Algorithm For Internet Of Things Research Articles

Traffic prediction for diverse edge IoT data using graph network

AbstractMore researchers are proposing artificial intelligence algorithms for Internet of Things (IoT) devices and applying them to themes such as smart cities and smart transportation. In recent years, relevant research has mainly focused on data processing and algorithm modeling, and most have shown good prediction results. However, many algorithmic models often adjust parameters for the corresponding datasets, so the robustness of the models is weak. When different types of data face other model parameters, the prediction performance often varies a lot. Thus, this work starts from the perspective of data processing and algorithm models. Taking traffic data as an example, we first propose a new data processing method that processes traffic data with different attributes and characteristics into a dataset that is more common for most models. Then we will compare different types of datasets from the perspective of multiple model parameters, and further analyze the precautions and changing trends of different traffic data in machine learning. Finally, different types of data and ranges of model parameters are explored, together with possible reasons for fluctuations in forecast results when data parameters change.

A new intrusion detection system based on SVM–GWO algorithms for Internet of Things

A new intrusion detection system based on SVM–GWO algorithms for Internet of Things

Data analysis algorithm for internet of things based on federated learning with optical technology

Data analysis algorithm for internet of things based on federated learning with optical technology

Machine Learning Algorithms for Internet of Things: Unleashing the Power of Smart Connectivity

The combination of Machine Learning (Machine learning) algorithms and the Internet of Things (IoT) has transformed how we engage with our environment. The ability to analyze and collect enormous volumes of data generated by networked devices has created new opportunities for optimization, Smart decision-making, and automation. This chapter will focus on different machine learning algorithms that can help improve the capabilities of IoT devices. We will examine the key algorithms and their applications in the IoT sector, ranging from supervised and unsupervised learning techniques to deep learning and reinforcement learning models. Keyword: Machine Learning, Internet of things (IOT), K- Means, Linear Regression, Logistic Regression, PCA, Deep Learning, Smart city, CNN, LDA.

Energy efficient clustering-based routing algorithm for internet of things

Routing process is one of the most critical processes in wireless sensor network (WSN). Due to WSN is mainly used in many applications in internet of things (IoT), routing algorithm can affect the performance of these applications. Thus, the usage of inefficient routing algorithm may lead to losing the data collected by sensors. Moreover, it will cause the sensors to waste energy. This paper proposes an energy efficient clustering-based routing algorithm that is based on tunicate swarm algorithm (TSA). TSAbased clustering algorithm selects the optimal cluster head by calculating the remaining energy, the distance to the base station (BS), the distance to each cluster member and balancing the load between the created clusters. TSAbased routing algorithm is used to create paths from cluster heads to the BS using relay nodes. The TSA-based routing algorithm creates the paths based on the path length, the count of relay nodes in the path, and the number of cluster members of each relay node. The result shows that the proposed algorithm is promising in respect of extending the lifetime of the network and conserving the energy.

Security defense strategy algorithm for Internet of Things based on deep reinforcement learning

Currently, important privacy data of the Internet of Things (IoT) face extremely high risks of leakage. Attackers persistently engage in continuous attacks on terminal devices to obtain private data of crucial importance. Although significant progress has been made in recent years in deep reinforcement learning defense strategies, most defense methods still face problems such as low defense resource allocation efficiency and insufficient defense coordination capabilities. To solve the above problems, this paper constructs a novel adversarial security scenario and proposes a security game model that integrates defense resource allocation and patrol inspection. Regarding the above game model, this paper designs a deep reinforcement learning algorithm named SDSA to calculate its security defense strategy. SDSA calculates the allocation strategy of the best patrolling strategy that is most suitable for the defender by searching the policy on a multi-dimensional discrete action space, and enables multiple defense agents to cooperate efficiently by training a multi-intelligent Dueling Double Deep Q-Network (D3QN) with prioritized experience replay. Finally, the experimental results show that the SDSA-learned security defense strategy can provide a feasible and effective security protection strategy for defenders against attacks compared to the MADDPG and OptGradFP methods.

Energy and Distribution-Aware Cooperative Clustering Algorithm in Internet of Things (IoT)-Based Federated Learning

In Internet of Things (IoT)-based federated learning (FL), if IoT devices are located far from the base station (BS), they consume lots of energy to transmit the updated parameters to BS, which can cause energy depletion of IoT devices. To mitigate this problem, IoT devices can be clustered and the clustered IoT devices transmit the updated parameters to their cluster headers (CHs) instead of BS. However, if the aggregated data distribution in each cluster is non-independent and identically distributed (non-IID), the desired accuracy of the model cannot be achieved. In this paper, we propose an energy and distribution-aware cooperative clustering algorithm (EDA-CCA) where several IoT devices with a sufficient energy level are selected as CHs. By considering the distance to these CHs and BS and the data distribution of IoT devices, other IoT devices are clustered, and then a hierarchical parameter aggregation (i.e., sequential aggregation within each cluster and synchronous aggregation between CHs and FL server) is conducted. Evaluation results demonstrate that EDA-CCA can make the model having the desired accuracy with the lowest energy consumption among the comparison schemes.

An edge-aided parallel evolutionary privacy-preserving algorithm for Internet of Things

Data sanitization in the context of Internet of Things (IoT) privacy refers to the process of permanently and irreversibly hiding all sensitive information from vast amounts of streaming data. Taking into account the dynamic and real-time characteristics of streaming IoT data, we propose a parallel evolutionary Privacy-Preserving Data Mining (PPDM), called High-performance Evolutionary Data Sanitization for IoT (HEDS4IoT), and implement two mechanisms on a Graphics Processing Units (GPU)-aided parallelized platform to achieve real-time streaming protected data transmission. The first mechanism, the Parallel Indexing Engine (PIE), generates retrieval index lists from the dataset using GPU blocks. These lists are used in place of the dataset during the PPDM process. The second mechanism, called Parallel Fitness Function Engine (PF2E), parallelizes the index lists on the GPU threads to speed up the computation of the quality of solutions generated by the evolutionary algorithm, in which deferential evolution is adopted as the evolutionary algorithm. In addition to the ability for Big data, the HEDS4IoT can be adaptively adjusted for dynamic nature of IoT where new streaming data is considered for data sanitization. Our experimental results with extensive benchmarks show that, at the kernel level, the PIE and PF2E mechanisms are averagely 33.5x and 53.7x faster than their CPU-implemented version, respectively. At the application level, our findings demonstrate that the HEDS4IoT can perform the PPDM process 47.7x faster than some of the state-of-art methods.

Optimization of Energy-Efficient Cluster Head Selection Algorithm for Internet of Things in Wireless Sensor Networks

The Internet of Things (IoT) now uses the Wireless Sensor Network (WSN) as a platform to sense and communicate data. The increase in the number of embedded and interconnected devices on the Internet has resulted in a need for software solutions to manage them proficiently in an elegant and scalable manner. Also, these devices can generate massive amounts of data, resulting in a classic Big Data problem that must be stored and processed. Large volumes of information have to be produced by using IoT applications, thus raising two major issues in big data analytics. To ensure an efficient form of mining of both spatial and temporal data, a sensed sample has to be collected. So for this work, a new strategy to remove redundancy has been proposed. This classifies all forms of collected data to be either relevant or irrelevant in choosing suitable information even before they are forwarded to the base station or the cluster head. A Low-Energy Adaptive Clustering Hierarchy (LEACH) is a cluster-based routing protocol that uses cluster formation. The LEACH chooses one head from the network sensor nodes, such as the Cluster Head (CH), to rotate the role to a new distributed energy load. The CHs were chosen randomly with the possibility of all CHs being concentrated in one locality. The primary idea behind such dynamic clustering was them resulted in more overheads due to changes in the CH and advertisements. Therefore, the LEACH was not suitable for large networks. Here, Particle Swarm Optimization (PSO) and River Formation Dynamics are used to optimize the CH selection (RFD). The results proved that the proposed method to have performed better compared to other methods.

Parameter-Sharing-Based Average-Consensus Time Synchronization in IoT Networks

Average-consensus protocol is one of the ways to develop distributed time-synchronization algorithms in Internet of Things (IoT) networks. However, the large number of iteration leads to a common time notion issue in nodes. This poses a critical challenge in the convergence of the time-synchronization algorithm and resulting asymptotic convergence in the average consensus protocol. In this paper, a parameter-sharing-based average-consensus time-synchronization (PACTS) algorithm is proposed. For fast convergence, the proposed PACTS quickly forwards the time information to multi-hop nodes and employs multi-hop average-consensus instead of single-hop average-consensus. Specifically, a node asynchronously and periodically broadcasts the relative clock offset estimation of neighbors with its local time information. Meanwhile, the relative clock offset estimation of the multi-hop node is calculated and used to estimate the average value. Consequently, an average-consensus among local multi-hop nodes is obtained. As a result, the iteration number and convergence time are significantly reduced over the network. Finally, the experimental results indicate that the proposed PACTS algorithm has low complexity, high accuracy, and quick convergence.

A comprehensive analysis for crowd counting methodologies and algorithms in Internet of Things

A comprehensive analysis for crowd counting methodologies and algorithms in Internet of Things

Network Intrusion Detection Method Using Stacked BILSTM Elastic Regression Classifier with Aquila Optimizer Algorithm for Internet of Things (IoT)

Globally, over the past ten years, computer networks and Internet of Things (IoT) networks have grown significantly due to the increasing amount of data that has been collected, ranging from zettabytes to petabytes. As a result, as the network has expanded, security problems have also emerged. The large data sets involved in these types of attacks can make detection difficult. The developing networks are being used for a multitude of sophisticated purposes, such as smart homes, cities, grids, gadgets, and objects, as well as e-commerce, e-banking, and e-government. As a result of the development of numerous intrusion detection systems (IDS), computer networks are now protected from security and privacy threats. Data confidentiality, integrity, and availability will suffer if IDS prevention efforts fail. Complex attacks can't be handled by traditional methods.  There has been a growing interest in advanced deep learning techniques for detecting intrusions and identifying abnormal behavior in networks. This research aims to propose a novel network namely stacked BiLSTM elastic regression classifier (Stack_BiLSTM-ERC) with Aquila optimizer algorithm for feature selection. This optimization method computes use of a cutting-edge transition function that enables it to be transformed into a binary form of the Aquila optimizer. A better solution could be secured once number of possible solutions are found from diverse regions of the search space utilizing the Aquila optimizer method. NSL-KDD and UNSW-NB15 are two datasets that enable learning characteristics from the raw data in order to detect harmful prerequisites characteristics and effective framework patterns. The proposed Stack_BiLSTM-ERC achieves 98.l3% of accuracy, 95.1% of precision, 94.3% of recall and 95.4 of F1-score for NSL-KDD dataset. Moreover, 98.6% of accuracy, 97.2% of precision, 98.5 of recall and 97.5% of F1-score.

Fault classification algorithm for internet of things devices based on deep learning

Fault classification algorithm for internet of things devices based on deep learning

A Hybrid Delay Aware Clustered Routing Approach Using Aquila Optimizer and Firefly Algorithm in Internet of Things

Protocols for clustering and routing in the Internet of Things ecosystem should consider minimizing power consumption. Existing approaches to cluster-based routing issues in the Internet of Things environment often face the challenge of uneven power consumption. This study created a clustering method utilising swarm intelligence to obtain a more even distribution of cluster heads. In this work, a firefly optimization method and an aquila optimizer algorithm are devised to select the intermediate and cluster head nodes required for routing in accordance with the NP-Hard nature of clustered routing. The effectiveness of this hybrid clustering and routing approach has been evaluated concerning the following metrics: remaining energy, mean distances, number of hops, and node balance. For assessing Internet of things platforms, metrics like network throughput and the number of the living node are crucial, as these systems rely on battery-operated equipment to regularly capture environment data and transmit specimens to a base station. Proving effective, the suggested technique has been found to improve system energy usage by at least 18% and increase the packet delivery ratio by at least 25%.

Elastic Hop Count Trickle Timer Algorithm in Internet of Things

The Internet of Things (IoT) is a technology that allows machines to communicate with each other without the need for human interaction. Usually, IoT devices are connected via a network. A wide range of network technologies are required to make the IoT concept operate successfully; as a result, protocols at various network layers are used. One of the most extensively used network layer routing protocols is the Routing Protocol for Low Power and Lossy Networks (RPL). One of the primary components of RPL is the trickle timer method. The trickle algorithm directly impacts the time it takes for control messages to arrive. It has a listen-only period, which causes load imbalance and delays for nodes in the trickle algorithm. By making the trickle timer method run dynamically based on hop count, this research proposed a novel way of dealing with the difficulties of the traditional algorithm, which is called the Elastic Hop Count Trickle Timer Algorithm. Simulation experiments have been implemented using the Contiki Cooja 3.0 simulator to study the performance of RPL employing the dynamic trickle timer approach. Simulation results proved that the proposed algorithm outperforms the results of the traditional trickle algorithm, dynamic algorithm, and e-trickle algorithm in terms of consumed power, convergence time, and packet delivery ratio.

Smart Distributed Multi-Ledger Construction Algorithm for Internet of things (IoT)

The Internet of Objects (IoT) could be a new paradigm for the linked, data-exchanging nature of things and their parts through a network supported by nodes. IoT has risen to the forefront of emerging technologies in a very type of applications, including peer-to-peer networks, smart energy grids, home and building automation, vehicle-to-vehicle communication, and wearable computing devices, because of the widespread use of small devices and embedded sensor frameworks. thanks to its tremendous expansion and widespread use, it's posed security issues that may prevent it from getting used during a style of new applications. The growing number of networked gadgets creates several access points for attackers, also as security issues. due to the fragile nature of IoT applications including health, automation, and energy grids, security concerns can't be tolerated. DLT (Distributed Ledger Technology) is one such technology which will help to mitigate IoT security threats. The vulnerability of a central node, which can compromise the whole system, are often reduced by adopting a distributed ledger to eliminate the necessity for one node. Blockchain, a distributed ledger technology, has sparked plenty of interest and generated real-world value. Here we construct a distributed multi ledger construction algorithm for peer-to-peer data transaction which involves key generation for each transaction.

Heterogeneous data mining algorithm of Internet of things based on Spark of biotech and artificial intelligence architecture

Due to the limitation of memory, heterogeneous data mining algorithm of Internet of things takes too long to process large-scale data of biotech companies. This paper proposes a heterogeneous data mining algorithm for Internet of things based on Spark artificial intelligence architecture. The heterogeneous data is abstracted into the characteristic value of the unified dimension related to the target task, the attribute is established and the preprocessing result is output. A distributed parallel computing framework is established by using Spark artificial intelligence architecture, and the preprocessed data is distributed to different execution points. The data of each execution point is converted into bit matrix to calculate the attribute association degree, and the data is partitioned according to the association result. According to association rules of biotech companies, local and global frequent pattern trees are generated to complete heterogeneous data mining. The experimental results show that the execution time of the heterogeneous data mining algorithm designed in this paper is 1892s, which is 1147s, 1991s and 624s less than HUIM-ACO algorithm, FP-Growth algorithm and ENFP-Growth algorithm, respectively. The algorithm designed in this paper can effectively shorten data processing time and improve the execution efficiency.

Secure Clustering Strategy Based on Improved Particle Swarm Optimization Algorithm in Internet of Things.

This paper proposes a secure clustering strategy based on improved particle swarm optimization (PSO) in the environment of the Internet of Things (IoT). First, in the process of cluster head election, by considering the residual energy and load balance of nodes, a new fitness function is established to evaluate and select better candidate cluster head nodes. Second, the optimized adaptive learning factor is used to adjust the location update speed of candidate cluster head nodes, expand the local search, and accelerate the convergence speed of global search. Finally, in the stage of forwarding node election and data transmission, in order to reduce the energy consumption of forwarding nodes, each cluster head node elects a forwarding node among the ordinary nodes in its cluster, so that the elected forwarding nodes have the optimal energy and location relationship. Experiments show that the proposed method effectively prolongs the network lifetime compared with the comparison methods. The average node degree of the proposed method is less than 2.5.

Intelligent Load Balancing Algorithms for Internet of Things - A Review

Abstract: The Internet of Things has witnessed an upsurge in the number of sensors communicating with each other over the Internet, and this number, currently in billions, is increasing at an expeditious pace. However, this augmentation in the number of interlaced devices can lead to profusion and jamming of the network, thereby degrading energy, latency, and throughput. Load balancing of the network is one of the techniques which could alleviate this issue. This paper reviews the methods that have been employed for load balancing of the Internet of Things, thereby serving the research community two-fold. Firstly, it gives a comprehensive introduction to the classification of load balancing algorithms. Secondly, it offers researchers the prospect of developing intelligent novel algorithms catering to the load balancing predicament.

A Load Forecasting Algorithm for Power Internet of Things Using Training Data Dimension Expansion and Ensemble Learning Technique

With the development of the power internet of things (IOT), load forecasting will play an important role the power system. It can optimize the power generation planning and improve the economical operation of power IOT. In this paper, a new loading forecasting algorithm for power IOT is proposed using training data dimension expansion and ensemble learning. In the offline phase, the obtained meteorological data and time information is normalized to remove the unit effect at first. Then, the Hampel filter is used to cope with the outliers of the meteorological data from sensors. Through the preprocessing, the fingerprint of the training data is constructed. Next, the matrix multiplication method is proposed to expand the dimension of training data fingerprint information. Finally, the ensemble learning combining multiple long short-term memory (LSTM) networks are proposed to obtain multiple power load forecasting models and corresponding weight coefficients. In the online phase, the obtained meteorological data and time information are preprocessed to form the input of and power load forecasting models. The final power load forecasting is obtained by linear weighted sum method with intermediate forecasting result. In the proposed algorithm, more features of training data can be obtained by the data dimension expansion. Moreover, the ensemble learning using LSTM can make fully use of the timing sequence of training and improve the generalization performance of offline training. Experiment results illustrate that the proposed algorithm has better forecasting performance than existing methods.