The MQTT protocol is one of the Internet of Things (loT) application protocols. It is based on publishing/subscribing messaging patterns. This protocol uses a broker to manage the communication between the subscribers and the publishers over the internet. There were two problems associated with the implementation of this protocol. The first one, there are many sensors generating huge data using the very simple IoT architecture. These big data may lead to congestion on the internet and thus will cause several disadvantages such as decreasing throughput, packet loss, and delay, which in turn may affect the performance of the MQTT broker, thus affecting the mechanism of the performance of the IoT application. The second problem, the MQTT protocol does not contain a priority management algorithm which is considered a very important and special issue in health applications. In this paper, a smart gateway is designed to enhance the MQTT networks. This work introduced the smart e-health gateway used in MQTT based remote Electrocardiogram (ECG) monitoring system. A new architecture has been implemented that contains two new components, the local broker and the publisher /subscriber (pub/sub) models. The local broker works in the local network and the publisher /subscriber (pub/sub) model is responsible for the process of smart in the gateway. One of the smart applications was used is a filter application. We’ve taken the data filtering side and give it a priority, as an example in order to know the possibility of achieving the architecture of the gateway that was created in our work. And is this architecture valid to work or invalid. Therefore, we conducted a test using the application of ECG signal filtration and give priority to patients according to the case of an urgent for each patient. The R-peaks were calculated from the ECG signals-peaks for the identification of heart rate. The urgent level will be classified depending on the beats rate feature. The proposed smart gateway function is to manage the data rate that will pass through to the internet. In the case of high urgent level, all the packets will be passed, while in case of a low level, the least possible data will be passed. The proposed system scenarios are designed and evaluated by using the OMNET simulation environment and Inet4 framework. The simulation results are shown by the evaluation methods used (delay, throughput, packets sent, and packets received). These results proved the success of the architectural work that was implemented in the system.

Internet of things (IoT) is the paradigm for internetworking devices for data exchange and control. One of the important problems facing the IoT networks is the congestion; many researchers have discussed this problem. In this research, a comparison between two congestion control mechanisms used for internet of things the (CoAP and CoCoA) is presented. The cooja simulator was used for simulating different topologies and scenarios and the packets transmitted was captured using wireshak utility program. The captured packets were analyzed using MATLAB tools. The analysis showed that the CoCoA outperform CoAP in terms of goodput, number of dropped packets and bandwidth utilization.

Abstract— Health monitoring based on the internet of things (IoT) and cloud computing is regarded as a hot topic to research. However, such systems often face issues with delay and throughput due to the large amount of data that must be transmitted from sensors to the cloud. One important type of data for health monitoring is Electrocardiogram (ECG) signals, which generate a large amount of data to be transmitted. This research treats this problem by modelling these signals in order to reduce their size using Gaussian approximation. The cloud server is an MQTT broker to which the sensors publish their data via a gateway. The Gaussian parameters are calculated in the gateway, which act as a Fog layer, before published to the broker. The monitoring devices can subscribe to the broker and access the transmitted data. Our experiments were conducted using the MIT–BIH dataset and a real broker (HiveMQ). The results showed that the system was able to significantly reduce delay in transmitting data and prevent loss of information. Without using the Gaussian approximation technique, the system was only able to monitor a limited number of patients (17 for Qos1 and 23 for Qos0) without losing information. However, when using the Gaussian approximation model with five functions, the system was able to monitor many more patients (78 for Qos1 and 100 or more for Qos0) without losing any data.

This research deals with measurement of the density of vehicles traffic. The traffic density is estimated from an image captured using the ordinary optical camera. An image processing methods is used and the edge of the objects is extracted. A two dimensional wavelet transform is used as a feature extraction. The extracted features were reduced by Multiple Region Centroid Estimation. A neural network is trained using many sets of images with different Traffic densities then it is used for traffic measurement. A classification rate of 98% can be achieved.

A wireless local area network (WLAN) based on IEEE802.11 protocol is the essential part for connection to the internet .and it is widely used in our life today and tomorrow. The growth in the number of nodes per small area likes home, office, and so on. This increase in density led to the degradation in performance in terms of throughput and delay experienced by the end-users. So many amendments proposed in order to improve the performance. Then the performance evaluation of WLAN is an important research topic. In this paper, the performance of WLAN working at 2.4GHz and 5 GHz assessed in a home-like area under the widely used applications the assessment is in terms of throughput and delay. The evaluation conducted by using the optimum networks (OPNET) 14.5 modeler simulator. In this work, we concluded that: using a single AP showed degradation in performance due to congestion. Because all nodes use the same transmission channel which showed clearly in the case of the data rate of 6 Mbps in both (a and g) standards. The effectiveness is about (1.1%, 1.3%) respectively. While using two APs improves the performance for the same case to about (28% and 65%) since it mitigates the congestion because each AP uses a different communication channel.