PoIR: A Node Selection Mechanism in Reputation-Based Blockchain Consensus Using Bidirectional LSTM Regression Model

This research presents a reputation-based blockchain consensus mechanism called Proof of Intelligent Reputation(PoIR)as an alternative to traditional Proof of Work(PoW).PoIR addresses the limitations of existing reputationbased consensus mechanisms by proposing a more decentralized and fair node selection process.The proposed PoIR consensus combines Bidirectional Long Short-Term Memory(BiLSTM)with the Network Entity Reputation Database(NERD)to generate reputation scores for network entities and select authoritative nodes.NERD records network entity profiles based on various sources,i.e.,Warden,Blacklists,DShield,AlienVault Open Threat Exchange(OTX),and MISP(Malware Information Sharing Platform).It summarizes these profile records into a reputation score value.The PoIR consensus mechanism utilizes these reputation scores to select authoritative nodes.The evaluation demonstrates that PoIR exhibits higher centralization resistance than PoS and PoW.Authoritative nodes were selected fairly during the 1000-block proposal round,ensuring a more decentralized blockchain ecosystem.In contrast,malicious nodes successfully monopolized 58%and 32%of transaction processes in PoS and PoW,respectively,but failed to do so in PoIR.The findings also indicate that PoIR offers efficient transaction times of 12 s,outperforms reputation-based consensus such as PoW,and is comparable to reputation-based consensus such as PoS.Furthermore,the model evaluation shows that BiLSTM outperforms other Recurrent Neural Network models,i.e.,BiGRU(Bidirectional Gated Recurrent Unit),UniLSTM(Unidirectional Long Short-Term Memory),and UniGRU(Unidirectional Gated Recurrent Unit)with 0.022 Root Mean Squared Error(RMSE).This study concludes that the PoIR consensus mechanism is more resistant to centralization than PoS and PoW.Integrating BiLSTM and NERD enhances the fairness and efficiency of blockchain applications.

A Fast Global Node Selection Algorithm for Bearings-only Target Localization

In the target tracking, the nodes aggregate their observations of the directions of arrival of the target. The network then uses an extended Kalman filter （EKF） to combine the measurements from multiple snapshots to track the target. In order to rapidly select the best subset of nodes to localize the target with the minimum mean square position error and low power consumption, this paper proposes a simple algorithm, which uses the location information of the target and the network. The lower botmd of localization error is utilized according to the distances between the target and the selected active nodes. Furthermore, the direction likelihoods of the active nodes is predicted by way of the node/target bearing distributing relationships.

An Area Optimization Based Cooperative Localization Algorithm with Node Selection

In recent years,position information has become a key feature to drive location and context aware services in mobile communication.Researchers from all over the world have proposed many solu-tions for indoor positioning over the past several years.However,due to weak signals,multipath or non-line-of-sight signal propagation,accurately and efficiently localizing targets in harsh indoor environments re-mains a challenging problem.To improve the perfor-mance in harsh environment with insufficient anchors,cooperative localization has emerged.In this paper,a novel cooperative localization algorithm,named area optimization and node selection based sum-product al-gorithm over a wireless network(AN-SPAWN),is de-scribed and analyzed.To alleviate the high compu-tational complexity and build optimized cooperative cluster,a node selection method is designed for the cooperative localization algorithm.Numerical experi-ment results indicate that our proposed algorithm has a higher accuracy and is less impacted by NLOS errors than other conventional cooperative localization algo-rithms in the harsh indoor environments.

The Next-Hop Node Selection Based GPSR in Vehicular Ad Hoc Networks

Vehicular Ad hoc Networks (VANETs) which is a special form of Mobile Ad hoc Networks (MANETs) has promising application prospects in the future. Due to the rapid changing of topology structure, how to find a route which can guarantee Quality of Service (QoS) is an important issue in VANETs. This paper presents an improved Greedy Perimeter Stateless Routing (GPSR) protocol based on our proposed next-hop node selection mechanism. Firstly, we define the link reliability in two cases which take the movement direction angle between two vehicles into consideration. Then we propose a next-hop node selection mechanism based on a weighted function which consists of link reliability between the sender node and next-hop candidate node, distance between next-hop candidate node and the destination, movement direction angle of next-hop candidate node. At last, an improved GPSR protocol is proposed based on the next-hop node selection mechanism. Simulation results are presented to evaluate the performance of the improved GPSR protocol, which shows that the performance including packet delivery ratio and average end-to-end delay of the proposed protocol is better in some situations.

Management of lateral pelvic lymph nodes in rectal cancer:Is it time to reach an Agreement?

In this editorial,we proceed to comment on the article by Chua et al,addressing the management of metastatic lateral pelvic lymph nodes(mLLN)in stage II/III rectal cancer patients below the peritoneal reflection.The treatment of this nodal area sparks significant controversy due to the strategic differences followed by Eastern and Western physicians,albeit with a higher degree of convergence in recent years.The dissection of lateral pelvic lymph nodes without neoadjuvant therapy is a standard practice in Eastern countries.In contrast,in the West,preference leans towards opting for neoadjuvant therapy with chemoradiotherapy or radiotherapy,that would cover the treatment of this area without the need to add the dissection of these nodes to the total mesorectal excision.In the presence of high-risk nodal characteristics for mLLN related to radiological imaging and lack of response to neoadjuvant therapy,the risk of lateral local recurrence increases,suggesting the appropriate selection of strategies to reduce the risk of recurrence in each patient profile.Despite the heterogeneous and retrospective nature of studies addressing this area,an international consensus is necessary to approach this clinical scenario uniformly.

Extracting Multiple Nodes in a Brain Region of Interest for Brain Functional Network Estimation and Classification

Purpose: Brain functional networks (BFNs) has become important approach for diagnosis of some neurological or psychological disorders. Before estimating BFN, obtaining blood oxygen level dependent (BOLD) representative signals from brain regions of interest (ROIs) is important. In the past decades, the common method is generally to take a ROI as a node, averaging all the voxel time series inside it to extract a representative signal. However, one node does not represent the entire information of this ROI, and averaging method often leads to signal cancellation and information loss. Inspired by this, we propose a novel model extraction method based on an assumption that a ROI can be represented by multiple nodes. Methods: In this paper, we first extract multiple nodes (the number is user-defined) from the ROI based on two traditional methods, including principal component analysis (PCA), and K-means (Clustering according to the spatial position of voxels). Then, canonical correlation analysis (CCA) was issued to construct BFNs by maximizing the correlation between the representative signals corresponding to the nodes in any two ROIs. Finally, to further verify the effectiveness of the proposed method, the estimated BFNs are applied to identify subjects with autism spectrum disorder (ASD) and mild cognitive impairment (MCI) from health controls (HCs). Results: Experimental results on two benchmark databases demonstrate that the proposed method outperforms the baseline method in the sense of classification performance. Conclusions: We propose a novel method for obtaining nodes of ROId based on the hypothesis that a ROI can be represented by multiple nodes, that is, to extract the node signals of ROIs with K-means or PCA. Then, CCA is used to construct BFNs.

Optimizing Service Stipulation Uncertainty with Deep Reinforcement Learning for Internet Vehicle Systems

Fog computing brings computational services near the network edge to meet the latency constraints of cyber-physical System(CPS)applications.Edge devices enable limited computational capacity and energy availability that hamper end user performance.We designed a novel performance measurement index to gauge a device’s resource capacity.This examination addresses the offloading mechanism issues,where the end user(EU)offloads a part of its workload to a nearby edge server(ES).Sometimes,the ES further offloads the workload to another ES or cloud server to achieve reliable performance because of limited resources(such as storage and computation).The manuscript aims to reduce the service offloading rate by selecting a potential device or server to accomplish a low average latency and service completion time to meet the deadline constraints of sub-divided services.In this regard,an adaptive online status predictive model design is significant for prognosticating the asset requirement of arrived services to make float decisions.Consequently,the development of a reinforcement learning-based flexible x-scheduling(RFXS)approach resolves the service offloading issues,where x=service/resource for producing the low latency and high performance of the network.Our approach to the theoretical bound and computational complexity is derived by formulating the system efficiency.A quadratic restraint mechanism is employed to formulate the service optimization issue according to a set ofmeasurements,as well as the behavioural association rate and adulation factor.Our system managed an average 0.89%of the service offloading rate,with 39 ms of delay over complex scenarios(using three servers with a 50%service arrival rate).The simulation outcomes confirm that the proposed scheme attained a low offloading uncertainty,and is suitable for simulating heterogeneous CPS frameworks.

Optimization of Load Balancing Algorithm for Virtual Machine Dynamic Migration under Mobile Cloud Computing

As one of the key technologies of cloud computing,the virtualization technology can virtualize all kinds of resources and integrate them into the unified planning of the cloud computing management platform.The migration of virtual machines is one of the important technologies of virtual machine applications.However,there are still many deficiencies in the implementation of load balancing by virtual machine dynamic migration in cloud computing.Traditional triggering strategy thresholds are mostly fixed.If there is an instantaneous peak,it will cause migration,which will cause a waste of resources.In order to solve this problem,based on improving the dynamic migration framework,this paper proposes node selection optimization algorithm and node load balancing strategy and designs a prediction module,which uses a one-time smooth prediction to avoid the shortcoming of peak load moment.The simulation experiments and conclusions analysis results show that the fusion algorithm has performance advantages obvious.

Lightweight Direct Acyclic Graph Blockchain for Enhancing Resource-Constrained IoT Environment

Blockchain technology is regarded as the emergent security solution for many applications related to the Internet of Things(IoT).In concept,blockchain has a linear structure that grows with the number of transactions entered.This growth in size is the main obstacle to the blockchain,which makes it unsuitable for resource-constrained IoT environments.Moreover,conventional consensus algorithms such as PoW,PoS are very computationally heavy.This paper solves these problems by introducing a new lightweight blockchain structure and lightweight consensus algorithm.The Multi-Zone Direct Acyclic Graph(DAG)Blockchain(Multizone-DAG-Blockchain)framework is proposed for the fog-based IoT environment.In this context,fog computing technology is integrated with the IoT to offload IoT tasks to the fog nodes,thus preserving the energy consumption of the IoT devices.Both IoT and fog nodes are initially authenticated using a non-cloneable physical function-based validationmechanism(DPUF-VM)inwhichmultiple authentication certificates are verified in the blockchain.Each transaction is stored in a hash function in the blockchain using the lightweight CubeHash algorithm and signed by the Four-Q-Curve algorithm.In the cloud,sensitive data is stored as ciphertext.Fog nodes provide data security to avoid the energy consumption and complexity of IoT nodes.The fog node first performs a redundancy analysis using the Jaccard Similarity(JS)measure and sensitivity analysis using the Neutrosophic Neural Intelligent Network(N2IN)algorithm.A lightweight proof-of-authentication(PoAh)algorithm is presented and executed by the optimal consensus node selected by the bi-objective spiral optimization(BoSo)algorithm for transaction validation.The proposed work is modeled in Network Simulator 3.26(ns-3.26),and the performance is evaluated in terms of energy consumption,storage cost,response time,and throughput.

QoS Constrained Network Coding Technique to Data Transmission Using IoT

The research work presents,constrained network coding technique to ensure the successful data transmission based composite channel cmos technology using dielectric properties.The charge fragmentation and charge splitting are two components of the filtered switch domino(FSD)technique.Further behavior of selected switching is achieved using generator called conditional pulse generator which is employed in Multi Dynamic Node Domino(MDND)technique.Both FSD and MDND technique need wide area compared to existing single nodekeeper domino technique.The aim of this research is to minimize dissipation of power and to achieve less consumption of power.The proposed research,works by introducing the method namely Interference and throughput aware Optimized Multicast Routing Protocol(IT-OMRP).The main goal of this proposed research method is to introduce the system which can forward the data packets towards the destination securely and successfully.To achieve the bandwidth and throughput in optimized data transmission,proposed multicast tree is selected by Particle Swarm Optimization which will select the most optimal host node as the branches of multi cast tree.Here node selection is done by considering the objectives residual energy,residual bandwidth and throughput.After node selection multi cast routing is done with the concern of interference to ensure the reliable and successful data transmission.In case of transmission range size is higher than the coverage sense range,successful routing is ensured by selecting secondary host forwarders as a backup which will act as intermediate relay forwarders.The NS2 simulator is used to evaluate research outcome from which it is proved that the proposed technique tends to have increased packet delivery ratio than the existing work.

Research on reference nodes placement and selection of ubiquitous computing locating service

Obtaining the location of an unknown node accurately is a key problem of a locating service under a ubiquitous computing environment.The paper proposes and proves three theorems of location reference node placement according to the analysis of the location error produced during location using a polygon location method and three important characteristics of chaos dynamics.Based on the three theorems,the location reference node selection(LRNS)algorithm is proposed by improving on the traditional polygon location algorithm.The simulation results indicate that the reference node placement theorems and the LRNS algo-rithm can meet the requirements of a ubiquitous terminal’s real-time location and possess a preferable precision in location.

Mutual-information based weighted fusion for target tracking in underwater wireless sensor networks

Underwater wireless sensor networks(UWSNs)can provide a promising solution to underwater target tracking.Due to limited energy and bandwidth resources,only a small number of nodes are selected to track a target at each interval.Because all measurements are fused together to provide information in a fusion center,fusion weights of all selected nodes may affect the performance of target tracking.As far as we know,almost all existing tracking schemes neglect this problem.We study a weighted fusion scheme for target tracking in UWSNs.First,because the mutual information(MI)between a node’s measurement and the target state can quantify target information provided by the node,it is calculated to determine proper fusion weights.Second,we design a novel multi-sensor weighted particle filter(MSWPF)using fusion weights determined by MI.Third,we present a local node selection scheme based on posterior Cramer-Rao lower bound(PCRLB)to improve tracking efficiency.Finally,simulation results are presented to verify the performance improvement of our scheme with proper fusion weights.