Autonomous Self-Rerouting for Multi-Wormhole Mitigation in Wireless Sensor Networks using XGBoost Ensemble Learning

Wireless sensor networks (WSNs) are receiving more popularity in mission critical and delay sensitive industrial applications because they offer low latency and reliable message transmission. In applications like gas leakage detection, monitoring of pressure and industrial process control etc. reliable communication between sink and the sensing nodes is very important. In wireless sensor networks sensing nodes are placed very densely in different environments and mostly with no defined network topology. In industrial setup, the placement of the sensing nodes plays a very important role, most importantly it increases overall system throughput by efficiently transmitting the calibrated readings and providing maximum security to the industrial devices. In this paper we aim to investigate, how different topological settings effects packet delivery ratio (PDR) and End-to-End delay in wireless sensor networks? This paper also focuses on the performance study of three different network topological settings for mission critical applications. We evaluated the performance of wireless sensor network (WSN) by placing the sensor nodes in three different topological designs namely Linear, Tier one and Split Tier one.

PurposeThis study aims to present a novel system for detection and prevention of black hole and wormhole attacks in wireless sensor network (WSN) based on deep learning model. Here, different phases are included such as assigning the nodes, data collection, detecting black hole and wormhole attacks and preventing black hole and wormhole attacks by optimal path communication. Initially, a set of nodes is assumed for carrying out the communication in WSN. Further, the black hole attacks are detected by the Bait process, and wormhole attacks are detected by the round trip time (RTT) validation process. The data collection procedure is done with the Bait and RTT validation process with attribute information. The gathered data attributes are given for the training in which long short-term memory (LSTM) is used that includes the attack details. This is used for attack detection process. Once they are detected, those attacks are removed from the network using the optimal path selection process. Here, the optimal shortest path is determined by the improvement in the whale optimization algorithm (WOA) that is called as fitness rate-based whale optimization algorithm (FR-WOA). This shortest path communication is carried out based on the multi-objective function using energy, distance, delay and packet delivery ratio as constraints.Design/methodology/approachThis paper implements a detection and prevention of attacks model based on FR-WOA algorithm for the prevention of attacks in the WSNs. With this, this paper aims to accomplish the desired optimization of multi-objective functions.FindingsFrom the analysis, it is found that the accuracy of the optimized LSTM is better than conventional LSTM. The energy consumption of the proposed FR-WOA with 35 nodes is 7.14% superior to WOA and FireFly, 5.7% superior to grey wolf optimization and 10.3% superior to particle swarm optimization.Originality/valueThis paper develops the FR-WOA with optimized LSTM detecting and preventing black hole and wormhole attacks from WSN. To the best of the authors’ knowledge, this is the first work that uses FR-WOA with optimized LSTM detecting and preventing black hole and wormhole attacks from WSN.

In nigh application, it is indispensable to discover or supervise the behavior of the surrounding situation at a fine resolution over large spatial-temporal scales therefore WSN (Wireless Sensor Network) is well-advised. Because of smaller size and low power requirement of sensor device use of WSN for watching the surrounding action is increased time to time. Numeral of sensor inside WSN is proportionate to the WSN accuracy. As WSN may be operated in unsupervised and aggressive environment because of this probability of failure of sensor node inside WSN is increased. If any of the sensor node is failed, this will lead to the change the WSN topology, also create network separation, increases the distance between a node pair and reduce secondary available shortest routes. Due to this quality of service (QoS) of WSN will be degraded, to improve QoS of WSN, we have to find out faults within the network. This paper represents the easiest way for finding the defective sensing element inside the network. This method measure Round Trip Time (RTT) and throughput of the Round Trip Path (RTP) and compare the calculated RTT and throughput with threshold values (threshold value for RTT (Th (rtt)) and throughput (Th (throughput))) based on a comparison result we will find a faulty sensor within the network.

Network Control Systems (NCS), including Wireless Sensor Networks (WSN) are broadly deployed across different application areas. A prominent dispute in WSNs is the liability to wormhole attacks, which lead to routing errors, degradation of sensor network lifetime, and disruption of network topology. Despite the development of numerous Wormhole attack detection methods, many of these techniques require additional hardware or consume significant system resources, limiting their practicality. This paper introduces a novel detection framework leveraging the ResNeXt architecture in conjunction with a Deep Stacked Autoencoder (ResNeXt-DSAE) for effective wormhole attack detection in NCSs. The framework begins with the simulation of a WSN, where routing is functioned using the Low Energy Adaptive Clustering Hierarchy (LEACH) protocol. The detection process is structured into multiple phases, starting with the evaluation of the Neighbor Ratio Threshold (NRT), followed by the wormhole attack detection through out-of-band and in-band detection strategies. In the out-of-band detection phase, the transmission range is analyzed, while in-band detection assesses Round-Trip Time (RTT) and Packet Delivery Ratio (PDR). The wormhole attack classification is subsequently performed using the ResNeXt-DSAE framework to distinguish between out-of-band and in-band attacks. Investigational outcomes demonstrate that the devised ResNeXt-DSAE framework accomplishes superior proficiency, with a maximum throughput of 97.55 Mbps, Packet Deliver Ratio (PDR) of 99.58%, network lifetime of 0.945, and a minimal delay of 0.815 s, network activity energy consumption of 0.255 J, and computational cost of 17.99 s for 200 nodes. Furthermore, the proposed method attains a detection rate of 0.958, an accuracy of 0.958, a precision of 0.940, a recall of 0.970, an F1-score of 0.955, a False Positive Rate (FPR) of 0.058, and a False Negative Rate (FNR) of 0.077 thereby transcending existing wormhole attack detection approaches.

The new advances of the Internet of Things (IoT) technology can be utilized to promote service delivery in several real-life applications such as healthcare systems. The Routing Protocol for Low Power and Loss Network (RPL) is a routing protocol designed to serve as a proper routing protocol for packets in Wireless Sensor Networks (WSN). Among the most prominent issues exist in the RPL protocol are packet loss within the WSN and sensors power consumption especially in healthcare WSNs. Multiple Objective Functions (OF) in RPL intended to find the routes from source nodes to a destination node. This paper presents an evaluation to discover which OF is more efficient for a WSN in a healthcare scenario where the Packet Delivery Ratio (PDR) of WSN and the sensors' power consumption are prominent concerns. Expected transmission Count (ETX) and Objective Function Zero (OF0) of RPL were examined in various network densities and network topologies such as the grid and random topology. The simulation outcomes revealed that the OF0 is more efficient regarding the PDR and power consumption compared to the ETX in random

Wireless sensor network will get wide applicability and acceptance if improved energy efficient routing protocol is implemented. Most of the cluster based hierarchical routing protocols use base station (BS) as a main controller to select cluster heads (CHs) which increases energy consumption and round trip time delay. In this paper, we propose an improved energy efficient routing protocol to maximize the network life time and reduce data delay time. In this proposed protocol, new CH is selected by the current CH instead of the BS based on the maximum residual energy and minimum distance between the CH and a node among all the nodes in a cluster. Unlike the modified LEACH (MLEACH), in the proposed protocol re-clustering is occurred only when the residual energy of the current CHs lower than a threshold value. From the comparison of the simulation results it is proved that the proposed protocol provides lower cluster formation time and network failure with better network fault tolerance and packet delivery ratio than the MLEACH.

Inventions in electronics and communication industries increased rapidly the utilization of wireless sensor networks, still the selection of the routing algorithm is done application wise. The existing routing protocols are having many problems and limitations in terms of Quality of Service parameters, like energy consumption is more, delay and jitter are high, throughput and packet delivery ratio are minimum. Security wise also they are not much secure. This paper proposes to overcome the problems and the limitations present in the existing routing protocols. The problem can be solved by developing a secure, energy wise proficient and high velocity routing protocol. The paper describes the enhancement in the utility of the wireless communication sensor network applications using routing techniques by optimization of QOS parameters. Hence optimization of Quality of Service parameters leads to optimization of the wireless communication sensor network system for power efficient application. This paper focuses on better performance of routing protocol by optimization in the QOS parameters with the help of Traffic Sensitive Queue management. The routing protocol is analyzed on the base of following QOS parameters: Throughput, Jitter, Packet Delivery Ratio, Delay, and Energy consumption. The enhanced graphs subsequent to application of Queue Management are discussed with the help of simulation results.

Wireless sensor networks have variety of applications in military and civilian tracking, habitat monitoring, patient monitoring and industrial control and automation. Many protocols have been developed to support these applications. For applications such as gas leakage detection system, volcanic activities alerts, fire safety systems, border surveillance and tsunami alert systems where apart from energy saving, timely information delivery is also important, an efficient MAC protocol is required. These are termed as mission critical applications. Reducing energy consumption, efficient utilization of bandwidth, Throughput, Latency, Scalability and Adaptability, Reliability, and Degree of Intelligence are the most important parameters of a good MAC protocol designed for mission critical applications. The degree of intelligence is the parameter which is novel to these protocols and will be provided by introducing the Machine learning and Artificial Intelligence. The chapter addresses the design issues for MAC layer, different MAC protocols designed for wireless sensor networks, mission Critical Applications of WSNs and the performance parameters required for Mission Critical MAC Protocols. Various MAC protocols based on contention based and contention free channel access mechanism are discussed in detail in the chapter. Now we are in the era, where each application demands intelligence and automation. For this purpose, there is need to design smart protocols adaptive to critical scenarios. In the chapter the existing MAC protocols and the performance parameters for a mission critical MAC protocol such as throughput, packet delivery ratio, packet loss rate, efficient bandwidth utilization, scalability and adaptability are discussed. A review of machine learning techniques is also done which shows that MAC protocols may be enhanced for their suitability in mission critical scenarios. The chapter also discussed the case study of one mission critical MAC protocol and its comparison with SMAC protocol. The application of mission critical MAC protocol in pipeline leakage detection system is also discussed with its design model. Finally the chapter ends with discussion of recent issues and challenges and future scope of intelligent ML based MAC protocol design.

Wireless Sensor Networks (WSNs) have gained significant importance in a variety of applications, from environmental monitoring to healthcare and industrial automation. The simulation of these networks plays a crucial role in evaluating performance metrics such as energy consumption, throughput, packet delivery ratio (PDR), and delay, which are essential for network optimization. This paper presents a comparative study of two popular simulators, NS-3 and OMNeT++, for simulating WSNs under varying node densities (20, 50, and 100 nodes) to assess their performance in terms of the aforementioned metrics. The primary objective of the study was to evaluate and compare the efficiency, scalability, and effectiveness of NS-3 and OMNeT++ in simulating WSNs, focusing on energy efficiency, throughput, packet delivery ratio, and delay, which are critical in large-scale WSN deployments. The methods involved simulating a range of network scenarios in both NS-3 and OMNeT++ using identical network topologies and parameters. Energy consumption, throughput, PDR, and delay were measured and compared across different node densities. Statistical analyses, including paired t-tests, ANOVA, and regression analysis, were used to assess the significance of the differences between the simulators. The results revealed that OMNeT++ consistently outperformed NS-3 in terms of energy efficiency, throughput, PDR, and delay. OMNeT++ showed better scalability, with a more stable throughput and a lower delay, particularly as node density increased. Statistical analysis confirmed the significance of these differences, particularly in energy consumption and throughput. These findings suggest that OMNeT++ is better suited for simulating large-scale WSNs where energy efficiency, scalability, and low latency are crucial. In conclusion, OMNeT++ is recommended for large-scale WSN simulations, while NS-3 remains valuable for detailed protocol simulations in smaller networks. A hybrid approach combining both simulators could be explored for optimizing WSN performance in future research.

Wireless sensor networks (WSN) MAC protocol has been the active research area since last few years because of application specific nature of these networks. This paper studies popular contention based SMAC protocol in multihop scenario and analyzes its suitability in mission critical WSN applications. Along with the residual energy, the throughput and packet delivery ratio are considered as the important parameters for mission critical applications. Improvements are suggested in the SMAC protocol with the simulation results in NS-2.

In wireless sensor networks (WSNs), sensor nodes are randomly distributed to transmit sensed data packets to the base station periodically. These sensor nodes, because of constrained battery power and storage space, cannot utilize conventional security measures. The widely held challenging issues for the network layer of WSNs are the packet-dropping attacks, mainly sinkhole and wormhole attacks, which focus on the routing pattern of the protocol. This thesis presents an improved version of the second level of the guard to the system, intrusion detection systems (IDSs), to limit the hostile impact of these attacks in a Low Energy Adaptive Clustering Hierarchy (LEACH) environment. The proposed system named multipath intrusion detection system (MIDS) integrates an IDs with ad hoc on-demand Multipath Distance Vector (AOMDV) protocol. The IDS agent uses the number of packets transmitted and received to calculate intrusion ratio (IR), which helps to mitigate sinkhole attacks and from AOMDV protocol round trip time (RTT) is computed by taking the difference between route request and route reply time to mitigate wormhole attack. MATLAB simulation results show that this cooperative model is an effective technique due to the higher packet delivery ratio (PDR), throughput, and detection accuracy. The proposed MIDS algorithm is proven to be more efficient when compared with an existing LEACH-based IDS system and MS-LEACH in terms of overall energy consumption, lifetime, and throughput of the network.

Wireless sensor networks (WSNs) with efficient and accurate design to increase the quality of service (QoS) have become a hot area of research. Implementing the efficient and accurate WSNs requires deployment of the large numbers of portable sensor nodes in the field. The quality of service of such networks is affected by lifetime and failure of sensor node. In order to improve the quality of service, the data from faulty sensor nodes has to be ignored or discarded in the decision-making process. Hence, detection of faulty sensor node is of prime importance. In the proposed method, discrete round trip paths (RTPs) are compared on the basis of round trip delay (RTD) time to detect the faulty sensor node. RTD protocol is implemented in NS2 software. WSNs with circular topology are simulated to determine the RTD time of discrete RTPs. Scalability of the proposed method is verified by simulating the WSNs with various sensor nodes.

 Abstract—We design a networked control system (NCS) with discrete-time state predictor where the communication between the controller output and the plant input takes place over a wireless sensor network (WSN). In order to measure time delays between the controller output and the plant input in real time, we design an algorithm to measure round trip time (RTT) between WSN nodes, and implement it into TinyOS of WSN. By using the measured time delays, we construct the discrete-time state predictor to compensate the time delays between the controller output and the plant input in real-time. For the real time experiment, we simulate the dynamic plant model, the controller, and WSN interface using Real-Time Windows Target provided in MATLAB. The WSN interface in the Simulink model consists of serial ports, which connect the controller output and the plant input with WSN nodes. The experiment results show that the time delays between the controller output and the plant input are precisely measured in real time; the discrete-time state predictor appropriately compensates the time delays; and the stability is achieved in the closed-loop of the NCS. In this paper, we design an NCS with discrete-time state predictor where the communication between the controller output and the plant input takes place over WSNs. In order to measure time delays between the controller output and the plant input in real time, we design an algorithm to measure round trip time (RTT) between WSN nodes, and implement it into TinyOS of WSN. By using the measured time delays as a parameter, we construct the discrete-time state predictor (6), (7) to compensate the time delays between the controller output and the plant input in real time. The discrete-time state predictor suitably compensates measured time delays in the feedback loop. The experiment results show that the time delays between the controller output and the plant input are precisely measured in real time; the discrete-time state predictor appropriately compensates the time delays; and the closed-loop of the NCS is made to be stable.

We design a networked control system (NCS) where the communication between sensors and controllers takes place over a wireless sensor network (WSN). In order to measure time delays between sensors and controllers in real time, we design an algorithm to measure round trip time (RTT) between WSN nodes, and implement it into TinyOS of WSN. By using the measured time delays, we construct the Smith predictor to compensate the time delays between sensors and controllers in real-time. For the real time experiment, we simulate the dynamic plant model, controller, and WSN interface using Real-Time Windows Target provided in MATLAB. The WSN interface in the Simulink model consists of serial ports, which connect the plant output and controller with WSN nodes. The experiment results show that the time delays between sensors and controllers are precisely measured in real time; the Smith predictor appropriately compensates the time delays; and the stability of the designed NCS is achieved.

Wireless sensor networks (WSN) constitute a current field of interest in which the major concerns are related to mobility, spatial distribution, connectivity and dynamic creation of networks between autonomous nodes for cooperative detection and data transfer in diverse areas (e.g., healthcare, environmental or industrial monitoring). To this end, the present work describes the theoretical and practical development of a communication protocol for WSNs based on Bluetooth. The interaction between mobile nodes is performed with a multi-hop scheme in response to traffic needs without requiring a scatternet formation procedure. The interest of this algorithm—based on the concept of routing vector—is that it was designed to withstand changes in the node distribution for high-mobility scenarios, thus allowing the implementation of a robust data routing in low-resource microcontrolled devices with no operability loss. As the main contribution, we present the hardware and software implementation of the communication protocol in real devices along several case studies. With this aim, a long-term experimentation in a dense scenario has been carried out through an intelligent agent-based approach to formally validate the protocol considering three different performance metrics: packet delivery ratio, feedback overhead and round-trip time.