A Privacy‑Preserving Edge Intelligence Framework for Real‑Time

Multimodal Threat Detection in Smart Urban Surveillance Systems

Jubber Nadaf

, Amol K.

, Vinayak Patil

, P. Mathiyalagan

, S. K. Lokesh Naik

and Indira R.

Department of Computer Engineering, Bharati Vidyapeeth (Deemed to be University) College of Engineering, Pune, India

Department of Computer Science and Business Systems, Bharati Vidyapeeth (Deemed to be University) College of

Engineering, Pune, India

Department of Mechanical Engineering, Bharati Vidyapeeth College of Engineering Navi Mumbai, Maharashtra, India

Department of Mechanical Engineering, J.J. College of Engineering and Technology, Tiruchirappalli, Tamil Nadu, India

Department of Computer Science and Engineering, MLR Institute of Technology, Hyderabad, Telangana, India

Department of CSE, New Prince Shri Bhavani College of Engineering and Technology, Chennai, Tamil Nadu, India

Keywords: Edge Computing, Smart Surveillance, Real‑Time Threat Detection, Urban Safety, Privacy‑Preserving AI.

Abstract: Amidst increasingly complex urban safety challenges, the demand for intelligent, scalable and privacy-aware

surveillance system has become urgent. In this paper, we present a new edge computing architecture for the

real-time multimodal threat detection in smart cities. By fusing lightweight deep learning models onto the

edge device, the solution brings in place video analytics at the edge able to identify unusual behaviors, object

defacement, or intrusions with no or little delay. Compared with traditional cloud-based models, data security

is guaranteed due to the on-device learning process in the proposed model, and dynamic adaptability to dense

and unpredictable urban environments is also provided. The empirical results reveal that the proposed system

achieves high threat detection accuracy with a manageable low computational cost, thus indicating its

potential in enabling it on diverse smart urban infrastructures.

1 INTRODUCTION

The transformation of city life into smart cities has

demanded sophisticated surveillance tools that can

protect lives instantly. As urban areas continue to

become increasingly dense and complex, legacy

surveillance solutions many of which depend on

centralized cloud networks are finding it difficult to

meet the need for instantaneous threat detection and

response. However, these short-comings - high

latency, bandwidth restrictions, and data privacy

considerations - limit their efficacy for dynamic and

sensitive scenarios like densely populated public

areas, transport hubs or critical infrastructure

installations.

In this regard, edge computing has been proposed

as disruptive approach, providing on-site processing

and effectively reducing latency and response time.

By offloading cloud-based server-based

computational intelligence to distributed edge nodes

located near data source, such systems facilitate real

time threat analysis without sending sensitive data

across public networks. This not only improves real-

time processing but also adheres to strict privacy

laws, as it reduces the dependency on the cloud.

We present a general edge intelligence technology

platform for real time multimodal threats finding in

urban surveillance system in this paper. With use of

lightweight AI models, the framework can run

effectively on energy-limited edge devices, and

achieve high accuracy detection under different

threat scenarios, such as abnormal human behaviour,

object anomaly and potential intruding. Such an

intelligent architecture is capable of handling

dynamically changing conditions in an urban

environment, ensuring compatibility for future

public safety infrastructural systems.

We present a practical and scalable model in... which

overcomes the limitations of current surveillance

models by leveraging edge-based computation,

privacy preservation, and multimodal analytics,

enabling securer and more responsive smart city

ecosystems.

Nadaf, J., K., A., Patil, V., Mathiyalagan, P., Naik, S. K. L. and R., I.

A Privacy-Preserving Edge Intelligence Framework for Real-Time Multimodal Threat Detection in Smart Urban Surveillance Systems.

DOI: 10.5220/0013858400004919

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies (ICRDICCT‘25 2025) - Volume 1, pages

117-123

ISBN: 978-989-758-777-1

117

2 PROBLEM STATEMENT

Today's urban areas face a growing array of

challenging and diverse security threats requiring

agile, trusted, and intelligent surveillance solutions.

Legacy cloud video monitoring systems have been

challenged by serious delays, scalability limitations

and privacy-related red flags and can hardly meet the

requirement of real-time threat detection in dynamic

and crowded urban environments. These systems are

also not sufficiently flexible to accommodate

multimodal security threat scenarios, such as e.g.,

spontaneous violence, abandoned objects and

suspicious behavior that dictate real-time local

processing and decision-making. The lack of

privacy-preserving and resource-limited frameworks

that allow to perform complex analytics directly at the

edge-top of the fog also adds to the issue. It is

therefore essential to design a decentralized,

lightweight, on-the-fly adaption to urban

surveillance, to achieve low-latency detection while

protecting the sensitive video data and dealing with

complex urban threats in a responsive way.

3 LITERATURE SURVEY

Adopting edge computing for surveillance system has

received much attention for its capability to overcome

the drawbacks of cloud-oriented architectures,

especially including latency and privacy. Patrikar and

Parate (2021) surveyed the anomaly detection

methods to evaluate the edge video surveillance but

focussed on theoretical models and lacked of the real-

world deployment in a volunteered city level. Nikouei

et al. (2019), the authors proposed: I-SAFE, however

it only employed fuzzy decision-making for

suspicious activity generation and did not use deep

learning models which are very important for

processing complex threat patterns. Similarly, Isern et

al. (2020) implemented a reconfigurable cyber-

phsical system for critical infrastructure but lacked

the capabilities for real-time edge-based video

analysis.

Wang et al. (2020) developed SurveilEdge

offering a hybrid cloud-edge video query system.

Unfortunately, the need to use cloud components

caused a delay and led to lost opportunities in the

development of edge computing being fully

autonomous. Saponara et al. (2020) and Yu et al.

(2020) addressed fire and crowd detection,

respectively, using CNN-based approaches, however

these systems were application specific and were not

general enough for multi-threat detection.

Zhou et al. extensively investigated edge

intelligence. (2021) suggested that AI should be

moved to the edge, but the work was more of a

concept. Li, Ota, and Dong (2021) and Chen, Hao,

and Hwang (2021) talked about fogs and edges in

manufacturing and QoE optimization; however, with

the word “surveillance”, they have ignored the harsh

real-time constraint in public domain. Zhang and

Zhang (2021) presented a review concentrating on

edge computing in smart grids, but the challenges in

dealing with large-scale video streaming were

ignored in these papers.

Green Computing Practices, Huang et al. (2022),

is focused on energy efficiency but does not address

latency-sensitive applications such as urban

surveillance. Other studies, e.g., Wang and Liu

(2022) and Khan et al. (2022) gave basic knowledge

regarding edge applications in smart cities, yet it did

not touch multimodal threat detection and its real-

time nature.

Healthcare-oriented edge models such as Alam

and Saini (2022) and domain-specific adaptations in

agriculture, manufacturing, and logistics (Wang & Li,

2023; Liu & Chen, 2023; Chen & Wang, 2024)

exhibit edge capability, albeit these cannot be directly

applied toward public safety applications. Li and

Wang (2022) presented a general survey to video

surveillance combined with edge computing, whereas

it should be noted that the privacy issues and the

restrictions of edge model existed in a dynamic

environment.

The ITS of Zhang and Liu (2023) featured urban

deployment and could only support the traffic

management. References: Chen and Zhang 2023;

Zhao and Sun 2023; and Yang and Wang (2024):

These works focused on edge computing in industrial

automation, smart smart grids, and home aut o

mation, but they did not incorporate behavioral

analytics for threat detection.

Other applications in retail and education (Zhang

& Li, 2024; Li & Zhang, 2024) showed customer

tracking and activity monitoring at the edge, however

were not flexible enough for the unpredictable public

safety events. Although the reviewed papers validate

the relevance of edge computing in various fields, a

significant gap is the provision of unified, privacy-

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aware, multimodal real-time threat detection system

that is also tailored to the latency-critical,

unpredictable nature of modern urban environments.

This work extends those basic studies and

sidesteps those limitations by introducing a dedicated

setup that exploits edge-based video analytics,

lightweight AI models and privacy-preserving

architectures to aid in the real-time surveillance over

urban scenarios.

4 METHODOLOGY

The proposed method provides a decentralized edge

computing scheme, which utilizes real-time

multimodal threat detection in urban surveillance

scenarios. At the heart of the architecture, there is

edge intelligence model to support local video

analysis via small deep learning model, and thus, to

reduce the burden to be exert on centralized cloud

resources. The system structure is composed of 3

integrated sections referred to as the sensing, the edge

processing, and the response coordination layers.

Table 1 show the Dataset Description and Threat

Classes.

Table 1: Dataset Description and Threat Classes.

Dataset

Name

Source

Reso

lutio

Threat

Classes

Detected

Total

Images/F

rames

UrbanS

afeSet

Custo

m +

Open

640x

480

Intrusion,

Loitering,

Object

Abandonme

25,000

Surveill

anceX

Public

Reposi

tory

720p

Fighting,

Running,

Vandalism

12,500

SafeCit

CCTV

City

Camer

1080

Trespassing,

Aggression,

Crowd

Forming

9,800

Live video feeds from the sensing layer are also

fed to multiple high-definition video cameras

throughout key urban areas such as traffic

intersections, public transportation locations, and

high pedestrian traffic zones. These raw video

streams are sent to the edge computing nodes located

in the surveillance sites for operation. Two hundred

edge nodes consist of resource-constrained devices

(e.g., Jetson Nano, Raspberry Pi) configured for the

target applications such as CNN or various object

detection models (e.g., MobileNet-SSD and

YOLOv5-Lite). Figure 1 show the Real-Time Edge-

Based Threat Detection Workflow.

Figure 1: Real-Time Edge-Based Threat Detection

Workflow.

The edge processing layer executes real-time

analytics directly on the edge node. This involves

object detection, motion tracking and behavior (or

activity) classification of multimodal data like visual

patterns, spatio-temporal object trajectories and

contextual scene understanding. By using quantized

neural networks and model pruning techniques, both

the amount of memory and the power consumption

are minimized to obtain high inference performance.

Threat scenarios like Unattended baggage,

aggression, unauthorized access or loitering are

detected through our own trained datasets

appropriate for urban safety use-cases. Table 2 show

the Dataset Description and Threat Classes

In order to protect data privacy and guarantee

data compliance, privacy-preserving architectures are

developed. All your video is processed locally and

only meta data (such as threat/zone types, location

coordinates, timestamp) is relayed to a central control

room for viewing. In particular, no raw data goes out

of the edge node, which preserves personal identity

and reduces the bandwidth consumption.

A Privacy-Preserving Edge Intelligence Framework for Real-Time Multimodal Threat Detection in Smart Urban Surveillance Systems

119

Table 2: Dataset Description and Threat Classes.

Dataset

Name

Source

Resol

ution

Threat

Classes

Detected

Total

Images/F

rames

UrbanS

afeSet

Custo

m +

OpenC

640x

480

Intrusion,

Loitering,

Object

Abandonme

25,000

Surveill

anceX

Public

Reposi

tory

720p

Fighting,

Running,

Vandalism

12,500

SafeCit

CCTV

City

Camer

1080

Trespassing,

Aggression,

Crowd

Forming

9,800

The response coordination level is responsible

for processing edge-triggered alerts. Pre-defined rules

can be used to automate intervention schemes

including law enforcement real-time alerts, the

activation of alarms, or the illumination of

emergency lights in smart infrastructure, based on the

severity of the threat. The system also has log and

audit trails for incident review, feedback-based

learning, and system retraining.

This approach guarantees the scalability, energy

efficiency, and robustness of the presented

framework when it comes to the real-time AI-enabled

surveillance of urban environments. It utilizes the

advantage of edge computing to maintain a low-

latency, secure, and smart system that can support a

customized size to responding smart cities.

5 RESULTS AND DISCUSSION

The edge-based surveillance framework was tested in

the simulated and semi-real-world urban scenes to

ascertain 1) its capability of real-time detection of

threats, 2) the reduction in the latency and 3) the

preservation of privacy while 4) achieving model

accuracy. Table 3 show the Model Performance

Metrics on Edge DevicesThe experimental

configuration consisted of several edge nodes

connected with HD IP cameras for constantly

streaming live video footage in countering scenarios

similar to public transit stations, crowded markets

and gated premises. Figure 2 show the Model

Accuracy Comparison on Edge Devices.

Table 3: Model Performance Metrics on Edge Devices.

Model

Used

Edge

Devi

Accu

racy

(%)

Inferenc

e Time

(ms/fra

me)

FPS

Achi

eved

Power

Consum

ption

(W)

YOL

Ov5-

Lite

Jetso

Nano

91.7

180

8.5

Mobil

eNet-

SSD

Rasp

berry

Pi 4

88.2

240

5.9

YOL

Ov4-

Tiny

Jetso

Nano

93.1

210

9.1

Figure 2: Model Accuracy Comparison on Edge Devices.

The performance metrics showed that the system

attained an average detection latency of 180 ms per

frame, this was considerably better than cloud-based

architectures where if a cloud-based architecture was

used it would suffer from a 730 ms (cloud dependent

architecture) or 900 ms (dependent on the data rate)

loss in latency due to the network delay and data

transmission overhead. The edge model,

implemented with quantized YOLOv5-Lite and

MobileNet-SSD, preserved the inference accuracy of

91.7% under varied threat scenarios, such as

unauthorized entry, object left, and violent act, under

low-light or occluded environment. Table 4 show the

Threat Detection Response Time Analysis.

Table 4: Threat Detection Response Time Analysis.

Threat

Type

Detectio

n Time

(ms)

Alert

Trigger

Time (ms)

Total

Response

Time (ms)

Intrusion

170

250

Object

Anomaly

190

100

290

Suspicio

Loitering

160

230

Aggressi

Behavior

200

290

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Besides speed and accuracy, the system showed

good privacy compliance by analyzing all video

frames locally at the edge node. Only threat metadata

was sent to central command, and the bandwidth

usage was reduced by 85%, compared to

conventional cloud offloading systems. Not only did

this improve data protection, but it also allowed real-

time monitoring when the network failed or when

bandwidth fluctuated.

Figure 3: Threat Detection and Alert Response Time.

The scalable nature of the system was examined by

deploying a total of 10 edge nodes in various regions

of a simulative smart city grid. The distributed

processing architecture was able to process multi-

stream inputs in a timely fashion, without generating

central bottlenecks. The resource usage also kept in

the 70% CPU and 50% GPU boundaries, which again

confirms the efficiency of the lightweight model and

the thermal-aware edge deployment. Figure 3 show

the Threat Detection and Alert Response Time

Real-time alerting was another big win. Upon

recognizing an unusual incident, the system can alert

law enforcement or emergency responders in less

than 250 milliseconds. Such responsiveness is crucial

for high-risk situations like abandoned luggage in

transit stations or violent activity in public places.

Figure 4 show the Network Bandwidth Usage

Comparison

Cross-comparison with cloud-centric solutions

further demonstrated that the edge is the place to be,

particularly for fast-evolving dynamic scenarios

where latency, privacy, and quick decision-making

are indispensable. The flexibility of the architecture

further enables for the retraining and updating of the

model with minimal effort as threat patterns change,

providing long-term adaptability of the surveillance

ecosystem. Table 5 show the Network Load and

Bandwidth Utilization Comparison.

Table 5: Network Load and Bandwidth Utilization Comparison.

Process

ing

Type

Avg

Bandwidt

h Used

(Mbps)

Data

Transmitted

per Hour

(GB)

Raw

Vide

Sent

Priv

acy

Leve

Cloud-

Based

12.8

5.4

Yes

Low

Edge-

Only

(Propos

ed)

1.9

0.3

High

Figure 4: Network Bandwidth Usage Comparison.

In all, these results validate that the presented edge

intelligence framework successfully addresses the

fundamental challenges of latency, privacy, and

multimodal threat analysis for urban surveillance, and

it represents a practical, future-aware method for

securing smart city infrastructure. Figure 5 show the

Privacy Assurance: Cloud vs Edge.

Figure 5: Privacy Assurance: Cloud Vs Edge.

6 CONCLUSIONS

The increasing complexity and size of cities require

surveillance systems that will not be only intelligent

and reactive but also respectful to individuals’

A Privacy-Preserving Edge Intelligence Framework for Real-Time Multimodal Threat Detection in Smart Urban Surveillance Systems

121

privacy while maintaining security levels. In this

study, a new edge computing-based architecture has

been developed for the real-time multimodal threat

detection in the context of a smart urban surveillance

system. Through distributing the computational

intelligence to edge nodes, they can get rid of the

latency and privacy problems of the cloud-based

system. By utilizing small and efficient deep learning

models at the edge, the framework achieves accurate

and low-latency detection of a rich set of threat

scenarios which include intrusions, object anomalies

and behavioral anomalies in complex urban

environments.

The performance evaluation results show the high

detection accuracy and the low processing delay and

bandwidth requirement of the proposed system,

which can use in secure data transmission without

encryption. The modular, scalable, and adaptable

nature of this network makes the integration of the

proposed architecture to diverse urban infrastructures

possible, without the dependance on high end

centralized resources. Also, the ability to analyze

sensitive video streams at the edge lends itself to

privacy-compliant deployments in heavily data-

regulated areas.

In summary, by presenting this edge intelligence

framework, this work provides an innovative solution

for the next generation intelligent urban surveillance

system. By addressing those technological

limitations, Hexnode beyond a doubt, can emerge as

a significant enabler for the future smart city security

solutions.

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