Internet of Robotic Things in Surveillance

These days a new field has emerged known as IoRT which is a combination of IOT and Robotics and known as Internet of Robotic Things. Through IORT, intelligent devices can monitor events, fuse sensor data from a variety of sources, use local and distributed intelligence to determine a best course of action, and then act to control or manipulate objects in the physical world and physically moving through that world. This paper mainly focuses on application of IoRT as a surveillance robot with audio and video features in the domain of security. Keywords: IOT, Robotics, Surveillance Robot, Ardino, Sensors, Raspberry Pi, Robotic control.

A 3-CS Distributed Federated Transfer Learning Framework for Intelligent Edge Optical Networks

With the rapid development of optical network and edge computing, the operation efficiency of the edge optical network has become more and more important, requiring an intelligent approach to enhance the network performance. To enhance the intelligence of the edge optical network, this article firstly provides the demand for the development of edge optical networks. Then, a cross-scene, cross-spectrum, and cross-service (3-CS) architecture for edge optical networks is presented. Finally, a federated transfer learning (FTL) framework, realizing a distributed intelligence edge optical network, is proposed. The usability of the proposed framework is verified by simulation.

Distributed Intelligence Model for IoT Applications Based on Neural Networks

Increasing the implication of IoT data puts a focus on extracting the knowledge from sensors’ raw data. The management of sensors’ data is inefficient with current solutions, as studies have generally focused on either providing cloud-based IoT solutions or inefficient predefined rules. Cloud-based IoT solutions have problems with latency, availability, security and privacy, and power consumption. Therefore, Providing IoT gateways with relevant intelligence is essential for gaining knowledge from raw data to make the decision of whether to actuate or offload tasks to the cloud. This work proposes a model that provides an IoT gateway with the intelligence needed to extract the knowledge from sensors’ data in order to make the decision locally without needing to send all raw data to the cloud over the Internet. This speeds up decisions and actions for real-time data and overcomes the limitations of cloud-based IoT solutions. When the gateway is unable to process a task locally, the data and task are offloaded to the cloud.