A Multi-Level Strategy for Energy Efficient Data Aggregation in Wireless Sensor Networks

In this paper, we have proposed energy efficient multi-level aggregation strategy which considers data sensing as continuous stochastic process. Our proposed strategy performs filtration of sensed data by removing the redundancy in the sensed data pattern of the sensor node using Brownian motion. Further, the filtered data at the sensor node undergoes entropy-based processing prior to the transmission to cluster head. The head node performs wavelet-based truncation of the received entropy in order to select higher information bearing packets before transmitting them to the sink. Overall, our innovative approach reduces the redundant packets transmissions yet maintaining the fidelity in the aggregated data. We have also optimized the number of samples that should be buffered in an aggregation period. In addition, the power consumption analysis for individual sensors and cluster heads is performed that considers the communicational and computational cost as well. Simulation of our proposed method reveals quality performance than existing data aggregation method based on wavelet entropy and entropy based data aggregation protocols respectively. The evaluation criteria includes—cluster head survival, aggregation cycles completed during simulation, energy consumption and network lifetime. The proposed scheme reflects high potential on practical implementation by improving the life prospects of the sensor network commendably.     

BECPA: Bandwidth Efficient Cluster Based Packet Aggregation in Wireless Sensor Network

In recent years, energy consumption and data gathering is a foremost concern in many applications of wireless sensor networks (WSNs). The major issue in WSNs is effective utilization of the resource as energy and bandwidth with a large gathering of data from the monitoring and control applications. This paper proposes novel Bandwidth Efficient Cluster based Packet Aggregation algorithm for heterogeneous WSNs. It combines the idea of variable packet generation rate of each node with random data. The nodes are randomly distributed with different energy level and are equal in numbers. It uses the perfectly compressible aggregation function at cluster head based on the correlation of packets and data generated by each node. Compare to state-of-the-art solutions, the algorithm shows 4.43 % energy savings with reduced packet delivery ratio (62.62 %) at the sink. It shows better bandwidth utilization in packet aggregation than data aggregation.     

无线传感器网络安全数据汇聚算法

针对无线传感器网络数据汇聚与传输过程面临的安全威胁,文中提出一种基于安全数据汇聚与信息重构的多路由数据传输算法——MDT.该算法允许网络中存在妥协节点,也允许丢失部分数据.通过理论分析与仿真实验,证实该算法在不增加网络整体能耗的前提下,能有效地抵御侦听、数据篡改和拒绝服务攻击,全面提高系统的安全性和可靠性.     

An Integrated Trust Assisted Energy Efficient Greedy Data Aggregation for Wireless Sensor Networks

Wireless Personal Communications - Wireless sensor networks (WSN) gathers information pertaining to sensitive data. As because the sensor nodes in WSN are remote and unattended for a longer period...     

Achieving Energy Conservation by Cluster Based Data Aggregation in Wireless Sensor Networks

In Wireless Sensor Networks (WSN), one of the major issues is to maximize the network lifetime. Since all sensor nodes directly send the data to the Base station, the energy requirement is very high. This reduces the lifetime of the network. One of the solutions is to partition the network into various clusters which avoids direct communication. In this paper we propose an Energy efficient Cluster Based Data Aggregation (ECBDA) scheme for sensor networks. In this algorithm, Cluster members send the data only to its corresponding local cluster head, there by communication overhead is reduced. Data generated from neighboring sensors are often redundant and highly correlated. So the cluster head performs data aggregation to reduce the redundant packet transmission. In our approach, clusters are formed in a non-periodic manner to avoid unnecessary setup message transmissions. Re-clustering is performed only when CH needs to balance the load among the nodes. The simulation results show that our approach effectively reduces the energy consumption and hence the network lifetime is also increased.     

基于Mobile Agent的无线传感器网络管理框架

无线传感器网络是一种全新的技术,能够广泛应用于恶劣环境和军事领域.为解决数据从节点到处理中心过程中传输耗能大,节点的电池能量消耗不均衡等问题,文章提出了一种基于Mobile Agent的管理框架,并根据这个管理框架设计实现了Mobile Agent服务管理中心和Mobile Agent实体.实验表明,此框架能有效减少和平衡能量消耗.     

Two Tier Cluster Based Data Aggregation (TTCDA) for Efficient Bandwidth Utilization in Wireless Sensor Network

Wireless Sensor Networks are often used for monitoring and control applications where sensor nodes collect data and send it to the sink. Direct transmission of data packets to the sink from nodes in the network causes increased communication costs in terms of energy, network lifetime and bandwidth utilization. In this context, this paper proposes Two Tier Cluster-based Data Aggregation (TTCDA) algorithm for the randomly distributed nodes in the network to minimize computation and communication cost. The TTCDA effectively considers the packet and data aggregation using additive and divisible aggregation functions at cluster head and sink. The aggregation functions are applied according to spatial and temporal correlation of packets and data generated by each node. It also prevents transmission of redundant data by improving energy consumption and bandwidth utilization as compared with state-of-the-art solution. The performance of the algorithm is validated using examples and simulations. Also, it is seen that packet aggregation in TTCDA is better for the bandwidth utilization as it reduces average energy consumption by 3.13 % as compared to data aggregation.     

Energy Harvesting Based Efficient Routing Scheme for Wireless Sensor Network

Wireless Sensor Network were deployed in a complex environment where the wide range of complex application is mandatory for the services. Such application includes military, agriculture, healthcare, defense, monitoring, surveillance etc. In general sensor nodes were spatially distributed and deployed in remote fashion, usually they are powered up by batteries. These battery powered sensor nodes are pruned to failure due to its power constrained nature. This led many researchers to explore energy efficient context aware routing for Wireless Sensor Networks. Hence a novel energy harvesting based efficient routing scheme is desirable to address the above stated problem. The key idea is to harvest the energy source from the deployed environment. The proposed routing scheme is tested and validated in MATLAB based simulation test bed. The experimental results shows that the proposed routing scheme is robust and meet all the requirements of routing and promising results for energy usage.     

Fault-Tolerance Data Aggregation for Clustering Wireless Sensor Network

The hierarchical cluster-based topology is commonly accepted as an optimal structure for sensor network to increase communication scalability, prolong network lifetime, and reduce data redundancy. However, the data privacy and security are challenging the proliferation of clustering wireless sensor network (CWSN) due to its highly constrained resources and violably deployed environments, which make it infeasible to directly apply traditional cryptography and therefore vulnerable to various attacks. This article proposes a scheme that provides efficient privacy-preserving data fusion as well as malicious data tolerance by mining concealed data within groups. And the dynamically organized groups in each cluster improves resilience against large number of node compromise comparing with the existing data aggregation schemes. The simulation results and mathematical comparison show the effectiveness and fitness of our scheme for CWSN in terms of fault tolerance and process efficiency, which costs a little of additional overheads in memory and communication.     

Intelligent Query-Based Data Aggregation Model and Optimized Query Ordering for Efficient Wireless Sensor Network

Data aggregation algorithms play a primary role in WSN, as it collects and aggregates the data in an energy efficient manner so that the life expectancy of the network is extended. This paper intends to develop a query-based aggregation model for WSN using the advanced optimization algorithm called group search optimization (GSO). The proposed model is constructed in such a way that the querying order (QO) can be ranked based on latency and throughput. Accordingly, the main objective of the proposed GSO-based QO is to minimize the latency and maximize the throughput of WSN. The proposed data aggregation model facilitates the network administrator to understand the best queries so that the performance of the base station can be improved. After framing the model, it compares the performance of GSO-based QO with the traditional PSO-based QO, FF-based QO, GA-based QO, ABC-based QO and GSO-based QO in terms of idle time and throughput. Thus the data aggregation performance of proposed GSO-based QO is superior to the traditional algorithms by attaining high throughput and low latency.     

无线传感器网络中有效的安全数据融合机制

在无线传感器网络中,数据融合是实现有效传输和节省能源的一个重要途径,许多应用都需要可靠并且可信的数据来进行融合.针对上述要求,提出了一个新的安全数据融合算法来保证融合数据的机密性和完整性.算法使用端到端加密和逐跳加密相结合的方式进行数据传输,通过认证过程进行恶意节点及伪造数据的检测.仿真表明,提出的算法能够有效地检测出恶意节点,并保证融合结果的准确性.     

Energy Efficient Data Gathering Technique Based on Optimal Mobile Sink Node Selection for Improved Network Life Time in Wireless Sensor Network (WSN)

Wireless Personal Communications - In this paper, reinforcement learning (RL) based cognitive anti-jamming system employing full duplex tactical radio is investigated under electromagnetic spectrum...     

WSN中基于多分辨率和压缩感知的数据融合方案

当前基于压缩感知的传感器网络数据融合方案中,不论数据字段有何特征,均假设网络具有固定而均匀的压缩阈值,从而导致数据通信量过高,能耗浪费较大。提出一种基于多分辨率和压缩感知的数据融合方案。首先,对传感器网络进行配置,以生成多个层次类型不同的簇结构,用于过渡式数据收集,在该结构上,最低层的叶节点只传输原始数据,其他层的数据收集簇进行压缩采样;然后将其测量值向上发送,当母数据收集簇收到测量值时,利用基于反向DCT和DCT模型的CoSaMP算法恢复原始数据;最后,在SIDnet-SWANS平台上部署了该方案,并在不同的二维随机部署传感器网络规模下进行了测试。实验结果表明,随着分层位置的变化,大部分节点的能耗均显著降低,与NCS方案相比,能耗下降50%~77%,与HCS方案相比,能耗下降37%~70%。     

Double Cluster Based Energy Efficient Routing Protocol for Wireless Sensor Network

Reducing the energy consumption of sensor nodes and prolonging the life of the network is the central topic in the research of wireless sensor network (WSN) protocol. The low-energy adaptive clustering hierarchy (LEACH) is one of the hierarchical routing protocols designed for communication in WSNs. LEACH is clustering based protocol that utilizes randomized rotation of local cluster-heads to evenly distribute the energy load among the sensors in the network. But LEACH is based on the assumption that each sensor nodes contain equal amount of energy which is not valid in real scenarios. A developed routing protocol named as DL-LEACH is proposed. The DL-LEACH protocol cluster head election considers residual energy of nodes, distance from node to the base station and neighbor nodes, which makes cluster head election reasonable and node energy consumption balance. The simulation results of proposed protocols are compared for its network life time in MATLAB with LEACH protocol. The DL-LEACH is prolong the network life cycle by 75 % than LEACH.     

Wheel Based Event Triggered Data Aggregation and Routing in Wireless Sensor Networks: Agent Based Approach

Event triggered data aggregation and routing minimizes the amount of energy and bandwidth required to transmit the data from the event affected area. This paper proposes a Wheel based Event Triggered data aggregation and routing (WETdar) scheme in Wireless Sensor Networks (WSNs) by employing a set of static and mobile agents. A wheel with spokes is constructed by WSN nodes around an event node (a sensor node where an event occurs). Gathering and aggregation of the information is performed along the spokes of a wheel in Spoke Aggregator (SA) nodes and sent to an event node, which routes to a sink node. Spoke generation and identification of SA nodes along the spokes is performed by using a mobile agent, based on parameters such as Euclidean distance, residual energy, spoke angle and connectivity. Mobile agent and its clones discover multiple paths to a sink node from an event node. The scheme is simulated in various WSN scenarios to evaluate the effectiveness of the approach. The performance parameters analyzed are number of SAs, SA selection time, aggregation time, aggregation energy, energy consumption, number of isolated nodes and network life time. We observed that proposed scheme outperforms as compared to the existing aggregation scheme.     

能量有效的多级无线传感网络数据采集方法

The majority of the energy consumption by the sensors is the energy requirement for data transmission in Wireless Sensor Networks (WSNs). Therefore, introducing mobile collectors to collect data instead of multi-hop data relay is essential. However, for many proposed data gathering approaches, long data delay is the main problem. Hence, the problem of how to decrease the energy consumption and the data delay needs to be solved. In this paper, a low delay data collection mechanism using multiple mobile collectors is proposed. First, a self-organization clustering algorithm is designed. Second, sensor nodes are organized into three-level clusters. Then a collection strategy based on the hierarchical structure is proposed, which includes two rules to dispatch mobile collectors rationally. Simulation results show that the proposed mechanism is superior to other existing approaches in terms of the reduction in energy expenditure and the decrease in data delay.     

DEED:一种无线传感器网络中高效节能的数据通信协议

无线传感器网络由许多具有低功率无线收发装置的传感器节点组成,能够有效地从不同环境监测收集周边环境信息并传送到远处的基站进行处理.由于传感器节点的电池能量极为有限,因此节点的通信应有效的利用能量,以延长网络的生命周期.LEACH^[4]协议是一种典型的能有效延长网络生命周期的节能通信协议.本文提出了一种分布的、高效节能的通信协议DEED,DEED利用了数据聚合技术,以聚类的方式组织节点,聚类首领在网络中均匀分布并组织成路由树,由根节点与基站直接通信.实验结果显示DEED性能远好于LEACH.     

Research on Data Aggregation Scheme of Wireless Sensor Network

针对分簇网络拓扑结构中簇头节点能量消耗过快,综合考虑了节点的密集程度和剩余能量,采用节点自适应的簇头选择算法,选择部署越集中和剩余能量越大的节点作为簇头节点.同时节点引入了新鲜性信息熵模型,通过比较前后两次接收到的数据的差别程度,设置一个参考阈值来判断是否转发数据.这种数据汇聚算法有效地降低了数据的冗余,减少了能量消耗,增加了带宽利用率,延长了网络的生存期.     

无线传感器网络数据传输及融合技术

多跳传输原始测量数据的能耗较大,将数据进行分布式网内压缩后再传输的节能方案逐渐为人们所关注.在此从数据融合的角度,对无线传感器网络中各种数据传输技术进行了较全面的考察,着重介绍网内分布式小波变换算法(DWT_IRR).最后,列表分析比较了几种常见的数据融合技术,并对该领域的进一步发展提出了一些思路.