In wireless sensor networks, the energy source is limited to the capacity of the sensor node’s battery. Clustering in WSN can help with reducing energy consumption because transmission energy is related to the distance between sender and receiver. In this paper, we propose a fuzzy logic model for cluster head election. The proposed model uses five descriptors to determine the opportunity for each node to become a CH. These descriptors are: residual energy, location suitability, density, compacting, and distance from the base station. We use this fuzzy logic model in proposing the Fuzzy Logic-based Energy-Efficient Clustering for WSN based on minimum separation Distance enforcement between CHs (FL-EEC/D). Furthermore, we adopt the Gini index to measure the clustering algorithms’ energy efficiency in terms of their ability to balance the distribution of energy through WSN sensor nodes. We compare the proposed technique FL-EEC/D with a fuzzy logic-based CH election approach, a k-means based clustering technique, and LEACH. Simulation results show enhancements in energy efficiency in terms of network lifetime and energy consumption balancing between sensor nodes for different network sizes and topologies. Results show an average improvement in terms of first node dead and half nodes dead.

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SUMMARYMultiuser diversity (MUD) cooperative wireless networks combine the features of the MIMO systems without confronting the physical layer constraints by providing multiple copies of the transmitted signal from the source to the destination with the help of the relay node. Cooperative wireless networks have attracted the full attention in the last few years and are implemented widely in many wireless communication systems to adapt for the fading impairments, provide higher data rates, and improve the performance of the wireless communication systems. In this paper, we present an informative study for the reason of evaluating the performance of the MUD in the multiuser two‐hop cooperative multi‐relay networks using maximal ratio combining. Furthermore, we derive tight closed‐form expressions of outage probability and symbol error probability for the amplify‐and‐forward and fixed decode‐and‐forward protocols with the MUD. Additionally, we conduct a simulation study to show to what extent our analytical and simulation results agree with each other. It is worthy to mention that our analytical and simulation results agree fairly with each other under high average signal‐to‐noise ratio, whereas they show that our proposed system with multiple relays provides significant improvements over those previously proposed systems having only one relay. Copyright © 2013 John Wiley & Sons, Ltd.

In the recent years, Denial-of-Service (DoS) or Distributed Denial-of-Service (DDoS) attack has spread greatly and attackers make online systems unavailable to legitimate users by sending huge number of packets to the target system. In this paper, we proposed two methodologies to detect Distributed Reflection Denial of Service (DrDoS) attacks in IoT. The first methodology uses hybrid Intrusion Detection System (IDS) to detect IoT-DoS attack. The second methodology uses deep learning models, based on Long Short-Term Memory (LSTM) trained with latest dataset for such kinds of DrDoS. Our experimental results demonstrate that using the proposed methodologies can detect bad behaviour making the IoT network safe of Dos and DDoS attacks

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