Relay Node Placement Research Articles

Optimizing relay node placement and routing in WBANs using free search krill herd and harmony search algorithm

Optimizing relay node placement and routing in WBANs using free search krill herd and harmony search algorithm

Multi-objective Evolutionary Algorithms for Coverage and Connectivity Aware Relay Node Placement in Cluster-Based Wireless Sensor Networks

Multi-objective Evolutionary Algorithms for Coverage and Connectivity Aware Relay Node Placement in Cluster-Based Wireless Sensor Networks

Optimal relay nodes placement with game theory optimization for Wireless Sensor Networks

Wireless Sensor Networks (WSN) play a major role in the wide variety of applications like underground pipeline and leaks monitoring, temperature distribution monitoring in industrial cyber systems, military, forest life monitoring, and environmental and geographical monitoring. Sensors are widely used in these different applications. The number of sensors and the application concerned mainly decides the energy consumption, network lifetime. In this process relay nodes may help the sensors as backbone to connect with sink node or base stations. In this paper, we introduce a new approach for relay node selection in WSN to minimize the energy consumption of the network. It uses channel aware relay selection technique using game theory optimization and act as a virtual backbone in connecting to the base station. However, the relay nodes are varied to check the optimal number of relays required for the small, medium and large number of nodes deployed in the network. Simulations are carried out using Network Simulator NS-2.35 and network is analyzed in wide variety of scenarios. Results show that the proposed relay node selection algorithm reduces energy consumption, improves lifetime, throughput of the network.

Energy-Efficient Relay Node Placement in Wireless Heterogeneous Networks with Capacity Constraints

In wireless heterogeneous networks (HetNets), rational relay node (RN) placement can not only expand network coverage and capacity, but also significantly reduce energy consumption of the network. However, existing RN placement schemes mainly focus on improving network performance, and do not take the network energy consumption and capacity constraints of RNs into consideration, which makes their solutions impractical. Therefore, in this paper, we investigate energy-efficient RN placement (EERPL) problem, and try to reduce energy consumption of a wireless HetNet within the range of RN’s capacity constraints. More specifically, we first present a fine-grained energy consumption model and formulate the EERPL problem, which can be translated to the degree constrained minimum Steiner tree problem. Then, we propose a heuristic RN-placement algorithm HBAPS, which involves two steps, i.e., generating an initial minimum-energy RN placement solution and adjusting the solution to be with in the capacity constraints. Finally, extensive simulations are conducted in both randomly generated scenarios and real trace-based scenarios. Experimental results demonstrate that HBAPS achieves nearly optimal results when the scenario is small, and even in a large scenario, the proposed algorithm still obtains proper solutions within the acceptable time, and outperforms the other two existing heuristic algorithms.

A Voronoi Diagram and Q-Learning based Relay Node Placement Method Subject to Radio Irregularity

Industrial Wireless Sensor Networks (IWSNs) have been widely used in industrial applications that require highly reliable and real-time wireless transmission. A lot of works have been done to optimize the Relay Node Placement (RNP), which determines the underlying topology of IWSNs and hence impacts the network performance. However, existing RNP algorithms use a fixed communication radius to compute the deployment result at once offline, while ignoring that the radio environment may vary drastically across different locations, also known as radio irregularity. To address this limitation, we propose a Voronoi diagram and Q-learning based RNP (VQRNP) method in this article. Instead of using a fixed communication radius, VQRNP employs the Q-learning algorithm to dynamically update the radio environment of measured areas, uses a Voronoi diagram based method to estimate the radio environment of unmeasured areas, and proposes a coverage extension location selection algorithm to place RNs so as to extend the coverage of the deployed network based on the results estimated by Voronoi diagram based Graph Generating (VGG). In this way, the VQRPN method can adapt itself well to the variation of radio environment and largely speed up the deployment process. Extensive simulations verify that VQRNP significantly outperforms existing RNP algorithms in terms of reliability.

A novel relay placement method for smart energy IoT systems in offices with genetic algorithm

<p>Smart energy in large offices and organizations is an important research area of the Internet of Things (IoT). The energy efficiency of buildings is vital for the environment and global sustainability. To achieve satisfactory performance for this goal, WiFi access point (AP) indoor coverage is of high importance. As it costs a lot to add more WiFi to have good coverage in all parts of the office’s buildings, we consider relay node (RN) instead of adding more APs. So in this paper, we propose a novel RN placement in order to improve the indoor coverage of offices considering the signal attenuation using a path-loss model as the main measure for determining positions. The main problem is the placement of these RNs and the required number of them by considering existing APs. At first, we obtain the radio propagation model parameters by considering the data that are collected from the AP. Then based on these parameters, the proposed solution uses a genetic algorithm (GA) for RN placement optimization problem. The experimental results display the effectiveness of the proposed solution for the RN placement problem.</p>

Energy efficiency scheme for relay node placement in heterogeneous networks

Relay node (RN) placement expands the network coverage and capacity and significantly reduces the energy consumption of heterogeneous networks (HetNets). Energy efficiency is the system design parameter in HetNets as it determines network operators' energy consumption and economic value. Relay is one of the energy-saving methods, where it can reduce the transmit power by breaking a long transmission distance into several short transmissions. However, placing an RN without a proper transmission distance may lead to a waste of energy. Thus, investigating an optimum RN placement in HetNets is crucial to ensure energy efficiency and maintain network performance. This paper presents an energy efficiency scheme for the RN based on four commonly used network topologies of indoor HetNets. The minimum energy consumption algorithm is proposed based on a comparison of distance and links of the RN. The results show that the circular network topology is an optimal network model with an efficiency factor increase of 6% that can be used to design the energy efficiency indoor HetNet.

Relay node placement in wireless sensor network for manufacturing industry

Relay nodes are necessary to maintain scalability and increase longevity as the number of manufacturing industrial sensors grows. In a fixed-budget circumstance, however, the cost of purchasing the bare minimum of relay nodes to connect the network may exceed the budget. Although it is hard to establish a network that connects all sensor nodes, in this case, a network with a high level of connection is still desirable. This paper proposes two metrics for determining the connectedness of a disconnected graph of sensor nodes and determining the optimum deployment method for relay nodes in a network with the highest connectedness while staying within a budget restriction. The metrics are the number of connected graph components and the size of the most significant connected graph component. Prim's algorithm and the approximation minimum spanning tree algorithm are applied to construct a disconnected graph and discover the best relay node placement to solve these two criteria. Compared to the other metrics, simulation findings suggest that prioritizing the most significant connected components in the disconnected graph can yield superior outcomes by deploying the fewest number of relay nodes while retaining the connectedness of the graph.

A DL-Enabled Relay Node Placement and Selection Framework in Multicellular Networks

A DL-Enabled Relay Node Placement and Selection Framework in Multicellular Networks

Energy efficiency scheme for relay node placement in heterogeneous networks

Energy efficiency scheme for relay node placement in heterogeneous networks

An improved algorithm for delay constrained relay node deployment in wireless sensor networks

Given that Delay Constrained Relay Node Placement (DCRNP) problem is getting more and more attention in time-critical domains, this paper proposes an Improved Greedy-based Selection Algorithm (IGSA) to tackle such problem under delay and budget constraints. IGSA first employs communication cost matrix and Dijkstra’s algorithm to detect whether the problem is solvable, then selects the amount and locations of deployed relay nodes (RNs) layer by layer by continuously calling the greedy coverage algorithm until all sensors have a feasible path to the sink under a preset delay constraint, and finally removes redundant RNs to reach the approximate optimal solution. Extensive simulations show that our approach can save 15%–30% of deployed RNs than the Two-phase Set-Covering-based Algorithm (TSCA), which is the state-of-the-art algorithm for the DCRNP problem, only with a slight sacrifice of solving time. Meanwhile, Ad Hoc On-Demand Distance Vector (AODV) routing protocol is applied to further examine the performance of the deployment results solved by TSCA and IGSA, respectively. Results show that every RN deployed by IGSA is involved in data forwarding, while IGSA deploys leisure RNs which do not participate in the data forwarding. Besides, compared with TSCA, the network formed by IGSA has lower network delay in the data transmission process, and the energy consumption of the central node and throughput of the two topologies are almost at the same level.

Intelligent Fault-Tolerance Data Routing Scheme for IoT-Enabled WSNs

Wireless sensor networks (WSNs) have become one of the essential components of the Internet of Things (IoT). In any IoT application, different sensor-based devices gather data from physical objects and transmit the sensed information to the base station (BS). The BS analyzes this information depending on the sensor location. A high-performance intelligent WSNs is essential for any IoT-based application. In this article, high-performance intelligent WSNs are referred to as IoT-enabled WSNs. In IoT-enabled WSNs, fault occurrence probability is much more than traditional networks. Faulty nodes and broken links affect the reliability of the IoT-enabled WSNs. Various fault-tolerance algorithms enhance the network’s reliability using multipath transmission, relay node placement, and backup node selection. However, these algorithms suffer from huge data transmission delays, packet overhead, and less detection accuracy. In this work, a multiobjective-deep reinforcement-learning (DRL)-based algorithm is proposed for fault tolerance in IoT-enabled WSNs. The main objective of this work is to detect the faulty nodes with high accuracy and less overhead. Furthermore, this work focuses on reliable data transmission after fault detection. Finally, a mobile sink (MS) is used for energy-efficient data gathering that significantly improves the network lifetime. Extensive simulations and theoretical analysis prove that the proposed algorithm outperformed as compared to the state-of-the-art algorithms in terms of fault detection accuracy (FDA), false alarm rate (FAR), false-positive rate (FPR), network lifetime, and throughput.

Deploying Hierarchical Mesh Networks for Supporting Distributed Computing in Industrial Internet of Things

The centralized computing model in industrial Internet of Things (IIoT) leads to large delay and unbalanced traffic, which strictly restricts the adoption of IIoT in industrial applications demanding high network performance. To cope with the problem, researchers resort to the in-network computation model in which the computation capability is distributed among nodes in IIoT. Existing works on the in-network computation assume that the network connectivity built in advance meets the performance requirement of the in-network computation model. Nevertheless, no node placement methods have been proposed to build network connectivity supporting in-network computation. For this reason, we propose an in-network-computation-oriented node placement (INP) algorithm. The INP algorithm first decomposes the whole problem into several delay constrained relay node placement problems, and then, solves them sequentially. Moreover, we prove that the INP algorithm ensures explicit time complexity and approximation ratio. Finally, we verify the efficiency of this work through extensive simulations and preliminary experiments.

CGARP: Chaos genetic algorithm-based relay node placement for multifaceted heterogeneous wireless sensor networks

CGARP: Chaos genetic algorithm-based relay node placement for multifaceted heterogeneous wireless sensor networks

An Efficent Coverage and Maximization of Network Lifetime in WSN Through Metaheuristics

<span>In wireless sensor networks (WSNs), energy, connectivity, and coverage are the three most important constraints for guaranteed data forwarding from every sensor node to the base station. Due to continuous sensing and transmission tasks, the sensor nodes deplete more quickly and hence they seek the help of data forwarding nodes, called relay nodes. However, for a given set of sensor nodes, finding optimal locations to place relay nodes is a very challenging problem. Moreover, from the earlier studies, the relay node placement is defined as a non-deterministic polynomial tree hard (NP-Hard) problem. To solve this problem, we propose a multi-objective firefly algorithm-based relay node placement (MOFF-RNP) to deploy an optimal number of relay nodes while considering connectivity, coverage, and energy constraints. To achieve network lifetime, this work adopted energy harvesting capabilities to the sensor nodes and backup relay strategy such that every sensor node is always connected to at least one relay to forward the data. The optimal relay placement is formulated as an objective function and MOFF is applied to achieve a better solution. Extensive Simulations are carried out over the proposed model to validate the performance and the obtained results are compared with state-of-art methods)</span>

A new algorithm inspired by Impala Mexican Wave with variable stride for relay node placement as a nested reverse p-median problem in disjoint wireless sensor networks

In the wireless sensor network (WSN) environment, large-scale failure due to the explosion, energy depletion, harsh or hostile environments, can cause disjoint partitions and disconnect the network. In this case, relay nodes (RNs) can deploy to restore connectivity. This paper has proposed a new algorithm inspired by Impala Mexican Wave with variable stride (IMWVS) for relay placement. This method solves the nested reverse p-median problem. The purpose of this method is to reduce the number of deployed RNs and increase the robustness of the network. Also, the coverage and reliability of the method can be satisfied. In terms of the number of deployed RNs, IMWVS has 50.49 and 68.18% improvement averagely in comparison with CRP and RRLC-GBP methods for large-scale multiple partitions resulted from multiple failures, respectively.

Optimum Placement of Relay Nodes in WBANs for Improving the QoS of Indoor RPM System

Pandemic situation such as COVID-19 boosted the demand for remote patient monitoring (RPM) system. The medical sensors attached to the human body in RPM system experience varying channel quality due to body movements. This paper analyzes the signal received from RPM sensors when a patient rotates by different angles while sitting on a chair as well as heed the use of a relay node placed on his/her body. Literature suggests many relay-based communication protocols to deliver physio-signals efficiently in an RPM application. However, limited studies have focused on the position of a relay node on the human body. In this paper, we empirically analyze the off-body communication path of sensor nodes by collecting data from different body orientations in a residential room. We estimate the path loss parameters for underweight, normal and overweight body mass index (BMI) categories. The estimated parameters are then used to simulate the physical layer of a home-based indoor RPM application. We inspect different relay node positions on the human body and allude an optimal position of the relay node that cover the transmission range of all sensors and provides an improved channel quality. We improve the Quality of Service (QoS) during non-line-of-sight (NLOS) situation and design an adaptive cross-layer communication protocol for WBANs.

Enhanced Particle Swarm Optimization for Effective Relay Nodes Deployment in Wireless Sensor Networks

One of the critical design problems in Wireless Sensor Networks (WSNs) is the Relay Node Placement (RNP) problem. Inefficient deployment of RNs would have adverse effects on the overall performance and energy efficiency of WSNs. The RNP problem is a typical example of an NP-hard optimization problem which can be addressed using metaheuristics with multi-objective formulation. In this paper, we aimed to provide an efficient optimization approach considering the unconstrained deployment of energy-harvesting RNs into a pre-established stationary WSN. The optimization was carried out for three different objectives: energy consumption, network coverage, and deployment cost. This was approached using a novel optimization approach based on the integration of the Particle Swarm Optimization (PSO) algorithm and a greedy technique. In the optimization process, the greedy algorithm is an essential component to provide effective guidance during PSO convergence. It supports the PSO algorithm with the required information to efficiently alleviate the complexity of the PSO search space and locate RNs in the spots of critical significance. The evaluation of the proposed greedy-based PSO algorithm was carried out with different WSN scenarios of varying complexity levels. A comparison was established with two PSO variants: the classical PSO and a PSO hybridized with the pattern search optimizer. The experimental results demonstrated the significance of the greedy algorithm in enhancing the optimization process for all the considered PSO variants. The results also showed how the solution quality and time efficiency were considerably improved by the proposed optimization approach. Such improvements were achieved using a simple integration technique without adding to the complexity of the system and introducing additional optimization stages. This was more evident in the RNP scenarios of considerably large search spaces, even with highly complex and challenging setups.

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

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

Efficient Placement of an Aerial Relay Drone for Throughput Maximization

Unmanned aerial vehicle (UAV) communication can be used in overcrowded areas and either during or postdisaster situations as an evolving technology to provide ubiquitous connections for wireless devices due to its flexibility, mobility, and good condition of the line of sight channels. In this paper, a single UAV is used as an aerial relay node to provide connectivity to wireless devices because of the considerable distance between wireless devices and the ground base station. Specifically, two path loss models have been utilized; a cellular‐to‐UAV path loss for a backhaul connection and an air‐to‐ground path loss model for a downlink connection scenario. Then, the tradeoff introduced by these models is discussed. The problem of efficient placement of an aerial relay node is formulated as an optimization problem, where the objective is to maximize the total throughput of wireless devices. To find an appropriate location for a relay aerial node that maximizes the overall throughput, we first use the particle swarm optimization algorithm to find the drone location; then, we use three different approaches, namely, (1) the equal power allocation approach, (2) water filling approach, and (3) modified water filling approach to maximize the total users’ throughput. The results show that the modified water filling outperforms the other two approaches in terms of the average sum rate of all users and the total number of served users. More specifically, in the best‐case scenario, it was observed that the average sum rate of the modified water filling is better than the equal power allocation and ensuring 100% coverage. In contrast, the water filling provides a very close average sum rate to the modified water filling, but it only provides a 28% user coverage.