Mitigating Denial of Service Attacks in Fog-Based Wireless Sensor Networks Using Machine Learning Techniques

Wireless sensor networks are considered to be among the most significant and innovative technologies in the 21st century due to their wide range of industrial applications. Sensor nodes in these networks are susceptible to a variety of assaults due to their special qualities and method of deployment. In WSNs, denial of service attacks are common attacks in sensor networks. It is difficult to design a detection and prevention system that would effectively reduce the impact of these attacks on WSNs. In order to identify assaults on WSNs, this study suggests using two machine learning models: decision trees and XGBoost. The WSNs dataset was the subject of extensive tests to identify denial of service attacks. The experimental findings demonstrate that the XGBoost model, when applied to the entire dataset, has a higher true positive rate (98.3%) than the Decision tree approach (97.3%) and a lower false positive rate (1.7%) than the Decision tree technique (2.7%). Like this, with selected dataset assaults, the XGBoost approach has a higher true positive rate (99.01%) than the Decision tree technique (97.50%) and a lower false positive rate (0.99%) than the Decision tree technique (2.50%)

Enhanced priority-based adaptive energy-aware mechanisms for wireless sensor networks

Wireless Sensor Networks (WSN) continues to find its use in our lives. However, research has shown that it has barely attained an optimal performance, particularly in the aspects of data heterogeneity, data prioritization, data routing, and energy efficiency, all of which affects its operational lifetime. The IEEE 802.15.4 protocol standard, which manages data forwarding across the Data Link Layer (DLL) does not address the impact of heterogeneous data and node Battery-Level (BL) which is an indicator for node battery life. Likewise, mechanisms proposed in the literature – TCP-CSMA/CA, QWL-RPL and SSRA have not proffered optimal solution as they encourage excessive computational overhead which results in shortened operational lifetime. These problems are inherited on the Network Layer (NL) where data routing is implemented. Mitigating these challenges, this research presents an Enhanced Priority-based Adaptive Energy-Aware Mechanisms (EPAEAM) for Wireless Sensor Networks. The first mechanism is the Optimized Backoff Mechanism for Prioritized Data (OBMPD) in Wireless Sensor Networks. This mechanism proposed the Class of Service Traffic Priority-based Medium Access Control (CSTP-MAC). The CSTP-MAC is implemented on the DLL. In this mechanism, unique backoff period expressions compute backoff periods according to the class and priority of the heterogeneous data. This approach improved network performances which enhanced network lifetime. The second mechanism is the Shortest Path Priority-Based Objective Function (SPPB-OF) for Wireless Sensor Networks. SPPB-OF is implemented across the NL. SPPB-OF implements a unique shortest path computation algorithm to generate energy-efficient shortest path between the source and destination nodes. The third mechanism is the Cross-Layer Energy-Efficient Priority-based Data Path (CL-EEPDP) for Wireless Sensor Networks. CL-EEPDP is implemented across the DLL and NL with considerations for node battery-level. A unique mathematical expression, Node Battery-Level Estimator (NBLE) is used to estimate the BL of neighbouring nodes. The knowledge of the BL together with the priority of data are used to decide an energy-efficient next-hop node. Benchmarking the EPAEAM with related mechanisms - TCP-CSMA/CA, QWL-RPL and SSRA, results show that EPAEAM achieved improved network performance with a packet delivery ratio (PDR) of 95.4%, and power-saving of 90.4%. In conclusion, the EPAEAM mechanism proved to be a viable energy-efficient solution for a multi-hop heterogeneous data WSN deployment with support for extended operational lifetime. The limitations and scope of these mechanisms are that their application is restricted to the data-link and network layers, moreover, only two classes of data are considered, that is; High Priority Data (HPD) and Low Priority Data (LPD)

Hybridization of enhanced ant colony system and Tabu search algorithm for packet routing in wireless sensor network

In Wireless Sensor Network (WSN), high transmission time occurs when search agent focuses on the same sensor nodes, while local optima problem happens when agent gets trapped in a blind alley during searching. Swarm intelligence algorithms have been applied in solving these problems including the Ant Colony System (ACS) which is one of the ant colony optimization variants. However, ACS suffers from local optima and stagnation problems in medium and large sized environments due to an ineffective exploration mechanism. This research proposes a hybridization of Enhanced ACS and Tabu Search (EACS(TS)) algorithm for packet routing in WSN. The EACS(TS) selects sensor nodes with high pheromone values which are calculated based on the residual energy and current pheromone value of each sensor node. Local optima is prevented by marking the node that has no potential neighbour node as a Tabu node and storing it in the Tabu list. Local pheromone update is performed to encourage exploration to other potential sensor nodes while global pheromone update is applied to encourage the exploitation of optimal sensor nodes. Experiments were performed in a simulated WSN environment supported by a Routing Modelling Application Simulation Environment (RMASE) framework to evaluate the performance of EACS(TS). A total of 6 datasets were deployed to evaluate the effectiveness of the proposed algorithm. Results showed that EACS(TS) outperformed in terms of success rate, packet loss, latency, and energy efficiency when compared with single swarm intelligence routing algorithms which are Energy-Efficient Ant-Based Routing (EEABR), BeeSensor and Termite-hill. Better performances were also achieved for success rate, throughput, and latency when compared to other hybrid routing algorithms such as Fish Swarm Ant Colony Optimization (FSACO), Cuckoo Search-based Clustering Algorithm (ICSCA), and BeeSensor-C. The outcome of this research contributes an optimized routing algorithm for WSN. This will lead to a better quality of service and minimum energy utilization

Enhancing graph-routing algorithm for industrial wireless sensor networks

Industrial Wireless Sensor Networks (IWSNs) are gaining increasing traction, especially in domains such as the Industrial Internet of Things (IIoT), and the Fourth Industrial Revolution (Industry 4.0). Devised for industrial automation, they have stringent requirements regarding data packet delivery, energy consumption balance, and End-to-End Transmission (E2ET) time. Achieving effective communication is critical to the fulfilment of these requirements and is significantly facilitated by the implementation of graph-routing – the main routing method in the Wireless Highway Addressable Remote Transducer (WirelessHART), which is the global standard of IWSNs. However, graph-routing in IWSN creates a hotspot challenge resulting from unbalanced energy consumption. This issue stems from the typical configuration of WirelessHART paths, which transfers data packets from sensor nodes through mesh topology to a central system called the Network Manager (NM), which is connected to a network gateway. Therefore, the overall aim of this research is to improve the performance of IWSNs by implementing a graph-routing algorithm with unequal clustering and optimisation techniques. In the first part of this thesis, a basic graph-routing algorithm based on unequal clustering topologies is examined with the aim of helping to balance energy consumption, maximise data packet delivery, and reduce the number of hops in the network. To maintain network stability, the creation of static clusters is proposed using the WirelessHART Density-controlled Divide-and-Rule (WDDR) topology. Graph-routing can then be built between Cluster Heads (CHs), which are selected according to the maximum residual energy rate between the sensor nodes in each static cluster. Simulation results indicate that graph-routing with the WDDR topology and probabilistic unequal clustering outperforms mesh topology, even as the network density increased, despite isolated nodes found in the WDDR topology. The second part of this thesis focuses on using the Covariance-Matrix Adaptation Evolution Strategy (CMA-ES) algorithm. This addresses the three IWSN requirements that form the focus of this research, by proposing three single-objective graph-routing paths: minimum distance (PODis), maximum residual energy (POEng), and minimum end-to-end transmission time (POE2E). The research also adapts the CMA-ES to balance multiple objectives, resulting in the Best Path of Graph-Routing with a CMA-ES (BPGR-ES). Simulation results show that the BPGR-ES effectively balances IWSN requirements, but single-objective paths of graph-routing does not achieve balanced energy consumption with mesh topology, resulting in a significant reduction in the efficiency of the network. Therefore, the third part of this thesis focuses on an Improvement of the WDDR (IWDDR) topology to avoid isolated nodes in the static cluster approaches. The IWDDR topology is used to evaluate the performance of the single-objective graph-routing paths (PODis, POEng, and POE2E). The results show that in IWDDR topology, single-objective graph-routing paths result in more balanced energy consumption