Fog computing is an emerging computing paradigm that uses processing and storage capabilities located at the edge, in the cloud, and possibly in between. Testing and benchmarking fog applications, however, is hard since runtime infrastructure will typically be in use or may not exist, yet. In this paper, we propose an approach that emulates such infrastructure in the cloud. Developers can freely design emulated fog infrastructure, configure performance characteristics, manage application components, and orchestrate their experiments. We also present our proof-of-concept implementation MockFog 2.0. We use MockFog 2.0 to evaluate a fog-based smart factory application and showcase how its features can be used to study the impact of infrastructure changes and workload variations.

IoT data are usually exchanged via pub/sub, e.g., based on the MQTT protocol. Especially in the IoT, however, the relevance of data often depends on the geo-context, e.g., the location of data source and sink. In this paper, we propose two inter-broker routing strategies that use this characteristic for the selection of rendezvous points. We evaluate analytically and through experiments with a distributed pub/sub prototype which strategy is best suited in three IoT scenarios. Based on simulation, we compare the performance and efficiency of our approach to the state of the art: Our strategies reduce the event delivery latency by up to 22 times compared to the only alternative that sends slightly fewer messages. Our strategies also require significantly less inter-broker messages than all other approaches while achieving at least the same performance.

Today, communication between IoT devices heavily relies on fog-based publish/subscribe (pub/sub) systems. Communicating via the cloud, however, results in a latency that is too high for many IoT applications. In this paper, we describe the design of a fog-based pub/sub system that integrates edge resources to improve communication latency between end devices in proximity. To this end, geo-distributed broker instances organize themselves in dynamically sized broadcast groups. Each broadcast group comprises a set of well connected edge brokers that communicate directly using flooding. This minimizes communication latency and copes well with frequently updated subscriptions and mobile end devices, which is required by many IoT applications. Messages between broadcast groups are routed via a massively scalable fog broker that pre-filters messages to reduce excess data dissemination. Our approach, therefore, manages the tradeoff between latency and excess data.

In the Internet of Things, the relevance of data often depends on the geographic context of data producers and consumers. Today’s data distribution services, however, mostly focus on data content and not on geo-context, which could help to reduce the dissemination of excess data in many IoT scenarios. In this paper, we propose to use the geo-context information associated with devices to control data distribution. We define what geo-context dimensions exist and compare our definition with concepts from related work. Furthermore, we designed GeoBroker, a data distribution service that uses the location of things, as well as geofences for messages and subscriptions, to control data distribution. This way, we enable new IoT application scenarios while also increasing overall system efficiency for scenarios where geo-contexts matter by delivering only relevant messages. We evaluate our approach based on a proof-of-concept prototype and several experiments.