Data to decision: pushing situational information needs to the edge of the network

Abstract

Obtaining a high level of situation awareness while maintaining optimal utilization of resources is becoming increasingly important, especially in the context of asymmetric warfare, where information superiority is crucial for maintaining the edge over the opponent. Obtaining an adequate level of situational information from an ISR system is dependent on sensor capabilities as well as the ability to cue the sensors appropriately based on the current information needs and the ability to utilize the collected data with suitable data processing methods. Applying the Data to Decision approach for managing the behavior of sensor systems facilitates optimal use of sensor assets while providing the required level of situational information. The approach presented in the paper combines the Data to Decision approach with the Fog Computing paradigm, where the computation is pushed to the edge of the network. This allows to take advantage of Big Data potentially generated by the sensor systems while keeping the resource requirements in terms of bandwidth manageable. We suggest a System of Systems approach for assembling the ISR system, where individual systems have a high level of autonomy and the computational resources to perform the necessary computation tasks. To facilitate a composition of a System of Systems of sensors for tactical applications the proactive middleware ProWare is applied. The work presented in the paper has been conducted as part of the European Defense Agency project IN4STARS, in the context of which an implementation of a sensor solution is being built, which facilitates on-line sensor cueing and collaboration between sensors by building upon the Fog Computing paradigm and utilizing the Data to Decision concepts.

Assessment of the Suitability of Fog Computing in the Context of Internet of Things

“…The importance and applicability of fog computing were assessed by Yannuzzi et al [41] and Preden et al [42] at a superficial level. In [43], the authors considered various computing paradigms inclusive of cloud computing, and investigated the feasibility of building up a reliable and fault-tolerant fog computing platform.…”

Fog Computing for Sustainable Smart Cities

“…Form factor limitation means that the amount of computational power that can be concentrated into a device always correlates to the size of the device. Some common characteristics of fog computing are "proximity to end-users and client objectives, dense geographical distribution and local resource pooling, latency reduction for quality of service (QoS) and edge analytics/stream mining, resulting in superior user-experience and redundancy in case of failure" [Forrest Stroud ;Preden et al 2015]. One of the common goals of fog computing is to make 'big data' smaller.…”

“…This indicates that many applications employ Fog computing to do small data analytics tasks but meanwhile, still rely on Cloud computing platforms to perform large tasks, which normally cannot be done on Fog devices. There also exist a few research efforts where Fog devices will take care of almost all the data analytics tasks, such as Gazis et al [Gazis et al 2015], Kulkarni et al [Kulkarni et al 2012], Preden et al [Preden et al 2015], and Oueis et al [Oueis et al 2015]. The reasons for these include real-time local decision making [Gazis et al 2015;Preden et al 2015], privacy [Kulkarni et al 2012], and load balancing [Gazis et al 2015;Oueis et al 2015].…”

Hybrid‐Vehfog: A robust approach for reliable dissemination of critical messages in connected vehicles

“…The Health fog combines the data from different sources with an adequate level of security. Preden et al combined the data design approach with fog computing to perform the computation at the edge of the network. Do et al addressed the issue of joint resource allocation and carbon footprint problem of video streaming with the help of fog computing.…”