Jamming detection approach based on fuzzy assisted multicriteria decision‐making system for wireless sensor networks

The jamming detection approach based on fuzzy assisted multicriteria decision‐making system (JDA) is proposed to detect the presence of jamming in downstream communication for Cluster based Wireless Sensor Network (CWSN). The proposed approach is deployed in cluster head (CH). The JDA functions in two aspects: First, the CH periodically measures the jamming detection metrics namely Packet Delivery Ratio (PDR) and Received Signal Strength Indicator (RSSI) of every node in the cluster to determine the behavior of the sensor nodes. In order to determine the behavior of members in the cluster, the CH compares the measured PDR with the PDR threshold. If the measured PDR is lesser than the PDR threshold, then CH applies the TOPSIS method on the PDR and RSSI metrics to determine the presence of jamming. These metrics are considered as the criteria and the nodes or the members are considered to be the alternatives. Next, the fuzzy logic is applied on the results obtained from the TOPSIS method to optimize the jamming detection metrics and identify the presence of jamming accurately. The proposed jamming detection approach detects well and arrives at 99.6% jamming detection rate as shown in simulation.     

A green cluster‐based routing scheme for large‐scale wireless sensor networks

In wireless sensor networks (WSNs), clustering has been shown to be an efficient technique to improve scalability and network lifetime. In clustered networks, clustering creates unequal load distribution among cluster heads (CHs) and cluster member (CM) nodes. As a result, the entire network is subject to premature death because of the deficient active nodes within the network. In this paper, we present clustering‐based routing algorithms that can balance out the trade‐off between load distribution and network lifetime “green cluster‐based routing scheme.” This paper proposes a new energy‐aware green cluster‐based routing algorithm to preventing premature death of large‐scale dense WSNs. To deal with the uncertainty present in network information, a fuzzy rule‐based node classification model is proposed for clustering. Its primary benefits are flexibility in selecting effective CHs, reliability in distributing CHs overload among the other nodes, and reducing communication overhead and cluster formation time in highly dense areas. In addition, we propose a routing scheme that balances the load among sensors. The proposed scheme is evaluated through simulations to compare our scheme with the existing algorithms available in the literature. The numerical results show the relevance and improved efficiency of our scheme.     

WSN中基于RSSI的加权质心定位算法的改进

自身节点定位是无线传感器网络的关键技术之一。本文对距离无关定位算法中的质心定位算法进行了分析,在基于RSSI的质心定位算法的基础上提出了一种新的校正RSSI测距值的加权定位算法。测距阶段将信标节点之间的距离和信号强度信息同时考虑在内进行RSSI值校正,权值选择阶段采用了修正传统权重的计算方法,权值取距离倒数之和。通过仿真证明,本文提出的算法相对于传统的加权质心定位算法有明显改进,获得较好的定位精度。     

A cluster‐based fault‐tolerant routing protocol for wireless sensor networks

Energy conservation and fault tolerance are two critical issues in the deployment of wireless sensor networks (WSNs). Many cluster‐based fault‐tolerant routing protocols have been proposed for energy conservation and network lifetime maximization in WSNs. However, these protocols suffer from high frequency of re‐clustering as well as extra energy consumption to tolerate failures and consider only some very normal parameters to form clusters without any verification of the energy sufficiency for data routing. Therefore, this paper proposes a cluster‐based fault‐tolerant routing protocol referred as CFTR. This protocol allows higher energy nodes to become Cluster Heads (CHs) and operate multiple rounds to diminish the frequency of re‐clustering. Additionally, for the sake to get better energy efficiency and balancing, we introduce a cost function that considers during cluster formation energy cost from sensor node to CH, energy cost from CH to sink, and another significant parameter, namely, number of cluster members in previous round. Further, the proposed CFTR takes care of nodes, which have no CH in their communication range. Also, it introduces a routing algorithm in which the decision of next hop CH selection is based on a cost function conceived to select routes with sufficient energy for data transfer and distribute uniformly the overall data‐relaying load among the CHs. As well, a low‐overhead algorithm to tolerate the sudden failure of CHs is proposed. We perform extensive simulations on CFTR and compare their results with those of two recent existing protocols to demonstrate its superiority in terms of different metrics.     

A survey on IP‐based wireless sensor network solutions

Wireless sensor networks (WSNs) are composed of thousands of smart‐sensing nodes, which capture environment data for a sink node. Such networks present new challenges when compared with traditional computer networks, namely in terms of smart node hardware constraints and very limited energy resources. Ubiquitous computing can benefit from WSNs from the perspective that sensed data can be used instead of the user without explicit intervention, turning ubiquitous computing into a reality. Internet connectivity in WSNs is highly desirable, featuring sensing services at a global scale. Two main approaches are considered: proxy based or sensor node stack based. This second approach turns sensors into data‐producing hosts also known as ‘The Internet of Things’. For years, the TCP/IP (Transmission Control Protocol/Internet Protocol) suite was considered inappropriate for WSNs, mainly due to the inherent complexity and protocol overhead for such limited hardware. However, recent studies made connecting WSNs to the Internet possible, namely using sensor node stack based approaches, enabling integration into the future Internet. This paper surveys the current state‐of‐the‐art on the connection of WSNs to the Internet, presents related achievements, and provides insights on how to develop IP‐based communication solutions for WSNs today. Copyright © 2010 John Wiley & Sons, Ltd.     

A lifetime enhancement protocol in cluster‐based wireless sensor networks with guaranteed delay

Wireless sensor networks nowadays find application in all the fields of the world. Rare event detection is an important application in which the wireless sensor network is used. In the case of rare event detection, event of interest or the important event occurs very rarely. Battery‐powered sensor nodes are deployed to detect the event and report to the base station. Sensing and communicating the low priority events happen in major portion of the lifetime for the sensor nodes. However, if the event occurs, then it should be detected and communicated at the earliest to the base station or to the sink node. To reduce the network traffic due to low priority data, we propose a cross layered algorithm to improve the lifetime of the sensor nodes in the case of clustered architecture. In spite of the increase in the network lifetime, the time to detect and communicate to the base station is maintained as that of the traditional clustering approach. The proposed algorithm is simulated, and results show significant improvement in the lifetime of the sensor nodes with guaranteed latency. In this paper, we also suggest methods to support the latency to sensor nodes on priority basis for continuous reporting.     

基于数据融合的无线传感器网络路由算法

在分簇协议LEACH和链状协议PEGASIS的基础上,提出一种新的基于数据融合的分簇路由算法.簇首节点采用多跳方式传输数据,并根据周围节点的密集程度构造不同大小的簇;簇内节点计算上行和下行节点构造数据融合树,采用时分复用调度算法进行多跳路由.NS2仿真结果表明该路由算法均衡了各个节点的能量消耗,延长了网络存活时间,并降低了网络延迟.     

采用自适应退避机制的无线传感网分簇算法

传统的LEACH算法因为簇首选举的随机性而导致节点能量分布不均,从而影响网络寿命和系统吞吐量,在此基础之上提出了一种基于自适应退避机制的分簇算法,通过设定一个能量门限来衡量簇首的健康度,并采用退避机制来使那些趋于衰亡的簇首能找到合适的继任者来担当簇首,保证数据传输的可靠性.仿真实验显示,该算法能够较LEACH获得更长的网络寿命,并提高了网络的吞吐量.     

Square partition‐based node scheduling algorithm for wireless passive sensor networks

Wireless passive sensor networks play an important role in solving the energy limitation of nodes in the Internet of Things, and node scheduling is a significant method used to improve the energy utilization of nodes. In this work, an unused energy model based on analyzing the energy consumption characteristics of passive nodes is proposed because no unified model of passive sensor nodes is reported in previous studies. A rapid square partition clustering method is proposed according to the analysis of the relation between the sensing and communication radii of nodes, and the secondary grouping and node scheduling in each cluster are implemented to ensure the coverage rate of networks. Experimental results show that the state distribution of nodes in the proposed algorithm is favorable. The performance of the proposed algorithm is significantly affected by the P ratio between the working and charging powers of nodes. When the value of P is less than 100, the network coverage and connectivity rate are maintained at more than 95% and 90%, respectively, and are both higher than the existing algorithm.     

FUCA: Fuzzy‐based unequal clustering algorithm to prolong the lifetime of wireless sensor networks

Wireless sensor network comprises billions of nodes that work collaboratively, gather data, and transmit to the sink. “Energy hole” or “hotspot” problem is a phenomenon in which nodes near to the sink die prematurely, which causes the network partition. This is because of the imbalance of the consumption of energy by the nodes in wireless sensor networks. This decreases the network's lifetime. Unequal clustering is a technique to cope up with this issue. In this paper, an algorithm, “fuzzy‐based unequal clustering algorithm,” is proposed to prolong the lifetime of the network. This protocol forms unequal clusters. This is to balance the energy consumption. Cluster head selection is done through fuzzy logic approach. Input variables are the distance to base station, residual energy, and density. Competition radius and rank are the two output fuzzy variables. Mamdani method is employed for fuzzy inference. The protocol is compared with well‐known algorithms, like low‐energy adaptive clustering hierarchy, energy‐aware unequal clustering fuzzy, multi‐objective fuzzy clustering algorithm, and fuzzy‐based unequal clustering under different network scenarios. In all the scenarios, the proposed protocol performs better. It extends the lifetime of the network as compared with its counterparts.     

A novel fault detection and recovery technique for cluster‐based underwater wireless sensor networks

The performance of underwater wireless sensor network gets affected by the working of a cluster in the network. The cluster head (CH) or cluster member (CM) fails because of energy depletion or hardware errors that increase delay and message overhead of the network. To recover the affected cluster, a technique is required to identify the failed CH or CM. We propose a fault detection and recovery technique (FDRT) for a cluster‐based network in this paper. Primarily, while selecting the CH, a backup cluster head (BCH) is selected using fuzzy logic technique based on parameters such as node density, residual energy, load, distance to sink, and link quality. Then, failure of CH, BCH, and CM is detected. If fault is detected at CH, then the BCH will start performing the task of failed CH. Simultaneously, when BCH failed, any other CM will be elected as BCH. If any of the CM appears to be nonperforming, then CH will detect the communication failure and request BCH to transfer the data from the failed CM to CH. The comparison of proposed FDRT is performed with existing FDRTs EDETA, RCH, and SDMCGC on the basis of packet drop, end‐to‐end delay, energy consumption, and delivery ratio of data packets. By simulation results, it is shown that FDRT for cluster‐based underwater wireless sensor network results in quicker detection of failures and recovery of the network along with the reduction in energy consumption, thereby increasing the lifespan of the network.     

无线传感器网络的神经网络模型

无线传感器网络是复杂的无线网络。无线传感器网络拥有大量的网络节点。网络节点是无线传感器网络的基础。为了研究复杂的无线传感器网络，采用了神经元描述了WSN的网络节点，用神经元模型表示了无线传感器网络。给出了无线待感器网络节点的神经元模型和无线传感器网络的神经网络模型，并将神经网络应用于无线传感器网络的数据融合应用。结果表明，基于神经网络的无线传感器网络研究可以使得复杂研究变得简单，利于开展WSN的深入研究。     

Fuzzy logic based clustering in wireless sensor networks: a survey

Wireless sensor networks (WSNs) have limited resources, thus extending the lifetime has always been an issue of great interest. Recent developments in WSNs have led to various new fuzzy systems, specifically designed for WSNs where energy awareness is an essential consideration. In several applications, the clustered WSN are known to perform better than flat WSN, if the energy consumption in clustering operation itself could be minimised. Routing in clustered WSN is very efficient, especially when the challenge of finding the optimum number of intermediate cluster heads can be resolved. Fortunately, several fuzzy logic based solutions have been proposed for these jobs. Both single- and two-level fuzzy logic approaches are being used for cluster head election in which several distinguished features of WSN have been considered in making a decision. This article surveys the recent fuzzy applications for cluster head selection in WSNs and presents a comparative study for the various approaches pursued.     

Distributed fuzzy logic based energy‐aware and coverage preserving unequal clustering algorithm for wireless sensor networks

In an energy‐constrained wireless sensor networks (WSNs), clustering is found to be an effective strategy to minimize the energy depletion of sensor nodes. In clustered WSNs, network is partitioned into set of clusters, each having a coordinator called cluster head (CH), which collects data from its cluster members and forwards it to the base station (BS) via other CHs. Clustered WSNs often suffer from the hot spot problem where CHs closer to the BS die much early because of high energy consumption contributed by the data forwarding load. Such death of nodes results coverage holes in the network very early. In most applications of WSNs, coverage preservation of the target area is a primary measure of quality of service. Considering the energy limitation of sensors, most of the clustering algorithms designed for WSNs focus on energy efficiency while ignoring the coverage requirement. In this paper, we propose a distributed clustering algorithm that uses fuzzy logic to establish a trade‐off between the energy efficiency and coverage requirement. This algorithm considers both energy and coverage parameters during cluster formation to maximize the coverage preservation of target area. Further, to deal with hot spot problem, it forms unequal sized clusters such that more CHs are available closer to BS to share the high data forwarding load. The performance of the proposed clustering algorithm is compared with some of the well‐known existing algorithms under different network scenarios. The simulation results validate the superiority of our algorithm in network lifetime, coverage preservation, and energy efficiency.     

UEEDA: Uniform and energy‐efficient deployment algorithm for wireless sensor networks

Efficient and accurate sensor deployment is a critical requirement for the development of wireless sensor networks. Recently, distributed energy‐efficient self‐deployment algorithms, such as the intelligent deployment and clustering algorithm (IDCA) and the distributed self‐spreading algorithm (DSSA), have been proposed to offer almost uniform distribution for sensor deployment by employing a synergistic combination of cluster structuring and a peer‐to‐peer deployment scheme. However, both DSSA and IDCA suffer from unnecessary movements that have arisen from an inappropriate design in partial force. To improve the performance of self‐deployment algorithms, a uniform and energy‐efficient deployment algorithm (UEEDA) is proposed in this paper. Simulation results demonstrate that the proposed UEEDA outperforms both DSSA and IDCA in terms of uniformity and algorithm convergence speed. Copyright © 2007 John Wiley & Sons, Ltd.     

基于二次质心的无线传感器网络定位算法

节点的定位是无线传感器网络中的一种重要技术。提出了一种新的无线传感器网络定位算法——基于二次质心算法的定位算法,与以往的基于三边测量的加权质心方法不同,该算法改进了对未知节点位置的估算方法,一定程度上避免了因多次估算质心而产生的累积误差,提高了定位精度。仿真表明,该算法的定位精度较之前的三边测量方法提高了约19%。     

Density‐adaptive network reprogramming protocol for wireless sensor networks

Network reprogramming is a process used to update program codes of sensor nodes that are already deployed. To deal with potentially unstable link conditions of wireless sensor networks, the epidemic approach based on 3‐way advertise‐request‐data handshaking is preferred for network reprogramming. Existing epidemic protocols, however, require a long completion period and high traffic overhead in high‐density networks, mainly due to the hidden terminal problem. In this paper, we address this problem by dynamically adjusting the frequency of advertisement messages in terms of the density of sensor nodes, which is the number of sensor nodes in a certain area. We compare the performance of the proposed scheme, called DANP (Density‐Adaptive Network Reprogramming Protocol), with a well‐known epidemic protocol, Deluge. Simulations indicate that, in the grid topologies, DANP outperforms Deluge by about 30% in terms of the completion time and about 50% in terms of the traffic overhead. Significant performance gain is observed in random topologies as well. The performance of DANP is further confirmed via measurements in an experimental test bed. Copyright © 2009 John Wiley & Sons, Ltd.     

Modulated relay based stable election protocol for large-scale wireless sensor networks

Internet of things (IoT) applications based on wireless sensor networks (WSNs) have recently gained vast momentum. These applications vary from health care, smart cities, and military applications to environmental monitoring and disaster prevention. As a result, energy consumption and network lifetime have become the most critical research area of WSNs. Through energy-efficient routing protocols, it is possible to reduce energy consumption and extend the network lifetime for WSNs. Using hybrid routing protocols that incorporate multiple transmission methods is an effective way to improve network performance. This paper proposes modulated R-SEP (MR-SEP) for large-scale WSN-based IoT applications. MR-SEP is based on the well-known stable election protocol (SEP). MR-SEP defines three initial energy levels for the nodes to improve the network energy distribution and establishes multi-hop communication between the cluster heads (CHs) and the base station (BS) through relay nodes (RNs) to reduce the energy consumption of the nodes to reach the BS. In addition, MR-SEP reduces the replacement frequency of CHs, which helps increase network lifetime and decrease power consumption. Simulation results show that MR-SEP outperforms SEP, LEACH, and DEEC protocols by 70.2%, 71.58%, and 74.3%, respectively, in terms of lifetime and by 86.53%, 86.68%, and 86.93% in terms of throughput.     

基于无线传感器网络的K均值算法研究

传统无线传感网一般由大量密集的传感器节点构成,存在节点计算能力、能源和带宽都非常有限的缺点,为了有效节能、延长网络寿命,介绍了基于聚类的K均值算法.该算法通过生成的簇头节点散播到网络的各个区域中,减少了每个区域内通信的能耗和可能会出现的一般节点过早死亡的情况,从而避免了网络对该区城提早失去监控.实验证明,该算法对各节点...     

A low‐cost node capture attack algorithm for wireless sensor networks

Wireless sensor networks (WSNs) have many micro devices that are easy to capture. In node capture attacks, the adversary physically captures sensors and extracts all information including key information from their memories, trying to compromise the system's security protection. However, the robust and random nature of many WSN security designs makes it difficult to compromise the system even with the capture of some sensors. In this paper, we approach WSN security from an adversarial point of view and investigate low‐cost and efficient algorithms to identify sensors in a WSN in the shortest time with the lowest cost. Instead of randomly capturing sensors, an intelligent attacker should choose the next target based on the known topology so far. Because the identification of such has been proven to be NP‐hard (non‐deterministic polynomial‐time hard), we propose to transform the problem into a set covering problem and develop a greedy minimum cost node capture attack algorithm (MCA) to lower cost of attack. Extensive simulations have been implemented to evaluate the performance of MCA and to compare it with several related schemes. It is shown that MCA lowers the cost of compromising WSNs by 16%. Copyright © 2015 John Wiley & Sons, Ltd.