Efficient Energy Utilization Through Optimum Number of Sensor Node Distribution in Engineered Corona-Based (ONSD-EC) Wireless Sensor Network

An optimum sensor node deployment in wireless sensor network can sense the event precisely in many real time scenarios for example forests, habitat, battlefields, and precision agriculture. Due to these applications, it is necessary to distribute the sensor node in an efficient way to monitor the event precisely and to utilize maximum energy during network lifetime. In this paper, we consider the energy hole formation due to the unbalanced energy consumption in many-to-one wireless sensor network. We propose a novel method using the optimum number of sensor node Distribution in Engineered Corona-based wireless sensor network, in which the interested area is divided into a number of coronas. A mathematical models is proposed to find out the energy consumption rate and to distribute the optimum number of sensor node in each corona according to energy consumption rate. An algorithm is proposed to distribute the optimum number of sensor nodes in corona-based networks. Simulation result shows that the proposed technique utilized 95 % of the total energy of the network during network lifetime. The proposed technique also maximizes the network lifetime, data delivery and reduce the residual energy ratio during network lifetime.     

无线传感器网络自适应目标跟踪节点调度算法

在无线传感器网络中,设计合理的节点调度算法是提高网络感知能力、降低系统能耗的关键。在分析节点能耗模型的基础上,针对移动目标跟踪型网络应用,提出一种高能效的无线传感器网络自适应节点调度算法ANSTT。该算法根据节点对移动目标的感知能力,以及节点的相对剩余能量水平,自动调整节点工作模式。仿真实验表明,ANSTT算法在维持低感知延时、高目标感知率的同时,可有效降低系统能耗,延长网络寿命。     

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。     

Dynamic power management in new architecture of wireless sensor networks

Dynamic power management (DPM) technology has been widely used in sensor networks. Though many specific technical challenges remain and deserve much further study, the primary factor currently limiting progress in sensor networks is not these challenges but is instead the lack of an overall sensor network architecture. In this paper, we first develop a new architecture of sensor networks. Then we modify the sleep state policy developed by Sinha and Chandrakasan in (IEEE Design Test Comput. 2001; 18 (2):62–74) and deduce that a new threshold satisfies the sleep‐state transition policy. Under this new architecture, nodes in deeper sleep states consume lower energy while asleep, but require longer delays and higher latency costs to awaken. Implementing DPM with considering the battery status and probability of event generation will reduce the energy consumption and prolong the whole lifetime of the sensor networks. We also propose a new energy‐efficient DPM, which is a modified sleep state policy and combined with optimal geographical density control (OGDC) (Wireless Ad Hoc Sensor Networks 2005; 1 (1–2):89–123) to keep a minimal number of sensor nodes in the active mode in wireless sensor networks. Implementing dynamic power management with considering the battery status, probability of event generation and OGDC will reduce the energy consumption and prolong the whole lifetime of the sensor networks. Copyright © 2008 John Wiley & Sons, Ltd.     

局部构造邻居最优能耗路的拓扑控制算法

无线传感器网络拓扑控制算法的首要任务是减少节点的能量消耗,从而延长整个网络的生存时间。而传感器节点大部分的能量消耗在无线通信模块。在分析了拓扑控制算法所基于的两种模型(UDG,MG)以及两种不同性质的网络(homogenous net,heterogeneous net)的基础上,考虑到在通常情况下,无线通信的能量消耗与通信距离的3次方成正比。基于此,针对能否设计一种选择邻居的标准,使邻居间的链接都是最小能耗链接这一问题,给出了一种构造最小能耗路的邻近图算法MEP(MG)。证明了算法具有连通性和1-spanner特性。     

多簇无线传感网的优化生存时间近邻功率控制算法

针对非均匀分布的无线传感网的生存时间问题,提出多簇无线传感网的优化生存时间近邻功率控制(NPCAOL_MC)算法。该算法采用K-means算法确定网络的簇个数和对应每个簇的节点,利用近邻算法评估每个簇的节点密度,确定簇的最优通信距离。结合Friss自由空间模型计算当前簇的最优发送功率。Sink节点广播通知其他节点,如果是同一簇内的节点相互通信,则采用簇最优功率发送数据,否则采用默认最大发送功率发送数据。仿真结果表明,利用NPCAOL_MC算法可以分析整个网络节点的位置信息,采用簇最优发送功率发送数据,从而提高生存时间,并使能耗经济有效。在密度分布不均的无线传感网中,NPCAOL_MC比采用固定发送功率的Ratio_w算法更优。     

QoS provisioning in wireless video sensor networks: a dynamic power management framework

Recent technological advances in microelectronics and nano-systems technologies have made it feasible to equip wireless sensor nodes with small low-cost cameras to capture and transmit video. Wireless video sensor networks are gaining popularity due to numerous potential applications such as video surveillance, environmental and habitat monitoring, and so on. However, due to the limited battery available in wireless video sensor nodes, provisioning of QoS in such a network is a challenging task. We provide a survey on the major issues related to QoS provisioning in wireless video sensor networks and possible solution approaches. A dynamic power management framework is proposed for a wireless video sensor node to improve energy saving performance so that the lifetime of the sensor node can be increased. This framework considers the video traffic arrival process in the sensor node, the sleep and wakeup processes in the camera and wireless transceiver electronics, the queue status, and the wireless channel condition. Performance analysis results show that the proposed mechanism can achieve considerable energy saving in a sensor node while providing a target level of QoS performance.     

一种基于冗余度的无线传感器网络节能策略

在无线传感器网络中,传感节点由于采用电池供电,因此寿命有限。如何有效节省传感器节点的能量,延长网络的使用寿命,一直是广泛研究的焦点。文章提出一种适用于高冗余度布置的无线传感器网络结构中,节省传感器节点能量消耗的方法-接续调度法。该方法通过协调点对小区域内节点的调度,使区域内节点依次分时段工作。通过这种接续调度,避免了节点间的冲突和串扰,达到延长整体网络寿命的效果。     

Target localization based on energy considerations in distributed sensor networks

Wireless distributed sensor networks are important for a number of strategic applications such as coordinated target detection, surveillance, and localization. Energy is a critical resource in wireless sensor networks and system lifetime needs to be prolonged through the use of energy-conscious sensing strategies during system operation. We propose an energy-aware target detection and localization strategy for cluster-based wireless sensor networks. The proposed method is based on an a posteriori algorithm with a two-step communication protocol between the cluster head and the sensors within the cluster. Based on a limited amount of data received from the sensor nodes, the cluster head executes a localization procedure to determine the subset of sensors that must be queried for detailed target information. This approach reduces both energy consumption and communication bandwidth requirements, and prolongs the lifetime of the wireless sensor network. Simulation results show that a large amount of energy is saved during target localization using this strategy.     

An optimised fuzzy clustering for wireless sensor networks

Minimising energy consumption has always been an issue of crucial importance in sensor networks. Most of the energy is consumed in data transmission from sensor nodes to the base station due to the long distance of nodes from the base station. In the recent past, a number of researchers have proposed that clustering is an efficient way of reducing the energy consumption during data transmission and enhancing the lifetime of wireless sensor networks. Many algorithms have been already proposed for cluster head selection. In this work, we analyse and compare the lifetime of the network with three different fuzzy-based approaches of cluster head selection. The three strong parameters which play an important role in lifetime enhancement – energy, centrality and node density – are considered for cluster head selection in our proposed fuzzy approaches. In the first approach, energy and centrality are considered simultaneously in a fuzzy system to select the cluster heads. In the second approach, energy and node density have been taken in a fuzzy system to select the cluster heads. In the third approach, node density and centrality are considered simultaneously by a fuzzy system to select the cluster heads. Simulation results of these fuzzy logic-based approaches show that all the three approaches are superior to the Low-Energy Adaptive Clustering Hierarchy (LEACH). Simulation results also show that the energy-centrality-based fuzzy clustering scheme gives best performance among all the three fuzzy-based algorithms and it enhances the lifetime of wireless sensor networks by a significant amount.     

多媒体传感网中一种基于优化覆盖及压缩代价评估的节能机制

为降低无线多媒体传感网能耗,利用贪婪算法调整节点传感方向,实现优化覆盖并降低了传感开销.同时,对节点数据压缩代价进行评估,使其以最小的能量代价传输数据,并根据多媒体事件流规律,优化网络生存时间.仿真结果表明,本方法在延长网络生存时间方面表现出了较好性能.     

一种基于无交集节点分组的无线传感器网络覆盖算法

无线传感器网络是一种无线自组织网络,它由大量能量有限的传感器节点组成.能量消耗和网络覆盖是无线传感器网络的两个核心问题,网络覆盖决定了无线传感器网络对物理世界的监测能力,能量消耗则决定了无线传感器网络的生存时间.本文研究了一种改进的基于无交集节点分组算法,针对随机选取节点实现无交集节点分组方式获得的分组个数少且节点通信...     

Topology control in the presence of jammers for wireless sensor networks

In this paper, the problem of ensuring packet delivery ratio and high network lifetime in wireless sensor networks in the presence of single or multiple jammers is studied using single‐leader‐multiple‐followers Stackelberg game theory. A topology control scheme is proposed, in which the sink node, which acts as the leader, identifies the set of jamming affected nodes. On the other hand, the sensor nodes, which act as followers, need to decide an optimum transmission power level, while ensuring an optimal set of neighbor nodes covered. A scheme, named TC‐JAM, for ensuring packet delivery ratio, while avoiding jammers and increasing network lifetime in wireless sensor networks, is proposed. In existing literatures, the sensor nodes are envisioned to be equipped with multiple interfaces, while having access for multiple channels. However, in TC‐JAM, the sensor nodes have simple hardware with single interface for communication, ie, the sensor nodes have single channel for communication. Additionally, in the proposed scheme, TC‐JAM, each sensor node has a provision to vary its transmission power according to the chosen strategies. Using TC‐JAM, the energy consumption of the overall network reduces by up to 62%, and the network lifetime increases by 56% to 73%.     

Distributed Temporal Data Prediction Model for Wireless Sensor Network

Sensor networks are critical for building smart environments for monitoring various physical and environmental conditions. Several automated tasks involving continuous and critical practically becomes infeasible for humans to perform with precision. Therefore, wireless sensor networks have emerged as the next-generation technology to permeate the technological upgradations into our daily activities. Such intelligent networks, embedded with sensing expertise, however, are severely energy-constrained. Sensor networks have to process and transmit large volumes of data from sensors to sink or base station, requiring a lot of energy consumption. Since energy is a critical resource in the sensor network to drive all its basic functioning, hence, it needs to be efficiently utilized for elongating network lifetime. This makes energy conservation primarily significant in sensor network design, especially at the sensor node level. Our research proposes an On-balance volume indicator-based Data Prediction (ODP) model for predicting the temperature in the sensor network. Our proposed model can be used to predict temperature with a permissible error of tolerance. This helps in reducing excessive power consumption expended in redundant transmissions, thereby increasing the network lifetime. The proposed data prediction model is compared with existing benchmark time series prediction models, namely Linear Regression (LR) and Auto-Regressive Integrated Moving Average (ARIMA). Experimental outcomes endorsed that our proposed prediction model outperformed the existing counterparts in terms of prediction accuracy and reduction in the number of transmissions in clustered architecture.

     

Maximum lifetime routing in wireless sensor networks

A routing problem in static wireless ad hoc networks is considered as it arises in a rapidly deployed, sensor based, monitoring system known as the wireless sensor network. Information obtained by the monitoring nodes needs to be routed to a set of designated gateway nodes. In these networks, every node is capable of sensing, data processing, and communication, and operates on its limited amount of battery energy consumed mostly in transmission and reception at its radio transceiver. If we assume that the transmitter power level can be adjusted to use the minimum energy required to reach the intended next hop receiver then the energy consumption rate per unit information transmission depends on the choice of the next hop node, i.e., the routing decision. We formulate the routing problem as a linear programming problem, where the objective is to maximize the network lifetime, which is equivalent to the time until the network partition due to battery outage. Two different models are considered for the information-generation processes. One assumes constant rates and the other assumes an arbitrary process. A shortest cost path routing algorithm is proposed which uses link costs that reflect both the communication energy consumption rates and the residual energy levels at the two end nodes. The algorithm is amenable to distributed implementation. Simulation results with both information-generation process models show that the proposed algorithm can achieve network lifetime that is very close to the optimal network lifetime obtained by solving the linear programming problem.     

基于发布/订阅模型的WSN设计

为降低通信频率对节点能耗的影响同时提高大规模节点下传感器网络的连通率,本文设计了一种以发布/订阅模型作为应用层数据交互方式的无线传感器网络。该网络利用6LoWPAN技术实现组网,并以Contiki操作系统为平台完成了基于发布/订阅模型的MQTT-S应用层协议设计。测试结果表明,该设计能够有效降低节点的通信流量,实现对节点功耗以及网络通信优化,并能与互联网实现无缝连接减少开发成本,对大规模传感器网络的设计与应用具有重要意义。     

A new algorithm for cluster head selection in LEACH protocol for wireless sensor networks

In wireless sensor network, a large number of sensor nodes are distributed to cover a certain area. Sensor node is little in size with restricted processing power, memory, and limited battery life. Because of restricted battery power, wireless sensor network needs to broaden the system lifetime by reducing the energy consumption. A clustering‐based protocols adapt the use of energy by giving a balance to all nodes to become a cluster head. In this paper, we concentrate on a recent hierarchical routing protocols, which are depending on LEACH protocol to enhance its performance and increase the lifetime of wireless sensor network. So our enhanced protocol called Node Ranked–LEACH is proposed. Our proposed protocol improves the total network lifetime based on node rank algorithm. Node rank algorithm depends on both path cost and number of links between nodes to select the cluster head of each cluster. This enhancement reflects the real weight of specific node to success and can be represented as a cluster head. The proposed algorithm overcomes the random process selection, which leads to unexpected fail for some cluster heads in other LEACH versions, and it gives a good performance in the network lifetime and energy consumption comparing with previous version of LEACH protocols.     

基于WirelessHART无线传感器网络系统的设计与实现

针对传统无线传感器网络系统精度低、能耗高、通信距离短等问题。提出一种基于WirelessHART技术的新型无线传感器网络系统,该系统以WirelessHART标准为平台,在此基础上设计了主板电路、无线通信以及传感器等模块。并对WirelessHART设备节点进行有障碍传输、精度以及能耗测试。测试结果表明：设计的WirelessHART设备节点可自动克服障碍传输,精度高达98%,相比ZigBee终端节点可降低31天的能耗。该系统设计的节点具有体积小,功耗低,精度高,抗扰能力强等优点。     

基于空间相关性的事件驱动无线传感器网络分簇算法

分簇算法是传感器网络中减少能量消耗的一种关键技术,它能够增强网络的扩展性和延长网络的生存时间。针对传感器节点数据的空间相关性,该文提出了一种新的基于空间相关性的事件驱动传感器网络分簇算法。算法根据用户要求的误差门限及结合节点数据的空间相关性马尔可夫模型,将事件感知区域划分成虚拟极坐标等价层。每个等价层选取层内当前剩余能量最大的节点作为簇头,网络通过移动代理收集簇头感知信息,该方法减少了传输数据量,有效节省了网络能量。     

Optimal load balanced clustering in homogeneous wireless sensor networks

Balancing the load among sensor nodes is a major challenge for the long run operation of wireless sensor networks. When a sensor node becomes overloaded, the likelihood of higher latency, energy loss, and congestion becomes high. In this paper, we propose an optimal load balanced clustering for hierarchical cluster‐based wireless sensor networks. We formulate the network design problem as mixed‐integer linear programming. Our contribution is 3‐fold: First, we propose an energy aware cluster head selection model for optimal cluster head selection. Then we propose a delay and energy‐aware routing model for optimal inter‐cluster communication. Finally, we propose an equal traffic for energy efficient clustering for optimal load balanced clustering. We consider the worst case scenario, where all nodes have the same capability and where there are no ways to use mobile sinks or add some powerful nodes as gateways. Thus, our models perform load balancing and maximize network lifetime with no need for special node capabilities such as mobility or heterogeneity or pre‐deployment, which would greatly simplify the problem. We show that the proposed models not only increase network lifetime but also minimize latency between sensor nodes. Numerical results show that energy consumption can be effectively balanced among sensor nodes, and stability period can be greatly extended using our models.