Energy-efficient Data Collection Scheme Research Articles

FlyingLoRa: Towards energy efficient data collection in UAV-assisted LoRa networks

Energy efficiency is an increasingly crucial consideration due to the battery-powered end devices in LoRa networks. With the adoption of chirp spread spectrum modulation, end devices far apart from the gateway have to use a high spreading factor and transmit power to send uplink packets, which causes longer transmission time and higher energy consumption compared to the end devices near the gateway, and further causes unfairness issues on energy efficiency. To tackle this problem, we investigate a novel energy-efficient data collection scheme named FlyingLoRa, where an unmanned aerial vehicle carrying one gateway is dispatched to harvest packets from end devices. The goal of FlyingLoRa is to minimize the energy consumption of end devices for packet transmission by jointly optimizing the 3D UAV trajectory, scheduling strategies, and transmission parameters of end devices. We formulate our design as a mixed-integer non-convex optimization problem and propose an efficient iterative algorithm to find a sub-optimal solution. The proposed approach is numerically simulated and the results show that FlyingLoRa improves energy efficiency by 16.13× on average compared with the existing fixed gateway schemes. In addition, we realize FlyingLoRa in real scenarios and evaluate its performance by presenting the actual packet reception rate (PRR) and transmission energy consumption, which shows its effectiveness.

Energy-Efficient UAV-Aided Ocean Monitoring Networks: Joint Resource Allocation and Trajectory Design

The Internet of Underwater Things (IoUT) plays a key role in maritime monitoring systems, but energy-efficient data-uploading has been a challenging task owing to energy-constrained and expensive facilities, such as buoys and underwater sensors. In this article, we present an energy-efficient data collection scheme for unmanned aerial vehicle (UAV)-aided ocean monitoring networks (OMNs), where underwater acoustic and aerial radio frequency (RF) links are considered collaboratively. Our goal is to maximize energy efficiency (EE) of the entire OMN by jointly optimizing the transmit power of buoys and sensors, scheduling their transmissions, as well as designing the UAV's trajectory; the objective function is constrained by minimum throughput thresholds, power consumption budgets, and the UAV's kinematic conditions. Furthermore, we introduce a tradeoff between the energy consumption of buoys and sensors to bridge the gap between acoustic and RF links. The formulated problem is decomposed into three subproblems and they are solved alternatively. In each iteration, we leverage Dinkelbach's method and successive convex approximation (SCA) technique to tackle the fractional program (FP) and transform an original subproblem into a convex form, respectively. Extensive simulations confirm the convergence of our proposed scheme, reveal the influence of the tradeoff on EE, and show that our scheme outweighs other benchmarks in different scenarios.

An Energy-efficient Data Collection Scheme by Mobile Element based on Markov Decision Process for Wireless Sensor Networks

An Energy-efficient Data Collection Scheme by Mobile Element based on Markov Decision Process for Wireless Sensor Networks

Deep Learning-Based Ground Vibration Monitoring: Reinforcement Learning and RNN–CNN Approach

This letter studies deep learning-based efficient ground vibration monitoring systems. In this work, artificial intelligence (AI) techniques are adopted to effectively deal with practical issues of data collection and classification. Specifically, we develop a novel energy-efficient data collection scheme by adopting deep Q-network-based reinforcement learning. Also, we propose an enhanced joint recurrent neural network (RNN) and convolutional neural network (CNN) approach for ground vibration classification. The performance of the proposed scheme is evaluated using real-world ground vibration data. The experimental results show that the proposed classification scheme outperforms the best existing scheme with CNN by more than 13% in terms of classification accuracy. It is also shown that the proposed energy management scheme can improve the accuracy of the proposed ground vibration monitoring system by 7.6% over the comparable scheme using equal power allocation.

Unmanned aerial vehicle-assisted energy-efficient data collection scheme for sustainable wireless sensor networks

Recently, wireless sensor networks (WSNs), consisted of battery-powered sensor nodes, are widely adopted by various civilian/military applications for implementing real-time monitoring or long-term surveillance tasks. However, due to limited battery capacity, sensor lifetime is limited, which in turn affects the working time of the whole system. Therefore, solving the problem of improving energy efficiency is the premise for constructing a sustainable/energy-efficient WSN. To address this problem, by taking advantage of the emerging unmanned aerial vehicle (UAV) techniques, we propose a novel UAV-assisted data collection scheme for improving the energy efficiency of the deployed WSN. To meet the performance requirements of systems with different scales, our algorithm has two working modes: single- and multiple-UAV scenarios. For the small-scale system, a single UAV considered data-collector is adopted to access and collect data from the deployed nodes. For the large-scale system, based on a newly designed clustering method, multiple data collectors are utilized to gathering sensed data from the deployed nodes for conserving their energy and reducing the data forwarding latency. To evaluate our proposed work, intensive simulation experiments are conducted. The results demonstrate that the proposed algorithm achieved better performance than the control group regarding system-wide energy efficiency and average end-to-end delay.

Energy Efficient Data Collection Algorithm for Mobile Wireless Sensor Network

Wireless sensor network (WSN) with mobile sink serves a lot of industrial and agricultural monitoring applications. The data collection with WSN has been popularly known in many applications such as smart environments, health monitoring, habitat monitoring, surveillance and tracking systems. The mobile sink moves to the corresponding cluster and gathers the data and moves to the next cluster head (CH) for the same. The data loss and waiting time of the data in the CH results from energy consumption and work repetition. This article provides a novel energy efficient data collection scheme. The data collection is done by the sink in a Polling based M/G/1 server model. The cluster member (CM) sends the data in N threshold model to the CH. The CH gathers the data from the CM and reports to sink once it arrives nearby. The energy efficient cluster head selection algorithm is evaluated with the proposed model and the waiting time is analyzed. The data arrival from the CM to the CH is considered as Poisson in nature. The sink mobility with respect to polling system is analyzed. The sink data collection is done through dynamic polling mechanism based on the CH arrival rate. The proposed algorithm outperforms the modified low energy adaptive clustering hierarchy and energy-aware multi-hop routing protocol (M-GEAR) protocols in terms of lifetime, waiting time and throughput. The proposed algorithm provides high resistance to energy hole and HOTSPOT problem.

Energy-Efficient Data Collection Scheme for Environmental Quality Management in Buildings

How to efficiently collect sensory data for supporting energy-efficient operation of buildings has become a great challenge, especially for large-scale networks in buildings. In this paper, a spatio-temporal compression-based optimized clustering scheme is proposed for energy-efficient environmental data collection in buildings. In the scheme, first, according to the establishment of a cluster, an adaptive dynamic cluster head selection method for prolonging the lifetime of sensory nodes in buildings is developed. Meanwhile, to further reduce energy consumption, we construct the dynamic optimal quantity of the cluster heads solution method to achieve minimum energy consumption. Moreover, the spatio-temporal correlations of sensory data are explored in the data sampling and transmission processes, which can decrease the amount of data transmission and further extend the lifetime of the entire data collection network. The proposed scheme provides sensing data with high quality for environmental quality management in buildings and supports energy-efficient building operation. Finally, the simulation results show that the proposed scheme obtains obvious advantages in energy consumption compared with other related schemes, and the lifetime of the proposed scheme is also longer than others when it maintains high reconstruction precision.

On the Design of an Energy Efficient Data Collection Scheme for Body Area Nano Networks

With the recent advancements in nanotechnologies, Body Area Nanonetworks (BANNETs) are expected to be a promising solution for many critical biomedical applications. Due to the extremely small size of nanomachines, serious energy limitation becomes a challenging roadblock stunting the development of BANNETs. As an initial step towards this end, this paper focuses on the design of an energy-efficient data collection scheme in BANNETs. First, a sleep/wake-up mechanism is introduced to avoid the unnecessary energy consumption when no external request comes. Then, with a careful consideration of both node available energy and transmission energy consumption, we design a new node selection strategy to further reduce the energy consumption in the data collection process. Finally, we conduct extensive simulations for both the proposed data collection scheme and the benchmark greedy scheme to illustrate the energy efficiency of our scheme as well as to discuss the impacts of network parameters on network performance.

On the Design of an Energy Efficient Data Collection Scheme for Body Area Nano Networks

With the recent advancements in nanotechnologies, Body Area Nanonetworks (BANNETs) are expected to be a promising solution for many critical biomedical applications. Due to the extremely small size of nanomachines, serious energy limitation becomes a challenging roadblock stunting the development of BANNETs. As an initial step towards this end, this paper focuses on the design of an energy-efficient data collection scheme in BANNETs. First, a sleep/wake-up mechanism is introduced to avoid the unnecessary energy consumption when no external request comes. Then, with a careful consideration of both node available energy and transmission energy consumption, we design a new node selection strategy to further reduce the energy consumption in the data collection process. Finally, we conduct extensive simulations for both the proposed data collection scheme and the benchmark greedy scheme to illustrate the energy efficiency of our scheme as well as to discuss the impacts of network parameters on network performance.

Secure Energy Efficient Data Collection Scheme Over Wireless Multimedia Sensor Networks

A secure data collection in wireless multimedia sensor networks (WMSNs) has given attention to one of security issues. WMSNs pose unique security challenges due to their inherent limitations in communication and computing, which makes vulnerable to various attacks. For the energy efficiency, WMSNs adopt mobile sinks to collect data from sensor nodes. Thus, how to gather data securely and efficiently is an important issue WMSNs. In this paper, we propose a secure energy efficient data collection scheme over WMSNs, which are based on Bilinear pairing and symmetric key cryptosystem. First of all, we devise a security model based on a hierarchical key structure for the security mechanisms, authentication, key agreement, confidentiality, and integrity. Based on the model, we propose a secure energy efficient data collection scheme, which could establish secure session in one round. The proposed scheme could efficiently remedy security and efficiency problems in the previous data collection schemes over WMSNs. It has only about 18% of overhead for the security but also has energy efficiency compared with the other related schemes.