15,478 research outputs found
Decentralized Smart Surveillance through Microservices Platform
Connected societies require reliable measures to assure the safety, privacy,
and security of members. Public safety technology has made fundamental
improvements since the first generation of surveillance cameras were
introduced, which aims to reduce the role of observer agents so that no
abnormality goes unnoticed. While the edge computing paradigm promises
solutions to address the shortcomings of cloud computing, e.g., the extra
communication delay and network security issues, it also introduces new
challenges. One of the main concerns is the limited computing power at the edge
to meet the on-site dynamic data processing. In this paper, a Lightweight IoT
(Internet of Things) based Smart Public Safety (LISPS) framework is proposed on
top of microservices architecture. As a computing hierarchy at the edge, the
LISPS system possesses high flexibility in the design process, loose coupling
to add new services or update existing functions without interrupting the
normal operations, and efficient power balancing. A real-world public safety
monitoring scenario is selected to verify the effectiveness of LISPS, which
detects, tracks human objects and identify suspicious activities. The
experimental results demonstrate the feasibility of the approach.Comment: 2019 SPIE Defense + Commercial Sensin
A Microservice-enabled Architecture for Smart Surveillance using Blockchain Technology
While the smart surveillance system enhanced by the Internet of Things (IoT)
technology becomes an essential part of Smart Cities, it also brings new
concerns in security of the data. Compared to the traditional surveillance
systems that is built following a monolithic architecture to carry out lower
level operations, such as monitoring and recording, the modern surveillance
systems are expected to support more scalable and decentralized solutions for
advanced video stream analysis at the large volumes of distributed edge
devices. In addition, the centralized architecture of the conventional
surveillance systems is vulnerable to single point of failure and privacy
breach owning to the lack of protection to the surveillance feed. This position
paper introduces a novel secure smart surveillance system based on
microservices architecture and blockchain technology. Encapsulating the video
analysis algorithms as various independent microservices not only isolates the
video feed from different sectors, but also improve the system availability and
robustness by decentralizing the operations. The blockchain technology securely
synchronizes the video analysis databases among microservices across
surveillance domains, and provides tamper proof of data in the trustless
network environment. Smart contract enabled access authorization strategy
prevents any unauthorized user from accessing the microservices and offers a
scalable, decentralized and fine-grained access control solution for smart
surveillance systems.Comment: Submitted as a position paper to the 1st International Workshop on
BLockchain Enabled Sustainable Smart Cities (BLESS 2018
EIQIS: Toward an Event-Oriented Indexable and Queryable Intelligent Surveillance System
Edge computing provides the ability to link distributor users for multimedia
content, while retaining the power of significant data storage and access at a
centralized computer. Two requirements of significance include: what
information show be processed at the edge and how the content should be stored.
Answers to these questions require a combination of query-based search, access,
and response as well as indexed-based processing, storage, and distribution. A
measure of intelligence is not what is known, but is recalled, hence, future
edge intelligence must provide recalled information for dynamic response. In
this paper, a novel event-oriented indexable and queryable intelligent
surveillance (EIQIS) system is introduced leveraging the on-site edge devices
to collect the information sensed in format of frames and extracts useful
features to enhance situation awareness. The design principles are discussed
and a preliminary proof-of-concept prototype is built that validated the
feasibility of the proposed idea
BlendMAS: A BLockchain-ENabled Decentralized Microservices Architecture for Smart Public Safety
Thanks to rapid technological advances in the Internet of Things (IoT), a
smart public safety (SPS) system has become feasible by integrating
heterogeneous computing devices to collaboratively provide public protection
services. While a service oriented architecture (SOA) has been adopted by IoT
and cyber-physical systems (CPS), it is difficult for a monolithic architecture
to provide scalable and extensible services for a distributed IoT based SPS
system. Furthermore, traditional security solutions rely on a centralized
authority, which can be a performance bottleneck or single point failure.
Inspired by microservices architecture and blockchain technology, this paper
proposes a BLockchain-ENabled Decentralized Microservices Architecture for
Smart public safety (BlendMAS). Within a permissioned blockchain network, a
microservices based security mechanism is introduced to secure data access
control in an SPS system. The functionality of security services are decoupled
into separate containerized microservices that are built using a smart
contract, and deployed on edge and fog computing nodes. An extensive
experimental study verified that the proposed BlendMAS is able to offer a
decentralized, scalable and secured data sharing and access control to
distributed IoT based SPS system.Comment: Submitted to the 2019 IEEE International Conference on Blockchain
(Blockchain-2019
A Scalable Platform for Distributed Object Tracking across a Many-camera Network
Advances in deep neural networks (DNN) and computer vision (CV) algorithms
have made it feasible to extract meaningful insights from large-scale
deployments of urban cameras. Tracking an object of interest across the camera
network in near real-time is a canonical problem. However, current tracking
platforms have two key limitations: 1) They are monolithic, proprietary and
lack the ability to rapidly incorporate sophisticated tracking models; and 2)
They are less responsive to dynamism across wide-area computing resources that
include edge, fog and cloud abstractions. We address these gaps using Anveshak,
a runtime platform for composing and coordinating distributed tracking
applications. It provides a domain-specific dataflow programming model to
intuitively compose a tracking application, supporting contemporary CV advances
like query fusion and re-identification, and enabling dynamic scoping of the
camera network's search space to avoid wasted computation. We also offer
tunable batching and data-dropping strategies for dataflow blocks deployed on
distributed resources to respond to network and compute variability. These
balance the tracking accuracy, its real-time performance and the active
camera-set size. We illustrate the concise expressiveness of the programming
model for tracking applications. Our detailed experiments for a network of
1000 camera-feeds on modest resources exhibit the tunable scalability,
performance and quality trade-offs enabled by our dynamic tracking, batching
and dropping strategies
iFogSim: A Toolkit for Modeling and Simulation of Resource Management Techniques in Internet of Things, Edge and Fog Computing Environments
Internet of Things (IoT) aims to bring every object (e.g. smart cameras,
wearable, environmental sensors, home appliances, and vehicles) online, hence
generating massive amounts of data that can overwhelm storage systems and data
analytics applications. Cloud computing offers services at the infrastructure
level that can scale to IoT storage and processing requirements. However, there
are applications such as health monitoring and emergency response that require
low latency, and delay caused by transferring data to the cloud and then back
to the application can seriously impact their performances. To overcome this
limitation, Fog computing paradigm has been proposed, where cloud services are
extended to the edge of the network to decrease the latency and network
congestion. To realize the full potential of Fog and IoT paradigms for
real-time analytics, several challenges need to be addressed. The first and
most critical problem is designing resource management techniques that
determine which modules of analytics applications are pushed to each edge
device to minimize the latency and maximize the throughput. To this end, we
need a evaluation platform that enables the quantification of performance of
resource management policies on an IoT or Fog computing infrastructure in a
repeatable manner. In this paper we propose a simulator, called iFogSim, to
model IoT and Fog environments and measure the impact of resource management
techniques in terms of latency, network congestion, energy consumption, and
cost. We describe two case studies to demonstrate modeling of an IoT
environment and comparison of resource management policies. Moreover,
scalability of the simulation toolkit in terms of RAM consumption and execution
time is verified under different circumstances.Comment: Cloud Computing and Distributed Systems Laboratory, The University of
Melbourne, June 6, 201
Minor Privacy Protection Through Real-time Video Processing at the Edge
The collection of a lot of personal information about individuals, including
the minor members of a family, by closed-circuit television (CCTV) cameras
creates a lot of privacy concerns. Particularly, revealing children's
identifications or activities may compromise their well-being. In this paper,
we investigate lightweight solutions that are affordable to edge surveillance
systems, which is made feasible and accurate to identify minors such that
appropriate privacy-preserving measures can be applied accordingly. State of
the art deep learning architectures are modified and re-purposed in a cascaded
fashion to maximize the accuracy of our model. A pipeline extracts faces from
the input frames and classifies each one to be of an adult or a child. Over
20,000 labeled sample points are used for classification. We explore the timing
and resources needed for such a model to be used in the Edge-Fog architecture
at the edge of the network, where we can achieve near real-time performance on
the CPU. Quantitative experimental results show the superiority of our proposed
model with an accuracy of 92.1% in classification compared to some other face
recognition based child detection approaches.Comment: Accepted by the 2nd International Workshop on Smart City
Communication and Networking at the ICCCN 202
A Study on Smart Online Frame Forging Attacks against Video Surveillance System
Video Surveillance Systems (VSS) have become an essential infrastructural
element of smart cities by increasing public safety and countering criminal
activities. A VSS is normally deployed in a secure network to prevent access
from unauthorized personnel. Compared to traditional systems that continuously
record video regardless of the actions in the frame, a smart VSS has the
capability of capturing video data upon motion detection or object detection,
and then extracts essential information and send to users. This increasing
design complexity of the surveillance system, however, also introduces new
security vulnerabilities. In this work, a smart, real-time frame duplication
attack is investigated. We show the feasibility of forging the video streams in
real-time as the camera's surroundings change. The generated frames are
compared constantly and instantly to identify changes in the pixel values that
could represent motion detection or changes in light intensities outdoors. An
attacker (intruder) can remotely trigger the replay of some previously
duplicated video streams manually or automatically, via a special quick
response (QR) code or when the face of an intruder appears in the camera field
of view. A detection technique is proposed by leveraging the real-time
electrical network frequency (ENF) reference database to match with the power
grid frequency.Comment: To Appear in the 2019 SPIE Defense + Commercial Sensin
Design Challenges of Multi-UAV Systems in Cyber-Physical Applications: A Comprehensive Survey, and Future Directions
Unmanned Aerial Vehicles (UAVs) have recently rapidly grown to facilitate a
wide range of innovative applications that can fundamentally change the way
cyber-physical systems (CPSs) are designed. CPSs are a modern generation of
systems with synergic cooperation between computational and physical potentials
that can interact with humans through several new mechanisms. The main
advantages of using UAVs in CPS application is their exceptional features,
including their mobility, dynamism, effortless deployment, adaptive altitude,
agility, adjustability, and effective appraisal of real-world functions anytime
and anywhere. Furthermore, from the technology perspective, UAVs are predicted
to be a vital element of the development of advanced CPSs. Therefore, in this
survey, we aim to pinpoint the most fundamental and important design challenges
of multi-UAV systems for CPS applications. We highlight key and versatile
aspects that span the coverage and tracking of targets and infrastructure
objects, energy-efficient navigation, and image analysis using machine learning
for fine-grained CPS applications. Key prototypes and testbeds are also
investigated to show how these practical technologies can facilitate CPS
applications. We present and propose state-of-the-art algorithms to address
design challenges with both quantitative and qualitative methods and map these
challenges with important CPS applications to draw insightful conclusions on
the challenges of each application. Finally, we summarize potential new
directions and ideas that could shape future research in these areas
Omega Model for Human Detection and Counting for application in Smart Surveillance System
Driven by the significant advancements in technology and social issues such
as security management, there is a strong need for Smart Surveillance System in
our society today. One of the key features of a Smart Surveillance System is
efficient human detection and counting such that the system can decide and
label events on its own. In this paper we propose a new, novel and robust
model, The Omega Model, for detecting and counting human beings present in the
scene. The proposed model employs a set of four distinct descriptors for
identifying the unique features of the head, neck and shoulder regions of a
person. This unique head neck shoulder signature given by the Omega Model
exploits the challenges such as inter person variations in size and shape of
peoples head, neck and shoulder regions to achieve robust detection of human
beings even under partial occlusion, dynamically changing background and
varying illumination conditions. After experimentation we observe and analyze
the influences of each of the four descriptors on the system performance and
computation speed and conclude that a weight based decision making system
produces the best results. Evaluation results on a number of images indicate
the validation of our method in actual situation
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