Intelligent Aquila Optimization Algorithm-Based Node Localization Scheme for Wireless Sensor Networks

In recent times, wireless sensor network (WSN) finds their suitability in several application areas, ranging from military to commercial ones. Since nodes in WSN are placed arbitrarily in the target field, node localization (NL) becomes essential where the positioning of the nodes can be determined by the aid of anchor nodes. The goal of any NL scheme is to improve the localization accuracy and reduce the localization error rate. With this motivation, this study focuses on the design of Intelligent Aquila Optimization Algorithm Based Node Localization Scheme (IAOAB-NLS) for WSN. The presented IAOAB-NLS model makes use of anchor nodes to determine proper positioning of the nodes. In addition, the IAOAB-NLS model is stimulated by the behaviour of Aquila. The IAOAB-NLS model has the ability to accomplish proper coordinate points of the nodes in the network. For guaranteeing the proficient NL process of the IAOAB-NLS model, widespread experimentation takes place to assure the betterment of the IAOAB-NLS model. The resultant values reported the effectual outcome of the IAOAB-NLS model irrespective of changing parameters in the network.     

基于遗传算法的三维无线传感器网络定位新算法

针对三维无线传感器网络节点自身定位问题,提出了一种基于遗传算法的新定位算法。该算法通过分析未知节点与它的无线射程范围内的已知节点之间的通讯约束和距离测量,对未知节点建立数学模型;针对此数学模型利用遗传算法求解,把该解作为未知节点的估计位置。理论分析和试验结果表明,该算法具有很强的健壮性,未知节点的失效和新节点的加入不会影响算法的性能,并且算法定位精度高,条件简单,适合各种规模的无线传感器网络的节点定位。     

Three Dimensional Optimum Node Localization in Dynamic Wireless Sensor Networks

Location information plays an important role in most of the applications in Wireless Sensor Network (WSN). Recently, many localization techniques have been proposed, while most of these deals with two Dimensional applications. Whereas, in Three Dimensional applications the task is complex and there are large variations in the altitude levels. In these 3D environments, the sensors are placed in mountains for tracking and deployed in air for monitoring pollution level. For such applications, 2D localization models are not reliable. Due to this, the design of 3D localization systems in WSNs faces new challenges. In this paper, in order to find unknown nodes in Three-Dimensional environment, only single anchor node is used. In the simulation-based environment, the nodes with unknown locations are moving at middle & lower layers whereas the top layer is equipped with single anchor node. A novel soft computing technique namely Adaptive Plant Propagation Algorithm (APPA) is introduced to obtain the optimized locations of these mobile nodes. These mobile target nodes are heterogeneous and deployed in an anisotropic environment having an Irregularity (Degree of Irregularity (DOI)) value set to 0.01. The simulation results present that proposed APPA algorithm outperforms as tested among other meta-heuristic optimization techniques in terms of localization error, computational time, and the located sensor nodes.     

Type II Fuzzy Logic Based Cluster Head Selection for Wireless Sensor Network

Wireless Sensor Network (WSN) forms an essential part of IoT. It is embedded in the target environment to observe the physical parameters based on the type of application. Sensor nodes in WSN are constrained by different features such as memory, bandwidth, energy, and its processing capabilities. In WSN, data transmission process consumes the maximum amount of energy than sensing and processing of the sensors. So, diverse clustering and data aggregation techniques are designed to achieve excellent energy efficiency in WSN. In this view, the current research article presents a novel Type II Fuzzy Logic-based Cluster Head selection with Low Complexity Data Aggregation (T2FLCH-LCDA) technique for WSN. The presented model involves a two-stage process such as clustering and data aggregation. Initially, three input parameters such as residual energy, distance to Base Station (BS), and node centrality are used in T2FLCH technique for CH selection and cluster construction. Besides, the LCDA technique which follows Dictionary Based Encoding (DBE) process is used to perform the data aggregation at CHs. Finally, the aggregated data is transmitted to the BS where it achieves energy efficiency. The experimental validation of the T2FLCH-LCDA technique was executed under three different scenarios based on the position of BS. The experimental results revealed that the T2FLCH-LCDA technique achieved maximum energy efficiency, lifetime, Compression Ratio (CR), and power saving than the compared methods.     

无线传感器网络中基于信誉的恶意节点检测

提出一种基于信誉的恶意节点检测方法——RMDMN,在分簇的网络结构基础上,对节点的行为属性(如丢包率、转发率、位置匹配信息等)和网络攻击进行建模,结合阈值比较法动态地更新节点信誉值并进行恶意节点判断.实验仿真显示,该方法具有一定的恶意节点检测能力.     

An Energy-Efficient Wireless Power Transmission-Based Forest Fire Detection System

Compared with the traditional techniques of forest fires detection, wireless sensor network (WSN) is a very promising green technology in detecting efficiently the wildfires. However, the power constraint of sensor nodes is one of the main design limitations of WSNs, which leads to limited operation time of nodes and late fire detection. In the past years, wireless power transfer (WPT) technology has been known as a proper solution to prolong the operation time of sensor nodes. In WPT-based mechanisms, wireless mobile chargers (WMC) are utilized to recharge the batteries of sensor nodes wirelessly. Likewise, the energy of WMC is provided using energy-harvesting or energy-scavenging techniques with employing huge, and expensive devices. However, the high price of energy-harvesting devices hinders the use of this technology in large and dense networks, as such networks require multiple WMCs to improve the quality of service to the sensor nodes. To solve this problem, multiple power banks can be employed instead of utilizing WMCs. Furthermore, the long waiting time of critical sensor nodes located outside the charging range of the energy transmitters is another limitation of the previous works. However, the sensor nodes are equipped with radio frequency (RF) technology, which allows them to exchange energy wirelessly. Consequently, critical sensor nodes located outside the charging range of the WMC can easily receive energy from neighboring nodes. Therefore, in this paper, an energy-efficient and cost-effective wireless power transmission (ECWPT) scheme is presented to improve the network lifetime and performance in forest fire detection-based systems. Simulation results exhibit that ECWPT scheme achieves improved network performance in terms of computational time (12.6%); network throughput (60.7%); data delivery ratio (20.9%); and network overhead (35%) as compared to previous related schemes. In conclusion, the proposed scheme significantly improves network energy efficiency for WSN.     

蚁群循环定位算法在WSNs节点定位中的应用

针对无线传感网络中测距和定位算法复杂、精度不高的问题,提出了一种基于蚁群算法的循环定位算法。首先,利用接收信号强度指示(RSSI)方法测量距离,并建立信号衰减模型来计算测距公式。然后,利用循环定位算法对8个锚节点进行循环定位,将定位结果的平均值作为盲节点的最佳位置。最后,将该算法与加权质心定位算法和三边测量法进行对比,实验结果表明,循环定位算法的定位精度和稳定性更佳。     

基于局部网络拓扑特征的鲁棒节点定位算法

针对无线传感器网络的较大测距误差严重影响定位算法精度和鲁棒性的问题,利用节点均匀部署网络的拓扑特征,提出了一种基于局部网络拓扑特征的鲁棒节点定位算法(LFLS算法).该算法通过构建节点测距高估粗差阈值参数和测距低估粗差阈值参数,在对未知节点1跳测距数据集进行粗差识别及剔除等预处理滤波的基础上,使用高斯加权最小二乘定位算法实现节点定位.仿真结果表明,基于局部网络拓扑特征的鲁棒节点定位算法的定位精度明显优于未采用局部网络拓扑特征进行粗差预处理的加权最小二乘定位算法,其中粗差测距直接相关节点的定位精度改进尤为明显.     

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.     

一种新的分布式无线传感器网络定位方法

针对无线传感器网络节点能源受限的特征,以系统最小硬件开销为设计原则,提出了一种适用于基于测距的分布式定位方法(3/2-NANDB),该方法可在不增加单个独立节点硬件开销的情况下,利用附加的外部控制系统发射一个旋转定向波束充分挖掘节点间的冗余信息,有效排除节点位置的模糊性,从而可完全确定只有两个邻居节点的节点位置和部分只有一个邻居节点的节点位置,达到减少GPS携带节点数量、最大化网络内部可定位节点数目、扩大网络观察范围和延长无线传感器网络存活时间等目的.而利用该方法的节点二义性排除算法,还可以辅助其他现有的基于三邻居(3-NA)的定位算法提高整体定位性能.     

Remaining Capacity Measurement and Analysis of Alkaline Batteries for Wireless Sensor Nodes

In most applications, wireless sensor networks (WSNs) will deploy a large number of distributed sensor nodes in remote or inhospitable places, making batteries their main source of energy; thus, the stored energy is a key resource of a WSN. Sensor nodes should balance their limited resources to increase the lifetime of the network. The knowledge of the available amount of energy becomes an important requirement for the maintenance, implementation of self-management techniques, and viability of the WSN. Therefore, the research of the State-of-Charge (SoC), or the remaining capacity estimation, is of key importance. This paper presents an energy-efficient battery-remaining capacity-estimation technique. The experiments were conducted using the MICA2 wireless sensor node platform, which shows that the voltage-only-based estimation presented an available 18% of the battery maximum capacity, although the battery had been fully discharged, and a current-based estimation technique is presented with minimal hardware intervention.      

Photovoltaics in the assessment of wireless sensor network reliability with changing environmental conditions

This paper presents a reliability assessment of a wireless sensor network (WSN) equipped with mini photovoltaic cells (PV‐WSN) under natural environmental conditions while accounting for different types of system failures. In particular, our assessment considers the hardware specifications of the sensors, photovoltaic (PV) specifications, the use of rechargeable batteries, communication protocols, and various elements required for efficient detection of environmental conditions. We accomplished this by developing a simulator that generated data for 2 broad WSN conditions: (1) WSN without PV and (2) WSN with PV. The dynamic source routing protocol was employed for these simulations, and the following variables were assessed for both conditions: WSN reliability, the impact of energy consumption on the network, and the types of failures that lead to sensor unavailability. The following assumptions were made to run the simulation: the distribution of WSN nodes is random, with 1 sink node per rectangular cluster, the sensor nodes are structurally and functionally identical, environmental interference and suboptimal orientation impair PV cell recharge capacity randomly, and no communication loss occurs. Our reliability assessment assumed extreme environmental conditions and further made assessments of component reliability that included the following parameters: sensor and PV cell hardware specifications, the rechargeable nature of PV cell batteries for different sensor activity states, the availability of sunlight for powering PV cells, and the energy efficiency of PV cells. We found that network lifetime was prolonged for the PV‐WSN condition over the WSN without PV condition, introducing a role for PV cells as potential energy sources for WSNs.     

Locating ineffective sensor nodes in wireless sensor networks

Ineffective sensor node (InESN) in a wireless sensor network (WSN) is defined as one whose position cannot be estimated by traditional localisation methods. Incremental localisation method is investigated and the existence of the InESNs is confirmed. By analysing the existing characteristics, the InESNs are classified into three categories: InESNs connecting with one known node, InESNs connecting with two known nodes and InESNs standing alone. It is impossible to locate the InESNs of the third category because they cannot receive any information from the known nodes. With a moving target in the WSN, a constrained least-squares formulation is developed to estimate the InESNs of the first two categories. Numerical evaluations are carried out to examine the performance of the proposed method and show that it is indeed effective for locating the InESNs. By incorporating the InESNs in the tracking applications, the performance of the target tracking can be greatly enhanced.     

Wireless sensor node localization

For most wireless sensor network (WSN) applications, the positions of the sensor nodes need to be known. Global positioning systems have not fitted into WSNs very well owing to their price, power consumption, accuracy and limitations in their operating environment. Hence, the last decade has brought about a large number of proposed methods for WSN node localization. They show tremendous variation in the physical phenomena they use, the signal properties they measure, the resources they consume, as well as in their accuracy, range, advantages and limitations. This paper provides a high-level, comprehensive overview of this very active research area.     

Statistical Analysis with Dingo Optimizer Enabled Routing for Wireless Sensor Networks

Security is a vital parameter to conserve energy in wireless sensor networks (WSN). Trust management in the WSN is a crucial process as trust is utilized when collaboration is important for accomplishing trustworthy data transmission. But the available routing techniques do not involve security in the design of routing techniques. This study develops a novel statistical analysis with dingo optimizer enabled reliable routing scheme (SADO-RRS) for WSN. The proposed SADO-RRS technique aims to detect the existence of attacks and optimal routes in WSN. In addition, the presented SADO-RRS technique derives a new statistics based linear discriminant analysis (LDA) for attack detection, Moreover, a trust based dingo optimizer (TBDO) algorithm is applied for optimal route selection in the WSN and accomplishes secure data transmission in WSN. Besides, the TBDO algorithm involves the derivation of the fitness function involving different input variables of WSN. For demonstrating the enhanced outcomes of the SADO-RRS technique, a wide range of simulations was carried out and the outcomes demonstrated the enhanced outcomes of the SADO-RRS technique.     

一种低计算复杂度的无线传感器网络分簇定位算法

针对已有的集中式定位算法定位精度低,而分布式定位算法计算复杂度高、通信量大的问题,提出了一种适用于无线传感器网络的计算复杂度低的节点分簇定位算法.首先,提出满足最大连通度的多边界节点分簇算法,采用此算法把网络划分为若干个簇,各簇分别进行簇内节点定位;其次,各簇进行融合,最终实现全网节点的定位.仿真结果表明,这种分簇定位算法比分布式定位算法计算复杂度低、通信量小、定位精度相当或略差,比集中式定位算法计算复杂度低、通信量小、定位精度高.采用该算法可以降低传感器网络节点定位过程中的能耗,提高计算效率,延长网络寿命.     

Data Reliability and Sensors Lifetime in Bridge Health Monitoring using LoRaWAN-Zigbee

The Wireless Sensor Network (WSN) is regarded as the fastest expanding technological trend in recent years due its application in a variety of sectors. In the monitoring region, several sensor nodes with various sensing capabilities are installed to gather appropriate data and communicate it to the gateway. The proposed system of the heterogeneous WSN employing LoRaWAN-Zigbee based hybrid communication is explored in this research study. To communicate in a network, two Long–Range Wide Area Network (LoRaWAN) sensor clusters and two Zigbee sensor clusters are employed, together with two Zigbee and LoRaWAN converters. The suggested Golden eagle shepherd optimization (GESO) method then forms Zigbee as well as LoRaWAN networking clusters. Furthermore, depending on energy usage and data packet size, the fitness of each sensor node is assessed using the Dynamic Intelligent Reasoning Based Neural (DIRN) approach. MATLAB software is used to implement and execute this study. When the Zigbee network’s transmission distance is 650 m and the LoRaWAN network’s transmission range is 3.5 km, the system can function with a packet loss rate of less than 0.04 percent. This study shows significant gains in the performance of the system when compared to traditional approaches based on digital findings obtained on software solutions.     

基于非对称往返测距的海洋无线传感网络节点定位算法

提出一种新的基于非对称往返测距的海洋无线传感网络节点定位(LMARR)算法,该算法利用节点间非对称的接收与发送测距信息的时间差,推算出节点间海水声速以及未知节点与其邻居参考节点之间的距离,将三维距离信息转换成二维,运用最小二乘法完成定位计算。与SWN和ARTL算法相比较,仿真结果表明:LMARR算法能有效地提高节点定位的精度,特别是在深度为20～120m海水声速持续变化的区域,定位精度比SWN算法提高了20%,比ARTL算法提高了33%;此外,LMARR算法还具有较高的稳定性。     

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.     

Hybrid Swarm Intelligence Based QoS Aware Clustering with Routing Protocol for WSN

Wireless Sensor Networks (WSN) started gaining attention due to its wide application in the fields of data collection and information processing. The recent advancements in multimedia sensors demand the Quality of Service (QoS) be maintained up to certain standards. The restrictions and requirements in QoS management completely depend upon the nature of target application. Some of the major QoS parameters in WSN are energy efficiency, network lifetime, delay and throughput. In this scenario, clustering and routing are considered as the most effective techniques to meet the demands of QoS. Since they are treated as NP (Non-deterministic Polynomial-time) hard problem, Swarm Intelligence (SI) techniques can be implemented. The current research work introduces a new QoS aware Clustering and Routing-based technique using Swarm Intelligence (QoSCRSI) algorithm. The proposed QoSCRSI technique performs two-level clustering and proficient routing. Initially, the fuzzy is hybridized with Glowworm Swarm Optimization (GSO)-based clustering (HFGSOC) technique for optimal selection of Cluster Heads (CHs). Here, Quantum Salp Swarm optimization Algorithm (QSSA)-based routing technique (QSSAR) is utilized to select the possible routes in the network. In order to evaluate the performance of the proposed QoSCRSI technique, the authors conducted extensive simulation analysis with varying node counts. The experimental outcomes, obtained from the proposed QoSCRSI technique, apparently proved that the technique is better compared to other state-of-the-art techniques in terms of energy efficiency, network lifetime, overhead, throughput, and delay.