In recent years, the tremendous growth of interconnected devices results in a new technology called Internet of Things (IoT). Cloud computing assists the IoT applications to store the data and perform computation in order to control and manage the vast amount of data generated by these IoT devices. But the major challenge in Cloud computing is to meet requirements of many real-time applications of Internet of Things. Whereas, Edge is a computing architecture that helps to communicate, manage, store, and processes the data that quickly returns the response. This is made possible by moving these functionalities closer to the end users. Edge computing and cloud computing are independent as wells as mutually beneficial to many applications. This paper discusses the overview of IoT, communication technologies and protocols required for IoT, data transfer in IoT. Cloud services to store, process and to analyze the data generated from IoT devices are explored.

Edge computing is a new paradigm enabling intelligent applications for the Internet of Things (IoT) using mobile, low-cost IoT devices embedded with data analytics. Due to the resource limitations of Internet of Things devices, it is essential to use these resources optimally. Therefore, intelligence needs to be applied through an efficient deep learning model to optimize resources like memory, power, and computational ability. In addition, intelligent edge computing is essential for real-time applications requiring end-to-end delay or response time within a few seconds. We propose decentralized heterogeneous edge clusters deployed with an optimized pre-trained yolov2 model. In our model, the weights have been pruned and then split into fused layers and distributed to edge devices for processing. Later the gateway device merges the partial results from each edge device to obtain the processed output. We deploy a convolutional neural network (CNN) on resource-constraint IoT devices to make them intelligent and realistic. Evaluation was done by deploying the proposed model on five IoT edge devices and a gateway device enabled with hardware accelerator. The evaluation of our proposed model shows significant improvement in terms of communication size and inference latency. Compared to DeepThings for 5 X 5 fused layer partitioning for five devices, our proposed model reduces communication size by ~ 14.4% and inference latency by ~16%.

Internet of Things (IoT) is an innovative and an emerging field, which supports global infrastructure for exchange and sharing of data by interconnecting the uniquely identifiable physical and virtual devices without any intervention of humans. It also provides advanced services for practical applications. IoT globally puts a light on to a future life of effective ubiquitous computing and is also expected to provide advanced connectivity of devices and services for variety of applications. The practical applications of IoT have disadvantages with 3-layered and 4-layered architecture in meeting certain requirements. Hence to emphasize the practical and intelligent IoT applications, we introduce a 5-layered IoT architecture which interprets the functionalities of IoT in an effective way. This paper presents an overview of IoT by summarizing its Evolution, Definition, 5-layered architecture, Technologies and IoT Applications. Security threat adds a weakness to IoT, which must be taken into consideration to have a secured data exchange and sharing. Hence this paper further highlights the key privacy challenges and the security challenges with respect to layers of IoT architecture, which must be considered for the development of IoT.

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