A survey of communication protocols for internet of things and related challenges of fog and cloud computing integration

The fast increment in the number of IoT (Internet of Things) devices is accelerating the research on new solutions to make cloud services scalable. In this context, the novel concept of fog computing as well as the combined fog-to-cloud computing paradigm is becoming essential to decentralize the cloud, while bringing the services closer to the end-system. This article surveys e application layer communication protocols to fulfill the IoT communication requirements, and their potential for implementation in fog- and cloud-based IoT systems. To this end, the article first briefly presents potential protocol candidates, including request-reply and publish-subscribe protocols. After that, the article surveys these protocols based on their main characteristics, as well as the main performance issues, including latency, energy consumption, and network throughput. These findings are thereafter used to place the protocols in each segment of the system (IoT, fog, cloud), and thus opens up the discussion on their choice, interoperability, and wider system integration. The survey is expected to be useful to system architects and protocol designers when choosing the communication protocols in an integrated IoT-to-fog-to-cloud system architecture.Peer ReviewedPostprint (author's final draft

Engineering and Experimentally Benchmarking a Container-based Edge Computing System

While edge computing is envisioned to superbly serve latency sensitive applications, the implementation-based studies benchmarking its performance are few and far between. To address this gap, we engineer a modular edge cloud computing system architecture that is built on latest advances in containerization techniques, including Kafka, for data streaming, Docker, as application platform, and Firebase Cloud, as realtime database system. We benchmark the performance of the system in terms of scalability, resource utilization and latency by comparing three scenarios: cloud-only, edge-only and combined edge-cloud. The measurements show that edge-only solution outperforms other scenarios only when deployed with data located at one edge only, i.e., without edge computing wide data synchronization. In case of applications requiring data synchronization through the cloud, edge-cloud scales around a factor 10 times better than cloud-only, until certain number of concurrent users in the system, and above this point, cloud-only scales better. In terms of resource utilization, we observe that whereas the mean utilization increases linearly with the number of user requests, the maximum values for the memory and the network I/O heavily increase when with an increasing amount of data

Fog Computing Architecture for Indoor Disaster Management

Most people spend their time indoors. Indoors have a higher complexity than outdoors. Moreover, today's building structures are increasingly sophisticated and complex, which can create problems when a disaster occurs in the room. Fire is one of the disasters that often occurs in a building. For that, we need disaster management that can minimize the risk of casualties. Disaster management with cloud computing has been extensively investigated in other studies. Traditional ways of centralizing data in the cloud are almost scalable as they cannot cater to many latency-critical IoT applications, and this results in too high network traffic when the number of objects and services increased. It will be especially problematic when in a disaster that requires a quick response. The Fog infrastructure is the beginning of the answer to such problems. This research started with an analysis of literature and hot topics related to fog computing and indoor disasters, which later became the basis for creating a fog computing-based architecture for indoor disasters. In this research, fog computing is used as the backbone in disaster management architecture in buildings. MQTT is used as a messaging protocol with the advantages of simplicity and speed. This research proposes a disaster architecture for indoor disasters, mainly fire disasters

The Effects of Cloud Computing and Internet of Things on the Next Generation Internet

Two separate yet crucial technologies that are influencing our lives more and more are cloud computing and IoT. It is anticipated that they will be widely adopted, making them essential elements of the Future Internet (FI). IoT improves our daily life by enabling connectivity and communication across several devices. However, flexible network access offered by cloud computing makes it possible to integrate dynamic data from several sources. Nonetheless, there are a number of difficulties in integrating IoT and cloud computing in the FI. Our goal in this research paper is to present and analyze the fundamental ideas behind cloud computing and the Internet of Thing

Data delivery performance on MongoDB generated by WSN over high data load

Wireless sensor networks (WSN) Environment is a collection of nodes that sensing and sending data to other nodes in a large number of nodes. One of the WSN concepts is continuous or periodic data collection, then the data obtained can be further processed. From this process, it is possible to produce large data, so the process of sending and storing data becomes a high load. Data obtained from WSN nodes vary greatly, which affects on sending and storing of data. Therefore, a study is needed to analyze WSN's ability to send and store large amounts of data. Message query telemetry protocol (MQTT) is used because it could save resources on communication and MongoDB used because it does not have the concept of tables and rows, this is very suitable for variations in data generated by WSN. In this study, it can be concluded that the performance of MongoDB on high data amount is acceptable, so MongoDB is highly recommended for WSN implementation

Learning Platform for Smart City Application Development

With the emerging trend of the Internet of Things and Smart City environments, new trends have arisen in building applications, too. The current approach to application development, known as monolithic architecture, is not suitable for the newest appliances. Recently, the microservice-based application architecture has emerged as a potential solution that could meet the imposed challenges. In order to educate new generations of software and IT engineers to be able to develop such microservice-based applications, educators at universities should establish an efficient platform to ensure this process. This article presents the portable and lightweight platform for teaching application development based on microservices. The platform is focused on supporting lightweight publish/subscribe messaging protocols, such as MQTT, and built-on open-source hardware development boards such as Arduino/Genuino. This Smart City learning platform is designed to enable the collaborative development of systems, applications and services for the cities of the future, and it should be efficient enough to support the learning of all the necessary skills that can be used for the development of complex and large-scale systems. The architecture of the proposed platform, as well as its elements are presented in this article

Benchmarking Buffer Size in IoT Devices Deploying REST HTTP

A few potential IoT communication protocols at the application layer have been proposed, including MQTT, CoAP and REST HTTP, with the latter being the protocol of choice for software developers due to its compatibility with the existing systems. We present a theoretical model of the expected buffer size on the REST HTTP client buffer in IoT devices under lossy wireless conditions, and validate the study experimentally. The results show that increasing the buffer size in IoT devices does not always improve performance in lossy environments, hence demonstrating the importance of benchmarking the buffer size in IoT systems deploying REST HTTP.Comment: This paper is uploaded here for research community, thus it is for non-commercial purpose

Enhanced IoT Wi-Fi protocol standard’s security using secure remote password

In the Internet of Things (IoT) environment, a network of devices is connected to exchange information to perform a specific task. Wi-Fi technology plays a significant role in IoT based applications. Most of the Wi-Fi-based IoT devices are manufactured without proper security protocols. Consequently, the low-security model makes the IoT devices vulnerable to intermediate attacks. The attacker can quickly target a vulnerable IoT device and breaches that vulnerable device's connected network devices. So, this research suggests a password protection based security solution to enhance Wi-Fi-based IoT network security. This password protection approach utilizes the secure remote password protocol (SRPP) in Wi-Fi network protocols to avoid brute force attack and dictionary attack in Wi-Fi-based IoT applications. The performance of the IoT security solution is implemented and evaluated in the GNS3 simulator. The simulation analysis report shows that the suggested password protection approach supports scalability, integrity and data protection against intermediate attacks

Computation Offloading and Scheduling in Edge-Fog Cloud Computing

Resource allocation and task scheduling in the Cloud environment faces many challenges, such as time delay, energy consumption, and security. Also, executing computation tasks of mobile applications on mobile devices (MDs) requires a lot of resources, so they can offload to the Cloud. But Cloud is far from MDs and has challenges as high delay and power consumption. Edge computing with processing near the Internet of Things (IoT) devices have been able to reduce the delay to some extent, but the problem is distancing itself from the Cloud. The fog computing (FC), with the placement of sensors and Cloud, increase the speed and reduce the energy consumption. Thus, FC is suitable for IoT applications. In this article, we review the resource allocation and task scheduling methods in Cloud, Edge and Fog environments, such as traditional, heuristic, and meta-heuristics. We also categorize the researches related to task offloading in Mobile Cloud Computing (MCC), Mobile Edge Computing (MEC), and Mobile Fog Computing (MFC). Our categorization criteria include the issue, proposed strategy, objectives, framework, and test environment.