FAMACROW: Fuzzy and ant colony optimization based combined mac,routing, and unequal clustering cross-layer protocol for wireless sensor networks

This paper presents Fuzzy and Ant Colony Optimization Based Combined MAC, Routing, and Unequal Clustering Cross-Layer Protocol for Wireless Sensor Networks (FAMACROW) consisting of several nodes that send sensed data to a Master Station. FAMACROW incorporates cluster head selection, clustering, and inter-cluster routing protocols. FAMACROW uses fuzzy logic with residual energy, number of neighboring nodes, and quality of communication link as input variables for cluster head selection. To avoid hot spots problem, FAMACROW uses an unequal clustering mechanism with clusters closer to MS having smaller sizes than those far from it. FAMACROW uses Ant Colony Optimization based technique for reliable and energy-efficient inter-cluster multi-hop routing from cluster heads to MS. The inter-cluster routing protocol decides relay node considering its: (i) distance from current cluster head and that from MS (for energy-efficient inter-cluster communication), (ii) residual energy (for energy distribution across the network), (iii) queue length (for congestion control), (iv) delivery likelihood (for reliable communication). A comparative analysis of FAMACROW with Unequal Cluster Based Routing [33], Unequal Layered Clustering Approach [43], Energy Aware Unequal Clustering using Fuzzy logic [37] and Improved Fuzzy Unequal Clustering [35] shows that FAMACROW is 41% more energy-efficient, has 75–88% more network lifetime and sends 82% more packets compared to Improved Fuzzy Unequal Clustering protocol.     

An energy-driven unequal clustering protocol for heterogeneous wireless sensor networks

Due to the limitation of energy resources, energy efficiency is a key issue in wireless sensor networks (WSNs). Clustering is proved to be an important way to realize hierarchical topology control, which can improve the scalability and prolong the lifetime of wireless sensor networks. In this paper, an energy-driven unequal clustering protocol (EDUC) for heterogeneous wireless sensor networks is proposed. EDUC includes an unequal clustering algorithm and an energy-driven adaptive cluster head rotation method. The unequal size of clusters can balance the energy consumption among clusters, and the energy-driven cluster head rotation method can achieve the balance of energy consumption among nodes within a cluster, which reduces the waste of energy. Simulation experiments show that EDUC balances the energy consumption well among the cluster heads and prolongs the network lifetime.     

一种分布式能量有效的无线传感器网络分簇路由协议

提出了一种分布式能量有效的无线传感器网络分簇路由协议DEEC(Distributed Energy-efficient Clustering Algorithm)。该协议采用基于时间的簇首选择算法,广播时间取决于自身剩余能量和其邻居节点的剩余能量。在数据传输阶段,采用簇内单跳与簇间多跳相结合的方式,引入权值函数优化簇首中继节点的选择。仿真实验结果表明,与LEACH,PEGASIS协议相比,DEEC能够有效地节约单个节点能量、均衡网络能耗、延长网络生存周期。     

一种能量均衡的无线传感器网络分簇算法*

为了延长网络的生存时间,提出了一种能量均衡的无线传感器网络分簇算法（EBCA）,该算法优先选择剩余能量较多的节点作为簇首,以平衡节点的能量消耗。仿真实验结果表明：无论同构网还是异构网,该算法都能显著地推迟网络第一个节点的死亡时间,其性能明显优于LEACH算法。     

一种能量高效的无线传感器网络分簇路由算法

无线传感器网络中节点的能量有限,提高能量的有效性便成为无线传感器网络路由协议设计的首要目标。设计了一种能量高效的分簇路由算法,它提出让候选节点在一定的覆盖范围内以剩余能量为标准来竞选簇头,以使簇头分布均匀;处于簇类交界的节点则根据能量和距离来选择归属的簇头,以平衡网络负载;新算法还采用多跳的簇间通信方式来降低大部分簇头节点的通信负载。仿真结果表明:新算法能够有效降低网络能耗,延长网络生存时间。     

能耗均衡的自供能无线传感器网络分簇路由算法

当前的采能技术已经能够让传感器节点自动从环境中获得适量的能量补给,针对现有自供能无线传感器网络分簇路由算法中未考虑位于不同地理区域的节点所获补给能量大小的不同,而导致能量补给少区域的簇头数过少、簇规模过大、全网能耗不均衡等问题,本文提出了一种能耗均衡的自供能无线传感器网络分簇路由算法-EBCS（energy balanced clustering with self-energized）,该算法结合实际能量补给场景对簇头选举机制进行了改进,并采用了一种自适应式簇间通信机制,充分保存与利用补给能量。理论和仿真实验表明：EBCS算法能够较好维持预设的簇头比例,在网络平均剩余能量、当前可用节点数量等性能方面优于另外两种现有算法。     

无线传感器网络中基于分层的非均衡分簇算法

降低网络能量消耗、延长网络寿命是无线传感器网络设计的重要目标,分簇是实现该目标的主要方法之一。针对矩形传感器网络,提出一种基于分层的非均衡分簇算法。算法根据节点的能量消耗情况计算出了每层的宽度,可以有效实现网络的能耗均衡。将非均衡分簇算法与LEACH协议进行了比较,仿真实验表明:基于分层的非均衡分簇算法可以更好地实现能耗均衡,能有效延长网络的寿命。     

无线传感器网络分簇方法的优化

现有的分布式分簇算法都是针对节点分布均匀的传感器网络设计的,不适用于节点分布不均匀的传感器网络,簇负载均衡和降低能耗成了节点不均匀部署的传感器网络的一个重要挑战。针对节点分布不均匀网络提出了一种基于区域密度的分布式分簇算法（RDCA）,算法中节点测试其所在区域的节点密度,根据区域节点密度,调节其通信的覆盖范围,从而使成簇后簇负载达到平衡。仿真实验结果显示,在节点部署不均匀的传感器网络中,本算法与LEACH、HEED相比较,可以更好地实现簇的负载平衡,有效地提高成簇后网络的稳定周期。     

无线传感器网络中一种能量高效的分布式分簇算法

提出了一种适用于无线传感器网络的能量高效的分布式分簇算法（ＥＥＤＣ）,该算法使具有较高剩余能量及距离基站较近的节点有更大的机会成为簇头．理论分析表明该算法通信开销较小,而且有效地均衡了节点的能量消耗．为了确保ＥＥＤＣ 的正确性、完整性和可靠性,利用形式化方法———着色网对其关键属性进行建模和分析．仿真结果表明,ＥＥＤＣ 有效地延长了网络生命周期,提高了网络的能耗效率．     

无线传感器网络中能量有效的多层分簇算法*

针对无线传感器网络中每轮能耗和簇内节点负载不均衡问题,本文提出了一种能量有效的多层分簇算法(EEMLC)。基站在首轮按照各层的最优簇头数对整个网络逐层进行虚拟分区,在接着的轮次中,各个分区根据本轮节点的剩余能量来选取下一轮簇头,前者使得网络每轮总能耗最小并有效保证各轮的能耗均衡,后者确保分区内节点的负载平衡,从而最大限度地延长网络的生命周期。实验仿真结果表明,与LEACH算法相比,EEMLC算法的每轮能耗均衡性有了显著提高,网络的生命周期延长了11.3%。     

Secure Routing Protocol based on Multi-objective Ant-colony-optimization for wireless sensor networks

To solve the problem of achieving the maximum network security goal with lower energy consumption in wireless sensor networks, this paper proposes a Secure Routing Protocol based on Multi-objective Ant-colony-optimization (SRPMA) for wireless sensor networks. The ant colony algorithm is improved to be a multi-objective routing algorithm with considering the residual energy of nodes and the trust value of a rout path as two optimization objectives, in which a rout path is produced through the multi pheromone information and the multi heuristic information consisting of two objective functions. The node trust evaluation model is established by using an improved D-S evidence theory with confliction preprocessing to evaluate nodes trust degree. The multi-objective routing result is obtained by using the Pareto optimal solution mechanism by using the external archive method with a crowding distance criterion. The simulation results conducted with NS2 show that the proposed algorithm can achieve desired performance against the black hole attack in WSN routing.     

A novel differential evolution based clustering algorithm for wireless sensor networks

Clustering is an efficient topology control method which balances the traffic load of the sensor nodes and improves the overall scalability and the life time of the wireless sensor networks (WSNs). However, in a cluster based WSN, the cluster heads (CHs) consume more energy due to extra work load of receiving the sensed data, data aggregation and transmission of aggregated data to the base station. Moreover, improper formation of clusters can make some CHs overloaded with high number of sensor nodes. This overload may lead to quick death of the CHs and thus partitions the network and thereby degrade the overall performance of the WSN. It is worthwhile to note that the computational complexity of finding optimum cluster for a large scale WSN is very high by a brute force approach. In this paper, we propose a novel differential evolution (DE) based clustering algorithm for WSNs to prolong lifetime of the network by preventing faster death of the highly loaded CHs. We incorporate a local improvement phase to the traditional DE for faster convergence and better performance of our proposed algorithm. We perform extensive simulation of the proposed algorithm. The experimental results demonstrate the efficiency of the proposed algorithm.     

一种新颖的传感器网络聚类路由协议

提出了一种能量有效的基于聚类的传感器网络路由协议—EEHCA（an Energy-Efficient Hierarchical Clustering Algorithm for wireless sensor networks）。该协议通过最小化通信能量消耗并在所有节点之间实现能量消耗负载平衡的方式,达到了延长传感器网络生存时间的目的。协议提出了一种新颖的簇首确定机制,该机制可以避免感知区域内的节点进行频繁的簇首选举,从而节约了能量。为提高传感器网络的容错性能,引入了备用簇首的概念。在簇首与基站通信方面,采用多跳传输的方式进行,从而避免了距离基站较远的簇首进行长距离通信时所造成的能量过早耗尽的问题。仿真结果表明提出的协议拥有比LEACH和HEED协议更长的网络生存时间。     

基于TSK模糊系统的非均匀分簇算法在无线传感网中的应用

无线传感网作为一个重要的数据调度工具,是人类同自然交互的有效途径。然而,无线传感网中的传感器数量有限,同时还极易被无关因素影响。因此,研究提出一种基于区间二型TSK模糊系统和高效数据融合的非均匀分簇路由算法,分簇是为了最大程度地减小网络损耗,延长其生存时间,一般的分簇方法,可能会导致负载失衡,为解决这种热区现象,研究使用区间二型TSK模糊逻辑算法,进行非均匀分级分簇。同时引入高效数据融合技术,将调度过程划分为几个周期,利用时间间隙进行数据采集,并进行降维操作,进一步提升数据传输效率。研究在MATLAB平台,对该算法以及自适应分簇层次等其余四种算法进行对照分析实验,并将其分为200m×200m和1000m×1000m的监测范围,实验结果表明,在不同大小的监测区域中,研究使用算法的分簇效果、网络吞吐量以及节点损耗率指标,都明显优于其他算法。其中,在小范围区域内,其剩余能力均值比自适应分簇层次算法提升了49.7%;在大范围区域内,该算法的HND指标比自适应分簇层次算法提升了98.7%。因此,研究采用算法具有极佳的性能。     

A power- and coverage-aware clustering scheme for wireless sensor networks

A common and critical operation for wireless sensor networks is data gathering. The efficient clustering of a sensor network that can save energy and improve coverage efficiency is an important requirement for many upper layer network functions. This study concentrates on how to form clusters with high uniformity while prolonging the network lifetime. A novel clustering scheme named power- and coverage- aware clustering (PCC) is proposed, which can adaptively select cluster heads according to a hybrid of the nodes' residual energy and loyalty degree. Additionally, the PCC scheme is independent of node distribution or density, and it is free of node hardware limitations, such as self-locating capability and time synchronization. Experiment results show that the scheme performs well in terms of cluster size (and its standard deviation), number of nodes alive over time, total energy consumption, etc.     

无线传感器网络中一种能量均衡的分簇策略

以无线传感器网络中的能量消耗模型为基础,提出了一种能量均衡的无线传感器网络分簇路由协议EECHS(energy-effficient cluster-head selection)。该协议通过节点的剩余能量和节点距离基站的距离来调节其成为簇首的概率,并进一步调节簇的大小。仿真结果表明,与改进后的DCHS协议相比,该策略使网络的生命周期和稳定周期分别提高了31%和45%以上。     

Distributed anomaly detection for industrial wireless sensor networks based on fuzzy data modelling

Modern infrastructure increasingly depends on large computerized systems for their reliable operation. Supervisory Control and Data Acquisition (SCADA) systems are being deployed to monitor and control large scale distributed infrastructures (e.g. power plants, water distribution systems). A recent trend is to incorporate Wireless Sensor Networks (WSNs) to sense and gather data. However, due to the broadcast nature of the network and inherent limitations in the sensor nodes themselves, they are vulnerable to different types of security attacks. Given the critical aspects of the underlying infrastructure it is an extremely important research challenge to provide effective methods to detect malicious activities on these networks. This paper proposes a robust and scalable mechanism that aims to detect malicious anomalies accurately and efficiently using distributed in-network processing in a hierarchical framework. Unsupervised data partitioning is performed distributively adapting fuzzy c-means clustering in an incremental model. Non-parametric and non-probabilistic anomaly detection is performed through fuzzy membership evaluations and thresholds on observed inter-cluster distances. Robust thresholds are determined adaptively using second order statistical knowledge at each evaluation stage. Extensive experiments were performed and the results demonstrate that the proposed framework achieves high detection accuracy compared to existing data clustering approaches with more than 96% less communication overheads opposed to a centralized approach.     

一种新的无线传感器网络分簇模型

从工业现场应用的角度对无线传感器网络进行研究,提出了一种新的双簇头分级模型。该模型在单簇头模型的基础上增加了一个冗余簇头节点,在簇头节点电池耗尽或出现故障之时,冗余簇头节点能够实时切换成簇头节点以维持簇稳定工作。介绍了双簇头分级模型的工作原理、覆盖范围和能耗管理,并且对该模型的性能进行了实验仿真,实验结论证明双簇头分级模型比之单簇头分级模型有更好的稳定性和安全性,以及长的生存时间而更适合应用于工业现场。     

无线传感器网络中一种有效的分布式簇划分算法

提出了一种快速有效的分布式簇划分算法，为每个节点设定一个初始时间，最先到期的节点成为簇头。考虑到簇头选举的合理性，时间衰减与节点连通度相关，并辅以随机化的方法消除时间同步对算法的影响。通过仿真验证该簇划分算法的有效性，并定量分析了通信半径与平均簇头个数的关系。     

无线传感器网络动态重传算法

无线传感器网络路由协议通过点到点的重传来提高数据传输的可靠性,其重传机制没有考虑不同业务数据的可靠性需求差异,统一设定一个静态的最大重传次数。本文提出了一种动态重传算法,为每种业务分别根据其可靠性需求动态设定最大重传次数。对于较低可靠性需求的业务,相比于传统重传机制减少了重传次数。仿真表明动态重传算法能有效降低网络能耗。