Energy Efficient Data Collection Research Articles

Energy-Efficient Data Aggregation and Collection for Multi-UAV-Enabled IoT Networks

Energy-Efficient Data Aggregation and Collection for Multi-UAV-Enabled IoT Networks

Remote Sensing Revolution: Mapping Land Productivity and Vegetation Trends with Unmanned Aerial Vehicles (UAVs)

Abstract: This review paper offers a comprehensive exploration of the multifaceted applications of Unmanned Aerial Vehicles (UAVs) in various domains, showcasing their transformative impact in addressing complex challenges. The evaluation of cloud-based UAV systems' stability reveals their robustness and reliability, underlining their significance in numerous industries. Additionally, their role in enhancing robot navigation in intricate environments signifies a substantial advancement in robotics and automation. The integration of blockchain technology for secure Internet of Things (IoT) data transfer emphasizes the critical importance of data integrity and confidentiality in the IoT era. Furthermore, the optimization of energy-efficient data collection in IoT networks through UAVs demonstrates their potential to revolutionize data-driven decision-making processes, particularly in fields reliant on data accuracy and timeliness. The paper also highlights the application of deep reinforcement learning to enhance UAV-assisted IoT data collection, showcasing the synergy between advanced machine learning techniques and UAV technology. Finally, the discussion underscores the pivotal role of UAVs in precision agriculture, where they facilitate ecological farming practices and monitor environmental conditions, contributing to the pursuit of sustainable and efficient agriculture. This review reaffirms UAVs' status as transformative tools, reshaping industries and unlocking new frontiers of innovation and problem-solving. With ongoing technological advancements, UAVs are poised to play an increasingly central role in a wide range of applications, promising a future marked by ground breaking possibilities. Key findings include the dominance of the United States and China in the field, exploration of characteristics such as crop production, and innovative UAV-based methods for grassland mapping, maize growth assessment, and Arctic plant species monitoring. The research underscores the potential of UAVs in bridging field data and satellite mapping, providing valuable insights into diverse applications, from soil analysis to yield predictions, highlighting their transformative role in environmental monitoring and precision agriculture

A PSO-based energy-efficient data collection optimization algorithm for UAV mission planning.

With the development of the Internet of Things (IoT), the use of UAV-based data collection systems has become a very popular research topic. This paper focuses on the energy consumption problem of this system. Genetic algorithms and swarm algorithms are effective approaches for solving this problem. However, optimizing UAV energy consumption remains a challenging task due to the inherent characteristics of these algorithms, which make it difficult to achieve the optimum solution. In this paper, a novel particle swarm optimization (PSO) algorithm called Double Self-Limiting PSO (DSLPSO) is proposed to minimize the energy consumption of the unmanned aerial vehicle (UAV). DSLPSO refers to the operational principle of PSO and incorporates two new mechanisms. The first mechanism is to restrict the particle movement, improving the local search capability of the algorithm. The second mechanism dynamically adjusts the search range, which improves the algorithm's global search capability. DSLPSO employs a variable population strategy that treats the entire population as a single mission plan for the UAV and dynamically adjusts the number of stopping points. In addition, the proposed algorithm was also simulated using public and random datasets. The effectiveness of the proposed DSLPSO and the two new mechanisms has been verified through experiments. The DSLPSO algorithm can effectively improve the lifetime of the UAV, and the two newly proposed mechanisms have potential for optimization work.

Evolution-based energy-efficient data collection system for UAV-supported IoT: Differential evolution with population size optimization mechanism

In recent years, unmanned aerial vehicles (UAVs) have been broadly employed as a data collection platform to assist in efficiently collecting data from IoT devices. However, the deployment optimization of UAVs has been challenged due to the need to minimize the energy consumption of UAVs and IoT devices. Several algorithms have been recently proposed for tackling this challenge, but they still have room for improvement due to their slow convergence speed and memory-wasting problems. Therefore, in this study, a new energy-aware approach has been proposed for accurately optimizing the entire deployment of UAVs, which could minimize the total energy consumption. This approach is based on presenting a new encoding mechanism, namely an optimized population size mechanism, for representing both location and number of stop points in an effective manner. In this mechanism, similar to some studies in the literature, the whole population is responsible for the entire deployment, and each individual is responsible for a stop point in this deployment. However, this mechanism presents a novel way to optimize the number of stop points based on adding an auxiliary variable to each stop point to determine whether it will be removed, inserted, or replaced in the newly generated deployment. This variable will be optimized by the optimization techniques during the optimization process to search for the optimal choice for each stop point that could achieve a better deployment. Two well-known optimization techniques, known as differential evolution (DE) and gradient-based optimizer (GBO), are adapted using this mechanism to present new variants, namely DEoPS and GBoPS, for accurately tackling the deployment optimization problem. Two energy consumption formulations are used in our work to investigate the performance of DEoPS and GBoPS. Several experiments have been conducted to compare the performance of both DEoPS and GBoPS with several algorithms on eleven instances. The experimental findings show the effectiveness of GBoPS for the first formulation and the effectiveness of DEoPS for the second formulation.

Energy Consumption Minimization in Unmanned Aerial Vehicle-Enabled Secure Wireless Sensor Networks

In wireless sensor networks (WSNs), unmanned aerial vehicles (UAVs) are considered an effective data collection tool. In this paper, we investigate the energy-efficient data collection problem in a UAV-enabled secure WSN without knowing the instantaneous channel state information of the eavesdropper (Eve). Specifically, the UAV collected the information from all the wireless sensors at the scheduled time and forward it to the fusion center while Eve tries to eavesdrop on this confidential information from the UAV. To surmount this intractable and convoluted mixed-integer non-convex problem, we propose an efficient iterative optimization algorithm using the block coordinate descent (BCD) method to minimize the maximum energy consumption of the ground sensor nodes (GSNs) under the constraints of secrecy outage probability (SOP), connection outage probability (COP), minimum secure data, information causality, and UAV trajectory. Numerical results demonstrate the superiority of the algorithm we proposed in energy consumption and secrecy rate compared with other schemes.

Height-Fixed UAV Enabled Energy-Efficient Data Collection in RIS-Aided Wireless Sensor Networks

Wireless sensor nodes (SNs) are usually energy-limited, a low energy efficiency of SN may be caused by the occlusion of transmission link in the unmanned aerial vehicle (UAV)-enabled data collection. Reconfigurable intelligent surfaces (RISs) provide potential communication opportunities by reflecting and enhancing the signal, thus increasing SNs’ energy efficiency. This paper investigates energy-efficient data collection aided by <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> RIS elements, where a height-fixed and flight time-constrained UAV is considered as the collector, a tractable air-to-ground channel model is established considering the cascaded RIS channel and time-varying trajectory of UAV, and energy consumption of SNs is minimized. Specifically, coupling two intractable fourth-order moment variables, the air-to-ground channel model is processed into an easily computable expression through the Gamma function. Furthermore, based on the proposed two RIS deployment schemes, maximum energy consumption of SNs minimization problem is formulated as a non-convex mixed-integer nonlinear programming (NMNP) problem with optimization of UAV trajectory and SNs wake-up schedules. Additionally, through the slack technique and Taylor expansion, the NMNP problem is decoupled as linear programming and quadratically constrained quadratic programming, and an effective algorithm is designed to obtain the sub-optimal solution. Simulations prove the validity of our algorithm and RIS deployment insights are given for SNs energy saving.

Energy-efficient data collection over underwater MI-assisted acoustic cooperative MIMO WSNs

Underwater magnetic induction (MI)-assisted acoustic cooperative multiple-input-multiple-output (MIMO) has been recently proposed as a promising technique for underwater wireless sensor networks (UWSNs). For the more, the energy utilization of energy-constrained sensor nodes is one of the key issues in UWSNs, and it relates to the network lifetime. In this paper, we present an energy-efficient data collection for underwater MI-assisted acoustic cooperative MIMO wireless sensor networks (WSNs), including the formation of cooperative MIMO and relay link establishment. Firstly, the cooperative MIMO is formed by considering its expected transmission range and the energy balance of nodes with it. Particularly, from the perspective of the node's energy consumption, the expected cooperative MIMO size and the selection of master node (MN) are proposed. Sequentially, to improve the coverage of the networks and prolong the network lifetime, relay links are established by relay selection algorithm that using matching theory. Finally, the simulation results show that the proposed data collection improves its efficiency, reduces the energy consumption of the master node, improves the networks' coverage, and extends the network lifetime.

Fuzzy clustering and optimal routing in wireless sensor network

A wireless sensor network (WSN) is a collection of numerous independent sensor nodes that can sense, process, and manipulate data. WSN is grouped into clusters for energy-efficient data collection. A clustering and aggregation technique automatically extends the lifetime of a WSN by collecting data within the cluster to the cluster head, reduces the amount of data through processing, and transmitting. WSN routing protocols are also required for completing all types of operations in a Internet of things (IOT) environment, such as sensing, controlling, and transmitting packets. In this paper, a novel Fuzzy Clustering and Optimal Routing (FCOR) method is proposed in order to lessen the energy consumption, delay, and improve network lifetime and node density. The proposed FCOR method is executed in two stages. The initial stage consists of clustering and cluster head selection using modified Fuzzy c-means algorithm (MFCM). This algorithm will efficiently cluster the nodes and select the optimal cluster head. The second phase consists of optimal routing using a normalized whale optimization algorithm (NWOA), that select the optimal route and thus improve the lifetime of the nodes. The efficiency of the proposed FCOR approach has been determined using the evaluation metrics such as energy efficiency, packet delivery, and network lifetime. The experimental results reveals that the proposed FCOR model achieves less energy consumption of 67.8%, 54.4%, 60% and 6.67% than existing FRNSEER, E-ALWO, ACI-GSO and CRSH respectively.

Energy-efficient mobile sink data collection in wireless sensor networks using LEACH with rendezvous nodes

In Wireless Sensor Networks (WSNs), energy consumption management poses a formidable challenge due to the energy-intensive nature of data transmission and processing. WSNs consist of densely deployed sensor nodes, and data collection is carried out by cluster heads. To address the issue of transmitting data to the mobile sink (MS) and extend the network's lifetime, rendezvous nodes(RNs) are utilized. However, existing approaches have not sufficiently improved network longevity. To tackle this problem, a novel protocol called Energy Efficient Mobile Sink Data Collection (EEMSDC) is proposed. This protocol aims to balance energy usage, prevent sensor node overload, and facilitate data delivery to the MS through RNs. The proposed approach optimizes the MS's tour in WSNs by employing predetermined TDMA slots for efficient data transmission with RNs. The proposed protocol outperforms existing protocols such as LEACH, EAMMH, LEACH-C, MOD-LEACH, MGEAR, and TDEEC in terms of network lifetime, energy consumption, and data loss. By reducing network overhead and ensuring balanced energy consumption among sensor nodes, the proposed approach significantly extends the network's lifetime. The effectiveness of the proposed protocol is validated through mathematical analysis and simulations conducted using MATLAB 2021a. The simulations show a remarkable reduction in energy consumption by up to 68% and an impressive increase in network lifetime by 73% compared to LEACH. These findings underscore the substantial advantages of the proposed approach in enhancing the performance of WSNs.

An Enhanced Energy-Efficient Data Collection Optimization Algorithm for UAV Swarm in the Intelligent Internet of Things

In the case of limited endurance of unmanned aerial vehicles (UAVs), in order to further improve UAV data collection efficiency, this paper puts forward EDC-UAVIIoT: an enhanced energy-efficient data collection optimization algorithm for UAV swarm in the intelligent Internet of Things. First of all, the algorithm optimizes the UAV cruise path through the intelligent Internet of Things routing mechanism, avoids the occurrence of data errors in the packet transmission process, and uses the end-to-end transmission error probability model. The error probability of data packets in the transmission process is calculated to improve the efficiency of data collection tasks and data throughput. Secondly, considering the relationship between energy harvesting and energy consumption balance, this paper uses semi-definite programming and a convex approximation algorithm to transform the non-convex optimization problem into a convex optimization problem and realize the mapping relationship between the UAV cluster node and the target node coordinates, which reduces the computational complexity. Finally, the simulation results show that the EDC-UAVIIoT algorithm is compared with other algorithms in network energy consumption, running time, network delay, and network throughput. The numerical values are increased by 7.03%, 10.16%, 12.39%, and 8.82%, respectively, thus verifying the effectiveness and stability of the proposed EDC-UAVIIoT algorithm.

Remote Data Acquisition Using UAVs and Custom Sensor Node Technology

Precision agriculture technology has advanced rapidly in the 21st century. Despite this, the vast majority of US farmers do not employ any form of precision agriculture. Reasons for this include the high initial cost, lack of internet connectivity in rural areas, and complex setup and operation. The basis of this project was to create a low-cost, energy-efficient data collection system using an unmanned aerial vehicle (UAV) as a mobile sink node in a local wireless system. This was accomplished through the design and manufacture of custom sensor nodes and a custom drone-mounted wireless receiver node. The sensor node and drone node enclosures were 3D printed and assembled using low-cost materials and internal components. The system was successfully tested in a field where it collected soil data, including soil moisture, soil temperature, and electrical conductivity. The cost and scalability of the system are discussed, as well as potential improvements and comparisons with existing technologies. The system was concluded to have many potential applications in its current state but with room to expand and improve its operation and features.

Dynamic data collection algorithm based on mobile edge computing in underwater internet of things

The Underwater Internet of Things (UIoT) has emerged as one of the prominent technologies in the development of future ocean monitoring systems, where mobile edge elements (such as autonomous underwater vehicles (AUVs)) provide a promising method for the data collection from sensor nodes. However, as an important part of the UIoT, underwater wireless sensor networks (UWSNs) are severely affected by the underwater dynamic environment. For instance, node locations change continuously, which significantly increases the difficulty of data collection. To solve this problem, the concept of an inevitable communication space (ICS) is proposed. The ICS is calculated by analyzing the variation in the position of nodes and the communication range. Furthermore, an ICS-based dynamic data collection algorithm (ICS-DDCA) for UIoT is proposed to collect underwater data. This method utilizes the ICS instead of the initial location of the node for data collection to further improve the performance of the algorithm and shorten the data collection time. The simulation results demonstrate that compared with the energy-efficient data collection over AUV-assisted (EEDA) and data collection algorithms based on probabilistic neighborhood (PNCS-GHA), ICS-DDCA can effectively reduce the collection time, while ensuring the full completion of data collection.

Three-Dimensional Trajectory Design for Unmanned Aerial Vehicle-Based Secure and Energy-Efficient Data Collection

Fixed-wing unmanned aerial vehicles (UAVs) provide a practical means for data collection in many Internet-of-Things (IoT) applications. Secrecy and energy efficiency are crucial to the data collection, but have never been well addressed in the lack of appropriate propulsion energy models for three-dimensional (3D) flights of fixed-wing UAVs. This paper delivers a new approach to the joint optimization of the 3D flight trajectory and data collection schedule of a fixed-wing UAV for secure and energy-efficient data collection under an eavesdropping attack. A critical aspect is that we derive a new propulsion energy model for fixed-wing UAVs flying 3D trajectories. By using equivalent transformation, difference-of-convex techniques, and successive convex approximation, we convexify the model and demonstrate its viability to the design of energy-efficient 3D trajectories. Another key aspect is that we further optimize the trajectory and schedule of the UAV for secure and energy-efficient collection of secret data from sensor nodes on the ground. This is non-trivial due to the convexification of the non-smooth secrecy constraints in addition to the new 3D propulsion energy model. Simulations validate the new model and demonstrate considerable energy saving of 3D trajectories, compared to two-dimensional (2D) trajectories optimized based on the state-of-the-art 2D propulsion energy model.

Towards Energy-Efficient Data Collection by Unmanned Aerial Vehicle Base Station With NOMA for Emergency Communications in IoT

For emergency communications in an internet of thing (IoT) network, a large number of gateways are distributed to gather the data traffic. Considering the practical difficulty of deploying multiple territorial base stations (TBSs) in a wide range, unmanned aerial vehicle base station (UAV-BS) can fly to a specific point and hover above there to collect data traffic from gateways. In this paper, we aim to maximize the UAV-BS energy efficiency under the constraints of total serving delay, UAV-BS flying speed, and the maximum available transmitting power of gateways, etc. Firstly, we propose a distributed gateway cluster (GC) algorithm to group gateways into multiple GCs based on the distances among gateways. Next, the UAV-BS flies and hovers above each GC, where the gateways in the GC simultaneously transmit data to the UAV-BS by non-orthogonal multiple access (NOMA). By analyzing the NOMA feature, we propose theorems optimizing the UAV-BS hovering height to minimize the transmitting power of the gateway with the maximum transmitting power among the gateways in a GC. Based on the proposed theorems, we formulate the joint optimization problem to maximize the UAV-BS energy efficiency with only the variables of UAV-BS flying speed and the serving time for each GC. The optimization problem is effectively solved by the geometric programming (GP) method. Finally, we verify the effectiveness of the proposed algorithms by extensive simulation results.

Energy-Efficient Routing Using Novel Optimization with Tabu Techniques for Wireless Sensor Network

Wireless Sensor Network (WSN) consists of a group of limited energy source sensors that are installed in a particular region to collect data from the environment. Designing the energy-efficient data collection methods in large-scale wireless sensor networks is considered to be a difficult area in the research. Sensor node clustering is a popular approach for WSN. Moreover, the sensor nodes are grouped to form clusters in a cluster-based WSN environment. The battery performance of the sensor nodes is likewise constrained. As a result, the energy efficiency of WSNs is critical. In specific, the energy usage is influenced by the loads on the sensor node as well as it ranges from the Base Station (BS). Therefore, energy efficiency and load balancing are very essential in WSN. In the proposed method, a novel Grey Wolf Improved Particle Swarm Optimization with Tabu Search Techniques (GW-IPSO-TS) was used. The selection of Cluster Heads (CHs) and routing path of every CH from the base station is enhanced by the proposed method. It provides the best routing path and increases the lifetime and energy efficiency of the network. End-to-end delay and packet loss rate have also been improved. The proposed GW-IPSO-TS method enhances the evaluation of alive nodes, dead nodes, network survival index, convergence rate, and standard deviation of sensor nodes. Compared to the existing algorithms, the proposed method outperforms better and improves the lifetime of the network.

A reliable routing mechanism with energy-efficient node selection for data transmission using a genetic algorithm in wireless sensor network

Energy-efficient and reliable data routing is critical in Wireless Sensor Networks (WSNs) application scenarios. Due to oscillations in wireless links in adverse environmental conditions, sensed data may not be sent to a sink node. As a result of wireless connectivity fluctuations, packet loss may occur. However, retransmission-based approaches are used to improve reliable data delivery. These approaches need a high quantity of data transfers for reliable data collection. Energy usage and packet delivery delays increase as a result of an increase in data transmissions. An energy-efficient data collection approach based on a genetic algorithm has been suggested in this paper to determine the most energy-efficient and reliable data routing in wireless sensor networks. The proposed algorithm reduced the number of data transmissions, energy consumption, and delay in network packet delivery. However, increased network lifetime. Furthermore, simulation results demonstrated the efficacy of the proposed method, considering the parameters energy consumption, network lifetime, number of data transmissions, and average delivery delay.

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 Consumption Minimization for Data Collection From Wirelessly-Powered IoT Sensors: Session-Specific Optimal Design With DRL

Reliable and energy-efficient data collection from resource-limited sensors is essential to the success of future Internet of Things (IoT). In this article, we study the energy consumption minimization problem during the data collection from a generic wirelessly-powered sensor. Specifically, we determine the optimal data collection parameters, in terms of charging duration and charging power as well as sensor transmission rate, in real time according to the instantaneous channel condition while satisfying a certain latency constraint. For the scenario of ideal rate-adaptive transmission with linear energy harvesting, we derive closed-form expressions for optimal transmission parameters. We also establish the condition on channel quality for successful data collection under a latency constraint. For the more practical case of finite block-length transmission with nonlinear energy harvesting, we develop a deep reinforcement learning (DRL) solution for efficient online implementation. We also propose an online tuning scheme to cater for model inaccuracy and environment variation. The accuracy and effectiveness of our proposed approaches are verified by comparing with benchmark schemes. Our DRL-based approach has broad applicability and can solve other real-time optimal design problems in wireless communications.

Optimized scheduling method in 6TSCH wireless networks

IEEE802.15.4e-TSCH is a mode exploited by the Internet of Things. Time Slotted Channel Hopping (TSCH) presents an upgrade to the IEEE 802.15.4 to build a Medium Access Control (MAC) for low power and loss network applications in IoT. This norm defines the concept of TSCH based on channel hopping and reservation of bandwidth to achieve energy efficiency, as well as consistent transmissions. Centralized approaches have been proposed for planning TSCH. They have succeeded in increasing network efficiency and reducing latency, but the scheduling length remains not reduced. However, distributed solutions appear to be more stable in the face of change, without creating a priori assumptions about the topology of the network or the amount of traffic to be transmitted. A distributed scheduling allowing neighboring nodes to decide on a coordination system operated by a minimal scheduling feature is currently proposed by the 6TiSCH working group. This scheduling allows sensor nodes to determine when data is to be sent or received. However, the details of scheduling time intervals are not specified by the TSCH-mode IEEE802.15.4e standard. In this work, we propose a distributed Optimized Minimum Scheduling Function (OMSF) that is based on the 802.15.4e standard TSCH mode. For this purpose, a distributed algorithm is being implemented to predict the scheduling requirements over the next slotframe, focused on the Poisson model and using a cluster tree topology. As a consequence, it will reduce the negotiation operations between the pairs of nodes in each cluster to decide on a schedule. This prediction allowed us to deduce the number of cells needed in the next slotframe. Clustering decreases, the overhead processing costs that produce the prediction model. So, an energy-efficient data collection model focused on clustering and prediction has been proposed. As a result, the energy consumption, traffic load, latency, and queue size in the network, have been reduced.

Dynamic monitoring frequency for energy-efficient data collection in Internet of Things

With growth of Internet of Things, number of connected sensors increases as well, along with data being collected by those sensors. Most sensors are battery powered and commonly collect data in short and equally spaced time periods resulting with large amount of redundant and often irrelevant data. In this paper, we propose a dynamic monitoring frequency (DMF) algorithm that aims at collecting data only when sensor readings change by more than a predefined value between consecutive readings. Thus, a sensor is turned on only when a change in monitored phenomenon value exceeds a predefined threshold. Two algorithms are analyzed, namely statistical and machine learning. DMF shows notable performance, resulting either with up to ∼70% less missed readings, or it collects up to ∼40% less data compared to the baseline algorithm that collects data with static monitoring frequency.