An Energy-Balanced Data Gathering Algorithm for Linear Wireless Sensor Networks

Aiming at the data unbalance of sensor nodes in the wireless sensor networks (WSN), this paper mainly studies the data gathering algorithm for linear WSNs. As the data amount varies from the time spent on data gathering of entire network minimal, it is a key factor to balance energy consumption and further prolong the network lifetime. Therefore, this paper proposes a TDMA scheduling algorithm for general k-hop networks, and takes detail performance analysis on the algorithm. Furthermore, we present the method of selecting the optimal hop-count and its formula as well as the formula of the number of timeslots required for converge-cast in order to maximize network lifetime. Finally, we obtain some general conclusions of network optimization based on the theoretical analysis and simulations. Compared with the 1-hop algorithm in (C. Florens and R. McEliece, Packets Distribution Algorithms for Sensor Networks, IEEE INFOCOM, San Diego, pp. 1063–1072, 2003), the TDMA scheduling algorithm for the general k-hop network proposed in our paper is more universal and has more practical application value.     

Energy optimized micro genetic algorithm based LEACH protocol for WSN

This article presents the design, analyses and implementation of the novel routing protocol for energy optimization based on LEACH for WSN. Network Lifetime is the major problem in various routing protocols used in WSN. In order to overcome that problem, our proposed routing protocol is developed, which is a combination of Micro Genetic algorithm with LEACH protocol. Our proposed µGA-LEACH protocol, strengthen the cluster head (CH) selection and also reduce the energy consumption of the network when compared to existing protocols. This paper shows the improvement of network lifetime and energy consumption with the optimal CH selection based on a micro genetic algorithm and also compared the results with an existing hierarchical routing protocol like LEACH, LEACH-C, LEACH GA and GADA LEACH routing protocol with various packet sizes, and initial energy.

     

A hybrid approach for the optimization of quality of service metrics of WSN

The core objective behind this research paper is to implement a hybrid optimization technique along with proactive routing algorithm to enhance the network lifetime of wireless sensor networks (WSN). The combination of two soft computing techniques viz. genetic algorithm (GA) and bacteria foraging optimization (BFO) techniques are applied individually on destination sequence distance vector (DSDV) routing protocol and after that the hybridization of GA and BFO is applied on the same routing protocol. The various simulation parameters used in the research are: throughput, end to end delay, congestion, packet delivery ratio, bit error rate and routing overhead. The bits are processed at a data rate of 512 bytes/s. The packet size for data transmission is 100 bytes. The data transmission time taken by the packets is 200 s i.e. the simulation time for each simulation scenario. Network is composed of 60 nodes. Simulation results clearly demonstrates that the hybrid approach along with DSDV outperforms over ordinary DSDV routing protocol and it is best suitable under smaller size of WSN.

     

Sandpiper optimization algorithm with cosine similarity based cross-layer routing protocol for smart agriculture in wireless sensor network assisted internet of things systems

The Internet of Things (IoT) has recently attained a prominent role in enabling smooth and effective communication among various networks. Wireless sensor network (WSN) is utilized in IoT to collect peculiar data without interacting with humans in specific applications. Energy is a major problem in WSN-assisted IoT applications, even though better data communication is achieved through cross-layer models. This paper proposes a new cross-layer-based clustering and routing model to provide a scalable and energy-efficient long data communication in WSN-assisted IoT systems for smart agriculture. Initially, the fuzzy k-medoids clustering approach is used to split the network into various clusters since the formation of clusters plays an important role in energy consumption. Then, a new swarm optimization known as enhanced sparrow search algorithm (ESSA), which is the combination of SSA and chameleon swarm algorithm (CSA), has been introduced for optimal cluster head (CH) selection to solve the energy-hole problems in WSN. A cross-layer strategy has been preferred to provide efficient data transmission. Each sensor node parameter of the physical layer, network layer and medium access control (MAC) is considered for processing routing. Finally, a new bio-inspired algorithm is known as the sandpiper optimization algorithm (SOA), and cosine similarity (CS) has been employed to determine the optimal route for efficient data transmission and retransmission. The simulation of the proposed protocol is implemented by network simulator (NS2), and the simulation results are taken in terms of end-to-end delay, PDR, communication overhead, communication cost, average consumed energy, and network lifetime.     

Energy Efficient Cluster Based Routing Protocol Using Charged System Harmony Search Algorithm in WSN

In general, Wireless Sensor Networks (WSNs) is developed with a group of distributed and locative sensor nodes for sensing different environmental conditions. The primary challenges faced by WSN are: low network time and transmission data delay. In crucial applications like monitoring the ecosystem, military and disaster management, and data routing, the incorporation of WSN is very critical. Henceforth, a Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol was proposed but it was found to be uneconomical for energy management. Also, the optimization of Cluster Head (CH) is considered as NP hard problem. This research work deals the issues in optimal path selection in routing of wireless sensor networks to increase the network lifetime. Various techniques are available in metaheuristics, such as the Charged System Search (CSS), that effectively used to resolve the routing problem. Despite of this, most of the meta-heuristics suffer from local optima issues. A charged system search and harmony search algorithm based routing protocol is presented in this research work. Experimental results present the efficient performance of proposed HS model with increased cluster structures, improved network lifetime and reduced end-to-end delay and average packet loss rate.

     

Developing multi-path routing protocol in MANET using hybrid SM-CSBO based on novel multi-objective function

One of the infrastructure-free networks is mobile ad hoc networks (MANETs) that are built with limited battery life using wireless mobile devices. This restricted battery capability in MANETs creates the necessity of considering the energy-awareness constraint in designing them. As routing protocols, the major aim of MANETs is to create the energy awareness in the network; it improves the network's lifetime through effectively utilizing the available restricted energy. Moreover, it creates some limitations like the mobility constraint, wireless link's sensitivity to environmental impacts, and restricted transmission range and residual energy of nodes that causes rapid modifications in the network topology and frequent link failure. By taking those problems, this paper plans to develop a new multipath routing protocol, where the hybrid optimization algorithm with the integration of cuckoo search optimization (CSO) and butterfly optimization algorithm (BOA) is proposed and named sensory modality-based cuckoo search butterfly optimization (SM-CSBO) for determining the optimal path between the source and destination. The main goal is to select the path with better link quality and more stable links to guarantee reliable data transmission. The multi-objective function is considered with the factors regarding distance, normalized energy, packet delivery ratio, and control overhead to develop an effective routing protocol in MANET. The proposed model of SM-CSBO algorithm has superior than 5.8%, 30.4%, 36.7%, and 39.3%, correspondingly maximized than PSO, SFO, CSO, and SFO algorithms while considering the number of nodes as 150. The simulation outcomes proved that it enhances network performance when compared with the other traditional protocols.     

An Adaptive Fuzzy C Means with Seagull Optimization Algorithm for Analysis of WSNs in Agricultural Field with IoT

In recent years, the environmental monitoring in agriculture field is an essential required application. To achieve the environmental monitoring of agriculture fields, the wireless sense networks (WSN) and internet of things is utilized. In the WSN, the energy consumption is a main issue to access the medium and transfer the networks. Hence, in this paper, adaptive fuzzy C means clustering and seagull optimization algorithm is developed for monitoring environmental conditions in agriculture field. Two main objective functions are utilized to empower the presentation of the WSN such as load balancing and energy efficient operation. The proposed method is a combination of fuzzy C means clustering and seagull optimization algorithm (SOA). The energy efficient and load balancing is achieved by optimal routing scheme by proposed method. The fuzzy C-means clustering is utilized to empower the energy efficient operation and load balancing. In the fuzzy C-means clustering, the SOA is utilized to select the optimal path selection. The proposed method is executed by NS2 simulator and performances are compared with existing methods such as atom search optimization and emperor penguin optimization respectively. The performance metrics are delay, drop, throughput, energy consumption, network lifetime, overhead and delivery ratio.

     

一种环境感知的无线Mesh网络自适应QoS路径选择算法

本文针对如何改善无线多跳Mesh网络的服务质量,满足无线多媒体业务对数据传输的带宽、时延、抖动的要求等问题,研究了一种基于无线信道状态和链路质量统计的MAC层最大重传次数的自适应调整算法。该算法通过对无线Mesh网络的无线信道环境的动态感知,利用分层判断法区分无线分组丢失的主要原因是无线差错还是网络拥塞导致,实时调整MAC层的最佳重传次数,降低无线网络中的分组冲突概率。基于链路状态信息的统计和最大重传策略,提出了一种启发式的基于环境感知的QoS路由优化机制HEAOR。该算法通过动态感知底层链路状态信息,利用灰色关联分析法自适应选择最优路径,在不增加系统复杂度的基础上,减少链路误判概率,提高传输效率。NS2仿真结果表明,HEAOR算法能有效减少重路由次数,降低链路失效概率,提高网络的平均吞吐率。本文提出的方法不仅能够优化MAC层的重传,而且通过发现跨层设计的优化参数实现对路径的优化选择。      

Iterative approach to optimizing convergence routing priorities

This paper shows how to optimize the routing decisions in a nondeterministic routing algorithm called convergence routing in which routes may change depending on the traffic conditions. The routing algorithm guarantees a loss-free delivery of data packets from bursty sources, and a deterministic bound on the route length in arbitrary topology networks. The routing decisions are based on assigning routing priorities to the links such that a packet is forwarded to the highest priority link which is not blocked. Routing priorities are assigned using a local-greedy metric which minimizes the distance (number of hops) to the destination. This work shows that routing decisions using a local-greedy metric are not optimal, and the performance of the algorithm can be improved substantially by using new measures. Thus, various look-ahead metrics which take into account the potential gain on the other switching nodes toward the destination of a packet are suggested. The contributions of this work are: (1) a new analytical model to capture the behavior of a switching node; (2) an iterative optimization technique to set routing priorities according to various look-ahead measures; and (3) heuristics to ensure the stability of the routing priorities. The optimization objective is to maximize the throughput by minimizing the maximum total flow carried on a link in the network under static traffic model. The performance is studied computationally on various networks and traffic matrices. It is shown that up to a 50% performance increase can be obtained by optimizing the routing priorities     

基于分解协调的SWCC-OBS网络路由选择研究

针对稀疏分布有限波长转换能力的光突发交换(SWCC-OBS)网络容易出现的高丢包率和负载不均 衡问题,建立了稀疏分布OBS全网丢包率的数学模型。为了克服模型求解过程中多 变量带来的非 线性问题,对影响全网突发丢包率的参变量进行分解预估和反馈更新,求得全网对应业务流 模型下的优化 路由路径,获得了全网突发数据的近似最低丢包率。仿真实验表明,在SWCC-OBS网络中, 本文提出的路由 选择模型及分解协调的路由选择算法,不仅能有效地避免突发数据包间的相互冲突、降低了 全网丢包率,而 且能很好地适应突发数据业务的动态变化,保证了网络的负载均衡和较低的丢包率。     

NHRPA: a novel hierarchical routing protocol algorithm for wireless sensor networks

Considering severe resources constraints and security threat hierarchical routing protocol algorithm. The proposed routing of wireless sensor networks （WSN）, the article proposed a novel protocol algorithm can adopt suitable routing technology for the nodes according to the distance of nodes to the base station, density of nodes distribution, and residual energy of nodes. Comparing the proposed routing protocol algorithm with simple direction diffusion routing technology, cluster-based routing mechanisms, and simple hierarchical routing protocol algorithm through comprehensive analysis and simulation in terms of the energy usage, packet latency, and security in the presence of node protocol algorithm is more efficient for wireless sensor networks. compromise attacks, the results show that the proposed routing     

Network energy optimization and intelligent routing in WSN applicable for IoT using self-adaptive coyote optimization algorithm

The Internet of Things (IoT) is a recent wireless telecommunications platform, which contains a set of sensor nodes linked by wireless sensor networks (WSNs). These approaches split the sensor nodes into clusters, in which each cluster consists of an exclusive cluster head (CH) node. The major scope of this task is to introduce a novel CH selection in WSN applicable to IoT using the self-adaptive meta-heuristic algorithm. This paper aids in providing the optimal routing in the network based on direct node (DN) selection, CH selection, and clone cluster head (CCH) selection. DNs are located near the base station, and it is chosen to avoid the load of CH. The adoption of the novel self-adaptive coyote optimization algorithm (SA-COA) is used for the DN selection and CCH selection. When the nodes are assigned in the network, DN and CCH selection is performed by the proposed SA-COA. Then, the computation of residual energy helps to select the CH, by correlating with the threshold energy. CCH is proposed to copy the data from the CH to avoid the loss of data in transmitting. By forming the CCH, the next CH can be easily elected with the optimal CCH using SA-COA. From the simulation findings, the best value of the designed SA-COA-LEACH model is secured at 1.14%, 3.17%, 1.18%, and 7.33% progressed than self-adaptive whale optimization algorithm (SAWOA), cyclic rider optimization algorithm (C-ROA), krill herd algorithm (KHA), and COA while taking several nodes 50. The proposed routing of sensor networks specifies better performance than the existing methods.     

传感器网络簇间通信自适应节能路由优化算法

提出了一种用于无线传感器网络簇间通信的自适应节能路由优化算法。网络中以总体通信能耗最小为首要原则建立端到端之间的路由,并对簇间通信提出了一种优化算法,使得簇首之间相互通信时,可以在直接、中继以及协作3种不同方式中自适应地选择最节能的通信方式。所提出的路由算法可确保源节点与基站通信过程中选择到最优或次优的节能路由,并可降低由于通信覆盖盲区造成的通信中断的概率。仿真结果表明,与其他传感器网络路由算法相比,所提出的路由算法在相同吞吐量和误码率前提下每一轮都消耗更少的能量,并且在不同的节点密度下都可有效地延长网络的生命周期和工作时间。     

E‐TDGO: An encrypted trust‐based dolphin glowworm optimization for secure routing in mobile ad hoc network

Mobile ad hoc networks (MANETs) are independent networks, where mobile nodes communicate with other nodes through wireless links by multihop transmission. Security is still an issue to be fixed in MANETs. Hence, a routing protocol named encrypted trust‐based dolphin glowworm optimization (DGO) (E‐TDGO) is designed using Advanced Encryption Standard‐128 (AES‐128) and trust‐based optimization model for secure routing in MANET. The proposed E‐TDGO protocol includes three phases, namely, k‐path discovery, optimal path selection, and communication. At first, k paths are discovered based on the distance and the trust level of the nodes. From the k paths discovered, the optimal path is selected using a novel algorithm, DGO, which is developed by combining glowworm swarm optimization (GSO) algorithm and dolphin echolocation algorithm (DEA). Once the optimal path is selected, communication begins in the network such that E‐TDGO protocol ensures security. The routing messages are encrypted using AES‐128 with shared code and key to offer security. The experimental results show that the proposed E‐TDGO could attain throughput of 0.11, delay of 0.01 second, packet drop of 0.44, and detection rate of 0.99, at the maximum number of rounds considered in the network of 75 nodes with attack consideration.     

一种基于LEACH的无线传感器网络路由算法及仿真

在低功耗自适应分簇（LEACH,Low Energy Adaptive Clustering Hierarch）算法中,由于每一轮循环都要重新构造簇,距离较远的簇头节点可能会因长距离发送数据而过早耗尽自身能量,能量较低的节点当选为簇头节点时将会加速该节点的死亡,影响整个网络的生命周期。针对LEACH算法分簇机制中存在的不足,提出了一种改进的路由算法。仿真结果表明,改进算法通过考虑节点的剩余能量与固定分簇的方法,有效的改善了网络能量均衡,提高了网络生存时间。     

基于混合CS的WSN六边形格状优化分簇路由算法研究

建立基于混合CS的六边形格状WSN分簇模型,定量分析网络数据传输次数与数据压缩比例和分簇大小的关系,并求解最优网络分簇个数。提出基于混合CS的WSN六边形格状优化分簇路由算法,均衡网络通信开销的同时减少数据传输次数。通过仿真实验验证所提出的优化分簇模型与算法优于传统分簇模型,能有效降低网络数据传输次数。建立基于混合CS的六边形格状WSN分簇模型,定量分析网络数据传输次数与数据压缩比例和分簇大小的关系,并求解最优网络分簇个数。提出基于混合CS的WSN六边形格状优化分簇路由算法,均衡网络通信开销的同时减少数据传输次数。通过仿真实验验证所提出的优化分簇模型与算法优于传统分簇模型,能有效降低网络数据传输次数。     

无线多跳网络快速跨层资源优化分配算法

针对背压路由算法容易造成大量队列积压和收敛速度慢的缺陷,该文研究了无线多跳网络中节点功率受限情况下的联合拥塞控制、路由和功率分配的跨层优化问题。以最大化网络效用为目标,以流平衡条件、功率等为约束条件建模,基于牛顿法提出了一种具有超线性收敛性能的算法,并运用矩阵分裂技术使该算法能够分布式实施。仿真结果表明,该算法在实现网络效用最大化的同时,能够有效提高网络中的能量效用,且能将网络中的队列长度稳定在一个较低水平,降低包传输延时。     

Simulation‐based real‐time routing protocol with load distribution in wireless sensor networks

In wireless sensor networks (WSNs), sensors gather information about the physical world and the base station makes decision and then performs appropriate actions upon the environment. This technology enables a user to effectively sense and monitor from a distance in real‐time. WSNs demand real‐time forwarding which means messages in the network are delivered according to their end‐to‐end deadlines (packet lifetime). This paper proposes a novel real‐time routing protocol with load distribution (RTLD) that ensures high packet throughput with minimized packet overhead and prolongs the lifetime of WSN. The routing depends on optimal forwarding (OF) decision that takes into account of the link quality (LQ), packet delay time and the remaining power of next hop sensor nodes. The proposed mechanism has been successfully studied through simulation work. Copyright © 2009 John Wiley & Sons, Ltd.     

Optimal Routing and Data Aggregation for Maximizing Lifetime of Wireless Sensor Networks

An optimal routing and data aggregation scheme for wireless sensor networks is proposed in this paper. The objective is to maximize the network lifetime by jointly optimizing data aggregation and routing. We adopt a model to integrate data aggregation with the underlying routing scheme and present a smoothing approximation function for the optimization problem. The necessary and sufficient conditions for achieving the optimality are derived and a distributed gradient algorithm is designed accordingly. We show that the proposed scheme can significantly reduce the data traffic and improve the network lifetime. The distributed algorithm can converge to the optimal value efficiently under all network configurations.