Multiple Mobile Research Articles

An Omnidirectional Transportation System With High Terrain Adaptability and Flexible Configurations Using Multiple Nonholonomic Mobile Robots

An Omnidirectional Transportation System With High Terrain Adaptability and Flexible Configurations Using Multiple Nonholonomic Mobile Robots

An Efficient and Secure Method for Simultaneous Ownership Transfer of Multiple Mobile Readers

In recent years, radio frequency identification technology has developed rapidly and has been applied in supply chains. Products in supply chains are varied and may belong to several different owners. Ownership transfer requires the consent of a majority of owners. This article proposed a method suitable for simultaneously transferring the ownership of a large number of goods. The method does not require a trusted third party and can securely and efficiently transfer group ownership for multiple owners with multiple tags. The old and new owner groups can be classified as different authorities. This method can be used in mobile readers to transfer one, some, or all tags in a group. It includes mutual authentication between tags, readers, and backend servers, and can ensure that only assigned owners can obtain the tag ownership. The method proved can resist most known attacks, such as secret disclosure attacks and replay attacks. It can also prevent attacks from the dishonest original owners. Finally, through experiments, we compared the proposed method with other many-to-many ownership transfer methods and demonstrated that the proposed method has better security and fewer transmitted messages than other methods.

AVX-TSCHA: Leaking information through AVX extensions in commercial processors

Modern x86 processors support an AVX instruction set to boost performance. However, this extension set may also cause security issues. We discovered that there are vulnerable properties in the implementation of the masked load/store instructions. First, these instructions can suppress exceptions caused by invalid or inaccessible memory access. Second, the execution time of these instructions leaks the current state of the page mappings, permissions, and TLB states.Based on this, we present a novel AVX timing side-channel attack that can defeat address space layout randomization. We demonstrate the significance of our side-channel attack by showing User and Kernel ASLR breaks on the recent Intel and AMD processors in various environments, including cloud computing systems (Amazon AWS, Google GCP, and Microsoft Azure), an SGX enclave (a fine-grained ASLR break), and major OSes (Linux, Windows, and macOS). Our attack can identify the Linux kernel's base address in 0.29 ms as well as those of loaded kernel modules in 2.24 ms, with a near-zero error rate. We further demonstrate that our attack can be used to infer user behavior, such as mouse movements and data transmissions over the network. Our evaluation results on multiple mobile, desktop, and server processors (a total of 26 Intel and AMD CPUs) show that 1) the AVX timing side-channel works on the vast majority of Intel processors (from the Sandy Bridge microarchitecture) as well as AMD processors (from the Zen microarchitecture onward) and 2) our KASLR breaks are very fast and reliable. To the best of our knowledge, our attack is the first to demonstrate a KASLR break on both the recent Intel Alder Lake and AMD Zen 3 CPUs. We highlight that more robust isolation or fine-grained randomization should be adopted to mitigate our presented attacks successfully.

Force-reflecting event-triggered predefined-time cooperative control for teleoperation of NMMs via data-driven based observer

This paper investigates the cooperative control problem for the teleoperation system of networked mobile manipulators (NMMs), in which each mobile manipulator is subject to nonholonomic constraints, model uncertainty and external disturbances. The control objective of such a teleoperation system is to synchronize the task-space position between the human-controlled master manipulator and the slave mobile manipulators, and meanwhile enable the slave mobile manipulators to achieve a formation. For this purpose, a hierarchical predefined-time cooperative control (HPTCC) framework involving predefined-time distributed event-triggered (PTDET) estimator, predefined-time stability (PTS) controller and data-driven based (DDB) observer, is proposed. The PTDET estimator is used to generate the information that the slave mobile manipulators need to track and enable multiple slave mobile manipulators to interact through an event-triggered communication network. The PTS controller is presented to ensure the predefined-time stability of the master manipulator and the slave ones. Moreover, for improving the telepresence of the considered teleoperation system, the DDB observer is proposed to reestablish the interaction force between the slave mobile manipulators and the complex operating environment (i.e., the environmental force) on the human operator side. Based on the hierarchical control analysis as well as Lyapunov theory, several sufficient conditions are derived to guarantee the predefined-time stability for the teleoperation system of NMMs. Finally, several simulations are conducted to demonstrate the effectiveness of the proposed control scheme.

Exploration of a Simple Navigation Method for Swarm Robots Pioneered by Heterogeneity

In recent years, research has been conducted on swarm robot systems in which multiple autonomous mobile robots cooperate to perform tasks. Swarm robot systems are expected to perform high functionality as a group by cooperating with each other, in spite of the limited capabilities of the individual robots. This paper explores a method of simplifying swarm robot controllers as much as possible for swarm robot navigation. If we can achieve autonomous navigation of swarm robots to a target area with minimal resource consumption, they only need to implement the task execution function in that area. This leads to lower costs for swarm robot design and more efficient system architecture design. To address the above aims, this study focuses on heterogeneity. Specifically, we introduce a navigator robot that indirectly guides swarm robots named the worker robots. Heterogeneity in this paper refers to the worker robots and the navigator robots. We design the interaction between the navigator robot and the worker robots to provide a system that guides the worker robots to the destination.

Performance analysis and optimization of multiple IIoT devices radio frequency energy harvesting NOMA mobile edge computing networks

In this day and age, the Industrial Internet of Things (IIoT) has been considered to revolutionize industrial manufacturing by capturing and accessing massive data sources with incredible speed and efficiency than before. Combined with it, Mobile Edge Computing (MEC) is a comprehensive Digital Transformation tendency to solve the problems Cloud computing faces. However, the fundamental challenges of energy and latency make deploying IIoT MEC networks difficult. Accordingly, this paper considers the efficient design of time allocation for successful computation probability (SCP) maximization for multiple energy-constrained mobile devices (MD) and multiple antennas access point (AP) in uplink radio frequency energy harvesting (RF EH) non-orthogonal multiple access (NOMA) IIoT network. Specifically, multiple MDs need to receive the energy and compute support of a MEC server placed in a multiple antenna wireless AP to complete the task immediately. Accordingly, a four-phase communication protocol is proposed to ensure system performance. The system follows the cluster head (CH) scheme based on the channel state information (CSI) to harvest RF energy from the AP. To ensure the highest system performance, we propose two algorithms for determining the optimal EH time for two CHs: SCPM-GSS and SCPM-GA. In addition, we derive the closed-form expressions for the SCP of the system and each CH. Monte Carlo simulations are used to verify the results of the analysis. The numerical results demonstrate the effects of crucial system parameters of our proposed NOMA scheme with those of conventional orthogonal multiple access (OMA) schemes. Furthermore, the proposed optimization algorithms allow the system to avoid outages like the random parameters setting approach and improve the SCP by 3 to 30% compared to the fixed parameters set when the transmit power is low and medium.

Distributed Neural-Network-Based Cooperation Control for Teleoperation of Multiple Mobile Manipulators Under Round-Robin Protocol.

This article addresses the distributed cooperative control design for a class of sampled-data teleoperation systems with multiple slave mobile manipulators grasping an object in the presence of communication bandwidth limitation and time delays. Discrete-time information transmission with time-varying delays is assumed, and the Round-Robin (RR) scheduling protocol is used to regulate the data transmission from the multiple slaves to the master. The control task is to guarantee the task-space position synchronization between the master and the grasped object with the mobile bases in a fixed formation. A fully distributed control strategy including neural-network-based task-space synchronization controllers and neural-network-based null-space formation controllers is proposed, where the radial basis function (RBF) neural networks with adaptive estimation of approximation errors are used to compensate the dynamical uncertainties. The stability and the synchronization/formation features of the single-master-multiple-slaves (SMMS) teleoperation system are analyzed, and the relationship among the control parameters, the upper bound of the time delays, and the maximum allowable sampling interval is established. Experiments are implemented to validate the effectiveness of the proposed control algorithm.

A Behavior-based Adaptive Dynamic Programming Method for Multiple Mobile Manipulators Coordination Control

A Behavior-based Adaptive Dynamic Programming Method for Multiple Mobile Manipulators Coordination Control

Essential not Supplemental: Medicare Advantage Members’ Use of Non-Emergency Medical Transportation (NEMT)

BackgroundOver five million people in the USA miss or delay medical care because of a lack of transportation. Transportation barriers are especially relevant to Medicare Advantage (MA) health plan enrollees, who are more likely to live with multiple chronic conditions and experience mobility challenges. Non-Emergency Medical Transportation (NEMT) helps to address transportation gaps by providing rides to and from routine medical care (for example, medical appointments, laboratory tests, and pharmacy visits) and has been added as a supplemental benefit to some MA health plans.ObjectiveWe aimed to characterize MA enrollees’ experiences with supplemental NEMT benefits.DesignQualitative interviews focused on participants’ experiences with existing NEMT benefits, transportation, and mobility.ParticipantsTwenty-one MA enrollees who used their MA NEMT benefit in 2019 and who remained eligible for ongoing transportation benefits through 2021.ApproachUsing purposive sampling from a list of eligible participants, we recruited individuals who used their MA NEMT benefit in 2019 and who remained eligible for benefit-covered transportation services through 2021.Key ResultsParticipants considered NEMT an essential service, particularly because these services helped them decrease social isolation, reduce financial insecurity, and manage their own medical needs. Navigating logistical challenges associated with arranging NEMT services required participants to commit considerable time and energy and limited the effectiveness and reliability of NEMT.ConclusionParticipants described NEMT as a valued service essential to their ability to access health care. They suggested ways to increase service flexibility and reliability that could inform future NEMT policy and practice. As health systems and payers learn how to best address social risks, particularly as the US population ages, our findings underscore the importance of NEMT services and highlight opportunities to advance comprehensive transportation solutions for MA participants.

A 51 Gb/s Reconfigurable mmWave Fiber-Wireless C-RAN Supporting 5G/6G MNO Network Sharing

The growing demand for ubiquitous broadband connectivity in 5G/6G Radio Access Networks (RANs) has turned Centralized RAN (C-RAN) and reconfigurable Point-to-MultiPoint (PtMP) network topologies into the mainstream approach for keeping pace with the vastly increasing traffic capacities and connection densities. Relying on an all-passive Arrayed Waveguide Grating Router (AWGR), we propose and demonstrate reconfigurable allocation of multiple parallel Fiber-Wireless (FiWi) millimeter wave (mmWave) analog (Intermediate Frequency over Fiber) IFoF interfaces. The proposed technology provides a framework for the implementation of reconfigurable multiple FiWi backhaul /midhaul/fronthaul (X-haul) connections. The use of AWGR enables wavelength slicing and the co-existence of multiple transport links over the same X-haul network infrastructure. The proposed concept is experimentally validated by two sets of experimental measurements. More specifically a 4λ- Wavelength-Division Multiplexed (WDM) signals modulated by IFoF signals are routed through the AWGR and wirelessly transmitted over the air across a 1m V-band distance by two different pairs of mmWave 60 GHz antennas. In the first experiment, a Phased Array Antenna (PAA) operating at 60 GHz is utilized achieving a 2.5 Gb/s data rate per wavelength and beam pair with an Error Vector Magnitude (EVM) of less than 12.5% and an aggregate 10 Gb/s traffic capacity. The achieved performance meets for the first time the respective 5G Key Performance Indicator (KPI) requirements for EVM quality and peak data traffic while supporting the link reconfiguration wavelength routing and mmWave beam direction. In the second experiment, the PAA is replaced by a point-to-point 60 GHz link utilizing a high-end mm-wave transmitter, thus achieving a data rate of 12.75 Gb/s per wavelength-beam with an EVM of less than 6.5% and an aggregate capacity of 51 Gb/s. To this end, the proposed solution is shown to form a promising roadmap toward flexible and reconfigurable 5G/6G mmWave C-RAN architectures where multiple Mobile Network Operators (MNOs) can share the same network infrastructure and dynamically allocate fiber, antenna beams and wavelengths.

Enhancing Data Discretization for Smoother Drone Input Using GAN-Based IMU Data Augmentation

This study investigates the use of generative adversarial network (GAN)-based data augmentation to enhance data discretization for smoother drone input. The goal is to improve unmanned aerial vehicles’ (UAVs) performance and maneuverability by incorporating synthetic inertial measurement unit (IMU) data. The GAN model is employed to generate synthetic IMU data that closely resemble real-world IMU measurements. The methodology involves training the GAN model using a dataset of real IMU data and then using the trained model to generate synthetic IMU data. The generated synthetic data are then combined with the real data for data discretization. The resulting improved data discretization is evaluated using statistical metrics and a similarity evaluation. The improved data discretization demonstrates enhanced drone performance in terms of flight stability, control accuracy, and smoothness of movements when compared to standard data discretization methods. These results highlight the potential of GAN-based data augmentation for enhancing data discretization and improving drone performance. The proposition of improved data discretization offers a tangible benefit for the successful integration of Advanced Air Mobility (AAM) systems. Enhancing the accuracy and reliability of data acquisition and processing in UAS makes UAS operations safer and more reliable. This improvement is crucial for achieving the goal of automated and autonomous operations in diverse settlement environments, encompassing multiple mobility modes such as ground and air transportation.

The Value of Illegality: Venezuelan Migrants in Ecuador and the (Re)Entrenchment of Neoliberal Capitalism

Despite Ecuadorians’ own extensive experiences as international migrants, as Venezuelan migration into Ecuador has increased sharply in the last five years, the Ecuadorian government has enacted a barrage of restrictive and exclusionary policies at and within the country's borders. Consequently, Venezuelans in Ecuador are forced into illegality, a spatialized social condition that entails simultaneous marginalization and inclusion in the contemporary socioeconomic order. This paper draws on theorizations of governmentality, destitution economies, and status value to argue that the illegalization of migrants produces various forms of value for the government and broader public. First, illegality works as a governmental mechanism to reconcile neoliberal capitalism with the inequalities it exacerbates. At the national scale, illegalization of Venezuelan migrants facilitates political legitimation for the Ecuadorian government, and at the local scale, illegality makes migrants hyper-visible in ways that obscure the negative consequences of neoliberal economic strategies. Second, illegality facilitates strategies for economic exploitation of migrants by private actors at the local scale. Exploring how illegality produces value in Ecuador is useful for understanding other cases of South–South migration and draws attention to the disciplinary functions of migration management outside the Global North. This article provides a case study of multiple mobilities in the context of growing poverty, historical inequalities, and the entrenchment of neoliberal capitalism.

Risks of nutrients and metal(loid)s mobilization triggered by groundwater recharge containing reactive organic matter

The potential risk of multiple contaminants mobilization from sediment was evaluated under the infiltration of reactive organic matter (OM) during groundwater recharge. Significant NH4-N, PO43−, As, Fe, and Mn release were observed which show elevated peaks in the initial stage (0–19 d) and declines afterwards until reach quite low and stable content (20–37 d). When re-oxidize with SO42− (38–65 d), the reducing condition is stimulated to release more Fe and Mn however no As, NH4-N and PO43−. The microbial reductive Fe oxides dissolution is the main driving force for NH4-N, PO43−, As, Fe, and Mn release. The NH4-N and PO43− release was attributed by passive mobilization from Fe/Mn oxides due to its dissolution. The cation exchange also works significantly on NH4+ release while desorption works on PO43− as well. Microorganism responses to the infiltration reactive OM and further impacts the mobilization of multiple contaminants. The precipitation of secondary Fe/Mn minerals accompanied by co-precipitation and/or re-absorption lower down their aqueous levels. However, this aqueous contamination risk was hidden in solid reactive secondary minerals when oxidizer like SO42− perturbs the recurrence of Fe and Mn contamination occurs. This shows the perennial release of nutrients and trace elements should be paid attention in areas with reactive OM infiltration followed by redox/groundwater chemistry fluctuations, potentially in hyporheic zone of river/lake, managed aquifer recharge zone, etc.

Collaborative Energy Optimization of Multiple Chargers Based on Node Collaborative Scheduling

Wireless rechargeable sensor network (WRSN) uses mobile chargers (MCs) to charge sensor nodes wirelessly to solve the energy problems faced by traditional wireless sensor network. In WRSN, mobile charging schemes with multiple MCs supplementing energy are quite common. How to properly plan the MC’s moving path to reduce the charge energy loss and deploy nodes to improve network coverage rate has become a huge research challenge. In this paper, a collaborative energy optimization algorithm (CEOA) is proposed for multiple chargers based on k-mean++ and node collaborative scheduling. The CEOA combines internal energy optimization and external device power supply, effectively prolongs network lifetime, and improves network coverage rate. It uses the k-mean++ to cluster nodes in the network; then, the nodes in the network are scheduled to sleep based on the confident information coverage (CIC) model. Finally, the CEOA uses a main mobile charger to carry multiple auxiliary mobile chargers to charge all the nodes in the cluster. Simulation results show that the proposed algorithm increases the network lifetime by more than 8 times and the coverage rate by about 20%.

Mobility Assisted Adaptive Clustering Hierarchy for IoT Based Sensor Networks in 5G and Beyond

One of the massive machine type communication (mMTC) applications for monitoring and sensing in 5G cellular network is the Internet of Things (IoT) based wireless sensor network (WSN). Non uniform battery usage by the nodes in these networks often results in creating energy holes or voids in the network making the network disconnected. An effective solution is to deploy multiple mobile nodes throughout the network, however finding optimal path for these mobile nodes is reported to be an NP hard problem. This paper proposes MAACH (Mobility Assisted Adaptive Clustering Hierarchy), an efficient mobility assisted clustering and routing framework for IoT based sensor network in 5G and beyond. Also, an elaborate method to calculate the exact path optimally for multiple mobile nodes is presented to alleviate non uniform energy dissipation of the sensing nodes. Simulation results show that our algorithm effectively finds the optimal trajectory of multiple mobile nodes in a distribute manner and also improves network stability period by 60-70% and the network lifetime by 70-90% across multiple network deployments.

Hierarchical finite-time cooperative control for teleoperation of networked disturbed mobile manipulators

This paper investigates the teleoperation problem of networked disturbed mobile manipulators (NDMMs), in which the human operator remotely controls multiple slave mobile manipulators through a master manipulator. Each individual of the slave ones consisted of a nonholonomic mobile platform and a holonomic constrained manipulator that is mounted on the nonholonomic mobile platform. The cooperative control objective of the considered teleoperation problem includes: (1) synchronizing the states of the slave manipulators to the human-controlled master one; (2) forcing the mobile platforms of the slave ones to form a user-defined formation; (3) controlling the geometric center of all the platforms to track a reference trajectory. We present a hierarchical finite-time cooperative control (HFTCC) framework to achieve the cooperative control goal in a finite time. The presented framework includes the distributed estimator, the weight regulator and the adaptive local controller, where the estimator generates the estimated states of the desired formation and trajectory, the regulator selects the slave robot that the master one needs to track, as well as the presented adaptive local controller guarantees the finite-time convergence of the controlled states with model uncertainties and disturbances. Additionally, for improving the telepresence, a novel super twisting observer is presented to reconstruct the interaction force between the salve mobile manipulators and the remote operating environment on the master (i.e., the human) side. Finally, the effectiveness of the proposed control framework is demonstrated by several simulation results.

Providing Meaning to Violence: Multiple Mobilizations and Dynamics of Conflict Escalation from November 2013 until February 2014 in Ukraine

AbstractThe article examines the aspects of contention and conflict escalation before and during the period from November 2013 till February 2014 in Ukraine that have not yet received due attention in research. It studies the contention between the government and the opposition and the concomitant Maidan protest mobilizations by groups advocating unity with Russia, and opposing the visions of political community of the radical groups making up part of the Maidan coalition. Conflict escalation is studied as a combination of structural conditions, choices and actions taken by conflict agents, and evolving discursive factors that enable political violence. The analysis indicates that while structural conditions played a role, conflict escalation is a nonlinear and agency-driven process, evolving through mutually influencing choices and actions of the competing parties, that either drive escalation or lead to deradicalization. The article suggests that the modes of contention and radicalization between the government and the opposition opened opportunities for groups supporting unity with Russia to escalate their demands, to radicalize their visions of political community, and to build leverage with Russia. In conclusion, several key narratives and discursive processes enabling the legitimization of the use of force and the implications for peacebuilding are discussed. The findings help to understand better the environment in which violent conflict further escalated in 2014.

MMSCM: A multiple mobile sinks coverage maximization based hierarchical routing protocol for mobile wireless sensor networks

AbstractMobile wireless sensor network (MWSN) adapts to the requirements of mobility of civil and military network devices and has received a lot of attention. Designing an efficient, low‐energy, and fault‐tolerant routing protocol for the frequently changing topology of highly mobile MWSNs is challenging. Most routing protocols today only support the movement of sensor nodes or only sink. This paper proposes a Multiple Mobile Sinks Coverage Maximization based hierarchical routing protocol for mobile wireless sensor networks (MMSCM), which allows sensor nodes and sinks to move simultaneously. The MMSCM divides the monitoring area into virtual grids and deploys a movable sink in each grid cell. Sink selects the appropriate next hop for each cluster head in the area to build an optimal routing forest and collects data from all routing tree root nodes at a uniform speed along the specified path with maximum coverage. Simulation results show that, compared with current advanced routing algorithms, the MMSCM protocol can better solve the network delay problem and improve data throughput while extending the network life cycle, which is more suitable for time‐sensitive application scenarios.

Toward the Minimal Wait-for Delay for Rechargeable WSNs with Multiple Mobile Chargers

Nowadays, the flourish of the internet of things incurs a great demand for progressive technologies to prolong the lifetime of Wireless Sensor Networks. Exploiting a fleet of Mobile Chargers (MCs) to replenish the energy-critical sensor nodes provides a new dimension to maintain long-term network operations, but may suffer from high charging delay due to MC’s limited mobility. Most existing studies focus on the reduction of server-oriented delay, i.e., the overall time taken by MCs (servers) to carry out sensor charging and travel inside the sensing field. However, these solutions may not be robust enough as some energy-critical sensor nodes will run out of the stored energy before the charger’s arrival. In this article, we address this challenge by reducing the client-oriented delay—referred to as the wait-for delay —which is defined as the “arrival times” at the to-be-charged sensor nodes (clients). To this end, we first formulate a novel wait-for charging delay minimization problem under the multi-node energy charging scheme. We then prove the NP-hardness of the proposed problem. Inspired by empirical observations, we devise an efficient approximation algorithm with a provable approximation ratio for the problem. We have evaluated the proposed algorithm using real-life system settings. The experimental results suggest that the proposed algorithm certainly performs better than the existing benchmarks; it could reduce the wait-for delay by up to 87.4 percent.

Polymer composition optimization approach based on feature extraction of bound and free water using time-domain nuclear magnetic resonance

As global environmental sustainability becomes increasingly emphasized, the development of eco-friendly materials, including solutions to the issue of marine plastics, is thriving. However, the material parameter space is vast, making efficient search a challenge. Time-domain nuclear magnetic resonance offers material property information through the complex T2 relaxation curves resulting from multiple mobilities. In this research, we used the Carr–Purcell–Meiboom–Gill (CPMG) pulse sequence to evaluate the binding state of water (water affinity) in polymers synthesized with various monomer compositions, which were immersed in seawater. We also assessed the T2 relaxation property of the polymers using the magic sandwich echo, double quantum filter, and magic-and-polarization echo filter techniques. We separated the T2 relaxation curves of CPMG into free and bound water for polymers by employing semisupervized nonnegative matrix factorization. By employing the features of separated bound water and polymer properties, a polymer composition optimization method offered crucial factors to monomers through random forests, predicted the components of the polymer using generative topography mapping regression, and determined expected values using Bayesian optimization for polymer composition candidates with the desired high water affinity and high rigidity.