A Secure Zone-Based Routing Protocol for Mobile Ad Hoc Networks

In this thesis, we proposed a secure hybrid ad hoc routing protocol, called Secure Zone Routing Protocol (SZRP), which aims at addressing the above limitations by combining the best properties of both proactive and reactive approaches. The proposed protocol is based on the concept zone routing protocol (ZRP). It employs an integrated approach of digital signature and both the symmetric and asymmetric key encryption techniques to achieve the security goals like message integrity, data confidentiality and end to end authentication at IP layer. The thesis details the design of the proposed protocol and analyses its robustness in the presence of multiple possible security attacks that involves impersonation, modification, fabrication and replay of packets caused either by an external advisory or an internal compromised node within the network. The security and performance evaluation of SZRP through simulation indicates that the proposed scheme successfully defeats all the identified threats and achieves a good security at the cost of acceptable overhead. Together with existing approaches for securing the physical and MAC layer within the network protocol stack, the Secure Zone Routing Protocol (SZRP) can provide a foundation for the secure operation of an ad hoc network

P2FLF: Privacy-Preserving Federated Learning Framework Based on Mobile Fog Computing

Mobile IoT devices provide a lot of data every day, which provides a strong base for machine learning to succeed. However, the stringent privacy demands associated with mobile IoT data pose significant challenges for its implementation in machine learning tasks. In order to address this challenge, we propose privacy-preserving federated learning framework (P2FLF) in a mobile fog computing environment. By employing federated learning, it is possible to bring together numerous dispersed user sets and collectively train models without the need to upload datasets. Federated learning, an approach to distributed machine learning, has garnered significant attention for its ability to enable collaborative model training without the need to share sensitive data. By utilizing fog nodes deployed at the edge of the network, P2FLF ensures that sensitive mobile IoT data remains local and is not transmitted to the central server. The framework integrates privacy-preserving methods, such as differential privacy and encryption, to safeguard the data throughout the learning process. We evaluate the performance and efficacy of P2FLF through experimental simulations and compare it with existing approaches. The results demonstrate that P2FLF strikes a balance between model accuracy and privacy protection while enabling efficient federated learning in mobile IoT environments

Recruitment Algorithm in Edge-Cloud Servers based on Mobile Crowd-Sensing in Smart Cities

As more and more mobile devices rely on cloud services since the introduction of cloud computing, data privacy has emerged as one of the most pressing security concerns. Users typically encrypt their important data before uploading it to cloud servers to safeguard data privacy, which makes data usage challenging. On the other side, this also increases the possibility of brand-new issues in cities. A clever, effective and efficient urban monitoring system is required to address possible challenges that may arise in urban settings. In the smart city concept, which makes use of sensors, one strategy that might be used in IoT and cloud computing is to monitor and gather data on problems that develop in cities in real-time. However, it will take a while and be rather expensive to install IoT and sensors throughout the city. The Mobile Crowd-Sensing (MCS) method is proposed to be used in this study to retrieve and gather data on issues that arise in metropolitan areas from citizen reports made using mobile devices. And we suggest a budget-constrained, reputation-based collaborative user recruitment (RCUR) procedure for a MCS system. To construct an edge-assisted MCS system in urban situations, we first integrate edge computing into MCS. We also examine how user reputation affects user recruitment. Finally, we create a collaborative sensing approach using the edge nodes’ sensing capabilities

Optimizing horizontal scalability in cloud computing using simulated annealing for Internet of Things

The Internet of Things (IoTs) is a technology that connects sensor devices to the Internet to enable smarter and more intelligent communication. Today, many industries are using various IoT devices to create smart and intelligent environments. However, the sudden increase in demand has created a major challenge for IoT connections, known as scalability. Scalability refers to increasing and expanding the number of internet-connected devices for a specific application. To address this issue, we propose simulated annealing-based horizontal scaling to achieve faster and more efficient scaling to accommodate IoT devices. We explore different horizontal scaling methods and propose a Markov chain process to model the scaling. We then use simulated annealing to optimize the scaling visualized by the Markov chain process. Our goal is to focus on the flexible nature of horizontal scalability for adding various IoT devices and resources as needed. We have compared our proposed horizontal scalability optimization with vertical scalability, which has a built-in feature of elasticity. We have evaluated several parameters, such as cost, service rate, and transfer rate, and found that our proposal outperforms existing methods