无线传感器网络中终端节点的休眠算法

针对无线传感器网络中节点能量有限等特点,提高能量利用效率和延长网络寿命是无线传感器网络设计的目标。由于终端节点只负责数据采集,不需要转发其它节点的数据,因此可以减少终端节点的空闲侦听时间,从而进入休眠状态。仿真结果验证了所提方案的能量消耗低于传统非休眠方案的能量消耗,该休眠算法能有效延长网络的生存时间。     

基于休眠和扇区的传感器网络拓扑控制算法

拓扑控制算法对节省无线传感器网络节点能量、延长网络寿命具有重要意义。无线传感器网络节省节点能量有两种方法：层次型拓扑结构控制和功率控制；但目前还没有算法将以上两种方法相结合。丈中是结合周期性休眠和拓扑控制的一种新型拓扑控制策略：基于休眠和扇区的无线传感器网络拓扑控制算法，将拓扑控制算法和节点的休眠结合起来。仿真结果表明：算法在不降低网络吞吐量条件下节省了网络能量消耗，有效延长网络寿命。     

认知无线传感器网络中基于GSC的协作传输机制

针对无线传感器网络中单个节点能量和发射功率均受限的特点,在发送端已知信道状态信息(CSI)的条件下提出了一种基于广义选择合并算法(GSC)的协作传输机制。根据GSC算法和节点的功率受限条件来决定具体参与协作传输的节点个数,并结合信道状态和节点的剩余能量来对节点进行调度。理论分析和仿真结果表明该机制能够有效地延长网络寿命。     

基于能量探测的WSN节点调度算法研究

针对无线传感器网络在节点传输过程中的延迟与链路质量问题,设计一种基于能量探测的节点休眠调度算法EAS,通过对节点插入苏醒时隙来减小端到端之间的延迟,并根据每个节点的剩余能量值进行能量探测,使整个网络中各节点的能量相对均衡地消耗,在保证传输质量的基础上达到延长网络的生命周期的目的。实验结果表明,对比现有算法,EAS算法能够在保证使WSN网络在延迟一定的情况下明显降低能耗,延长整个网络的工作寿命。     

一种无线传感器网络全连通群的休眠调度算法

该文针对无线传感器网络的覆盖性和连通性问题,在假设传感器节点地理位置信息已知的条件下,设计了一种包含全连通群的建立和维护以及群内节点休眠调度的全新算法。该算法采用保证群内节点彼此一跳可达的全连通群分群方法,以及分布式节能的休眠调度策略,最大程度上减少传感器网络的能量消耗,延长了网络寿命。仿真结果表明:该算法能较好地保证无线传感器网络的覆盖性和连通性,且能耗较低。     

无线传感器网络的中继节点自适应选择休眠机制

针对无线传感器网络节点能量有限的问题,设计一种中继节点自适应休眠方法,对网络节点的工作时间进行优化调度。在路由链路建立中,根据网络中节点的地理位置、剩余能量选择中继节点的方法进行节点休眠调度。网络工作时通过计算下一跳最佳节点位置,选取其附近区域内剩余能量多的节点作为下一跳的转发节点,设计出中继节点自适应选择的节点休眠机制。仿真结果表明,所设计的休眠机制提高了节点能量利用效率、延长了网络工作时间。     

基于冗余节点休眠和分阶段唤醒策略的传感器网络三维覆盖控制方法

该文针对无线传感器网络中节点能量有限且密集布点时存在大量冗余节点的情况,提出了基于冗余节点休眠和分阶段唤醒策略的无线传感器网络3维覆盖控制方法。在3维待监测区域中随机配置大量传感器节点,达到高密度分布,使冗余节点处于休眠状态,等待活跃节点能量耗尽之后,分阶段唤醒休眠节点,直至整个传感器网络中所有节点的能量都耗尽为止。仿真结果表明,该方法提高了传感器网络的网络性能,且对相同的传感器节点数,分阶段唤醒策略优于不分阶段的唤醒策略,先使冗余节点休眠之后再唤醒方法的网络性能高于直接唤醒方法的网络性能。     

传感器网络的任务双效节能调度研究

能源供应有限性是局限传感器网络的性能和存活寿命的重要因素,本文从传感器网络节点的任务调度出发,提出动态能量管理DPM和动态电压/频率调节DV/FS的双效处理器节能调度算法,即DV/FS-RM和DV/FS-EDF调度算法;在DPM动态控制空闲任务进入休眠的同时,在保证节点的实时性的前提下,通过DV/FS-RM或DV/FS-EDF算法降低处理器频率,达到更好的节能效果.实验显示,该节能任务调度算法使以电池为能源的传感器网络节点的生存期成倍地延长.     

基于节点休眠的水下无线传感器网络覆盖保持分簇算法

为有效延长水下无线传感器网络的生命周期、保持网络覆盖率,该文提出一种基于节点休眠的覆盖保持分簇算法。首先计算网络节点的覆盖冗余度,并对覆盖冗余度高的节点执行休眠策略,然后以网络覆盖率及节点能耗均衡性为目标,采用多目标算法进行求解,再利用TOPSIS法从非支配解集中选出较优解,当有节点死亡时,通过唤醒策略保持网络覆盖率。仿真结果表明,与目前较好的网络规划算法相比,该文算法能够更好地降低网络能耗,延长网络生命周期并保持网络对环境的覆盖率。     

基于全网休眠的ZigBee网络节能算法

无线传感器网络节点一般由电池供电而且部署后需要长时间工作,这就导致节点的能量成为网络运行的瓶颈。针对无线传感器网络节点能量受限的问题,提出了一种基于全网休眠的节能新算法。该算法使终端节点、路由节点和汇聚节点都能得到休眠,从而为网络中每一种节点节约能量;同时,为路由节点设计了长、短两种休眠策略,在不影响数据收发的前提下减少能耗。基于CC2630芯片的实验结果表明:与现有相关算法相比,该新算法使节点能耗降低10%。     

DRP: An energy‐efficient routing protocol for underwater sensor networks

The features of transmissions in underwater sensor networks (UWSNs) include lower transmission rate, longer delay time, and higher power consumption when compared with terrestrial radio transmissions. The negative effects of transmission collisions deteriorate in such environments. Existing UWSN routing protocols do not consider the transmission collision probability differences resulting from different transmission distances. In this paper, we show that collision probability plays an important role in route selection and propose an energy‐efficient routing protocol (DRP), which considers the distance‐varied collision probability as well as each node's residual energy. Considering these 2 issues, DRP can find a path with high successful transmission rate and high‐residual energy. In fact, DRP can find the path producing the longest network lifetime, which we have confirmed through theoretical analysis. To the best of our knowledge, DRP is the first UWSN routing protocol that uses transmission collision probability as a factor in route selection. Simulation results verify that DRP extends network lifetime, increases network throughput, and reduces end‐to‐end delay when compared with solutions without considering distance‐varied collision probability or residual energy.     

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%。     

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.     

Energy-optimized cluster head selection based on enhanced remora optimization algorithm in underwater wireless sensor network

With the technological advancements, wireless sensor network (WSN) has played an impeccable role in monitoring the underwater applications. Underwater WSN (UWSN) is supported by WSN but subjected to data dissemination in an acoustic medium. Due to challenging conditions in underwater scenario, the limited battery resources of these sensor nodes stem to a crucial research problem that needs to address the energy-efficient routing in UWSN. In this research work, we intend to propose an energy-optimized cluster head (CH) selection based on enhanced remora optimization algorithm (ECERO) in UWSN. Since CH devours the maximum energy among the nodes, we perform selection of CH based on EROA while considering energy, Euclidean distance from sink, node density, network's average energy, acoustic path loss model and lastly, the adaptive quantity of CHs in the network. Further, to reduce the load on CH node, we introduce the concept of sleep scheduling among the closely located cluster nodes. The proposed work improves the performance of recently proposed EOCSR algorithm by great magnitude which claims to mitigate hot-spot problem, but EOCSR still suffers from the same due to relaying a large magnitude of data.     

面向混合业务的无线传感器网络能量有效接入策略

研究了在实时业务和非实时业务同时存在的混合背景下,非实时业务的无线传感器节点自适应侦听和睡眠的动态接入机制。网络节点处于睡眠状态时所需的能量很低,节约了无线传感器网络节点的平均能量消耗;但是,过长的睡眠时间可能使得网络节点错失传输机会。因此,根据信道的使用情况,合理地设定无线传感器网络节点的睡眠时间,能够在网络能量消耗和传输效率之间进行调整,从而最大化无线传感器网络的能量传输效率。首先,利用连续时间 Markov 方法对问题进行建模,并利用基于摄动分析理论对系统模型进行分析,获得求解无线传感器网络能量效率最大化的最优睡眠时间梯度算法。最后通过理论结果和计算机仿真模拟的对比,验证了推荐方法的可行性。     

HSCA: a novel harmony search based efficient clustering in heterogeneous WSNs

Clustering objective reasons scalability, fault tolerance, data aggregation or fusion, load balancing of cluster heads, stabilized network topology, maximal network lifetime, increased connectivity, reduced routing delay, collision avoidance and utilizing sleeping schemes in wireless sensor networks. Load balanced clustering effectively organize the network into a connected hierarchy. Clustering is a discrete problem that can have more than one solution under different operating constraints. In this scenario, meta-heuristic algorithms are found suitable because they give set of solutions in acceptable time constraints. In the literature, several analytical and meta-heuristic approaches have been developed for load balanced clustering. In this paper, a novel harmony search based energy efficient load balanced clustering algorithm is presented and it is tested on a large sample network. Results demonstrated that the proposed approach has faster convergence and gives reliable and efficient load balanced clustering as compared to conventional harmony search algorithm (HSA) and several other methods in the literature. Moreover, the robustness of the proposed approach is also verified for different cases of fixed and variable parameters of HSA.     

Signaling game approach to improve the MAC protocol in the underwater wireless sensor networks

Underwater sensor networks (UWSNs) are instructed for critical applications like military surveillance and underwater oil spills that conducted in a very massive three‐dimensional (3‐D) space that needs many underwater nodes (UNs) to cover the target area. Those UNs are not easy to recharge and cannot exploit solar power. MAC protocols deployed for UWSN ought to consider the energy efficiency, so as, to extend the network lifetime with total connectivity and significant throughput. Terrestrial MAC protocols could not be used for UWSN due to long and unpredictable propagation delay. Consequently, the development of a new MAC protocol for the harsh environment as underwater is a challenging task. In this study, we focus on the deployment of TDMA in UWSN for this, two schemes entitled TDMA slot sharing (TSS) and free time slots reallocation (FTSR) are proposed. Received data stored in the buffer waiting for processing and forwarding might lead to an unlimited data transfer latency those results in the buffer overflow. Otherwise, free time slots appearing during the communication process resulting from dead nodes increase uselessly sleep time for the rest of the nodes. Both schemes based on signaling game are proposed to overcome those problems, TSS is used to enable the slot sharing between UNs during the communication process to reduce the buffer overflow. FTSR scheme aims to increase the throughput of UNs by allowing the reuse of free time slots. Numerical results conducted in this work show good improvement in the network performance concerning throughput.     

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。     

MRL-SCSO: Multi-agent Reinforcement Learning-Based Self-Configuration and Self-Optimization Protocol for Unattended Wireless Sensor Networks

Resource-constrained nodes in unattended wireless sensor network (UWSN) operate in a hostile environment with less human intervention. Achieving the optimal quality of service (QoS) in terms of packet delivery ratio, delay, energy, and throughput is crucial. In this paper, we propose a topology control and data dissemination protocol that uses multi-agent reinforcement learning (MRL) and energy-aware convex-hull algorithm, for effective self-configuration and self-optimization (SCSO) in UWSN, called MRL-SCSO. MRL-SCSO maintains a reliable topology in which the effective active neighbor nodes are selected using MRL. The network boundary is determined using convex-hull algorithm to maintain the connectivity and coverage of the network. The boundary nodes transmit data under high traffic load conditions. The performance of MRL-SCSO is evaluated for various nodes count and under different load conditions by using the Contiki’s Cooja simulator. The results showed that MRL-SCSO stabilizes the performance and improves QoS.     

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.