Continuous monitoring of chronic patients improves their quality of life and reduces the economic costs of the sanitary system. However, in order to ensure a good monitoring, high bandwidth and low delay are needed. The 5G technology offers higher bandwidth and lower delays and packets loss than previous technologies. This paper presents an architecture for smart eHealth monitoring of chronic patients. The architecture elements include wearable devices, to take measures from the body, and a smartphone in the patient side and a DataBase with an intelligent system which is able to send an alarm when it detects that it is happening something anomalous. The intelligent system uses machine learning in BigData taken from different hospitals and the data taken from the patient to diagnose and generate alarms. Experiment tests have been done to simulate the traffic from many users to the DataBase in order to evaluate the suitability of 5G in our architecture. When there is low number of users, like 100 or 200 users, we do not find big differences of round trip time between 4G and 5G, but when there are more users, like 1000 users, it increases considerably reaching 4 times more in 4G. The Packet Loss is almost null in 4G until 300 users while in 5G is possible to keep it null until 700 users. Our results point out that in order to have high number of patients continuously monitored, it is necessary to use 5G network because it offers low delays and guarantees the availability of bandwidth for all users.

As cities continue to grow, numerous initiatives for Smart Cities are being conducted. The concept of Smart City encompasses several concepts being governance, economy, management, infrastructure, technology and people. This means that a Smart City can have different communication needs. Wireless technologies such as WiFi, ZigBee, Bluetooth, WiMax, 4G or LTE (Long Term Evolution) have presented themselves as solutions to the communication needs of Smart City initiatives. However, as most of them employ unlicensed bands, interference and coexistence problems are increasing. In this paper, the wireless technologies available nowadays for IoT (Internet of Things) in Smart Cities are presented. Our contribution is a review of wireless technologies, their comparison and the problems that difficult coexistence among them. In order to do so, the characteristics and adequacy of wireless technologies to each domain are considered. The problems derived of over-crowded unlicensed spectrum and coexistence difficulties among each technology are discussed as well. Finally, power consumption concerns are addressed. 24 As the initiatives taken to turn a city into a smart city may be varied and have different requirements, employing one unified technology throughout the city may not be efficient. Although wired solutions present a reliable channel to transmit data, the cost of deploying a wired network that connects all devices is extremely high and the result would be considerably inefficient. Wireless technologies have presented themselves as a solution to the rapid growth of devices that are connected to the network as well as the increasing demand of services that allow monitoring cities. IoT is gaining rapid popularity and it is being utilized for transport, health, environment, animal monitoring and smart metering applications [4,5]. Wireless Sensor Networks (WSN) are being employed all over the world as a low-cost and low-energy consuming method to provide a communication mechanism [6]. However, wireless technologies are varied and their utilization should be considered depending on the application. Traffic type, distance, energy consumption or number of nodes are some of the factors that should be considered when deciding how to transmit the gathered data. Moreover, the abuse of wireless technologies may derive in some problems.The majority of the wireless technologies available nowadays employ the 2.4 GHz ISM (Industrial, Scientific and Medical) band. It has not been a great problem until wireless devices started growing at a fast pace. Cable connections between computers and peripherals are being replaced by technologies such as Bluetooth [7]. It is also being employed by home automation systems or telemedicine applications [8]. ZigBee is popular for monitoring systems where a great number of nodes and low energy consumption are required. Services that require interaction with humans are usually utilized with PCs, smartphones or tablets, such as Ambient Assisted Living (AAL) and healthcare systems, and employ WiFi or mo...

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Internet of Things (IoT) is rapidly gaining momentum in the scenario of telecommunications. Conventional networks allow for interactivity and data exchange, but these networks have not been designed for the new features and functions of IoT devices. In this paper, an algorithm is proposed to share common recourse among Things, that is, between different types of smart appliances. This proposal is based on an IoT network with centralized management architecture, controlled by an Artificial Intelligence (AI). The AI controller uses an algorithm which based on machine learning techniques, collecting information on the network through an information protocol. Every smart thing that connects to the network is announces through a protocol message called Function and Service Discovery Protocol (DFSP) over the queued message telemetry transport protocol (MQTT). The proposed algorithm is responsible for discovering and allocating resources in the networks. As a result, using our proposed algorithm in communication system provides the outperform efficiency and availability than that used in conventional communication systems for the integrate IoT devices.

IP Multimedia Subsystem (IMS) is a robust multimedia service. IMS becomes more important when delivering multimedia services. Multimedia service providers can benefit from IMS to ensure a good QoE (Quality of Experience) to their customers with minimal resources usage. In this paper, we propose an intelligent media distribution IMS system architecture for delivering video streaming. The system is based primarily on uploading a multimedia file to a server in the IMS. Later, other users can download the uploaded multimedia file from the IMS. In the system, we also provide the design of the heuristic decision methods and models based on probability distributions. Thus, our system takes into account the network parameters such as bandwidth, jitter, delay and packet loss that influence the QoE of the end-users. Moreover, we have considered the other parameters of the energy consumption such as CPU, RAM, temperature and number connected users that impact the result of the QoE. All these parameters are considered as input to our proposal management system. The measurements taken from the real test bench show the real performance and demonstrate the success of the system about ensuring the upload speed of the multimedia file, guaranteeing the QoE of end users and improving the energy efficiency of the IMS.

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