Edge server registration phase. by Naif Alzahrani (19992885)

“…The proof of correctness of the proposed protocol has been scrutinized through a well-known and widely used Real-Or-Random (RoR) model, ProVerif validation, and attacks’ discussion, demonstrating the thoroughness of the proposed protocol. …”

Network model. by Naif Alzahrani (19992885)

“…The proof of correctness of the proposed protocol has been scrutinized through a well-known and widely used Real-Or-Random (RoR) model, ProVerif validation, and attacks’ discussion, demonstrating the thoroughness of the proposed protocol. …”

Comparative Analysis (Performance Metrics). by Naif Alzahrani (19992885)

“…The proof of correctness of the proposed protocol has been scrutinized through a well-known and widely used Real-Or-Random (RoR) model, ProVerif validation, and attacks’ discussion, demonstrating the thoroughness of the proposed protocol. …”

SN/IoT or user registration phase. by Naif Alzahrani (19992885)

“…The proof of correctness of the proposed protocol has been scrutinized through a well-known and widely used Real-Or-Random (RoR) model, ProVerif validation, and attacks’ discussion, demonstrating the thoroughness of the proposed protocol. …”

Communication costs in bits. by Naif Alzahrani (19992885)

“…The proof of correctness of the proposed protocol has been scrutinized through a well-known and widely used Real-Or-Random (RoR) model, ProVerif validation, and attacks’ discussion, demonstrating the thoroughness of the proposed protocol. …”

Communication costs. by Naif Alzahrani (19992885)

“…The proof of correctness of the proposed protocol has been scrutinized through a well-known and widely used Real-Or-Random (RoR) model, ProVerif validation, and attacks’ discussion, demonstrating the thoroughness of the proposed protocol. …”

Mutual authentication phase. by Naif Alzahrani (19992885)

“…The proof of correctness of the proposed protocol has been scrutinized through a well-known and widely used Real-Or-Random (RoR) model, ProVerif validation, and attacks’ discussion, demonstrating the thoroughness of the proposed protocol. …”

Notations used and their descriptions. by Naif Alzahrani (19992885)

“…The proof of correctness of the proposed protocol has been scrutinized through a well-known and widely used Real-Or-Random (RoR) model, ProVerif validation, and attacks’ discussion, demonstrating the thoroughness of the proposed protocol. …”

Comparative Analysis (Security Functionalities). by Naif Alzahrani (19992885)

“…The proof of correctness of the proposed protocol has been scrutinized through a well-known and widely used Real-Or-Random (RoR) model, ProVerif validation, and attacks’ discussion, demonstrating the thoroughness of the proposed protocol. …”

Storage Cost of the Proposed Protocol. by Naif Alzahrani (19992885)

“…The proof of correctness of the proposed protocol has been scrutinized through a well-known and widely used Real-Or-Random (RoR) model, ProVerif validation, and attacks’ discussion, demonstrating the thoroughness of the proposed protocol. …”

Comparison with existing work. by Khondokar Oliullah (21553289)

“…<div><p>Authentication is a critical challenge in fog computing security, especially as fog servers provide services to many IoT users. The conventional authentication process often requires disclosing sensitive personal information, such as usernames, emails, mobile numbers, and passwords that end users are reluctant to share with intermediary services (i.e., Fog servers). …”

Registration phase. by Khondokar Oliullah (21553289)

“…<div><p>Authentication is a critical challenge in fog computing security, especially as fog servers provide services to many IoT users. The conventional authentication process often requires disclosing sensitive personal information, such as usernames, emails, mobile numbers, and passwords that end users are reluctant to share with intermediary services (i.e., Fog servers). …”

Performance comparison of different ML models. by Khondokar Oliullah (21553289)

“…<div><p>Authentication is a critical challenge in fog computing security, especially as fog servers provide services to many IoT users. The conventional authentication process often requires disclosing sensitive personal information, such as usernames, emails, mobile numbers, and passwords that end users are reluctant to share with intermediary services (i.e., Fog servers). …”

Classification Report for the proposed model. by Khondokar Oliullah (21553289)

“…<div><p>Authentication is a critical challenge in fog computing security, especially as fog servers provide services to many IoT users. The conventional authentication process often requires disclosing sensitive personal information, such as usernames, emails, mobile numbers, and passwords that end users are reluctant to share with intermediary services (i.e., Fog servers). …”

ROC curve of the proposed model. by Khondokar Oliullah (21553289)

“…<div><p>Authentication is a critical challenge in fog computing security, especially as fog servers provide services to many IoT users. The conventional authentication process often requires disclosing sensitive personal information, such as usernames, emails, mobile numbers, and passwords that end users are reluctant to share with intermediary services (i.e., Fog servers). …”

Fog-IoT architecture. by Khondokar Oliullah (21553289)

“…<div><p>Authentication is a critical challenge in fog computing security, especially as fog servers provide services to many IoT users. The conventional authentication process often requires disclosing sensitive personal information, such as usernames, emails, mobile numbers, and passwords that end users are reluctant to share with intermediary services (i.e., Fog servers). …”

Computational cost of the proposed approach. by Khondokar Oliullah (21553289)

“…<div><p>Authentication is a critical challenge in fog computing security, especially as fog servers provide services to many IoT users. The conventional authentication process often requires disclosing sensitive personal information, such as usernames, emails, mobile numbers, and passwords that end users are reluctant to share with intermediary services (i.e., Fog servers). …”

Confusion Matrix of different ML models. by Khondokar Oliullah (21553289)

“…<div><p>Authentication is a critical challenge in fog computing security, especially as fog servers provide services to many IoT users. The conventional authentication process often requires disclosing sensitive personal information, such as usernames, emails, mobile numbers, and passwords that end users are reluctant to share with intermediary services (i.e., Fog servers). …”

Performance metrics of applied ML models. by Khondokar Oliullah (21553289)

“…<div><p>Authentication is a critical challenge in fog computing security, especially as fog servers provide services to many IoT users. The conventional authentication process often requires disclosing sensitive personal information, such as usernames, emails, mobile numbers, and passwords that end users are reluctant to share with intermediary services (i.e., Fog servers). …”

An Ecological Benchmark of Photo Editing Software: A Comparative Analysis of Local vs. Cloud Workflows by Pierre-Alexis DELAROCHE (22092572)

“…Experimental Methodology Framework Local Processing Pipeline Architecture Data Flow: Storage I/O → Memory Buffer → CPU/GPU Processing → Cache Coherency → Storage I/O ├── Input Vector: mmap() system call for zero-copy file access ├── Processing Engine: OpenMP parallelization with NUMA-aware thread affinity ├── Memory Management: Custom allocator with hugepage backing └── Output Vector: Direct I/O bypassing kernel page cache Cloud Processing Pipeline Architecture Data Flow: Local Storage → Network Stack → TLS Tunnel → CDN Edge → Origin Server → Processing Grid → Response Pipeline ├── Upload Phase: TCP window scaling with congestion control algorithms ├── Network Layer: Application-layer protocol with adaptive bitrate streaming ├── Server-side Processing: Containerized microservices on Kubernetes orchestration ├── Load Balancing: Consistent hashing with geographic affinity routing └── Download Phase: HTTP/2 multiplexing with server push optimization Dataset Schema and Semantic Structure Primary Data Vectors Field Data Type Semantic Meaning Measurement Unit test_type Categorical Processing paradigm identifier {local_processing, cloud_processing} photo_count Integer Cardinality of input asset vector Count avg_file_size_mb Float64 Mean per-asset storage footprint Mebibytes (2^20 bytes) total_volume_gb Float64 Aggregate data corpus size Gigabytes (10^9 bytes) processing_time_sec Integer Wall-clock execution duration Seconds (SI base unit) cpu_usage_watts Float64 Thermal design power consumption Watts (Joules/second) ram_usage_mb Integer Peak resident set size Mebibytes network_upload_mb Float64 Egress bandwidth utilization Mebibytes energy_consumption_kwh Float64 Cumulative energy expenditure Kilowatt-hours co2_equivalent_g Float64 Carbon footprint estimation Grams CO₂e test_date ISO8601 Temporal execution marker RFC 3339 format hardware_config String Node topology identifier Alphanumeric encoding Statistical Distribution Characteristics The dataset exhibits non-parametric distribution patterns with significant heteroscedasticity across computational load vectors. …”