Node Load Research Articles

Learning automaton-based energy-efficient and fault-tolerant topology evolution algorithm for underwater acoustic sensor network

With the development of Marine Internet of Things, underwater acoustic sensor network (UASN) has become a research focus. However, the energy of UASN nodes is limited, and the complex underwater environment is easy to cause the failure of UASN nodes, which will affect the normal operation of UASN. To address this issue, in this paper, an UASN energy-efficient and fault-tolerant topology is generated to prolong the node lifetime and improve the fault tolerance for node failure. Initially, the learning automaton is introduced and improved to optimize the transmission power of each node. Based on the optimized power of nodes, the node lifetime considering node load is analyzed. Then the preferential growth mechanism is improved to present the energy-efficient and fault-tolerant topology evolution algorithm. Consequently, the energy-efficient and fault-tolerant UASN topology is generated. In the simulation experiments, the selection criteria of parameters about transmission power and node load under different actual requirements is obtained, and the performance of generated topology is validated. Simulation results show that the fault tolerance and energy efficiency of generated topology in this paper outperforms the TCEB and PG-OSTCG topologies.

A Novel Voltage/var Sensitivity Calculation Method to Partition the Distribution Network Containing Renewable Energy

Abstract: As the current calculation method of voltage/var sensitivity (VVS) could not reflect the temporal variations of power sources and load, the paper proposes a novel VVS calculation method to partition the distribution network containing renewable energy. Firstly, the defects of the existing VVS calculation methods are analyzed, and a novel VVS calculation method is proposed by adding reactive power output perturbation to the reactive power source. Secondly, the VVS matrix is fuzzified to obtain the membership matrix between each node. Based on the membership relationship between the reactive power source nodes, the nodes with a strong coupling relationship are merged to determine the initial number of partitioning. Then, the final partitioning result is determined according to the affiliation between each load node and each reactive power source. Finally, the partitioning result is evaluated based on the partitioning membership indexes. The proposed approach is tested on the IEEE 33-node distribution test system, and numerical simulations verify the high efficiency of partitioning. The simulation results show that the proposed method can reflect the temporal variations of power sources and load, and ensure the reasonable distribution of voltage and reactive power in each zone. Background: The voltage/var control of the power system is an important means to ensure the safe and economically stable operation of the system. Objective: The paper proposes a novel VVS calculation method to partition the distribution network containing renewable energy. Method: Firstly, the existing problems of electrical distance index are analyzed. A novel VVS matrix calculation method is proposed, which calculates the VVS of each reactive power source to the remaining nodes by adding voltage/var output perturbation to the reactive power source. Secondly, the VVS matrix is fuzzified to obtain the membership matrix between each node. Based on the membership relationship between the reactive power source nodes, the power supply nodes with strong coupling relationship are merged to determine the initial number of the partitioning. Then, the final partitioning result is determined according to the affiliation between each load node and each reactive power source. Finally, the partitioning result is evaluated based on the sensitivity index of membership degree. Results: The proposed approach is tested on the IEEE 33-node distribution test system and numerical simulations verify high efficiency of the partitioning. The simulation results show that the proposed method can reflect the temporal variations of power sources and load, and ensure the reasonable distribution of voltage and reactive power in each zone. Conclusion: The proposed method can reflect the temporal variations of power sources and load, and ensure the reasonable distribution of voltage and reactive power in each zone. This paper proposed a fuzzy clustering partitioning based on a novel calculation method of VVS, which is suitable for the problem of variable voltage /var running state of the system under high-proportion DG access.

Evaluation of cross-immunity among major porcine circovirus type 2 genotypes by infection with PCV2b and PCV2d circulating strains

There are three main genotypes of porcine circovirus type 2 (PCV2), namely PCV2a, PCV2b and PCV2d, of which PCV2b and PCV2d are currently the most common. There are antigenic differences between these different genotypes. To explore the effect of PCV2 antigen differences on the immune protection provided by vaccines, a cross-immune protection test was carried out in pigs. Three genotype strains, PCV2a-CL, PCV2b-MDJ and PCV2d-LNHC, were inactivated and emulsified to prepare inactivated vaccines to immunize pigs, who were then challenged with the circulating strains PCV2b-BY and PCV2d-LNHC. Immunoperoxidase monolayer assays (IPMAs) and micro-neutralization assays were used to detect antibodies against the three different genotypes of PCV2. The results showed that the three genotype vaccines induced pigs to produce antibodies against the same and different genotypes of PCV2, but the levels of IPMA and neutralizing antibodies against the same genotype were higher than those against different genotypes. Quantitative Polymerase Chain Reaction (qPCR), virus titration and immunohistochemistry were used to detect PCV2 genomic DNA, live virus and antigen, respectively, in inguinal lymph nodes of experimental pigs. Following challenge with the PCV2b-BY strain, the viral DNA load in the inguinal lymph nodes of pigs immunized with the three genotype vaccines was reduced by more than 99 % compared to the unimmunized group. Following challenge with the PCV2d-LNHC strain, the viral DNA loads in the inguinal lymph nodes of pigs immunized with PCV2a, PCV2b and PCV2d genotype vaccines were reduced by 93.8 %, 99.8 % and 98.3 %, respectively, compared to unimmunized controls. In addition, neither live PCV2 virus nor antigen were detected in the inguinal lymph nodes of pigs immunized with any of the genotype vaccines (0/18), but both were detected in the lymph nodes of experimental pigs in the unimmunized control group (6/6). These findings suggest that, although the antigenic differences of the three genotype strains induce significant differences in antibody levels, they seem to have little effect on cross-protection between different genotypes.

Multi-agent deep reinforcement learning for user association and resource allocation in integrated terrestrial and non-terrestrial networks

Integrating the terrestrial network with non-terrestrial networks to provide radio access as anticipated in the beyond 5G networks calls for efficient user association and resource allocation strategies. In this work, a weighted sum single objective optimization problem that maximizes the total network data rate while minimizing the mobility-induced handoffs and prioritizing service provisioning of mission-critical users in the integrated terrestrial and non-terrestrial network is formulated. The problem’s complexity is reduced by solving the defined problem in two phases; the user association followed by the resource distribution phase. Several proposed approaches to the user association sub-problem utilize a central node that requires nearly-complete information, which may not be available in real time. On the contrary, this paper proposes a centralized training and distributed execution multi-agent dueling double deep Q network (MA3DQN) solution. Each user collects the channel state and access node loading information in this approach and makes an association decision that considers its quality of service requirements. The algorithm’s performance is validated through comparison with the genetic algorithm (GA), the integer linear programming (ILP), a heuristic approximation-based solution, the greedy approach, and the random user association (RUA) algorithm. Moreover, the paper simulates the multi-agent deep Q network solution as an additional benchmark algorithm. Simulation results reveal that as the number of users in the network increases, the acquired data rate of the MA3DQN is within 0.48% and 0.4% of that achieved by the GA and ILP, respectively, and outperforms all other algorithms. Notably, the proposed MA3DQN algorithm presents the best running time, attaining a gain of 99.9% over the GA algorithm, which performs the poorest among the algorithms characterized by polynomial worst-case time complexity. Besides, the MA3DQN approach maintains a handoff probability of zero, unlike the ILP, the approximation-based, greedy, and RUA solutions.

Method of Site Selection and Capacity Setting for Battery Energy Storage System in Distribution Networks with Renewable Energy Sources

The reasonable allocation of the battery energy storage system (BESS) in the distribution networks is an effective method that contributes to the renewable energy sources (RESs) connected to the power grid. However, the site and capacity of BESS optimized by the traditional genetic algorithm is usually inaccurate. In this paper, a power grid node load, which includes the daily load of wind power and solar energy, was studied. Aiming to minimize the average daily distribution networks loss with the power grid node load connected with RESs, a site selection and capacity setting model of BESS was built. To solve this model, a modified simulated annealing genetic algorithm was developed. In the developed method, the crossover probability and the mutation probability were modified by a double-threshold mutation probability control, which helped this genetic method to avoid trapping in local optima. Moreover, the cooling mechanism of simulated annealing method was presented to accelerate the convergence speed of the improved genetic algorithm. The simulation results showed that the convergence speed using the developed method can be accelerated in different number BESSs and the convergence time was shortened into 35 iteration times in view of networks loss, which reduced the convergence time by about 30 percent. Finally, the required number of battery system in BESS was further built according to the real batteries grouping design and the required capacity of BESS attained using the developed method.

0.38 kV Electrical Network With Three-phase Loads and Capacitor Shunt-balancing Device

The issue of reducing the current asymmetry coefficient and the current unbalance coefficient (in the absence of KSHSU, KSHSU in the load node, KSHSU on transformer buses) with the help of shunt-balancing devices is considered. The proposed symmetrical device has an additional two power stages and allows to increase the efficiency of symmetry due to adjustable, automatically changeable parameters that vary depending on the level of current asymmetry, i.e. on the magnitude of the zero sequence current.

Invulnerability Improvement of Multipath Protocols in Different Scenarios

Developments in computer communications and networks enabled the deployment of exciting new areas. Meanwhile, Efficient network and communication technologies also promote the development of routing protocols. AODV(Ad hoc On-Demand Distance Vector Routing, AODV) routing protocol has invulnerability to different mobile attributes of agent nodes and network damage scenarios. However, this invulnerability performance can not meet the needs of mobile multi-agent cooperative communication. Therefore, this paper proposes an improved responsive multipath routing protocol MD_AODV(Multipath Destroy-resistance AODV, MD_AODV). This routing protocol is based on AODV routing protocol. It is mainly improved in three aspects: multichannel concurrency mechanism, node load balancing mechanism and shortest path maintenance mechanism. Firstly, in this research, an improved multipath routing protocol is modeled and implemented, which is based on OPNET Network simulation platform. Then, network models with different degrees of impairment are built. Combines the mobile attributes of mobile multi-agent task coordination to carry out invulnerability simulation experiments . These experiments study the invulnerability of the improved multipath routing protocol. Finally, this paper evaluates and compares the routing invulnerability of MD_AODV, AODV and AOMDV(Ad-hoc On-Demand Multipath Distance Vector, AOMDV). The evaluation results show that MD_AODV routing has better invulnerability when the network is severely damaged

Provision of optimal dispatching scenarios for regional power systems in the face of uncontrollable power shortages

As of 2023, substantiation of the criteria for optimal load balancing of territorial energy islands in the face of uncontrolled power shortages due to unpredictable failure (destruction as a result of military bombardment) of the upper (trunk) level energy infrastructure is relevant. The research aims to substantiate approaches to ensuring the controlled operation of regional power systems under conditions of power shortages. The study applied the theory and methods of mathematical analysis of complex multicomponent systems in the form of mixed Boolean linear programming. A Mathematical Programming Language and modelling methods of the NEOS server based on the Gurobi solver were used to describe the modes of operation of power grids. A proportional power distribution method between load nodes of the post-emergency network configuration was used to determine the direction of electricity transit with the least losses. An algorithm and a program for solving the problem of addressing flows and power losses in multi-node regional power systems are proposed. The optimisation problem is formulated in the form of a mixed Boolean linear programming model with the criterion of minimising the power momentum, considering balance constraints and upper limits on the power between supply and load nodes. The developed transport matrix made it possible to find the optimal power distribution for emergency and post-emergency modes according to the criterion of minimum network losses. Algorithms and scenarios for the response of dispatching services were formed, considering the capacity of power grids and determining temporary power supply schemes, the configuration of which will ensure the “survivability” of energy islands. The levels of efficiency of the Boolean linear programming model associated with the connectivity of the cycles of the transport problem and the order of traversal of its vertices for test examples depending on the constraints imposed on the components of the network structure are established. The process of tracking electricity flows will allow for establishing routes connecting specific electricity supply nodes with load nodes and determining their shares in covering the energy island’s demand under conditions of power shortage

Static Voltage Stability Zoning Analysis Based on a Sensitivity Index Reflecting the Influence Degree of Photovoltaic Power Output on Voltage Stability

The large-scale integration of photovoltaic (PV) power can bring a greatly negative influence on the grid-connected system’s voltage stability. To study the static voltage stability (SVS) of PV grid-connected systems, the traditional SVS index, L-index, was re-examined. It was firstly derived and proved that the PV active output Ppv is proportional to the voltage phase angle of the PV station’s POI (Point of Interconnection), based on a simplified two-node system integrated with a PV station operating in PV (active power—voltage) mode or PQ (active power—reactive power) mode with unit power factor. Then a novel voltage stability sensitivity index LPAS-index was proposed that takes the derivative of the L-index with respect to the POI’s voltage phase angle, so as to reflect the influence degree of Ppv on the SVS of each load node. A SVS zoning analysis method for the PV grid-connected system was designed according to the classification results of load nodes based on the proposed LPAS-index, the power grid can be zoned into three kinds of areas that reflect different correlations between the SVS and Ppv: strong correlation, moderate correlation and weak correlation. Since the LPAS-index is less impacted by Ppv, the SVS zoning results are relatively unchanged. On the basis of a classic 14-node system with PV, the practicability of the zoning analysis method was verified. The simulation results show that the PV access point in general falls within the strongly or moderately associated area with Ppv. When most of the load nodes fall within the weakly associated area with Ppv, it is not necessary to consider the impact of Ppv and load power is still the main influencing factor on the SVS. In the multi-PV case, owing to the expansion of areas more affected by Ppv, an excessive Ppv can cause adverse influence on the SVS of the whole power grid; and an effective PV power-shedding measure is proposed to solve this problem. The proposed SVS zoning analysis method can be used for reference by power grid dispatchers.

Clearance of bacteria from lymph nodes in sheep immunized with Brucella suis S2 vaccine is associated with M1 macrophage activation

Ovine brucellosis is a global zoonotic disease of sheep caused by Brucella melitensis, which inflicts a significant burden on human and animal health. Brucella suis strain S2 (B. suis S2) is a smooth live attenuated vaccine for the prevention of ovine brucellosis in China. However, no previous studies have assessed the immunogenicity of B. suis S2 vaccine after oral immunization in sheep. Here, we attempted to evaluate the ovine immune response over the course of B. suis S2 immunization and to identify in vivo predictors for vaccine development. Body temperature, serum Brucella antibodies, serum cytokines (IL-12p70 and interferon [IFN]-γ), and bacterial load in the mandibular lymph nodes (LN), superficial cervical LN, superficial inguinal LN, and spleen were investigated to determine the safety and efficacy of the vaccine. The abnormal body temperature of sheep occurred within 8 days post-infection (dpi). Brucella suis S2 persisted for a short time (< 21 dpi) in the mandibular LN. The highest level of IL-12p70 was observed at 9 dpi, whereas serum IFN-γ levels peaked at 12 dpi. Transcriptome analysis and quantitative reverse transcription PCR were performed to determine gene expression profiles in the mandibular LN of sheep. Antigen processing and presentation pathway was the dominant pathway related to the dataset. Our studies suggest that the immune response in ovine LN resembled type 1 immunity with the secretion of IL-12p70 and IFN-γ after B.suis S2 immunization and the vaccine may eliminate Brucella via stimulation of M1 macrophages through the course of Th cells.

A Simulation Study of the Resiliency of Mobile Energy Storage Networks

Resilience is regarded as an essential design objective of a wide range of systems in modern society. This work is based on a vision that networks of mobile energy storage systems could provide an alternative off-grid power system design for rural and underdeveloped regions. To evaluate the resiliency of networked energy storage systems under overload failure, a model of concurrent cascading failure and healing processes is developed and demonstrated. Two resilience metrics are used to evaluate the resilience of a real-world network, namely the recovery level at a specified time and the recovery time. The simulations generate system trajectories at each time step. We explore the dependence of the system behavior on different model parameters that capture key recovery strategies. The success probability of the recovery of a failed node needs to be high enough for the network to restore its original functionality. Similarly, the increase in recovery budget parameter also leads to faster and higher recovery levels. However, in most cases, there appears to be upper limits for both parameters, beyond which any further increase could not improve the recovery performance. There is an optimum portion of the loads of the active neighboring nodes that will be carried by the newly recovered node that results in the shortest recovery times or highest recovery levels. Our work sheds light on how to enhance networked systems resiliency by considering the optimization of various model parameters.

Active and Reactive Power Coordination Optimization of the Active Distribution Network

The operation and control of the active distribution network are faced with great challenges due to a mass of tunable and controllable devices connected to the network, resulting in large active power loss and voltage deviation. In this paper, a method of active and reactive power coordination optimization for the active distribution network based on a one-dimensional convolutional neural network (1D-CNN) is proposed. This method can mine valuable information from the historical data of distribution networks, and use one-dimensional convolutional neural networks to map the complex nonlinear relationship between node load and optimization strategy. The simulation results of the modified IEEE33 node distribution network system show that the active power loss and the node voltage deviation of the proposed method are significantly reduced.

Joint offloading decision and resource allocation in vehicular edge computing networks

With the rapid development of Intelligent Transportation Systems (ITS), many new applications for Intelligent Connected Vehicles (ICVs) have sprung up. In order to tackle the conflict between delay-sensitive applications and resource-constrained vehicles, computation offloading paradigm that transfers computation tasks from ICVs to edge computing nodes has received extensive attention. However, the dynamic network conditions caused by the mobility of vehicles and the unbalanced computing load of edge nodes make ITS face challenges. In this paper, we propose a heterogeneous Vehicular Edge Computing (VEC) architecture with Task Vehicles (TaVs), Service Vehicles (SeVs) and Roadside Units (RSUs), and propose a distributed algorithm, namely PG-MRL, which jointly optimizes offloading decision and resource allocation. In the first stage, the offloading decisions of TaVs are obtained through a potential game. In the second stage, a multi-agent Deep Deterministic Policy Gradient (DDPG), one of deep reinforcement learning algorithms, with centralized training and distributed execution is proposed to optimize the real-time transmission power and subchannel selection. The simulation results show that the proposed PG-MRL algorithm has significant improvements over baseline algorithms in terms of system delay.

Evaluating distribution network optimal structure with respect to solar hosting capacity

Optimal design of distribution networks has become an important topic for analysis due to the growing share of photovoltaics (PV). Low and medium voltage networks should undergo structural changes to accommodate widespread PV generation and optimal operation. In this paper, the impact of the network structure on the solar hosting capacity (HC) is analyzed with respect to the role of low and medium voltage networks in power delivery. A given set of load nodes is simulated with multiple feeding substations and varying peak power and number of PV plants. The slime mold algorithm is utilized for numerous topology generations and measured load time series represent regions ranging from rural to urban. The results reveal that networks should go through significant structural changes to cope with larger PV generation and even more so to increase the HC. On the other hand, voltage control measures, such as on-load tap changers, PV reactive power control and curtailment, provide a competitive solution to varying size of distribution networks in hosting solar power. Finally, the analysis in this study provides evidence to a possible need in the change of residential PV policies in order to sustain the current pace of adopting PV plants in Finland.

Analysis of cascading failures caused by mobile overload attacks in scale-free networks

With the rapid network development, static-mobile networks have recently attracted much attention. To solve the problem of cascading failures in the static-mobile network, a cascading failures model is proposed based on mobile overload attacks. In this model, we use a Gaussian Markov model to generate the trajectories of the mobile nodes and define their load using the adjustable parameter β. Cascading failures are caused by mobile nodes attacking static nodes. Based on this triggering mechanism, we develop three different attack modes (i.e., high-degree overload attacks, low-degree overload attacks, and all overload attacks) in scale-free networks. The results show that: multiple processes of cascading failures occur at several different moments; reducing both the number and load of mobile nodes can increase the robustness of the network; among the three attack modes, the low-degree overload attacks cause the most significant scale of failure, followed by the high-degree overload attacks and finally all overload attacks. The experimental results can provide theoretical guidance for cascading failures in static-mobile networks.

Parameter Identification of Asynchronous Load Nodes

Asynchronous loads (AL), because of their low negative-sequence resistance, produce the effect of reduced unbalance at their connection points. Therefore, proper modeling of unbalanced load flows in power supply systems requires properly accounting for AL. Adequate models of the induction motor can be realized in the phase frame of reference. The effective use of such models is possible only if accurate data on the parameters of induction motor equivalent circuits for positive and negative sequences are available. Our analysis shows that the techniques used to determine these parameters on the basis of reference data can yield markedly disparate results. It is possible to overcome this difficulty by applying parameter identification methods that use the phase frame of reference. The paper proposes a technique for parameter identification of models of individual induction motors and asynchronous load nodes. The results of computer-aided simulation allow us to conclude that by using parameter identification, we can obtain an equivalent model of an asynchronous load node, and such a model provides high accuracy for both balanced and unbalanced load flow analysis. By varying load flow parameters, we demonstrate that the model proves valid over a wide range of their values. We have proposed a technique for the identification of asynchronous load nodes with such asynchronous loads, including electrical drives equipped with static frequency converters. With the aid of the AL identification models proposed in this paper, it is possible to solve the following practical tasks of management of electric power systems: increasing the accuracy of modeling their operating conditions; making informed decisions when taking measures to reduce unbalance in power grids while accounting for the balancing adjustment effect of AL.

Predicting the cascading dynamics in complex networks via the bimodal failure size distribution.

Cascading failure as a systematic risk occurs in a wide range of real-world networks. Cascade size distribution is a basic and crucial characteristic of systemic cascade behaviors. Recent research works have revealed that the distribution of cascade sizes is a bimodal form indicating the existence of either very small cascades or large ones. In this paper, we aim to understand the properties and formation characteristics of such bimodal distribution in complex networks and further predict the final cascade size. We first find that the bimodal distribution is ubiquitous under certain conditions in both synthetic and real networks. Moreover, the large cascades distributed in the right peak of bimodal distribution are resulted from either the failure of nodes with high load at the first step of the cascade or multiple rounds of cascades triggered by the initial failure. Accordingly, we propose a hybrid load metric (HLM), which combines the load of the initial broken node and the load of failed nodes triggered by the initial failure, to predict the final size of cascading failures. We validate the effectiveness of HLM by computing the accuracy of identifying the cascades belonging to the right and left peaks of the bimodal distribution. The results show that HLM is a better predictor than commonly used network centrality metrics in both synthetic and real-world networks. Finally, the influence of network structure on the optimal HLM is discussed.

Vulnerable Line Identification of Cascading Failure in Power Grid Based on New Electrical Betweenness

In this brief, a new electrical betweenness method is proposed to identify the vulnerable lines of the power grid. This method can better balance the accuracy and efficiency of identifying vulnerable lines. The proposed power percentage of generator-load is used as an indicator to evaluate the stability of power grid, and the dynamic influence of generator and load node removal on power grid is considered. The nonlinear cascading failure model of complex networks with overload and weighted edges is introduced, and the three states of normal-failure-overload of lines are fully considered. The model used is more in line with the actual power system. The simulation analysis on IEEE 39 and 118-bus system shows that the change of power loss caused by removing a certain proportion of vulnerable lines and deliberately attacking high new electrical betweenness in turn is compared. The feasibility and effectiveness of this method in identifying vulnerable lines in cascading failure model can be verified.

A New Restoration Strategy in Microgrids after a Blackout with Priority in Critical Loads

The danger of a total blackout in a wide area or, even worse, in a country is always present. The restoration methods after a blackout mainly focus on the strategy that the dispatchers in the control centers of the Transmission System Operator will follow than the abilities that the distribution’s microgrids have. This study suggests a restoration technique to improve distribution system resilience following a blackout, using distributed generation for the restoration of important loads. The goal of the restoration problem is to maximize the number of critical loads that are restored following the catastrophic incident. Under the restrictions of the DGs and the network, the DGs with good black start capability are restored first. Load weight and node importance degree are suggested during the recovery path selection procedure, while taking node topological importance and load importance into account. A mixed-integer linear program (MILP) is used to simulate the issue, and the modified IEEE 39-bus test system is used to verify the efficacy of the suggested restoration approach.

Multihop Clustering Routing Protocol Based on Improved Coronavirus Herd Immunity Optimizer and Q-Learning in WSNs

Wireless sensor networks (WSNs) are composed of a large number of spatially distributed sensor nodes to monitor and transmit information from the environment. However, the batteries used by these sensor nodes have limited energy and cannot be charged or replaced due to the harsh deployment environment. This energy limitation will seriously affect the lifetime of the network. Therefore, the purpose of this research is to reduce energy consumption and balance the load of sensor nodes by clustering routing protocols, so as to prolong the lifetime of the network. First, the coronavirus herd immune optimizer is improved and used to optimize the network clustering. Second, the cluster heads (CHs) are selected according to the energy and location factors in the clusters, and a reasonable CH replacement mechanism is designed to avoid the extra communication energy consumption caused by the frequent replacement of CHs. Finally, a multihop routing mechanism between the CHs and the base station is constructed by Q-learning. Simulation results show that the proposed work can improve the structure of clusters, enhance the load balance of nodes, reduce network energy consumption, and prolong the network lifetime. The appearance time of the first energy-depleted node is delayed by 25.8%, 85.9%, and 162.2% compared with IGWO, ACA-LEACH, and DEAL in the monitoring area of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$300\times300$ </tex-math></inline-formula> m, respectively. In addition, the proposed protocol shows better adaptability in varying dynamic conditions.