Connected IoT Devices Research Articles

Edge Intelligence in the Cognitive Internet of Things: Improving Sensitivity and Interactivity

A new network paradigm, CIoT, has been proposed by applying cognitive computing technologies, which is derived from cognitive science and artificial intelligence in combination with the data generated by connected IoT devices and the actions that these devices perform. The development of cognitive computing is very important in the above process to meet key technical challenges, such as generation of big sensory data, efficient computing/storage at the CIoT edge, and integration of multiple data sources and types. On the other hand, to evolve with the new computing and communication paradigms, the CIoT ecosystem has to update by absorbing new capabilities such as deep learning, the CIoT sensing system, data analytics, and cognitiion in providing human-like intelligence.

Edge Intelligence—On the Challenging Road to a Trillion Smart Connected IoT Devices

Editor’s note: This article provides an industry and government perspective to the problem of intelligence resource constrained IoT nodes and presents the vision of the future and important technologies that might play a strong role in enabling the vision of trillion smart connected sensors. — Shreyas Sen, Purdue University

Optimal SIC Ordering and Computation Resource Allocation in MEC-Aware NOMA NB-IoT Networks

Nonorthogonal multiple access (NOMA) and mobile edge computing (MEC) have been emerging as promising techniques in narrowband Internet of Things (NB-IoT) systems to provide ubiquitously connected IoT devices with efficient transmission and computation. However, the successive interference cancellation (SIC) ordering of NOMA has become the bottleneck limiting the performance improvement for the uplink transmission, which is the dominant traffic flow of NB-IoT communications. Also, in order to guarantee the fairness of task execution latency across NB-IoT devices, the computation resource of MEC units has to be fairly allocated to tasks from IoT devices according to the task size. For these reasons, we investigate the joint optimization of SIC ordering and computation resource allocation in this paper. Specifically, we formulate a combinatorial optimization problem with the objective to minimize the maximum task execution latency required per task bit across NB-IoT devices under the limitation of computation resource. We prove the NP-hardness of this joint optimization problem. To tackle this challenging problem, we first propose an optimal algorithm to obtain the optimal SIC ordering and computation resource allocation in two stages: the convex computation resource allocation optimization followed by the combinatorial SIC ordering optimization. To reduce the computational complexity, we design an efficient heuristic algorithm for the SIC ordering optimization. As a good feature, the proposed low-complexity algorithm suffers a negligible performance degradation in comparison with the optimal algorithm. Simulation results demonstrate the benefits of NOMA in reducing the task execution latency.

Object detection mechanism based on deep learning algorithm using embedded IoT devices for smart home appliances control in CoT

Object detection and recognition is commonly used in diverse computer vision based applications and many algorithms are proposed in literature. However, application of object detection algorithms in real-time systems demand minimal computation time and comprehensive performance analysis needs to be conducted before deployment. This paper aims to conduct performance analysis of deep learning based algorithm i.e. single shot detector (SSD) in IoT based embedded devices for smart home appliances control. We have developed a smart home automation system using object detection algorithm based on model view controller (MVC) architecture deployed on Cloud of Things (CoT) i.e. Amazon Web Service (AWS) cloud for users to remotely monitor their homes. Message queuing telemetry transport (MQTT) protocol is used for communication with connected IoT devices. For load-balancing, we have proposed the concept of distributed broker to support high number of publishers and subscribers. For experimental analysis, we have connected a camera with Raspberry Pi for object detection based on deep learning algorithm (SSD) using OpenCV library in our proposed system. Experiments are conducted to evaluate the performance of object detection algorithm under varying environmental conditions by changing the light intensity level, distance of object from camera, and frame size of video. Results show that communication delay is very low (i.e. 0.2 s) as compared to processing delay in Raspberry Pi. Furthermore, changing environmental conditions have very low/insignificant impact on the processing delay of object detection algorithm i.e. average delay of 1.7 s (stdev. 0.18) and 1.8 s (stdev. 0.24) in bright and dark lighting levels, respectively. However, accuracy is deteriorated under low lighting intensity level and increased frame sizes i.e. from 95–100 to 80–85%. Selected embedded device, camera model and object detection algorithm limits the performance of object detection in real-time systems and shall be carefully selected to fulfill the requirement.

Security and Privacy for Smart, Connected, and Mobile IoT Devices and Platforms

Security and Privacy for Smart, Connected, and Mobile IoT Devices and Platforms

Blockchain based hybrid network architecture for the smart city

Recently, the concept of “Smart Cities” has developed considerably with the rise and development of the Internet of Things as new form of sustainable development. Smart cities are based on autonomous and distributed infrastructure that includes intelligent information processing and control systems heterogeneous network infrastructure, and ubiquitous sensing involving millions of information sources Due to the continued growth of data volume and number of connected IoT devices, however, issues such as high latency, bandwidth bottlenecks, security and privacy, and scalability arise in the current smart city network architecture. Designing an efficient, secure, and scalable distributed architecture by bringing computational and storage resources closer to endpoints is needed to address the limitations of today’s smart city network. In this paper, we propose a novel hybrid network architecture for the smart city by leveraging the strength of emerging Software Defined Networking and blockchain technologies. To achieve efficiency and address the current limitations, our architecture is divided into two parts: core network and edge network. Through the design of a hybrid architecture, our proposed architecture inherits the strength of both centralized and distributed network architectures. We also propose a Proof-of-Work scheme in our model to ensure security and privacy. To evaluate the feasibility and performance of our proposed model, we simulate our model and evaluate it based on various performance metrics. The result of the evaluation shows the effectiveness of our proposed model.

대규모 IoT 컴퓨팅 환경에서 동적 클러스터링 기반 효율적 관리 기법

IoT devices that collect information for end users and provide various services like giving traffic or weather information to them that are located everywhere aim to raise quality of life. Currently, the number of devices has dramatically increased so that there are many companies and laboratories for developing various IoT devices in the world. However, research about IoT computing such as connecting IoT devices or management is at an early stage. A server node that manages lots of IoT device in IoT computing environment is certainly needed. But, it is difficult to manage lots of devices efficiently. However, anyone cannot surly know about how many servers are needed or where they are located in the environment. In this paper, we suggest a method that is a way to dynamic clustering IoT computing environment by logical distance among devices. With our proposed method, we can ensure to manage the quality in large-scale IoT environment efficiently.