional ACO algorithm delays not suitable for high real-time WSNs applications. In this paper, we propose an improved ACO algorithm approach for WSNs that use mobile sinks by considering CH distances. In this research, the network is divided into several clusters and each cluster has one CH. While the distance between CHs is considered under the traditional ACO algorithm, the mobile sink node finds an optimal mobility trajectory to communicate with CHs under our improved ACO algorithm. Simulation results show that the proposed algorithm can significantly improve wireless sensor network performance compared to other routing algorithms.     

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.