Data Gathering In Sensor Networks Research Articles

Wireless sensor network data gathering using a multi-fold gravitational search algorithm with a mobile agent

Wireless sensor network data gathering using a multi-fold gravitational search algorithm with a mobile agent

Joint Sampling and Reconstruction of Time-Varying Signals Over Directed Graphs

Vertex-domain and temporal-domain smoothness of time-varying graph signals are cardinal properties that can be exploited for effective graph signal reconstruction from limited samples. However, existing approaches are not directly applicable when the signal's frequency occupancy changes with time. Moreover, while e.g., sensor network applications can benefit from directed graph models, the non-orthogonality of the graph eigenvectors can challenge spectral-based signal reconstruction algorithms. In this context, here we consider <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$K$</tex-math></inline-formula> -sparse time-varying signals with unknown frequency supports. By exploiting the smoothness of the varying graph frequency supports and employing shift operations over directed graphs, we study joint sampling of multiple varying signals based on Schur decomposition to reconstruct each signal by orthogonal frequency components. Firstly, joint frequency support of the multiple signals is identified by proposing a two-stage Individual-Joint sampling scheme. Based on the estimated frequency support, the GFT coefficients of each signal can be recovered using data collected in individual sampling stage. Greedy algorithms are proposed for vertex set selection and graph shift order selection, which enable a robust signal reconstruction against additive noise. Considering the signals in applications may be approximately <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$K$</tex-math></inline-formula> -sparse, we further exploit the samples in both individual and joint sampling stages and investigate the optimal signal reconstruction as a convex optimization problem with adaptive frequency support selection. The proposed optimal sampling and reconstruction algorithms outperform several existing schemes in random network and sensor network data gathering.

Network Lifetime Improved Optimal Routing in Wireless Sensor Network Environment

In the field of Wireless Sensor Networks, major problem arises due to the unbalanced consumption of energy. The unbalanced consumption will reduce the lifetime of a network performance. In the existing work, replacement of node is a difficult process and conservation of network parameters is not concentrated. It leads to the failure of network in different situations. These issues are resolved by introducing a density aware optimal clustering which is used to perform clustering to ensure the data transmission in a reliable manner. In the proposed work, Energy Balancing and Optimal Routing Based Secured Data Transmission based on clustering is introduced. It enhances the lifetime uniformly deployed data-gathering sensor networks using a balanced energy consumption technique. Ant Colony Optimization method is used to select the optimum head of the cluster to enhance lifetime of a network. Different techniques have been proposed for optimal edge disjoint routing. The data packets are forwarded in an optimum way by these techniques. Hybrid Genetic Particle Swarm Optimization algorithm is used to select the optimum edge disjoint route paths. The parameters of Quality of Service like reliability, capacity of processing, bandwidth and energy are considered to make an optimum selection. Based on the similarity values, sensor data’s are grouped and these data are aggregated to ensure the consumption energy which makes an optimum data transmission.

Joint Optimization of Mobile Charging and Data Gathering for Wireless Rechargeable Sensor Networks

Recent advances in radio frequency (RF) power transfer provide a promising technology to power sensor nodes. Adoption of mobile chargers to replenish the nodes’ energy has recently attracted a lot of attention and the mobility assisted energy replenishment provides predictable and sustained power service. In this paper, we study the joint optimization of mobile charging and data gathering in sensor networks. A wireless multi-functional vehicle (WMV) is employed and periodically moves along specified trajectories, charge the sensors and gather the sensed data via one-hop communication. The objective of this paper is to maximize the uplink throughput by optimally allocating the time for the downlink wireless energy transfer by the WMV and the uplink transmissions of different sensors. We consider two scenarios where the WMV moves in a straight line and around a circle. By time discretization, the optimization problem is formulated as a 0-1 programming problem. We obtain the upper and lower bounds of the problem by converting the original 0-1 programming problem into a linear programming problem and then obtain the optimal solution by using branch and bound algorithm. We further prove that the network throughput is independent of the WMV’s velocity under certain conditions. Performance of our proposed algorithm is evaluated through extensive simulations. The results validate the correctness of our proposed theorems and demonstrate that our algorithm outperforms two baseline algorithms in achieved throughput under different settings.

An Aggregate Signature Based Trust Routing for Data Gathering in Sensor Networks

An Aggregate Signature based Trust Routing (ASTR) scheme is proposed to guarantee safe data collection in WSNs. In ASTR scheme, firstly, the aggregate signature approach is used to aggregate data and keep data integrity. What is more important, a light aggregate signature based detour routing scheme is proposed to send abstract information which includes the data sending time and ID of data, nodes’ ID to the sink over different paths which can verify whether the data reaches the sink safely. In addition, the trust of a path is evaluated according to the success rate of the path. The trust of paths susceptible to frequent attack will be lowered and the path with high trust will be selected for data routing to avoid data gathering through low trust path and thereby increase the success rate of data gathering. Our comprehensive performance analysis has shown that, the ASTR scheme is able to effectively ensure an increase in success rate of data transmission by 23.23%, reduce the data amount loaded by the node by 53.59%, reduce the redundant data by 41.70%.

An Efficient Data Gathering Protocol for Mobile Wireless Sensor Networks

Data gathering is a hot research issue of mobile wireless sensor networks. In order to ensure maximum data gathering, minimize the total energy consumption and optimize network reliability, from Sink dynamic selection of movement path to the life cycle of the entire sensor network. Through the study found that the problem is NP- hard. We then propose a mobile wireless sensor network data gathering strategy based on ant colony algorithm. Before Sink carries out the next round of data gathering, ant colony algorithm is used to estimate and selectively optimize the next hop destination according to the current network environment parameters and moving strategy. Simulation results show that compared with other data gathering methods, the proposed algorithm can produce balanced network energy, reduce the total energy consumption, improve network efficiency and reliability, prolong network lifetime.

Energy efficient distributed compressed data gathering for sensor networks

With advances in sensing technologies and wireless sensor networks, energy consumption is a key factor for data collection in sensor network. Compressive sensing has attracted more attention for reducing energy cost due to its enabled sub-Nyquist sampling rate. In this paper, the JSM-2 model was introduced for data compressed collection for sensor network based on distribution compressive sensing theory. And we deem that the quantization configuration is also a critical factor for energy efficiency for data communication. Then, we constructed energy consumption configuration model joint distribution compressive sensing and quantization compressive sensing. The experimental results showed that the proposed algorithm can gain more energy performance than conventional QCS architecture for sensor network data collection.

A Cloud-based Architecture for the Internet of Things targeting Industrial Devices Remote Monitoring and Control

The process of acquiring, analysing and managing data obtained by sensors and actuators in industrial environments can benefit from modern Cloud-based platforms towards a complete implementation of the Industrie 4.0 concept. The analysis of huge data sets produced by these sensors (Big Data) could allow quick and accurate decision making. For example, productivity improvements can be achieved by analysing device performance and degradation for real-time feedback on configuration and optimization. This work proposes a Cloud-based architecture for Internet of Things (IoT) applications to improve the deployment of smart industrial systems based on remote monitoring and control. By using specific technologies available as a service, we demonstrate the proposed architecture on an automated electric induction motor use case. This approach includes layers for sensor network data gathering, data transformation between standard protocols, message queuing, real-time data analysis, reporting for further analysis, and real-time control. Particularly, by using the proposed architecture, we remotely monitored, controlled and processed data produced by sensors and actuators coupled to the motor. Preliminary results indicate this foundation can support predictive methods and management of automated systems in the Industrie 4.0 context.

A Comparative Study and Analysis of Data Aggregation Techniques in WSN

Background: Wireless Sensor Network (WSN) often consists of large number sensor nodes which co-operatively send sensed data to base station. These networks are used in various applications scenarios such as habitat monitoring, disaster management, security and military, etc. As the sensor nodes are generally operated by battery driven, an efficient utilization of power is essential. Therefore, to enhance the life time of sensor network, power efficient methods has to be adapted to gather and aggregate data. It's mainly because majority of the sensor node energy is consumed during transmission than computation. Data aggregation is one of the widely recognized solutions to gather and reduce the redundant number of packets to be sent to base station by aggregating them. Literature survey shows that several application specific sensor network data gathering protocols exists in enhancing the network lifetime. Methods: This paper provides a comprehensive survey of different data aggregation algorithms in wireless sensor networks. The different algorithm are compared and contrasted based on the performance measures such as network type, mobility, Data (D) or Node (N) centric protocol, computation characteristics, data delivery model, communication pattern, query based support, energy usage and application type. To evaluate the performance of some of the algorithms surveyed in this work, simulation experiments on Matlab were conducted. Results: It was observed from the results that that the network topology has a high impact on the routing protocols to perform data aggregation. There exist few other protocols using contemporary methodologies such as Tree, Grid and Hybrid approaches which are also discussed in this paper. Application: The survey will help to identify the data aggregation algorithm suitable for a specific application.

Improving Energy efficiency in ClusteredWireless Sensor Networks

One of the most critical issues in designing wireless sensor network is to minimize the energy consumption and increase longevity. In Wireless Sensor Network, data fusion can reduce data redundancy among sensed data and optimal sensor routing algorithm can provide strategy for data gathering with as minimum energy as possible. We combined data fusion and clustering method together to reduce the energy. In this paper we propose K-means algorithm for clustering. We design a novel routing algorithm called an Adaptive algorithm(AA) for energy efficient data gathering in sensor network that optimizes data transmission cost and the data fusion cost. Simulation result is given for the proposed protocol with K-means Algorithm. Simulation result shows that AA achieves better performance than MFST.

Improved structures for data collection in static and mobile wireless sensor networks

In this paper we consider the problem of efficient data gathering in sensor networks for arbitrary sensor node deployments. The efficiency of the solution is measured by a number of criteria: total...

A Review on Lifetime Enhancement in Wireless Sensor Networks

Wireless sensor networks are used to monitor physical or environmental conditions like temperature, pressure, sound etc.Increasing the lifetime of wireless sensor networks is a major challenge because the nodes are equipped with low power battery.Energy efficient routing helps in increasing the lifetime of sensor nodes by maximizing the overall performance of the nodes. Generally in routing algorithm best path is chosen for transmission from source to destination,but if the same path is used for a long period of time for quick transmission time the nodes in the selected path will get drained fast.This paper discusses about the different methods used for increasing the lifetime of wireless sensor networks. Index terms: sensor node,wireless sensor networks, routing. I. Introduction Wireless sensor networks are collections of compact size and relatively inexpensive computational nodes that measure local environmental conditions or other parameters and forward such information to the base station for appropriate processing.The basic unit in a sensor network is a sensor node. Wireless sensor networks can sense the environment, communicate with neighbouring nodes and can also perform basic computations on the data being collected.Recent developments in sensor technology and wireless communication have helped in the deployment of large scale wireless sensor networks for a variety of applications including environmental monitoring of habitat,data collection of temperature, pressure, sound ,humidity, light,vibration etc.For such type of applications hundreds or thousands of low cost sensor nodes can be deployed over the area to be monitored.In data gathering sensor network each sensor node must periodically report its sensed data to the sink.

An Energy-Efficient Data Gathering Method for Sensor Networks with Mixed Communication Modes

One critical issue in wireless sensor networks is how to gather sensed information in an energy-efficient way since the energy is a scarce resource in a sensor node. Cluster-based architecture is an effective architecture for data-gathering in wireless sensor networks. In this paper, by taking the nodes energy consumption into account, we first derive the optimal number of clusters for data gathering in sensor networks. To balance the cluster heads energy consumption, we propose a mixed communication modes where the cluster heads can transmit data to the base station in either single-hop or multi-hop. We then develop a data gathering method based on the optimal number of clusters and mixed communication modes. The simulation results show that the proposed method outperforms LEACH and HEED in terms of network lifetime by balancing energy dissipation.

SERA: a secure energy reliability aware data gathering for sensor networks

Wireless sensor networks are used in many applications in military, ecology, health, and other areas. These applications often include the monitoring of sensitive information making the security issue one of the most important aspects to consider in this field. However, most of protocols optimize for the limited capabilities of sensor nodes and the application specific nature of the networks, but they are vulnerable to serious attacks. In this paper, a Secure Energy and Reliability Aware data gathering protocol (SERA) is proposed, which provides energy efficiency and data delivery reliability as well as security. The proposed protocol's security was confirmed by a formal verification carried out using the AVISPA tool and analysis of the most common network layer attacks such as selective forwarding, sinkhole, Sybil, wormhole, HELLO flood, and acknowledgment spoofing attacks. Additionally, a visual simulation environment was developed to evaluate the performance of the proposed protocol.

Optimal Stop Points for Data Gathering in Sensor Networks with Mobile Sinks

Given a wireless sensor network (WSN) in which a mobile sink is used to collect data from the sensor nodes, this paper addresses the problem of selecting a set of stop points that results in low energy usage by the sensor nodes. This paper assumes an approach in which a mobile sink travels along a fixed path and uses a stop-and-collect protocol since this has previously been shown to be an efficient WSN data collection method. The problem of selecting an optimal set of stop points is shown to be an NP-hard problem. Then, an Integer Linear Programming (ILP) formulation is used to derive an optimal algorithm that can be used for small problem instances. Next, a polynomial-time Tabu-search-based heuristic algorithm is proposed. Simulations are used to compare the energy consumption values, computation times and expected network lifetimes when using the optimal ILP algorithm, the proposed heuristic algorithm and several other possible heuristic algorithms. The results show that the proposed heuristic algorithm results in near-optimal energy usage values with low computation times, thereby making it suitable for large-sized WSNs.

On Reducing Communication Energy Using Cross-Sensor Coding Technique

This paper addresses the uneven communication energy problem in data gathering sensor networks where the nodes closer to the sink tend to consume more energy than those of the farther nodes. Consequently, the lifetime of a network is significantly shortened. We propose a cross-sensor coding technique using On-Off keying which exploits (a) the tradeoff between delay and energy consumption and (b) the network topology in order to alleviate the problem of unequal energy consumption. We formulate our coding problem as an integer linear programming problem and show how to construct a number of codes based on different criteria. We show that the proposed technique can extend the lifetime of a small sensor network.

Implementation Of Hybrid Protocol For Routing As Well As Clustering

WSN consisting of a large number of small sensors with low power transceivers can be an effective tool for gathering data in a variety of environments. As sensor nodes are deployed in sensing field, they can help people to monitor and aggregate data. Researchers also try to find more efficient ways of utilizing limited energy of sensor node in order to give longer life time of WSNs. Network lifetime, scalability, and load balancing are important requirements for many data gathering sensor network applications. Therefore, many protocols are introduced for better performance. The lack of infrastructure and dynamic nature of mobile ad hoc networks demand new networking strategies to be implemented in order to provide efficient end-to-end communication. Some researches have been proposed to organize the network into groups called clusters and use different routing protocols for inter and intra clusters to propagate an information. But with these solutions, the network first needs to be organized into cluster and next, we need to construct each routing table[16]. In the available literature, multi-hop routing protocol is well known for power saving in data gathering [5]. Researchers have used such types of the cluster-based (e.g., LEACH, EERP), the chain-based (e.g. PEGASIS) and the tree-based (e.g. TREEPSI) to establish their energy efficient routing protocols. In this paper, we propose an improved version which uses both cluster and tree based protocols. The proposed protocol improves the power consumption by improving FND.

Real-time data gathering in sensor networks

Wireless sensor networks represent a new generation of real-time traffic communications and high data rate sensor applications, such as structural health monitoring and control. We study some problems related to data gathering in sensor networks when the sensors collect the sensed data about their environment and this information should be delivered to a collecting central Base Station. We prove that scheduling messages through the network to minimize the maximal delivery time with restrictions on the total idle time allowed is NP-hard. We also refer to a special case of linear network topology for which we present two polynomial time optimization algorithms: One is for minimizing the maximal lateness and maximal delay, while the other is for minimizing the number of tardy messages.

Balancing Energy Consumption to Maximize Network Lifetime in Data-Gathering Sensor Networks

Unbalanced energy consumption is an inherent problem in wireless sensor networks characterized by multihop routing and many-to-one traffic pattern, and this uneven energy dissipation can significantly reduce network lifetime. In this paper, we study the problem of maximizing network lifetime through balancing energy consumption for uniformly deployed data-gathering sensor networks. We formulate the energy consumption balancing problem as an optimal transmitting data distribution problem by combining the ideas of corona-based network division and mixed-routing strategy together with data aggregation. We first propose a localized zone-based routing scheme that guarantees balanced energy consumption among nodes within each corona. We then design an offline centralized algorithm with time complexity O(n) (n is the number of coronas) to solve the transmitting data distribution problem aimed at balancing energy consumption among nodes in different coronas. The approach for computing the optimal number of coronas in terms of maximizing network lifetime is also presented. Based on the mathematical model, an energy-balanced data gathering (EBDG) protocol is designed and the solution for extending EBDG to large-scale data-gathering sensor networks is also presented. Simulation results demonstrate that EBDG significantly outperforms conventional multihop transmission schemes, direct transmission schemes, and cluster-head rotation schemes in terms of network lifetime.

EDGES: Efficient data gathering in sensor networks using temporal and spatial correlations

In this paper, we present an approximate data gathering technique, called EDGES, for sensor networks that utilizes temporal and spatial correlations. The goal of EDGES is to efficiently obtain the sensor reading within a certain error bound. To do this, EDGES utilizes the multiple model Kalman filter, which is for the non-linear data distribution, as an approximation approach. The use of the Kalman filter allows EDGES to predict the future value using a single previous sensor reading in contrast to the other statistical models such as the linear regression and multivariate Gaussian. In order to extend the lifetime of networks, EDGES utilizes the spatial correlation. In EDGES, we group spatially close sensors as a cluster. Since a cluster header in a network acts as a sensor and router, a cluster header wastes its energy severely to send its own reading and/or data coming from its children. Thus, we devise a redistribution method which distributes the energy consumption of a cluster header using the spatial correlation. In some previous works, the fixed routing topology is used or the roles of nodes are decided at the base station and this information propagates through the whole network. But, in EDGES, the change of a cluster is notified to a small portion of the network. Our experimental results over randomly generated sensor networks with synthetic and real data sets demonstrate the efficiency of EDGES.