Mobile Edge Computing (MEC) has emerged as a viable means to improve processing efficiency by reducing distance between computation and Internet of Things (IoT) devices, results in lower latency and energy expenditure. Although MEC appears to be a good candidate for improving task distribution efficiency, there remain issues surrounding task offloading, high computational latencies, and the heterogeneity of IoT devices that make centralized resource allocation seamless. This research presents an improved task offloading paradigm, with an emphasis on reducing latency and improving efficiency in an IoT-MEC context. Incoming tasks are classified into four categories based on levels of complexity. Type 1 tasks can be executed on the IoT device, while Type 4 task are fully offloaded into a cloud. Type 2 and Type 3 tasks utilize a parallel execution approach, where tasks are partially executed on the IoT device and partially satisfies the MEC server. The model in this study incorporates matching and queuing theory to improve task allocation from the device to the server. An additional branch based heuristic method is also introduced to further reduce task processing efficiency. Algorithm used was implemented using Python libraries through simulations and real-world cases. Comparative analysis with baseline task offloading methods demonstrates substantial performance improvements, achieving reductions in task computation latency of 40%−70%. These results demonstrate that the proposed methodology is efficient and effective in optimizing the overall execution of task in MEC-enabled IoT networks.

Intrusion detection systems (IDSs) are deployed to detect anomalies in real time. They classify a network’s incoming traffic as benign or anomalous (attack). An efficient and robust IDS in software-defined networks is an inevitable component of network security. The main challenges of such an IDS are achieving zero or extremely low false positive rates and high detection rates. Internet of Things (IoT) networks run by using devices with minimal resources. This situation makes deploying traditional IDSs in IoT networks unfeasible. Machine learning (ML) techniques are extensively applied to build robust IDSs. Many researchers have utilized different ML methods and techniques to address the above challenges. The development of an efficient IDS starts with a good feature selection process to avoid overfitting the ML model. This work proposes a multiple feature selection process followed by classification. In this study, the Software-defined networking (SDN) dataset is used to train and test the proposed model. This model applies multiple feature selection techniques to select high-scoring features from a set of features. Highly relevant features for anomaly detection are selected on the basis of their scores to generate the candidate dataset. Multiple classification algorithms are applied to the candidate dataset to build models. The proposed model exhibits considerable improvement in the detection of attacks with high accuracy and low false positive rates, even with a few features selected.