To explore the Internet of things logistics system application, an Internet of things big data clustering analysis algorithm based on K-mans was discussed. First of all, according to the complex... Show More

To explore the Internet of things logistics system application, an Internet of things big data clustering analysis algorithm based on K-mans was discussed. First of all, according to the complex event relation and processing technology, the big data processing of Internet of things was transformed into the extraction and analysis of complex relational schema, so as to provide support for simplifying the processing complexity of big data in Internet of things (IOT). The traditional K-means algorithm was optimized and improved to make it fit the demand of big data RFID data network. Based on Hadoop cloud cluster platform, a K-means cluster analysis was achieved. In addition, based on the traditional clustering algorithm, a center point selection technology suitable for RFID IOT data clustering was selected. The results showed that the clustering efficiency was improved to some extent. As a result, an RFID Internet of things clustering analysis prototype system is designed and realized, which further tests the feasibility.

In order to make the gateway of the Internet of things access to the public network effectively, using a Software-Defined Network (SDN) concept, a sensor network was studied. When performing... Show More

In order to make the gateway of the Internet of things access to the public network effectively, using a Software-Defined Network (SDN) concept, a sensor network was studied. When performing hierarchical design, the gateway of the Internet of things was layered. It was divided into the policy layer and the forwarding layer. The policy layer distributed the policy, to receive instructions from the upper server. At the same time, it managed the nodes and matches the forwarding rules. The forwarding layer is only responsible for forwarding data packets. This control and data flow separation structure could effectively improve the system transmission efficiency. At the same time, the software structure design and hardware design of the sensor gateway of the Internet of things were given. The design and function of the gateway was analyzed and studied. A comprehensive solution of the gateway system was proposed. The results showed that this method could further improve the performance of the sensor network gateway. Therefore, the design of the Internet of things sensing information gateway based on the concept of SDN has a high practical value.

In view of the limitations of the traditional intelligent interaction, the new needs brought by intelligent interaction of Internet of things (IoT) and the context-aware technology were analyzed. An... Show More

In view of the limitations of the traditional intelligent interaction, the new needs brought by intelligent interaction of Internet of things (IoT) and the context-aware technology were analyzed. An intelligent interactive architecture based on context aware and IoT-oriented was proposed. The intelligent interaction and context expression model in the IoT environment were constructed. The results showed that the proposed architecture and method could achieve intelligent interaction between human and materials in the IoT environment. To sum up, the architecture can improve the flexibility of intelligent interaction system.

Internet of things is a network of objects mainly supported by electronic devices and electronic components such as sensors and electronic cards. These objects can be physical and virtual devices... Show More

Internet of things is a network of objects mainly supported by electronic devices and electronic components such as sensors and electronic cards. These objects can be physical and virtual devices, sensors or actuators, are autonomous and have their own intelligence characteristics. On the other hand, smart environments are those in which sensors and actuators have been integrated, to react to events and to adapt to those present. The environment acquires intelligence through its intelligent components, or through the intelligence resulting from its interaction with other components. Our contribution is a proposal of Cognitive IoT (CIoT) devices structure by adding an agent layer to the device. Such layer provides the device with agent characteristics (intelligence, autonomy, cooperation and organization).

Past natural hazards have produced numerous biological and physical indicators that can be used to predict similar instances in the future. These indicators can be sensed dynamically underwater or... Show More

Past natural hazards have produced numerous biological and physical indicators that can be used to predict similar instances in the future. These indicators can be sensed dynamically underwater or on land to generate real time alerts. This article proposes the first validated fuzzified system to predict tsunamis (FABETP) using an overlap-based algorithm. This proposed algorithm can predict seismicity based on underwater marine animal's anomalous behavior, characterized and implemented as biological indicators (i.e., aquatic animal behavioral attributes). Relevant information is extracted from these attributes and used to design fuzzy rules that generate opinion-based alerts. More precisely, the proposed algorithm, Overlap-based Fuzzified rated Marine Behavior, (OBF_MB), derives alert rules when executed on a sea turtle behavior dataset obtained from an online repository. The deployed underwater sensor-collected dataset includes the following measurements: induced electromagnetic field, undersea turtle count, and angle of deviation (in terms of the turtles' navigation direction formulated per month and per day). These values are used as the inputs to the proposed system. To generate an opinion, an information gain-based opinion score is used to calculate the opinion deviations from the generated opinions of the default rule. For future data values, 2004 is used here as the default opinion year and the scenarios is the default rule. This paper formulates three classes of opinions using the proposed algorithm: Alert, Pre-Alert and No-Alert. These opinions can be used in the future to generate real-time alerts based on aquatic animal behavior.

Recently developments in wireless sensor networks (WSNs) have raised numerous challenges, node localization is one of these issues. The main goal in of node localization is to find accurate position... Show More

Recently developments in wireless sensor networks (WSNs) have raised numerous challenges, node localization is one of these issues. The main goal in of node localization is to find accurate position of sensors with low cost. Moreover, very few works in the literature addressed this issue. Recent approaches for localization issues rely on swarm intelligence techniques for optimization in a multi-dimensional space. In this article, we propose an algorithm for node localization, namely Moth Flame Optimization Algorithm (MFOA). Nodes are located using Euclidean distance, thus set as a fitness function in the optimization algorithm. Deploying this algorithm on a large WSN with hundreds of sensors shows pretty good performance in terms of node localization. Computer simulations show that MFOA converge rapidly to an optimal node position. Moreover, compared to other swarm intelligence techniques such as Bat algorithm (BAT), particle swarm optimization (PSO), Differential Evolution (DE) and Flower Pollination Algorithm (FPA), MFOA is shown to perform much better in node localization task.