Biblio

With the continues growth of our technology, majority in our sectors are becoming smart and one of its great applications is in agriculture, which we call it as smart farming. The application of sensors, IoT, artificial intelligence, networking in the agricultural setting with the main purpose of increasing crop production and security level. With this advancement in farming, this provides a lot of privileges like remote monitoring, optimization of produce and too many to mention. In light of the thorough systematic analysis performed in this study, it was discovered that Edge-of-things is a potential computing scheme that could boost an artificial intelligence for intelligent remote farm security and intrusion detection pre-alarm system over other computing schemes. Again, the purpose of this study is not to replace existing cloud computing, but rather to highlight the potential of the Edge. The Edge architecture improves end-user experience by improving the time-related response of the system. response time of the system. One of the strengths of this system is to provide time-critical response service to make a decision with almost no delay, making it ideal for a farm security setting. Moreover, this study discussed the comparative analysis of Cloud, Fog and Edge in relation to farm security, the demand for a farm security system and the tools needed to materialize an Edge computing in a farm environment.

Intelligent, smart, Cloud, reconfigurable manufac-turing, and remote monitoring, all intersect in modern industry and mark the path toward more efficient, effective, and sustain-able factories. Many obstacles are found along the path, including legacy machineries and technologies, security issues, and software that is often hard, slow, and expensive to adapt to face unforeseen challenges and needs in this fast-changing ecosystem. Light-weight, portable, loosely coupled, easily monitored, variegated software components, supporting Edge, Fog and Cloud computing, that can be (re)created, (re)configured and operated from remote through Web requests in a matter of milliseconds, and that rely on libraries of ready-to-use tasks also extendable from remote through sub-second Web requests, constitute a fertile technological ground on top of which fourth-generation industries can be built. In this demo it will be shown how starting from a completely virgin Docker Engine, it is possible to build, configure, destroy, rebuild, operate, exclusively from remote, exclusively via API calls, computation networks that are capable to (i) raise alerts based on configured thresholds or trained ML models, (ii) transform Big Data streams, (iii) produce and persist Big Datasets on the Cloud, (iv) train and persist ML models on the Cloud, (v) use trained models for one-shot or stream predictions, (vi) produce tabular visualizations, line plots, pie charts, histograms, at real-time, from Big Data streams. Also, it will be shown how easily such computation networks can be upgraded with new functionalities at real-time, from remote, via API calls.

Distributed computation and AI processing at the edge has been identified as an efficient solution to deliver real-time IoT services and applications compared to cloud-based paradigms. These solutions are expected to support the delay-sensitive IoT applications, autonomic decision making, and smart service creation at the edge in comparison to traditional IoT solutions. However, existing solutions have limitations concerning distributed and simultaneous resource management for AI computation and data processing at the edge; concurrent and real-time application execution; and platform-independent deployment. Hence, first, we propose a novel three-layer architecture that facilitates the above service requirements. Then we have developed a novel platform and relevant modules with integrated AI processing and edge computer paradigms considering issues related to scalability, heterogeneity, security, and interoperability of IoT services. Further, each component is designed to handle the control signals, data flows, microservice orchestration, and resource composition to match with the IoT application requirements. Finally, the effectiveness of the proposed platform is tested and have been verified.

Internet of things (IoT) and Fog computing applications deal with sensitive data and need security tools to be protected against attackers. CoAP (Constrained Application Protocol), combined with DTLS (Datagram Transport Layer Security), provides security to IoT/Fog applications. However, processing times need to be considered when using this combination due to IoT/Fog environment constraints. Our work presents a CoAP with DTLS application and analyzes the performance of Raspberry Pi 3 during DTLS handshakes, data encryption and data decryption with the most relevant cipher suites. The performance of confirmable and non-confirmable CoAP POST requests is also measured and discussed in our work. We discovered that cipher suites that use RSA as an authentication method on handshake are slightly faster than cipher suites that use ECDSA, while symmetric key encryption with AES256(128)GCM are 40% faster than AES256(128) default modes. Our study also suggests CoAP modifications to obtain higher efficiency, and it might help future IoT/Fog application developers to understand CoAP and DTLS union, providing an application example and performance metrics.

Authentication is a challenging and emerging issue for Fog-IoT security paradigms. The fog nodes toward large-scale end-users offer various interacted IoT services. The authentication process usually involves expressing users' personal information such as username, email, and password to the Authentication Server (AS). However, users are not intended to express their identities or information over the fog or cloud servers. Hence, we have proposed an Anonymous User Authentication Scheme for Fog-IoT (AUASF) to keep the anonymity existence of the IoT users and detect the intruders. To provide anonymity, the user can send encrypted credentials such as username, email, and mobile number through the Cloud Service Provider (CSP) for registration. IoT user receives the response with a default password and a secret Id from the CSP. After that, the IoT user submits the default password for first-time access to Fog Service Provider (FSP). The FSP assigns a One Time Password (OTP) to each user for further access. The developed scheme is equipped with hash functions, symmetric encryptions, and decryptions for security perceptions across fog that serves better than the existing anonymity schemes.

Monitoring for security and well-being in highly populated areas is a critical issue for city administrators, policy makers and urban planners. As an essential part of many dynamic and critical data-driven tasks, situational awareness (SAW) provides decision-makers a deeper insight of the meaning of urban surveillance. Thus, surveillance measures are increasingly needed. However, traditional surveillance platforms are not scalable when more cameras are added to the network. In this work, a smart surveillance as an edge service has been proposed. To accomplish the object detection, identification, and tracking tasks at the edge-fog layers, two novel lightweight algorithms are proposed for detection and tracking respectively. A prototype has been built to validate the feasibility of the idea, and the test results are very encouraging.

In the present generation internet plays a major role. The data being sent by the user is created by the things like pc, mobiles, sensors etc. and these data are sent to the cloud system. When a data from the IOT devices are sent to the cloud, there is a question of privacy and security. To provide security for the data well-known security algorithms are used in fog layer and are successful in transferring the data without any damage. Here different techniques used for providing security for IOT data are discussed.

The rapid development of the Internet of Things (IoT) results in generating massive amounts of data. Significant portions of these data are sensitive since they reflect (directly or indirectly) peoples' behaviors, interests, lifestyles, etc. Protecting sensitive IoT data from privacy violations is a challenge since these data need to be communicated, processed, analyzed, and stored by public networks, servers, and clouds; most of them are untrusted parties for data owners. We propose a solution for protecting sensitive IoT data called Policy Enforcement Fog Module (PEFM). The major task of the PEFM solution is mandatory enforcement of privacy policies for sensitive IoT data-wherever these data are accessed throughout their entire lifecycle. The key feature of PEFM is its placement within the fog computing infrastructure, which assures that PEFM operates as closely as possible to data sources within the edge. PEFM enforces policies directly for local IoT applications. In contrast, for remote applications, PEFM provides a self-protecting mechanism based on creating and disseminating Active Data Bundles (ADBs). ADBs are software constructs bundling inseparably sensitive data, their privacy policies, and an execution engine able to enforce privacy policies. To prove effectiveness and efficiency of the proposed module, we developed a smart home proof-of-concept scenario. We investigate privacy threats for sensitive IoT data. We run simulation experiments, based on network calculus, for testing performance of the PEFM controls for different network configurations. The results of the simulation show that-even with using from 1 to 5 additional privacy policies for improved data privacy-penalties in terms of execution time and delay are reasonable (approx. 12-15% and 13-19%, respectively). The results also show that PEFM is scalable regarding the number of the real-time constraints for real-time IoT applications.