Data Mules Research Articles

FloatingBlue: A Delay Tolerant Networks-Enabled Internet of Things Architecture for Remote Areas Combining Data Mules and Low Power Communications.

Monitoring vast and remote areas like forests using Wireless Sensor Networks (WSNs) presents significant challenges, such as limited energy resources and signal attenuation over long distances due to natural obstacles. Traditional solutions often require extensive infrastructure, which is impractical in such environments. To address these limitations, we introduce the "FloatingBlue" architecture. This architecture, known for its superior energy efficiency, combines Bluetooth Low Energy (BLE) technology with Delay Tolerant Networks (DTN) and data mules. It leverages BLE's low power consumption for energy-efficient sensor broadcasts while utilizing DTN-enabled data mules to collect data from dispersed sensors without constant network connectivity. Deployed in a remote agricultural area in the Amazon region, "FloatingBlue" demonstrated significant improvements in energy efficiency and communication range, with a high Packet Delivery Ratio (PDR). The developed BLE beacon sensor achieved state-of-the-art energy consumption levels, using only 2.25 µJ in sleep mode and 11.8 µJ in transmission mode. Our results highlight "FloatingBlue" as a robust, low-power solution for remote monitoring in challenging environments, offering an energy-efficient and scalable alternative to traditional WSN approaches.

Wireless sensor network for structural health monitoring using RFID based data mules

The wireless system did the huge cost-cutting in monitoring the structure so, now it can be used permanently as an integral part of the system as a smart infrastructure that will give them real-time information the structure. The wireless devices transmit the collected data about cracks, displacement, and excess vibration in slab-tracks. The train which will collect the data and train will be used as a data mule in this paper which will upload the information to a remote-control centre. The data which will be collected stored in the database and to know the status of the track a query will be fired from an application. In this paper, many design for communication systems are proposed which are efficient, with fine accuracy, and most importantly it is a low-cost system.

Performance Evaluation of Enhanced Slotted AlohaCA Protocol on Planet Mars

The launch and successful operation of the Mars Cube One (MarCO) CubeSat in May 2018 heralded a new era in solar system exploration and the setup of the first Interplanetary CubeSat Network (ICN). The success of this mission could give rise of an Interplanetary DTN–Based CubeSat network, in which the CubeSat Nanosatellite, as DTN custody node, plays the role of Data Mule to collect data from rovers on a planet such as Mars. In order to maximize the contact volume which is the amount of transmitting data from rovers to the CubeSat during its pass over their service zone, we will need to design an efficient MAC protocol. This research focuses on the simulation and evaluation of the performance of the Slotted AlohaCA MAC Protocol on the planet Mars compared to Earth taking into account the different properties between the two planets, such as radius, mass and speed of rotation of the Nanosatellite in its orbital at the same altitude. We have conducted many simulations using the NS2 simulator that takes into consideration the spatial dynamic behavior of the Nanosatellite, which is dependent on motion of the Nanosatellite in its orbit. Three appropriate performance measures are evaluated: Throughput, stability and power consumption. The obtained simulation results on the planet Mars show an improvement on performance of the Slotted AlohaCA on the planet Mars compared to Earth.

InDaMul: Incentivized Data Mules for Opportunistic Networking Through Smart Contracts and Decentralized Systems

The rise of Internet-of-Things enables the development of smart applications devoted to improving the quality of life in urban and rural areas, thus fostering the creation of smart territories. However, some dislocated areas are underprivileged in providing such services due to the lack, inefficiency, or excessive cost of Internet access. Opportunistic networking techniques might aid in surmounting these problems. In this article, we propose a framework that relies on an untrusted Data Mule to carry data from an offline source to an online destination. In particular, we present a framework that enables the communication between different actors and a reward mechanism using Distributed Ledger Technologies, Smart Contracts, and Decentralized File Storage. The protocol involved in bringing a Client’s message online and getting back a response is thoroughly explained in all its steps and then discussed on the most important trust and security issues. Finally, we evaluate such a protocol and the whole framework through a series of communication latency tests, an analysis of the Smart Contract usage, and simulations in which buses act as Data Mules. Our results suggest the feasibility of our proposal in a smart territory scenario.

Energy Aware Trajectory Optimization of Solar Powered AUVs for Optical Underwater Sensor Networks

Visible light communication (VLC) provides an alternative underwater wireless connectivity solution with its low latency and high data rates albeit at relatively shorter distances in the order of tens of meters. In the context of underwater sensor networks (USNs), VLC is particularly suitable to establish connectivity between “data mule” autonomous underwater vehicles (AUVs) and sensor nodes since communications is enabled only when the sensor node and mule AUV are in close proximity. In this paper, we consider a USN scenario where a solar-powered AUV gathers data from the sensor nodes using VLC signaling. We formulate a three-dimensional trajectory optimization for solar-powered AUVs with the goal of maximizing the harvested energy under constraints imposed by the data transmission. The optimization constraints include the minimum required data transfer rate, therefore a corresponding transmission distance, between the sensors and the AUV. We formulate the problem as a bilevel optimization problem. The lower-level objective function is in the form of traveling salesman problem which determines the optimum sequence order of the sensor nodes to be visited while the upper-level objective function is the optimization of the trajectory between each pair of adjacent nodes for the given order of node visits. Our numerical results demonstrate that the proposed trajectory significantly prolongs the mission time and autonomous operation of the AUV without the need to return to home base. Furthermore, since the proposed trajectory optimization is reactive to ocean currents, it brings reductions in the energy consumption of the AUVs.

Approximation and polynomial algorithms for the data mule scheduling with handling time and time span constraints

In this paper, we address the data mule scheduling problem with time constraints (DMSTC) in which the aim is to dispatch from a depot the minimum number of data mules to serve target sensors located on a network. Each target sensor is associated with a handling time and each dispatched data mule must return to the depot before time span D. Our main contribution is as follows. First, we give the first constant-factor 2-approximation algorithm and a bicriteria polynomial time approximation scheme (PTAS) for the DMSTC defined on a tree. The former result resolves an open problem proposed in the literature (Chen et al., 2020 [4]). This is achieved by an approximation preserving reduction from the DMSTC to the distance constrained vehicle routing problem, i.e. a special case of the DMSTC with zero handling times. Second, we show that our approximation preserving reduction can be extended to the multi-depot version of the DMSTC and derive a similar bicriteria PTAS for the multi-depot DMSTC on a tree if the number of depots is a fixed constant. Finally, we consider the uniform DMSTC, which is a particular case of the DMSTC with all handling times identical, and develop the first non-trivial polynomial algorithms for the uniform DMSTC define on several classes of special networks, including spiders, paths and cycles.

Shortest path planning of a data mule in wireless sensor networks

Shortest path planning of a data mule in wireless sensor networks

Cycler Orbits and Solar System Pony Express

A network of small satellites is designed to ferry data from Mars to Earth. The small satellites, or data mules, are assumed to have laser communicators and maintain cycler orbits. The concept exploits the fact that two nearby optical communicators can achieve extremely high data rates and that cycler orbits carry a satellite between Mars and Earth regularly. The use of a network of satellites on phase-shifted cycler orbits ensures that transfers from Mars to Earth happen with regular cadence. The cycler approximations, combinations, and cross-link analysis steps are outlined and shown to produce favorable results achieving petabit-scale transfers approximately yearly with as few as six satellites without significantly impacting regular Deep Space Network operations, for an average data rate that is much better than direct downlink alone.

Real-Time and Intelligent Flood Forecasting Using UAV-Assisted Wireless Sensor Network

The Wireless Sensor Network (WSN) is a promising technology that could be used to monitor rivers’ water levels for early warning flood detection in the 5G context. However, during a flood, sensor nodes may be washed up or become faulty, which seriously affects network connectivity. To address this issue, Unmanned Aerial Vehicles (UAVs) could be integrated with WSN as routers or data mules to provide reliable data collection and flood prediction. In light of this, we propose a fault-tolerant multi-level framework comprised of a WSN and a UAV to monitor river levels. The framework is capable to provide seamless data collection by handling the disconnections caused by the failed nodes during a flood. Besides, an algorithm hybridized with Group Method Data Handling (GMDH) and Particle Swarm Optimization (PSO) is proposed to predict forthcoming floods in an intelligent collaborative environment. The proposed water-level prediction model is trained based on the real dataset obtained from the Selangor River in Malaysia. The performance of the work in comparison with other models has been also evaluated and numerical results based on different metrics such as coefficient of determination (), correlation coefficient (), Root Mean Square Error (), Mean Absolute Percentage Error (), and are provided.

Mobile Data-Mule Optimal Path Planning for Wireless Sensor Networks

This article is concerned with collecting stored sensor data from a static Wireless Sensor Network utilizing a group of mobile robots that act as data mules. In this scenario, the static sensor nodes’ locations are known a priori, and the overall optimization problem is formulated as a variation of the Traveling Salesman Subset-tour Problem (TSSP). The constraints that are taken into account include: (a) the pairwise distance between static nodes, (b) the maximum time interval between consecutive visits of each static node, (c) the service time that is required for the collection of the sensor data from the mobile element that visits this sensor node, and (d) the energy efficiency for the mobile nodes. The optimal mobile robot paths are computed using an enhanced Mobile Element Scheduling scheme. This scheme extracts the sequential paths of the mobile elements in an attempt to eliminate any sensor data loss.

An Intelligent Edge-Traffic Routing Architecture for Vehicular Data-Mule Services

Using vehicles as a data mule in vehicular networks contributes to a greater capacity for data offloading from network devices in urban computing scenarios. The biggest obstacle in this type of solution resides in the complexity of managing a communication infrastructure composed of elements spread over different levels of the network. This work proposes the intelligent Edge-Traffic Routing (iETR) architecture, which provides geocast data mule services in urban computing to transport high volumes of data, based on requirements defined by the application. We implement and evaluated iETR through simulation. The results show that it can successfully reduce data transfer times and ensure application scalability, through vehicles distributed at the network edge.

A Novel Simulation Platform for Underwater Data Muling Communications Using Autonomous Underwater Vehicles

Autonomous Underwater Vehicles (AUVs) are seen as a safe and cost-effective platforms for performing a myriad of underwater missions. These vehicles are equipped with multiple sensors which, combined with their long endurance, can produce large amounts of data, especially when used for video capturing. These data need to be transferred to the surface to be processed and analyzed. When considering deep sea operations, where surfacing before the end of the mission may be unpractical, the communication is limited to low bitrate acoustic communications, which make unfeasible the timely transmission of large amounts of data unfeasible. The usage of AUVs as data mules is an alternative communications solution. Data mules can be used to establish a broadband data link by combining short-range, high bitrate communications (e.g., RF and wireless optical) with a Delay Tolerant Network approach. This paper presents an enhanced version of UDMSim, a novel simulation platform for data muling communications. UDMSim is built upon a new realistic AUV Motion and Localization (AML) simulator and Network Simulator 3 (ns-3). It can simulate the position of the data mules, including localization errors, realistic position control adjustments, the received signal, the realistic throughput adjustments, and connection losses due to the fast SNR change observed underwater. The enhanced version includes a more realistic AML simulator and the antenna radiation patterns to help evaluating the design and relative placement of underwater antennas. The results obtained using UDMSim show a good match with the experimental results achieved using an underwater testbed. UDMSim is made available to the community to support easy and faster evaluation of underwater data muling oriented communications solutions and to enable offline replication of real world experiments.

A secure data collection strategy using mobile vehicles joint UAVs in smart city

Recruiting mobile vehicles (MVs) has been proved as an effective and low-cost strategy to collect data from sensing devices (SDs) in the smart city. However, few works consider data security when using MVs as data mules. In our previous work, we have proposed a Consistent Trust Verification for MVs (CTV-MV), which builts trust through recommendation relationships, but this method was vulnerable to collusion attack, that is, multiple MVs provided consistent fake data to deceive the system. Therefore, a Cross Trust Verification for MVs joint UAVs (CTV-MVU) data collection strategy is proposed in this paper, where the Unmanned Aerial Vehicles (UAVs) are deployed to collect data from specific SDs which are used as baseline data to realize trust reasoning mechanism. Besides, the UAVs can also sense the SDs that are difficult to be collected by MVs due to their limitations of coverage, and thus improve the data collection ratio. Furthermore, a Trust Priority Recruitment (TPR) strategy for CTV-MVU is also proposed to prioritize the recruitment of high-trust MVs. Experiment results show that the proposed CTV-MVU strategy outperforms the CTV-MV one in terms of the excellent ratio, trust, data collection ratio, and robustness.

Improving Data-Throughput in Energy Harvesting Wireless Sensor Networks Using a Data Mule

Background: In Energy Harvesting Wireless Sensor Networks (EH-WSNs), sensors are harvesting energy from the renewable environment to make their operations endless and uninterrupted. However, in such a network, the time-varying nature of harvesting imposes a challenging issue in obtaining improved data-throughput. The use of a static-sink in EH-WSNs to improve data- throughput is less reliable because there is no assurance of the network connectivity. To alleviate such shortcomings, a Data Mule (MDM) has been introduced in EH-WSN for collecting sensors’ data. In this article, the MDM-based distance constrained tour finding problem is formulated such that the data-throughput can be improved within a given delay constraint. Methods: To solve the problem, we devise two different heuristic algorithms based on two different metrics. Results: The obtained experimental results demonstrate that the devised algorithms are more effective than the existing algorithms in terms of data-throughput. Conclusion: The data-throughput values of the first proposed algorithm are about 6.14% and 3.56% better than the other for two different data gathering time durations of 100 sec and 800 sec. The data-throughput values of the second proposed algorithm are about 5.03% and 5.25% better than the other for two different data gathering time durations of 100 sec and 800 sec.

Mobile Collectors for Opportunistic Internet of Things in Smart City Environment with Wireless Power Transfer

In the context of Internet of Things (IoT) for Smart City (SC) applications, Mobile Data Collectors (MDCs) can be opportunistically exploited as wireless energy transmitters to recharge the energy-constrained IoT sensor-nodes placed within their charging vicinity or coverage area. The use of MDCs has been well studied and presents several advantages compared to the traditional methods that employ static sinks. However, data collection and transmission from the hundreds of thousands of sensors sparsely distributed across virtually every smart city has raised some new challenges. One of these concerns lies in how these sensors are being powered as majority of the IoT sensors are extremely energy-constrained owing to their smallness and mode of deployments. It is also evident that sensor-nodes closer to the sinks dissipate their energy faster than their counterparts. Moreover, battery recharging or replacement is impractical and incurs very large operational costs. Recent breakthrough in wireless power transfer (WPT) technologies allows the transfer of energy to the energy-hungry IoT sensor-nodes wirelessly. WPT finds applications in medical implants, electric vehicles, wireless sensor networks (WSNs), unmanned aerial vehicles (UAVs), mobile phones, and so on. The present study highlights the use of mobile collectors (data mules) as wireless power transmitters for opportunistic IoT-SC operations. Specifically, mobile vehicles used for data collection are further exploited as wireless power transmitters (wireless battery chargers) to wirelessly recharge the energy-constrained IoT nodes placed within their coverage vicinity. This paper first gives a comprehensive survey of the different aspects of wireless energy transmission technologies—architecture, energy sources, IoT energy harvesting modes, WPT techniques and applications that can be exploited for SC scenarios. A comparative analysis of the WPT technologies is also highlighted to determine the most energy-efficient technique for IoT scenarios. We then propose a WPT scheme that exploits vehicular networks for opportunistic IoT-SC operations. Experiments are conducted using simulations to evaluate the performance of the proposed model and to investigate WPT efficiency of a power-hungry opportunistic IoT network for different trade-off factors.

Energy-Efficient and Fast Data Collection in UAV-Aided Wireless Sensor Networks for Hilly Terrains

Energy-constrained sensor nodes are often deployed in remote, hilly, and hard-to-reach areas for civilian and military purposes. In such wireless sensor networks (WSNs), an unmanned aerial vehicle (UAV) can be used to collect data from the sensor nodes. Low-altitude UAVs can be utilized to reduce the energy consumption of WSNs by optimizing the data collection position. In this study, we designed an energy-efficient and fast data collection (EFDC) scheme in UAV-aided WSNs for hilly areas with the help of a UAV as a data mule. First, we proposed a central bias hybrid energy-efficient distributed clustering algorithm for grouping the sensors. Then, we applied a modified tabu search algorithm to optimize the UAV position for collecting data from a cluster. To achieve fast data collection, we developed the traveling salesman problem with the derived data collection positions and solved it by applying a modified genetic algorithm. Based on our simulation results, the proposed EFDC scheme outperforms the conventional ones in terms of energy consumption, scalability, control overhead, delay, and load balancing.

Gathering Big Data in Wireless Sensor Networks by Drone.

The benefits of using mobile sinks or data mules for data collection in Wireless Sensor Network (WSN) have been studied in several works. However, most of them consider only the WSN limitations and sensor nodes having no more than one data packet to transmit. This paper considers each sensor node having a relatively larger volume of data stored in its memory. That is, they have several data packets to send to sink. We also consider a drone with hovering capability, such as a quad-copter, as a mobile sink to gather this data. Hence, the mobile collector eventually has to hover to guarantee that all data will be received. Drones, however, have a limited power supply that restricts their flying time. Hence, the drone’s energy cost must also be considered to increase the amount of collected data from the WSN. This work investigates the problem of determining the best drone tour for big data gathering in a WSN. We focus on minimizing the overall drone flight time needed to collect all data from the WSN. We propose an algorithm to create a subset of sensor nodes to send data to the drone during its movement and, consequently, reduce its hovering time. The proposed algorithm guarantees that the drone will stay a minimum time inside every sensor node’s radio range. Our experimental results showed that the proposed algorithm surpasses, by up to 30%, the state-of-the-art heuristics’ performance in finding drone tours in this type of scenario.

Secure Transmission of IoT mHealth Patient Monitoring Data from Remote Areas Using DTN

In remote rural areas without continuous Internet connectivity, it is hard to envisage the use of mHealth applications for remote patient monitoring. In such areas, patients need to travel long distances to reach the nearest health center. In this article, we propose an approach that solves this problem by transmitting mHealth monitoring data, collected using IoT sensors, using DTN. Thus, buses or other vehicles acting as data mules transmit the mHealth data from remote rural areas to a medical center or hospital in the nearest urban area. The proposed approach includes methods to preserve the security of the data through encryption and secure key exchange, and to authenticate the patients through appropriate hashing of selected information. It allows preserving the privacy of the patients, and it takes into account the intermittent nature of the network by adding redundancy to avoid data loss.

BEE-DRONES: Ultra low-power monitoring systems based on unmanned aerial vehicles and wake-up radio ground sensors

Nowadays, Unmanned Aerial Vehicles (UAVs) represent a significant aid on scenarios where fixed, ground infrastructures are temporarily or permanently not available; this is the case of large-scale applications of the Internet of Things (IoTs), e.g. smart city and agriculture 3.0, where the UAVs can be employed as mobile data mules and gather the data from Wireless Ground Sensors (WGSs). UAV-aided wireless sensor networks (WSNs) introduce considerable advantages both in terms of performance and costs since they avoid the need of error-prone multi-hop communications, and also the installation of static gateways; at the same time, they pose formidable research challenges for their implementation, like the synchronization issue between the UAV and the WGS and the path planning, which should take into account the extremely limited flight autonomy of the UAVs. In this paper, we address both the issues above by proposing BEE-DRONES, a novel framework for large-scale, ultra low-power UAV-aided WSNs. In order to mitigate the synchronization problem, we investigate the utilization of passive Wake-up Radio (WR) technology on the WGSs, and of wireless power transfer from the UAVs: by harvesting the energy from the UAV hovering over it, the WGS is activated only for the short time required to transfer the data toward the mobile sink, while it experiences zero-consumption in sleep mode. We investigate the performance of passive WR-based WGS through real measurements, under different WGS-UAV distances and antenna orientations. Then, based on such results, we formulate the joint WGS scheduling and UAV path planning problem, where the goal is to determine the optimal trajectory of the UAVs activating the WR-based WGSs while taking into account the Value of the Sensing (VoS) as well as the total lifetime of the WSN. The original problem is transformed into a multi-commodity flow problem, and both centralized and distributed heuristics over the multi-graph are proposed. Finally, we evaluate the proposed algorithms through extensive OMNeT++ simulations; the results demonstrate the gain of BEE-DRONES in terms of extended lifetime compared to traditional, non WR-based solutions (e.g. duty-cycle), and in terms of reduced data-correlation compared to non VoS-aware path planning solutions.

Sustainable Vehicle-Assisted Edge Computing for Big Data Migration in Smart Cities

Smart cities are based on connected devices generating large quantities of data every instant. These data can be stored at a nearby edge location for initial processing but later sending the data to the backend data centers for storage and further analysis consumes considerable network bandwidth. In this article, we propose a large-scale data migration framework using vehicles. The framework uses a neural network to identify suitable vehicles as data mules, ones moving toward the data destination, potentially reducing the load from backend networks in terms of bandwidth usage and overall energy consumption. We compare the framework with data transfers using the traditional Internet and an approach without machine intelligence. The proposed framework performs well in terms of data loss, transfer time, energy, and CO2 emissions. From experiments, we demonstrate that the approach achieves a 67% success rate with data transfers $193\times $ faster than the average Internet bandwidth of 21.28 Mb/s. Moreover, the resulting CO2 emissions for 30-TB data transfers stood at 6.403 kg, which is significantly lower compared to 1172.8 kg for the Internet.