Problem Of Data Gathering Research Articles

Networking of Sensors With Fixed Transmission Ranges: Distributed Data Processing Over Graphs

In this letter, we address the data gathering problem in low-power wireless sensor networks (WSNs) with sensors of fixed transmission ranges. We model the NP-hard problem as an integer linear program that can be applied to networks of small sizes. Further, we propose a novel graphical model that apprehends the features of such networks, and design a polynomial-time approximation algorithm using the proposed graphical model for networks of large sizes. The performance is evaluated under data compaction strategies based on compressive sensing. Simulation results demonstrate the superior performance of the proposed method over its peer protocols.

Beyond Bio-Inspired Robotics: How Multi-Robot Systems Can Support Research on Collective Animal Behavior.

In the study of collective animal behavior, researchers usually rely on gathering empirical data from animals in the wild. While the data gathered can be highly accurate, researchers have limited control over both the test environment and the agents under study. Further aggravating the data gathering problem is the fact that empirical studies of animal groups typically involve a large number of conspecifics. In these groups, collective dynamics may occur over long periods of time interspersed with excessively rapid events such as collective evasive maneuvers following a predator’s attack. All these factors stress the steep challenges faced by biologists seeking to uncover the fundamental mechanisms and functions of social organization in a given taxon. Here, we argue that beyond commonly used simulations, experiments with multi-robot systems offer a powerful toolkit to deepen our understanding of various forms of swarming and other social animal organizations. Indeed, the advances in multi-robot systems and swarm robotics over the past decade pave the way for the development of a new hybrid form of scientific investigation of social organization in biology. We believe that by fostering such interdisciplinary research, a feedback loop can be created where agent behaviors designed and tested in robotico can assist in identifying hypotheses worth being validated through the observation of animal collectives in nature. In turn, these observations can be used as a novel source of inspiration for even more innovative behaviors in engineered systems, thereby perpetuating the feedback loop.

Energy Efficient Data Gathering in IoT Networks With Heterogeneous Traffic for Remote Area Surveillance Applications: A Cross Layer Approach

In this paper, the problem of energy-efficient data gathering in an Internet of Things (IoT) based remote area surveillance application is addressed by designing a suitable MAC layer uplink solution. We follow the 3GPP specified scheduled access scheme for narrowband IoT (NB-IoT) and consider presence of both delay sensitive and delay tolerant traffic in the network. First, we present a mixed integer non-linear programming (MINLP) based optimization framework to minimize the IoT devices’ cumulative transmission energy per uplink frame under the constraints of finite resource blocks, mean queue stability and residual energy levels of IoT nodes. A cross layer approach has been adopted. Precisely, using Lyapunov optimization, we propose a dynamic, distributed transmit power allocation for IoT nodes and a centralized node scheduling scheme. Further, assuming Poisson model for traffic generation at delay tolerant traffic generating nodes, we suggest a distributed probabilistic sleep scheduling scheme to improve the average delay experience of delay sensitive traffic while improving the overall energy conservation. Simulation results suggest impressive performance of our proposed solution over the existing uplink solution for NB-IoT, in terms of delay experience of delay sensitive traffic, buffer length requirement and nodes’ total energy consumption at high traffic load.

WDAT‐OMS: A two‐level scheme for efficient data gathering in mobile‐sink wireless sensor networks using compressive sensing theory

This study addresses the problems of energy and delay in wireless sensor networks equipped with mobile sinks. The authors jointly consider the compressive sensing (CS) theory, cluster-based routing, and sink mobility to propose a data collection method named ‘weighted data aggregation trees with optimal mobile sink(s) (WDAT-OMS)’. The proposed scheme relies on a two-level architecture in which sensors are clustered at the first level. WDAT-OMS uses the CS theory along with load-balanced data aggregation trees to route packets from sensors to the corresponding cluster heads (CHs). In this regard, they present an efficient metric named ‘energy-and distance-aware CH selection’ to fairly distribute the energy consumption among different sensors. At the second level, one or more sinks traverse the network to collect the aggregated data of CHs. As an advantage, WDAT-OMS not only balances the energy consumption among different sensors but also increases the network scalability. Numerical results demonstrate that the proposed algorithm reduces energy consumption in comparison with ‘centralised clustering algorithm’, ‘energy-aware CS-based data aggregation’, and ‘energy-balanced high-level data aggregation tree ‘by 66%, 62%, and 63% for an average number of clusters, respectively. It also decreases the sink delay in comparison with the ‘single-hop data-gathering problem’ by 10%.

Sensor Network Topology Design and Analysis for Efficient Data Gathering by a Mobile Mule

In this paper, we study the problem of data gathering in ad-hoc sensor networks using a mobile entity called mule. The mule traverses the children of failed sensors, to prevent loss of data. Our objective is to define the optimal communication tree, and the mule’s placement such that the mule’s overall traveling distance is minimized. We explore this problem in several network topologies including: unit disc graph on a line (UDL), general unit disc graph (UDG), and a complete graph with failing probabilities on the nodes (CGFP). We provide an optimal solution for the UDL problem and three approximation algorithms for the UDG problem. For the CGFP problem we outline the two possible structures of an optimal solution and provide near optimal approximation algorithms.

Energy Efficient Bigdata Gathering Using Mobile Sink in Wireless Sensor Network

In Customary Wireless sensor systems designs comprise of static hubs which are thickly sent over a detecting region. Another information gathering component for huge scale remote sensor arranges by bringing portability into the system. A Mobile information gatherer begins the information get-together visit intermittently from the information sinks which have static characteristics, surveys every sensor whereas navigating its diffusion run, at that point legitimately gathers information from the sensor in single-jump interchanges, lastly ship the information to the static sink. The main challenge is in shrinking the piece of every tour of data gathering and it is single hop data-gathering problem. Distance and time constraints are satisfied using data gathering algorithm. Results reveal that the projected algorithm for gathering the data can greatly shorten the poignant space between collectors which appreciably extends life time of the network.

Approximation Algorithms for Barrier Sweep Coverage

Time-varying coverage, namely sweep coverage is a recent development in the area of wireless sensor networks, where a few mobile sensors sweep or monitor a comparatively large number of locations periodically. In this article, we study barrier sweep coverage with mobile sensors where the barrier is considered as a finite length continuous curve on a plane. The coverage at every point on the curve is time-variant. We propose an optimal solution for sweep coverage of a finite length continuous curve. Usually, energy source of a mobile sensor is a battery with limited power, so energy restricted sweep coverage is a challenging problem for long running applications. We propose an energy-restricted sweep coverage problem where every mobile sensor must visit an energy source frequently to recharge or replace its battery. We propose a [Formula: see text]-approximation algorithm for this problem. The proposed algorithm for multiple curves achieves the best possible approximation factor 2 for a special case. We propose a 5-approximation algorithm for the general problem. As an application of the barrier sweep coverage problem for a set of line segments, we formulate a data gathering problem. In this problem a set of mobile sensors is arbitrarily monitoring the line segments one for each. A set of data mules periodically collects the monitoring data from the set of mobile sensors. We prove that finding the minimum number of data mules to collect data periodically from every mobile sensor is NP-hard and propose a 3-approximation algorithm to solve it.

HAS⁴: A Heuristic Adaptive Sink Sensor Set Selection for Underwater AUV-Aid Data Gathering Algorithm.

In this paper, we target solving the data gathering problem in underwater wireless sensor networks. In many underwater applications, it is not quick to retrieve sensed data, which gives us the opportunity to leverage mobile autonomous underwater vehicles (AUV) as data mules to periodically collect it. For each round of data gathering, the AUV visits part of the sensors, and the communication between AUV and sensor nodes is a novel high-speed magnetic-induction communication system. The rest of the sensors acoustically transmit their sensed data to the AUV-visit sensors. This paper deploys the HAS (Heuristic Adaptive Sink Sensor Set Selection) algorithm to select the AUV-visited sensors for the purpose of energy saving, AUV cost reduction and network lifetime prolonging. By comparing HAS with two benchmark selection methods, experiment results demonstrate that our algorithm can achieve a better performance.

Guarding against excess: a frequently ignored ethical principle in medical education research

This article was migrated. The article was marked as recommended. In the 21st century, medical ethics has been combined with informatics ethics to form medical informatics ethics, and medical education researchers need to be aware of medical informatics ethics principles. An important medical informatics ethics principle is guarding against excess: gathering only those data that are required, and using all the data that have been gathered. An example of excessive data gathering can be found in the gathering of demographic data: it is considered excessive when these data are gathered for no explicit reason, used minimally in the Results, and mostly ignored in the Discussion. This paper details the problem of excessive data gathering, and then outlines five steps requiring the cooperation of institutional ethics review boards, researchers, journal editors and journal reviewers that should be followed in order to guard against excess in medical education research.

Data Gathering With Minimum Number of Relay Packets in Wireless Sensor Networks

The data gathering problem is an important issue in wireless sensor networks (WSNs). In general, mobile elements are used to collect the sensed data of sensors to reduce the problem of unbalanced energy consumption caused by multi-hop transmission. However, in time-sensitive applications of WSNs where mobile elements are used to collect time-sensitive data, we must pay particular attention to the length of the traveling path to ensure that data are still useful after delivered to the sink. In this paper, we focus on how to plan a traveling path that meets the delay requirement of time-sensitive applications for data collection and reduces the amount of relay packets in the WSNs. The proposed algorithm is called Timeliness Traveling Path Planning (TTPP) algorithm. Based on the least squares curve approach, the proposed TTPP algorithm can find the best-fitting curve for any given set of sensors by reducing the amount of relay packets in the WSNs. The effectiveness of the proposed TTPP algorithm is confirmed through extensive simulations.

Near Optimal Data Gathering in Rechargeable Sensor Networks with a Mobile Sink

We study data gathering problem in Rechargeable Sensor Networks (RSNs) with a mobile sink, where rechargeable sensors are deployed into a region of interest to monitor the environment and a mobile sink travels along a pre-defined path to collect data from sensors periodically. In such RSNs, the optimal data gathering is challenging because the required energy consumption for data transmission changes with the movement of the mobile sink and the available energy is time-varying. In this paper, we formulate data gathering problem as a network utility maximization problem, which aims at maximizing the total amount of data collected by the mobile sink while maintaining the fairness of network. Since the instantaneous optimal data gathering scheme changes with time, in order to obtain the globally optimal solution, we first transform the primal problem into an approximate network utility maximization problem by shifting the energy consumption conservation and analyzing necessary conditions for the optimal solution. As a result, each sensor does not need to estimate the amount of harvested energy and the problem dimension is reduced. Then, we propose a Distributed Data Gathering Approach (DDGA), which can be operated distributively by sensors, to obtain the optimal data gathering scheme. Extensive simulations are performed to demonstrate the efficiency of the proposed algorithm.

Low-latency Data Gathering with Reliability Guaranteeing in Heterogeneous Wireless Sensor Networks

In order to achieve low-latency and high-reliability data gathering in heterogeneous wireless sensor networks (HWSNs), the problem of multi-channel-based data gathering with minimum latency (MCDGML), which associates with construction of data gathering trees, channel allocation, power assignment of nodes and link scheduling, is formulated as an optimization problem in this paper. Then, the optimization problem is proved to be NP-hard. To make the problem tractable, firstly, a multi-channel-based low-latency (MCLL) algorithm that constructs data gathering trees is proposed by optimizing the topology of nodes. Secondly, a maximum links scheduling (MLS) algorithm is proposed to further reduce the latency of data gathering, which ensures that the signal to interference plus noise ratio (SINR) of all scheduled links is not less than a certain threshold to guarantee the reliability of links. In addition, considering the interruption problem of data gathering caused by dead nodes or failed links, a robust mechanism is proposed by selecting certain assistant nodes based on the defined one-hop weight. A number of simulation results show that our algorithms can achieve a lower data gathering latency than some comparable data gathering algorithms while guaranteeing the reliability of links, and a higher packet arrival rate at the sink node can be achieved when the proposed algorithms are performed with the robust mechanism.

Maximising energy efficiency using data aggregation in sensor networks

Wireless sensor networks (WSNs) is a class of wireless ad hoc networks in which sensor nodes sense, process, and communicate data acquired from the physical environment to an external-base-station (BS). But the fundamental challenge in the design of wireless sensor networks (WSNs) is to maximise their lifetimes especially when they have a limited and non-replenish able energy supply. Energy-efficient communication is necessary to increase the lifetime of the sensor network. In this work solutions are for the data gathering and routing problem with in network aggregation in WSNs. The aim of our work is to focus on data gathering and redundancy elimination with in-network aggregation in WSNs. The objective is to maximise the network lifetime by utilising data aggregation with redundancy removal by increasing accuracy thereby improving packet delivery ratio and reducing energy consumption. This work focuses on the maximum lifetime data gathering and routing to avoid packet loss in wireless sensor networks.

Gathering big data for teamwork evaluation with microworlds

We identify some of the challenges related with conducting research into teamwork, addressing in particular the data gathering problem, where researchers face multiple tensions derived from different viewpoints regarding what data to gather and how to do it. To address this problem, we propose a microworld approach for conducting research into teamwork. We present the main requirements guiding the microworld development, and discuss a set of components that realise the requirements. Then, we discuss a study that used the developed microworld to evaluate a groupware tool, which was designed to support team activities related to infrastructure maintenance. The paper emphasises the range of data gathered with the microworld, and how it contributed to simultaneously evaluate team behaviour and tool design. The paper reflects on the major contributions brought by the microworld approach, emphasising in particular the capacity to gather diverse data, and to combine behaviour and design evaluations. This research contributes to consolidate the microworld approach in teamwork research. It also contributes to reduce the gap between behavioural-oriented and design-oriented research. The combination of the behaviour-oriented and design-oriented views is of particular importance to design science, since it is founded on iterative cycles of development and evaluation.

Data Gathering in Wireless Sensor Networks Using Unmanned Aerial Vehicles

In an IoT world sensor-enabled systems are all around us and accessible for management at any time and place. Besides other technological components, small unmanned aerial vehicles are also expected to have an important role in IoT as they fly at low-altitude becoming suitable data acquisition vehicles in certain situations. In this article we focus on data gathering using unmanned aerial vehicles for applications having delivery limit constraints. The problem is to design an efficient set of paths to gather sensor data at specific places, and to deliver it at the sink node, while accomplishing the delivery limit associated with data. After formalizing the problem, a heuristic approach is developed that incorporates solution improvement mechanisms suitable for data gathering purposes. Results show that the proposed approach is suitable to solve the data gathering problem and clues on how to adjust parameters, according to the nature of the data set, are given.

Zero-effort projection for sensory data reconstruction in wireless sensor networks

Compressive sensing is a promising technique for data gathering in large-scale wireless sensor networks. Existing compressive sensing–based data gathering techniques still follow sampling than compression paradigm. In this article, we proposed a random sampling zero-encoding data gathering scheme for wireless sensor networks, which exploits virtual Gaussian energy diffusion model to obtain sampling and compression data gathering. Our proposed data gathering model not only can make simultaneous sampling and compression but also do not need to assign projection matrix to each sensor node. Our scheme can efficiently resolve two types of sensor networks’ data gathering problems: recover missing sensory data and extend monitoring field using incomplete random sampling. Extensive experimental results show that our proposed random sampling zero-encoding data gathering model has good performance for reconstructing the sensory data in wireless sensor networks.

Data Gathering Techniques for Wireless Sensor Networks: A Comparison

We study the problem of data gathering in wireless sensor networks and compare several approaches belonging to different research fields; in particular, signal processing, compressive sensing, information theory, and networking related data gathering techniques are investigated. Specifically, we derived a simple analytical model able to predict the energy efficiency and reliability of different data gathering techniques. Moreover, we carry out simulations to validate our model and to compare the effectiveness of the above schemes by systematically sampling the parameter space (i.e., number of nodes, transmission range, and sparsity). Our simulation and analytical results show that there is no best data gathering technique for all possible applications and that the trade-off between energy consumptions and reliability could drive the choice of the data gathering technique to be used. In this context, our model could be a useful tool.

Flow-based scheme for time-constrained data gathering in wireless sensor networks

We consider the problem of data gathering in a wireless sensor network using a single mobile element. The solution must involve a combination of a mobile element visiting a subset of the nodes caching points, while other nodes communicate their data to the caching points in a multi-hop communication paradigm. The caching point nodes are selected with the aim of reducing the number of hops, a message originated from a node must travel to reach the nearest caching point. In this paper, we propose a flow-based algorithm that iteratively constructs the mobile element tour and the multi-hop routing trees. By transforming the topology of the network into maximum flow instance, our approach aims to construct its solution to reduce the overall traffic in the network. We evaluate our algorithm by comparing its performance with best comparable well-known algorithms from the literature.

Network Coding-Aware Compressive Data Gathering for Energy-Efficient Wireless Sensor Networks

This article investigates the joint application of compressive sensing (CS) and network coding (NC) to the problem of energy-efficient data gathering in wireless sensor networks. We consider the problem of optimally constructing forwarding trees to carry compressed data to projection nodes. Each compressed dataset refers to a weighted aggregation (or sum) of sensed measurements from network sensors collected at one projection node. Projection nodes then forward their received compressed data to the sink, which subsequently recovers the original measurements. This aggregation technique, based on CS, is shown to reduce significantly the number of transmissions in the network. We observe that the presence of multiple forwarding trees gives rise to many-to-many communication patterns in sensor networks that, in turn, can be exploited to perform NC on the compressed data being forwarded on these trees. Such a technique will further reduce the number of transmissions required to gather the measurements, resulting in a better network-wide energy efficiency. This article addresses the problem of NC--aware construction of forwarding/aggregation trees. We present a mathematical model to optimally construct such forwarding trees, which encourage NC operations on the compressed data. Owing to its complexity, we further develop algorithmic methods (both centralized and distributed) for solving the problem and analyze their complexities. We show that our algorithmic methods are scalable and accurate, with worst-case optimality gap not exceeding 3.96% in the studied scenarios. We also show that, when bothNC and compressive data gathering are considered jointly, performance gains (reduction in number of transmissions) of up to 30% may be attained. Finally, we show that the proposed methods distribute the workload of data gathering throughout the network nodes uniformly, resulting in extended network life times.

Joint Virtual MIMO and Data Gathering for Wireless Sensor Networks

Virtual multi-input multi-output (MIMO) or vMIMO is becoming an attractive technology to achieve spatial diversity in wireless networks without using additional antennas, and to reduce power consumption by cooperation among multiple nodes. As data gathering is one of the most important operations in many sensor network applications, this paper studies energy-efficient data gathering in wireless sensor networks using vMIMO. We define the joint vMIMO and data gathering (vMDG) problem, which is NP-hard. We also propose a distributed method called D-vMDG as an approximation algorithm. This algorithm first constructs a tree-like topology by taking the unique features of vMIMO into account. Then, an energy-efficient routing protocol based on dynamic programming is proposed for each node on the constructed topology. Our theoretical analysis shows that D-vMDG can achieve an approximation ratio of O(1). Our simulations show that D-vMDG decreases the energy consumption by 81 and 36 percent compared to the well-known MDT [26] and MIMO-LEACH [19] algorithms respectively.