Green Computing for Industrial Wireless Sensor Networks: Energy oriented Cross Layer Modelling

Background: Enabling industrial environment with automation is growing trend due to the recent developments as industry 4.0 centric production. The industrial wireless sensor network environments have a number of constraints, including densely deployed nodes, delay constraint for mechanical operation, and access constraints due to node position within instruments. The related literature have applied existing models of wireless sensor network in industrial environment without appropriate updating in the different layers of communication, which results in performance degradation in realistic industrial scenario. Method: This paper presents a framework for Energy Oriented Cross Layer Data Dissemination Path (E-CLD2 P) towards enabling green computing in industrial wireless sensor network environments. It is a cross-layer design approach considering deployment of sensors at the physical layer up to data dissemination at the network layer and smart services at application layer. In particular, an energy centric virtual circular deployment visualization model is presented focusing on physical layer signal transmission characteristics in industrial WSNs scenario. A delay centric angular striping is designed for cluster based angular transmission to support deadline constrained industrial operation in the WSNs environments. Algorithms for energy centric delivery path formulation and node’s role transfer are developed to support green computing in restricted access industrial WSNs scenario. Results: The green computing framework is implemented to evaluate the performance in a realistic industrial WSNs environment. Conclusion: The performance evaluation attests the benefits in terms of number of metrics in realistic industrial constrained environments.

Multivariable Heuristic Approach to Intrusion Detection in Network Environments

The Internet is an inseparable part of our contemporary lives. This means that protection against threats and attacks is crucial for major companies and for individual users. There is a demand for the ongoing development of methods for ensuring security in cyberspace. A crucial cybersecurity solution is intrusion detection systems, which detect attacks in network environments and responds appropriately. This article presents a new multivariable heuristic intrusion detection algorithm based on different types of flags and values of entropy. The data is shared by organisations to help increase the effectiveness of intrusion detection. The authors also propose default values for parameters of a heuristic algorithm and values regarding detection thresholds. This solution has been implemented in a well-known, open-source system and verified with a series of tests. Additionally, the authors investigated how updating the variables affects the intrusion detection process. The results confirmed the effectiveness of the proposed approach and heuristic algorithm.

Cache Pollution Detection Method Based on GBDT in Information-Centric Network

There is a new cache pollution attack in the information-centric network (ICN), which fills the router cache by sending a large number of requests for nonpopular content. This attack will severely reduce the router cache hit rate. Therefore, the detection of cache pollution attacks is also an urgent problem in the current information center network. In the existing research on the problem of cache pollution detection, most of the methods of manually setting the threshold are used for cache pollution detection. The accuracy of the detection result depends on the threshold setting, and the adaptability to different network environments is weak. In order to improve the accuracy of cache pollution detection and adaptability to different network environments, this paper proposes a detection algorithm based on gradient boost decision tree (GBDT), which can obtain cache pollution detection through model learning. Method. In feature selection, the algorithm uses two features based on node status and path information as model input, which improves the accuracy of the method. This paper proves the improvement of the detection accuracy of this method through comparative experiments.