Reliable Edge Computing Architectures for Crowdsensing Applications

Edge computing has opened the door to a wide range of opportunities in the field of crowdsensing. Processing that happens close to the end devices has many advantages over those that take place at the cloud, such as lower latency and near real-time computation. However, research on the reliability of edge-computing paradigms is important if we wish to maintain the Quality of Service (QoS). This paper aims to review research studies that emphasize reliability in edge-computing architectures. We will discuss various approaches that researchers have taken to tackle resilience in Internet of Things (IoT) networks. Thus, we will be able to gauge the status of IoT reliability research. We have divided the research studies based on the approach taken by researchers in proposing suitable models. The majority of literature analyzes networks based on layers, such as edge, fog, cloud, etc. These proposed architectures can support edge-computing practices. Crowdsensing can utilize reliable edge-computing architectures to develop improved systems and procedures. Studies on reliability are crucial since edge-computing architectures have found applications in mission-critical areas such as healthcare. Reliability studies is also important for the manufacture of energy-efficient systems. This is because if a system has a reliable design, then less time will be spent on its maintenance. Hence, the applications of this study are not limited to crowdsensing.