Multi-hop Communication Research Articles

On Providing Energy Harvested Multi-hop D2D Communication with UAV Bridge in Disaster Area

On Providing Energy Harvested Multi-hop D2D Communication with UAV Bridge in Disaster Area

Rapid and Resilient LoRa Leap: A Novel Multi-Hop Architecture for Decentralised Earthquake Early Warning Systems.

We introduce a novel LoRa-based multi-hop communication architecture as an alternative to the public internet for earthquake early warning (EEW). We examine its effectiveness in generating a meaningful warning window for the New Zealand-based decentralised EEW sensor network implemented by the CRISiSLab operating with the adapted Propagation of Local Undamped Motion (PLUM)-based earthquake detection and node-level data processing. LoRa, popular for low-power, long-range applications, has the disadvantage of long transmission time for time-critical tasks like EEW. Our network overcomes this limitation by broadcasting EEWs via multiple short hops with a low spreading factor (SF). The network includes end nodes that generate warnings and relay nodes that broadcast them. Benchmarking with simulations against CRISiSLab's EEW system performance with internet connectivity shows that an SF of 8 can disseminate warnings across all the sensors in a 30 km urban area within 2.4 s. This approach is also resilient, with the availability of multiple routes for a message to travel. Our LoRa-based system achieves a 1-6 s warning window, slightly behind the 1.5-6.75 s of the internet-based performance of CRISiSLab's system. Nevertheless, our novel network is effective for timely mental preparation, simple protective actions, and automation. Experiments with Lilygo LoRa32 prototype devices are presented as a practical demonstration.

Deep Learning-Based Joint Beamforming Design for Multi-Hop Reconfigurable Intelligent Surface (RIS)-Aided Communication Systems

Reconfigurable intelligent surface (RIS) is one of the promising technologies for sixth generation communications due to its advantages including energy saving, high spectral efficiency, etc. However, the non-convex joint beamforming design is a challenge, especially in the multi-hop RIS-assisted communication system. This paper proposes a deep learning-based joint beamforming (DLBF) design, aiming to maximize the system data rate for multi-hop RIS-aided communication systems. The proposed DLBF design consists of the reflection matrices design of all RISs and the transmit beamforming design at the base station, which has a reduced computational complexity. Numerical results show that the proposed DLBF can achieve 1.8 bit/s/Hz sum rate gain compared to the conventional beamforming method for the two-user scenario, which can be enhanced by large-scale users. The sum rate performance can be improved by increasing the number of RISs due to the reflection gain, and corresponding results provide a guidance of the multi-hop number selection for further investigation.

Threshold-driven K-means sector clustering algorithm for wireless sensor networks

The clustering algorithm is an effective method for developing energy efficiency routing protocol for wireless sensor networks (WSNs). In clustered WSNs, cluster heads must handle high traffic, thus consuming more energy. Therefore, forming balanced clusters and selecting optimal cluster heads are significant challenges. The paper proposes a sector clustering algorithm based on K-means called KMSC. KMSC improves efficiency and balances the cluster size by employing symmetric dividing sectors in conjunction with K-means. For the selection of cluster heads (CHs), KMSC uses the residual energy and distance to calculate the weight of the node, then selects the node with the highest weight as CH. A hybrid single-hop and multi-hop communication is utilized to reduce long-distance transmissions. Furthermore, the impact of the number of sectors, the threshold for clustering, and the network size on the performance of KMSC has been explored. The simulation results show that KMSC outperforms EECPK-means, K-means, TSC, LSC, and SEECP in terms of FND, HND, and LND.

Node Role Selection and Rotation Scheme for Energy Efficiency in Multi-Level IoT-Based Heterogeneous Wireless Sensor Networks (HWSNs).

The emergence of Internet of Things (IoT)-based heterogeneous wireless sensor network (HWSN) technology has become widespread, playing a significant role in the development of diverse human-centric applications. The role of efficient resource utilisation, particularly energy, becomes further critical in IoT-based HWSNs than it was in WSNs. Researchers have proposed numerous approaches to either increase the provisioned resources on network devices or to achieve efficient utilisation of these resources during network operations. The application of a vast proportion of such methods is either limited to homogeneous networks or to a single parameter and limited-level heterogeneity. In this work, we propose a multi-parameter and multi-level heterogeneity model along with a cluster-head rotation method that balances energy and maximizes lifetime. This method achieves up to a 57% increase in throughput to the base station, owing to improved intra-cluster communication in the IoT-based HWSN. Furthermore, for inter-cluster communication, a mathematical framework is proposed that first assesses whether the single-hop or multi-hop inter-cluster communication is more energy efficient, and then computes the region where the next energy-efficient hop should occur. Finally, a relay-role rotation method is proposed among the potential next-hop nodes. Results confirm that the proposed methods achieve 57.44%, 51.75%, and 17.63% increase in throughput of the IoT-based HWSN as compared to RLEACH, CRPFCM, and EERPMS, respectively.

A novel unified interference management scheme for multicellular MIMO communication with instantaneous relay

SummaryIn the world of emerging wireless networks, interference poses a significant challenge to reliable wireless communication. Additionally, these networks are prone to path loss and blockages, which can be addressed by utilizing the advanced technology of multihop communication with instantaneous relay (IR). However, scenarios involving IR‐assisted networks are considered instances of multihop communications that face potential obstacles caused by interference. As a result, multiple interference management approaches exist to tackle this interference issue, among which aligned interference neutralization (AIN) is a state‐of‐the‐art technology that seamlessly unifies two established interference management strategies: interference alignment (IA) and interference neutralization (IN). Therefore, this paper presents a novel tristaged AIN scheme to mitigate interference in a multicellular multiple‐input multiple‐output (MIMO) interference multiple access channel (IMAC). In the proposed scheme, the initial stage‐1 involves transmitting message signals from individual transmitters or users to the IR and the receiving base stations (BSs). In stage‐2, the IR neutralizes half of the interference signals by performing IN. Finally, in stage‐3, IA is carried out at the receiver BS terminals, aligning the remaining interference signals equally within the available dimensions. Based on this proposed approach, we determined that for an IR‐aided multicellular MIMO IMAC, the achievable degree of freedom (DoF) is 2N. The proposed approach's robustness and effectiveness have been analyzed through extensive simulations, and these simulation results indicated that the proposed approach outperforms other benchmark interference management techniques in terms of DoF and sum rate, thereby improving user performance.

A mobile data collection method for balancing energy consumption and delay in strip-shaped wireless sensor networks with branches

Strip-shaped Wireless Sensor Networks (WSNs) with branches are commonly used in various long and narrow applications, such as mines, factories, subways, and pipelines, and they face serious energy hole problems caused by multi-hop communication. The Mobile Data Collector (MDC) can alleviate the energy hole problem. Current solutions have two limitations: one is the balance between energy consumption and delay, and the other is the overly ideal network model, e.g., the square region or circular area. This paper focuses on strip-shaped networks and proposes a novel mobile data collection method to find a trade-off between energy preservation and data delivery delay. Firstly, the MDC path is planned by solving the diameter of the tree in the network, resulting in reduced delay. Secondly, the network energy consumption is further reduced by clustering and optimal transmission distance adjustment. Then, a network lifetime balancing mechanism is designed to balance network energy between backbone and branches. Finally, the performance of the algorithm proposed in this paper has been studied in four types of strip-shaped WSNs and compared with four existing MDC methods with evaluation metrics of maximum node energy consumption, network delay and weighted sum of both. The simulation results demonstrate that the proposed algorithm is applicable to different types of strip-shaped WSNs with branches and achieves excellent network performance, which can effectively balance network energy consumption and data collection delay.

Energy efficient routing protocol for enhancing the network lifetime in wireless sensor network

Wireless sensor networks (WSNs) confront significant challenges related to battery capacity, as sensor nodes operate on limited energy resources. To address this issue, low energy adaptive clustering hierarchy (LEACH) protocol is commonly employed for power management in WSNs. LEACH is commonly used for power management. Here, sensing region is divided into clusters and sectors, placing a gateway node at the center to minimize energy consumption during data transmission. It employs one-hop, two-hop, or three-hop pathways based on node proximity to the base station (BS) to optimize energy usage. Network performance is assessed using rounds, throughput, and energy usage. MATLAB simulations compare the proposed approach with dual layer LEACH (DL-LEACH) and LEACH, showing significant improvements in network lifetime. The proposed scheme outperforms LEACH by 515% and 347% for 20% and 50% node depletion, respectively. Compared to DL-LEACH, it extends network lifetime by 27% and 59% under similar scenarios. Sectoring, clustering, and multi-hop communication reduce energy consumption, enhancing network lifetime and addressing WSN challenges effectively.

Efficient multi-agent cooperation: Scalable reinforcement learning with heterogeneous graph networks and limited communication

This paper addresses the challenge of scalable multi-agent reinforcement learning (MARL) under partial observability and communication constraints. An Efficient Multi-Agent Cooperation (EMAC) approach is proposed, which comprises a Heterogeneous Graph Network (HGN) and a Frame-wise Communication Reduction Algorithm (FCRA). HGN can effectively extract semantic information from local observations, while FCRA utilizes historical embeddings to achieve an approximation of multi-hop communication with 1-hop communication. Through comparative experiments, EMAC showed a 30% improvement in efficiency over the best-performing benchmark, scalability to arbitrary numbers of agents, and adaptation to unstructured environments. A t-SNE analysis of agent embeddings further reveals EMAC’s capability to understand and leverage semantic similarities among agents. These results highlight EMAC’s potential as a robust framework for decentralized multi-agent decision-making in complex and dynamic environments.

Enabling resilient UAV swarms through multi-hop wireless communications

In the last decade, the popularity of UAVs, colloquially known as drones, has increased tremendously. Nowadays, drones are used in a wide range of use-cases, such as precision agriculture, surveillance, and photography. Many of these use-cases can be made more efficient if multiple UAVs are used cooperatively (i.e., in a swarm). To achieve this, communication between the UAVs is paramount. To ensure communication, many works rely on the existing infrastructure (e.g., 4G). However, in many rural areas, this infrastructure does not exist. In those cases, an ad hoc (Wi-Fi) network is the most adequate alternative. Yet, due to the limited communication range of Wi-Fi, it is not possible to let UAVs in a swarm to communicate over a long distance. To solve this issue a relay approach is necessary. Despite general solutions to relay messages between (mobile) nodes already exist, many UAV swarms rely on master–slave communication. Thus, a specific solution for this type of communication might be more efficient. Hence, in this work, we propose a strategy to efficiently relay messages for UAV swarms adopting the master–slave communication paradigm. Our approach seeks to introduce a very small message overhead to avoid congestion of the network, and to provide more bandwidth for the actual applications of the UAV swarm. We tested our approach using a realistic UAV simulator called ArduSim. Our results show that our approach is capable of detecting all the nodes in the network within a few seconds. Furthermore, we applied our message relay approach on an existing swarm application (where a swarm of UAVs had to follow a mission), and our results show that, now, the communication range of the UAVs can be much larger, without impacting other aspects of the mission (such as flight time).

“A lightweight authentication scheme for 6LoWPAN-based Internet-of -things”

ABSTRACT IPv6-based IoT networks, i.e. 6LoWPAN network, require authentication for security. The existing authentication schemes on symmetric-key based approaches for 6LoWPAN consume huge computational and processing overhead in the literature. In a large network, pre-deployment of the key leads to huge memory consumption due to storing many keys. Furthermore, in asymmetric key cryptography, operations with the public key are computationally expensive, which is unsuitable for resource-constrained devices. We proposed a lightweight key exchange and authentication scheme for 6LoWPAN to mutually authenticate the sensor nodes, the gateway, and the server with less computational overhead and without compromising any security credentials. The authentication scheme helps to generate a session key without storing any secrets in the 6LoWPAN devices. We utilized a one-way hash function and XOR operation to reduce overhead in multi-hop communication. The AVISPA verification tool and the formal verification show that the proposed method is resistant to well-known attacks. The proposed scheme outperforms relevant methods in computational overhead, communication cost, and computation time.

TCV-D: An Approach for Path Selection in Vehicular Task Offloading

The vehicular task offloading technology enhances the transportation system by offloading tasks of task-requesting vehicles (TRV) to either vehicular fog nodes (VFN) or road-side units (RSU) using single-hop or multi-hop communication. Due to the limited availability of VFN/RSU within a single-hop of TRV, a multi-hop path is employed for offloading tasks from TRV to VFN/RSU. However, the multi-hop path selection approaches have several issues, such as frequent re-connections due to varying vehicle speed, lower successful offloading when task deadline is missed, increased outage time when no VFN/RSU is within communication range of TRV, and inefficient vehicle selection to offload task leading to longer path lifetime. To address these issues, we have proposed a novel approach called Time-Computation-Variance based deadline sensitive path selection (TCV-D), which considers contextual information from k-hop neighbors. The approach offers four offloading modes: Direct mode, RSU mode, VFN mode, and Search mode, depending on the availability of RSUs/VFNs in single and multi-hop scenarios. To ensure tasks are delivered within deadlines, the proposed approach executes tasks by task forwarding vehicle or neighbor of task forwarding vehicle instead of designated VFN/RSU if delivering the task directly to the destination would exceed the deadline constraint. Extensive result analysis demonstrates substantial improvements compared to the existing k-hop-limited offloading time-based path selection (k-hop-limited-OTS) approach, including a 60% reduction in re-connections, a 35% decrease in path life time, a 30% decrease in outage time, and an 84% increase in successful offloading ratio, approximately.

DRAMS: Double-RIS assisted multihop routing scheme for device-to-device communication

Reconfigurable intelligent surfaces (RISs) is a promising solution for enhancing the performance of multihop wireless communication networks. In this paper, we propose a double-RIS assisted multihop routing scheme for a device-to-device (D2D) communication network. Specifically, the scheme is dependent on the already deployed RISs and users in the surroundings. Besides the RISs, the emphasis of this work is to make more use of the existing intermediate users (IUs), which can act as relays. Hence, the density of RIS deployment in the surroundings can be reduced, which leads to the avoidance of resource wastage. However, we cannot solely depend on the IUs because this implies complete dependence on their availability for relaying and as a result, the aspect of reliability in terms of delay-constrained information transfer cannot be guaranteed. Moreover, the IUs are considered capable of energy harvesting and as a result, they do not waste their own energy in the process of volunteering to act as a relay for other users. Numerical results demonstrate the advantage of the proposed scheme over some existing approaches and lastly, useful insights related to the scheme design are also drawn, where we characterize the maximum acceptable delay at each hop under different set-ups.

Novel compressed linear network coding vectors for multihop communication networks

Novel compressed linear network coding vectors for multihop communication networks

FLSTRA: Federated Learning in Stratosphere

We propose a federated learning (FL) in stratosphere (FLSTRA) system,where a high altitude platformstation (HAPS) facilitates a large number of terrestrial clients to collaboratively learn a global model without sharing the training data. FLSTRA overcomes the challenges faced by FL in terrestrial networks, such as slow convergence and high communication delay due to limited client participation and multi-hop communications. HAPS leverages its altitude and size to allow the participation of more clients with line-of-sight (LOS) links and the placement of a powerful server. However, handling many clients at once introduces computing and transmission delays. Thus, we aim to obtain a delay-accuracy trade-off for FLSTRA. Specifically, we first develop a joint client selection and resource allocation algorithm for uplink and downlink to minimize the FL delay subject to the energy and quality-of-service (QoS) constraints. Second, we propose a communication and computation resource-aware (CCRA-FL) algorithmto achieve the target FL accuracy while deriving an upper bound for its convergence rate. The formulated problemis non-convex; thus, we propose an iterative algorithmto solve it. Simulation results demonstrate the effectiveness of the proposed FLSTRA system, compared to terrestrial benchmarks, in terms of FL delay and accuracy.

Alleviating Distortion Accumulation in Multi-Hop Semantic Communication

Alleviating Distortion Accumulation in Multi-Hop Semantic Communication

A Reinforcement Learning-Based Incentive Scheme for Multi-Hop Communications in Vehicular Networks

A Reinforcement Learning-Based Incentive Scheme for Multi-Hop Communications in Vehicular Networks

Trust Aware Nero Fuzzy Based Agglomerative Hierarchical Clustering with Secure Whale Optimization Routing for Enhancing Energy Efficiency in WSN

Wireless sensor networks (WSNs) comprise a network of dispersed, carefully positioned sensor nodes in their deployment environment to monitor and collect data on natural phenomena. These sensor nodes collaborate to transmit data via multi-hop communication, ultimately reaching a central base station for processing. However, WSNs face significant challenges due to the resource-constrained nature of these devices and the harsh, open environments in which they operate. Addressing energy optimization and ensuring secure communication are primary concerns in the successful operation of WSNs. This paper introduces anovelTrust aware Neuro Fuzzy Clustering head selection (TNFCH) and agglomerative hierarchical clustering approach (AHC) with Secure Whale Optimization (SWO) Algorithm Routing to enhance energy-efficient transmission in WSNs. Our proposed protocol (TNFCH-AHWO) efficiently organizes nodes by utilizing neural network and Fuzzy logic then securely transfers the data into the communication network. We employ a Trust calculation algorithm in our system to ensure Trust and data integrity, facilitating efficient lightweight operations such as key generation, encryption, decryption, and verification. This ensures hop-to-hop authentication among the nodes in WSNs. To assess the performance of our proposed protocol, we conducted simulations using the NS3 simulator. The findings of the simulation show that the suggested protocol greatly enhances various performance metrics, including energy consumption analysis, throughput, network delay, network lifetime, and packet delivery ratio when compared to existing protocols.

Joint Optimizing High-Speed Cruise Control and Multi-Hop Communication in Platoons: A Reinforcement Learning Approach

Joint Optimizing High-Speed Cruise Control and Multi-Hop Communication in Platoons: A Reinforcement Learning Approach

A Novel Hybridization of ML Algorithms for Cluster Head Selection in WSN

Generally, Wireless Sensor Networks (WSNs) are infrastructure-less networks with thousands of sensor nodes that sense or monitor the physical and environmental changes and forward the collected data to a central node. Besides, WSN has become the most efficient technology for handling Internet of Things (IoT) devices. Still, challenges such as node failures, high traffic among the nodes, link failures, etc., limit the performance of WSNs. To solve the challenges in WSN, this paper aims to develop a novel non-uniform clustering model, where the Cluster Heads (CHs) are selected based on the candidate CH selection strategy that transfers the data. Moreover, unbalanced energy utilization and data redundancy are eliminated via multi-hop communication. For attaining the non-uniform clustering model, the routing among the data packets is done by the efficiency of the hybridization of the Machin Learning (ML) algorithms viz Genetic Algorithm (GA) and Lion Algorithm (LA) with the consideration of energy, cost, time, network lifetime, and data accuracy. Finally, the performance of the proposed model is verified and validated through a comparative study with the existing models.