Limited Communication Range Research Articles

Group key agreement protocol among terminals of the intelligent information system for mobile edge computing

Security communication and information sharing among mobile devices are important application technologies of the intelligent information system (IIS). Because IIS is vulnerable to attacks, so the security of information sharing among mobile devices is seriously threatened. Thence, it is necessary to establish a secure channel for communication among mobile devices of IIS over an opening network. Group key agreement (GKA) can establish a secure channel among mobile devices of IIS by encryption technology. Due to the resource-constraints of mobile devices, such as weak computing power, small storage capacity, and limited communication range. To address these issues, an asymmetric GKA protocol among terminals of IIS for mobile edge computing (GKA–IIS–MEC) network is proposed in this paper. Adopted asymmetric GKA to achieve the group secure communication mechanisms that message sender unfettered in this protocol; the protocol also uses edge computing environment to migrate the computation and communication loads of mobile devices of IIS to edge nodes, thereby ensuring that mobile devices have lightweight computation and communication loads; and the members participating in the GKA can verify whether the group session keys they calculated are correctness. Under the hardness assumption of bilinear inverse Diffie–Hellman problem, the proposed protocol is proven that it can resist negative attacks. After evaluating the performance of the protocol, GKA–IIS–MEC has higher efficiency than the referred works in terms of time cost, communication consumption, and computation consumption.

Self-powered 5G NB-IoT system for remote monitoring applications

In the new era of 5G and artificial intelligence of things (AIoT), remote monitoring and early warning based on widely distributed self-powered wireless sensing system are particularly important. However, the high power consumption, limited communication range and operation lifespan of this system are serious obstacles in practical applications. Here, we report a self-powered 5G narrowband IoT (NB-IoT) system for remote fire monitoring, driven by a magnetic-assisted noncontact energy harvester (MN-EH) which hybridizes piezoelectric and electromagnetic generator. The rotational MN-EH device can efficiently convert wind energy into electrical power while NB-IoT makes it possible to trade off the low-power and long-range transmission. With the magnetic coupling mechanism, the device can avoid degradation and abrasion to improve the reliability. Through optimizing a magnetic arrangement, the rotating peak torque can be reduced more than 93%. The MN-EH is further developed to power the NB-IoT system which can acquire temperature and CO concentration and transmit them to the cloud server every ~13 min. This work provides an effective strategy and paves the way for practical applications of self-powered remote monitoring system.

Multivehicle Flocking With Collision Avoidance via Distributed Model Predictive Control.

Flocking control has been studied extensively along with the wide applications of multivehicle systems. In this article, the distributed flocking control strategy is studied for a network of autonomous vehicles with limited communication range. The main difference from the existing methods lies in that collision avoidance is considered a necessary condition while the vehicles are driven to follow a common desired trajectory under the proximity network. The sufficient conditions for system feasibility and stability are given by the proposed strategy. First, a centralized standard model predictive control (MPC) scheme is adopted to formulate the multivehicle flocking control problem by setting collision avoidance as an optimization constraint under the proximity network. Further, an equivalent distributed MPC (DMPC) is developed based on the consensus of local controllers under the existing framework of the alternating direction method of multiplier (ADMM). However, it may require infinite time to achieve consensus for all vehicles and, thus, the local controllers resulting in a limited number of ADMM iterations may not satisfy the given constraints. The constraints for each local controller are then modified so that the collision between vehicles is avoided all of the time. The feasibility and stability of the proposed method are analyzed under practical conditions. Simulation and experimental results show that the flocking of vehicles can track the common desired trajectory stably with no collisions by the proposed method.

Consensus of Multiagent Systems With Relative State Saturations

Within the multiagent systems framework, the relative states between neighbors can be acquired by some on-board sensors, and then the relative state saturations inevitably occur due to the limited sensing capabilities. This paper investigates the consensus problem of nonlinear multiagent systems subject to the relative state saturations. Utilizing the incidence matrix and the edge Laplacian, the consensus problem of nonlinear multiagent systems with the relative state saturations can be cast into the stabilization problem of edge dynamics operating on the constrained set. Three types of protocols, namely continuous, intermittent, and adaptive state feedback protocols are, respectively, proposed for achieving the constrained consensus, and meanwhile yielding consensus values. A consensus analysis is provided by virtue of state saturation theory, switched system theory, adaptive theory, and Lyapunov stability theory. Output feedback protocol is also designed. Finally, the obtained results are applied to connectivity preservation for first-order nonlinear multiagent systems, despite the presence of limited communication range and input constraints. The theoretical results are validated by two simulation examples.

EdgeGO: A Mobile Resource-Sharing Framework for 6G Edge Computing in Massive IoT Systems

With the remarkable development of the 5G technologies, more and more real-time and complex computational tasks from the Internet-of-Things (IoT) systems can be fulfilled by 5G edge servers. While the ultradense deployment is required for 5G edge services, in the upcoming era of 6G with an even more limited communication range, it is almost impossible to achieve 6G service coverage with dense deployments. To address this fundamental limit, we propose EdgeGO, a mobile resource-sharing framework that employs mobile edge servers to provide a cost-effective deployment of 6G edge computing, which enables edge resource sharing for massive IoT devices. Unlike traditional mobile cloudlets, EdgeGO exploits the asynchronization between requests receiving and results returning to decouple the stringent delay and resource requirements for edge computing. As a result, the server moving and task processing could be paralleled. Besides, EdgeGO incorporates a two-layer iterative updating algorithm, which jointly optimizes path planning and task scheduling to improve the overall task efficiency. Extensive simulation results show that by careful managing mobility and task execution of the edge servers, EdgeGO is able to drastically increase the resource utilization by 166.67% and decrease the deployment cost of 6G edge computing by 25.58%.

Robust platoon control of underactuated autonomous underwater vehicles subjected to nonlinearities, uncertainties and range and angle constraints

This paper studies the three-dimensional platoon control of multiple underactuated autonomous underwater vehicles (AUVs) subjected to environmental disturbances and model uncertainties. The main control objective is to design a tracking controller to force a platoon of AUVs to construct a convoy-like formation along feasible trajectories while each consecutive pair preserves a desired line-of-sight distance with limited communication range and every possible collision is avoided between consecutive vehicles. To achieve this objective, the prescribed performance function (PPF) methodology is employed to constrain the relative distance and angles between successive pairs during their motion. A robust neural network (NN), hyperbolic tangent function, and dynamic surface control technique are simultaneously utilized to propose the prescribed performance-based controller which is robust against completely unknown parameters, nonlinear hydrodynamic damping, actuators saturation nonlinearity, NN approximation errors and time-varying environmental disturbances. Lyapunov's direct method is used to prove that all signals of the closed-loop control system are bounded and the relative distance and angles converge to a neighbourhood of the origin. Finally, numerical computer simulations verify the proposed controller performance for offshore applications.

L-Platooning: A Protocol for Managing a Long Platoon With DSRC

Vehicle platooning is an automated driving technology that enables a group of vehicles to travel very closely together as a single unit to improve fuel efficiency and driver safety and reduces CO2 emission. The significant benefits of platooning attracted huge interests from academia and industry, especially from logistics companies for utilizing platoons of "long-body" trailer trucks because of the huge cost savings. In this paper, we demonstrate that existing DSRC-based platooning solutions, however, fail to support formation of such "long" platoons consisting of typical trailer trucks because of the limited communication range of DSRC. To address this problem, we propose L-Platooning, the first platooning protocol that enables seamless, reliable, and rapid formation of a long platoon. We introduce a novel concept called Virtual Leader that refers to a vehicle that acts like a platoon leader to extend the coverage of the original platoon leader. A virtual leader election algorithm is developed to effectively designate a virtual leader based on the novel metric called the Virtual Leader Quality Index (VLQI) which quantifies the effectiveness of a vehicle serving as a platoon leader. We also develop mechanisms for L-Platooning to support the vehicle join and leave maneuvers specifically for a long platoon. Through extensive simulations using the combination of Veins (Plexe) and SUMO, we demonstrate that L-Platooning enables long-body trailer trucks to form a long platoon effectively and maintain the desired inter-vehicle distance precisely. We also show that L-Platooning handles seamlessly the vehicle join and leave maneuvers for a long platoon.

An intelligent scheme for big data recovery in Internet of Things based on Multi-Attribute assistance and Extremely randomized trees

Due to the inherent characteristics of sensor nodes in Internet of Things, such as constrained energy, data redundancy, limited communication range and computing capabilities, the data loss problem becomes one key issue for applications which depends heavily on the data completeness. Some of the current solutions, such as interpolation, are designed to use data correlation for the data recovery problem. Spatiotemporal correlation is an important characteristic of sensory data since the nodes are generally deployed to observed similar physical phenomenon. However, it is very difficult to extract data correlation especially in case that the coupling degree between different perceptual attributes is low. Machine learning is an efficient auto-learning method that can obtain the inherent rules automatically. This paper has proposed an intelligent recovery scheme for big data in Internet of Things based on Multi-Attribute assistance and Extremely randomized Trees (MAET). Firstly, the collected dataset is denoised by detecting and removing the outliers. Secondly, the slave attributes are chosen whose correlations are high with the target attribute by using the Spearman correlation coefficient. Thirdly, the proposed scheme is trained by using extremely randomized trees with slave attributes. Finally, the missing data can be recovered with the trained model as well as the help of other attributes whose data is not lost. Experiment shows that the proposed scheme with multiple attributes is efficient and can improve the accuracy of recovered data compared with other algorithms.

Energy-Efficient Node Deployment in Heterogeneous Two-Tier Wireless Sensor Networks With Limited Communication Range

We study a heterogeneous two-tier wireless sensor network in which N heterogeneous access points (APs) collect sensing data from densely distributed sensors and then forward the data to M heterogeneous fusion centers (FCs). This heterogeneous node deployment problem is modeled as an optimization problem with the total power consumption of the network as its cost function. The necessary conditions of the optimal AP and FC node deployment are explored in this paper. We provide a variation of Voronoi Diagram as the optimal cell partition for this network and show that each AP should be placed between its connected FC and the geometric center of its cell partition. In addition, we propose a heterogeneous two-tier Lloyd algorithm to optimize the node deployment. Furthermore, we study the sensor deployment when the communication range is limited for sensors and APs. Simulation results show that our proposed algorithms outperform the existing clustering methods like Minimum Energy Routing, Agglomerative Clustering, Divisive Clustering, Particle Swarm Optimization, Relay Node placement in Double-tiered Wireless Sensor Networks, and Improved Relay Node Placement, on average.

Vehicle Platooning With Non-Ideal Communication Networks

The performance and effectiveness of a vehicle platoon rely on the topology of information flow and quality of communications, such as delays and dropouts. In this paper, we investigate the homogeneous and constant-time-headway-spacing-policy-based vehicle platoon longitudinal control problem, where communication impairments including the limited communication range, random packet losses, and time-varying communication delays are considered. Here, internal stability characterizes the system stability without disturbance while string stability is concerned with the error amplification when the vehicle platoon is affected by external disturbances. First, for the case when each vehicle utilizes the position, velocity, and acceleration information of multiple preceding and following vehicles, we obtain sufficient conditions to guarantee the internal stability of the vehicle platoon system based on the stability of matrix polynomials and the matrix eigenvalue perturbation theory. Then, considering random packet losses, we find that the effectiveness of platoon control relies on the frequency of packets being successfully received, or the delay till a new packet is received. We also obtain the upper bound for delays on all communication links such that the internal stability of the vehicle platoon can still be maintained when time-varying communication delays are within the bound. The $L_2$ -string stability is also analyzed through the transfer function for both ideal communication cases, and cases with uniform and constant delays. Extensive simulink-based numerical results validate our analysis, which reveals how communication impairments affect platoon control with a realistic information flow topology.

An efficient data collection path planning scheme for wireless sensor networks with mobile sinks

Wireless sensor networks with mobile sinks enable a mobile device to move into the sensing area for the purpose of collecting the sensing data. Mobile sinks increase the flexibility and convenience of data gathering in such systems. Taking the energy consumption of the mobile sink into account, the moving distance of the mobile sink must be reduced efficiently. Hence, it is important and necessary to develop an efficient path planning scheme for mobile sinks in large-scale wireless sensor network systems. According to several greedy-based algorithms, we adopt an angle bisector concept to create the moving path for the mobile sink. In this paper, a novel and efficient data collection path planning scheme is proposed to reduce the moving distances and to prolong the lifetimes of mobile sinks in wireless sensor networks. Considering the communication range limitations of sensor nodes and the obstacles within sensing areas, we design an inner center path planning algorithm to reduce the moving distance for the mobile sink. A back-routing avoidance method is included to address the moving path backpropagation problem. We account for the obstacles in sensing area. The reference point of obstacle avoidance is employed to address the obstacle problem. The proposed scheme makes an adaptive decision for creating the moving path of the mobile sink. A suitable moving path planning scheme can be achieved, and the moving distance of the mobile sink can be reduced. The proposed scheme is promising in large-scale wireless sensor networks. When the number of sensor nodes in the sensing area is increased by 50, the proposed scheme yields an average moving distance that is 1.1 km shorter than that of the heuristic tour-planning algorithm, where the sensing area is 5 km × 5 km. Simulation results demonstrate that the proposed data collection path planning scheme outperforms the previously developed greedy-based scheme in terms of the moving paths and moving distances of mobile sinks in wireless sensor networks.

Optimized Particle Swarm Optimization Algorithm for the Realization of an Enhanced Energy-Aware Location-Aided Routing Protocol in MANET

Mobile Ad-hoc Network (MANETs) is a wireless network topology with mobile network nodes and movable communication routes. In addition, the network nodes in MANETs are free to either join or leave the network. Typically, routing in MANETs is multi-hop because of the limited communication range of nodes. Then, routing protocols have been developed for MANETs. Among them, energy-aware location-aided routing (EALAR) is an efficient reactive MANET routing protocol that has been recently obtained by integrating particle swarm optimization (PSO) with mutation operation into the conventional LAR protocol. However, the mutation operation (nonuniform) used in EALAR has some drawbacks, which make EALAR provide insufficient exploration, exploitation, and diversity of solutions. Therefore, this study aims to propose to apply the Optimized PSO (OPSO) via adopting a mutation operation (uniform) instead of nonuniform. The OPSO is integrated into the LAR protocol to enhance all critical performance metrics, including packet delivery ratio, energy consumption, overhead, and end-to-end delay.

Robust prescribed performance-based control of autonomous tractor-trailers convoy with limited communication range

This paper investigates the problem of a nonlinear platoon controller design for a group of autonomous tractor-trailers with limited communication ranges in order to increase the load transportation capacity. The main control objective is to force each consecutive pair of tractor-trailers to follow each other in a convoy-like formation based on their posture and velocity measurements in the presence of unmodelled dynamics and external disturbances. The prescribed performance strategy is effectively applied to the relative range and angle between each successive pair in the group to preserve their connectivity without any possible collision by defining some preassigned performance characteristics on the transient and steady-state responses. A high-gain observer, an adaptive robust controller and a three-layer neural network are heuristically utilised to develop an efficient robust output-feedback platoon controller as well. The stability of the entire state-feedback and output-feedback control system is investigated by the Lyapunov stability method and some numerical simulations are presented to validate the controller efficiency in an autonomous transportation application.

Multi-UAV cooperative target tracking with bounded noise for connectivity preservation

We investigate cooperative target tracking of multiple unmanned aerial vehicles (UAVs) with a limited communication range. This is an integration of UAV motion control, target state estimation, and network topology control. We first present the communication topology and basic notations for network connectivity, and introduce the distributed Kalman consensus filter. Then, convergence and boundedness of the estimation errors using the filter are analyzed, and potential functions are proposed for communication link maintenance and collision avoidance. By taking stable target tracking into account, a distributed potential function based UAV motion controller is discussed. Since only the estimation of the target state rather than the state itself is available for UAV motion control and UAV motion can also affect the accuracy of state estimation, it is clear that the UAV motion control and target state estimation are coupled. Finally, the stability and convergence properties of the coupled system under bounded noise are analyzed in detail and demonstrated by simulations.

Nearly Optimal Sensor Selection for TDOA-Based Source Localization in Wireless Sensor Networks

In a large-scale wireless sensor network, it is typically impossible to use all the sensors to localize a source due to the limited communication range and battery power of the sensor nodes, thereby a sensor selection procedure is required. In this paper, the sensor selection problem for time-difference-of-arrival (TDOA) based localization is addressed. We propose two nearly optimal sensor selection methods: one is based on the original TDOA model and the other is based on the transformed time-of-arrival (TOA) model. Both methods aim to minimize the trace of the Cramer-Rao lower bound (CRLB) under the condition of fixed number of selected sensors. For the first method, we introduce a special vector to distinguish the reference sensor with the non-reference sensors. We then formulate a non-convex optimization problem with a Boolean vector as the optimization variable. For the second method, we first prove that the CRLB for TDOA-based localization is exactly the same with that for TOA-based localization where the start transmission time is unknown. We then formulate a non-convex optimization problem to minimize the CRLB for TOA-based localization. Both non-convex optimization problems are relaxed as convex semidefinite programs (SDPs) by applying semidefinite relaxation, followed by the Gaussian randomization procedure to improve the quality of the SDP solutions if necessary. Simulation results show that the performance of proposed methods approaches that of the exhaustive search method.

Multi-Agent Deep Reinforcement Learning-Based Flexible Satellite Payload for Mobile Terminals

Information dissemination in mobile networks turns out to be a problem when the network is sparse. Mobile networks begin to establish a separate cluster attributable to the limited communication range of terminals. The multi-beam satellite communication systems can play a significant role in providing direct-to-user satellite mobile services and connecting the separated clusters. This paper focuses on how to efficiently schedule limited satellite-based radio resources to enhance transmission efficiency and meet the requested traffic with low complexity. Taking the inter-beam interference and resource utilization variance into consideration, we build a game-theoretic based model for bandwidth allocation in the forward link. As the size of satellite beams increases, the size of the action space for deep reinforcement learning based on a single agent becomes large, resulting in high time complexity. Thus, we extend the single-agent deep reinforcement learning to the multi-agent context and then propose a cooperative multi-agent deep reinforcement learning method to achieve the optimal bandwidth allocation strategy. Each beam works as a player who is willing to satisfy the request traffic with flexible payloads. We built a multi-beam satellite platform using real historical data. The experimental results show that this approach is capable of enhancing transmission efficiency and can be flexible to achieve the desired goal with low complexity.

Assigning Multiple AUVs to Form Arrays Under Communication Range Limitations Based on the Element Zero Method

The array formed by multiple AUVs can effectively improve target detection capabilities when each AUV is equipped with a sonar unit. However, due to the limited data processing capabilities of an AUV and its communication range limitations, efficiently forming and transforming arrays are challenging. Therefore, this article innovatively uses the optimal assignment in operations research to solve this problem and proposes an element zero method to efficiently assign multiple AUVs to arrays. First, a cost matrix consisting of the distance cost of each AUV for reaching their different assigned positions in the array is generated. Second, the preprocessing and postprocessing steps are combined to increase the number of zeros in the generated cost matrix. Third, the space tree method based on the depth-first search rule is used to find <b xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</b> independent zeros of different rows and columns. Fourth, the optimal assignment is adjusted after considering the communication range limitations. To verify the feasibility and effectiveness of the proposed method, the simulation experiments of array formation and transformation are performed for multiple numbers of AUVs. The results of this article show that the proposed method has significant advantages over the Hungarian method, and thus suggests that the proposed method is effective.

You Foot the Bill! Attacking NFC With Passive Relays

Imagine when you line up in a store, the person in front of you can make you pay her bill by using a passive wearable device that forces a scan of your credit card or mobile phones without your awareness. An important assumption of today's near-field communication (NFC)-enabled cards is the limited communication range between the commercial reader and the NFC cards. Previous approaches effectively used mobile phones and active relays to break the range limit of NFC propagation for the NFC attack. However, these approaches require a power supply and protocol modification when mobile phones or active relays transmit NFC signals. We propose ReCoil, a system that uses passive relays to attack NFC-enabled mobile phones or cards by expanding the communication range of NFC to 49.6 cm, an obvious improvement over its intended commercial distance. ReCoil is a magnetically coupled resonant wireless power transfer system, which optimizes the energy transfer by searching the optimal geometry parameters. Specifically, we first narrow down the feasible area reasonably and design the ReCoil-greedy algorithm such that the relays absorb the maximum energy from the reader. In order to reroute the signal to pass over the surface of the human body, we then design a half waistband by carefully analyzing the impact of the distance and orientation between two coils on the mutual inductance. Then, three more coils are added to the system to keep enlarging the communication range. Finally, extensive experiment results validate our analysis, showing that our passive relays consisting of common copper wires and tunable capacitors can expand the range of NFC to 49.6 centimeters.

Stability Analysis of Vehicle Platooning With Limited Communication Range and Random Packet Losses

Control performance of vehicle platooning relies on the information flow topology and quality of wireless communications. In this article, we investigate the constant-time-headway-spacing-policy-based vehicle platooning problem, where multiple predecessors' information is used by the following vehicles and communication impairments, i.e., limited communication range and random packet losses, are considered. In this article, first, when the leading vehicle moves at a constant speed, we obtain the sufficient and necessary conditions on sampling time, control gains, and internal lag, to ensure the stability of the vehicle platoon based on matrix polynomials' stability for ideal communications. Second, for time-independent homogeneous random packet losses, we provide the upper bound for the loss rate to maintain convergence in expectation by matrix eigenvalue perturbation theory when no input is set for lossy information. We also provide sufficient conditions to guarantee mean-square convergence for heterogeneous time-independent random packet losses and show the convergence time for any given accuracy and probability. Third, when historically latest information is used for input, the sufficient and necessary conditions are provided to ensure the internal stability and string stability by Markov jump linear system theory. Furthermore, we discuss the controller design when no feasible solution exists to guarantee the string stability. Extensive numerical results validate our analysis.

Distributed Tracking Control for Connectivity-Preserving and Collision-Avoiding Formation Tracking of Underactuated Surface Vessels with Input Saturation

This paper investigates the formation tracking control problem of a group of underactuated surface vessels (USVs) in the presence of model uncertainties and environmental disturbances. Additional constraints, such as collision avoidance, heterogeneous limited communication range and input saturation are also considered. A modified barrier Lyapunov function (BLF) is introduced to achieve the connectivity preservation, the collision avoidance and the distributed formation tracking. Extended state observer (ESO) is employed to estimate total disturbances consisting of environmental disturbances and model uncertainties. Auxiliary variables are introduced to deal with the underactuated problem and input saturation. A distributed controller is developed for each USV. Using the Lyapunov method analyze the stability of the system, it is proven that all signals are bounded and tracking errors converge to a neighborhood of the origin. Simulation results show that the proposed controller is practicable and effective.