Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

6LoWPAN in Wireless Sensor Network with IoT in 5G Technology for Network Secure Routing and Energy Efficiency

Today, interconnection and routing protocols must discover the best solution for secure data transformation with a variety of smart devices due to the growing influence of information technology, such as Internet of Things (IoT), in human life. In order to handle routing concerns with regard to new interconnection approaches like the 6LoWPAN protocol, it is required to offer an improved solution. This research propose novel technique in 6LoWPAN network secure routing and energy efficiency (EE) for WSN in IoT application based on 5G technology. Here the energy optimization has been carried out using clustered channel aware least square support vector machine (Cl_CHLSSVM). Then the secure routing has been carried out using fuzzy based Routing Protocol for low-power and Lossy Networks with kernel-particle swarm optimization (Fuz_RPL_KPSO). To serve needs of IoT applications, proposed method is cognizant of both node priorities as well as application priorities. Applications' sending rate allocation is modeled as a constrained optimization issue.Pxperimental analysis is carried out in terms of throughput of 96%, weighted fairness index of 77%, end-to-end delay of 59%, energy consumption of 86%, and buffer dropped packets of 51%

An Unsupervised Approach for Automatic Activity Recognition based on Hidden Markov Model Regression

Using supervised machine learning approaches to recognize human activities from on-body wearable accelerometers generally requires a large amount of labelled data. When ground truth information is not available, too expensive, time consuming or difficult to collect, one has to rely on unsupervised approaches. This paper presents a new unsupervised approach for human activity recognition from raw acceleration data measured using inertial wearable sensors. The proposed method is based upon joint segmentation of multidimensional time series using a Hidden Markov Model (HMM) in a multiple regression context. The model is learned in an unsupervised framework using the Expectation-Maximization (EM) algorithm where no activity labels are needed. The proposed method takes into account the sequential appearance of the data. It is therefore adapted for the temporal acceleration data to accurately detect the activities. It allows both segmentation and classification of the human activities. Experimental results are provided to demonstrate the efficiency of the proposed approach with respect to standard supervised and unsupervised classification approache