Mesh Networks Research Articles

Optimization method for short circuit current reduction in extensive meshed LV network

This paper focuses on the analysis of suitable optimization methods applied to large meshed low-voltage networks. The introduced methods aim to minimize the SC-current contribution by simultaneously fulfilling well-defined operational constraints. The high number of binary variables (134) used in the worst case to determine the possible network configurations generates 1040 possible solutions. For this reason, the paper focuses only on methods capable of reducing the necessary steady-state calculations to a tractable size. Depending on the case under study, the deterministic method turns to be faster than the other ones at the price of reaching only a local minimum. In contrast, if a longer computation time is tolerated, then evolutionary algorithms succeed in finding the global optimum.

AC Transmission Network Expansion Planning Using the Line-Wise Model for Representing Meshed Transmission Networks

In this paper, a Line-Wise (LW) formulation of the AC Transmission Network Expansion Planning (AC-TNEP) problem for meshed transmission networks is proposed. The proposed formulation is then relaxed using a novel Quadratic Convex (QC) relaxation that can handle the mixed-integer nature of the problem and be solved efficiently using commercial solvers. The exact and relaxed formulations have been integrated into an algorithm for solving the AC-TNEP problem that has been introduced as an enhancement of an existing algorithm in the literature for obtaining feasible solutions of the AC-TNEP problem upon the use of convex relaxations to solve it. The AC-TNEP problem is solved for different scenarios that involve test cases ranging from 6 to 118 buses. The obtained solutions for the 6-bus to 46-bus test cases are shown to be globally optimal or reinforced to identical or better than the available solutions in the literature. Moreover, as distinguished from solutions in the literature, the proposed algorithm does not utilize approximations and heuristic constraints that are used to ease solving the AC-TNEP problem and were found to potentially lead to locally optimal solutions. Furthermore, the proposed algorithm is used to solve the AC-TNEP problem for the 87-bus and 118-bus test cases to establish its ability to solve for larger test cases. Extensions of the proposed algorithm to account for Reactive Power Planning (RPP) and dynamic planning are studied to further demonstrate its merits.

JECP: Joint Energy Conservation and Collision Avoidance for Path Construction in Bluetooth Networks

With the growing elderly population of the world population, the number of elderly people who live alone is continuously growing. Providing them with high-quality healthcare has become a major concern nowadays. In order to detect the elderly people’s sudden danger of elderly living alone, this paper proposes a path construction mechanism, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">JECP</i> , which aims to conserve energy and avoid collision for the emergent sensors in Bluetooth mesh networks. In case that the emergent sensors are deployed in the same room, the proposed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">JECP</i> constructs disjoint paths to avoid collisions, which are triggered by the same emergent event at the same time. The other case is that the emergent sensors are deployed in different rooms. The probability that these emergent sensors are triggered by the same event is very small. Hence the proposed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">JECP</i> constructs the best emergent path that shares the general sensors as much as possible to save energy consumption. The experimental results show that the proposed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">JECP</i> outperforms the existing mechanisms in terms of transmission delay and energy consumption for the emergent sensors in Bluetooth Low Energy (BLE) networks.

Transfer Learning Approaches for Gait-Based Wireless Person Identification

The human gait is a biometric feature that is unique to any individual. Since the human body affects the propagation of wireless signals, wireless networks can be used for identification of persons walking nearby. This biometric identification approach has been object of research in several studies described in the literature. In this work, we present a gait-based wireless user identification system that outperforms these existing solutions in terms of cost, effort and accuracy. Firstly, using a mesh network of low-cost wireless sensor nodes, the identification error rate could be reduced by more than 80in comparison to the best of prior studies. For a set of six persons to be identified, the identification accuracy could thus be improved to more than 99. Secondly, using the technique of Transfer Learning, the capabilities of the proposed system can be easily transferred to identify unknown persons and locations with only a few minutes of training effort. Training the system on the identification of six unknown persons, it has been found that the training effort can be reduced by about 96without a significant loss of accuracy.

Evaluation of LoRa Mesh Networks for Disaster Response

Evaluation of LoRa Mesh Networks for Disaster Response

Experimentally realized in situ backpropagation for deep learning in photonic neural networks.

Integrated photonic neural networks provide a promising platform for energy-efficient, high-throughput machine learning with extensive scientific and commercial applications. Photonic neural networks efficiently transform optically encoded inputs using Mach-Zehnder interferometer mesh networks interleaved with nonlinearities. We experimentally trained a three-layer, four-port silicon photonic neural network with programmable phase shifters and optical power monitoring to solve classification tasks using "in situ backpropagation," a photonic analog of the most popular method to train conventional neural networks. We measured backpropagated gradients for phase-shifter voltages by interfering forward- and backward-propagating light and simulated in situ backpropagation for 64-port photonic neural networks trained on MNIST image recognition given errors. All experiments performed comparably to digital simulations ([Formula: see text]94% test accuracy), and energy scaling analysis indicated a route to scalable machine learning.

Joint Channel Assignment and Bandwidth Reservation Using Improved FireFly Algorithm (IFA) in Wireless Mesh Networks (WMN)

Joint Channel Assignment and Bandwidth Reservation Using Improved FireFly Algorithm (IFA) in Wireless Mesh Networks (WMN)

Mapping sub-wavelength service with heterogenous security demand over physical-layer secured optical transport networks

In optical transport networks, client signals are usually aggregated to the lightpath channels for long-haul transport, and the aggregation process is referred to as service mapping. In the context of physical-layer secured optical networks, in which the lightpaths are encrypted, network operators need to consider not only the bandwidth demand but also the security demand of client services in the service mapping process. This paper mainly explores the service mapping issue, which decides the solution for aggregating service into the physical-layer secured optical transport network. First, we introduce the physical-layer secured optical transport networks and illustrate their differences from the traditional optical network. Then, we formulate the service mapping problem as an integer linear programming (ILP) model to maximize the number of successfully mapped services. To address the scalability issue of the ILP model, we further propose two security-aware service mapping algorithms based on the security constraints. Simulation results show that the ILP model can reach a 98.8% successful service mapping rate in a six-node mesh network. The successful service mapping rate of the heuristics is about 0.5% lower than the ILP model, which proves that the performance of the heuristics is very close to the ILP.

Efficient Integration of Fixed-Step Capacitor Banks and D-STATCOMs in Radial and Meshed Distribution Networks Considering Daily Operation Curves

The problem regarding the optimal integration of efficient reactive power compensation in radial and meshed distribution networks using fixed-step capacitor banks and distribution static compensators (D-STATCOMs) is addressed in this research paper by proposing a master–slave optimization methodology. Radial and meshed distribution topologies are considered for the grid structure while including variable active and reactive demand curves. An economic analysis is performed, considering the net present value of the optimization plan, as well as the costs of energy losses and the capacitor banks’ acquisition, installation, and operation. In the case of the D-STATCOMs, an annualized costs analysis is presented. In the master stage, the discrete version of the generalized normal distribution optimization (GNDO) algorithm selects the nodes and the sizes of the capacitor banks. In the slave stage, the successive approximations power flow approach is implemented. Numerical results in the IEEE 33-bus grid (with both radial and meshed topologies) and the IEEE 85-bus grid (with a radial configuration) demonstrated the proposed master–slave optimization’s effectiveness in minimizing the project’s expected net present value for a planning period of five years. Moreover, a simulation in the IEEE 69-bus grid under peak operation conditions showed that the GNDO approach is an excellent optimization technique to solve the studied problem when compared to combinatorial and exact optimization methods. In addition, numerical validations considering D-STATCOMs in the IEEE 85-bus grid confirmed the effectiveness and robustness of the GNDO approach in addressing problems associated with optimal reactive power compensation in medium-voltage distribution systems.

GraphSAGE-Based Multi-Path Reliable Routing Algorithm for Wireless Mesh Networks

Wireless mesh networks (WMN) promise to be an effective way to solve the “last mile” access problem on the Internet of Things (IoT) and the key to next-generation wireless networks. The current routing algorithms of WMN are difficult to adapt to complex environments and guarantee the reliable transmission of services. Therefore, this paper proposes a reliable routing algorithm that combines the improved breadth-first search and a graph neural network, namely GraphSAGE. The algorithm consists of two parts: (1) A multi-path routing algorithm based on the improved breadth-first search. This algorithm can continuously iterate link information based on network topology and output all shortest paths. (2) A GraphSAGE-based performance optimization algorithm. This algorithm creates a method to generate network labels for supervised training of GraphSAGE. Then, the network labels and GraphSAGE are used to learn graph features to obtain the value of network performance for each shortest path. Finally, the path with the best network performance is selected for data transmission. Simulation results show that in the face of complex environments, the proposed algorithm can effectively alleviate network congestion, improve throughput, and reduce end-to-end delay and packet loss rate compared with the traditional shortest-path routing algorithm and the Equal-Cost Multi-Path routing (ECMP).

An Extensive Power and Performance Analysis for High Dimensional Mesh and Torus Interconnection Networks

The next generation parallel computers are the keys to achieve exascale performance, whereas sequential computers have already been saturated. In order to achieve this mighty target of exascale computing, one of the main challenges is the reduction of power consumption along with achieving suitable performance. Energy efficiency is a key feature to ensure the trade-off between the performances over the required power usages. Hence, to focus on those issues, the target of this article is to analyze the performance versus power usage trade-off for the conventional networks like- Mesh and Torus. High degree networks show much better performance than the low degree of networks. However, high degree networks require higher power usage for their high degree of interconnected links. This article showed that with zero load latency, the 3D Torus could show about 57.07% better performance than a 2D Torus. On the other hand, a 2D Mesh network requires about 24.22% less router power usage than the 3D Mesh, &amp; 5D Torus requires about 66.8% higher router power usage than a 3D Torus network.

Robust expansion planning and hardening strategy of meshed multi-energy distribution networks for resilience enhancement

As an integrated energy system (IES) incorporated with electricity, gas, and heating networks, the meshed multi-energy distribution network (MMDN) can operate more economically with higher energy utilization efficiency. To achieve high reliability, MMDN is mesh-constructed and radial-operated, complicating the operation scenarios involved in planning. Existing coordinated planning models seldom consider network reconfiguration and hardening strategies collaboratively for extreme contingencies, which is not consistent with actual conditions. In addition, considering the multi-stage discreteness of the expansion planning problem, the existing robust algorithms may feedback invalid cuts. The neglect of such issues could introduce a momentous impact on the optimality of solutions. This paper presents a resiliency-oriented expansion planning and hardening model of MMDN, and the hierarchical algorithm is developed. Specifically, the model is formulated as a min–max-min optimization problem including investment and operation levels. The investment level problem optimizes planning schemes constrained with the robustness verification of the operation level problem. The operation level problem examines the operational cost and feeds back iterative information to the investment level. An additional cuts generation method is developed in the operation level, which effectively deals with invalid feedback information caused by the existing binary variable parameterization. Furthermore, accelerating strategies including infeasible region reduction, multi-cut feedback, and contingency set reduction are developed. To be solved by off-the-shelf solvers, the model is cast as a mixed-integer linear programming (MILP) problem by adopting the convex hull relaxation and linearization techniques for operational constraints with high accuracy. Numerical test results justify the effectiveness of the model.

Cost-Aware Optimization of Optical Add-Drop Multiplexers Placement in Packet-Optical xHaul Access Networks

This work concentrates on the problem of optimizing the cost of a passive wavelength division multiplexing (WDM) optical network used as a transport network for carrying the xHaul packet traffic between a set of remote radio sites and a central hub in a 5G radio access network (RAN). In this scope, we investigate the flexible use of optical add-drop multiplexers (OADMs) for the aggregation of traffic from a number of remote sites, where the type/capacity of optical devices—OADMs and optical multiplexers (MUXs)—is selected in accordance with the traffic demand. The approach is referred to as Flex-O. To this end, we formulate the xHaul network planning problem consisting in the joint provisioning of transmission paths (TPs) between the remote sites and the hub with optimized selection and placement of OADMs on the paths and proper selection of MUXs at the ends of the TPs. The problem formulation takes into accounts the optical power budget that limits the maximum transmission distance in a function of the amount and type of optical devices installed on the TPs. The network planning problem is modeled and solved as a mixed-integer linear programming (MILP) optimization problem. Several network scenarios are analyzed to evaluate the cost savings from the flexible (optimized) use of OADMs. The scenarios differ in terms of the availability of OADMs and the capacity of the WDM devices applied on the TPs. The numerical experiments performed in three mesh networks of different size show that the cost savings of up to between 35 and 45% can be achieved if the selection of OADMs is optimized comparing to the networks in which either single-type OADMs are used or the OADMs are not applied.

Genetic Algorithms Optimized Adaptive Wireless Network Deployment

Advancements in UAVs have enabled them to act as flying access points that can be positioned to create an interconnected wireless network in complex environments. The primary aim of such networks is to provide bandwidth coverage to users on the ground in case of an emergency or natural disaster when existing network infrastructure is unavailable. However, optimal UAV placement for creating an ad hoc wireless network is an NP-hard and challenging problem because of the UAV’s communication range, unknown users’ distribution, and differing user bandwidth requirements. Many techniques have been presented in the literature for wireless mesh network deployment, but they lack either generalizability (with different users’ distributions) or real-time adaptability as per users’ requirements. This paper addresses the UAV placement and control problem, where a set of genetic-algorithm-optimized potential fields guide UAVs for creating long-lived ad hoc wireless networks that find all users in a given area of interest (AOI) and serve their bandwidth requirements. The performance of networks deployed using the proposed algorithm was compared with the current state of the art on several experimental simulation scenarios with different levels of communication among UAVs, and the results show that, on average, the proposed algorithm outperforms the state of the art by 5.62% to 121.73%.

Performance Analysis of HWMP Protocol in Wireless Mesh Network

Performance Analysis of HWMP Protocol in Wireless Mesh Network

Wireless Mesh Network and Its Application in Sports Agility Tests

Wireless Mesh Network and Its Application in Sports Agility Tests

Development of Non-Intrusive Low-Power Digital Water Meter Reading System Based On Wireless Mesh Network and Internet of Things

The application of WMN and IoT technology to the metering infrastructure is proven to provide convenience in data collection, access to information, and management of metering devices. Unfortunately, the use of this technology is still minimal in developing countries like Indonesia. In this study, the development of a digital meter reading system that focuses on low-power non-intrusive reading devices and the application of WMS in the water meter reading system is carried out. This system consists of a series of meter reader sensors that are connected mesh to a gateway where meter data can be monitored in real-time and remotely. This sensor circuit is an integrated circuit added to the existing mechanical water meter which allows recording the volume of water consumption digitally. This circuit consists of an optical sensor, real-time clock (RTC), microcontroller, Lora Transmitter, and power supply. The use of solar cells to supply energy for meter reading devices is another interesting aspect of the development of this system. A gateway is a circuit built to bridge the transmission of meter data from a radio-based sensor network to the internet. The result of this research is the establishment of a meter reading system capable of accurately reading water consumption, low power where the meter data is easily accessible using a device connected to the internet. This system has been evaluated for every part of development such as reading water consumption, power consumption, and also communication in the mesh network. The contribution of this research is that it can be used as a reference for developing a digital meter system that is more effective, has low power, and has easy access to meter data

시분할 다중 접속 기반 무선 메쉬 네트워크에서 브로드캐스트 스케줄링 문제를 위한 타부서치 알고리즘

시분할 다중 접속 기반에서 브로드캐스트 스케줄링 문제는 타임 슬롯을 사용하여 모든 단말이 충돌 없이 전송할 수 있도록 설계하는 문제이다. 본 논문에서는 시분할 다중 접속 기반의 무선 메쉬 네트워크에서 브로드캐스트를 사용하는 노드의 전송 스케줄을 최적화하여 전체 전송 횟수와 타임 슬롯 이용률을 최대화하는 브로드캐스트 스케줄링 최적화 알고리즘을 제안한다. 제안된 최적화 알고리즘은 메타휴리스틱 방식 중 하나인 타부서치 알고리즘을 사용한다. 제안된 타부서치 알고리즘은 전체 전송 횟수를 최대화하는 목적함수를 사용하여 효율적인 이웃해 생성방식을 설계하였다. 본 논문에서는 네트워크에서 발생하는 모든 브로드캐스팅에 대하여 전체 전송 횟수와 타임 슬롯 이용률 관점에서 제안된 타부서치 알고리즘의 성능평가를 수행하였다. 성능평가 결과에서 제안된 타부서치 알고리즘은 이전에 제안된 다른 최적화 알고리즘보다 더 우수한 결과를 나타내었다.

Intrusion detection model using optimized quantum neural network and elliptical curve cryptography for data security

Secure data transmission in wireless mesh networks is a necessary attribute for machine learning-based intrusion detection systems (IDS). Numerous attacks may have an adverse effect on the system's computing efficiency. In order to accurately detect an attack and enable data protection, Whale with Cuckoo search optimization (WCSO) based quantum neural network (QNN) and elliptical curve cryptography (ECC) is presented. Whale optimization algorithm (WOA) is used to choose the features in the network data that aid in precisely detecting intrusions. To identify attacks, the optimized quantum network which combines the WOA approach with the feed-forward and backpropagation algorithms, is used. Sensitive data retrieving requires an encryption procedure that is enabled by the ECC algorithm, which could safely save the data files in the server, in order to, secure the documentation with security measures. In the event that the data owner maintains sensitive data on a server, the document is encrypted using the encryption method. To determine the best key optimized ECC is used. The QNN with WOA-based IDS framework is a solid option for real-time intrusion detection analysis with high accuracy of 98.5%. Thus, the study has demonstrated that the suggested effort will also provide better secure data storage, resolving security concerns.

Active mesh and neural network pipeline for cell aggregate segmentation

Segmenting cells within cellular aggregates in 3D is a growing challenge in cell biology due to improvements in capacity and accuracy of microscopy techniques. Here, we describe a pipeline to segment images of cell aggregates in 3D. The pipeline combines neural network segmentations with active meshes. We apply our segmentation method to cultured mouse mammary gland organoids imaged over 24 h with oblique plane microscopy, a high-throughput light-sheet fluorescence microscopy technique. We show that our method can also be applied to images of mouse embryonic stem cells imaged with a spinning disc microscope. We segment individual cells based on nuclei and cell membrane fluorescent markers, and track cells over time. We describe metrics to quantify the quality of the automated segmentation. Our segmentation pipeline involves a Fiji plugin that implements active mesh deformation and allows a user to create training data, automatically obtain segmentation meshes from original image data or neural network prediction, and manually curate segmentation data to identify and correct mistakes. Our active meshes-based approach facilitates segmentation postprocessing, correction, and integration with neural network prediction.