Gateway Selection Research Articles

Multi-Objective Optimization of Gateway Location Selection in Long-Range Wide Area Networks: A Tradeoff Analysis between System Costs and Bitrate Maximization

LoRaWANs play a critical role in various applications such as smart farming, industrial IoT, and smart cities. The strategic placement of gateways significantly influences network performance optimization. This study presents a comprehensive analysis of the tradeoffs between system costs and bitrate maximization for selecting optimal gateway locations in LoRaWANs. To address this challenge, a rigorous mathematical model is formulated to incorporate essential factors and constraints related to gateway selection. Furthermore, we propose an innovative metaheuristic algorithm known as the M-VaNSAS algorithm, which effectively explores the solution space and identifies favorable gateway locations. The Pareto front and TOPSIS methods are employed to evaluate and rank the generated solutions, providing a robust assessment framework. Our research findings highlight the suitability of a network model comprising 144 gateways tailored for the Ubon Ratchathani province. Among the evaluated algorithms, the M-VaNSAS method demonstrates exceptional efficiency in gateway location selection, outperforming the PSO, DE, and GA methods.

Online Learning Based Joint Gateway Selection and User Scheduling in Non-Stationary Air-Ground Networks

Online Learning Based Joint Gateway Selection and User Scheduling in Non-Stationary Air-Ground Networks

Frozen Base Gateway Discovery Algorithm for Mobile Ad-Hock Network with Internet Connectivity Using AODV + Routing Algorithm

A mobile ad-hoc network (MANET) is a self-organized, auto-configured network and the mobile nodes have been free to move. To enable the MANET to connect to the internet it uses an intermediate bridge called a gateway. The MANET and the internet gateway is a heterogeneous network, which needs a gateway discovery. There are three types of gateway discovery mechanisms: proactive, reactive, and hybrid gateway discovery mechanism. The main challenge in the integration of the MANET and the Internet is, that there are high link disconnection results from node mobility and limited energy. In addition link disconnection, packet drop, and end-to-end delay. This study examines the impact of node mobility and energy in the frozen path from the source node to the destination node of MANET In this study, the Ad-hoc on-demand distance vector routing (AODV) routing protocol is used and modified to examine the gateway selection for MANET and the Internet. Moreover, the AODV+ control message RREQ and RREP and the routing table are improved to store the energy factor, path queue length, and frozen factor of the path. The proposed gateway discovery algorithm is known as frozen-based gateway AODV+ (F_AODV+), The stability factors of the path from the source node to the gateway, are calculated using a simple additive weighting method of the energy factor, average path queue node, and the path with the Maximum energy factor and minimum queue length is the more Frozen to select Frozen gateway from the source node to distension node.A mobile ad-hoc network (MANET) is a self-organized, auto-configured network and the mobile nodes have been free to move. To enable the MANET to connect to the internet it uses an intermediate bridge called a gateway. The MANET and the internet gateway is a heterogeneous network, which needs a gateway discovery. There are three types of gateway discovery mechanisms: proactive, reactive, and hybrid gateway discovery mechanism. The main challenge in the integration of the MANET and the Internet is, that there are high link disconnection results from node mobility and limited energy. In addition link disconnection, packet drop, and end-to-end delay. This study examines the impact of node mobility and energy in the frozen path from the source node to the destination node of MANET In this study, the Ad-hoc on-demand distance vector routing (AODV) routing protocol is used and modified to examine the gateway selection for MANET and the Internet. Moreover, the AODV+ control message RREQ and RREP and the routing table are improved to store the energy factor, path queue length, and frozen factor of the path. The proposed gateway discovery algorithm is known as frozen-based gateway AODV+ (F_AODV+), The stability factors of the path from the source node to the gateway, are calculated using a simple additive weighting method of the energy factor, average path queue node, and the path with the Maximum energy factor and minimum queue length is the more Frozen to select Frozen gateway from the source node to distension node.

Information aggregation and data compression based on PLC for distributed photovoltaic integration

The massive integration of distributed photovoltaics (PVs) into power grid causes rapid growth of data collection, which has put significant pressure on power line communication (PLC). Edge computing enables efficient data compression on edge-side gateway to reduce data size. However, it still faces challenges such as the coupling between transmission delay and compression delay, as well as the coupling between data compression ratio selection and gateway selection. In this paper, we formulate the information aggregation and data compression problem to minimize the total delay, and develop a delay-aware upper confidence bound (UCB) algorithm. It sets the negative value of delay as the learning reward to achieve delay-aware learning, and jointly optimizes gateway and data compression ratio selection based on upper confidence bound ranking and Karush-Kuhn-Tucker (KKT) conditions. Simulation results demonstrate the superior performance of the proposed algorithm.

An Adaptive Gateway Selection Mechanism for MANET-IoT Applications in 5G Networks

An Adaptive Gateway Selection Mechanism for MANET-IoT Applications in 5G Networks

An integrated Fuzzy-BWM, Fuzzy-LBWA and V-Fuzzy-CoCoSo-LD model for gateway selection in fog-bolstered Internet of Things

This study proposes a fog gateway selection strategy based on a hybrid Multi-criteria Decision-Making (MCDM) optimization model to avoid overwhelming the fog–cloud network in fog computing architecture. The study suggests that only authorized fog gateways should communicate with the cloud to prevent prolonged response times and significant data loss. To prioritize the selection criteria, the proposed model integrates subjective weights obtained from Fuzzy Best–Worst Method (Fuzzy-BWM) and Fuzzy Level Based Weight Assessment (Fuzzy-LBWA) using an extended nonlinear weighted synthesis approach. A Vector-normalized Fuzzy Combined Compromise Solution with Later Defuzzification (V-Fuzzy-CoCoSo-LD) model is used to rank the alternative gateways. The study constructs a taxonomy of selection criteria and finds that Quality-of-service and Level-of-security are the most important criteria with weights of 0.3731 and 0.3452, respectively. The hybridized weighting method addresses the weaknesses of individual methods. The study also conducts sensitivity analysis of the impact of fuzzy weighting parameters α̃ and β̃ on the decision-making process, and the results show that the most viable alternative remains the same regardless of the changes. The sensitivity analysis of the most sensitive criterion as well as comparative and correlation analyses also validate the effectiveness and robustness of the proposed model. The study concludes that the vector normalization method is the best for Fuzzy-CoCoSo MCDM model, with Spearman’s and Pearson’s correlation coefficient mean values of 0.9404 and 0.9868, respectively, justifying the choice in this study. Finally, the study provides design implications for the practical application of the proposed model in fog computing services. The proposed model can assist fog service providers in making informed decisions, and this study presents a case study to demonstrate the effectiveness of the model.

An Efficient Mobile Gateway Selection and Discovery Based-Routing Protocol in Heterogeneous LTE-VANET Networks

Coupling cellular communication networks with vehicular ad hoc networks (VANET) can be a very interesting way out for providing Internet access to vehicles in the road. However, due to the several specific characteristics of VANETs, making an efficient multi-hop routing from vehicular sources to the Internet gateways through Long Term Evolution (LTE) technology is still challenging. In this paper, an Internet mobile gateway selection scheme is proposed to elect more potential vehicles to behave as gateways to Internet in VANETs. Therefore, the discovery and the selection of route to those mobiles gateways is carried out via an efficient multiple metrics-based relay selection mechanism. The objective is to select the more reliable route to the mobile gateways, by reducing the communication overhead and performing seamless handover. The proposed protocol is compared with one recent protocol based on packet delivery ratio, average end-to-end delay and overhead. The results show that the proposed protocol ameliorates significantly the network performance in the contrast of the other protocol.

On Softwarization of Intelligence in 6G Networks for Ultra-Fast Optimal Policy Selection: Challenges and Opportunities

The emerging Sixth Generation (6G) communication networks promising 100 to 1000 Gbps rates and ultra-low latency (1 millisecond) are anticipated to have native, embedded Artificial Intelligence (AI) capability to support a myriad of services, such as Holographic Type Communications (HTC), tactile Internet, remote surgery, etc. However, these services require ultra-reliability, which is highly impacted by the dynamically changing environment of 6G heterogeneous tiny cells, whereby static AI solutions fitting all scenarios and devices are impractical. Hence, this article introduces a novel concept called the softwarization of intelligence in 6G networks to select the most ideal, ultra-fast optimal policy based on the highly varying channel conditions, traffic demand, user mobility, and so forth. Our envisioned concept is exemplified in a Multi- Armed Bandit (MAB) framework and evaluated within a use case of two simultaneous scenarios (i.e., Neighbor Discovery and Selection (NDS) in a Device-to-Device (D2D) network and aerial gateway selection in an Unmanned Aerial Vehicle (UAV)- based under-served area network). Furthermore, our concept is evaluated through extensive computer-based simulations that indicate encouraging performance. Finally, related challenges and future directions are highlighted.

A novel traffic aware reliable gateway selection in wireless mesh network

A novel traffic aware reliable gateway selection in wireless mesh network

Optimal Association Strategy of Multi-Gateway Wireless Sensor Networks Against Smart Jammers

The Internet of Things (IoT) immensely benefits from wireless sensor networks (WSNs). Unfortunately, WSNs, by virtue of being low-power, are vulnerable to physical jamming attacks. This letter focuses on WSNs with multiple gateways where potentially many sensors are under the attack of a smart jammer. The jammer can observe the communication links and selectively jam the transmission of some sensors such that the throughput of the WSN is minimized. The proposed defense method is to develop a gateway selection and sensors’ associations policy that is aware of the jammer’s existence. First, the conflict interaction between the WSN and the jammer is modeled as a Stackelberg game. Then the WSN’s strategy, leading to a Nash equilibrium, is derived which is both stable and optimal. The numerical results show that compared to existing alternative approaches, the proposed method, with only four gateways, can increase the throughput by more than 200% and saves around 30 dB in sensors’ power consumption.

An Optimal Cluster Head and Gateway Node Selection with Fault Tolerance

In Mobile Ad Hoc Networks (MANET), Quality of Service (QoS) is an important factor that must be analysed for the showing the better performance. The Node Quality-based Clustering Algorithm using Fuzzy-Fruit Fly Optimization for Cluster Head and Gateway Selection (NQCAFFFOCHGS) has the best network performance because it uses the Improved Weighted Clustering Algorithm (IWCA) to cluster the network and the FFO algorithm, which uses fuzzy-based network metrics to select the best CH and entryway. However, the major drawback of the fuzzy system was to appropriately select the membership functions. Also, the network metrics related to the path or link connectivity were not considered to effectively choose the CH and gateway. When learning fuzzy sets, this algorithm employs a new Continuous Action-set Learning Automata (CALA) approach that correctly modifies and chooses the fuzzy membership functions. Despite the fact that it extends the network’s lifespan, it does not assist in the detection of defective nodes in the routing route. Because of this, a new Fault Tolerance (NQCAEFFFOCHGS-FT) mechanism based on the Distributed Connectivity Restoration (DCR) mechanism is proposed, which allows the network to self-heal as a consequence of the algorithm’s self-healing capacity. Because of the way this method is designed, node failures may be utilised to rebuild the network topology via the use of cascaded node moves. Founded on the fractional network information and topologic overhead related with each node, the DCR is suggested as an alternative to the DCR. When compared to the NQCAFFFOCHGS algorithm, the recreation results display that the proposed NQCAEFFFOCHGS-FT algorithm improves network performance in terms of end-to-end delay, energy consumption, Packet Loss Ratio (PLR), Normalized Routing Overhead (NRO), and Balanced Load Index (BLI).

A Duty Cycle-Based Gateway Selection Algorithm for LoRaWAN Downlink Communication

Long Range Wide Area Network (LoRaWAN) has been developed to meet the requirements for the enormous device-to-device communication of Internet of Things (IoT) networks, which consist of a large number of participating devices spread over large coverage areas with low data rates and low power consumption. It supports communications in both directions, uplink, and downlink directions. However, the downlink communication in the current LoRaWAN raises the bottleneck issue at gateways due to the used gateway selection algorithm. This paper proposes a novel gateway selection algorithm based on the duty cycle time-off values for the existing gateways, Duty Cycle Gateway Selection (DCGS), to direct acknowledgment packets as downlink traffic towards the most suitable gateway. Thus, the proposed system avoids subsequent retransmission of previously sent traffic that leads to excessive traffic overloading the network. The proposed system avoids exhausting a gateway duty cycle with downlink traffic by distributing the downlink traffic among available gateways based on the duty cycle time off. DCGS is evaluated using FloRa and INET frameworks in the well-known network simulator OMNeT++. The result shows the superior performance of the proposed approach over the existing Signal-to-Noise ratio (SNR) based selection mechanism. It clearly indicates that the DCGS maintains a better confirmed packet delivery rate while reducing number of retransmissions, collisions, and power consumption.

Fuzzy Fruit Fly Optimized Node Quality-Based Clustering Algorithm for Network Load Balancing

Recently, the fundamental problem with Hybrid Mobile Ad-hoc Networks (H-MANETs) is to find a suitable and secure way of balancing the load through Internet gateways. Moreover, the selection of the gateway and overload of the network results in packet loss and Delay (DL). For optimal performance, it is important to load balance between different gateways. As a result, a stable load balancing procedure is implemented, which selects gateways based on Fuzzy Logic (FL) and increases the efficiency of the network. In this case, since gateways are selected based on the number of nodes, the Energy Consumption (EC) was high. This paper presents a novel Node Quality-based Clustering Algorithm (NQCA) based on Fuzzy-Genetic for Cluster Head and Gateway Selection (FGCHGS). This algorithm combines NQCA with the Improved Weighted Clustering Algorithm (IWCA). The NQCA algorithm divides the network into clusters based upon node priority, transmission range, and neighbour fidelity. In addition, the simulation results tend to evaluate the performance effectiveness of the FFFCHGS algorithm in terms of EC, packet loss rate (PLR), etc.

A Study on Development of Matrix Model of WMN for Mesh Clustering and its Validation

Tracking and tracing network nodes and monitoring network topology pose a huge challenge in Wireless Mesh Networks (WMNs). This becomes even more challenging in the scenario of multiple clusters, which keep performing hand-off handovers among each other for load management or link management purposes. In the dynamic and fragile WMN scenario, loss of connectivity is very common. In the case of multi-hop multi-gateway WMNs, such a scenario is very common. The architecture of the IEEE 802.11s WMN proposes a network wherein each node selects a gateway through which it routes its packets. A node selects the gateway based on its proximity in terms of the number of hop-distance and the Air Time Link Metric. Since the selection of gateway is done by a node on basis of its local information, more than often the scheduling of gateway becomes unfair. This leads to gateway congestion and a drop in the throughput of the network. In this paper, a model of a centralized gateway scheduling scheme is proposed. This paper proposes a matrix model of a clustered WMN. A well-defined gateway service set (cluster) is formed and then this is mapped onto the matrix model of the clustered WMN. In such a centralized scheme, an additional responsibility of tracking the nodes for connectivity is also addressed through this matrix model. This ensures no lost nodes or repeated nodes while the formation of clusters. Thereafter, the paper presents the process for validation of the integrity of WMN and its clusters through a mathematical model of WMN.

Adaptive QoS-Aware Multi-Metrics Gateway Selection Scheme for Heterogenous Vehicular Network

A heterogeneous vehicular network (HetVNET) is a promising network architecture that combines multiple network technologies such as IEEE 802.11p, dedicated short-range communication (DSRC), and third/fourth generation cellular networks (3G/4G). In this network area, vehicle users can use wireless fidelity access points (Wi-Fi APs) to offload 4G long-term evolution (4G-LTE) networks. However, when using Wi-Fi APs, the vehicles must organize themselves and select an appropriate mobile gateway (MGW) to communicate to the cellular infrastructure. Researchers are facing the problem of selecting the best MGW vehicle to aggregate vehicle traffic and reduce LTE load in HetVNETs when the Wi-Fi APs are unavailable for offloading. The selection process utilizes extra network overhead and complexity due to the frequent formation of clusters in this highly dynamic environment. In this study, we proposed a non-cluster adaptive QoS-aware gateway selection (AQAGS) scheme that autonomously picks a limited number of vehicles to act as LTE gateways based on the LTE network’s load status and vehicular ad hoc network (VANET) application’s QoS requirements. The present AQAGS scheme focuses on highway scenarios. The proposed scheme was evaluated using simulation of Urban mobility (SUMO) and network simulator version 2 (NS2) simulators and benchmarked with the clustered and non-clustered schemes. A comparison was made based on the end-to-end delay, throughput, control packet overhead (CPO), and packet delivery ratio (PDR) performance metrics over Voice over Internet Protocol (VoIP) and File Transfer Protocol (FTP) applications. Using VoIP, the AQAGS scheme achieved a 26.7% higher PDR compared with the other schemes.

Three‐dimensional quota matching‐based latency‐sensitive task offloading for multi‐mode green IoT in smart buildings

The green internet of things with heterogeneous communication technologies can provide data transmission and computing services for low-carbon operation of smart buildings. However, latency-sensitive task offloading in smart buildings for multi-mode green internet of things still faces several challenges such as coupling between multi-mode channel and multiple gateway selection, diversified quality of service requirement guarantee, and contradiction of long-term performance guarantee and short-term optimisation objectives. To address these challenges, a three-dimensional quota matching-based latency-sensitive task offloading algorithm is proposed to minimise the weighted difference between energy consumption and throughput under the long-term queuing delay constraints. Specifically, the minimisation problem is decoupled by Lyapunov optimisation. The three-dimensional quota matching among devices, gateways, and channels is employed to solve the conflicts between gateway selection and channel selection. Finally, the three-dimensional quota matching is converted to a two-side quota matching to further reduce complexity and solved iteratively. Numerical results demonstrate that compared with H3CG and MMCS, the proposed algorithm improves the weighted difference between energy consumption and throughput by 21.85% and 27.91%, respectively, and reduces the sensor-side average queuing delay by 30.82% and 16.83%, and gateway-side average queuing delay by 16.57% and 26.71%, respectively.

Adversarial learning‐based multi‐timescale network resource management in multi‐mode green IoT network for smart building

Multi-mode green internet of things (IoT) network that integrates multiple communication media can well meet data transmission and processing demands of low-carbon smart building. However, network resource management optimisation including joint optimisation of gateway and channel selection still faces technical challenges such as differentiated quality of service (QoS) demand guarantee, coupling between optimisation problems with different timescales, and adversary caused by multi-device competition. To address these challenges, an adversarial learning-based multi-timescale network resource management algorithm for multi-mode green IoT is proposed. Specifically, the minimisation problem of weighted difference between energy consumption and throughput under the long-term queuing delay constraints is formulated to achieve differentiated QoS guarantee. Large-timescale gateway selection is decoupled from small-timescale channel selection by establishing matching preferences based on empirical performance, and optimised by using bilateral matching with quota. Finally, an exponential-weight algorithm for exploration and exploitation (EXP3)-based small-timescale channel selection algorithm is proposed to achieve adversary awareness. Simulation results demonstrate that compared with asynchronous greedy matching algorithm and auction-based many-to-many matching algorithm, the proposed algorithm performs superior in terms of energy consumption and throughput.

A Novel Technique for Gateway Selection in Hybrid MANET Using Genetic Algorithm

A Novel Technique for Gateway Selection in Hybrid MANET Using Genetic Algorithm

Reinforcement Learning based Gateway Selection in VANETs

In vehicular ad hoc networks (VANETs), providing the Internet has become an urgent necessity, where mobile gateways are used to ensure network connection to all customer vehicles in the network. However, the highly dynamic topology and bandwidth limitations of the network represent a significant issue in the gateway selection process. Two objectives are defined to overcome these challenges. The first objective aims to maximize the number of vehicles connected to the Internet by finding a suitable gateway for them depending on the connection lifetime. The second objective seeks to minimize the number of connected vehicles to the same gateway to overcome the limitation of gateways' bandwidth and distribute the load in the network. For this purpose, A gateway discovery system assisted by the vehicular cloud is implemented to find a fair trade-off between the two conflicting objectives. Proximal Policy Optimization, a well-known reinforcement learning strategy, is used to define and train the agent. The trained agent was evaluated and compared with other multi-objective optimization methods under different conditions. The obtained results show that the proposed algorithm has better performance in terms of the number of connected vehicles, load distribution over the mobile gateways, link connectivity duration, and execution time.

Gateway selection algorithm for radio and optical hybrid satellites considering weather conditions

Gateway selection algorithm for radio and optical hybrid satellites considering weather conditions