Cloudlet-based cyber foraging framework for distributed video surveillance provisioning

Abstract

Continuous monitoring activities in surveillance system generate massive amount of data to be transferred from Internet-of-Things (IoT) devices (i.e., cameras and sensors) to a centralized processing unit for analysis. However, several issues need to be addressed, including data migration over bandwidth limited and high latency communication networks, and the heterogeneous nature of data obtained from the surveillance system. The aim of this paper is to develop a scalable and lightweight intelligent distributed surveillance system that make use of an integrated framework of Internet-of-Things (IoT) and cloud computing.It leverages on the pervasiveness in IoT and ubiquitous property of cloud computing technology. This paper introduces the concept of bringing cloud closer to the IoT in order to address the resource poverty of IoT and the dependency for massive data transmission to distant cloud. For providing real-time on-site object detection, this paper instantiates a cloudlet on resource-rich nearby infrastructure which is connected to the IoT devices for distributed retrieval and processing of critical and sensitive data.

Adaptive Edge and Fog Computing Paradigm for Wide Area Video and Audio Surveillance

“…In a dynamic IoT-based and Big Data driven environment like in the case of Wide Area Video and Audio Surveillance, there are still valid challenges with respect to efficient and reliable real-time processing. Several issues need to be addressed, including data migration over bandwidth limited and high latency communication networks, and the heterogeneous nature of data obtained from the surveillance systems [2]. Academic and commercially-successful platforms (Apache Hadoop, Apache Spark) with tremendous corporate backing (Amazon, Microsoft, Google) still present high barriers to entry [3] and, in fact, taking advantage of elasticity remains challenging for even sophisticated users, as the majority of these frameworks were designed to first target onpremise installations at large scale [4] and are not applicable for real-time processing due to their static nature [5].…”

“…In specific, A. Ali et al [2] proposed the concept of bringing cloud closer to the IoT, and in order to provide real-time onsite object detection, it is needed to instantiate a cloudlet on resource-rich nearby infrastructure which is connected to the IoT devices for distributed retrieval and processing of critical and sensitive data.…”

“…Applications run in stateless containers that are spawned based on triggering of events. In this model, a function is deployed once and is invoked repeatedly whenever new inputs arrive and elastically scales with input size [2].…”

“…For example, the storage needs that are created within the duration of one hour for 1000 video streams of video surveillance, tens of terabytes of data are generated and stored [1]. In modern, smart surveillance systems, the massive amount of data generated from the camera (or other sensors) has to be transferred from Internet-of-Things (IoT) devices level to a centralized processing unit for analysis [2] of the footage and only then proceed with the storage of the incident footage. Even with usage of video encoding standards like MPEG-4 and H.264 to achieve high compression rate, video remains a very resource hungry type of multimedia, especially if taken into account that video analytics nowadays receive as an input HD or Full HD video, increasing substantially the strain on the hosting infrastructure for real time processing and provision.…”

Privacy Mining from IoT-Based Smart Homes

“…IoT enables different smart environments with various smart devices [3][4] such as smart homes, smart buildings, smart communities, smart cities, and smart grids. IoT also enables numerous applications, including healthcare [4][5] [6], home automation [7], security [8][9] [10], and surveillance [11] [12][13] [14]. Among these applications, healthcare attracts a lot of attention in recent years [4] [15] due to the growth of the elderly population.…”