Predicting Internet of Things Data Traffic Through LSTM and Autoregressive Spectrum Analysis

The rapid increase of Internet of Things (IoT) applications and services has led to massive amounts of heterogeneous data. Hence, we need to re-think how IoT data influences the network. In this paper, we study the characteristics of IoT data traffic in the context of smart cities. Aiming at analyzing the influence of IoT data traffic on the access and core network, we generate various IoT data traffic according to the characteristics of different IoT applications. Based on the analysis of the inherent features of the aggregated IoT data traffic, we propose a Long Short-Term Memory (LSTM) model combined with autoregressive spectrum analysis to predict the IoT data traffic. In this model, the autoregressive spectrum analysis is used to estimate the minimum length of the historical data needed for predicting the traffic in the future, which alleviates LSTM's performance deterioration with the increase of sequence length. A sliding window enables predicting the long-term tendency of IoT data traffic while keeping the inherent features of the data traffic. The evaluation results show that the proposed model converges quickly and can predict the variations of IoT traffic more accurately than other methods and the general LSTM model.Peer reviewe

Predicting Internet of Things Data Traffic Through LSTM and Autoregressive Spectrum Analysis

The rapid increase of Internet of Things (IoT) applications and services has led to massive amounts of heterogeneous data. Hence, we need to re-think how IoT data influences the network. In this paper, we study the characteristics of IoT data traffic in the context of smart cities. Aiming at analyzing the influence of IoT data traffic on the access and core network, we generate various IoT data traffic according to the characteristics of different IoT applications. Based on the analysis of the inherent features of the aggregated IoT data traffic, we propose a Long Short-Term Memory (LSTM) model combined with autoregressive spectrum analysis to predict the IoT data traffic. In this model, the autoregressive spectrum analysis is used to estimate the minimum length of the historical data needed for predicting the traffic in the future, which alleviates LSTM's performance deterioration with the increase of sequence length. A sliding window enables predicting the long-term tendency of IoT data traffic while keeping the inherent features of the data traffic. The evaluation results show that the proposed model converges quickly and can predict the variations of IoT traffic more accurately than other methods and the general LSTM model.Peer reviewe

Using artificial intelligence to support emerging networks management approaches

In emergent networks such as Internet of Things (IoT) and 5G applications, network traffic estimation is of great importance to forecast impacts on resource allocation that can influence the quality of service. Besides, controlling the network delay caused with route selection is still a notable challenge, owing to the high mobility of the devices. To analyse the trade-off between traffic forecasting accuracy and the complexity of artificial intelligence models used in this scenario, this work first evaluates the behavior of several traffic load forecasting models in a resource sharing environment. Moreover, in order to alleviate the routing problem in highly dynamic ad-hoc networks, this work also proposes a machine-learning-based routing scheme to reduce network delay in the high-mobility scenarios of flying ad-hoc networks, entitled Q-FANET. The performance of this new algorithm is compared with other methods using the WSNet simulator. With the obtained complexity analysis and the performed simulations, on one hand the best traffic load forecast model can be chosen, and on the other, the proposed routing solution presents lower delay, higher packet delivery ratio and lower jitter in highly dynamic networks than existing state-of-art methods

A Fusion-Based Framework for Wireless Multimedia Sensor Networks in Surveillance Applications

Multimedia sensors enable monitoring applications to obtain more accurate and detailed information. However, the development of efficient and lightweight solutions for managing data traffic over wireless multimedia sensor networks (WMSNs) has become vital because of the excessive volume of data produced by multimedia sensors. As part of this motivation, this paper proposes a fusion-based WMSN framework that reduces the amount of data to be transmitted over the network by intra-node processing. This framework explores three main issues: 1) the design of a wireless multimedia sensor (WMS) node to detect objects using machine learning techniques; 2) a method for increasing the accuracy while reducing the amount of information transmitted by the WMS nodes to the base station, and; 3) a new cluster-based routing algorithm for the WMSNs that consumes less power than the currently used algorithms. In this context, a WMS node is designed and implemented using commercially available components. In order to reduce the amount of information to be transmitted to the base station and thereby extend the lifetime of a WMSN, a method for detecting and classifying objects on three different layers has been developed. A new energy-efficient cluster-based routing algorithm is developed to transfer the collected information/data to the sink. The proposed framework and the cluster-based routing algorithm are applied to our WMS nodes and tested experimentally. The results of the experiments clearly demonstrate the feasibility of the proposed WMSN architecture in the real-world surveillance applications

Mobile traffic modelling for wireless multimedia sensor networks in IoT

Wireless sensor networks suffer from some limitations such as energy constraints and the cooperative demands essential to perform multi-hop geographic routing for real-time applications. Quality of Service (QoS) depends to a great extent on offering participating nodes an incentive for collaborating. In this paper, we present a novel traffic model for a new-generation of sensor networks that supports a wide range of communication-intensive real-time multimedia applications. The model is used to investigate the effects of multi-hop communication on Intelligent Transportation Systems (ITS) via Markov discrete time M/M/1 queuing system. Moreover, an analytical formulation for the bit error rate (BER), and the critical path-loss model is presented. We address the degree of irregularity parameter for location-based switching with respect to two categories in distributed retransmission: the hop-by-hop and the end-to-end retransmission. Simulation results based on realistic case study and assumptions are performed to highlight the effects on the average packet delay, energy consumption, and network throughput. The findings presented in this work are of great help to designers of wireless multimedia sensor networks (WMSNs)

Mobile traffic modelling for wireless multimedia sensor networks in IoT

Wireless sensor networks suffer from some limitations such as energy constraints and the cooperative demands essential to perform multi-hop geographic routing for real-time applications. Quality of Service (QoS) depends to a great extent on offering participating nodes an incentive for collaborating. In this paper, we present a novel traffic model for a new-generation of sensor networks that supports a wide range of communication-intensive real-time multimedia applications. The model is used to investigate the effects of multi-hop communication on Intelligent Transportation Systems (ITS) via Markov discrete time M/M/1 queuing system. Moreover, an analytical formulation for the bit error rate (BER), and the critical path-loss model is presented. We address the degree of irregularity parameter for location-based switching with respect to two categories in distributed retransmission: the hop-by-hop and the end-to-end retransmission. Simulation results based on realistic case study and assumptions are performed to highlight the effects on the average packet delay, energy consumption, and network throughput. The findings presented in this work are of great help to designers of wireless multimedia sensor networks (WMSNs)