A Node Placement Algorithm Utilizing Mobile Nodes in WSN and IoT Networks

The operation of the Internet of Things (IoT) networks and Wireless Sensor Networks (WSN) is often disrupted by a number of problems, such as path disconnections, network segmentation, node faults, and security attacks. A method that gains momentum in resolving some of those issues is the use of mobile nodes or nodes deployed by mobile robots. The use of mobile elements essentially increases the resources and the capacity of the network. In this work, we present a Node Placement Algorithm with two variations, which utilizes mobile nodes for the creation of alternative paths from source to sink. The first variation employs mobile nodes that create locally-significant alternative paths leading to the sink. The second variation employs mobile nodes that create completely individual (disjoint) paths to the sink. We then extend the local variation of the algorithm by also accounting for the energy levels of the nodes as a contributing factor regarding the creation of alternative paths. We offer both a high-level description of the concept and also detailed algorithmic solutions. The evaluation of the solutions was performed in a case study of resolving congestion in the network. Results have shown that the proposed algorithms can significantly contribute to the alleviation of the problem of congestion in IoT and WSNs and can easily be used for other types of network problems.

A web interface for WMN-PSODGA hybrid intelligent simulation system: performance evaluation for different mesh client distributions

Purpose The purpose of this paper is to implement a web interface for a hybrid intelligent system. By using the implemented web interface, one can find optimal assignments of mesh routers in wireless mesh networks (WMNs). This study evaluates the implemented system considering three distributions of mesh clients to solve the node placement problem in WMNs. Design/methodology/approach The node placement problem in WMNs is well known to be a computationally hard problem. Therefore, intelligent algorithms are used for solving this problem. The implemented system is a hybrid intelligent system based on meta-heuristics algorithms: particle swarm optimization (PSO) and distributed genetic algorithm (DGA). The proposed system is called WMN-PSODGA. Findings This study carried out simulations using the implemented simulation system. From the simulations results, it was found that the WMN-PSODGA system performs better for chi-square distribution of mesh clients compared with Weibull and exponential distributions. Research limitations/implications For simulations, three different distributions of mesh clients were considered. In the future, other mesh client distributions, number of mesh nodes and communication distance need to be considered. Originality/value This research work, different from other research works, implemented a hybrid intelligent simulation system for WMNs. This study also implemented a web interface for the proposed system, which make the simulation system user-friendly.

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>

Router and gateway node placement in wireless mesh networks for emergency rescue scenarios

The focus of this paper is on base functionalities required for UAV-based rapid deployment of an ad hoc communication infrastructure in the initial phases of rescue operations. The main idea is to use heterogeneous teams of UAVs to deploy communication kits that include routers, and are used in the generation of ad hoc Wireless Mesh Networks (WMN). Several fundamental problems are considered and algorithms are proposed to solve these problems. The Router Node Placement problem (RNP) and a generalization of it that takes into account additional constraints arising in actual field usage is considered first. The RNP problem tries to determine how to optimally place routers in a WMN. A new algorithm, the RRT-WMN algorithm, is proposed to solve this problem. It is based in part on a novel use of the Rapidly Exploring Random Trees (RRT) algorithm used in motion planning. A comparative empirical evaluation between the RRT-WMN algorithm and existing techniques such as the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) and Particle Swarm Optimization (PSO), shows that the RRT-WMN algorithm has far better performance both in amount of time taken and regional coverage as the generalized RNP problem scales to realistic scenarios. The Gateway Node Placement Problem (GNP) tries to determine how to locate a minimal number of gateway nodes in a WMN backbone network while satisfying a number of Quality of Service (QoS) constraints.Two alternatives are proposed for solving the combined RNP-GNP problem. The first approach combines the RRT-WMN algorithm with a preexisting graph clustering algorithm. The second approach, WMNbyAreaDecomposition, proposes a novel divide-and-conquer algorithm that recursively partitions a target deployment area into a set of disjoint regions, thus creating a number of simpler RNP problems that are then solved concurrently. Both algorithms are evaluated on real-world GIS models of different size and complexity. WMNbyAreaDecomposition is shown to outperform existing algorithms using 73% to 92% fewer router nodes while at the same time satisfying all QoS requirements.

A comparison study of chi-square and uniform distributions of mesh clients for different router replacement methods using WMN-PSODGA hybrid intelligent simulation system

Wireless Mesh Networks (WMNs) are gaining a lot of attention from researchers due to their advantages such as easy maintenance, low upfront cost and high robustness. Connectivity and stability directly affect the performance of WMNs. However, WMNs have some problems such as node placement problem, hidden terminal problem and so on. In our previous work, we implemented a simulation system to solve the node placement problem in WMNs considering Particle Swarm Optimization (PSO) and Distributed Genetic Algorithm (DGA), called WMN-PSODGA. In this paper, we compare chi-square and uniform distributions of mesh clients for different router replacement methods. The router replacement methods considered are Constriction Method (CM), Random Inertia Weight Method (RIWM), Linearly Decreasing Inertia Weight Method (LDIWM), Linearly Decreasing Vmax Method (LDVM) and Rational Decrement of Vmax Method (RDVM). The simulation results show that for chi-square distribution the mesh routers cover all mesh clients for all router replacement methods. In terms of load balancing, the method that achieves the best performance is RDVM. When using the uniform distribution, the mesh routers do not cover all mesh clients, but this distribution shows good load balancing for four router replacement methods, with RIWM showing the best performance. The only method that shows poor performance for this distribution is LDIWM. However, since not all mesh clients are covered when using uniform distribution, the best scenario is chi-square distribution of mesh clients with RDVM as a router replacement method.

Low Latency Aware Fog Nodes Placement in Internet of Things Service Infrastructure

Fog Computing extends storage and computation resources closer to end-devices. In several cases, the Internet of Things (IoT) applications that are time-sensitive require low response time. Thus, reducing the latency in IoT networks is one of the essential tasks. To this end, fog computing is developed with a motive for the data production and consumption to always be within proximity; therefore, the fog nodes must be placed at the edge of the network, which is near the end devices, such that the latency is minimized. The optimal location selection for fog node placement within a network out of a very large number of possibilities, such as minimize latency, is a challenging problem. So, it is a combinatorial optimization problem. Hard combinatorial optimization problems (NP-hard) involve huge discrete search spaces. The fog node placement problem is an NP-hard problem. NP-hard problems are often addressed by using heuristic methods and approximation algorithms. Combinatorial optimization problems can be viewed as searching for the best element of some set of discrete items; therefore in principle, any metaheuristic can be used to solve them. To resolve this, meta-heuristic-based methods is proposed. We apply the Simulated Annealing (SA), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) technique to design fog node placement algorithms. Genetic Algorithm is observed to give better solutions. Since Genetic Algorithm may get stuck in local optima, Hybrid Genetic Algorithm, and Simulated Annealing (GA-SA), Hybrid Genetic Algorithm and Particle Swarm Optimization (GA-PSO) were compared with GA. By extensive simulations, it is observed that hybrid GA-SA-based for node placement algorithm outperforms other baseline algorithms in terms of response time for the IoT applications.