能量采集型无线传感器网络的高能效路由算法

在能量采集型无线传感器网络中,虽然有能量吸收,但是因能量依然非常珍贵,如何优化路由协议,提高能量利用率,延长网络寿命仍然是值得研究的问题.为求解高能效的路由,提出了一种采用遗传算法的高能效路由算法,建立考虑节点的吸收能量、剩余能量、消耗能量和浪费能量的适应函数,用遗传算法寻找全局最优路径.将该适应函数与3种其他适应函数作对比,其他3种适应函数分别为只考虑路径能耗最小的适应函数,考虑路径能耗与路径上节点的吸收能量、剩余能量的适应函数以及考虑路径能耗与网络中所有节点的浪费能量的适应函数.采用遗传算法解出4种路由,通过仿真分析可知,所提出的路由算法能量利用效率最高.     

Routing Technology in Wireless Sensor Network Based on Ant Colony Optimization Algorithm

With rapid development of wireless communication, sensor, micro power system and electronic technology, the research on wireless sensor network has attracted more and more attention. The work proposed routing algorithm in wireless sensor network based on ant colony optimization by analyzing routing protocol and utilizing advanced idea. Ant colony optimization algorithm has advantages in implementing local work, supporting multiple paths and integrating link quality into pheromone formation. In routing selection, the work calculated probability that node is selected as the next hop according to pheromone concentration on the path. With characteristics including self-organization, dynamic and multipath, ant colony optimization algorithm is suitable for routing in wireless sensor network. With low routing cost, good adaptability and multipath, the algorithm balanced energy consumption to prolong network lifetime. In terms of simulation and experiments, ant colony algorithm was proved to be suitable for finding optimal routing in wireless sensor network, thus achieving design goal of routing algorithm.     

EOMR: An Energy-Efficient Optimal Multi-path Routing Protocol to Improve QoS in Wireless Sensor Network for IoT Applications

The wireless sensor network based IoT applications mainly suffers from end to end delay, loss of packets during transmission, reduced lifetime of sensor nodes due to loss of energy. To address these challenges, we need to design an efficient routing protocol that not only improves the network performance but also enhances the Quality of Service. In this paper, we design an energy-efficient routing protocol for wireless sensor network based IoT application having unfairness in the network with high traffic load. The proposed protocol considers three-factor to select the optimal path, i.e., lifetime, reliability, and the traffic intensity at the next-hop node. Rigorous simulation has been performed using NS-2. Also, the performance of the proposed protocol is compared with other contemporary protocols. The results show that the proposed protocol performs better concerning energy saving, packet delivery ratio, end-to-end delay, and network lifetime compared to other protocols.

     

Load balancing clustering and routing for IoT-enabled wireless sensor networks

Internet of things (IoT) devices are equipped with a number of interconnected sensor nodes that relies on ubiquitous connectivity between sensor devices to optimize information automation processes. Because of the extensive deployments in adverse areas and unsupervised nature of wireless sensor networks (WSNs), energy efficiency is a significant aim in these networks. Network survival time can be extended by optimizing its energy consumption. It has been a complex struggle for researchers to develop energy-efficient routing protocols in the field of WSNs. Energy consumption, path reliability and Quality of Service (QoS) in WSNs became important factors to be focused on enforcing an efficient routing strategy. A hybrid optimization technique presented in this paper is a combination of fuzzy c-means and Grey Wolf optimization (GWO) techniques for clustering. The proposed scheme was evaluated on different parameters such as total energy consumed, packet delivery ratio, packet drop rate, throughput, delay, remaining energy and total network lifetime. According to the results of the simulation, the proposed scheme improves energy efficiency and throughput by about 30% and packet delivery ratio and latency by about 10%, compared with existing protocols such as Chemical Reaction Approach based Cluster Formation (CHRA), Hybrid Optimal Based Cluster Formation (HOBCF), GWO-based clustering (GWO-C) and Cat Swarm Optimization based Energy-Efficient Reliable sectoring Scheme with prediction algorithms (P_CSO_EERSS). The study concludes that the protocol suitable for creating IoT monitoring system network lifetime is an important criteria.     

基于AODV的无线多媒体传感器网络路由协议

研究了AODV路由协议,分析了多路径路由实现机制,提出了一种可应用于无线多媒体传感器网络的能量均衡多路径AODV路由协议。该协议建立从源节点到目的节点的多条路径,在路径的选择上综合考虑了路径跳数和节点剩余能量,用以保证负载均衡,延长网络生存期,该算法使用了分流的方式避免拥塞。通过使用NS2仿真软件对EEMP-AODV路由协议进行仿真,结果显示其在拥塞避免、实时性、吞吐量和网络生存期方面的性能有明显提升。     

Dynamic clustering approach with ACO-based mobile sink for data collection in WSNs

Enhancing the network lifetime of wireless sensor networks is an essential task. It involves sensor deployment, cluster formation, routing, and effective utilization of battery units. Clustering and routing are important techniques for adequate enhancement of the network lifetime. Since the existing clustering and routing approaches have high message overhead due to forwarding collected data to sinks or the base station, it creates premature death of sensors and hot-spot issues. The objective of this study is to design a dynamic clustering and optimal routing mechanism for data collection in order to enhance the network lifetime. A new dynamic clustering approach is proposed to prevent premature sensor death and avoid the hot spot problem. In addition, an Ant Colony Optimization (ACO) technique is adopted for effective path selection of mobile sinks. The proposed algorithm is compared with existing routing methodologies, such as LEACH, GA, and PSO. The simulation results show that the proposed cluster head selection algorithm with ACO-based MDC enhances the sensor network lifetime significantly.

     

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.     

Energy efficient fault-tolerant multipath routing scheme for wireless sensor networks

In wireless sensor network （MSN）, reliability is the main issue to design any routing technique. To design a comprehensive reliable wireless sensor network, it is essential to consider node failure and energy constrain as inevitable phenomena. In this paper we present energy efficient node fault diagnosis and recovery for wireless sensor networks referred as energy efficient fault tolerant multipath routing scheme for wireless sensor network. The scheme is based on multipath data routing. One shortest path is used for main data routing in our scheme and other two backup paths are used as alternative path for faulty network and to handle the overloaded traffic on main channel. Shortest pat data routing ensures energy efficient data routing. Extensive simulation results have revealed that the performance of the proposed scheme is energy efficient and can tolerates more than 60% of fault.     

A Novel Energy Efficient and Lifetime Maximization Routing Protocol in Wireless Sensor Networks

The energy consumption is a key design criterion for the routing protocols in wireless sensor networks (WSN). Some of the conventional single path routing schemes may not be optimal to maximize the network lifetime and connectivity. Thus, multipath routing schemes is an optimal alternative to extend the lifetime of WSN. Multipath routing schemes distribute the traffic across multiple paths instead of routing all the traffic along a single path. In this paper, we propose a multipath Energy-Efficient data Routing Protocol for wireless sensor networks (EERP). The latter keeps a set of good paths and chooses one based on the node state and the cost function of this path. In EERP, each node has a number of neighbours through which it can route packets to the base station. A node bases its routing decision on two metrics: state and cost function. It searches its Neighbours Information Table for all its neighbours concerned with minimum cost function. Simulation results show that our EERP protocol minimizes and balances the energy consumption well among all sensor nodes and achieves an obvious improvement on the network lifetime.     

Network Lifetime Maximization for Estimation in Multihop Wireless Sensor Networks

We consider the distributed estimation by a network consisting of a fusion center and a set of sensor nodes, where the goal is to maximize the network lifetime, defined as the estimation task cycles accomplished before the network becomes nonfunctional. In energy-limited wireless sensor networks, both local quantization and multihop transmission are essential to save transmission energy and thus prolong the network lifetime. The network lifetime optimization problem includes three components: i) optimizing source coding at each sensor node, ii) optimizing source throughput of each sensor node, and iii) optimizing multihop routing path. Fortunately, source coding optimization can be decoupled from source throughput and multihop routing path optimization, and is solved by introducing a concept of equivalent 1-bit MSE function. Based on the optimal source coding, the source throughput and multihop routing path optimization is formulated as a linear programming (LP) problem, which suggests a new notion of character-based routing. The proposed algorithm is optimal and the simulation results show that a significant gain is achieved by the proposed algorithm compared with heuristic methods.     

Up-Down Links Dualpath Greedy Routing Protocol for Wireless Sensor Networks

Greedy geographic routing is attractive in wireless sensor networks because of its efficiency and scalability. This paper presents an up-down links dualpath greedy routing (UDLDGR) protocol for wireless sensor networks. The routing protocol not only reserves the features of greedy forwarding algorithm, which is simple, efficient, but also uses different relay nodes to serve as routing nodes for up and down routing paths, makes the energy consumption more balanced. The greatest advantage of UDLDGR is it trades off only small cost for the source node to obtain two different transmission paths information. The multipath strengthens the network reliability, such as load balancing and robustness to failures. Our simulation results show that UDLDGR can improve system lifetime by 20–100% compared to single path approaches.     

An intelligent hybrid prairie dog optimization algorithm-based stable cluster reliable routing scheme for VANETs

Vehicular ad hoc network (VANET) is most significant for supporting intelligent transportation system (ITS)-based technologies, but it gets hurdled by sparse distribution of vehicles on highways, and dynamically challenging topology that arises due to increase in traffic. Hence, energy stable and optimized cluster construction maximizes the network lifetime. In this paper, Hybrid Prairie Dogs and Beluga Whale Optimization-based Node Clustering (HPDBWOA-NC) mechanism is proposed with the parameters of highway route, node velocity, number of vehicular nodes, and communication for achieving stable cluster construction in VANETs. It is proposed with the balanced exploration and exploitation potential of Prairie Dog Optimization Algorithm (PDOA) and Beluga Whale Optimization Algorithm (BWOA) for establishing optimal clusters that increase the network stability during the routing process. It integrated the exploration and exploitation capabilities of PDOA and BWOA and confirmed better optimized clusters which confirmed reliable data delivery by preventing the issue of premature convergence. It constructed clusters and selected cluster heads (CHs) depending on the fitness factors of energy, interdistance between vehicles, communication range, and vehicular density. The results of the proposed HPDBWOA-NC generated optimal number of CHs in the network which is comparatively 34.21% better than the benchmarked mechanisms. The mean throughput and packet delivery ratio (PDR) achieved by the proposed HPDBWOA-NC are identified to be significantly improved by 25.48% and 28.91% better than the investigated metaheuristic clustering protocols. The statistical study also guaranteed an increased factor of 81, during the processing of optimizing the clusters during the employment of ITS applications in VANETs.     

Connectivity constrained wireless sensor deployment using multiobjective evolutionary algorithms and fuzzy decision making

Deployment of sensor nodes is an important issue in designing sensor networks. The sensor nodes communicate with each other to transmit their data to a high energy communication node which acts as an interface between data processing unit and sensor nodes. Optimization of sensor node locations is essential to provide communication for a longer duration. An energy efficient sensor deployment based on multiobjective particle swarm optimization algorithm is proposed here and compared with that of non-dominated sorting genetic algorithm. During the process of optimization, sensor nodes move to form a fully connected network. The two objectives i.e. coverage and lifetime are taken into consideration. The optimization process results in a set of network layouts. A comparative study of the performance of the two algorithms is carried out using three performance metrics. The sensitivity analysis of different parameters is also carried out which shows that the multiobjective particle swarm optimization algorithm is a better candidate for solving the multiobjective problem of deploying the sensors. A fuzzy logic based strategy is also used to select the best compromised solution on the Pareto front.     

无线传感网络中基于蚁群的路由算法

In the wireless sensor networks, high efficient data routing for the limited energy resource networks is an important issue. By introducing Ant-colony algorithm, this paper proposes the wireless sensor network routing algorithm based on LEACH. During the construction of sensor network clusters, to avoid the node premature death because of the energy consumption, only the nodes whose residual energy is higher than the average energy can be chosen as the cluster heads. The method of repeated division is used to divide the clusters in sensor networks so that the numbers of the nodes in each cluster are balanced. The basic thought of ant-colony algorithm is adopted to realize the data routing between the cluster heads and sink nodes, and the maintenance of routing. The analysis and simulation showed that the proposed routing protocol not only can reduce the energy consumption, balance the energy consumption between nodes, but also prolong the network lifetime.     

DR-NAP: Data reduction strategy using neural adaptation phenomenon in wireless sensor networks

Internet of Things (IoT) has got significant popularity among the researchers' community as they have been applied in numerous application domains. Most of the IoT applications are implemented with the help of wireless sensor networks (WSNs). These WSNs use different sensor nodes with a limited battery power supply. Hence, the energy of the sensor node is considered as one of the primary constraints of WSN. Besides, data communication in WSN dissipates more energy than processing the data. In most WSNs applications, the sensed data generated from the same location sensor nodes are identical or time-series/periodical data. This redundant data transmission leads to more energy consumption. To reduce the energy consumption, a data reduction strategy using neural adaptation phenomenon (DR-NAP) has been proposed to decrease the communication energy in routing data to the BS in WSN. The neural adaptation phenomenon has been utilized for designing a simple data reduction scheme to decrease the amount of data transmitted. In this way, the sensor node energy is saved and the lifetime of the network is enhanced. The proposed approach has been implanted in the existing gravitational search algorithm (GSA)-based clustered routing for WSN. The sensed data are transmitted to CH and BS using DR-NAP. Real sensor data from the Intel Berkeley Research lab have been used for conducting the experiments. The experiment results show 47.82% and 51.96% of improvement in network lifetime when compared with GSA-based clustered routing and clustering scheme using Canada Geese Migration Principle (CS-CGMP) for routing, respectively.     

基于ERPMT改进启发式方法的WSN寿命最大化算法

针对传感器节点的电池容量限制导致无线传感网络寿命低的问题,基于容量最大化(CMAX)、线上最大化寿命(OML)两种启发式方法以及高效路由能量管理技术(ERPMT),提出了基于ERPMT改进启发式方法的无线传感网络寿命最大化算法。首先,通过启发式方法初始化每个传感器节点,将节点能量划分为传感器节点起源数据和其它节点数据延迟;然后利用加入的一种优先度量延迟一跳节点的能量消耗;最后,根据路径平均能量为每个路由分配一个优先级,并通过ERPMT实现最终的无线传感网络优化。针对不同分布类型网络寿命的实验验证了本文算法的有效性及可靠性,实验结果表明,相比较为先进的启发式方法CMAX及OML,本文算法明显增大了无线传感网络的覆盖范围,并且大大地延长了网络的寿命。     

Analysis,design and simulation of Internet of Things routing algorithm based on ant colony optimization

In this paper, a routing algorithm to optimize the selection of the best path for the transmitted data within the Internet of Things (IoT) system is proposed. The algorithm controls the use of ant colony ideas in the IoT system to obtain the best routing benefit. It divides the IoT environment into categorized areas depending on network types. Then, it applies the most suitable ant colony algorithm to the concerned network within each area. Furthermore, the algorithm considers routing problem in intersected areas that may arise in case of IoT system. Finally, Network Simulator 2 is used to evaluate the proposed algorithm performance. Simulation results demonstrate the effectiveness of the proposed routing algorithm in terms of end‐to‐end delay, packet loss ratio, bandwidth consumption, throughput, overhead of control bits, and energy consumption ratio. Copyright © 2016 John Wiley & Sons, Ltd.     

Multi-objective-derived energy efficient routing in wireless sensor networks using hybrid African vultures-cuckoo search optimization

Wireless Sensor Network (WSN) plays an essential role in consumer electronics, remote monitoring, an electromagnetic signal, and so forth. The functional capacity of WSN gets enhanced everyday with different technologies. The rapid development of wireless communication, as well as digital electronics, provides automatic sensor networks with low cost and power in various functions, but the challenge faced in WSN is to forward a huge amount of data between the nodes, which is a highly complex task to provide superior delay and energy loss. To overcome these issues, the development of a routing protocol is used for the optimal selection of multipath to perform efficient routing in WSN. This paper developed an energy-efficient routing in WSNs utilizing the hybrid meta-heuristic algorithm with the help of Hybrid African Vultures-Cuckoo Search Optimization (HAV-CSO). Here, the designed method is utilized for choosing the optimal cluster heads for progressing the routing. The developed HAV-CSO method is used to enhance the network lifetime in WSN. Hence, the hybrid algorithm also helps select the cluster heads by solving the multi-objective function in terms of distance, intra-cluster distance, delay, inter-cluster distance, throughput, path loss, energy, transmission load, temperature, and fault tolerance. The developed model achieved 7.8% higher than C-SSA, 25.45% better than BSO-MTLBO, 23.21% enhanced than AVOA, and 1.29% improved than CSO. The performance of the suggested model is validated, and the efficacy of the developed work is proved over other existing works.     

An Energy-Efficient Routing Algorithm Based on Relative Identification and Direction for Wireless Sensor Networks

In studies of wireless sensor networks (WSNs), routing protocols in network layer is an important topic. To date, many routing algorithms of WSNs have been developed such as relative direction-based sensor routing (RDSR). The WSNs in such algorithm are divided into many sectors for routing. RDSR could simply reduce the number of routes as compared to the convention routing algorithm, but it has routing loop problem. In this paper, a less complex, more efficient routing algorithm named as relative identification and direction-based sensor routing (RIDSR) algorithm is proposed. RIDSR makes sensor nodes establish more reliable and energy-efficient routing path for data transmission. This algorithm not only solves the routing loop problem within the RDSR algorithm but also facilitates the direct selection of a shorter distance for routing by the sensor node. Furthermore, it saves energy and extends the lifetime of the sensor nodes. We also propose a new energy-efficient algorithm named as enhanced relative identification and direction-based sensor routing (ERIDSR) algorithm. ERISDR combines triangle routing algorithm with RIDSR. Triangle routing algorithm exploits a simple triangle rule to determine a sensor node that can save more energy while relaying data between the transmitter and the receiver. This algorithm could effectively economize the use of energy in near-sensor nodes to further extend the lifetime of the sensor nodes. Simulation results show that ERIDSR get better performance than RDSR, and RIDSR algorithms. In addition, ERIDSR algorithm could save the total energy in near-sensor nodes more effectively.     

An energy‐efficient clustering and multipath routing for mobile wireless sensor network using game theory

Most of the current generation sensor nodes of mobile wireless sensor network (MWSN) are designed to have heterogeneous mobility to adapt itself in the applied environment. Energy optimization in MWSN with heterogeneous mobility is very challenging task. In this paper, a heterogeneous game theoretical clustering algorithm called mobile clustering game theory–1 (MCGT‐1) is proposed for energy optimization in a heterogeneous mobile sensor environment. Energy optimization is achieved through energy‐efficient cluster head election and multipath routing in the network. A heterogeneous clustering game is modelled with varying attributes and located an asymmetric equilibrium condition for a symmetric game with mixed strategies. The real‐time parameters, namely, predicted remaining energy, distance between a base station and nodes, distance between nodes, and mobility speed, were used to calculate the probability to elect the cluster head (CH). The efficient multipath routing is achieved through prior energy prediction strategy. It has mitigated the generation of “hot spots,” reducing its delay and improving the overall residual energy of the network. Simulation results showed that the average lifetime of MCGT‐1 has increased by 6.33 %, 13.1% and 14.2% and the PDR has improved by 4.8%,11.8%, and 17.2% than MCGT, LEACH‐ME and LEACH‐M respectively. The hot spot delay is reduced to 0.063025 seconds, improving the efficiency of the network.