Energy Efficient Clustering Protocol to Enhance Network Lifetime in Wireless Sensor Networks

In this paper, the energy conservation in the ununiform clustered network field is proposed. The fundamental reason behind the methodology is that in the process of CH election, nodes Competition Radius (CR) task is based on not just the space between nodes and their Residual Energy (RE), which is utilized in Energy-Aware Distributed Unequal Clustering (EADUC) protocol but also a third-degree factor, i.e., the nearby multi-hop node count. In contrast, a third-factor nearby nodes count is also used. This surrounding data is taken into account in the clustering feature to increase the network’s life span. The proposed method, known as Energy Conscious Scattered Asymmetric Clustering (ECSAC), self-controls the nodes’ energy utilization for equal allotment and un-equal delivery. Besides, extra attention is agreed to energy consumption in the communication process by applying a timeslot-based backtracking algorithm for increasing the network’s lifetime. The proposed methodology reduces the clustering overhead and node communication energy consumption to extend the network’s lifetime. Our suggested method’s results are investigated against the classical techniques using the lifetime of the network, RE, alive hop count and energy consumption during transmission as the performance metric.     

一种实时数据采集与监控系统

分布式数据采集和控制系统是一种特别适用于工业现场控制、仪器仪表控制的网络系统。本文主要介绍了基于分布式的仪表的远程数据采集和控制系统；该系统主要包括硬件系统组成结构、软件系统设计和网络的构造，并以实际项目验证了其可行性。     

Unmanned vehicles for environmental data collection

Unmanned vehicles have already proved invaluable in environmental field studies by providing levels of spatial–temporal sampling resolution which could have not been attained before. Recent trends show that the levels of spatial–temporal sampling resolutions attained with individual vehicles are feasible for wide areas through the operation of persistent vehicle networks. The possibility of persistent sampling over wide areas has the potential to revolutionize environmental field studies. The roles of unmanned vehicle systems in future environmental field studies are discussed in the light of the recent technological developments and trends, along with the major challenges associated to this vision. The discussion is illustrated with examples of developments from the Underwater Systems and Technologies Laboratory from Porto University, Portugal.     

Research on Time Synchronization Method Under Arbitrary Network Delay in Wireless Sensor Networks

To cope with the arbitrariness of the network delays, a novel method, referred to as the composite particle filter approach based on variational Bayesian (VB-CPF), is proposed herein to estimate the clock skew and clock offset in wireless sensor networks. VB-CPF is an improvement of the Gaussian mixture kalman particle filter (GMKPF) algorithm. In GMKPF, Expectation-Maximization (EM) algorithm needs to determine the number of mixture components in advance, and it is easy to generate overfitting and underfitting. Variational Bayesian EM (VB-EM) algorithm is introduced in this paper to determine the number of mixture components adaptively according to the observations. Moreover, to solve the problem of data packet loss caused by unreliable links, we propose a robust time synchronization (RTS) method in this paper. RTS establishes an autoregressive model for clock skew, and calculates the clock parameters based on the established autoregressive model in case of packet loss. The final simulation results illustrate that VB-CPF yields much more accurate results relative to GMKPF when the network delays are modeled in terms of an asymmetric Gaussian distribution. Moreover, RTS shows good robustness to the continuous and random dropout of time messages.     

Hybrid Sensor Selection Technique for Lifetime Extension of Wireless Sensor Networks

Energy conservation is a crucial issue to extend the lifetime of wireless sensor networks (WSNs) where the battery capacity and energy sources are very restricted. Intelligent energy-saving techniques can help designers overcome this issue by reducing the number of selected sensors that report environmental measurements by eliminating all replicated and unrelated features. This paper suggests a Hybrid Sensor Selection (HSS) technique that combines filter-wrapper method to acquire a rich-informational subset of sensors in a reasonable time. HSS aims to increase the lifetime of WSNs by using the optimal number of sensors. At the same time, HSS maintains the desired level of accuracy and manages sensor failures with the most suitable number of sensors without compromising the accuracy. The evaluation of the HSS technique has adopted four experiments by using four different datasets. These experiments show that HSS can extend the WSNs lifetime and increase the accuracy using a sufficient number of sensors without affecting the WSN functionality. Furthermore, to ensure HSS credibility and reliability, the proposed HSS technique has been compared to other corresponding methodologies and shows its superiority in energy conservation at premium accuracy measures.

     

Intelligent Real-Time Adaptation for Power Efficiency in Sensor Networks

This paper presents an intelligent, dynamic power conservation scheme for sensor networks in which the sensor network operation is adaptive to both changes in the objects under measurement and the network itself. The conservation scheme switches sensor nodes between a sleep and an active mode in a manner such that the nodes can maximize the time they spend in a power-efficient sleep state, which corresponds to a nonmeasuring and/or nontransmitting mode, while not missing important events. A switching decision is made based on changes (or their absence) in the signals sensed from the environment by an intelligent agent that has been trained to determine whether or not a special event has occurred. This intelligent agent is based on a novel neural network topology that allows for a significant reduction in the resource consumption required for its training and operation without compromising its change detection performance. The scheme was implemented to control a sensor network built from a number of Telos rev. B motes currently available on the market. A few new utilities including an original neural network-based intelligent agent, a “visualizer,” a communication manager, and a scheduler have been designed, implemented, and tested. Power consumption measurements taken in a laboratory environment confirm that use of the designed system results in a significant extension of sensor network lifetime (versus “always on” systems) from a few days to a few years.      

Reputation-Enabled Self-Modification for Target Sensing in Wireless Sensor Networks

Wireless sensor networks provide new tools for sensing physical environments. However, the general existence of faulty sensor measurements in networks will cause degradation of the network service quality and huge burden of the precious energy. While cryptography-based approaches are helpless of information generation, reputation systems are demonstrated of positive results. In this paper, we investigate the benefits of a distributed reputation system in target localization. A node reputation is defined as its measurement performance and is computed by the Dirichlet distribution. By assuming the sensing model of each node to be mixed Gaussian, we use reputation to estimate parameters of the sensing model and modify a node's original measurement. We also develop a reputation-based local voting algorithm to filter the untrustworthy data and then estimate the target location by a particle swarm optimization algorithm. To assure energy efficiency of the proposed approach, we use a reputation-based model to indicate the information importance of each data packet and ensure that a more important packet can be delivered with higher reliability. Finally, we experimentally evaluate the reputation system and demonstrate its accuracy and energy efficiency.      

Illumimote: Multimodal and High-Fidelity Light Sensor Module for Wireless Sensor Networks

We describe the system requirements, design, system integration, and performance evaluation of the Illumimote, a new light-sensing module for wireless sensor networks. The Illumimote supports three different light-sensing modalities: incident light intensity, color intensities, and incident light angle (the angle of ray arrival from the strongest source); and two situational sensing modalities: attitude and temperature. The Illumimote achieves high performance, comparable to commercial light meters, while conforming to the size and energy constraints imposed by its application in wireless sensor networks. We evaluated the performance of our Illumimote for light intensity, color temperature, and incident light angle measurements and verified the function of the attitude sensor. The Illumimote consumes about 90 mW when all features on board are activated. We describe our design and the experiment design for the performance evaluation.     

USB Sensor Network for Industrial Applications

This paper proposes a universal serial bus (USB) solution for sensor networking. First, network architecture has been presented to have, as its primary objective, its integration with existing infrastructure. For this reason, a USB-to-Ethernet gateway has been introduced. Then, many aspects of industrial applications have been considered to realize a suitable solution. Insulation problems and low-cost implementation have been tackled. Working prototypes of each network component have been defined and realized: USB host gateway, USB insulator, and USB hub. Several commercial USB devices can be used as sensors. Finally, some experiments have been carried out: Timing performances, network activities, and power consumption have been tested.     

一种高效的网格监控数据收集方法

为提高在面向服务的网格环境下以简单对象访问协议（SOAP）通信方式收集网格资源监控事件的效率,基于对监控事件依次传输时的低效率和收集者SOAP通信处理负载过大的现象的分析,针对具有较长生命期的监控事件提出了一种高效的收集方法——资源本地和资源间分别采用缓存和协同机制;在监控事件生命期允许的范围内先行跨资源汇聚尽量多的监控事件,再将它们通过单个SOAP消息一并发送至收集者。实验结果表明,所提方法不仅能使网格资源个体监控事件的SOAP传输开销降低50％～85％,还可将收集者上的SOAP通信处理负载降低75％左右。     

Wireless Sensor Network Modeling Using Modified Recurrent Neural Networks: Application to Fault Detection

This paper presents a dynamic model of wireless sensor networks (WSNs) and its application to sensor node fault detection. Recurrent neural networks (NNs) are used to model a sensor node, the node's dynamics, and interconnections with other sensor network nodes. An NN modeling approach is used for sensor node identification and fault detection in WSNs. The input to the NN is chosen to include previous output samples of the modeling sensor node and the current and previous output samples of neighboring sensors. The model is based on a new structure of a backpropagation-type NN. The input to the NN and the topology of the network are based on a general nonlinear sensor model. A simulation example, including a comparison to the Kalman filter method, has demonstrated the effectiveness of the proposed scheme.     

A Multi-Conditional Proxy Broadcast Re-Encryption Scheme for Sensor Networks

In sensor networks, it is a challenge to ensure the security of data exchange between packet switching nodes holding different private keys. In order to solve this problem, the present study proposes a scheme called multi-conditional proxy broadcast reencryption (MC-PBRE). The scheme consists of the following roles: the source node, proxy server, and the target node. If the condition is met, the proxy can convert the encrypted data of the source node into data that the target node can directly decrypt. It allows the proxy server to convert the ciphertext of the source node to a new ciphertext of the target node in a different group, while the proxy server does not need to store the key or reveal the plaintext. At the same time, the proxy server cannot obtain any valuable information in the ciphertext. This paper formalizes the concept of MC-PBRE and its security model, and proposes a MC-PBRE scheme of ciphertext security. Finally, the scheme security has been proved in the random oracle.     

Enhanced Archimedes Optimization Algorithm for Clustered Wireless Sensor Networks

Wireless sensor networks (WSN) encompass a set of inexpensive and battery powered sensor nodes, commonly employed for data gathering and tracking applications. Optimal energy utilization of the nodes in WSN is essential to capture data effectively and transmit them to destination. The latest developments of energy efficient clustering techniques can be widely applied to accomplish energy efficiency in the network. In this aspect, this paper presents an enhanced Archimedes optimization based cluster head selection (EAOA-CHS) approach for WSN. The goal of the EAOA-CHS method is to optimally choose the CHs from the available nodes in WSN and then organize the nodes into a set of clusters. Besides, the EAOA is derived by the incorporation of the chaotic map and pseudo-random performance. Moreover, the EAOA-CHS technique determines a fitness function involving total energy consumption and lifetime of WSN. The design of EAOA for CH election in the WSN depicts the novelty of work. In order to exhibit the enhanced efficiency of EAOA-CHS technique, a set of simulations are applied on 3 distinct conditions dependent upon the place of base station (BS). The simulation results pointed out the better outcomes of the EAOA-CHS technique over the recent methods under all scenarios.     

PUF-Based Key Distribution in Wireless Sensor Networks

Physical Unclonable Functions (PUFs) can be seen as kind of hardware oneway functions, who are easily fabricated but difficult to clone, duplicate or predict. Therefore, PUFs with unclonable and unpredictable properties are welcome to be applied in designing lightweight cryptography protocols. In this paper, a Basic Key Distribution Scheme (Basic-KDS) based on PUFs is firstly proposed. Then, by employing different deployment modes, a Random Deployment Key Distribution Scheme (RD-KDS) and a Grouping Deployment Key Distribution Scheme (GD-KDS) are further proposed based on the Basic-KDS for large scale wireless sensor networks. In our proposals, a sensor is not pre-distributed with any keys but will generate one by the embedded PUF when receiving a challenge from the gateway, which provides perfect resilience against sensor capture attacks. Besides, the unclonable and unpredictable properties of PUF guarantee the key uniqueness and two-way authentication. Analysis and experiment results show that our proposals have better performances in improving the resilience, secureconnectivity, and efficiency as compared to other schemes.     

Experimental Characterization of Wireless Sensor Networks for Industrial Applications

The effects of interference in the setup of wireless sensor networks (WSNs) represent a critical issue, and as such, it needs to be carefully addressed. To this aim, helpful information can be achieved through measurements to be carried out in advance on suitable prototypes and testbeds. In this paper, the measurement of industrial WSN performance is dealt with. In particular, a suitable testbed enlisting IEEE 802.15.4 wireless sensor nodes is presented along with the results of some experiments carried out even in the presence of interference. The purpose is to show how to evaluate some specific parameters of a WSN employed for industrial applications to obtain useful information for its setup optimization in the presence of interference. The analysis will show that from the measurement of these parameters (number of failed pollings, polling round-trip time, experimental cycle time, and alarm latency), interference effects can effectively be recognized, and the network setup can be optimized.      

Bandwidth-Efficient Geographic Multicast Routing Protocol for Wireless Sensor Networks

We present geographic multicast routing (GMR), a new multicast routing protocol for wireless sensor networks. It is a fully localized algorithm that efficiently delivers multicast data messages to multiple destinations. It does not require any type of flooding throughout the network. Each node propagating a multicast data message needs to select a subset of its neighbors as relay nodes towards destinations. GMR optimizes the cost over progress ratio where the cost is equal to the number of neighbors selected for relaying and the progress is the overall reduction of the remaining distances to destinations. Such neighbor selection achieves a good tradeoff between the bandwidth of the multicast tree and the effectiveness of the data distribution. Our cost-aware neighbor selection is based on a greedy set merging scheme achieving a O(Dnmin(D,n)³) computation time, where n is the number of neighbors of current node and D is the number of destinations. As in traditional geographic routing algorithms, delivery to all destinations is guaranteed by applying face routing when necessary. Our simulation results show that GMR outperforms position based multicast in terms of cost of the trees and computation time over a variety of networking scenarios     

水声传感器自动测试位移机构设计

在水声传感器研制过程和实际装机应用之前,需进行大量的试验来测试其性能及可靠性。根据水声传感器的性能测试需求,设计了能够搭载水声传感器进行水下测试的五自由度精密位移试验台,实现传感器在试验水槽内三个直线方向和两个回转角度方向上位置的全覆盖,并提出了一种可以快速检测梯形丝杠螺母传动回程误差的工程测量方法,通过软件补偿的方法来克服回程误差,实现机构运动的准确定位,满足了低成本模式下多自由度精密定位需求。最终,应用激光跟踪仪对试验设备进行了误差检测,保证测试过程中传感器相对位置的准确性。     

传感器网络时钟同步中基于多父节点的卡尔曼滤波算法

指出了节点每接收一个来自父节点的同步包等效于获得一个全局参考时钟的观测值,提出了使用矢量卡尔曼滤波器对来自多个父节点的观测值进行融合的算法.在多跳的无线传感器网络中,缓解了由于同步误差的逐层传递使全局时钟同步精度发生的恶化,避免网络边缘的节点产生较大的时钟误差,从而放宽了由误差传递导致的对网络规模的限制.硬件实现与数值仿真均表明相比现有协议,该算法能大幅度减小全局时钟误差,且随着节点跳数的增加而越发显著,有效地减轻了同步误差的传递问题.     

Hybrid BWO-IACO Algorithm for Cluster Based Routing in Wireless Sensor Networks

Wireless Sensor Network (WSN) comprises a massive number of arbitrarily placed sensor nodes that are linked wirelessly to monitor the physical parameters from the target region. As the nodes in WSN operate on inbuilt batteries, the energy depletion occurs after certain rounds of operation and thereby results in reduced network lifetime. To enhance energy efficiency and network longevity, clustering and routing techniques are commonly employed in WSN. This paper presents a novel black widow optimization (BWO) with improved ant colony optimization (IACO) algorithm (BWO-IACO) for cluster based routing in WSN. The proposed BWO-IACO algorithm involves BWO based clustering process to elect an optimal set of cluster heads (CHs). The BWO algorithm derives a fitness function (FF) using five input parameters like residual energy (RE), inter-cluster distance, intra-cluster distance, node degree (ND), and node centrality. In addition, IACO based routing process is involved for route selection in inter-cluster communication. The IACO algorithm incorporates the concepts of traditional ACO algorithm with krill herd algorithm (KHA). The IACO algorithm utilizes the energy factor to elect an optimal set of routes to BS in the network. The integration of BWO based clustering and IACO based routing techniques considerably helps to improve energy efficiency and network lifetime. The presented BWO-IACO algorithm has been simulated using MATLAB and the results are examined under varying aspects. A wide range of comparative analysis makes sure the betterment of the BWO-IACO algorithm over all the other compared techniques.