In recent years, Wireless Sensor Networks (WSNs) have demonstrated successful applications for both civil and military tasks. However, sensor networks are susceptible to multiple types of attacks because they are randomly deployed in open and unprotected environments. It is necessary to utilize effective mechanisms to protect sensor networks against multiple types of attacks on routing protocols. In this paper, we propose a lightweight intrusion detection framework integrated for clustered sensor networks. Furthermore, we provide algorithms to minimize the triggered intrusion modules in clustered WSNs by using an over‐hearing mechanism to reduce the sending alert packets. Our scheme can prevent most routing attacks on sensor networks. In in‐depth simulation, the proposed scheme shows less energy consumption in intrusion detection than other schemes. Copyright © 2009 John Wiley & Sons, Ltd.

Wireless sensor networks have many potential applications for both civil and military tasks. However, WSNs are susceptible to many types of attacks because they are deployed in open and unprotected environment. Selective forwarding attack is one of the easiest implement and damaged attacks in multi-hop routing protocols. In this paper, we proposed a lightweight detection algorithm based only on the neighborhood information. Our detection algorithm can detect selective forwarding attack with high accuracy and little overhead imposed on detection modules than previous works.

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In the era of heterogeneous 5G networks, Internet of Things (IoT) devices have significantly altered our daily life by providing innovative applications and services. However, these devices process large amounts of data traffic and their application requires an extremely fast response time and a massive amount of computational resources, leading to a high failure rate for task offloading and considerable latency due to congestion. To improve the quality of services (QoS) and performance due to the dynamic flow of requests from devices, numerous task offloading strategies in the area of multi-access edge computing (MEC) have been proposed in previous studies. Nevertheless, the neighboring edge servers, where computational resources are in excess, have not been considered, leading to unbalanced loads among edge servers in the same network tier. Therefore, in this paper, we propose a collaboration algorithm between a fuzzy-logic-based mobile edge orchestrator (MEO) and state-action-reward-state-action (SARSA) reinforcement learning, which we call the Fu-SARSA algorithm. We aim to minimize the failure rate and service time of tasks and decide on the optimal resource allocation for offloading, such as a local edge server, cloud server, or the best neighboring edge server in the MEC network. Four typical application types, healthcare, AR, infotainment, and compute-intensive applications, were used for the simulation. The performance results demonstrate that our proposed Fu-SARSA framework outperformed other algorithms in terms of service time and the task failure rate, especially when the system was overloaded.

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