The heterogeneous nature of the internet-of-thing (IoT) is gaining popularity and, simultaneously, faces rising security issues. The distributed denial of service (DDoS) attack is the most significant security threat addressed in the research. The research proposes edge-heterogeneous IoT (HetIoT) centric defense IDS that aids the HetIoT infrastructure in detecting and blocking victim traffic near the network edge. The Edge-HetIoT defense IDS helps to address significant issues such as performance and security due to proximity to the local network . The research focuses on six learning techniques, including five machine learning (ML) classifiers, namely, ID3, NB , RF , LR , and AdaBoost , and the proposed deep learning (DL)-based hybrid model (i.e., CNN+LSTM). These learning techniques are trained and tested using the real-time benchmark-dataset CICDDoS2019 and consider binary and multiclass (14 classes) classification. The performance is analyzed and evaluated against six classifiers to determine which classification model performs best in detecting and classifying various DDoS attacks. The proposed DL-based hybrid model outperforms when compared against ID3, NB, RF, LR, and AdaBoost. The proposed DL-based hybrid model successfully detects and classifies MSSQL , NetBIOS, TFTP, NTP, Syn, and Portmap attacks with 100% precision, recall, and f1-score. The overall weighted average precision, recall, and f1-score for the proposed DL-based hybrid model are 92%, 89%, and 90%, respectively.

The last few years have seen the promising growth in the application of wireless sensor networks (WSNs). The contribution of this paper is on a cluster-based time division multiple access (TDMA) scheduling algorithm to improve the performance of WSN applications in terms of energy efficiency, delay, throughput and scalability. Cluster-based scheduling improves the scalability by stabilizing the topology and it also improves the delay by increasing the reuse of slots. The paper proposes the Green Conflict Free (GCF) algorithm for finding a conflict free schedule across three-hop neighbours for inter- and intra-cluster communication. The algorithm is applied to a multi-hop cluster and uses a conflict graph to find the conflict free schedule. It helps to reduce the number of conflicts. Compared to state-of-the-art solutions, the algorithm shows better energy efficiency, average delay, scalability and better slot sharing through a reduced number of conflicts.