Optimal placement and routing strategies for resilient two‐tiered sensor networks

In hierarchical sensor networks using relay nodes, sensor nodes are arranged in clusters and higher powered relay nodes can be used as cluster heads. The lifetime of such a network is determined primarily by the lifetime of the relay nodes. In this paper, we propose two new integer linear programs (ILPs) formulations for optimal data gathering, which maximize the lifetime of the upper tier relay node network. Unlike most previous approaches considered in the literature, our formulations can generate optimal solutions under the non‐flow‐splitting model. Experimental results demonstrate that our approach can significantly extend network lifetime, compared to traditional routing schemes, for the non‐flow‐splitting model. The lifetime can be further enhanced by periodic updates of the routing strategy based on the residual energy at each relay node. The proposed rescheduling scheme can be used to handle single or multiple relay node failures. We have also presented a very simple and straightforward algorithm for the placement of relay nodes. The placement algorithm guarantees that all the sensor nodes can communicate with at least one relay node and that the relay node network is at least 2‐connected. This means that failure of a single relay node will not disconnect the network, and data may be routed around the failed node. The worst case performance of the placement algorithm is bounded by a constant with respect to any optimum placement algorithm. Copyright © 2008 John Wiley & Sons, Ltd.     

低能耗节点位置未知无线传感器网络控制方案

介绍了一种低能耗节点位置未知的网络控制方案,根据不同的网络运行轮数设定网络节点的通信半径,使网络具有良好的能量有效性.网络中基站经过构建阶段的启动过程、节点信息收集过程和节点信息上报过程,获得了整个网络节点的相对位置分布,然后整合节点-节点信息支路,得到具有回路链接的簇首节点集,其他节点根据自己邻居信息选择簇首节点,实现网络近似最小能耗拓扑的构建.通过仿真与同类典型算法LEACH-C、MCLB进行比较,结果显示该方案应用于网络运行时具有更长的网络生命周期、更少的信息总数和更低的网络构建代价.     

Adaptive concentric chains protocol for energy efficient routing in wireless sensor networks

This paper addresses the energy efficiency of data collection based on a concentric chain clustering topology for wireless sensor networks (WSNs). To conserve the energy dissipation of nodes spent in data routing, the paper attempts to take advantage of the two opportunities: (a) the impact of the relative positions of wireless nodes to the base station on the energy efficiency of the routing chain within each cluster; (b) the effect of the varying‐sized chains on the selection rule of cluster heads (CHs). To establish an energy‐efficient chain to connect all the nodes in a cluster, the paper proposes a principal vector projection approach, which takes into account both the position of each node and that of the base station, to determine the order to which a node can be linked into the chain in order to reduce the energy requirement of the chain. Since the CH selection rules in the concentric chains are mutually independent, solely based on their self‐cluster sizes, the multi‐hop path passing through all the CHs will consist of longer links and thus consume a significant fraction of the total energy. Thus, in order to suppress the effect of the unequal cluster sizes on decreasing the energy efficiency of the multi‐hop path of CHs, the paper offers an average‐cluster‐size‐based rule (ACSB) for each cluster in order to adapt the CH selection with both the number of active nodes in the current cluster and the average value of all cluster sizes. With these two proposed schemes, an adaptive concentric chain‐based routing algorithm is proposed which enables nodes to collaboratively reduce the energy dissipation incurred in gathering sensory data. By computer simulation, the results demonstrate that the proposed algorithm performs better than other similar protocols in terms of energy saved and lifetime increased capabilities for WSNs which deploy random sensor nodes. Copyright © 2010 John Wiley & Sons, Ltd.     

A faulty node detection scheme for wireless sensor networks that use data aggregation for transport

This paper presents a faulty node detection approach for wireless sensor networks that aggregate measurement data on their way toward the sink (base station). The approach is based on the idea of commanding sensor nodes on the aggregation paths to temporarily stop including their readings in the received aggregated readings from their upstream neighbors. The scheme is dependent on the ability of the sink to detect faulty nodes through changes in the received aggregated readings at the sink using a Markov Chain Controller (MCC). The algorithm that is run in the sink uses the MCC to assign a state to each sensor node based on transitions that are triggered by receiving aggregated path readings, and accordingly deduces the nodes that may be faulty. The experimental results show at least 98% detection rate at the cost of reasonable detection delays and generated wireless network traffic. Copyright © 2016 John Wiley & Sons, Ltd.     

A delay and energy aware coordination mechanism for WSAN

In this paper, a delay and energy aware coordination mechanism (DEACM) has been devised for wireless sensor–actor networks. In DEACM, a two‐level hierarchical K‐hop clustering mechanism is used to organize the sensors and actors for communication. In the first level, sensors form a K‐hop cluster using actors as cluster heads, and sink is made as the cluster head in the second level to form a cluster among actors. Sensor nodes, which are 1‐hop away from the actors, also called as relay nodes are elected as backup cluster head (BCH) based on the residual energy and node degree. BCH collects the data from sensors when an actor is away to perform actions in the affected area. The scheme is evaluated through exhaustive simulation in NS2 along with other existing schemes. Different parameters like average event waiting time, event reliability, and average energy dissipation are compared, varying the number of sensors, actors, and data transfer rate. In general, it is observed that the proposed DEACM outperforms other existing schemes. Copyright © 2016 John Wiley & Sons, Ltd.     

SGDD: self‐managed grid‐based data dissemination protocol for mobile sink in wireless sensor network

The reduction of energy consumption in order to increase network lifetime is one of the most major challenges in the design of wireless sensor networks. During data dissemination, the sensors that are located in the sink's neighborhood are responsible to relay data to the other nodes; hence, their energy is exhausted expeditiously. Therefore, the idea of utilizing mobile sinks can be so advantageous to decrease energy consumption during data dissemination process. In this paper, we propose self‐managed grid‐based data disseminating protocol for mobile sink in wireless sensor networks by using the idea of constructing a virtual grid. In self‐managed grid‐based data disseminating protocol, sink and nodes map their geographical position to a virtual location. In order to increase the performance, we have employed a cell head for each grid cell. Cell heads are selected based on two parameters, centralization and residual energy. Our data dissemination protocol is simple and has low overhead to construct and maintain. Also, we have presented a new method for sink location update, which leads to the least cost in data transfer. Simulation results illustrate that by utilizing hierarchical functionality and determining an optimal size for grid cells, energy consumption is decreased, which leads to increasing network lifetime. Copyright © 2015 John Wiley & Sons, Ltd.     

A lightweight,self‐adaptive lock gate designation scheme for data collection in long‐thin wireless sensor networks

Constrained by the physical environments, the long‐thin topology has recently been promoted for many practical deployments of wireless sensor networks (WSNs). In general, a long‐thin topology is composed of a number of long branches of sensor nodes, where along a branch each sensor node has only one potential parent node toward the sink node. Although data aggregation may alleviate excessive packet contention, the maximum payload size of a packet and the dynamically changing traffic loads may severely affect the amount of sensor readings that may be collected along a long branch of sensor nodes. In addition, many practical applications of long‐thin WSNs demand the exact sensor readings at each location along the deployment areas for monitoring and analysis purposes, so sensor readings may not be aggregated when they are collected. This paper proposes a lightweight, self‐adaptive scheme that designates multiple collection nodes, termed lock gates, along a long‐thin network to collect sensor readings sent from their respective upstream sensor nodes. The self‐adaptive lock gate designation scheme balances between the responsiveness and the congestion of data collection while mitigating the funneling effect. The scheme also dynamically adapts the designation of lock gates to accommodate the time‐varying sensor reading generation rates of different sensor nodes. A testbed of 100 Jennic sensor nodes is developed to demonstrate the effectiveness of the proposed lock gate designation scheme. Copyright © 2011 John Wiley & Sons, Ltd.     

A new load balancing and data collection algorithm for energy saving in wireless sensor networks

Data gathering is a major function of many applications in wireless sensor networks. The most important issue in designing a data gathering algorithm is how to save energy of sensor nodes while meeting the requirements of special applications or users. Wireless sensor networks are characterized by centralized data gathering, multi-hop communication and many to one traffic pattern. These three characteristics can lead to severe packet collision, network congestion and packet loss, and even result in hot-spots of energy consumption thus causing premature death of sensor nodes and entire network. In this paper, we propose a load balance data gathering algorithm that classifies sensor nodes into different layers according to their distance to sink node and furthermore, divides the sense zone into several clusters. Routing trees are established between sensor node and sink depending on the energy metric and communication cost. For saving energy consumption, the target of data aggregation scheme is adopted as well. Analysis and simulation results show that the algorithm we proposed provides more uniform energy consumption among sensor nodes and can prolong the lifetime of sensor networks.     

Performance analysis for relay networks with hierarchical support vector machines

In this paper, we consider a cooperative relay scheme for a mobile network with MIMO technology. The channel capacity for two well‐known relaying schemes are investigated: analogue relaying (amplify and forward) and digital relaying (decode and forward) from a mobile device to the base station through a relay node. In order to further increase the channel capacity, we propose an efficient hierarchical procedure based on support vector machine, namely hierarchical support vector machines (HSVM), to estimate the wireless networks condition approximately and design two ways (matched filter and minimum mean square error filter) of increasing the channel capacity according to the estimated wireless network condition. The proposed HSVM can estimate the wireless networks condition in much shorter time compared with the traditional minimum mean square error scheme without incurring much estimation error, which is spatial, useful for delay sensitive communication. For digital relaying, the effect of imperfect channel decode is also addressed. Our numerical results demonstrate the reduction of estimation complexity by adopting HSVM and the significant improvement of network capacity by applying the matched filter weight at relay nodes according to the network estimation. Copyright © 2011 John Wiley & Sons, Ltd.     

Joint Routing and Resource Allocation for Cluster Based Isolated Nodes in Cognitive Radio Wireless Sensor Networks

Clustering is an effective way to increase network lifetime but it leads to formation of isolated nodes in the wireless sensor network. These isolated sensor nodes forward data directly to sink and consume more energy which significantly reduces the network lifetime. In this article, we present how to maximize the network lifetime through joint routing and resource allocation with isolated nodes technique (JR-IN) between cluster head and isolated nodes in a cognitive based wireless sensor networks. In JR-IN technique the network area is divided into different layers and cluster size is formulated in each layer such that the size of the cluster remains unequal when it moves towards sink. Hence the cluster size is lager in the outermost layer compared to the cluster size in the inner most layer. To avoid inter cluster collision, we proposed different fixed channel to all the cluster heads in the network. For the intra cluster communication, the cluster member (sensor nodes) will lease the spectrum from the cluster head and forward data to their respective cluster head using TDMA technique. The periodical data gathering of cluster heads and forwarding the data to one hop cluster head may tend to lose energy faster and dies out quickly. We also propose in the JR-IN technique, the isolated nodes in the layer will take charge as a cluster head node and utilizes the resource allocated to the respective cluster head and forward the data to next hop cluster head. Simulation result shows that JR-IN outperforms the existing techniques, maximizes network lifetime and throughput and reduces the end to end delay.

     

Multi-factor and Distributed Clustering Routing Protocol in Wireless Sensor Networks

One of important issues in wireless sensor networks is how to effectively use the limited node energy to prolong the lifetime of the networks. Clustering is a promising approach in wireless sensor networks, which can increase the network lifetime and scalability. However, in existing clustering algorithms, too heavy burden of cluster heads may lead to rapid death of the sensor nodes. The location of function nodes and the number of the neighbor nodes are also not carefully considered during clustering. In this paper, a multi-factor and distributed clustering routing protocol MFDCRP based on communication nodes is proposed by combining cluster-based routing protocol and multi-hop transmission. Communication nodes are introduced to relay the multi-hop transmission and elect cluster heads in order to ease the overload of cluster heads. The protocol optimizes the election of cluster nodes by combining various factors such as the residual energy of nodes, the distance between cluster heads and the base station, and the number of the neighbor nodes. The local optimal path construction algorithm for multi-hop transmission is also improved. Simulation results show that MFDCRP can effectively save the energy of sensor nodes, balance the network energy distribution, and greatly prolong the network lifetime, compared with the existing protocols.     

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     

A genetic algorithm based approach for energy efficient routing in two-tiered sensor networks

Higher power relay nodes can be used as cluster heads in two-tiered sensor networks to achieve improved network lifetime. The relay nodes may form a network among themselves to route data towards the base station. In this model, the lifetime of a network is determined mainly by the lifetimes of these relay nodes. An energy-aware communication strategy can greatly extend the lifetime of such networks. However, integer linear program (ILP) formulations for optimal, energy-aware routing quickly become computationally intractable and are not suitable for practical networks. In this paper, we have proposed an efficient solution, based on a genetic algorithm (GA), for scheduling the data gathering of relay nodes, which can significantly extend the lifetime of a relay node network. For smaller networks, where the global optimum can be determined, our GA based approach is always able to find the optimal solution. Furthermore, our algorithm can easily handle large networks, where it leads to significant improvements compared to traditional routing schemes.     

An efficient cluster-based communication protocol for wireless sensor networks

A wireless sensor network is a network of large numbers of sensor nodes, where each sensor node is a tiny device that is equipped with a processing, sensing subsystem and a communication subsystem. The critical issue in wireless sensor networks is how to gather sensed data in an energy-efficient way, so that the network lifetime can be extended. The design of protocols for such wireless sensor networks has to be energy-aware in order to extend the lifetime of the network because it is difficult to recharge sensor node batteries. We propose a protocol to form clusters, select cluster heads, select cluster senders and determine appropriate routings in order to reduce overall energy consumption and enhance the network lifetime. Our clustering protocol is called an Efficient Cluster-Based Communication Protocol (ECOMP) for Wireless Sensor Networks. In ECOMP, each sensor node consumes a small amount of transmitting energy in order to reach the neighbour sensor node in the bidirectional ring, and the cluster heads do not need to receive any sensed data from member nodes. The simulation results show that ECOMP significantly minimises energy consumption of sensor nodes and extends the network lifetime, compared with existing clustering protocol.     

A Green Clustering Protocol for Mobile Sensor Network Using Particle Swarm Optimization

Energy consumption of sensor nodes is one of the crucial issues in prolonging the lifetime of wireless sensor networks. One of the methods that can improve the utilization of sensor nodes batteries is the clustering method. In this paper, we propose a green clustering protocol for mobile sensor networks using particle swarm optimization (PSO) algorithm. We define a new fitness function that can optimize the energy consumption of the whole network and minimize the relative distance between cluster heads and their respective member nodes. We also take into account the mobility factor when defining the cluster membership, so that the sensor nodes can join the cluster that has the similar mobility pattern. The performance of the proposed protocol is compared with well-known clustering protocols developed for wireless sensor networks such as LEACH (low-energy adaptive clustering hierarchy) and protocols designed for sensor networks with mobile nodes called CM-IR (clustering mobility-invalid round). In addition, we also modify the improved version of LEACH called MLEACH-C, so that it is applicable to the mobile sensor nodes environment. Simulation results demonstrate that the proposed protocol using PSO algorithm can improve the energy consumption of the network, achieve better network lifetime, and increase the data delivered at the base station.     

A Pre-Determined Nodes Deployment Strategy of Two-Tiered Wireless Sensor Networks Based on Minimizing Cost

Nodes deployment is a fundamental factor in determining the connectivity, coverage, lifetime and cost of wireless sensor networks. In this paper, a two-tiered wireless sensor networks consisting of sensor clusters and a base station is considered. Within a sensor cluster, there are many sensor nodes and a relay node. We focus on the deployment strategy for sensor nodes and relay nodes to minimize cost under some constraints. Several means are used. The regular hexagonal cell architecture is employed to build networks. Based on the analysis of energy consumption of sensors and cost of network, an integer programming model is presented to minimize the cost. By the model, number of layers of sensor cluster is determined. In order to balance the energy consumption of sensors on the identical layer, a uniform load routing algorithm is used. The numerical analysis and simulation results show that the waste of energy and cost of wireless sensor networks can be effectively reduced by using the strategy.     

An intelligent and knowledge‐based overlapping clustering protocol for wireless sensor networks

Overlapping is one of the topics in wireless sensor networks that is considered by researchers in the last decades. An appropriate overlapping management system can prolong network lifetime and decrease network recovery time. This paper proposes an intelligent and knowledge‐based overlapping clustering protocol for wireless sensor networks, called IKOCP. This protocol uses some of the intelligent and knowledge‐based systems to construct a robust overlapping strategy for sensor networks. The overall network is partitioned to several regions by a proposed multicriteria decision‐making controller to monitor both small‐scale and large‐scale areas. Each region is managed by a sink, where the whole network is managed by a base station. The sensor nodes are categorized by various clusters using the low‐energy adaptive clustering hierarchy (LEACH)‐improved protocol in a way that the value of p is defined by a proposed support vector machine–based mechanism. A proposed fuzzy system determines that noncluster heads associate with several clusters in order to manage overlapping conditions over the network. Cluster heads are changed into clusters in a period by a suggested utility function. Since network lifetime should be prolonged and network traffic should be alleviated, a data aggregation mechanism is proposed to transmit only crucial data packets from cluster heads to sinks. Cluster heads apply a weighted criteria matrix to perform an inner‐cluster routing for transmitting data packets to sinks. Simulation results demonstrate that the proposed protocol surpasses the existing methods in terms of the number of alive nodes, network lifetime, average time to recover, dead time of first node, and dead time of last node.     

An energy‐efficient node‐scheduling scheme for wireless sensor networks based on minimum dominating sets

One of the major requirements for new wireless sensor networks is to extend the lifetime of the network. Node‐scheduling techniques have been used extensively for this purpose. Some existing approaches rely mainly on location information through global positioning system (GPS) devices for designing efficient scheduling strategies. However, integration of GPS devices with sensor nodes is expensive and increases the cost of deployment dramatically. In this paper we present a location‐free solution for node scheduling. Our scheme is based on a graph theoretical approach using minimum dominating sets. We propose a heuristic to extract a collection of dominating sets. Each set consists of a group of working nodes which ensures a high level of network coverage. At each round, one set is responsible for covering the sensor field while the nodes in other sets are in sleep mode. We evaluate our solution through simulations and discuss our future research directions. Copyright © 2008 John Wiley & Sons, Ltd.     

Detouring dynamic routing holes in stationary wireless sensor networks in the presence of temporarily misbehaving nodes

In this work, we propose a scheme, named BRIDGE , to bypass dynamic routing holes arising in stationary wireless sensor networks in the presence of temporarily misbehaving nodes such as dumb 1 , 2 or transfaulty nodes. The affected nodes behave normally after the resumption of favorable environmental conditions. Therefore, both dumb and transfaulty behaviors of sensor nodes are dynamic in nature. The nodes in these networks get temporarily isolated from the network, when they behave as dumb or transfaulty. Because of the presence of nodes with such behavior, dynamic communication holes may occur in the network, which are formed or removed and thus increase or decrease in size with time. Connectivity re‐establishment procedures can mitigate holes by re‐connecting isolated nodes with the network after activating the intermediate sleep nodes, adjusting the communication range of intermediate nodes, or by using an alternative communication mode. However, such procedures cannot always re‐establish connectivity because of the lack of neighbor nodes in reduced or adjusted communication range. Therefore, routing schemes using greedy forwarding approaches need to bypass holes to avoid the data packets from getting stuck at the boundary nodes and efficiently delivering them to the sink. However, the existing hole avoidance schemes consider holes as static. The proposed scheme, BRIDGE , detects hole boundary and bypasses routing traffics in the dynamic hole scenario. In the proposed scheme, a boundary node selects the next hop based on the minimum distance from all the neighbor nodes to the destination node, although this minimum distance is more than the distance to the destination from the node itself. Simulation results show that the performance of the proposed scheme degrades with the increase in hole area. Copyright © 2015 John Wiley & Sons, Ltd.     

Delay-Constrained Optimal Data Aggregation in Hierarchical Wireless Sensor Networks

A lot of realistic applications with wireless sensor networks adopt hierarchical architecture in which sensor nodes are grouped into clusters, with each cluster relying on a gateway node for local data aggregation and long-distance radio transmission. Compared to normal sensor nodes, the gateway nodes, also called application nodes (ANs), are equipped with relatively powerful transceivers and have more energy. Nevertheless, since an AN is the main gateway for sensor nodes within its clusters, its energy may be depleted more quickly than normal sensor nodes. As such, it is important to find methods to save energy for ANs. This paper presents a Delay-Constrained Optimal Data Aggregation (DeCODA) framework that considers the unique feature of traffic patterns and information processing at ANs for energy saving. Mathematical models and analytical results are provided, and simulation studies are performed to verify the effectiveness of the DeCODA framework.