A Novel Hybrid Spotted Hyena-Swarm Optimization (HS-FFO) Framework for Effective Feature Selection in IOT Based Cloud Security Data

Internet of Things (IoT) has gained its major insight in terms of its deployment and applications. Since IoT exhibits more heterogeneous characteristics in transmitting the real time application data, these data are vulnerable to many security threats. To safeguard the data, machine and deep learning based security systems has been proposed. But this system suffers the computational burden that impedes threat detection capability. Hence the feature selection plays an important role in designing the complexity aware IoT systems to defend the security attacks in the system. This paper propose the novel ensemble of spotted hyena with firefly algorithm to choose the best features and minimise the redundant data features that can boost the detection system's computational effectiveness.  Firstly, an effective firefly optimized feature correlation method is developed.  Then, in order to enhance the exploration and search path, operators of fireflies are combined with Spotted Hyena to assist the swarms in leaving the regionally best solutions. The experimentation has been carried out using the different IoT cloud security datasets such as NSL-KDD-99 , UNSW and CIDCC -001 datasets and contrasted with ten cutting-edge feature extraction techniques, like PSO (particle swarm optimization), BAT, Firefly, ACO(Ant Colony Optimization), Improved PSO, CAT, RAT, Spotted Hyena, SHO and  BOC(Bee-Colony Optimization) algorithms. Results demonstrates the proposed hybrid model has achieved the better feature selection mechanism with less convergence  time and aids better for intelligent threat detection system with the high performance of detection

Edge Computing and Blockchain in Smart Agriculture Systems

The advancement of Internet-based technologies has made huge progress toward improving the accessibility of "smart agriculture." With the advent of unmanned and automatic management, smart agriculture is now able to accomplish monitoring, supervision, and real-time picture monitoring. It is not possible to know for sure that the data in a smart agriculture system is complete and secure from intrusion. This article investigates and assesses the potential of edge computing and blockchain for use in smart agriculture. We combine the advantages of blockchain technology and the edge computing framework to create a smart agriculture framework system that is based on a very straightforward analysis of the evolution of smart agriculture. The study proposes a thorough method for emphasizing the significance of agriculture and edge computing, as well as the advantages of incorporating blockchain technology in this context. This paper also proposes an intelligent agricultural product traceability system design: edge computing with blockchain for smart agriculture. The study concludes with a discussion of outstanding problems and difficulties that can arise during the creation of a blockchain-based edge computing system for smart agriculture systems

A Deadlock – Free Routing Algorithm for Torus Network

TORUS is a n-dimensional network topology. Each dimension will have k nodes.  A routing algorithm determines the sequence of channels for a packet to traverse from the source to destination. A new router design that significantly reduces the main drawback of worm hole switching – latency, is presented in this paper. Worm-hole switching is combined with virtual channel to provide better performance. Packet deadlock is avoided by verifying the freeness of the nodes before sending the packets to that node. The traditional ‘wormhole switching’ mechanism for routing in the torus network has the disadvantages such as link contention, message latency, need for large buffer size and finally a massive deadlock may appear. The recently proposed ‘clue’ algorithm, has the disadvantages such as difficulty in cut through the link by the packets, says nothing about loss of packets between a hop and storage overhead and complexity in dividing the virtual channels. We proposed an ‘Advanced Clue’ algorithm by combining the concepts of clue and flow controlled clue and also overcome the disadvantages of clue. We use two virtual channels and a buffer which gives a combination of clue and flow controlled clue. We also propose conditions that satisfy the reliability of the packet delivery between hops. The packet will be sent to the next hop and buffered in the current hop. The sending hop will set a timer and wait for the acknowledgement. If the acknowledgement is not arrived till the timer expired then, the packet will be resend, and otherwise the packet will be removed from the buffer. Keywords: Torus, Virtual channels, Cut – through Switching, Wormhole switching

Integrated publish/subscribe and push-pull method for cloud based IoT framework for real time data processing

Cloud-based IoT is a platform that connects smart devices to the cloud for real-time data analysis. Any device on the IoT platform can connect to the cloud through messaging. The integrated publish/subscribe and push/pull methods for a cloud-based IoT framework that is scalable for connecting IoT devices and processing the real-time data are proposed. The proposed framework uses a publish/subscribe messaging broker and a push-pull method for transmitting the data from the device to the cloud. The IoT devices publish the data via the broker, to which the cloud service providers subscribe. This publishing and transferring of data from the broker to the cloud is implemented with the help of a push-pull mechanism. In this mechanism, the broker makes the computations required to select the cloud service provider. Hence, the overhead of the device is reduced. All computations go in parallel, which reduces the latency of the system. The system is flexible for any number of devices, brokers, and cloud service providers, which shows that the system is scalable. The results demonstrated the effectiveness of the model, which was developed using a cloud-based IoT framework with a focus on scalability and latency

MDROGWL: modified deep reinforcement oppositional wolf learning for group key management in IoT environment

Securing confidential data against unauthorized users leads to access control policies with the rapid progression of Internet of Things (IoT) devices. Because of high mobility subscribers, the dynamic IoT environment is subjected to high signaling overhead which remains a challenging issue to guarantee data dissemination to legitimate users. The group's key management schemes are the central mechanism to deal with dynamic environments. But they are centralized concepts that cause scalability issues and suffer in handling large numbers of subscribers. Therefore, this paper proposes a modified deep reinforcement oppositional wolf learning-based group key management (MDROWL-GKM) system to monitor the data obtained in IoT properly. It does not maximize the network traffic as well as computational overhead when a group member leaves or joins. With the inclusion of an opposition-based learning gray wolf optimization algorithm, the overload issue of the modified deep reinforcement method is eliminated and the performance is enhanced. The efficacy of the proposed MDROWL-GKM system is investigated using different measures namely storage overhead, computation overhead, access response time, space complexity, re-evaluation time, policy adjustment accuracy, and communication overhead. The experimental analysis proves that the proposed MDROWL-GKM system is superior to other state-of-the-art techniques, particularly with high policy adjustment accuracy (96%), less communication overhead (8 μs) and area under curve (AUC) rate (0.982