Multi-hop Networks Research Articles

Optimization of packet forwarding scheduling in wireless ad-hoc networks: Deployment on a 3D environment

The key aim of a multi-hop wireless network is to increase service efficiency in terms of transmission scheduling, packet transfer volume, and other factors. In a practical deployment of Ad- Hoc wireless network in three-dimensional (3D) environments, we study the optimizing transmission scheduling problem by end-to-end latency minimization using the signal-to-interference-and-noise rate (SINR) mechanism. Two strategies are considered. The former directly deals with end-to-end latency and eliminates the constraint of frame periodicity. The latter combines two cooperative transmission and transmission interference suppression models. The properties of both strategies are analyzed, and mixed-integer programming (MIP) models and solution heuristics are developed. Simulation results are presented to clarify the end-to-end latency performance of strategies, especially in 3D environments.

GCN-BERT and Memory Network Based Multi-Label Classification for Event Text of the Chinese Government Hotline

In order to automatically generate multiple labels for the event text of the Chinese government hotline, this paper propose a multi-label classification framework based on graph convolutional network (GCN), BERT, and memory network. The framework consists of three modules: label count prediction module, label semantic insert module, and label selection module. In the label count prediction module, this paper constructs the event graph with the abstract meaning representation (AMR) and extract the event topic information vector with GCN. To predict the label count, this paper first use BERT to extract the event semantic information vector and then fuse it with the event topic information vector (GCN-BERT fusion vector) with a dynamic fusion gate. In the label semantic insert module, to obtain the event label candidate set, this paper uses a multi-hop memory network to store the event label semantic information, and then use the answer selection framework, which matches the GCN-BERT fusion vector with the event label semantic memory vector. In label selection module, this paper uses the label count based multi-label selection to sort the event label candidate set and guide to output the optimal multi-label set of the event. Comparison experimental results show that the proposed framework outperforms all baselines and ablation studies demonstrate the effectiveness of each module.

A Proposal of Communication Protocol to Improve the Throughput and Fairness of Multi-hop Wireless Networks and Its Evaluation

A Proposal of Communication Protocol to Improve the Throughput and Fairness of Multi-hop Wireless Networks and Its Evaluation

Implementation and Evaluation of Multi-hop LoRa Networks

A multi-hop approach can extend network coverage, including connectivity to difficult-to-access areas. This paper discusses the implementation and evaluation of the two-hop real-time LoRa (Two-hop RT-LoRa) protocol previously proposed to be used for industrial monitoring and control applications. The protocol was implemented on LoRa devices to support a multi-hop LoRa network that consists of one LoRa gateway and 40 end devices. It is shown with various campus deployment scenarios that the protocol improves reliability in data transmission significantly

Integrating Multi-Label Contrastive Learning With Dual Adversarial Graph Neural Networks for Cross-Modal Retrieval.

With the growing amount of multimodal data, cross-modal retrieval has attracted more and more attention and become a hot research topic. To date, most of the existing techniques mainly convert multimodal data into a common representation space where similarities in semantics between samples can be easily measured across multiple modalities. However, these approaches may suffer from the following limitations: 1) They overcome the modality gap by introducing loss in the common representation space, which may not be sufficient to eliminate the heterogeneity of various modalities; 2) They treat labels as independent entities and ignore label relationships, which is not conducive to establishing semantic connections across multimodal data; 3) They ignore the non-binary values of label similarity in multi-label scenarios, which may lead to inefficient alignment of representation similarity with label similarity. To tackle these problems, in this article, we propose two models to learn discriminative and modality-invariant representations for cross-modal retrieval. First, the dual generative adversarial networks are built to project multimodal data into a common representation space. Second, to model label relation dependencies and develop inter-dependent classifiers, we employ multi-hop graph neural networks (consisting of Probabilistic GNN and Iterative GNN), where the layer aggregation mechanism is suggested for using propagation information of various hops. Third, we propose a novel soft multi-label contrastive loss for cross-modal retrieval, with the soft positive sampling probability, which can align the representation similarity and the label similarity. Additionally, to adapt to incomplete-modal learning, which can have wider applications, we propose a modal reconstruction mechanism to generate missing features. Extensive experiments on three widely used benchmark datasets, i.e., NUS-WIDE, MIRFlickr, and MS-COCO, show the superiority of our proposed method.

Modeling Wireless Mesh Networks for Load Management

Developing a simulation model for multi-hop multi-gateway wireless mesh networks (WMNs) is a challenging task. In this paper, a multi-hop multi-gateway WMN simulation model is developed in a step-by-step approach. This paper presents a MATLAB Simulink-based simulation model of Wireless Mesh Network (WMN) designed for easy optimization of layer 2. The proposed model is of special utility for the simulation of scheduling of GateWay (GW) and packet within a multi-hop multi gateway wireless network. The simulation model provides the flexibility of controlling the flow of packets through the networks. Load management among the GWs of WMN is performed in a distributed manner wherein the nodes based on their local knowledge of neighborhood beacons optimize their path to a GW. This paper presents a centralized Load Management Scheme (LMS). The LMS is based on the formation of Gateway Service Sets (GSS). The GSS is formed on basis of equal load distribution among the GWs. The proposed LMS is then analyzed for throughput improvement by leveraging the MATLAB Simulink model developed in the paper. A throughput improvement of almost 600% and a 40% reduction in packet loss was observed through simulations thus indicating the efficacy of the proposed LMS. The uniqueness of the simulation model presented in this paper are its scalability and flexibility in terms of network topology parameters.

ICIC: A Dual Mode Intra-Cluster and Inter-Cluster Energy Minimization Approach for Multihop WSN

Energy minimization is one of the paramount issues in designing of the wireless sensor network and becomes more complex for multi-hop transmission. Furthermore, selection of multi hop route for the packets transmission is also a tedious task. In this paper, we propose an intra-cluster and inter-cluster (ICIC) energy minimization strategy for multihop wireless sensor network (WSN). In the proposed strategy, in intra cluster communication, a dynamic duty cycle allocation based on the cluster head (CH) node distance from the child nodes for the energy minimization is presented. In the inter cluster communication, minimum energy consumption route is chosen for the transmission of packets to base station (BS). The proposed strategy is compared with four state of the art techniques i.e., HEEMP, LEACH, MLEACH, and SEP protocols of clustering. The evaluation of the proposed and existing strategies is done in two different scenarios i.e., number of round and number of nodes. Results of the simulation show that the proposed strategy transmits more packets and has lower number of dead nodes than other strategies. Furthermore, proposed strategy utilizes more network energy and more energy for relaying.

Cognitive Communications System for Ultra-Low Size, Weight and Power (SWAP) Attributable Platforms

Communications resiliency and data distribution assurance has been compromised in highly competitive, dynamic, and stressing environments with the introduction of off the shelf, high performing jammers. This paper introduces a novel cognitive communications system that can be fielded on small attributable platforms; the architecture amalgamates a highly capable environmentally perceptive aperture, a software defined radio, and sophisticated networking techniques. The proposed cognitive communications system uses the 5G new radio waveform and applies groundbreaking machine learning methods to facilitate systems orchestration amongst its subsystems to transfer information effectively between nodes, and across large-scale multi-hop networks, essential for rapid strike missions. With the challenges imposed by mature and readily available jammers, a cognitive communications system can be used to maintain and sustain continuous communications to provide near real-time surveillance and situational awareness updates. In addition to describing the comprehensive cognitive communications system architecture, a jamming analysis will be presented. Performance results for an operational use case will be compared to existing trite architectures consisting of siloed apertures and standard radio systems to demonstrate an improvement of approximately 10 dB in communications margin with the auspicious technology offering of the cognitive communications system for low size, weight, and power attributable platforms.

Design of False Data Injection Attack on Distributed Process Estimation

Herein, design of false data injection attack on a distributed cyber-physical system is considered. A stochastic process with linear dynamics and Gaussian noise is measured by multiple agent nodes, each equipped with multiple sensors. The agent nodes form a multi-hop network among themselves. Each agent node computes an estimate of the process by using its sensor observation and messages obtained from neighboring nodes, via Kalman-consensus filtering. An external attacker, capable of arbitrarily manipulating the sensor observations of some or all agent nodes, injects errors into those sensor observations. The goal of the attacker is to steer the estimates at the agent nodes as close as possible to a pre-specified value, while respecting a constraint on the attack detection probability. To this end, a constrained optimization problem is formulated to find the optimal parameter values of a certain class of linear attacks. The parameters of linear attack are learnt on-line via a combination of stochastic approximation based update of a Lagrange multiplier, and an optimization technique involving either the Karush-Kuhn-Tucker (KKT) conditions or online stochastic gradient descent. The problem turns out to be convex for some special cases. Desired convergence of the proposed algorithms are proved by exploiting the convexity and properties of stochastic approximation algorithms. Finally, numerical results demonstrate the efficacy of the attack.

A Markov Chain Approach for Myopic Multi-Hop Relaying: Outage and Diversity Analysis

In this paper, a cooperative protocol is investigated for a multi-hop network consisting of relays with buffers of finite size, which may operate in different communication modes. The protocol is based on the myopic decode-and-forward strategy, where each node of the network cooperates with a limited number of neighboring nodes for the transmission of the signals. Each relay stores in its buffer the messages that were successfully decoded, in order to forward them through the appropriate channel links, based on its supported communication modes. A complete theoretical framework is investigated that models the evolution of the buffers and the transitions at the operations of each relay as a state Markov chain (MC). We analyze the performance of the proposed protocol in terms of outage probability and derive an expression for the achieved diversity-multiplexing tradeoff, by using the state transition matrix and the related steady state of the MC. Our results show that the proposed protocol outperforms the conventional multi-hop relaying scheme and the system's outage probability as well as the achieved diversity order depend on the degree of cooperation among neighboring nodes and the communication model that is considered for every relay of the network.

Design of coordinated transmission mechanism of heterogeneoustime-sensitive data flow for industrial internet

<p indent="0mm">Industrial internet needs to realize the integration of IT (information technology) and OT (operation technology). The construction of the field-level industrial network is key to truly realizing seamless access of production line data from OT to IT and the intelligent operation and maintenance of the production line from IT to OT. For OT, the data in the industrial system is highly time-sensitive, and the deterministic behavior of perceived data and control instructions in time is the foundation for reliable and stable industrial system operation. However, the industrial field is plagued by problems such as heterogeneous perceived data, incompatible protocols, and varying transmission requirements, which pose significant challenges to the deterministic transmission of heterogeneous time-sensitive flow. One feasible solution to this problem is to design a unified time-aware gating mechanism with low delay and jitter, allowing precise control of data flow transmission behavior; this is one of the key technologies in the time-sensitive networking (TSN) standard. This paper mainly studies the coordinated transmission of heterogeneous time-sensitive flow in multi-hop TSN networks. First, heterogeneous time-sensitive traffic is modeled into two categories: periodic time-sensitive (PTS) flow and sporadic time-sensitive (STS) flow. Then, the schedulability of the PTS flow set and the design method of the gate control lists are analyzed, the sufficient and necessary conditions of schedulability are defined, and a schedulability discriminant algorithm is designed to determine the schedulability of any decision set. Then, a coordinated transmission framework of heterogeneous time-sensitive flow is proposed, followed by the construction of the constraint set of heterogeneous data scheduling. This paper employs the design method of no-wait slot scheduling and constructs the heterogeneous time-sensitive flow scheduling problem with the optimization goal of minimizing the maximum slot occupation in the scheduling unit to achieve the minimum end-to-end deterministic delay of PTS flow and to reduce the impact of PTS scheduling mechanism on STS flow transmission as much as possible. Then, a uniform arrangement and filling (UAF) algorithm is developed to minimize slot occupation of scheduling units by uniformly arranging the flow of the same period and filling the idle time slots generated during pre-period scheduling. Finally, simulation and experiment results corroborate the effectiveness of the two kinds of heterogeneous time-sensitive flow coordinated transmission framework and the efficiency of the UAF scheduling algorithm based on ensuring the same scheduling performance.

Extending the Quality of Secure Service Model to Multi-Hop Networks

Rarely are communications networks point-to-point. In most cases, transceiver relay stations exist between transmitter and receiver end-points. These relay stations, while essential for controlling cost and adding flexibility to network architectures, reduce the overall security of the respective network. In an effort to quantify that reduction, we extend the Quality of Secure Service (QoSS) model to these complex networks, specifically multi-hop networks. In this approach, the quantification of security is based upon probabilities that adversarial listeners and disruptors gain access to or manipulate transmitted data on one or more of these multi-hop channels. Message fragmentation and duplication across available channels provides a security performance trade-space, with its consequent QoSS. This work explores that trade-space and the corresponding QoSS model to describe it.

Implementation and evaluation of a 2.4 GHz multi-hop WSN: LoS, NLoS, different floors, and outdoor-to-indoor communications

In this paper, the communication reliability of a 2.4 GHz multi-hop wireless sensor network (WSN) in various test scenarios is evaluated through experiments. First, we implement an autonomous communication procedure for a multi-hop WSN on Tmote sky sensor nodes; 2.4 GHz, an IEEE 802.15.4 standard. Here, all nodes including a transmitter node (Tx), forwarder nodes (Fw), and a base station node (BS) can automatically work for transmitting and receiving data. The experiments have been tested in different scenarios including: i) in a room, ii) line-of-sight (LoS) communications on the 2nd floor of a building, iii) LoS and non-line-of-sight (NLoS) communications on the 1st floor to the 2nd floor, iv) LoS and NLoS communications from outdoor to the 1st and the 2nd floors of the building. The experimental results demonstrate that the communication reliability indicated by the packet delivery ratio (PDR) can vary from 99.89% in the case of i) to 14.40% in the case of iv), respectively. Here, the experiments reveal that multi-hop wireless commutations for outdoor to indoor with different floors and NLoS largely affect the PDR results, where the PDR more decreases from the best case (i.e., the case of a)) by 85.49%. Our research methodology and findings can be useful for users and researchers to carefully consider and deploy an efficient 2.4 GHz multi-hop WSN in their works, since different WSN applications require different communication reliability level.

Deep Learning for Wireless Networking: The Next Frontier

With the growth of mobile technology in the last decade, wireless networks have become an integral part of our everyday lives. To meet the increasingly stringent application requirements, more and more network resources and features are becoming available, which requires innovative system designs such that the configuration and management of the networks can be performed automatically and autonomously. Due to its superior capability of discovering insightful knowledge in a data-driven manner, the emerging deep learning (DL) technology has shown great potential to fulfil this goal. This article systematically reviews recent efforts in leveraging DL for addressing wireless network optimization problems, presenting a fundamental understanding of where and how the supremacy of DL based approaches comes versus the conventional modeling based approaches. The basic research challenges and some promising research directions for fully exploiting the potential of DL in wireless network optimization are also discussed. The effectiveness of DL is illustrated with an innovative case study of integrating DL with multi-hop wireless network flow optimization.

Utility Maximization for Multihop Wireless Networks Employing BATS Codes

BATS (BATched Sparse) codes are a class of efficient random linear network coding variation that has been studied for multihop wireless networks mostly in scenarios of a single communication flow. Towards sophisticated multi-flow network communications, we formulate a network utility maximization (NUM) problem that jointly optimizes the BATS code parameters of all the flows and network scheduling. The NUM problem adopts a batch-wise packet loss model that can be obtained from the network local statistics without any constraints on packet loss patterns. Moreover, the NUM problem allows a different number of recoded packets to be transmitted for different batches in a flow, which is called adaptive recoding. Due to both the probably nonconcave objective and the BATS code-related variables, the algorithms developed for the existing flow optimization problems cannot be applied directly to solve our NUM problem. We introduce a two-step algorithm to solve our NUM problem, where the first step solves the problem with nonadaptive recoding schemes, and the second step optimizes adaptive recoding hop-by-hop from upstream to downstream in each flow. We perform various numerical evaluations and simulations to verify the effectiveness and efficiency of the algorithm.

Intrablock Interleaving for Batched Network Coding With Blockwise Adaptive Recoding

Batched network coding (BNC) is a low-complexity solution to network transmission in multi-hop packet networks with packet loss. BNC encodes the source data into batches of packets. As a network coding scheme, the intermediate nodes perform recoding on the received packets belonging to the same batch instead of just forwarding them. A recoding scheme that may generate more recoded packets for batches of a higher rank is also called adaptive recoding. Meanwhile, in order to combat burst packet loss, the transmission of a block of batches can be interleaved. Stream interleaving studied in literature achieves the maximum separation among any two consecutive packets of a batch, but permutes packets across blocks and hence cannot bound the buffer size and the latency. To resolve the issue of stream interleaver, we design an intrablock interleaver for adaptive recoding that can preserve the advantages of using a block interleaver when the number of recoded packets is the same for all batches. We use potential energy in classical mechanics to measure the performance of an interleaver, and propose an algorithm to optimize the interleaver with this performance measure. Our problem formulation and algorithm for intrablock interleaving are also of independent interest.

Throughput-Optimal Broadcast for Time-Varying Directed Acyclic Wireless Multi-Hop Networks With Energy Harvesting Constraints

In wireless multi-hop networks, a fundamental problem is to disseminate continuous data traffic from a source node to all other network nodes, which is known as the broadcast problem. Such a problem becomes even more complicated in wireless multi-hop networks with energy-harvesting capabilities at nodes when facing the interaction between stochastics of traffic arrivals at the source and randomness of energy-harvesting process at nodes. In such networks, the energy consumable at a node cannot exceed the amount of the energy harvested at the node. In this paper, we investigate the throughput-optimal broadcast problem in time-varying directed acyclic wireless multi-hop networks with such energy harvesting constraints. The topologies of such networks change dynamically with time while satisfying the directed acyclic property and the energy arrival time and harvested amount at a node are random, which causes the consumable energy in each time slot to fluctuate with time. Existing throughput-optimal broadcast algorithms did not consider such energy-harvesting constraints in their designs and therefore their throughput-optimal properties do not hold anymore in such a network. In this paper, we characterize the energy-harvesting uncertainties at nodes by using time-varying per-slot-based supportable transmission rates of wireless links. We consider the time-varying property of supportable link transmission rates caused by energy-harvesting dynamics in the per-slot transmission scheduling and propose an online max-weight broadcast algorithm. We derive a tight upper bound of broadcast capacity of the wireless networks under study in this paper and further prove that our proposed algorithm is throughput-optimal. We evaluate the throughput and latency performance of the proposed algorithm by simulations and the simulation results affirm our theoretical analysis.

Distributed TDMA Frame with Transmission Slot Estimation for Fully Connected Wireless Multihop Networks

Distributed TDMA Frame with Transmission Slot Estimation for Fully Connected Wireless Multihop Networks

The Wireless Control Bus: Enabling Efficient Multi-Hop Event-Triggered Control with Concurrent Transmissions

Event-triggered control ( ETC ) holds the potential to significantly improve the efficiency of wireless networked control systems. Unfortunately, its real-world impact has hitherto been hampered by the lack of a network stack able to transfer its benefits from theory to practice specifically by supporting the latency and reliability requirements of the aperiodic communication ETC induces. This is precisely the contribution of this article. Our Wireless Control Bus ( WCB ) exploits carefully orchestrated network-wide floods of concurrent transmissions to minimize overhead during quiescent, steady-state periods, and ensures timely and reliable collection of sensor readings and dissemination of actuation commands when an ETC triggering condition is violated. Using a cyber-physical testbed emulating a water distribution system controlled over a real-world multi-hop wireless network, we show that ETC over WCB achieves the same quality of periodic control at a fraction of the energy costs, therefore unleashing and concretely demonstrating its full potential for the first time.

A Performance–Consumption Balanced Scheme of Multi-Hop Quantum Networks for Teleportation

Teleportation is an important protocol in quantum communication. Realizing teleportation between arbitrary nodes in multi-hop quantum networks is of great value. Most of the existing multi-hop quantum networks are based on Bell states or Greeberger–Horne–Zeilinger (GHZ) states. Bell state is more susceptible to noise than GHZ states after purification, but generating a GHZ state consumes more basic states. In this paper, a new quantum multi-hop network scheme is proposed to improve the interference immunity of the network and avoid large consumption at the same time. Teleportation is realized in a network based on entanglement swapping, fusion, and purification. To ensure the robustness of the system, we also design the purification algorithm. The simulation results show the successful establishment of entanglement with high fidelity. Cirq is used to verify the network on the Noisy Intermediate-Scale Quantum (NISQ) platform. The robustness of the fusion scheme is better than the Bell states scheme, especially with the increasing number of nodes. This paper provides a solution to balance the performance and consumption in a multi-hop quantum network.