• The paper identifies the current trends in key security technologies for IoT-based smart farming through a systematic mapping study. • The paper assesses the technology readiness level (TRL) of each identified key security solution and the characteristics of a security product identified by ISO/IEC 25010. • Mapping of ISO/IEC 25010 security characteristics and Technology Readiness Level Smart farming systems sit at the intersection between three rapidly and independently advancing fields of IoT, Security, and Machine Learning. Its full realisation has tremendous positive impacts on food production; yet agricultural settings come with unique challenges that inhibit the rapid deployment of such state-of-the-art technologies. In this paper, we systematically study the current state of security for IoT-based smart farming research and development landscape and assess the proposed security solutions through the lens of technology readiness levels (TRL) and ISO/IEC 25010 security product evaluation framework. By analysing forty-eight primary studies, we identified the top security technologies under development, the critical security threats being addressed, and the most popularly used machine learning-based security solutions. Furthermore, we found that most of the ISO/IEC 25010 security characteristics considered by the security solutions are currently below TRL 6, indicating that they are well below the deployment readiness levels. Therefore, we recommend several supporting transitional technologies be developed to move the prototype development towards system validation and deployment to avoid the technology “valley of death”, such as farming-specific intrusion detection public datasets and large-scale IoT agriculture testbeds to validate the interoperability and transparency of security solutions at different layers. This systematic mapping study, together with a TRL assessment and ISO 25010 standard mapping, is the first of its kind, intending to provide a standardised comparison of the current state of security technologies for IoT-based smart farms to define a clear roadmap for future research and development. It provides a common terminology for the multidisciplinary stakeholders of smart farming to distinguish between theoretical security concepts and ready-to-deploy solutions, facilitating crucial decisions for investment, deployment, and commercialisation.

Federated learning (FL) has been widely adopted as a decentralized training paradigm that enables multiple clients to collaboratively learn a shared model without exposing their local data. As concerns over data privacy and regulatory compliance grow, machine unlearning, which aims to remove the influence of specific data from trained models, has become increasingly important in the federated setting to meet legal, ethical, or user-driven demands. However, integrating unlearning into FL introduces new challenges and raises largely unexplored security risks. In particular, adversaries may exploit the unlearning process to compromise the integrity of the global model. In this paper, we present the first backdoor attack in the context of federated unlearning, demonstrating that an adversary can inject backdoors into the global model through seemingly legitimate unlearning requests. Specifically, we propose BadFU, an attack strategy where a malicious client uses both backdoor and camouflage samples to train the global model normally during the federated training process. Once the client requests unlearning of the camouflage samples, the global model transitions into a backdoored state. Extensive experiments under various FL frameworks and unlearning strategies validate the effectiveness of BadFU, revealing a critical vulnerability in current federated unlearning practices and underscoring the urgent need for more secure and robust federated unlearning mechanisms.

Federated learning (FL), as an emerging artificial intelligence (AI) approach, enables decentralized model training across multiple devices without exposing their local training data. FL has been increasingly gaining popularity in both academia and industry. While research works have been proposed to improve the fault tolerance of FL, the real impact of unreliable devices (e.g., dropping out, misconfiguration, poor data quality) in real-world applications is not fully investigated. We carefully chose two representative, real-world classification problems with a limited numbers of clients to better analyze FL fault tolerance. Contrary to the intuition, simple FL algorithms can perform surprisingly well in the presence of unreliable clients.

One of the important characteristics of smart cities is ease in accessibility of information for residents and visitors alike. Language barriers, impairments and presence of crowd should not influence the context-awareness of the visitors in such cities. The information provided via advertisement boards play an important part in this regard. In this paper, we propose Smart ads-RFID based system for real time retrieval and translation of public advertisements. Candidate signs and advertisements are RFID-enabled and the electronic contents of such advertisements/signs are acquired and stored in a cloud database server. The system offers both real time and off-line text/voice translation into user's preferred language and communicates the translated contents to user's smart phone. The system also provides real time GIS map visualization of the RFID enabled advertisements in the city. It collects statistics regarding the number of users subscribed in the system, their locations, preferred language and most frequently used languages to help decision makers to decide when and where to post such advertisements. We described our developed pilot project and we investigated its usability, validity and usefulness using Fuzzy Rule-based System. We believe that such system could provide promising services within the context of smart cities.

Effective life time of any software can be increased many folds by proper and up to date maintenance. Automated software module clustering is a method used by software professionals to recover high-level structure of the system by decomposing the system into smaller manageable subsystems, containing interdependent modules. Once the structure of the system is clear, the understanding of any system for proper maintenance can be achieved. We have proposed an automated clustering approach based on the principles of Evolution Strategies to search a large solution space consisting of modules and their relationships. Our approach tries to achieve near optimal decompositions consisting of independent subsystems, containing interdependent modules. We have compared our proposed approach with a widely used Genetic Algorithm based clustering technique and our approach worked better in all test cases. In this paper, we are highlighting one distinguishing feature of our approach: the consistency in results. For any optimization algorithm, exactly similar results in different executions of the algorithm on same data cannot be achieved. However, the results should remain in close proximity and should not change drastically. We have carried out a comparative study of our approach and the Genetic Algorithm based approach using a set of test systems. The results with our approach are always consistent than those produced by the Genetic Algorithm based approach.

In this paper, we propose a novel approach for reducing the download time of large files over the Internet. Our approach, known as Parallelized File Transport Protocol (P-FTP), proposes simultaneous downloads of disjoint file portions from multiple file servers. P-FTP server selects file servers for the requesting client on the basis of a variety of QoS parameters, such as available bandwidth and server utilization. The sensitivity analysis of our file server selection technique shows that it performs significantly better than random selection. During the file transfer, P-FTP client monitors the file transfer flows to detect slow servers and congested links and adjusts the file distributions accordingly. P-FTP is evaluated with simulations and real-world implementation. The results show at least 50 percent reduction in download time when compared to the traditional file-transfer approach. Moreover, we have also carried out a simulation-based study to investigate the issues related to large scale deployment of our approach on the Internet. Our results demonstrate that a large number of P-FTP users has no adverse effect on the performance perceived by non-P-FTP users. In addition, the file servers and network are not significantly affected by large scale deployment of P-FTP.

The parallelized file transfer protocol (P-FTP) is a novel network resource aware parallel technique for improving file transfer performance on the Internet. Before starling the parallel file transfer sessions, it considers the available resources in the network (available bandwidth) and at the file servers (memory and CPU utilization). The client dynamically changes the file portions being downloaded from different file servers by monitoring the FTP flows and detecting slow servers and congested links. Early experimentation on Planet-Lab (2004) for a single P-FTP client suggests that the download time can be reduced by more than 50% for large files. In this paper, our goal is to evaluate whether P-FTP can be widely adopted within the Internet, To this end, we have carried out a simulation-based study to investigate the performance of P-FTP when it is adopted by a large user base. We find that, by virtue of its self-tuning capability, P-FTP continues to exhibit improved performance even with many simultaneous clients. Our results also demonstrate that introducing a large number of P-FTP users has no adverse effect on the performance perceived by users of the traditional single server file transfer. We attribute this improvement to the fact that P-FTP dynamically adapts the parallel sessions in response to changes in network state and server resources. This illustrates that P-FTP is highly scalable and is hence suitable for widespread deployment in the Internet.

Parallelized FTP (P-FTP) approach, attempts to solve the problem of slow downloads of large multimedia files while optimizing the utilization of mirror servers. The approach presented in this paper downloads a single file from multiple mirror servers simultaneously, where each mirror server transfers a portion of the file. P-FTP server calculates the optimum division of the file for efficient transfer. The dynamic monitoring ability of P-FTP maintains the file transfer process at the optimized level no matter how abruptly network and mirror server characteristics change.