Abstract
The increasing adoption of mobile personal devices and Internet of Things devices is leveraging the emergence of a wide variety of opportunistic sensing applications. However, the designers of this type of applications face a set of technical challenges related to the limitations and heterogeneity of the hardware and software platforms and to the dynamics of the scenarios where they are deployed. In this paper, we introduce a Semantic-Centric Fog-based framework aimed at effectively and efficiently supporting this type of applications. The proposed framework is composed of local and distributed algorithms that support the establishment and coordination of sensing tasks in the Fog. First, it performs ontology-driven in-network processing to locate the most adequate devices to carry out a given sensing task and then, it establishes efficient multihop routes that are used to coordinate relevant devices and to transport the collected sensory data to Fog sinks. We present a set of theorems that prove that the proposed algorithms are correct and the results of a series of detailed simulation-based experiments in NS3 that characterize the performance of the proposed platform. The results show that the proposed framework outperforms traditional sensing platforms that are based on centralized services.
Highlights
Fog computing is a distributed paradigm for transporting, storing, analyzing, and acting on data generated by a swarm of heterogeneous networked devices such as Internet of Things (IoT) [1] devices and personal mobile devices that are located at the network edge [2,3,4,5]
We present a platform for opportunistic sensing [12] in the Fog, where collections of heterogeneous networked devices (e.g., IoT devices, dedicated sensor networks, and personal mobile devices) can self-organize to collect relevant sensory data and to efficiently transport it to Fog sinks
In this centralized environment, when a sensing request arrives at a node, that particular node forwards the request to the ambient server that replies back with a list of devices that can fulfill the requirements specified in the sensing request
Summary
Fog computing is a distributed paradigm for transporting, storing, analyzing, and acting on data generated by a swarm of heterogeneous networked devices such as Internet of Things (IoT) [1] devices and personal mobile devices that are located at the network edge [2,3,4,5]. The main contributions of this paper are as follows: (1) an ontology-based semantic distance function, with an efficient implementation, that can be computed without accessing the whole ontology; (2) a semantic-driven distributed algorithm that uses in-network processing to locate a set of sensing devices that are able to perform a given sensing task at minimum network cost This way, sensing tasks can be opportunistically carried out by a combination of mobile devices, IoT artifacts, traditional sensor networks and Fog devices; and (3) an effective and efficient distributed algorithm that instantiates sensing Foglets by establishing and maintaining multihop paths connecting the best sensing devices in the environment to Fog sinks and that implements collaborative sensing schedules where multiple devices can share the load of implementing a sensing task.
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