Multi-hop Networks Research Articles

Evaluation of a Multi-Hop Wireless Internet-of-Things Network on Large Ships

IoT networks on large ships are known to face challenges, such as severe signal attenuation due to the complex steel bulkhead structure inside the ship. However, reliable connectivity is still required to monitor critical facilities such as engine rooms. This study presents an evaluation study of an IoT network using the IEEE 802.15.4 DSME MAC protocol for reliable data collection within a ship. We investigate the impact of ship-specific characteristics on signal propagation and analyze the feasibility of utilizing the DSME MAC protocol. We also compare the viability of 2.4 GHz and sub-1 GHz communication within a ship. In addition, we strategically select router locations and evaluate the stability and time sensitivity of the constructed network. The experimental results demonstrate the feasibility and reliability of the proposed multi-hop wireless network for seamless data transmission onboard ships.

Formula omitted]-Coverage Reliability for Wireless Multihop Network incorporating Boundary Effect

Coverage is one of the crucial performance measures for Wireless Multihop Networks (WMN) and is widely explored by researchers. The coverage performance of the network indicates how effectively the deployed network is able to provide the required coverage for a given period of time. The coverage performance of a finite network is influenced by different factors such as the presence of obstacles in the signal propagation path, the shape and size of the network deployment region, sensor node characteristics, and many others. Therefore, it is necessary to compute the performance of the network before its actual deployment in the desired region. The existing paper proposes an analytical solution for the network coverage considering sensor node failure and boundary effect using three different sensing models i.e., Boolean, Shadow Fading, and Elfes, separately. The Boolean Sensing Model (BSM) is deterministic and ignores the channel randomness, whereas the other two are probabilistic sensing models and are more realistic as they also consider the channel randomness caused by environmental factors. In the existing research, coverage estimation is determined separately for each individual model. However, coverage estimation for all three models and coverage reliability under boundary conditions have not been explored in the literature yet. Coverage reliability is another crucial metric determining how long a network can provide the required coverage. This paper aims to compute the Network Coverage Reliability (NCR) of a WMN deployed in given environments and with sensor node failure conditions. We have proposed an analytical solution to estimate the NCR of a WMN deployed in a finite circular region considering boundary effect using all three models viz., BSM, Shadowing & Multipath Fading (SMF), and Elfes Sensing Model (ESM) assuming sensor node failure condition. The Mean Time to Failure (MTTF) parameter has been determined for all three models and based on that the value of NCR is estimated. The proposed work considers κ-coverage reliability with 1≤κ≤3 where κ is the number of distinct sensor node required to cover the target and render desired NCR for different path fading and non-fading sensing models. The analytical results are validated through simulation runs and found to be consistent. Further, we have analyzed the impact of different parameters, such as the number of sensor node, sensing range, SMF parameters, sensor failure rate, and required reliability on the κ-coverage and κ-coverage reliability, referred to as NCR.

Reinforcement learning based multi-parameter joint optimization in dense multi-hop wireless networks

Carrier Sense Multiple Access with Collision Avoid (CSMA/CA) restricts the channel utilization efficiency although it always is regarded as a promising distributed channel access scheme, especially in dynamic dense wireless network scenarios. This paper investigates a Reinforcement Learning (RL) based multi-parameter joint optimization mechanism to build suitable access configuration for dense wireless multi-hop networks. Specifically, we propose a light-weight CSMA/CA mechanism based on Q-Learning (QL), namely QL-CSMA/CA, which jointly optimizes multiple channel access parameters, including Contention Window (CW), Maximum Backoff Exponent (MaxBE), and Maximum CSMA Backoffs (MaxCSMABackoffs), by setting mixed rewards and thus leading to higher network performance. Due to the limitation of the QL-CSMA/CA in large-scale state-space scenarios, we further present an enhanced CSMA/CA mechanism based on Deep Q-Network (DQN), namely DQN-CSMA/CA. Despite introducing a little complexity, the DQN-CSMA/CA can significantly improve transmission performance by utilizing more comprehensive network state information in complex dynamic network scenarios. Simulation results demonstrate the effectiveness of the proposed algorithms in terms of throughput, packet loss rate, delay, and collision rate, especially in high-density scenarios. Specifically, compared to SCAMA and traditional CSMA/CA algorithms, the QL-CSMA/CA algorithm achieves 9% and 5% performance improvements in terms of throughput, while DQN-CSMA/CA algorithm achieves 14% and 10% improvements in terms of throughput, respectively.

LoRaute: Routing Messages in Backhaul LoRa Networks for Underserved Regions

LoRa technology endows unprecedented ability to connect isolated geographical landscapes and build community networks that serve specific purposes. As the network grows, coherent routing of messages becomes imperative to meet the network’s objectives and Quality of Service (QoS) requirements. However, despite the recent rise in research and development centered around LoRa networks, not much research addresses the routing mechanisms in LoRa networks. Moreover, the LoRa routing mechanisms must run on low-power and resource-constrained devices, as these networks primarily target far-off locations or volatile environments such as volcanoes. Hence, this work proposes routing mechanisms (LoRaute) for LoRa networks that help route messages considering the messages’ QoS requirements. Also, a multi-purpose network hardware is proposed, which serves as a LoRa network base station or a WiFi to LoRa bridge for TCP/IP communication. Additionally, this work furnishes and assesses the implementation of the LoRa network and the routing mechanisms in a real environment. The implementation results indicate a seamless and rapid setup of multi-hop LoRa networks. Moreover, the implementation achieves a routing table record size of 9B, 24.45ms routing latency, and 171mA peak current consumption by the proposed LoRa node. Finally, the proposed system serves as a backhaul network for essential long-range communications, as demonstrated by the experimental setup.

Distributed Physical-layer Network Coding MAC protocol

Physical-layer Network Coding (PNC) was first proposed for a Two-Way Relay Channel (TWRC) to improve the spectrum efficiency since it allows nodes to transmit simultaneously via a relay node. This technique requires multiple nodes to transmit their packets with accurate synchronization. Therefore, in many works of literature, centralized scheduling with perfect synchronization has been assumed to be employed on top of PNC.Such assumptions are not applicable in general random access multi-hop wireless networks. Therefore, this paper proposes a distributed MAC protocol that supports PNC in static multi-hop wireless networks. The proposed MAC protocol is based on the Carrier Sense Multiple Access (CSMA) strategy, where RTS/CTS frames are used to detect PNC opportunities and to offer the appropriate scheduling of the involved transmissions that should occur simultaneously. This packet exchange process is coordinated by the relay node and was designed to guarantee compatibility with other conventional relaying schemes with specific concerns for the hidden node issues. Our solution was practically tested on a real testbed with different static wireless topologies and several physical settings.With numerical results, we investigate the effectiveness of PNC in distributed wireless multi-hop networks. Compared to the conventional CSMA/CA and the PNC opportunistic (PNCOPP) MAC protocols, the proposed protocol’s performances are advantageous in various scenarios, especially in large networks.

An Embedding-Driven Multi-Hop Spatio-Temporal Attention Network for Traffic Prediction

Traffic prediction is an important part of modern intelligent transportation systems (ITS), which helps transportation management and city planning. However, it is a very challenging task for modeling complex spatio-temporal dependencies, since the traffic data belongs to highly periodic multivariate time series which makes it hard to model accurate spatial dependencies only from time series and observed geolocation information of road segments. The existing research mainly focuses on finding ways of capturing dynamic spatial dependencies of road segments while neglecting the importance of periodicity, and few studies have explored a pure embedding-driven method that is robust to corrupted data to model periodicity. In this paper, we propose an embedding-driven multi-hop spatio-temporal attention network for traffic prediction (), which mainly focuses on leveraging the multi-scale periodicity of traffic data. Specifically, the proposed network applies a designed Fourier-series-based embedding, to capture the periodicity, which is more in line with real-world facts. Driven by the designed embedding, both local and global temporal dependencies are modeled properly by combining the attention-based methods and the convolution-based methods. Besides, we implement a trial that can hardly be seen in the existing traffic prediction works to combine the graph self-attention mechanism with a multi-hop diffusion process to explore the large-scale structural information on a designed set of graphs. Experiments on two real-world traffic datasets which contains traffic speed data for months show the effectiveness of our proposed methods. The experiments also suggest the methods can provide stable reasonable and smooth predictions for completely corrupted data.

Overloaded MIMO Bi-Directional Communication with Physical Layer Network Coding in Heterogeneous Multihop Networks

Overloaded MIMO Bi-Directional Communication with Physical Layer Network Coding in Heterogeneous Multihop Networks

Full-Loop AoI-Based Joint Design of Control and Deterministic Transmission for Industrial CPS

For improving the performance of industrial cyber-physical systems (ICPS), the joint design of control and transmission has been demonstrated as an efficient mechanism. However, the existing metrics in recent joint design works lack completeness and accuracy, which leads to the challenge of improving the stability and network resource utilization of ICPS. This paper proposes a full loop age of information (FL-AoI) based control and transmission joint design architecture for multi-subsystem ICPS integrating multi-hop network. The FL-AoI depicts the timeliness of information by state delay, input delay, and event-triggered status in full-loop ICPS. To avoid the instability caused by the input delay, we design an LQR-based controller by the FL-AoI and derive a feasible region of the FL-AoI for guaranteeing the stability of control systems. To ensure the accuracy of FL-AoI, we propose a routing and scheduling policy based on Time Sensitive Networking (TSN) for the deterministic bound of delay. Finally, we propose an optimal event-triggered policy based on FL-AoI for minimizing the data transmission amount while ensuring the system stability. The evaluation results show that our strategy improves the nonlinear and non-scalar control systems' stability while reducing the network burden of TSN compared with the traditional joint design strategies.

Enhancing cyber security in WSN using optimized self-attention-based provisional variational auto-encoder generative adversarial network

Wireless sensor network (WSN) is a multi-hop and self-organizing wireless network consists of fixed or moving sensors, this is one of the key components of the cyber physical system. It jointly senses, gathers, analyses, and transfer the data of detected objects in the network's service area before sending this data to the network's owner. The attacks like, Black hole, Gray hole, Flooding, scheduling are the usual WSN attacks that could quickly harm the system. A significant level of redundancy, network data's higher correlation, intrusion detection schemes for wireless sensor networks also have the drawbacks of poor identification rate, high computation overhead, and higher false alarm rate. MethodsInitially, the data's are taken from WSN-DS. In pre-processing, it confiscates the data redundancy and missing value restore sunder Color Wiener filtering (CWF). In feature selection, the optimal features are selected using tasmanian devil optimization (TDO) algorithm. Based on the optimum features, the intruders in WSN data are categorized into normal and anomalous data utilizing SAPVAGAN. Hence, honey badger algorithm (HBA) is proposed to optimize the SAPVAGAN, which detects the WSN intrusion accurately. ResultsThe proposed technique is performed in Python utilizing the WSN-DS dataset. Here, the performance measures, like recall, precision, f-measure, specificity, accuracy, RoC, computation time is evaluated. The proposed method provides 23.56%, 12.64%, and 15.63% higher accuracy, 23.14%, 16.78% and 20.04% lower computational time analyzed to the existing models, such as Intrusion Detection System in wireless sensor network using light GBM method (ECS-WSN-SLGBM), Intrusion Detection Scheme in wireless sensor network utilizing recurrent neural network (ECS-WSN-RNN) and Intrusion Detection Scheme for Wireless Sensor Networks utilizing whale optimized gate recurrent unit (ECS-WSN-WOGRU) respectively. ConclusionIt combines advanced techniques such as self-attention, provisional learning, and generative adversarial networks. By leveraging self-attention, the model captures important features and relationships in the WSN data. The provisional allows the model to adapt to changing network dynamics. The component generates realistic sensor data and accurately identifies malicious inputs. Overall, this innovative approach improves security and adaptability in WSNs.

The energy complexity of diameter and minimum cut computation in bounded-genus networks

This paper investigates the energy complexity of distributed graph problems in multi-hop radio networks, where the energy cost of an algorithm is measured by the maximum number of awake rounds of a vertex. Recent works revealed that some problems, such as broadcast, breadth-first search, and maximal matching, can be solved with energy-efficient algorithms that consume only polylog⁡n energy. However, there exist some problems, such as computing the diameter of the graph, that require Ω(n) energy to solve. To improve energy efficiency for these problems, we focus on a special graph class: bounded-genus graphs. We present algorithms for computing the exact diameter, the exact global minimum cut size, and a (1±ϵ)-approximate s-t minimum cut size with O˜(n) energy for bounded-genus graphs. Our approach is based on a generic framework that divides the vertex set into high-degree and low-degree parts and leverages the structural properties of bounded-genus graphs to control the number of certain connected components in the subgraph induced by the low-degree part.

Topology-based multi-jammer localization in wireless networks

Jamming attacks and unintentional radio interference are some of the most urgent threats harming the dependability of wireless communication and endangering the successful deployment of pervasive applications built on top of wireless networks. Unlike the traditional approaches focusing on developing jamming defense techniques without considering the location of jammers, we take a different viewpoint that the jammers’ position should be identified and exploited for building a wide range of defense strategies to alleviate jamming. In this paper, we address the problem of localizing multiple jamming attackers coexisting in wireless networks by leveraging the network topology changes caused by jamming. We systematically analyze the jamming effects and develop a framework that can partition network topology into clusters and can successfully estimate the positions of multiple jammers even when their jamming areas are overlapping. Our experiments on a multi-hop network setup using MicaZ sensor nodes validate the feasibility of real-time collection of network topology changes under jamming and our extensive simulation results demonstrate that our approach is highly effective in localizing multiple attackers with or without the prior knowledge of the order that the jammers are turned on.

Age of Information Optimization in Multi-Channel Based Multi-Hop Wireless Networks

The proliferation of IoT devices, with various capabilities in sensing, monitoring, and controlling, has prompted diverse emerging applications, highly relying on effective delivery of sensitive information gathered at edge devices to remote controllers for timely responses. To effectively deliver such information/status updates, this paper undertakes a holistic study of AoI in multi-hop networks by considering the relevant and realistic factors, aiming for optimizing information freshness by rapidly shipping sensitive updates captured at a source to its destination. In particular, we consider the multi-channel with OFDM (orthogonal frequency-division multiplexing) spectrum access in multi-hop networks and develop a rigorous mathematical model to optimize AoI at destination nodes. Real-world factors, including orthogonal channel access, wireless interference, and queuing model, are taken into account for the very first time to explore their impacts on the AoI. To this end, we propose two effective algorithms where the first one approximates the optimal solution as closely as we desire while the second one has polynomial time complexity, with a guaranteed performance gap to the optimal solution. The developed model and algorithms enable in-depth studies on AoI optimization problems in OFDM-based multi-hop wireless networks. Numerical results demonstrate that our solutions enjoy better AoI performance and that AoI is affected markedly by those realistic factors taken into our consideration.

Control-aware scheduling over multi-hop networks

Control-aware scheduling over multi-hop networks

Decentralized Configuration of TSCH-Based IoT Networks for Distinctive QoS: A Deep Reinforcement Learning Approach

The IEEE 802.15.4 Time-Slotted Channel Hopping (TSCH) is widely used as a reliable, low-power, and low-cost communication technology for many industrial Internet-of-Things (IoT) networks. In many applications, Quality-of-Service (QoS) requirements are different for heterogeneous nodes, necessitating non-equal parameter settings per node. This results in a very large configuration space making space exploration complex and time-consuming. Moreover, network state and QoS requirements may change over time. Thus, run-time configuration mechanisms are needed for making decisions about proper node settings to consistently satisfy diverse and dynamic QoS requirements. In this paper, we propose a run-time decentralized self-optimization framework based on Deep Reinforcement Learning (DRL) for parameter configuration of a multi-hop TSCH network. DRL adopts neural networks as approximate functions to speed up the process of converging to QoS-satisfying configurations. Simulation results show that our proposed framework enables the network to use the right configuration settings according to the diverse QoS demands of different nodes. Moreover, it is shown that the convergence time of the learning framework is in the order of a few minutes which is acceptable for many IoT applications.

Multi-modal multi-hop interaction network for dialogue response generation

Multi-modal multi-hop interaction network for dialogue response generation

Detecting the Most Vital Articulation Points in Wireless Multi-Hop Networks

Detecting the Most Vital Articulation Points in Wireless Multi-Hop Networks

Revenue Optimal Bandwidth Allocation in a Class of Multihop Networks: Algorithms and Asymptotic Optimality

We study Bandwidth Reservation (BR) policies for the Bandwidth on Demand (BoD) problem in a class of multihop networks. We motivate an Erlang fixed-point BR heuristic for the general BoD problem by first establishing the optimality of BR on a class of multihop networks. The motivating problem is a wireline network comprising <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$</tex-math> </inline-formula> links in tandem, each link of which is shared by two types of bandwidth demands, one type requiring one unit of bandwidth from every link, and the other type (being dedicated to the link) requiring one unit of bandwidth as well. First, for this <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$</tex-math> </inline-formula> -hop tandem network, when each link has unit bandwidth, we demonstrate that a policy of BR form is optimal. We then study the BoD problem for a more general <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$</tex-math> </inline-formula> -hop tandem network, in the “Kelly” limiting regime, where the arrival rates as well as the link bandwidths become large. For certain parameter regimes of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$</tex-math> </inline-formula> -hop tandem network, we show that an admission control policy of BR form is asymptotically optimal. Motivated by these results, we propose an Erlang fixed-point based, link-by-link, heuristic algorithm for computing a BR policy for the BoD problem in a general network. We, finally, evaluate this proposal numerically.

Secured Clustering Multilevel Underwater Acoustic Networks with Dynamic Routing Discovery (SCDRD) Protocol

Abstract: Due to multiple rising automated industry and combat field applications, researchers have developed a growing interest in the area of Underwater Acoustic Sensor Networks (UASNs) over the previous decade. Extending the lifetime of the energy and the consumption of energy in UASNs are enormous challenges. One of the primary difficulties in UASNs is the development of energy-saving routing mechanisms. In recent days the security of the data collected is the main concern because of widely accessible wireless sensors, because these low-power devices are incompatible with complicated encryption methods. As a result, for multi-hop Underwater Acoustic Sensor Networks (UASNs) with non-replenish able energy resources, lifetime optimization and security are two opposing design concerns. This work introduces an Efficient Energy Clustering with Dynamic Routing Discovery protocol (SCDRD protocol), which addresses two competing difficulties by taking into account several characteristics such as probability random walking and the energy balance control mechanism. The suggested protocol detects that energy consumption in the existing network topology is disproportional to energy deployment, which has an impact on the lifetime of wireless sensor networks. Through a quantitative security analysis, this work proposes a non-uniform efficient deployment technique to handle existing energy resource optimization problems and addresses the security location privacy concern. In all circumstances, the proposed Protocol produces the efficient trade-off between the energy balance and the routing mechanisms, allowing sensor networks to last substantially longer. In addition, the deployment of energy in the non-uniform manner results in the increased network’s lifetime and overall packets sent. The results of proposed work is compared with existing cost-aware routing protocols and outperform the existing protocol with respect to various performance parameters like energy, throughput and delivery ratio.

Optimize the Network Coding Paths to Enhance the Coding Protection in Wireless Multimedia Sensor Network

Efficient protection techniques for multimedia data transfer over Wireless Sensor Network (WSN) are very essential issues. In noisy Wireless Multimedia Sensor Networks (WMSN) Quality of Service (QoS) is a challenging task due to bandwidth and limited energy, and unpredictable channel conditions. Therefore, Forward Error Correction (FEC), a class of channel coding has been widely used in WSN. Nevertheless, the bulky size of multimedia data makes it more difficult to be transported over the noisy multi-hop wireless network. Moreover, the efficiency of FEC drops as the number of hops increases. In this paper, an optimized protection technique based on network coding and rateless code has been proposed to enhance the throughput and reduce overhead during data transfer in WMSN. The performance of NCP-OPR is enhanced via Optimal Network Path Model (ONPM) where the best available paths are optimally selected using Particle Swarm Optimization (PSO). In conjunction with the proposed protection scheme, the proposed ONPM is intended for limited power WSN by optimally distributing the power usage among the network paths so that the throughput can be improved.

Low-cost relay selection in multihop cooperative networks

A best relay selection algorithm for a cooperative multi-hop cross-layer single-input single-output (SISO) amplify-and-forward (AF) wireless relay network is analyzed in this work, with the application where exact channel state information (CSI) is known. We present algorithmic strategies to simplify a multi-hop parallel SISO relay network into a series multi-hop network by finding the best path having the maximum received signal-to-noise ratio (SNR). The best relay selection by using dynamic programming search entails high computations and large memory requirements, as well as involves the full CSI information, making this approach impractical for large-scale networks. The goal of the proposed low-cost near-optimal routing strategy in this work is to provide close to optimal performance with much less complexity compared to traditional routing. Next, we also propose a low-cost power allocation to further improve system performance over traditional power allocation found in the literature. Computer simulations show excellent performance improvement of our proposed methods in terms of bit error rate (BER) as well as outage probability over the traditional relaying and power allocation algorithms.