Data Gathering In Networks Research Articles

Climate Change Effects and Mitigation Measures in Uttarakhand Himalayas Region

One of the biggest environmental issues affecting the world's natural ecosystems is climate change. Based on the literature that is currently accessible, this article offers a succinct summary of how the Uttarakhand Himalayan Mountains' agriculture, water, and 15.61% forest are affected by climate change. Numerous research have been conducted on a variety of Himalayan ecosystem-related topics, but only a small number of local studies have been published that address factors connected to climate change. This is mainly because there is a dearth of organized and targeted data on the subject. Thus, the qualitative analysis shows that in order to meet the demands of research on many elements of climate change impacts, mitigation, and adaptation, it is imperative to strengthen the climate data gathering network. Over the last four decades, this region has been found in soil water content reduce, total precipitation and run-off, land-used change due to urbanization that are challenges for both community and government to mitigate and adaptation.

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.

Scheduling data gathering with background communications and a processing stage

A data gathering network is a distributed computer system where the workers have to transfer data to a node called the base station, which is responsible for their processing. Wireless sensor networks monitoring the environment or wired systems executing computations are examples of such networks. We analyze scheduling in data gathering networks with background communications. It is assumed that at most one worker can communicate with the base station at a time, and hence, the network works in a flow shop mode. The speed of each communication link may change in time because of various processes using the network. Communication and computation preemptions are possible. The objective is to minimize the time required to gather and process all data. We prove that this problem is strongly NP-hard and indicate several polynomially solvable cases. Heuristic algorithms are proposed and tested using computational experiments. On the basis of the obtained results, we identify the algorithms that perform best in specific types of networks.

Study on Gibbs Optimization-Based Resource Scheduling Algorithm in Data Aggregation Networks

In data aggregation networks (WSNs, ad hoc, mesh, etc.), it is key to schedule the network resources, such as channels and TDMA time slots, to minimize the communication conflict and optimize the network data-gathering performance. In this paper, the resources scheduling problem is formulated as a vertex coloring problem in graph theory. Then, a multi-channel TDMA scheduling algorithm based on the Gibbs optimization is proposed. By defining the Gibbs energy expression according to the objective function of the problem, the joint probability of channel and time slot can be computed for the optimized selection of channels and time slots. This algorithm is low-complexity and its convergence performance can be proven. Experiments with different network parameters demonstrate that the proposed algorithm can reduce the communication conflict, improve the network throughput, and effectively reduce the network transmission delay and scheduling length for the data aggregation networks.

Reprint of: Collecting experimental network data from interventions on critical links in workplace networks

This article describes and discusses challenges associated with interventionist network data gathering in organizational settings, with a special focus on dyadic interventions. While pointing out major risks of these approaches, we argue that collecting data in combination with dyadic network alteration methods can enable social network researchers to explore network mechanisms from a new angle and potentially benefit the members of the targeted social networks and the entire collectives, if certain research design and implementation principles are followed. We introduce a facilitated self-disclosure method for strengthening critical dyads in social networks in combination with longitudinal and retrospective network measurement. We assess the participants’ perceptions of the different stages of this process by qualitative interviews. The study illustrates that experimental network data collection includes some extra challenges in addition to the many challenges of observational network data collection but participants also reported practical benefits that would not be gained through observational network surveys alone. The results highlight the importance of early and continuous communication during the data collection process with all research participants, not just the management, and the benefits of sharing more of the preliminary results. The lessons learnt through this study can inform the design of experimental network data collection to prioritize the preferences of the participants and their benefits.

Exact and Approximation Algorithms for Minimizing Energy in Wireless Sensor Data Gathering Network with Data Compression

This article studies the problem of minimizing the total energy consumed in a heterogeneous wireless sensor data gathering network with data compression. In a wireless sensor data gathering network, a set of sensors is used to collect data and all the data are required to be transmitted to a single base station. Whether the base station is working in data receiving or idle mode, it consumes energy. To reduce the data transmission time, each sensor has the option to compress its collected data to decrease the original size before sending the data to the base station. However, compressing data takes some time delaying the data transmission starting time and also consuming energy. The task is to choose which sensors should compress their data and determine the data transmission order between the sensors and the base station with the goal of minimizing the total energy consumed. We prove that the studied problem is NP-hard, and propose a pseudo-polynomial dynamic programming exact algorithm. Furthermore, we present an approximation algorithm with the performance ratio that depends on the given energy consuming parameters for each unit time in different energy consuming activities.

Scheduling of step-improving jobs with an identical improving rate

Job processing times change over time in real-life production and manufacturing systems due to various factors including machine or worker learning, machine deterioration, production system upgrades or technological shocks. For step-improving processing times, job processing times are reduced by a certain rate if they start to process at, or after, a common critical date, which has wide applicability in real-world settings, such as data gathering networks and production systems with part-time workers. This paper considers single machine scheduling of minimizing total weighted completion time with step-improving jobs. The problem is shown to be intractable. Both exact and heuristic algorithms are developed, and the approximability of the heuristic algorithm is shown for a special case of the problem. Finally, computational experiments show that the proposed algorithms provide very effective and efficient solutions.

Collecting experimental network data from interventions on critical links in workplace networks

This article describes and discusses challenges associated with interventionist network data gathering in organizational settings, with a special focus on dyadic interventions. While pointing out major risks of these approaches, we argue that collecting data in combination with dyadic network alteration methods can enable social network researchers to explore network mechanisms from a new angle and potentially benefit the members of the targeted social networks and the entire collectives, if certain research design and implementation principles are followed. We introduce a facilitated self-disclosure method for strengthening critical dyads in social networks in combination with longitudinal and retrospective network measurement. We assess the participants’ perceptions of the different stages of this process by qualitative interviews. The study illustrates that experimental network data collection includes some extra challenges in addition to the many challenges of observational network data collection but participants also reported practical benefits that would not be gained through observational network surveys alone. The results highlight the importance of early and continuous communication during the data collection process with all research participants, not just the management, and the benefits of sharing more of the preliminary results. The lessons learnt through this study can inform the design of experimental network data collection to prioritize the preferences of the participants and their benefits.

A comparison of priority rules for minimizing the maximum lateness in tree data gathering networks

ABSTRACT This article considers scheduling in tree data gathering networks. The worker nodes of a network acquire datasets of known sizes at possibly different times. The datasets are sent to intermediate nodes which process them and then pass the results to the base station. Each dataset is assigned a due date by which it should arrive at the base station. The scheduling problem is to minimize the maximum dataset lateness. It is proved that this problem is strongly NP-hard, and heuristic algorithms based on priority rules are proposed. The algorithms are tested in a series of computational experiments, for a wide range of changing network parameters. The results show that, although most algorithms perform well only for certain instances, one of the proposed priority rules achieves very good results for all settings analysed.

On the Lifetime of Asynchronous Software-Defined Wireless Sensor Networks

In this article, we consider a software-defined wireless sensor network (WSN) architecture which conserves energy by applying asynchronous duty cycling. In asynchronous sensor networks, the overhearing adversely impacts the energy consumption of the nodes. Using a mathematical model, we compute the maximum lifetime of the network and accordingly propose a multichannel operation and transmit power control to reduce the effect of overhearing in asynchronous networks. Our comprehensive test results confirm that for the network with load-aware nonuniform sensor node deployment, the network lifetime is much higher compared to a uniform deployment. We also show that the use of data aggregation software-defined WSNs (SDWSNs) can improve the network lifetime as compared to the data gathering networks.

Scheduling in data gathering networks with background communications

In this work, we study scheduling in star data gathering networks with background communications. The worker nodes of the network hold datasets that have to be transferred directly to the base station. The communication speed of each link changes with time, due to other applications using the network, independently of the speeds of other links. Our goal is to gather all data within the minimum time. We show that while the preemptive version of this problem can be solved in polynomial time, the non-preemptive variant is strongly NP-hard. We propose for it an exact exponential algorithm based on dynamic programming, several polynomial time heuristics and local search algorithms. Theoretical performance guarantees and the existence of dominance relations between the heuristics are studied. The performance of the proposed algorithms is also tested in a series of computational experiments.

Heuristics for scheduling data gathering with limited base station memory

In this paper, we analyze scheduling in data gathering networks with limited base station memory. The network nodes hold datasets that have to be gathered and processed by a single base station. A dataset transfer can only start if sufficient amount of memory is available at the base station. As soon as a node starts sending a dataset, the base station allocates a block of memory of corresponding size. The memory is released when computations on the dataset finish. We prove that minimizing the total data gathering and processing time is strongly NP-hard. As this problem is a special case of a specific resource constrained flow shop scheduling problem, for which an exact exponential algorithm is known, we propose several simple polynomial-time heuristics and two groups of local search algorithms, and test their performance in computational experiments. We show that the local search algorithms produce very good schedules, and one of the simple heuristics delivers solutions of comparable quality in a very short time.

Binary Data Gathering With a Helper in Internet of Things: Distortion Analysis and Performance Evaluation

This paper focuses on one-helper assisted binary data gathering networks, for example, such as in Internet of Things, where a destination makes estimates of binary data relying on a number of agents and one helper. Due to the noise, corrupting errors already exist in the agent observations. To analyze the performance of this system, we formulate this system as a binary chief executive officer (CEO) problem with a helper. Initially, we use a successive decoding scheme to decompose the binary CEO problem with a helper into the multiterminal source coding and final decision problems. Then, we present an outer bound on the rate-distortion region for multiterminal source coding with binary sources and a helper. After solving a convex optimization problem formulated from the derived outer bound, we obtain the final distortion by substituting the minimized distortions of observation into the distortion propagating function, which is derived to bridge the relationship between the joint decoding results and final decision. Finally, we analyze the trade-off of rate-distortion through theoretical calculation and simulations. Both the theoretical and simulation results demonstrate that a helper can obviously reduce the signal-to-noise ratio threshold. We also have an in-depth discussion on the differences of system performance improvement between locating a helper and including an additional agent.

Communication scheduling in data gathering networks of heterogeneous sensors with data compression: Algorithms and empirical experiments

We consider a communication scheduling problem to address data compression and data communication together, arising from the data gathering wireless sensor networks with data compression. In the problem, the deployed sensors are heterogeneous, in that the data compression ratios, in terms of size reduction, the compression time, and the compression costs, in terms of energy consumption, on different sensors are different. The bi-objective is to minimize the total compression cost and to minimize the total time to transfer all the data to the base station. The problem reduces to two mono-objective optimization problems in two separate ways: in the original problem a time bound is given and the mono-objective is to minimize the total compression cost, and in the complementary problem a global compression budget is given and the mono-objective is to minimize the makespan. We present a unified exact algorithm for both of them based on dynamic programming; this exact algorithm is then developed into a fully polynomial time approximation scheme for the complementary problem, and a dual fully polynomial time approximation scheme for the original problem. All these approximation algorithms have been implemented and extensive computational experiments show that they run fast and return the optimal solutions almost all the time.

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.

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.

Scheduling for data gathering networks with data compression

This paper analyzes scheduling in a data gathering network with data compression. The nodes of the network collect some data and pass them to a single base station. Each node can, at some cost, preprocess the data before sending it, in order to decrease its size. Our goal is to transfer all data to the base station in given time, at the minimum possible cost. We prove that the decision version of this scheduling problem is NP-complete. Polynomial-time heuristic algorithms for solving the problem are proposed and tested in a series of computational experiments.

Communication scheduling in data gathering networks with limited memory

In this paper scheduling communications in data gathering networks is analyzed. We study collecting information by a set of sensors, each of which stores the data in its memory buffer and then passes them to a base station. The network lifetime ends as soon as the first node is out of memory. We use a divisible load model to propose a communication scheduling algorithm that maximizes the system lifetime, and hence, the total amount of gathered information. The influence of the sensor communication rates on the obtained schedules is exposed in a series of computational experiments.