无线传感器网络最优曲线压缩算法

针对无线传感器网络中的数据压缩问题,对DP压缩算法进行改进,提出一种最优曲线数据压缩算法OCDCA。减少压缩过程中对数据的扫描次数,以降低节点的能量消耗。采用最佳曲线拟合方法对监测数据点做直线优化拟合,以提高压缩精度。仿真结果表明,OCDCA算法程序时间复杂度较低,压缩精度和压缩效率较高,可降低无线传感器网络的能量消耗。     

无线传感器网络簇首备份机制研究

提出一种基于综合指标的无线传感器网络簇首备份机制。通过节点剩余能量、节点度数、通信代价三者构建一种有效的综合指标,通过对簇内成员节点的综合指标排序,选取具有最优综合指标值的成员节点作为备份簇首。对该备份机制的仿真结果表明,采用该机制的分簇无线传感器网络可有效地降低簇首故障所带来的损失,加强了分簇的稳定性,延长了网络的生命周期,提升了网络的整体性能。     

基于能耗的无线传感器网络最优簇首数研究

在无线传感器网络中,如何减少系统能耗、延长网络生存时间是最具挑战性的问题之一。LEACH路由协议通过网络分簇和簇首循环,使网络能量负载平衡,从而达到降低能耗的目的。该协议的关键是簇首数的选择,其最优值可使网络能耗降到最低。在深入研究LEACH协议及其能量模型的基础上,提出了一种最优簇首数的估算算法,并通过蒙特卡洛仿真验证了该算法的正确性。所得结论对无线传感器网络结构和路由协议的设计具有指导作用。     

无线传感器网络中一种簇首节点避免拥塞的方法

无线传感器网络的拥塞一般发生在有中继任务的簇首节点和汇聚节点部分。文中通过选用缓冲区占用率界定拥塞的发生,利用汇聚节点广播增加拥塞簇首节点的传输率,传输率由WLCA算法算出,仿真结果表明WLCA算法能在一定程度避免簇首节点的拥塞,但稳定性有待进一步加强。     

改进的无线传感器网络簇首选择策略及其路由算法

针对无线传感器网络中簇首能耗较大引起的能量黑洞问题,在不均匀分簇的基础上,从簇首选择和簇首更换两个方面提出相应的改进措施。在簇首选择时,将整个网络区域分为不均匀的簇,通过相关参数的设置,让每簇中能量最高的节点当选为簇首节点,网络运行时簇首维护本簇的能量信息表;在簇首更换时,采用局部更换测量,并由当前簇首在能量信息表中选择剩余能量最高的节点作为下一个簇首节点,从而改善了簇首能量效率和负载均衡。最后进行了仿真实验与对比,实验结果表明改进的路由协议能够有效提高网络性能,延长网络生命周期。     

基于簇首移动的无线传感器网络路由算法

以往的无线传感器网络分簇算法中,簇首位置固定无法移动,缺乏针对网络实时变化的灵活性,在均衡网络节点能量消耗的问题上存在着缺陷。鉴于此,提出一种簇首移动的无线传感器网络路由算法（MCHCA）。MCHCA算法将簇首设置为移动节点,通过网络区域大小及节点传输半径确定合理的移动簇首数目;根据簇内成员的位置坐标和剩余能量的信息,确定簇首每轮所需移动到的最佳位置;移动簇首收集簇内成员的数据并将其融合,传递给Sink节点。仿真结果表明,该算法可以有效地均衡网络节点负载的能耗,提高了网络的生命周期。     

无线传感器网络分簇拓扑控制算法

通过对经典的分簇算法HEED和EEUC进行研究与分析,对它们不足之处进行了改进,提出了一种新的基于双簇首节能的无线传感器网络分簇拓扑控制算法,即DCHEB算法。该算法提出了一种新的簇划分方案,通过此方案可以对无线传感器网络进行合理分簇,使得簇首节点位于合适的位置上,平均了各个簇的节点个数,可以避免簇内的边缘节点过早死亡。最后通过理论分析和仿真工具验证了该算法对减少无线传感器网络的能量消耗和延长其生存时间有很好的作用。     

无线传感器网络中一种能量自适应的簇首选择机制

提出一种能量自适应的簇首选择机制．算法通过综合考虑候选节点的剩余能量级、通信能量损耗等参数来优化簇首的选择，从而有效地避免了低能量节点被选为簇首的可能性，进一步保证网络内节点能量负载的均衡性．仿真结果表明，改进的簇首选择机制能够很好地均衡网络能量损耗分布，有效延长近30%的网络生存时间．     

基于K-means的无线传感器网络分簇算法研究

提出一种基于K-均值聚类的无线传感器网络分簇算法。从K-均值聚类算法中要解决的合理聚类数的确定、初始聚类中心的选择以及聚类性能对目标函数的依赖这三个问题入手,运用K-均值聚类算法来实现无线传感器网络分簇。仿真与性能分析结果表明,基于K-均值聚类的无线传感器网络分簇算法既能节省节点能量、延长网络生命,又能改善网络中的能耗均衡,并保证簇首分布的均匀性。     

基于簇首定向扩散的无线传感器网络路由协议研究

减少能量消耗,延长网络生存时间,是无线传感器网络中路由协议要考虑的重要问题;在对LEACH协议和定向扩散协议进行分析的基础上,针对这两种协议中存在的问题,提出一种基于簇首定向扩散(CHDD)的路由协议;该协议改进了簇首选择算法,在簇首与sink节点之间采用定向扩散算法,该协议可以有效地延长整个网络的生存时间,仿真结果证明该协议的性能改进。     

Practical data compression in wireless sensor networks: A survey

Power consumption is a critical problem affecting the lifetime of wireless sensor networks. A number of techniques have been proposed to solve this issue, such as energy-efficient medium access control or routing protocols. Among those proposed techniques, the data compression scheme is one that can be used to reduce transmitted data over wireless channels. This technique leads to a reduction in the required inter-node communication, which is the main power consumer in wireless sensor networks. In this article, a comprehensive review of existing data compression approaches in wireless sensor networks is provided. First, suitable sets of criteria are defined to classify existing techniques as well as to determine what practical data compression in wireless sensor networks should be. Next, the details of each classified compression category are described. Finally, their performance, open issues, limitations and suitable applications are analyzed and compared based on the criteria of practical data compression in wireless sensor networks.     

Multisignal 1-D compression by F-transform for wireless sensor networks applications

In wireless sensor networks a large amount of data is collected for each node. The challenge of transferring these data to a sink, because of energy constraints, requires suitable techniques such as data compression. Transform-based compression, e.g. Discrete Wavelet Transform (DWT), are very popular in this field. These methods behave well enough if there is a correlation in data. However, especially for environmental measurements, data may not be correlated. In this work, we propose two approaches based on F-transform, a recent fuzzy approximation technique. We evaluate our approaches with Discrete Wavelet Transform on publicly available real-world data sets. The comparative study shows the capabilities of our approaches, which allow a higher data compression rate with a lower distortion, even if data are not correlated.     

Energy optimal data propagation in wireless sensor networks

We propose an algorithm to compute the optimal parameters of a probabilistic data propagation algorithm for wireless sensor networks (WSN). The probabilistic data propagation algorithm we consider was introduced in previous work, and it is known that this algorithm, when used with adequate parameters, balances the energy consumption and increases the lifespan of the WSN. However, we show that in the general case achieving energy balance may not be possible. We propose a centralized algorithm to compute the optimal parameters of the probabilistic data propagation algorithm, and prove that these parameters maximize the lifespan of the network even when it is not possible to achieve energy balance. Compared to previous work, our contribution is the following: (a) we give a formal definition of an optimal data propagation algorithm: an algorithm maximizing the lifespan of the network. (b) We find a simple necessary and sufficient condition for the data propagation algorithm to be optimal. (c) We constructively prove that there exists a choice of parameters optimizing the probabilistic data propagation algorithm. (d) We provide a centralized algorithm to compute these optimal parameters, thus enabling their use in a WSN. (e) We extend previous work by considering the energy consumption per sensor, instead of the consumption per slice, and propose a spreading technique to balance the energy among sensors of a same slice. The technique is numerically validated by simulating a WSN accomplishing a data monitoring task and propagating data using the probabilistic data propagation algorithm with optimal parameters.     

Enabling energy-efficient and lossy-aware data compression in wireless sensor networks by multi-objective evolutionary optimization

Nodes of wireless sensor networks (WSNs) are typically powered by batteries with a limited capacity. Thus, energy is a primary constraint in the design and deployment of WSNs. Since radio communication is in general the main cause of power consumption, the different techniques proposed in the literature to improve energy efficiency have mainly focused on limiting transmission/reception of data, for instance, by adopting data compression and/or aggregation. The limited resources available in a sensor node demand, however, the development of specifically designed algorithms. To this aim, we propose an approach to perform lossy compression on single node based on a differential pulse code modulation scheme with quantization of the differences between consecutive samples. Since different combinations of the quantization process parameters determine different trade-offs between compression performance and information loss, we exploit a multi-objective evolutionary algorithm to generate a set of combinations of these parameters corresponding to different optimal trade-offs. The user can therefore choose the combination with the most suitable trade-off for the specific application. We tested our lossy compression approach on three datasets collected by real WSNs. We show that our approach can achieve significant compression ratios despite negligible reconstruction errors. Further, we discuss how our approach outperforms LTC, a lossy compression algorithm purposely designed to be embedded in sensor nodes, in terms of compression rate and complexity.     

CCS-MAC: Exploiting the overheard data for compression in wireless sensor networks

Both the overhearing and overhearing avoidance in a densely distributed sensor network may inevitably incur considerable power consumption. In this paper we propose a so-called CCS-MAC (collaborative compression strategy-based MAC) MAC protocol which facilitates to exploit those overheard data that is treated useless in traditional MAC protocols for the purpose of cost and energy savings. Particularly the CCS-MAC enables different sensor nodes to perform data compression cooperatively with regard to those overheard data, so that the redundancy of data prepared for the link layer transmission can be totally eliminated at the earliest. The problem of collaborative compression is analyzed and discussed along with a corresponding linear programming model formulated. Based on it a heuristic node-selection algorithm with a time complexity of (O(N²)) is proposed to the solve the linear programming problem. The node-selection algorithm is implemented in CCS-MAC at each sensor node in a distributed manner. The experiment results verify that the proposed CCS-MAC scheme can achieve a significant energy savings so as to prolong the lifetime of the sensor networks so far.     

Energy-efficient cooperative data aggregation for wireless sensor networks

Recently, cooperative communication mechanism is shown to be a promising technology to improve the transmit diversity only by a single transceiver antenna. Using this communication paradigm, multiple source nodes are able to coordinate their transmissions so as to obtain energy savings. As data aggregation is one of the most important operations in wireless sensor networks, this paper studies the energy-efficient data aggregation problem through cooperative communication. We first define the cooperative data aggregation (CDA) problem, and formally prove that this problem is NP-Hard. Due to the difficult nature of this problem, we propose a heuristic algorithm MCT for cooperative data aggregation. The theoretical analysis shows that this algorithm can reach the approximate performance ratio of 2. Moreover, the distributed implementation DMCT of the algorithm is also described. We prove that both centralized and distributed algorithms can construct the same topology for cooperative data aggregation. The experimental simulations show that the proposed algorithms will decrease the power consumption by about 12.5% and 66.3% compared with PEDAP and PEGASIS algorithms respectively.     

An energy-efficient protocol for data gathering and aggregation in wireless sensor networks

Data gathering is a major function of many applications in wireless sensor networks (WSNs). The most important issue in designing a data gathering algorithm is how to save energy of sensor nodes while meeting the requirement of applications/users such as sensing area coverage. In this paper, we propose a novel hierarchical clustering protocol (DEEG) for long-lived sensor network. DEEG achieves a good performance in terms of lifetime by minimizing energy consumption for in-network communications and balancing the energy load among all the nodes, the proposed protocol achieves a good performance in terms of network lifetime. DEEG can also handle the energy hetergenous capacities and guarantee that out-network communications always occur in the subregion with high energy reserved. Furthermore, it introduces a simple but efficient approach to cope with the area coverage problem. We evaluate the performance of the proposed protocol using a simple temperature sensing application. Simulation results show that our protocol significantly outperforms LEACH and PEGASIS in terms of network lifetime and the amount of data gathered.

Low power hardware-based image compression solution for wireless camera sensor networks

In this paper, we present and evaluate a hardware solution for user-driven and packet loss tolerant image compression, especially designed to enable low power image compression and communication over wireless camera sensor networks (WCSNs). The proposed System-on-Chip is intended to be designed as a hardware coprocessor embedded in the camera sensor node. The goal is to relieve the node microcontroller of the image compression tasks and to achieve high-speed and low power image processing. The interest of our solution is twofold. First, compression settings can be changed at runtime (upon reception of a request message sent by an end user or according to the internal state of the camera sensor node). Second, the image compression chain includes a (block of) pixel interleaving scheme which significantly improves the robustness against packet loss in image communication. We discuss in depth the internal hardware architecture of the encoder chip which is planned to reach high performance running in FPGAs and in ASIC circuits. Synthesis results and relevant performance comparisons with related works are presented.     

Topology control for delay-constraint data collection in wireless sensor networks

Data collection is one of the most important operations in wireless sensor networks. Many practical applications require the real-time data transmission, such as monitoring, tracking, etc. In this paper, we import and define the topology control problem for delay-constraint data collection (TDDC), and then formalize this problem into an integer programming problem. As NP-Hardness of this problem, we present a load-aware power-increased topology control algorithm (namely LPTC) to heuristically solve the problem. The theoretical analysis shows that this algorithm can reach O(1)-approximation ratio for the linear networks. And we also analyze the impact of the delay-constraint on the worst-case for the planar networks. Moreover, this paper designs two localized algorithms, called as SDEL and DDEL, based on the area division for TDDC problem. The experimental results show that LPTC algorithm can save at least 17% power consumptions compared with HBH algorithm in many situations.     

Energy-Aware and Density-Based Clustering and Relaying Protocol (EA-DB-CRP) for gathering data in wireless sensor networks

In this paper, we propose a novel energy-aware and density-based clustering and routing protocol (EA-DB-CRP) for gathering data in wireless sensor networks which basically aims at distributing the load among available sensor nodes which in turn balances the energy consumption in the network and consequently elongates the network lifetime. More precisely, we introduce a network model that ends up of having empirical expressions that describe how to partition the network field efficiently into equal-size layers and sub-layers. In each sub-layer, the role of cluster head is pivoted among all cluster individuals, in a round robin fashion, that are sorted in a list in a descending order based on an extremely effective cluster head weight. Additionally, there are a minimum number of cluster members maintained to guarantee the feasibility of clusters being formed and this is through proposing a cluster merge algorithm. Not only to this extent, but rather, we maintain the consideration of network density over created clusters to balance them and subsequently prolong the network lifetime. Lastly, an effective relaying algorithm is proposed in which cluster heads get aware of those sensor nodes located in a layer ahead toward the base station along with their relay-node weights whereas each cluster head picks the relay node that has the highest weight. Our proposed protocol is evaluated through conducting various MATLAB simulations. Strikingly, results demonstrated that our proposed protocol has a momentous change against other related works regarding network lifetime and energy use.