Flying Ad hoc Network (FANET) is a self-organizing wireless network that constitutes swarms of flying nodes, namely Unmanned Aerial Vehicles (UAV), and communicates in close proximity. It has various distinguishing characteristics that set it apart from other ad hoc networks, posing some issues, particularly in routing. UAVs are highly dynamic and have frequent topology changes. Hence, the network urges an efficient routing technique to coordinate the node swarms and enhance the evaluation metrics of the network. The biological behavior of various living organisms, such as animals, insects, microbes, and humans, inspires researchers to solve various routing problems in ad hoc networks. Decentralized self-organized swarms of UAVs closely resemble the biological system. Therefore, the Bio-Inspired Algorithms (BIA) resolve a wide range of routing challenges in FANET. A Systematic Literature Review (SLR) is adopted to survey FANET routing methods based on non-hybrid and hybrid BIAs to properly comprehend the existing bio-inspired strategies used in FANET routing. The review will be beneficial for the researchers in the specified area. To our knowledge, no SLR has been conducted about the FANET routing protocol that employs BIA. This paper examines (1) the characteristics and features of existing routing algorithms, (2) the need of both non-hybrid and hybrid BIA for effective and optimal routing, (3) an analysis of the method’s simulation tools, evaluation metrics, and mobility models, (4) the current issues and scope of the study related to the specified method.

Sensor nodes heterogeneity if not properly utilized could lead to uneven energy consumption and load imbalanced across the network, which degrades the performance of the network. Routing algorithms should try to achieve energy-efficiency and load-balancing among the heterogeneous nodes to prolong network lifetime. One of the solutions is by using duty-cycling in cluster-based routing such as in Sleep-awake Energy Efficient Distributed (SEED) clustering algorithm to minimize redundant transmission to achieve energy efficiency. However, this scheme suffers from idle listening problem, which lead to energy wastage across the network. Moreover, SEED cannot cope with an environment with sensor nodes with heterogeneous traffic rate. To cope with energy and traffic heterogeneity issues among sensor nodes, a traffic and energy aware routing protocol (TEAR) is proposed. TEAR avoids selecting node with low energy and high traffic rate for cluster head role to achieve load balancing. However, TEAR does not avoid redundant transmission from the sensor nodes that are in close distances. In this paper, we proposed a hybrid method called energy and traffic aware sleep-awake (ETASA) mechanism to improve energy efficiency and enhanced load balancing in heterogeneous wireless sensor network scenario. Unlike prior methods, in ETASA, the paired nodes alternate into sleep and awake mode based on node's energy and traffic rate. Moreover, we revised the conventional TDMA scheduling in SEED by allocating one slot for group of pairs in a cluster. This is done to address idle listening problem to minimize energy consumption. The proposed method improves the cluster head selection technique that selects high energy, low traffic and nodes with high number of pairs to improve balanced energy consumption. The proposed approach is evaluated and compared against the state-of-the-art baseline protocols. The result shows that the proposed ETASA has 16% and 15% lifetime improvements against TEAR and SEED.

In this article, a Host-Based Intrusion Detection System (HIDS) using a Modified Vector Space Representation (MVSR) N-gram and Multilayer Perceptron (MLP) model for securing the Internet of Things (IoT), based on lightweight techniques and using Fog Computing devices, is proposed. The Australian Defence Force Academy Linux Dataset (ADFA-LD), which contains exploits and attacks on various applications, is employed for the analysis. The proposed method is divided into the feature extraction stage, the feature selection stage, and classification modeling. To maintain the lightweight criteria, the feature extraction stage considers a combination of 1-gram and 2-gram for the system call encoding. In addition, a Sparse Matrix is used to reduce the space by keeping only the weight of the features that appear in the trace, thus ignoring the zero weights. Subsequently, Linear Correlation Coefficient (LCC) is utilized to compensate for any missing N-gram in the test data. In the feature selection stage, the Mutual Information (MI) method and Principle Component Analysis (PCA) are utilized and then compared to reduce the number of input features. Following the feature selection stage, the modeling and performance evaluation of various Machine Learning classifiers are conducted using a Raspberry Pi IoT device. Further analysis of the effect of MLP parameters, such as the number of nodes, number of features, activation, solver, and regularization parameters, is also conducted. From the simulation, it can be seen that different parameters affect the accuracy and lightweight evaluation. By using a single hidden layer and four nodes, the proposed method with MI can achieve 96% accuracy, 97% recall, 96% F1-Measure, 5% False Positive Rate (FPR), highest curve of Receiver Operating Characteristic (ROC), and 96% Area Under the Curve (AUC). It also achieved low CPU time usage of 4.404 (ms) milliseconds and low energy consumption of 8.809 (mj) millijoules.