Hot Spot Problem Research Articles

A novel cable-grasping planner for manipulator based on the operation surface

The manipulation of deformable linear objects is a hot spot and difficult problem in robotics research. Cables, as one kind of deformable linear objects, are characterized by elongation and uncertain shape. Although previous research has paid attention to deformable linear object’s manipulation, it still remains a challenge to detect and grasp cables in unstructured environments. In this paper, a new cable-grasping method framework by analysis of cable characteristics is proposed. The framework consists of cable pose measurement and cable-grasping path planning. In the cable pose measurement mission, we design a symmetric edge feature according to the cable image projection principle. Then, a semantic segmentation based edge detection technique and a center line fitting method based on sorting by principal axis are applied to measure the cable pose accurately. In the cable-grasping path planning mission, the grasping pose is determined considering the force directional manipulability . The cable operation surface is constructed after the characteristic analysis of the cable, on which basis, a two-phase path planner is proposed, with the second phase planner BG-RRT guided by motion directional manipulability to further optimize the planning path. The performance of the approach is verified by experiments in both the simulation environment and the actual environment. • Proposing a symmetric edge feature based on the cable image projection principle. • Designing an edge detection method based on semantic segmentation as well as a center line fitting method based on sorting by principal axis. • Constructing the cable operation surface according to the characteristics of cable. • A two-phase path planner based on operation surface for reducing collisions. • A new BG-RRT algorithm guided by motion directional manipulability for further improving the efficiency of cable grasping.

Uneven clustering and fuzzy logic based energy-efficient wireless sensor networks

Clustering is the fundamental issue in terms of ensuring long-term operation of wireless sensor networks (WSNs). The problem of hot spots remains the most prominent research challenge relating to the design of energy-efficient clustering algorithm. This paper proposed a protocol, namely an uneven clustering and fuzzy logic-based energy-efficient (UCFLEE), for prolonging network lifetime. Depending on the communication distance, the UCFLEE protocol divides the network into uneven clusters for suppressing the hot spot problem. The fuzzy logic selects the optimal cluster head in accordance with certain parameters. The advocated method adopts a dynamic energy threshold to chnage the cluster head. The UCFLEE protocol is dependent on the iterative deepening A (IDA) star algorithm for identifying the routing path from the cluster heads to the base station. The IDA-star method is reliant upon a cost bounded method to select the optimal solution for the base station. The UCFLEE protocol is tested and subsequently contrasted with other protocols. The results obtained from the UCFLEE protocol enable an energy consumption equilibrium, eradicates the hot spot challenge, while also attaining maximum network lifetime.

Energy efficient architecture for mitigating the hot-spot problem in wireless sensor networks

Energy efficient architecture for mitigating the hot-spot problem in wireless sensor networks

TEO-MCRP: Thermal exchange optimization-based clustering routing protocol with a mobile sink for wireless sensor networks

Sensor nodes consume their energy quickly due to their continuous perception, routing, and internal circuit mechanisms in Wireless Sensor Networks (WSNs). Especially, the nodes that are close to the sink node consume their energy more quickly, causing the hot spot problem. In this direction, the development of robust clustering and adaptive mobile routing methods is extremely significant to ensure energy efficiency in WSNs. For this purpose, a new mobile clustering routing protocol based on Thermal Exchange Optimization (TEO) inspired by Newton's cooling law, called TEO-MCRP, is presented for heterogeneous WSNs. In the present protocol, two different algorithms are proposed for Cluster Head (CH) selection and Mobile Sink (MS) path detection with objective functions including independent fitness parameters. With the manner of minimizing these objective functions, effective CH selection and MS trajectory determination have been performed for each mobile round using the temperature equations in the TEO algorithm. In parallel, an MS node gathers data from all CHs and transmits all data to the Base Station (BS). The performance results strikingly illustrate that the proposed protocol is better than other algorithms. Due to minimizing packet conflicts and losses, the packet delivery rate was achieved at 99.784% in the network

Dual-line data collection scheme for efficient mobile sink operation in solar-powered wireless sensor networks

Solar energy harvesting has been presented as a key solution to alleviate the lifespan problem of conventional battery-based wireless sensor networks, but it does not address the problem of uneven energy consumption across the network. In conventional sensor networks with a stationary sink node, the nodes around the sink node consume more energy than other nodes, which is known as the hotspot problem. In recent studies, mobile sink technology has been proved to be an effective solution to the problem, where a mobile sink visits sensor nodes and collects data. To reduce the energy consumption of the mobile sink, a subset of sensor nodes are designated as anchor nodes to collect data for a certain period, and the mobile sink visits only such anchor nodes. However, even with these approaches, small hotspots can still be formed near those anchor nodes, hence it is crucial to optimize the selection and management of anchor nodes. This study aims to propose a dual line-based anchor node selection scheme tailored to efficiently solve the hotspot problem in mobile sink-based solar-powered wireless sensor networks. The proposed scheme designates the areas, in which some nodes can be chosen as anchor nodes, in the form of two lines. This dual line-based anchor selection algorithm effectively operates for the best use of the solar energy harvested by each node in a balanced manner. The experimental results confirm that the proposed scheme minimizes hotspots in the network, reduces the blackout time of nodes, and improves the data collection rate.

Mobile sink-based data collection in event-driven wireless sensor networks using a modified ant colony optimization

The hotspot problem is one of the primary challenges in the wireless sensor networks (WSNs) because it isolates the sink node from the remaining part of the WSN. A mobile sink (MS)-based data acquisition strategy mitigates the hotspot problem, but the traditional MS-based data gathering approaches do not resolve the issue. However, the conventional techniques follow a fixed order of visits and static traversal of the MS. In this context, this paper uses a modified version of the ant colony optimization strategy for the data collected through a MS to mitigate the hotspot problem in the WSNs while improving the energy efficiency, network lifetime, throughput by reducing the packet loss and delay. In our work, we initially construct a forwarded load spanning tree to estimate the freight of each node in the WSN. Further, we choose RPs and their path simultaneously using the modified ACO algorithm by considering the forward loads, remaining energy, distance, etc. The proposed work also adopts the virtual RP selection strategy void unnecessary data exchanges between the nodes and RPs. Hence, it reduces the burden on relay nodes and optimize the energy usage among the nodes. We compare our approach with the recent ACO-based algorithms, and our approach outperforms them.

Energy Aware Seagull Optimization-Based Unequal Clustering Technique in WSN Communication

Wireless sensor network (WSN) becomes a hot research area owing to an extensive set of applications. In order to accomplish energy efficiency in WSN, most of the earlier works have focused on the clustering process which enables to elect CHs and organize unequal clusters. However, the clustering process results in hot spot problem and can be addressed by the use of unequal clustering techniques, which enables to construct of clusters of unequal sizes to equalize the energy dissipation in the WSN. Unequal clustering can be formulated as an NP-hard issue and can be solved by metaheuristic optimization algorithms. With this motivation, this paper presents a novel seagull optimization (SGO) based unequal clustering (SGOBUC) model to attain energy efficiency in WSN. The SGO algorithm is mainly inspired by the migrating and attacking behaviour of seagulls. They are formulated in a mathematical way and designed to highlight exploration as well as exploitation in a provided searching area. The SGOBUC technique derives a fitness involving different parameters in such a way that energy efficiency can be accomplished. A comprehensive simulation analysis takes place to showcase the enhanced outcomes of the SGOBUC technique. The simulation outcomes highlighted the betterment of the SGOBUC technique over the recent techniques interms of different dimensions.

Modeling and Performance Investigations of Partially Shaded Solar PV Arrays with Cell Partition Technique based Modules

Solar photovoltaic (PV) modules consist of solar cells connected in series to provide the required output power. The solar PV system is experiencing major challenges, which are mainly due to the partial shadows on the photovoltaic modules leading to mismatching power loss and hot spot problems. Hotspots have become a major cause of PV module failure. The Cell Partition Technique (CPT) is proposed to reduce hotspots and minimize mismatch losses caused by partial shadings. Specifically, each solar PV cell (Full cell) in a solar PV module is divided or partitioned into two half cells (known as Half-Cut Cells or HC) and three equal cells (known as Tri-Cut Cells or TC) in accordance with the proposed technique. The HC and TC types of cells are connected in a strings of series-parallel connection, and bypass diode is placed in middle of the solar PV module to ensure proper operation. The primary aim of this research is to model, evaluate, and investigate the performance of solar PV arrays using new PV modules are developed based on Cell Partition Technique (PVM-CPT), such as half-cut cell modules (HCM), and tri-cut cell modules (TCM) and compared with full-sized cell modules (FCM). These PVM-CPT are connected in Series–Parallel (SP), Total-Cross-Tied (TCT), and proposed static shade dispersion based TCT reconfiguration (SD-TCTR) for the array sizes of 3x4, 4x3 and 4x4, respectively. The purpose is to select the most appropriate solar PV array configurations in terms of the highest global maximum power and thus the lowest mismatch power losses under short and narrow, short and wide, long and narrow, long and wide type of cell level partial shadings. The Matlab/Simulink software is used to simulate and analyze all of the shading cases. The results show that, when compared to conventional module configurations under different shading conditions, the proposed static SD-TCTR arrangement with TC modules (SDTCTR-TCM) exhibits the lowest mismatch power losses and the greatest improvement in array power.

Energy-efficient sink relocation using whale optimisation technique in virtual grid-based wireless sensor networks

Wireless Sensor Network (WSN) is an efficient network for monitoring and recording the physical environment and transfers the monitored data into the central location using widely distributed sensor nodes. One of the main problems in WSN is the issue of developing an energy-efficient routing protocol that achieves less energy consumption and enhances the lifetime of the network. During the past decades, researchers have used the mobile sink to reduce the energy problem and hotspot problems. In this work, Virtual Grid Based Energy Efficient Sink Relocation (VGESR) is proposed to solve these issues. The grid clustering is achieved by employing K-means clustering. After clustering, the Leader Node (LN) selection is done by calculating the Acceptability Factor (AF). Acceptability factor is calculated based on the nodes residual energy, Available bandwidth and Received Signal Strength (RSS). Whale Optimisation Algorithm (WOA) technique is employed for the optimal sink relocation based on the fitness value of the nodes. The results obtained from the simulation prove that the proposed VGESR performs well in terms of life span and energy utilisation. The proposed VGESR simulation is performed using the OMNet++ tool.

Retracted] Econometric Analysis of the Hot Research of Marxist Theoretical Journals Based on Knowledge Map

In recent years, the research on Marxist theory has received a lot of attention. Many studies use quantitative research methods such as statistical keywords and constructing knowledge map to carry out Marxist theoretical analysis. In this paper, by constructing a self‐expanding Chinese word separation and self‐expanding address data knowledge graph, matching Marxist Chinese addresses based on the full‐text indexing knowledge graph, incorporating a weighted pinyin full‐text search mechanism to improve the matching accuracy of misspelled addresses, constructing a multiple matching mechanism for Marxist addresses by combining an online geographic parsing interface, and performing semisupervised matching for a small number of difficult addresses, a complete system of Marxist address matching methods is formed. By studying and analyzing the basic orientation and attention to academic hotspots, we can gain insight into the characteristics, rules, and problems of academic hotspots in Marxist theory journals.

Adaptive Data Collection Using UAV With Wireless Power Transfer for Wireless Rechargeable Sensor Networks

Wireless sensor networks (WSNs) are used to collect large amounts of data over a wide area. However, the sensor nodes used in WSNs have a short lifespan because they are generally battery-operated. Research is actively underway to increase the lifespan of WSNs via techniques such as energy harvesting and wireless power transfer (WPT). This study proposes a scheme for clustering and adjusting the WSN data collection rate to alleviate the hotspot problem and improve network connectivity. The proposed scheme is targeted at WSNs in which the sensor nodes harvest solar energy. In addition, an unmanned aerial vehicle (UAV) traverses along a given path and delivers energy to sensor nodes using WPT, while collecting data as a sink node. In this scheme, the number of cluster heads is determined by considering the number of nodes at each hop distance and the maximum amount of data that can be transferred with the available energy. Further, sensor nodes limit the amount of data they collect to reduce the relay load on intermediate nodes. To achieve this, nodes consider the amount of data transferred by parent nodes, especially nodes in the hotspot, and the number of child nodes. The sink node traverses along a given path, and collects the data accumulated in the cluster head while supplying its remaining energy to the cluster head. The amount of energy it transfers is determined by considering the number of cluster heads. Consequently, nodes in the hotspot are prevented from becoming blacked out from a lack of energy, and the energy harvesting efficiency is increased. Simulation results indicate that the proposed scheme reduces the number of blackout nodes in the hotspot areas near cluster heads. Furthermore, the data collection and monitoring performance increases with the increase in network connectivity.

Learning automata and lexical composition method for optimal and load balanced RPL routing in IoT

Low power and lossy network, internet of things (IoT) motivates energy-efficient and load-balanced routing in the network layer to extend network lifetime. IoT application scenarios exploit the routing protocol for low-power and lossy networks (RPL) due to the significant potentials. The core components of RPL are the trickle algorithm and objective functions (OF) for creating destination oriented directed acyclic graph (DODAG) and data forwarding. The RPL needs more attention to avoid hotspot problems and unnecessary energy depletion. Most of the existing routing protocols take a single either hop count or ETX, or multiple routing decision metrics. However, the RPL cannot select appropriate link metrics efficiently against the dynamic and lossy environment without considering the relationship between those metrics. Thus, the proposed methodology takes important routing metrics, such as hop count, expected transmission count, and traffic-related metric, and composites the metrics using learning automata and lexical composition method. The special attention on network energy balancing through expected transmission energy (ETT) avoids a hotspot issue and inefficient routing energy. The proposed work supports multiple metrics-based OF with considerable routing overhead by tuning the trickle parameter. Moreover, the proposed work is evaluated to show its advantages over the dynamic and lossy network, IoT.

A Green Data Collection & Transmission Method for IoT-Based WSN in Disaster Management

Being as an integral part of data exchange in disaster management, Internet of Thing (IoT) is an essential component to update the disaster management information to all the connected nodes. In such scenarios, the nodes are deployed with a limited battery, batteries of these nodes are non-rechargeable and such networks consume huge energy in data exchange. Therefore, the practical implementation of such type of networks is a difficult task as routing avoids the paths and consume a tremendous amount of energy during collection/transmission of data. This paper proposes an optimized Genetic Algorithm (GA) based green data collection/transmission method for IoT based WSN in disaster management by satisfying multiple constraints i.e., optimizing intra-cluster distance, systematic utilization of node’s energy in the cluster and reducing hop count. The proposed direct data collection/transmission and movable sink strategies shorten the communication distance between the sink and cluster head (CH) which diminishes the hotspot problem. The direct data collection helps in transmitting data directly to the sink when the sinks are nearer to the sensor nodes with respect to CH. Further, the incorporated dynamic sensing range minimizes overlapping of sensing range of CH along with a significant decrement in the transmission energy. The simulation results show that the proposed protocol outperforms the existing protocols on the performance metrics namely network’s remaining energy, lifetime, stability period, and throughput per rounds.

An Improved Genetic Algorithm Based Annulus-Sector Clustering Routing Protocol for Wireless Sensor Networks

In the clustering routing protocols for wireless sensor networks, uniform cluster formation and optimal routing paths finding are the two most important factors to minimize the network energy consumption and balance the network load. In this paper, an improved genetic algorithm based annulus-sector clustering routing protocol called GACRP is proposed. In GACRP, the circular network is divided into sectors with the same size for each annulus. The number of sectors is obtained by calculating the minimum energy consumption of the network. Each annulus-sector forms a cluster and the best node in this annulus-sector is selected as cluster head. Moreover, an improved genetic algorithm with a novel fitness function considering energy and load balance is presented to find the optimal routing path for each CH, and an adaptive round time is calculated to maintain the clusters. Simulation results show that GACRP can significantly improve the network energy efficiency and prolong the network lifetime as well as mitigate the hot spot problem.

DACOR: A distributed ACO‐based routing protocol for mitigating the hot spot problem in fog‐enabled WSN architecture

SummaryAnt colony optimization (ACO) and unequal clustering algorithms in wireless sensor networks (WSNs) prove their efficiency in protracting the network lifetime. However, the existing ACO and unequal clustering algorithms, respectively, do not consider jointly energy efficiency and reliability and focus only on some normal parameters to adjust the cluster radius, then neglecting the cluster head (CH) neighborhood information as it is wise to reduce the cluster radius when there are more neighbor CHs in order to balance the load and energy consumption. To resolve these problems, we propose a fault‐tolerant distributed ACO‐based routing (DACOR) protocol for mitigating the hot spot problem in fog‐enabled WSN architecture. To improve the performance of the network, we propose a multiple fog nodes (FNs) and unequal clustering‐based network model. The proposed model is energy efficient as it avoids repetitive clustering and affects CHs to FNs based on distance. Also, unlike the existing works which use either single FN/sink‐based unequal clustering or multiple FNs/sinks to mitigate hot spot problem, we propose to distribute unequal clustering to multiple FNs (partitions). Additionally, we formulate a different rule to calculate the cluster radius based on significant parameters ensuring energy efficiency and balancing. To route data from source to destination, we devise a new probabilistic formula which considers not only energy efficiency but also reliability. The performance of the proposed DACOR protocol has been investigated under different scenarios through simulations. The results show that the proposed DACOR protocol outperforms the existing protocols in terms of various main metrics.

Optimized data gathering in a heterogeneous Internet of Things network

SummaryRecent research introduces data gathering using mobile data collectors to conserve energy and elevate the hotspot problem. However, many of them suffer from buffer overflow due to the limited memory capacity of objects, even more, when dealing with heterogeneous Internet of Things (IoT) devices. Emerging bio‐inspiration technologies provide a novel direction for data collection and make it more intelligent and available. In this paper, we present an intelligent data gathering schema by taking into consideration buffer overflow called Center Gravity Mobile data collector based on Salp Swarm Algorithm (CGMSSA). To conduct data collection to improve the network performance, we adopt a hybridized distributed bio‐inspired technique to elect chefs and salp swarm intelligence to control data collector movement. First, we select chefs using grey wolf optimization. Then, several groups are formed. Then, a mobile data collector is adopted to access chefs, following the buffer overflow value and gather information. Considerable experiments are conducted to demonstrate that our solution can efficiently enhance the lifetime of the network and increase data throughput.

Fourier-like Thermal Relaxation of Nanoscale Explosive Hot Spots

We develop here an approach to directly determine the thermal transport properties of explosive hot spots with realistic initial structures through a combination of molecular dynamics (MD) and diffusive heat equation (HEq) modeling. Effective thermal conductivity values are determined by fitting HEq models to MD predictions of long timescale hot spot relaxation. The approach is applied to model hot spots in the molecular crystalline explosive TATB for a range of shock strengths and two limiting cases for impact orientation. Conductivity is found to be a strong function of density, which parametrically captures dependence on temperature, pressure, and material state. The approach provides a convenient foundation for determining the effective thermal conductivity for hot spot problems in other explosives and directly yields information on reasonable approximations that might be taken in higher-level models for those materials.

Research on Performance Optimization for Power Big Data Storage based on HBase

With the development of smart grid, the online monitoring of power equipment has become the focus. Due to the increasing of power data, data storage in power grid is faced with great challenge. HBase is widely used for storing and managing power data due to its excellent performance in distributed access to data. However, HBase is prone to write a hot spot problem when storing data. To address this problem, this paper proposes an optimization method for data storage performance based on a dynamic load balancing strategy, which is implemented by pre-partitioning processing, RowKey substitution, and WLC (Weighted Least Connection) algorithm adapted to HBase storage mechanism. Through the experimental analysis of insulator current data storage, the results show that the method improves the performance of HBase cluster parallel data storage.

Cost-effective and extensible LLC-resonant voltage-multiplier-based differential power processing optimizer for mismatched photovoltaic systems

Mismatched photovoltaic (PV) power systems due to the shadow effect will result in serious consequences such as reduced output power, hot spot problem, and low reliability. As one promising architecture to address this issue, differential power processing (DPP) optimizer has been extensively discussed to improve the actual energy yield through the charge redistribution among PV elements such as modules or sub-modules. However, since a large number of hardware components must be added in the conventional DPP optimizer, high cost and poor extensibility have become the bottleneck for the practical application of DPP technique in long-string photovoltaic systems. This paper presents an LLC-resonant voltage-multiplier-based DPP (LLC-VM-DPP) optimizer to address these problems. Specifically, the LLC resonant inverter with two switches is operating as a voltage equalizer to compensate the power reduction of shaded PV elements. The switch count is limited to two even for long-string photovoltaic systems. No feedback is required for the control implementation. Thus, the overall system performance such as the cost, circuit complexity, extensibility, power density, and efficiency can be improved. Simulation and experimental evaluation of the LLC-VM-DPP architecture for four-module-series-connection PV systems under different shading scenarios was carried out. Detailed power loss analysis was presented. With the LLC-VM-DPP architecture, the measured output power under severe shading scenario can be improved up to 32.9%while the presented DPP optimizer cost for four-module-series-connection PV systems can be reduced up to 45.41%.

Discussion of optimization of sham acupuncture setting based on a clinical trial about acupuncture treatment of migraine published in British Medical Journal

In the randomized controlled clinical trials of acupuncture, the setting of the control group (sham acupuncture) directly affected the interpretations about their outcomes (beyond placebo), and has been being the hot spot and difficult problem. In the present paper, we discussed various types of sham acupuncture (invade and non-invade needling) commonly used nowadays and made an in-depth analysis on the factors contributing to the successful blinding to patients with episodic migraine without aura in a clinical study published in British Medical Journal (2020, 368:m697). Moreover, we put forward some thoughts on how to optimize the setting of sham acupuncture in the treatment of pain diseases. These thoughts are 1) setting different placebo control group for different types of pain, 2) selecting conventional acupoints not associated with the disease, 3) selecting the most sui-table type of placebo acupuncture through pre-tests, 4) choosing the distal non-meridian and non-acupoint not in the same neuronal segment with the pain locus when using non-invade consolation needling, 5) trying best to reduce the patients' doubts about placebo acupuncture operation, 6) selecting subjects with little or without acupuncture experience for multicenter studies, and 7) trying best to select objective indicators and to avoid the subjects' report bias when evaluating the effects of acupuncture and consolation acupuncture.