Implementation and evaluation of a simulation system based on particle swarm optimisation for node placement problem in wireless mesh networks

With the fast development of wireless technologies, wireless mesh networks (WMNs) are becoming an important networking infrastructure due to their low cost and increased high speed wireless internet connectivity. This paper implements a simulation system based on particle swarm optimisation (PSO) in order to solve the problem of mesh router placement in WMNs. Four replacement methods of mesh routers are considered: constriction method (CM), random inertia weight method (RIWM), linearly decreasing Vmax method (LDVM) and linearly decreasing inertia weight method (LDIWM). Simulation results are provided, showing that the CM converges very fast, but has the worst performance among the methods. The considered performance metrics are the size of giant component (SGC) and the number of covered mesh clients (NCMC). The RIWM converges fast and the performance is good. The LDIWM is a combination of RIWM and LDVM. The LDVM converges after 170 number of phases but has a good performance.Peer ReviewedPostprint (author's final draft

Node placement in Wireless Mesh Networks: a comparison study of WMN-SA and WMN-PSO simulation systems

(c) 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.With the fast development of wireless technologies, Wireless Mesh Networks (WMNs) are becoming an important networking infrastructure due to their low cost and increased high speed wireless Internet connectivity. In our previous work, we implemented a simulation system based on Simulated Annealing (SA) for solving node placement problem in wireless mesh networks, called WMN-SA. Also, we implemented a Particle Swarm Optimization (PSO) based simulation system, called WMN-PSO. In this paper, we compare two systems considering calculation time. From the simulation results, when the area size is 32 × 32 and 64 × 64, WMN-SA is better than WMN-PSO. When the area size is 128 × 128, WMN-SA performs better than WMN-PSO. However, WMN-SA needs more calculation time than WMN-PSO.Peer ReviewedPostprint (author's final draft

Implementation of a new replacement method in WMN-PSO simulation system and its performance evaluation

(c) 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.With the fast development of wireless technologies, Wireless Mesh Networks (WMNs) are becoming an important networking infrastructure due to their low cost and increased high speed wireless Internet connectivity. In our previous work, we implemented the Linearly Decreasing Vmax Method (LDVM) for our WMN-PSO simulation system. In this paper, we implement a new replacement method for mesh routers called Rational Decrement of Vmax Method (RDVM). We use Size of Giant Component (SGC) and Number of Covered Mesh Clients (NCMC) as metrics for optimization. From the simulation results, we found that RDVM converges faster to best solution than LDVM.Peer ReviewedPostprint (author's final draft

An Optimal Routing Protocol Using a Multiverse Optimizer Algorithm for Wireless Mesh Network

Wireless networks, particularly Wireless Mesh Networks (WMNs), are undergoing a significant change as a result of wireless technology advancements and the Internet's rapid expansion. Mesh routers, which have limited mobility and serve as the foundation of WMN, are made up of mesh clients and form the core of WMNs. Mesh clients can with mesh routers to create a client mesh network. Mesh clients can be either stationary or mobile. To properly utilise the network resources of WMNs, a topology must be designed that provides the best client coverage and network connectivity. Finding the ideal answer to the WMN mesh router placement dilemma will resolve this issue MRP-WMN. Since the MRP-WMN is known to be NP-hard, approximation methods are frequently used to solve it. This is another reason we are carrying out this task. Using the Multi-Verse Optimizer algorithm, we provide a quick technique for resolving the MRP-WMN (MVO). It is also proposed to create a new objective function for the MRP-WMN that accounts for the connected client ratio and connected router ratio, two crucial performance indicators. The connected client ratio rises by an average of 16.1%, 12.5%, and 6.9% according to experiment data, when the MVO method is employed to solve the MRP-WMN problem, the path loss falls by 1.3, 0.9, and 0.6 dB when compared to the Particle Swarm Optimization (PSO) and Whale Optimization Algorithm (WOA), correspondingly

Node placement optimization using extended virtual force and cuckoo search algorithm in wireless sensor network

Node placement is one of the fundamental issues that affects the performance of coverage and connectivity in Wireless Sensor Network (WSN). In a large scale WSN, sensor nodes are deployed randomly where they are scattered too close or far apart from each other. This random deployment causes issues such as coverage hole, overlapping and connectivity failure that contributes to the performance of coverage and connectivity of WSN. Therefore, node placement model is develop to find the optimal node placement in order to maintain the coverage and guaranteed the connectivity in random deployment. The performance of Extended Virtual Force-Based Algorithm (EVFA) and Cuckoo Search (CS) algorithm are evaluated and EVFA shows the improvement of coverage area and exhibits a guaranteed connectivity compared to CS algorithm. Both algorithms have their own strength in improving the coverage performance. The EVFA approach can relocate the sensor nodes using a repulsive and attractive force after initial deployment and CS algorithm is more efficient in exploring the search of maximum coverage area in random deployment. This study proposed Extended Virtual Force and Cuckoo Search (EVFCS) algorithm with a combination of EVFA and CS algorithm to find an optimal node placement. A series of experimental studies on evaluation of proposed algorithm were conducted within simulated environment. In EVFCS, the algorithm searches the best value of threshold distance and relocated the new position of sensor nodes. The result suggested 18.212m is the best threshold distance that maximizes the coverage area. It also minimizes the problems of coverage hole and overlapping while guaranteeing a reasonable connectivity quality. It proved that the proposed EVFCS outperforms the EVFA approach and achieved a significant improvement in coverage area and guaranteed connectivity. The implementation of the EVFCS improved the problems of initial random deployment

Enhanced Dynamic Bandwidth Allocation Algorithm for Intelligent Home Networks

Internet of Things (IoT) has been seen playing a tremendous change in the Information Technology (IT) environments, and thus its importance has also been realized and played a vital role within Intelligent Home Networks (IHNs). This is because IoT establishes a connection between things and the Internet by utilizing different sensing devices to implement the intelligence to deal with the identification and management of the connected things. IHNs use intelligent systems to perform their daily operations. Meanwhile, these networks ensure comfort, safety, healthcare, automation, energy conservation, and remote management to devices and users. Apart from that, these networks provide assistance in self-healing for faults, power outages, reconfigurations, and more. However, we have realized that more and advanced devices and services continue to be introduced and used in these networks. This has led to competitions of the limited available network resources, services, and bandwidth. In this paper, therefore, we present the design and implementation of a Novel Dynamic Bandwidth Allocation (NoDBA) algorithm to solve the performance bottleneck incurred with IHNs. The proposed algorithm deals with the management of bandwidth and its allocation. In the proposed algorithm, this study integrates two algorithms, namely; Offline Cooperative Algorithm (OCA) and Particle Swarm Optimization (PSO) to improve Quality of Service (QoS). PSO defines the priority limits for subnets and nodes in the network. Meanwhile, OCA facilitates dynamic bandwidth allocation in the network. The Network Simulator-2 (NS-2) was used to simulate and evaluate the NoDBA and it showed improved results compared to the traditional bandwidth allocation algorithms. The obtained results show an average throughput of 92%, average delay of 0.8 seconds, and saves energy consumption of 95% compared to Dynamic QoS-aware Bandwidth Allocation (DQBA) and Data-Driven Allocation (DDA).   Keywords: IHNs, Dynamic Bandwidth Allocation, PSO, OCA, Qo

Robotic Wireless Sensor Networks

In this chapter, we present a literature survey of an emerging, cutting-edge, and multi-disciplinary field of research at the intersection of Robotics and Wireless Sensor Networks (WSN) which we refer to as Robotic Wireless Sensor Networks (RWSN). We define a RWSN as an autonomous networked multi-robot system that aims to achieve certain sensing goals while meeting and maintaining certain communication performance requirements, through cooperative control, learning and adaptation. While both of the component areas, i.e., Robotics and WSN, are very well-known and well-explored, there exist a whole set of new opportunities and research directions at the intersection of these two fields which are relatively or even completely unexplored. One such example would be the use of a set of robotic routers to set up a temporary communication path between a sender and a receiver that uses the controlled mobility to the advantage of packet routing. We find that there exist only a limited number of articles to be directly categorized as RWSN related works whereas there exist a range of articles in the robotics and the WSN literature that are also relevant to this new field of research. To connect the dots, we first identify the core problems and research trends related to RWSN such as connectivity, localization, routing, and robust flow of information. Next, we classify the existing research on RWSN as well as the relevant state-of-the-arts from robotics and WSN community according to the problems and trends identified in the first step. Lastly, we analyze what is missing in the existing literature, and identify topics that require more research attention in the future

Assessing the Performance of a Particle Swarm Optimization Mobility Algorithm in a Hybrid Wi-Fi/LoRa Flying Ad Hoc Network

Research on Flying Ad-Hoc Networks (FANETs) has increased due to the availability of Unmanned Aerial Vehicles (UAVs) and the electronic components that control and connect them. Many applications, such as 3D mapping, construction inspection, or emergency response operations could benefit from an application and adaptation of swarm intelligence-based deployments of multiple UAVs. Such groups of cooperating UAVs, through the use of local rules, could be seen as network nodes establishing an ad-hoc network for communication purposes. One FANET application is to provide communication coverage over an area where communication infrastructure is unavailable. A crucial part of a FANET implementation is computing the optimal position of UAVs to provide connectivity with ground nodes while maximizing geographic span. To achieve optimal positioning of FANET nodes, an adaptation of the Particle Swarm Optimization (PSO) algorithm is proposed. A 3D mobility model is defined by adapting the original PSO algorithm and combining it with a fixed-trajectory initial flight. A Long Range (LoRa) mesh network is used for air-to-air communication, while a Wi-Fi network provides air-to-ground communication to several ground nodes with unknown positions. The optimization problem has two objectives: maximizing coverage to ground nodes and maintaining an end-to-end communication path to a control station, through the UAV mesh. The results show that the hybrid mobility approach performs similarly to the fixed trajectory flight regarding coverage, and outperforms fixed trajectory and PSO-only algorithms in both path maintenance and overall network efficiency, while using fewer UAVs

GUARDIANS final report

Emergencies in industrial warehouses are a major concern for firefghters. The large dimensions together with the development of dense smoke that drastically reduces visibility, represent major challenges. The Guardians robot swarm is designed to assist fire fighters in searching a large warehouse. In this report we discuss the technology developed for a swarm of robots searching and assisting fire fighters. We explain the swarming algorithms which provide the functionality by which the robots react to and follow humans while no communication is required. Next we discuss the wireless communication system, which is a so-called mobile ad-hoc network. The communication network provides also one of the means to locate the robots and humans. Thus the robot swarm is able to locate itself and provide guidance information to the humans. Together with the re ghters we explored how the robot swarm should feed information back to the human fire fighter. We have designed and experimented with interfaces for presenting swarm based information to human beings