International Journal of Computer Science and Technology

International Journal of Computer Science and Technology | Vol. 4, No. 10, October 2023 | pp. 1–8

DOI: 10.46882/2023/IJCST/000225

Article Type: Original Research Paper

Title: Optimizing Spatial Query Performance in Distributed GIS Architectures via Multi-Level Grid Indexing

Names of Authors: Min-Ji Kim¹, David Miller²

Authors’ Affiliations: ¹Spatial Information Systems Lab, Pusan National University, Busan, South Korea; ²Geospatial Technologies Group, TechCorp Innovations, San Francisco, USA

Abstract: Distributed Geographic Information Systems (GIS) face major performance bottlenecks when executing complex spatial queries across multi-terabyte environmental datasets. Conventional indexing methods like standard R-trees encounter severe overlap issues when processing high-density urban coordinates, resulting in long query lookup delays. This paper proposes a multi-level grid indexing framework optimized for distributed spatial databases. The framework divides geographic coordinate spaces into hierarchical, non-overlapping grid cells, partitioning geospatial datasets balanced across cluster nodes. An adaptive spatial data placement algorithm balances workloads among servers based on historical query access frequencies. We validated this indexing architecture across a 16-node distributed cluster using a 500 GB global transport dataset. The experimental findings indicate a 44.1% reduction in total spatial query execution time compared to traditional distributed R-tree implementations. The cluster achieved a sustained query processing capacity of 12,400 queries per second, demonstrating its scalability for web-mapping infrastructures.

Keywords: Geographic Information Systems, Distributed Indexing, Spatial Queries, Grid Partitioning, Workload Balancing, Database Performance

Manuscript Timeline: Received: March 10, 2023; Revised: May 02, 2023; Accepted: June 15, 2023; Published: October 12, 2023

Citation: Kim, M. -J., & Miller, D. (2023). Optimizing Spatial Query Performance in Distributed GIS Architectures via Multi-Level Grid Indexing. International Journal of Computer Science and Technology, 4(10), 1–8. DOI: 10.46882/2023/IJCST/000225

International Journal of Computer Science and Technology | Vol. 4, No. 9, September 2023 | pp. 1–8

DOI: 10.46882/2023/IJCST/000224

Article Type: Original Research Paper

Title: Resilient Multi-UAV Swarm Coordination under Targeted Communication Jamming Conditions

Names of Authors: Carlos Silva¹, Amara Okafor²

Authors’ Affiliations: ¹Department of Systems Engineering, Technological Institute of Aeronautics, São José dos Campos, Brazil; ²Department of Computer Science, University of Ibadan, Ibadan, Nigeria

Abstract: Unmanned Aerial Vehicle (UAV) swarms require continuous peer-to-peer data coordination to execute spatial surveying operations safely and avoid mid-air collisions. However, targeted communication jamming attacks can sever inter-node links, leading to swarm fragmentation or mission failure. This study presents a resilient decentralized swarm coordination protocol developed to handle localized communication dropouts. The framework introduces a dynamic potential-field routing topology that recalculates vehicle trajectories using onboard optical flow data when communication signals are lost. This allows independent aerial units to infer neighboring maneuvers based on visual observation flags rather than relying on wireless data feeds. We tested this tracking mechanism within a simulated 3D operational space using 24 moving aerial vehicles under active signal jamming variables. The simulation results show that the swarm maintained a 91.6% structural cohesion level during active signal dropouts. The system completely avoided inter-vehicle collisions while restricting tracking trajectory errors to ±1.4 m during autonomous recovery phases.

Keywords: UAV Swarms, Communication Jamming, Decentralized Coordination, Optical Flow, Collision Avoidance, Network Resilience

Manuscript Timeline: Received: March 01, 2023; Revised: April 20, 2023; Accepted: May 29, 2023; Published: September 08, 2023

Citation: Silva, C., & Okafor, A. (2023). Resilient Multi-UAV Swarm Coordination under Targeted Communication Jamming Conditions. International Journal of Computer Science and Technology, 4(9), 1–8. DOI: 10.46882/2023/IJCST/000224

International Journal of Computer Science and Technology | Vol. 4, No. 8, August 2023 | pp. 1–8

DOI: 10.46882/2023/IJCST/000223

Article Type: Original Research Paper

Title: Optimizing Deep Neural Network Inference on Edge TPU Devices through Integer Quantization Protocols

Names of Authors: Amina Al-Mansoor¹, Dieter Schmidt²

Authors’ Affiliations: ¹Department of Computer Engineering, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia; ²Institute for Computer Architecture, Technical University of Munich, Munich, Germany

Abstract: Running complex object detection networks on localized edge hardware remains limited by the high memory requirements and power consumption profiles of floating-point processing layers. This research presents an integer quantization protocol designed to compress deep learning networks for edge-layer Tensor Processing Unit (TPU) deployments. Our methodology implements an optimized post-training quantization algorithm that maps 32-bit floating-point parameters (FP32) into 8-bit integer formats (INT8). The system uses a dynamic scaling factor calculation across intermediate network layers to minimize quantization noise accumulation. We evaluated this compression architecture using MobileNet-V3 and YOLO-V5 models running on embedded edge TPU modules. The experimental measurements indicate a 72.3% reduction in the total memory space required by the neural model. The total frame inference rate increased by 2.8x, achieving a processing latency of 14 ms per image frame. Crucially, the overall classification accuracy degraded by only 0.42% compared to the original uncompressed model framework.

Keywords: Edge TPU, Model Compression, Integer Quantization, Deep Neural Networks, Computer Vision, Resource Optimization

Manuscript Timeline: Received: February 22, 2023; Revised: April 15, 2023; Accepted: May 25, 2023; Published: August 14, 2023

Citation: Al-Mansoor, A., & Schmidt, D. (2023). Optimizing Deep Neural Network Inference on Edge TPU Devices through Integer Quantization Protocols. International Journal of Computer Science and Technology, 4(8), 1–8. DOI: 10.46882/2023/IJCST/000223

International Journal of Computer Science and Technology | Vol. 4, No. 7, July 2023 | pp. 1–8

DOI: 10.46882/2023/IJCST/000222

Article Type: Original Research Paper

Title: Quantifying Malicious Smart Contract Behavior via Automated Abstract Syntax Tree Feature Mapping

Names of Authors: Sanjay Nair¹, Takashi Sato²

Authors’ Affiliations: ¹Cyber Security Research Center, Indian Institute of Information Technology, Bangalore, India; ²School of Engineering, Tokyo Institute of Technology, Tokyo, Japan

Abstract: Decentralized application protocols running on public blockchain ecosystems remain highly vulnerable to smart contract vulnerabilities, exposing digital assets to targeted reentrancy and integer overflow exploits. Manual source-code validation is slow and cannot scale with the rapid deployment rate of smart contracts. This article introduces an automated vulnerability identification pipeline that uses structural feature mapping of Abstract Syntax Trees (ASTs). The pipeline extracts syntactic dependencies and data-flow pathways from smart contract source files, converting them into structured low-dimensional vector representations. A random forest classifier evaluates these feature maps to isolate architectural flaws before compilation or deployment. We tested this validation framework using a verification dataset of 4,500 checked Ethereum smart contracts. The analysis results prove that the tool achieves a true positive classification rate of 97.4%, surpassing standard rule-based pattern matching tools by 14.5%. The average analysis duration recorded was 240 ms per contract file, making it suitable for continuous integration (CI/CD) pipelines.

Keywords: Blockchain, Smart Contracts, Vulnerability Assessment, Abstract Syntax Tree, Automated Detection, Cybersecurity

Manuscript Timeline: Received: February 18, 2023; Revised: April 10, 2023; Accepted: May 20, 2023; Published: July 11, 2023

Citation: Nair, S., & Sato, T. (2023). Quantifying Malicious Smart Contract Behavior via Automated Abstract Syntax Tree Feature Mapping. International Journal of Computer Science and Technology, 4(7), 1–8. DOI: 10.46882/2023/IJCST/000222

International Journal of Computer Science and Technology | Vol. 4, No. 6, June 2023 | pp. 1–8

DOI: 10.46882/2023/IJCST/000221

Article Type: Original Research Paper

Title: Strategic Resource Optimization in 6G Multi-Tenant Slicing Networks Using Deep Q-Learning

Names of Authors: Chinedu Aliyu¹, Elena Rostova²

Authors’ Affiliations: ¹Department of Telecommunications Engineering, University of Nigeria, Nsukka, Nigeria; ²Network Systems Research Lab, Helsinki Institute of Technology, Helsinki, Finland

Abstract: The transition toward 6G mobile infrastructures requires the simultaneous management of highly distinct network slices tailored for ultra-reliable low-latency systems and massive machine-type deployments. Static network resource allocations result in severe underutilization or localized bandwidth exhaustion during peak periods. This paper presents a dynamic multi-tenant network slicing optimization model utilizing a Deep Q-Network (DQN) framework. The model tracks live performance telemetry parameters including instantaneous bitrates, queue sizes, and hardware utilization over a continuous time frame. It dynamically scales physical radio resources and virtual routing pathways among independent network operators based on localized demand variables. We simulated this network structure on an enterprise network platform with 20 distinct base stations and 1,000 mobile terminal nodes. The experimental results show a 34.7% improvement in total data throughout efficiency over standard static slicing models. Total network handoff latency was maintained below a strict boundary of 4.5 ms under high mobility stress conditions. A standard statistical evaluation confirmed the significance of this throughput improvement across testing runs (p < 0.01).

Keywords: 6G Infrastructure, Network Slicing, Deep Q-Learning, Resource Management, Multi-Tenant Systems, Telemetry

Manuscript Timeline: Received: February 10, 2023; Revised: April 02, 2023; Accepted: May 12, 2023; Published: June 06, 2023

Citation: Aliyu, C., & Rostova, E. (2023). Strategic Resource Optimization in 6G Multi-Tenant Slicing Networks Using Deep Q-Learning. International Journal of Computer Science and Technology, 4(6), 1–8. DOI: 10.46882/2023/IJCST/000221

International Journal of Computer Science and Technology | Vol. 4, No. 5, May 2023 | pp. 1–8

DOI: 10.46882/2023/IJCST/000220

Article Type: Original Research Paper

Title: Decentralized Provenance Auditing for Supply Chains Using Smart Contracts and Zero-Knowledge Proofs

Names of Authors: Liam O'Donnell¹, Yuki Tanaka²

Authors’ Affiliations: ¹Department of Software Architecture, Dublin Research Institute, Dublin, Ireland; ²Logistics Technology Infrastructure Division, Tokyo Systems Corp, Tokyo, Japan

Abstract: Global enterprise supply chains require verifiable product tracking mechanisms to combat counterfeiting and ensure compliance with regulatory standards. Traditional centralized databases are vulnerable to internal data tampering and lack transparency across independent suppliers. This paper presents a decentralized provenance auditing framework that combines public blockchain smart contracts with zero-knowledge proofs (ZKPs). The smart contracts log item transformations and custody transfers across production lines sequentially. To protect corporate privacy and trade secrets, the framework uses non-interactive zero-knowledge proofs (zk-SNARKs). This layer allows suppliers to verify regulatory compliance and product origins without revealing proprietary recipes, pricing structures, or specific logistics paths. We deployed and tested the cryptographic protocol on an enterprise Ethereum network setup. The validation tests confirm that creating an authenticity verification proof requires an average processing time of 84 ms. The smart contract transaction gas usage remained highly cost-effective for high-volume logistics tracking operations. The framework provides an absolute defense line against counterfeit data entries while maintaining corporate confidentiality.

Keywords: Supply Chain Provenance, Blockchain, Smart Contracts, Zero-Knowledge Proofs, Data Privacy, Auditing Systems

Manuscript Timeline: Received: June 15, 2022; Revised: July 22, 2022; Accepted: August 10, 2022; Published: May 07, 2023