Optimal load balanced clustering in homogeneous wireless sensor networks

Balancing the load among sensor nodes is a major challenge for the long run operation of wireless sensor networks. When a sensor node becomes overloaded, the likelihood of higher latency, energy loss, and congestion becomes high. In this paper, we propose an optimal load balanced clustering for hierarchical cluster‐based wireless sensor networks. We formulate the network design problem as mixed‐integer linear programming. Our contribution is 3‐fold: First, we propose an energy aware cluster head selection model for optimal cluster head selection. Then we propose a delay and energy‐aware routing model for optimal inter‐cluster communication. Finally, we propose an equal traffic for energy efficient clustering for optimal load balanced clustering. We consider the worst case scenario, where all nodes have the same capability and where there are no ways to use mobile sinks or add some powerful nodes as gateways. Thus, our models perform load balancing and maximize network lifetime with no need for special node capabilities such as mobility or heterogeneity or pre‐deployment, which would greatly simplify the problem. We show that the proposed models not only increase network lifetime but also minimize latency between sensor nodes. Numerical results show that energy consumption can be effectively balanced among sensor nodes, and stability period can be greatly extended using our models.     

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.     

Improving lifetime and network connections of 3D wireless sensor networks based on fuzzy clustering and particle swarm optimization

3D wireless sensor network (3D-WSN) has attracted significant interests in recent years due to its applications in various disciplinary fields such as target detection, object tracking, and security surveillance. An important problem in 3D WSN is the sensor energy optimization which determines a topology of sensors to prolong the network lifetime and energy expenditure. The existing methods for dealing with this matter namely low energy adaptive clustering hierarchy, LEACH-centralized, K-Means, single hop clustering and energy efficient protocol, hybrid-LEACH and fuzzy C-means organize the networks into clusters where non-cluster head nodes mainly carry out sensing tasks and send the information to the cluster head, while cluster head collect data from other nodes and send to the base station (BS). Although these algorithms reduce the total energy consumption of the network, they also create a large number of network disconnect which refers to the number of sensors that cannot connect to its cluster head and the number of cluster heads that cannot connect to the BS. In this paper, we propose a method based on fuzzy clustering and particle swarm optimization to handle this problem. Experimental validation on real 3D datasets indicates that the proposed method is better than the existing methods.     

A collaborative node management scheme for energy-efficient monitoring in wireless multimedia sensor networks

Clustering in sensor networks provides energy conservation, network scalability, topology stability, reducing overhead and also allows data aggregation and cooperation in data sensing and processing. Wireless Multimedia Sensor Networks are characterized for directional sensing, the Field of View (FoV), in contrast to scalar sensors in which the sensing area usually is more uniform. In this paper, we first group multimedia sensor nodes in clusters with a novel cluster formation approach that associates nodes based on their common sensing area. The proposed cluster formation algorithm, called Multi-Cluster Membership (MCM), establishes clusters with nodes that their FoVs overlap at least in a minimum threshold area. The name of Multi-Cluster Membership comes from the fact that a node may belong to multiple clusters, if its FoV intersects more than one cluster-head and satisfies the threshold area. Comparing with Single-Cluster Membership (SCM) schemes, in which each node belongs to exactly one cluster, because of the capability of coordination between intersected clusters, MCM is more efficient in terms of energy conservation in sensing and processing subsystems at the cost of adding complexity in the node/cluster coordination. The main imposed difficulty by MCM, is the coordination of nodes and clusters for collaborative monitoring; SCMs usually assign tasks in a round-robin manner. Then, as second contribution, we define a node selection and scheduling algorithm for monitoring the environment that introduces intra and inter-cluster coordination and collaboration, showing how the network lifetime is prolonged with high lifetime prolongation factors particularly in dense deployments.     

An energy-aware routing protocol for wireless sensor network based on genetic algorithm

Energy saving and effective utilization are an essential issue for wireless sensor network. Most previous cluster based routing protocols only care the relationship of cluster heads and sensor nodes but ignore the huge difference costs between them. In this paper, we present a routing protocol based on genetic algorithm for a middle layer oriented network in which the network consists of several stations that are responsible for receiving data and forwarding the data to the sink. The amount of stations should be not too many and not too few. Both cases will cause either too much construction cost or extra transmission energy consumption. We implement five methods to compare the performance and test the stability of our presented methods. Experimental results demonstrate that our proposed scheme reduces the amount of stations by 36.8 and 20% compared with FF and HL in 100-node network. Furthermore, three methods are introduced to improve our proposed scheme for effective cope with the expansion of network scale problem.     

Cluster head election techniques for coverage preservation in wireless sensor networks

Coverage preservation is one of the basic QoS requirements of wireless sensor networks, yet this problem has not been sufficiently explored in the context of cluster-based sensor networks. Specifically, it is not known how to select the best candidates for the cluster head roles in applications that require complete coverage of the monitored area over long periods of time. In this paper, we take a unique look at the cluster head election problem, specifically concentrating on applications where the maintenance of full network coverage is the main requirement. Our approach for cluster-based network organization is based on a set of coverage-aware cost metrics that favor nodes deployed in densely populated network areas as better candidates for cluster head nodes, active sensor nodes and routers. Compared with using traditional energy-based selection methods, using coverage-aware selection of cluster head nodes, active sensor nodes and routers in a clustered sensor network increases the time during which full coverage of the monitored area can be maintained anywhere from 25% to 4.5×, depending on the application scenario.     

无线传感器网络泊松分布最优簇首数目的研究

在无线传感器网络分簇路由协议中,簇首节点通常会消耗更多的能量。簇首节点过多或过少都会增加网络能耗。因此,为延长整个网络的生存周期,需要合理设置簇首节点的个数。在节点服从二维泊松分布的基础上,为最小化网络能耗,提出了一种求解网络最优簇首数目的方法。网络仿真表明,簇首个数为最优簇首数时,整个网络能量消耗最低。将求解的最优簇首数用于LEACH协议中,不仅延长了第一个节点的死亡时间,也提高了整个网络的生存周期。     

面向目标跟踪的无线传感器网络动态分簇

在考虑节点剩余能量和节点探测信号强度的基础上,提出了一种面向目标跟踪的无线传感器网络动态分簇方法。由于在簇首选择时考虑了节点的剩余能量,因此可以有效地避免簇首节点能量不足导致的跟踪失败。方法既能保证各个节点均衡地担任簇首节点,又能避免簇首切换过于频繁,因此可以延长网络的使用寿命。计算机仿真结果表明,在满足簇首节点能量需求和簇内节点大部分能观测到目标的情况下,应该取尽量小的能量阈值和尽量大的距离阈值。     

Clustering routing protocol based on improved PSO algorithm in WSN

Aiming at the problem that the location distribution of cluster head nodes filtered by wireless sensor network clustering routing protocol was unbalanced and the data transmission path of forwarding nodes was unreasonable,which would increase the energy consumption of nodes and shorten the network life cycle,a clustering routing protocol based on improved particle swarm optimization algorithm was proposed.In the process of cluster head election,a new fitness function was established by defining the energy factor and position equalization factor of the node,the better candidate cluster head node was evaluated and selected,the position update speed of the candidate cluster head nodes was adjusted by the optimized update learning factor,the local search and speeded up the convergence of the global search was expanded.According to the distance between the forwarding node and the base station,the single-hop or multi-hop transmission mode was adopted,and a multi-hop method was designed based on the minimum spanning tree to select an optimal multi-hop path for the data transmission of the forwarding node.Simulation results show that the clustering routing protocol based on improved particle swarm optimization algorithm can elect cluster head nodes and forwarding nodes with more balanced energy and location,which shortened the communication distance of the network.The energy consumption of nodes is lower and more balanced,effectively extending the network life cycle.     

A local dynamic cluster self-organization algorithm in wireless sensor networks for rainfall monitoring

Wireless Sensor Networks for Rainfall Monitoring (RM-WSNs) is a sensor network for the large-scale regional and moving rainfall monitoring, which could be controlled deployment. Delivery delay and cross-cluster calculation leads to information inaccuracy by the existing dynamic collaborative self-organization algorithm in WSNs. In this letter, a Local Dynamic Cluster Self-organization algorithm (LDCS) is proposed for the large-scale regional and moving target monitoring in RM-WSNs. The algorithm utilizes the resource-rich node in WSNs as the cluster head, which processes target information obtained by sensor nodes in cluster. The cluster head shifts with the target moving in chance and re-groups a new cluster. The target information acquisition is limited in the dynamic cluster, which can reduce information across-clusters transfer delay and improve the real-time of information acquisition. The simulation results show that, LDCS can not only relieve the problem of “too frequent leader switches” in IDSQ, also make full use of the history monitoring information of target and continuous monitoring of sensor nodes that failed in DCS.     

Heuristic algorithm for allocation of wavelength convertible nodesand routing coordination in all-optical networks

It is true that in all-optical networks, network performance can be improved by wavelength conversion. However, the switching node with wavelength conversion capability is still costly, and the number of such nodes should he limited in the network. In this paper, a performance optimization problem is treated in all-optical networks. We propose a heuristic algorithm to minimize an overall blocking probability by properly allocating a limited number of nodes with wavelength conversion capability. The routing strategy is also considered suitable to the case where the number of wavelength convertible nodes are limited. We validate the minimization level of our heuristic algorithm through numerical examples, and show that our algorithm can properly allocate nodes with conversion and decide routes for performance optimization     

Channel allocation algorithms for WLANs using distributed optimization

The performance of a wireless local area network (WLAN) depends on the channel assignments among interfering access points (APs). Due to the limited number of non-overlapping channels, severe interference scenarios may arise if no appropriated spectrum planning is employed. In our study we focus on WLANs scenarios where APs may belong to different administrative domains, which is actually a very common situation in dense urban deployments. In such cases the use of centralized algorithms is not feasible and the already proposed distributed methods does not guarantee optimal channel assignment. In this paper, therefore, we formalize the channel allocation as a distributed constraint optimization problem (DCOP) and propose a new distributed channel assignment algorithm named DCAA-O, which can find the optimal solution to the channel assignment problem for a group of APs. A suboptimal strategy denoted DCAA-S is also investigated, which aims at reducing the number of control messages to be exchanged between APs, while still achieving a suboptimal solution which is very close to the optimal one. The simulation results show that the proposed algorithms are able to outperform the best known techniques both in terms of solution quality and number of exchanged messages.     

Low-Energy Adaptive Unequal Clustering Protocol Using Fuzzy c-Means in Wireless Sensor Networks

Clustering technique in wireless sensor networks incorporate proper utilization of the limited energy resources of the deployed sensor nodes with the highest residual energy that can be used to gather data and send the information. However, the problem of unbalanced energy consumption exists in a particular cluster node in the network. Some more powerful nodes act as cluster head to control sensor network operation when the network is organized into heterogeneous clusters. It is important to assume that energy consumption of these cluster head nodes is balanced. Often the network is organized into clusters of equal size where cluster head nodes bear unequal loads. Instead in this paper, we proposed a new protocol low-energy adaptive unequal clustering protocol using Fuzzy c-means in wireless sensor networks (LAUCF), an unequal clustering size model for the organization of network based on Fuzzy c-means (FCM) clustering algorithm, which can lead to more uniform energy dissipation among the cluster head nodes, thus increasing network lifetime. A heuristic comparison between our proposed protocol LAUCF and other different energy-aware protocol including low energy adaptive clustering hierarchy (LEACH) has been carried out. Simulation result shows that our proposed heterogeneous clustering approach using FCM protocol is more effective in prolonging the network lifetime compared with LEACH and other protocol for long run.     

基于Voronoi的无线传感器网络覆盖控制优化策略

针对无线传感器网络运行状态中存在覆盖空洞的问题,提出了一种基于Voronoi有效覆盖区域的空洞侦测修复策略。该策略以满足一定网络区域覆盖质量为前提,在空洞区域内合理增加工作节点以提高网络覆盖率为优化目标,采用几何图形向量方法对节点感知范围和Voronoi多边形的位置特性进行理论分析,力求较准确地计算出空洞面积,找寻最佳空洞修复位置,部署较少的工作节点保证整个网络的连通性。仿真结果表明,该策略能有效地减少网络总节点个数和感知重叠区域,控制网络中冗余节点的存在,同时其收敛速度较快,能够获得比现有算法更高的目标区域空洞修复率,实现网络覆盖控制优化.     

Development of an indoor corridor localization system based on a WCL method with corridor area selection and distance compensation

In this paper, a received signal strength indicator (RSSI)-based weighted centroid localization (WCL) method for indoor corridor scenarios is developed and tested. The contribution of the proposed system is that, to scope the area of an unknown target position, the possible corridor area where the target node should be located is automatically selected, and only reference nodes deployed in such an area are applied for position estimation. Additionally, to improve the estimation precision, distance values converted from measured RSSIs and used for the WCL are also compensated to alleviate the RSSI variation problem caused by physical environments. Therefore, our methods can save a number of reference nodes to be used, while localization accuracy can also be achieved. Experiments in the corridor environment using a 2.4 GHz IEEE 802.15.4 wireless sensor network with four reference nodes deployed in the test field dimension of 22 m × 9.3 m have been performed. Experimental results demonstrate that the proposed method, which requires only two reference nodes, outperforms the original WCL method with four reference nodes by 53.053%, as indicated by average localization errors. The results also reveal that the proposed method provides error distances lower than the WCL method for all test target positions, while all estimated positions fall within the corridor areas by 100%.     

The traveling miser problem

Various monitoring and performance evaluation tools generate considerable amount of low priority traffic. This information is not always needed in real time and often can be delayed by the network without hurting functionality. This paper proposes a new framework to handle this low priority, but resource consuming traffic in such a way that it incurs a minimal interference with the higher priority traffic. Consequently, this improves the network goodput. The key idea is allowing the network nodes to delay data by locally storing it. This can be done, for example, in the Active Network paradigm. In this paper we show that such a model can improve the network's goodput dramatically even if a very simple scheduling algorithm for intermediate parking is used. The parking imposes additional load on the intermediate nodes. To obtain minimal cost schedules we define an optimization problem called the traveling miser problem. We concentrate on the on-line version of the problem for a predefined route, and develop a number of enhanced scheduling strategies. We study their characteristics under different assumptions on the environment through a rigorous simulation study. We prove that if only one link can be congested, then our scheduling algorithm is O(log/sub 2/B) competitive, where B is congestion time, and is 3-competitive, if additional signaling is allowed.     

Distance and clustering‐based energy‐efficient pseudonyms changing strategy over road network

To improve the fairness, the energy consumption changing pseudonyms needs to be taken into account. Existing works focus on changing velocity‐based pseudonyms changing strategy and short changes interval with limited coverage, but due to similar velocity and short changes, internal attacker guesses easily known communication and location information due to location information of vehicle on tracking, which may expose adversary private information, and frequently, pseudonyms changing occurs due to movement of vehicles' similar velocity and short coverage, which may cause serious attack of vehicle. To overcome this problem, distance and cluster can be performed. In this work, we proposed distance and cluster‐based energy pseudonyms changing method for road network. We proposed distance and energy‐based clustering routing service over road network, the cluster head elected to depend on random number of distance and energy to change pseudonyms of vehicles. An each interval to be establish cluster head vehicle deployed while selects the operation mode and informs the cluster members of the selected mode through beacon signal. The cluster head vehicle node performs the pseudonyms changing based on the predicted distance and energy of the cluster member to use clustering optimization. The data of whole network send to report server through these nodes while near the RSU, and the vehicles in this area will use less energy to change the pseudonyms. The simulation results show that the proposed method enhances pseudonyms changing strategy less consumption and delays sufficient privacy level each vehicle also our method has outperform compare with existing methods than we use Sumo simulation and Matlab tools to verify our proposed method. Our proposed method outperformed in terms of pseudonym changing energy efficiency to careful attention during the cluster formation process, stable and balanced clusters that prolong the network lifetime, increases distances to more CH vehicles connectivity to makes clustering group and changing their pseudonyms in terms of high level privacy and finally, CH nodes use Dijkstra's algorithm use MST among the vehicles nodes depend on existing road networks to follow shortest path selection roads in terms of high connectivity probability of CH and stable structure of the network decreases the topology changes and thus,the clustering overhead is reduced.     

分布式无线感知网络节点部署算法研究

在无线感知网络节点部署中,目标区域的覆盖率大小对信号检测的效果具有重要的意义,通过智能优化算法来提高区域覆盖率已成为当前无线感知网络节点部署领域的研究热点之一。为了提高分布式无线感知网络对目标区域内的重点区域的覆盖率和减少冗余感知节点的投放,论文提出了一种分布式无线感知网络节点部署算法。该算法首先通过随机部署满足连通性的少量感知节点后初次工作来定位和估计出重点区域,然后将估计出的重点区域融入到粒子群算法的目标函数和粒子更新方程中实现对感知节点的重新部署,从而更好的优化了重点区域的覆盖率和减少冗余感知节点数量。仿真结果表明,与标准粒子群算法及其他优化算法相比,论文所研究的算法有更高的覆盖率和更低的迭代次数。      

Transactions Papers - Device Placement for Heterogeneous Wireless Sensor Networks: Minimum Cost with Lifetime Constraints

Device placement is a fundamental factor in determining the coverage, connectivity, cost and lifetime of a wireless sensor network (WSN). In this paper, we explore the problem of relay node placement in heterogeneous WSN. We formulate a generalized node placement optimization problem aimed at minimizing the network cost with constraints on lifetime and connectivity. Depending on the constraints, two representative scenarios of this problem are described. We characterize the first problem, where relay nodes are not energy constrained, as a minimum set covering problem. We further consider a more challenging scenario, where all nodes are energy limited. As an optimal solution to this problem is difficult to obtain, a two-phase approach is proposed, in which locally optimal design decisions are taken. The placement of the first phase relay nodes (FPRN), which are directly connected to sensor nodes (SN), is modeled as a minimum set covering problem. To ensure the relaying of the traffic from the FPRN to the base station, three heuristic schemes are proposed to place the second phase relay nodes (SPRN). Furthermore, a lower bound on the minimum number of SPRN required for connectivity is provided. The efficiency of our proposals is investigated by numerical examples.     

改进粒子群算法优化低功耗自适应集簇分层的路由算法

针对LEA低C功H算法的簇头分布不合理、网络能耗不均的问题,在耗自适应集簇分层路由算法。首先将分割线的选择LE问A题CH转协议的基础上,提出了一种改进粒子群算法优化化成带约束的非线性优化问题,然后利用粒子群算法求解,并针对粒子群算法的不足进行相应的改进,最后采用仿真实验测试算法的性能。仿真结果表明,相对于其它改进LEACH路由算法,本文算法有效提高了网络的能量利用率,能够实现节点之间的能耗均衡,使无线传感器的网络生存时间得到延长。