Energy efficient path selection for mobile sink and data gathering in wireless sensor networks

Mobile sink (MS) has drawn significant attention for solving hot spot problem (also known as energy hole problem) that results from multihop data collection using static sink in wireless sensor networks (WSNs). MS is regarded as a potential solution towards this problem as it significantly reduces energy consumption of the sensor nodes and thus enhances network lifetime. In this paper, we first propose an algorithm for designing efficient trajectory for MS, based on rendezvous points (RPs). We next propose another algorithm for the same problem which considers delay bound path formation of the MS. Both the algorithms use k-means clustering and a weight function by considering several network parameters for efficient selection of the RPs by ensuring the coverage of the entire network. We also propose an MS scheduling technique for effective data gathering. The effectiveness of the proposed algorithms is demonstrated through rigorous simulations and comparisons with some of the existing algorithms over several performance metrics.     

Distributed trajectory design for data gathering using mobile sink in wireless sensor networks

Several studies have demonstrated the benefits of using a mobile sink (MS) to reduce energy consumption resulting from multi-hop data collection using a static sink in wireless sensor networks (WSNs). However, using MS may increase data delivery latency as it needs to visit each sensor node in the network to collect data. This is a critical issue in delay-sensitive applications where all sensed data must be gathered within a given time constraint. In this paper, we propose a distributed data gathering protocol utilizing MS for WSNs. The proposed protocol designs a trajectory for the MS, which minimizes energy consumption and delay. Our protocol operates in four main phases: data sensing, rendezvous point (RP) selection, trajectory design, and data gathering. In data sensing, a number of deployed sensor nodes keep sensing the target field for a specific period of time to capture events. Then, using a cluster-based RP selection algorithm, some sensor nodes are selected to become RPs based on local information. The selected RPs are then used to determine a trajectory for the MS. To do so, we propose three trajectory design algorithms that support different types of applications, namely reduced energy path (REP), reduced delay path (RDP), and delay bound path (DBP). The MS moves through the constructed path to accomplish its data gathering according to an effective scheduling technique that is introduced in this work. We validate the proposed protocol via extensive simulations over several metrics such as energy, delay, and time complexity.     

Efficient Approach for Maximizing Lifespan in Wireless Sensor Networks by Using Mobile Sinks

Recently, sink mobility has been shown to be highly beneficial in improving network lifetime in wireless sensor networks (WSNs). Numerous studies have exploited mobile sinks (MSs) to collect sensed data in order to improve energy efficiency and reduce WSN operational costs. However, there have been few studies on the effectiveness of MS operation on WSN closed operating cycles. Therefore, it is important to investigate how data is collected and how to plan the trajectory of the MS in order to gather data in time, reduce energy consumption, and improve WSN network lifetime. In this study, we combine two methods, the cluster‐head election algorithm and the MS trajectory optimization algorithm, to propose the optimal MS movement strategy. This study aims to provide a closed operating cycle for WSNs, by which the energy consumption and running time of a WSN is minimized during the cluster election and data gathering periods. Furthermore, our flexible MS movement scenarios achieve both a long network lifetime and an optimal MS schedule. The simulation results demonstrate that our proposed algorithm achieves better performance than other well‐known algorithms.     

蚁群优化和能量有效的Ad Hoc路由协议

为了延长Ad Hoc网络的生存周期,提出了一种基于蚁群优化和能量有效的Ad Hoc网络多路径动态路由算法ACOERA。该算法根据路径的有效能量率进行路由选择,路径建立后通过蚁群优化算法动态收集路径信息,并对路由表进行更新。仿真结果表明,该算法能有效延长网络生存时间,增强通信网络的自适应能力。     

Betweenness centrality based connectivity aware routing algorithm for prolonging network lifetime in wireless sensor networks

Network lifetime is the key design parameter for wireless sensor network protocols. In recent years, based on energy efficient routing techniques numerous methods have been proposed for enhancing network lifetime. These methods have mainly considered residual energy, number of hops and communication cost as route selection metrics. This paper introduces a method for further improvement in the network lifetime by considering network connectivity along with energy efficiency for the selection of data transmission routes. The network lifetime is enhanced by preserving highly connected nodes at initial rounds of data communication to ensure network connectivity during later rounds. Bassed on the above mentioned concept, a connectivity aware routing algorithm: CARA has been proposed. In the proposed algorithm, connectivity factor of a node is calculated on the basis of Betweenness centrality of a node and energy efficient routes are found by using fuzzy logic and ant colony optimization. The simulation results show that the proposed algorithm CARA performs better than other related state-of-the-art energy efficient routing algorithms viz. FML, EEABR and FACOR in terms of network lifetime, connectivity, energy dissipation, load balancing and packet delivery ratio.     

Virtual region based data gathering method with mobile sink for sensor networks

To solve the hotspot problem in wireless sensor networks, a type of virtual region based data gathering method (VRDG) with one mobile sink is proposed. Network is divided into several virtual regions consisting of three or less data gathering unit. One or more leaders are selected in each region according to their residual energy as well as the distance to all of the neighbors. Only the leaders upload data to sink in data gathering phase that effectively reduce energy consumption and end-to-end delay. Moreover, the “maximum step distance” could be calculated out by nodes to find out the best transmission path to the leader which further balance energy consumption of the whole network. Simulation results show that VRDG is energy efficient in comparing with MSE, SEP and LEACH. It also does well in prolonging network lifetime as well as in enhancing the efficiency of data collection.

     

A Tabu search based routing algorithm for wireless sensor networks

In this paper, a Tabu search based routing algorithm is proposed to efficiently determine an optimal path from a source to a destination in wireless sensor networks (WSNs). There have been several methods proposed for routing algorithms in wireless sensor networks. In this paper, the Tabu search method is exploited for routing in WSNs from a new point of view. In this algorithm (TSRA), a new move and neighborhood search method is designed to integrate energy consumption and hop counts into routing choice. The proposed algorithm is compared with some of the ant colony optimization based routing algorithms, such as traditional ant colony algorithm, ant colony optimization-based location-aware routing for wireless sensor networks, and energy and path aware ant colony algorithm for routing of wireless sensor networks, in term of routing cost, energy consumption and network lifetime. Simulation results, for various random generated networks, demonstrate that the TSRA, obtains more balanced transmission among the node, reduces the energy consumption and cost of the routing, and extends the network lifetime.     

Nature inspired discrete firefly algorithm for optimal mobile data gathering in wireless sensor networks

Nowadays wireless sensor networks enhance the life of human beings by helping them through several applications like precision agriculture, health monitoring, landslide detection, pollution control, etc. The built-in sensors on a sensor node are used to measure the various events like temperature, vibration, gas emission, etc., in the remotely deployed unmanned environment. The limited energy constraint of the sensor node causes a huge impact on the lifetime of the deployed network. The data transmitted by each sensor node cause significant energy consumption and it has to be efficiently used to improve the lifetime of the network. The energy consumption can be reduced significantly by incorporating mobility on a sink node. Thus the mobile data gathering can result in reduced energy consumption among all sensor nodes while transmitting their data. A special mobile sink node named as the mobile data transporter (MDT) is introduced in this paper to collect the information from the sensor nodes by visiting each of them and finally it sends them to the base station. The Data collection by the MDT is formulated as a discrete optimization problem which is termed as a data gathering tour problem. To reduce the distance traveled by the MDT during its tour, a nature-inspired heuristic discrete firefly algorithm is proposed in this paper to optimally collect the data from the sensor nodes. The proposed algorithm computes an optimal order to visit the sensor nodes by the MDT to collect their data with minimal travel distance. The proposed algorithm is compared with tree-based data collection approaches and ant colony optimization approach. The results demonstrate that the proposed algorithm outperform other approaches minimizing the tour length under different scenarios.     

An ant colony optimization based routing algorithm for extending network lifetime in wireless sensor networks

Reducing the energy consumption of network nodes is one of the most important problems for routing in wireless sensor networks because of the battery limitation in each sensor. This paper presents a new ant colony optimization based routing algorithm that uses special parameters in its competency function for reducing energy consumption of network nodes. In this new proposed algorithm called life time aware routing algorithm for wireless sensor networks (LTAWSN), a new pheromone update operator was designed to integrate energy consumption and hops into routing choice. Finally, with the results of the multiple simulations we were able to show that LTAWSN, in comparison with the previous ant colony based routing algorithm, energy aware ant colony routing algorithms for the routing of wireless sensor networks, ant colony optimization-based location-aware routing algorithm for wireless sensor networks and traditional ant colony algorithm, increase the efficiency of the system, obtains more balanced transmission among the nodes and reduce the energy consumption of the routing and extends the network lifetime.     

车载自组织网中基于蚁群算法的延迟感知路由协议

针对城市道路环境下车载自组织网( VANETs)中通信性能下降以及数据传输失败的问题,提出了一种基于蚁群算法的延迟感知路由( ACDR)协议。首先,建立双向车道的数学延迟模型;然后,根据提出的端点十字路口( EI)的概念,ACDR利用蚁群优化( ACO)寻找最佳路线,其中前向蚂蚁根据本地路段延迟以及当前十字路口与目的节点的端点十字路口之间的全局时延来选择路径,后向蚂蚁则负责在返回路径时更新信息素,同时,相邻十字路口之间利用贪婪转发算法进行数据包的传递。最后仿真比较了ACDR协议与连通性感知路由( CAR)协议的性能,结果表明提出的ACDR协议的数据包的传输延迟小,丢包率低,通信性能好。     

Minimum spanning tree–based delay‐aware mobile sink traversal in wireless sensor networks

Energy efficient data collection in a delay‐bound application is a challenging issue for mobile sink–based wireless sensor networks. Many researchers have proposed the concept of rendezvous points (RPs) to design the path for the mobile sink. Rendezvous points are the locations in the network where the mobile sink halts and collects data from the nearby sensor nodes. However, the selection of RPs for the design of path has a significant impact on timely data collection from the network. In this paper, we propose an efficient algorithm for selection of the RPs for efficient design of mobile sink trajectory in delay‐bound applications of wireless sensor networks. The algorithm is based on a virtual path and minimum spanning tree and shown to maximize network lifetime. We perform extensive simulations on the proposed algorithm and compare results with the existing algorithms to demonstrate the efficiency of the proposed algorithm of various performance metrics.     

基于ACS的无线传感器网络区分服务路由算法

针对无线传感器网络中数据传输的不同要求,将QoS分为3类,根据无线链路的特点提供区分服务。利用博弈论分析了数据传输在延迟、可靠性与网络能量开销之间的关系,基于改进的蚁群优化算法ACS(ant colony system),设计了区分服务路由算法ADSGR(ant colony system based differentiated service and game-theory routing),依据不同QoS要求,选择适当的路由,提高网络的整体性能和资源利用率。实验结果表明,与现有算法相比,该算法在数据传输的延迟、可靠性和能量开销上具有更好的性能。     

Energy equalizing routing for fast data gathering in wireless sensor networks

Energy saving and fast responding of data gathering are two crucial factors for the performance of wireless sensor networks. A dynamic tree based energy equalizing routing scheme (DTEER) was proposed to make an effort to gather data along with low energy consumption and low time delay. DTEER introduces a dynamic multi-hop route selecting scheme based on weight-value and height-value to form a dynamic tree and a mechanism similar to token passing to elect the root of the tree. DTEER can simply and rapidly organize all the nodes with low overhead and is robust enough to the topology changes. When compared with power-efficient gathering in sensor information systems (PEGASIS) and the hybrid, energy- efficient, distributed clustering approach (HEED), the simulation results show that DTEER achieves its intention of consuming less energy, equalizing the energy consumption of all the nodes, alleviating the data gathering delay, as well as extending the network lifetime perfectly.     

Improved ant colony-based multi-constrained QoS energy-saving routing and throughput optimization in wireless Ad-hoc networks简

In order to establish a route supporting multi-constrained quality of service（QoS）, increase network throughput and reduce network energy consumption, an improved ant colony-based multi-constrained QoS energy-saving routing algorithm（IAMQER） is proposed. The ant colony algorithm, as one of the available heuristic algorithms, is used to find the optimal route from source node to destination node. The proposed IAMQER algorithm, which is based on the analysis of local node information such as node queue length, node forwarding number of data packets and node residual energy, balances the relationship between the network throughput and the energy consumption, thus improving the performance of network in multi-constrained QoS routing. Simulation results show that this IAMQER algorithm can find the QoS route that reduce average energy consumption and improves network packet delivery ratio under the end-to-end delay and packet loss ratio constraints.     

Energy efficient dispatch strategy for the dual-functional mobile sink in wireless rechargeable sensor networks

Scavenging energy from radio-frequency (RF) signals has drawn significant attention in recent years. By introducing the technology of RF energy harvesting into wireless sensor networks, a new type of network named mobile data gathering based wireless rechargeable sensor network (MGWRSN) is considered in this paper. In the MGWRSN, a dual-functional mobile sink (MS) which has the abilities of data collecting and RF energy generating is employed. Data sensed by sensor nodes is gathered at several selected head nodes (HNs). Through using the RF energy supplied by the MS, the HNs deliver the gathered data to the MS arriving at the corresponding rendezvous points (RPs). In our works, the network energy consumption model of the MGWRSN is built, and the energy efficient dispatch strategy for the MS is studied, aiming at cutting down the total network energy consumption. For the simplest case, i.e., the one-HN MGWRSN, the optimal location of the RP is provided to minimize the total network energy consumption. After that, the researches are extended into the case of multi-HN MGWRSN and a heuristic dispatch strategy named HEEDS is proposed. Theoretical analysis and numerical results show that: (1) in the one-HN MGWRSN, the optimal location of the RP is close related to the data bulk to be transmitted, the unit mobility energy cost, the required bit error rate, the modulation scheme, and the departure position of the MS; (2) comparing with the existing algorithm WRP which directly dispatches the MS to the locations of HNs to collect data, the proposed strategy HEEDS is shown to be more energy efficient. Moreover, when a high energy transfer power is available at the MS, HEEDS renders shorter packet delay compared to WRP.     

Multi-constraints QoS routing optimization based on improved immune clonal shuffled frog leaping algorithm

Aiming at the multi-constraint routing problem,a mathematical model was designed,and an improved immune clonal shuffled frog leaping algorithm (IICSFLA) was proposed,which combined immune operator with traditional SFLA.Under the constraints of bandwidth,delay,packet loss rate,delay jitter and energy cost,total energy cost from the source node to the terminal node was computed.The proposed algorithm was used to find an optimal route with minimum energy cost.In the simulation,the performance of IICSFLA with adaptive genetic algorithm and adaptive ant colony optimization algorithm was compared.Experimental results show that IICSFLA solves the problem of multi-constraints QoS unicast routing optimization.The proposed algorithm avoids local optimum and effectively reduces energy loss of data on the transmission path in comparison with adaptive genetic algorithm and adaptive ant colony optimization algorithm.     

Energy-efficient routing for correlated data in wireless sensor networks

In this paper, we investigate the reduction in the total energy consumption of wireless sensor networks using multi-hop data aggregation by constructing energy-efficient data aggregation trees. We propose an adaptive and distributed routing algorithm for correlated data gathering and exploit the data correlation between nodes using a game theoretic framework. Routes are chosen to minimize the total energy expended by the network using best response dynamics to local data. The cost function that is used for the proposed routing algorithm takes into account energy, interference and in-network data aggregation. The iterative algorithm is shown to converge in a finite number of steps. Simulations results show that multi-hop data aggregation can significantly reduce the total energy consumption in the network.     

基于改进蚁群算法的无人机低空突防三维航迹规划

探讨了无人机低空突防三维航迹规划中地形、威胁以及航迹代价评估等模型的建立方法.将蚁群算法引入无人机航迹规划,针对传统蚁群算法易出现停滞现象,引入偏航角对启发信息进行调整改进;建立优先搜索集,使蚁群算法更快速有效地搜索到最优航迹.仿真实验结果证明了该规划方法的效率.     

基于蚁群算法的无线传感器网络数据聚合路由算法

传感器网络为减少冗余数据的传输耗能。降低延迟,需要在路由过程中采用数据聚合技术。文中采用定向传输方式,在消息路由机制基础上提出了一种基于蚁群算法的数据聚合路由算法。该算法主要思想在于将节点能耗、传输距离与聚合收益3方面作为启发因子,通过一组称为“蚂蚁”的人工代理寻找到达汇聚节点的最优路径。该算法利用蚁群算法的正反馈效应来达到数据汇集的目的,不需要网络节点维护全局信息,因此是一种实现数据聚合在能量与时延上折中的分布式路由算法。理论分析和仿真结果说明了新算法的有效性。     

基于改进蚁群算法的舰船航路规划研究

舰船在障碍物环境中航行,如果采用传统的人工绘制航线的方法,不仅费时费力,并且绘制的航线非常不准确,在障碍物位置发生变更的情况下,整条航线都要重新设计和绘制;其次,人工绘制的航线图,不便于保存,应用范围非常窄。为了弥补人工绘制航线的缺陷,采用一种基于改进蚁群算法的方法规划舰船的航线,并对改进的蚁群算法进行了仿真,获得了舰船在障碍物环境下的最优航线。