Fuzzy-based unequal clustering protocol for underwater wireless sensor networks

Underwater wireless sensor network (UWSN) is a network made up of underwater sensor nodes, anchor nodes, surface sink nodes or surface stations, and the offshore sink node. Energy consumption, limited bandwidth, propagation delay, high bit error rate, stability, scalability, and network lifetime are the key challenges related to underwater wireless sensor networks. Clustering is used to mitigate these issues. In this work, fuzzy-based unequal clustering protocol (FBUCP) is proposed that does cluster head selection using fuzzy logic as it can deal with the uncertainties of the harsh atmosphere in the water. Cluster heads are selected using linguistic input variables like distance to the surface sink node, residual energy, and node density and linguistic output variables like cluster head advertisement radius and rank of underwater sensor nodes. Unequal clustering is used to have an unequal size of the cluster which deals with the problem of excess energy usage of the underwater sensor nodes near the surface sink node, called the hot spot problem. Data gathered by the cluster heads are transmitted to the surface sink node using neighboring cluster heads in the direction of the surface sink node. Dijkstra's shortest path algorithm is used for multi-hop and inter-cluster routing. The FBUCP is compared with the LEACH-UWSN, CDBR, and FBCA protocols for underwater wireless sensor networks. A comparative analysis shows that in first node dies, the FBUCP is up to 80% better, has 64.86% more network lifetime, has 91% more number of packets transmitted to the surface sink node, and is up to 58.81% more energy efficient than LEACH-UWSN, CDBR, and FBCA.     

Doppler auxiliary time synchronization algorithm for underwater acoustic sensor network

Time synchronization problem in underwater acoustic sensor networks (UWSN) was studied.Due to the propagation of acoustic signals in underwater environment and nodes movement bring some problems to time synchronization.A distributed time synchronization algorithm was proposed based on Doppler method,called NU-Sync.NU-Sync solved the problem of uncertainty propagation delay caused by nodes movement through calculating relative velocity.And autonomous underwater vehicle (AUV) was used as beacon node which can save energy consumption in the process of calculation clock skew.Simulation resulted show NU-Sync achieves high level time synchronization precision.     

Distributed estimation of sensors position in underwater wireless sensor network

In this paper, a localisation method for determining the position of fixed sensor nodes in an underwater wireless sensor network (UWSN) is introduced. In this simple and range-free scheme, the node localisation is achieved by utilising an autonomous underwater vehicle (AUV) that transverses through the network deployment area, and that periodically emits a message block via four directional acoustic beams. A message block contains the actual known AUV position as well as a directional dependent marker that allows a node to identify the respective transmit beam. The beams form a fixed angle with the AUV body. If a node passively receives message blocks, it could calculate the arithmetic mean of the coordinates existing in each messages sequence, to find coordinates at two different time instants via two different successive beams. The node position can be derived from the two computed positions of the AUV. The major advantage of the proposed localisation algorithm is that it is silent, which leads to energy efficiency for sensor nodes. The proposed method does not require any synchronisation among the nodes owing to being silent. Simulation results, using MATLAB, demonstrated that the proposed method had better performance than other similar AUV-based localisation methods in terms of the rates of well-localised sensor nodes and positional root mean square error.     

UWSP：水下无线传感器网络节点休眠协议

休眠机制是传感器网络节点节约能量、延长工作寿命的重要手段之一。现存的水下传感器网络MAC协议主要考虑提高网络传输性能,对休眠机制的研究和涉及较少,并且仅有的一些休眠策略存在着因节点工作时间较为分散而导致的节点休眠-唤醒频繁的问题。节点频繁唤醒不仅会浪费额外的能量来启动电路,折损硬件寿命,还会增加数据传输冲突的概率。针对水声网络信道的独特性质,提出了一个基于树形拓扑结构的水下传感器网络节点休眠算法,该算法能够有效缩短节点唤醒次数,延长休眠时间,并保证端到端的传播延迟不受休眠时间的影响。该算法无冲突也无需预约信道,保证了较高的网络流量。最后,通过仿真实验验证了算法的可用性和效能。     

Energy harvesting-enabled energy-efficient routing using spotted hyena optimization in wireless sensor network

With the continuous proliferation of sensing technology, it has become possible to utilize energy harvesting (EH)-enabled sensor nodes for a variety of applications. However, conventional wireless sensor networks (WSNs), that is, those without EH-enabled nodes, still have limited applicability due to their limited battery resources. Further, the utilization of EH-enabled nodes in the network not only imposes a financial burden on the user but also limits its performance due to its dependence on environmental conditions. To address this concern, in this paper, we propose the EH-enabled energy-efficient routing (EHEER) technique for green communication in WSN. The predominant concern being addressed in this paper is the selection of cluster head (CH), which helps in gathering, aggregating and forwarding the data from the cluster-based routing paradigm. We use the spotted hyena optimizer (SHO) algorithm for optimizing the fitness parameters for CH selection, namely, energy ratio, distance considerations, node density, load balancing and the network's average energy. We use EH-enabled nodes in the network strategically so as to keep control over the costs incurred in the network. The simulation outcomes empirically prove the efficacy of the proposed work, as it effectively increases the network stability and operational period by a huge margin as compared to the existing techniques.     

基于运动预测的水下传感器网络MAC协议

 在海洋环境的影响下,水下无线传感器网络(UWSN)节点始终处于运动状态;UWSN采用声信号通信,缓慢的节点移动也会造成UWSN现有的多址接入技术(MAC)协议冲突避免机制失效。该文建立了水下节点的运动模型,基于AR运动预测模型减小水下节点的时空不确定性对于MAC协议的影响,提高发送信息在预约时隙到达的概率。仿真结果表明,采用AR(5)预测可以减小74.8%的时延探测误差。提出了基于预测的预约MAC协议：P-MAC。NS-2仿真结果表明该协议在海浪运动场景下能提高收包成功率(PRR)10%-15%。     

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.     

ROTEE: Remora Optimization and Tunicate swarm algorithm-based Energy-Efficient cluster-based routing for EH-enabled heterogeneous WSNs

Since the development of Wireless Sensor Networks (WSNs), the limited battery of the sensor nodes has been an unavoidable concern. Hence, to keep the WSNs operational for a longer possible duration, the recharging of node's battery through harvesting the ambient energy from surroundings (for an example, solar energy) has been proposed. In this work, we focus not only on utilizing the energy harvesting (EH)-enabled sensor nodes for routing purposes but also introduce a novel hybrid optimization ROATSA that uses Remora Optimization Algorithm (ROA) and Tunicate Swarm Algorithm (TSA) for energy-efficient cluster-based routing. The proposed work is termed as ROA and TSA-based Energy-Efficient Cluster-based Routing for EH-enabled WSN (ROTEE). Hybrid ROATSA is chosen due to enhanced convergence and exploitation capabilities. To reduce the financial burden on the network, we use only four EH-enabled nodes and locate them at each periphery of the network, equidistant to each other and the other nodes are 3-level energy heterogeneous sensor nodes. The selection of cluster head (CH) is optimized through ROATSA by considering profile index of each node by evaluating them at energy, distance, load balancing, node density, the delay involved, and network's average energy. The proposed work ROTEE shows supreme performance against the recently proposed clustering techniques.     

Assimilation of fuzzy clustering approach and EHO‐Greedy algorithm for efficient routing in WSN

The problems related to energy consumption and improvement of the network lifetime of WSN (wireless sensor network) have been considered. The base station (BS) location is the main concern in WSN. BSs are fixed, yet, they have the ability to move in some situations to collect the information from sensor nodes (SNs). Recently, introducing mobile sinks to WSNs has been proved to be an efficient way to extend the lifespan of the network. This paper proposes the assimilation of the fuzzy clustering approach and the Elephant Herding Optimization (EHO)‐Greedy algorithm for efficient routing in WSN. This work considers the separate sink nodes of a fixed sink and movable sink to decrease the utilization of energy. A fixed node is deployed randomly across the network, and the movable sink node moves to different locations across the network for collecting the data. Initially, the number of nodes is formed into the multiple clusters using the enhanced expectation maximization algorithm. After that, the cluster head (CH) selection done through a fuzzy approach by taking the account of three factors of residual energy, node centrality, and neighborhood overlap. A suitable collection of CH can extremely reduce the utilization of energy and also enhancing the lifespan. Finally, the routing protocol of the hybrid EHO‐Greedy algorithm is used for efficient data transmission. Simulation results display that the proposed technique is better to other existing approaches in regard to energy utilization and the system lifetime.     

Energy-efficient selection of relay for UWSNs in the Internet of underwater things

Internet of underwater things (IoUT) for underwater monitoring is known worldwide for smart interlinked underwater things that exhibit the capacity to monitor the vast unexplored waters of the oceans. Concept of IoUT has been derived from Internet of Things (IoT) in order to acquire the exquisite benefits of networking in underwater environment. IOT standards and technologies do not work well in underwater environment, such as infrared, Wi-Fi, and radio frequency (RF) due to high channel errors and limited range up to few meters. Acoustic waves, however, can be used to communicate both in shallow and deep oceans due to their low frequency (kHZ) signal. In context of IoUT, communication based on acoustic links enables different applications such as underwater exploration, environmental monitoring, and disaster prevention even without availability of GPS facility like free space environment. In unpredictable and changing underwater environment, energy efficiency becomes a major challenge during data routing along multiple devices. Batteries of the sensor nodes, autonomous underwater vehicle (AUV), and remotely operated vehicle (ROV) cannot be removed with easiness and difficult to recharge, and the only way out is efficient sensor node selection for relaying to save massive amount of energy. Energy aware channel routing protocol (ECARP) does not consider the depth of the node while selecting the relay nodes to forward the data. Relay node selection in underwater Internet of things (IOUT) is a primary problem addressed in this research based on channel state information (CSI) for establishing best path to relay information among IOUT devices. Our major focus was to develop better technique for the relay node selection using a CSI and select relay node by looking at its depth from ocean surface and residual energy in the proposed ED-CARP. Simulation results validate that proposed ED-CARP can decrease the communication cost and increase the network lifetime.     

Prolonging lifetime for wireless rechargeable sensor networks through sleeping and charging scheduling

Prolonging network lifetime is a fundamental requirement in wireless sensor network (WSN). Existing charging scheduling algorithms suffer from high node redundancy and energy consumption issues. In this paper, we study WSN charging problem from the perspectives of energy conservation combined with energy replenishment scheduling. Firstly, we detect the redundant nodes whose energy is wasted in the network functionality and develop a K‐covering redundant nodes sleeping scheduling algorithm (KRSS) for reducing energy. Secondly, we employed multiple wireless charging vehicles (WCVs) for energy replenishment and optimize the charging scheduling algorithm to prevent any exhaustion of nodes, and we proposed a distance and energy–oriented charging scheduling algorithm (DECS) with multiple WCVs. Simulation experiments are conducted to show the advantages of the proposed KRSS+DECS, confirming that our scheme is capable of removing redundant nodes, lowering node failures, and prolonging network lifetime.     

Improved bat optimization algorithm and enhanced artificial bee colony-based cluster routing scheme for extending network lifetime in wireless sensor networks

In this paper, improved bat and enhanced artificial bee colony optimization algorithm-based cluster routing (IBEABCCR) scheme is proposed for optimal cluster head (CH) selection with the merits of global diversity and improved convergence rate. It is proposed for achieving optimal CH selection by balancing the tradeoff between the phases of exploration and exploitation. It specifically targeted on the formulation of an ideal CH selection scheme using improved bat optimization algorithm (IBOA) for minimizing the energy depletion rate. It also focuses on the design of an enhanced artificial bee colony (EABC)-based sink node mobility scheme for determining the optimal points of deployment over which sink nodes can be moved to achieve better delivery of packets from CH to sink node. This CH selection and sink node mobility schemes are contributed for extending the network lifespan using the fitness function, which adopted the factors of node centrality, node degree, distance amid CH and base station (BS), distance among sensor nodes, and residual energy during CH selection process. The simulation experiments were performed using MATLAB version 2018, which confirmed that the number of alive nodes realized in the network is enhanced by 39.21% with the location of BS positioned at (100, 100). The number of rounds (network lifetime) is enhanced by 23.84% with different BS locations in the network. Furthermore, the packets received at the BS are also realized to be enhanced by 26.32% on an average in contrast to the baseline CH schemes used for investigation.     

An efficient UE cluster head selection algorithm in wireless sensor networks and cellular networks

Wireless Sensor Networks (WSNs) have been applied in many different areas. Energy efficient algorithms and protocols have become one of the most challenging issues for WSN. Many researchers focused on developing energy efficient clustering algorithms for WSN, but less research has been concerned in the mobile User Equipment (UE) acting as a Cluster Head (CH) for data transmission between cellular networks and WSNs. In this paper, we propose a cellular-assisted UE CH selection algorithm for the WSN, which considers several parameters to choose the optimal UE gateway CH. We analyze the energy cost of data transmission from a sensor node to the next node or gateway and calculate the whole system energy cost for a WSN. Simulation results show that better system performance, in terms of system energy cost and WSNs life time, can be achieved by using interactive optimization with cellular networks.     

Doppler shift with Archimedes Optimization Algorithm for localizing unknown nodes in underwater sensor networks

The issue of underwater sensor network (UWSN) localization has led to the aim of techniques presented in recent years. In this paper, we develop Doppler shift with Archimedes Optimization Algorithm for localizing unknown nodes in UWSN. The projected method predicts that sink node plays a major function in managing the computational load contrasted with the remaining nodes in the network of underwater. This node localization is proceeding with frequency shifts of sound waves contrasted toward real, which are present once observer in addition source can be mobile as they do in a marine atmosphere. The proposed technique is utilized to compute the estimated position of an unknown sensor node; here Archimedes' optimization algorithm is utilized to reduce the error during localization of nodes in UWSNs. This proposed technique can be enhancing the accuracy of the localization of nodes in UWSNs. This proposed methodology can be implemented and evaluated with the help of performance metrics. To validate the proposed technique's efficiency, it is contrasted with conventional techniques like Particle Swarm Optimization (PSO) and Whale Optimization Algorithm (WOA).     

水下无线传感器网络中一种安全定位算法

提出一种基于阵列传输结构的无线传感器网络安全定位算法（USA）。该算法主要解决水下无线传感器网络（UWSN）面临的一些安全威胁问题。以提高无线传感器网络安全性,特别是位置信息的安全性为设计目标。利用节点协作形成的阵列作为天线阵列进行相互通信,在不增加额外硬件成本的同时,还获得阵列天线给无线传感器网络带来的优势,如减小多径效应、提高接收端的信噪比、增加系统容量等。USA算法基于这种阵列结构使网络得到很高安全特性,特别是,对Wormhole攻击具有非常好的抵御性能。仿真实验证明该算法的有效性。     

Energy Efficient Energy Hole Repelling (EEEHR) Algorithm for Delay Tolerant Wireless Sensor Network

Reducing energy consumption and increasing network lifetime are the major concerns in Wireless Sensor Network (WSN). Increase in network lifetime reduces the frequency of recharging and replacing batteries of the sensor node. The key factors influencing energy consumption are distance and number of bits transmitted inside the network. The problem of energy hole and hotspot inside the network make neighbouring nodes unusable even if the node is efficient for data transmission. Energy Efficient Energy Hole Repelling (EEEHR) routing algorithm is developed to solve the problem. Smaller clusters are formed near the sink and clusters of larger size are made with nodes far from the sink. This methodology promotes equal sharing of load repelling energy hole and hotspot issues. The opportunity of being a Cluster Head (CH) is given to a node with high residual energy, very low intra cluster distance in case of nodes far away from the sink and very low CH to sink distance for the nodes one hop from the sink. The proposed algorithm is compared with LEACH, LEACH-C and SEP routing protocol to prove its novel working. The proposed EEEHR routing algorithm provides improved lifetime, throughput and less packet drop. The proposed algorithm also reduces energy hole and hotspot problem in the network.     

An energy efficient hole repair node scheduling algorithm for WSN

A sensor node in the wireless sensor network has limited energy and it normally cannot be replaced due to the random deployment, so how to prolong the network life time with limited energy while satisfying the coverage quality simultaneously becomes a crucial problem to solve for wireless sensor networks (WSN). In this work, we propose an energy efficient algorithm based on the sentinel scheme to reduce the sleeping node detection density by defining a new deep sleeping state for each sensor node. The average energy consumed by probing neighboring nodes is introduced as a factor to calculate the detection rate. In addition, after some theoretical analysis of the existence of coverage holes in WSN, a triangle coverage repair procedure is defined to repair coverage holes. Simulation results show that our proposed algorithm obtained better performance in terms of the coverage quality and network life time compared with some existing algorithms in the literature.     

CT-TDMA：水下传感器网络高效TDMA协议

针对水下无线传感器网络(UWSN,underwater sensor networks)提出以发送端为中心以连续时间为计量单位的冲突状态模型——局部冲突状态图及其分布式构建算法,并在此基础上设计了基于启发式规则的水下传感器网络TDMA协议(CT-TDMA,continuous time based TDMA)。CT-TDMA利用UWSN中同一接收节点与不同发送节点之间链路时延的差异性,减少在目的端的接收帧之间的空闲时间,从而提高网络流量;基于启发式规则的分配算法,能有效缩短连续时间轴上的时刻分配所花费的时间。模拟实验证明:CT-TDMA与以ST-MAC为代表的按时隙分配的TDMA方案相比,网络流量提高了20%,数据分组的端到端时延降低了18%;与由全局知识所计算出的最优分配策略相比,网络流量达到了80%,端到端时延仅延长了12%。     

LB-AGR: level-based adaptive geo-routing for underwater sensor network简

Underwater sensor network（UWSN） adopts acoustic communication with more energy-consumption and longer propagation-delay, which bring great challenges to protocol design. In this paper, we proposed level-based adaptive geo-routing（LB-AGR） protocol. LB-AGR divides traffics into four categories, and routes different types of traffic in accordance with different decisions. Packets upstream to the sink are forwarded unicast to the best next-hop instead of broadcasting to all neighbor nodes as in present UWSN routing protocols. LB-AGR defines an integrated forwarding factor for each candidate node based on available energy, density, location, and level-difference between neighbor nodes, which is used to determine the best next-hop among multiple qualified candidates. Through simulation experiments, we show the promising performance of LB-AGR.     

一种负载均衡且能量高效的水下传感网络分簇协议

针对水下无线传感网络能量效率低、生命周期短的问题,提出了一种负载均衡且能量高效的水下分簇（load balanced and energy efficient underwater clustering,LBEEUC）协议。该算法在分簇过程中首先根据节点的经验负载来确定节点所在区域簇头的比例,使经验负载大的区域分布较多的簇头,分担数据转发的任务,均衡网络的能耗;其次在节点入簇时,在簇内设置中继节点,用于均衡远离簇头节点的传输能耗,并提前进行数据融合,减少数据冗余;最后在建立簇间路由时,利用Q 学习算法根据路径消耗的总能量最小的原则选择最优传输路径。仿真结果表明,本算法有效地均衡了网络的能耗,提高了能量利用效率,进而提高了网络的生存时间。