Bio-Inspired Multi-Layer Quantum Swarm Immunization (QSI-Fusion) for Secure and Trust-Aware Routing in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are gaining immense popularity as a result of their wide potential applications in industry, military, and academia such as military surveillance, agricultural monitoring, industrial automation, and smart homes. Currently, WSN has garnered tremendous significance as it is has become the core component of the Internet of Things (IOT) area. Modern-day applications need a high level of security and quick response mechanism to deal with the emerging data trends where the response is measured in terms of latency, throughput, and scalability. Further, critical security issues need to be considered due to various types of threats and attacks WSNs are exposed to as they are deployed in harsh and hostile environments unattended in most of the mission critical applications. The fact that a complex sensor network consisting of simple computing units has similarities with specific animal communities, whose members are often very simple but produce together more sophisticated and capable entities. Thus, from an algorithmic viewpoint, bio-inspired framework such as swarm intelligence technology may provide valuable alternative to solve the large scale optimization problems that occur in wireless sensor networks. Self-organization, on the other hand, can be useful for distributed control and management tasks. In this chapter, swarm intelligence and social insects-based approaches developed to deal with a bio-inspired networking framework are presented. The proposed approaches are designed to tackle the challenges and issues in the WSN field such as large scale networking, dynamic nature, resource constraints, and the need for infrastructure-less and autonomous operation having the capabilities of self-organization and survivability. This chapter covers three phases of the research work carried out toward building a framework. First phase involves development of SIBER-XLP model, Swarm Intelligence Based Efficient Routing protocol for WSN with Improved Pheromone Update Model, and Optimal Forwarder Selection Function which chooses an optimal path from source to the sink to forward the packets with the sole objective to improve the network lifetime by balancing the energy among the nodes in the network and at the same time selecting good quality links along the path to guarantee that node energy is not wasted due to frequent retransmissions. The second phase of the work develops a SIBER-DELTA model, which represents Swarm Intelligence Based Efficient Routing protocol for WSN taking into account Distance, Energy, Link Quality, and Trust Awareness. WSNs are prone to behavior related attacks due to the misbehavior of nodes in forwarding the packets. Hence, trust aware routing is important not only to protect the information but also to protect network performance from degradation and protect network resources from undue consumption. Finally, third phase of the work involves the development of SIBER-DELTAKE hybrid model, an improved ACO-KM-ECC trust aware routing protocol based on ant colony optimization technique using K-Medoids (KM) algorithm for the formation of clusters with Elliptical Curve Cryptography (ECC). KM yields efficiency in setting up a cluster head and ECC mechanism enables secure routing with key generation and management. This model takes into account various critical parameters like distance, energy, link quality, and trust awareness to discover efficient routing.

Trust-aware routing is the significant direction for designing the secure routing protocol in Wireless Sensor Network (WSN). However, the trust-aware routing mechanism is implemented to evaluate the trustworthiness of the neighboring nodes based on the set of trust factors. Various trust-aware routing protocols are developed to route the data with minimum delay, but detecting the route with good quality poses a challenging issue in the research community. Therefore, an effective method named Sunflower Sine Cosine (SFSC)-based stacked autoencoder is designed to perform Electroencephalogram (EEG) signal classification using trust-aware routing in WSN. Moreover, the proposed SFSC algorithm incorporates Sunflower Optimization (SFO) and Sine Cosine Algorithm (SCA) that reveals an optimal solution, which is the optimal route used to transmit the EEG signal. Initially, the trust factors are computed from the nodes simulated in the network environment, and thereby, the trust-based routing is performed to achieve EEG signal classification. The proposed SFSC-based stacked autoencoder attained better performance by selecting the optimal path based on the fitness parameters, like energy, trust, and distance. The performance of the proposed approach is analyzed using the metrics, such as sensitivity, accuracy, and specificity. The proposed approach acquires 94.708%, 94.431%, and 95.780% sensitivity, accuracy, and specificity, respectively, with 150 nodes.

ABSTRACTThe Internet of Things (IoT) has emerged as a revolutionary technology, connecting a vast number of people to the internet through various devices such as smartphones, laptops, sensors, and more. An essential component of IoT, the wireless sensor network (WSN), enables automation in processes like sensing, node monitoring, and data transmission. However, the full potential of IoT is hindered by cyber threats and unreliable communication. Although several security algorithms exist, they are not suitable for energy‐constrained sensor nodes. To address this issue, this paper proposes an energy‐efficient security mechanism called adaptive clustering and trust aware routing (ACTAR) for IoT‐WSNs. ACTAR operates in three phases: adaptive and hybrid clustering (AHC), multiobjective function‐based cluster head selection (MOCH), and trust aware routing (TAR). First, AHC utilizes a nonuniform clustering mechanism to categorize the network into nearby and distant clusters. Next, the selection of cluster heads is based on four metrics: coverage, communication cost, residual energy, and node proximity. Finally, TAR calculates the trust degree of sensor nodes by evaluating their direct and indirect behavior in terms of communication interactions and energy consumption. The node with the highest trust degree is selected as the next‐hop forwarding node, followed by the route with the highest trust degree. Extensive simulations of ACTAR demonstrate its performance in terms of malicious detection rate, false‐positive rate, residual energy, and packet delivery ratio. Comparative analysis shows that ACTAR outperforms existing methods, proving its superiority.

Security attacks has become major obstacles in Wireless Sensor Networks (WSN) and Trust Aware Routing is second line of defense. With an aim to improve on the existing routing mechanisms, in this paper, we propose Interactive, Onlooker and Capability Trust Aware Routing (IOC-TAR), a multi-trust attribute framework for trust management in WSNs. IOC-TAR employs three trust features to establish a trustworthy relationship between sensor nodes for their cooperation. Interactive trust uses communication interactions, onlooker trust uses neighbor node’s opinions and capability trust uses stability and fault tolerance for trust assessment. For, each node, one composite trust factor is formulated and decides its trustworthiness. Extensive simulation experiments are conducted to evaluate the effectiveness and efficiency of proposed IOC-TAR in the identification of malicious nodes and the provision of attack resilience. The results declare that the IOC-TAR enhances the attack resilience by improving Malicious Detection rate and reducing False Positive Rate.

Internet of Things (IoT) and Wireless Sensor Networks (WSNs), a core component of IoT, face numerous cyber threats and unreliable communication, which limit the potential benefits of IoT. Existing security algorithms cannot be applied to energy-constrained sensor nodes. This paper proposes an energy-efficient security provision mechanism called Adaptive Clustering and Trust Aware Routing (ACTAR) for IoT-WSNs. ACTAR operates in three phases: Adaptive and Hybrid Clustering (AHC), Multi-objective Function-based Cluster Head Selection (MOCH), and Trust Aware Routing (TAR). At first, AHC, which is a non-uniform clustering mechanism, divides the entire network into two types of clusters: nearby clusters and distant clusters. Then, Cluster Head selection is done based on four metrics: Coverage, Communication Cost, Residual Energy, and Node Proximity. Lastly, TAR computes the trust degree of sensor nodes based on their direct and indirect behavior in terms of communication interactions and energy consumption. The node with the highest trust degree is chosen as the next hop forwarding node, followed by the route with the highest trust degree. Simulation experiments demonstrate the superiority of ACTAR over existing methods. It achieves an impressive energy efficiency of 76.5%, throughput of 1.27 Mbps, packet delivery ratio of 94.5%, and malicious detection rate of 93.0%.

Trust-aware routing in wireless sensor networks (WSNs) is a crucial problem that has drawn the attention of researchers. The motivation for tackling this problem arises directly from the highly constrained nature of a WSN and its easy exposure to insecure conditions. In this regard, reputation-based solutions are used to provide trust-aware routing. However, this approach requires that a node needs to continuously monitor its environment to detect misbehaviour events. This is considered to be a costly operation for WSN nodes because of its resource scarcity. Here, the authors propose a reputation system-based solution for trust-aware routing, which implements a new monitoring strategy called an efficient monitoring procedure in a reputation system (EMPIRE). EMPIRE is a probabilistic and distributed monitoring methodology that tries to reduce the monitoring activities per node while maintaining the ability to detect attacks at a satisfactory level. The proposed procedure has been evaluated using the Monte Carlo simulation. New evaluation methodologies are introduced to test and explore the efficiency of our proposed procedure. Simulation results of the reputation system show that reducing monitoring activities with EMPIRE does not have a significant impact on system performance in terms of security.

In this chapter, we discuss a bio-inspired computational model that utilizes heuristic techniques. This model is robust and possesses optimization capabilities to address obscure and substantiated problems. Swarm intelligence is an integral part of this bio-inspired model, functioning within groups. The nature of these algorithms is non-centralized, drawing inspiration from self-management to solve real-life complex computational problems. Examples include the traveling salesman problem, the shortest path problem, optimal fitness functions, security systems, and the use of optimal computational resources in various areas. The deployment of a Wireless Sensor Network involves a group of sensor nodes, typically implemented at remote locations to observe environmental behaviors. However, these sensor nodes operate on batteries, making replacement or recharge nearly impossible once deployed. Energy is a crucial resource for wireless sensor networks to extend their lifetime. While numerous concepts have been proposed to improve the lifespan of wireless sensor networks, many issues in Wireless Sensor Networks (WSN) are designed as multi-dimensional optimization problems. The bio-inspired model offers a solution to overcome these challenges. Swarm Intelligence proves to be a simple, efficient, and effective computational methodology for addressing various issues in wireless sensor networks, including node localization, clustering, data aggregation, and deployment. The Swarm Intelligence methodology encompasses several algorithms such as Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Reactive Search Optimization (RSO), Fish Swarm Algorithm (FSA), Genetic Algorithm (GA), Bacterial Foraging Algorithm (BFA), and Differential Evolution (DE). This chapter introduces Swarm Intelligence-based optimization algorithms and explores the impact of PSO in wireless sensor networks.

A wireless sensor networks encompasses battery powered sensor nodes with extremely limited processing capabilities. One of the major challenges in WSN is to overcome the attacks like Sinkhole, Wormhole and Sybil, Dos attacks, Selective forwarding etc. which reduces the performance of the network, since the Cryptographic techniques are not much effective in solving this problem. So in order to protect wireless sensor networks from attacks and to perform improvement in some of the aspects we have implemented a low overhead trust aware routing with energy efficient network protocol. This protocol is designed by incorporating the trust aware routing frame work with adaptive periodic threshold sensitive energy efficient network protocol. By minimizing the attacks, the network provides a better QOS with trustworthy and energy efficient route which leads to increase the network life time.

The main purpose of the study is to review the evolution of wireless sensor network security and routing techniques. Recent years have seen tremendous growth in Wireless Sensor Networks (WSNs). As WSN's become more and more crucial to everyday life, their security and trust become a primary concern. However because of the nature of WSNs, security design can be challenging. Trust-aware routing protocols play a vital role in security of Wireless Sensor Networks (WSNs). The review study provides an overview of Wireless Sensor Network (WSN) and discusses security issues and the routing techniques for high quality of service and efficient performance in a WSN. In order to identify gaps and propose research directions in WSN security and routing techniques, the study surveys the existing body of literature in this area. The main focus is on trust concepts and trust based approaches for wireless sensor networks. The study also highlights the difference between trust and security in the context of WSNs. The trust and security are interchangeable with each other when we elaborate a secure system and not same. Various surveys conducted about trust and reputation systems in ad hoc and sensor networks are studied and compared. Finally we summarize the different trust aware routing schemes.

Many trust-aware routing algorithms have been proposed in order to reliably deliver data packets from sensor nodes to the base station in wireless sensor networks (WSNs) where there exist inside attackers. In these approaches, a trust mechanism is adopted for each node to measure its neighbors' trustworthiness so the node can send data packets only to the trustworthy neighbors. A false alarm occurs when a good node is considered as untrustworthy. We propose a False Alarm DEtection and Recovery (FADER) technique which enables us to identify and reuse these false alarmed nodes. By doing so, we can improve the performance of the trust-aware routing protocol in terms of many metrics such as the network lifetime, the packet delivery rate, and many routing performance measures. We have conducted extensive OPNET simulations and the results confirm these claimed advantages of our proposed FADER approach over a representative trust-aware routing algorithm. The results show that FADER is able to recover 60-70% of the false alarms without recovering any of the attackers.

Wireless Sensor Networks (WSNs) often need to operate under strict requirements on energy consumption and be capable of self-adapting to the presence of non-trusted nodes which do not fully cooperate in the packet forwarding operation. In such an environment, the mechanism employed for the calculation of routing paths of minimum cost in terms of the number of transmissions executed for the reliable communication between the data source and the data sink is essential to prevent unnecessary nodes' energy depletion and help prolong the network's lifetime. In this paper, we study a routing metric, TXPFI, that captures the expected number of frame transmissions - including retransmissions - needed for the successful delivery of data from the source to the destination in the presence of malicious nodes and lossy links, and validate its applicability to the IETF RPL routing protocol. Through extensive simulations we evaluate the capability of TXPFI to compute routing paths of minimum transmission count and compare it against a number of metrics suitable for transmission count-efficient and trust-aware routing in WSNs. The results show that the use of TXPFI in RPL can lead to significant transmission count savings.

Wireless sensor networks (WSN) are the ideal domain for applications involving critical security events like military surveillance and detection of forest fire. Wireless sensor networks involve multi-hop routing and it offer minor security against identity deception through replaying routing information. The trust factor in the routing environment plays an important role in the military surveillance and related applications. Secured data aggregation is an important criterion that attracts serious research work. The factors reasonable in such harsh WSN are increased complexity, high overhead and poor link quality in case of various cryptographic techniques. These problems need to be addressed and overcome with the help of appropriate framework mechanisms. The analysis is further enhanced by mobile and harsh network conditions. Current trust-aware routing protocols using traditional cryptographic techniques are not capable of effectively tackling this serious problem. To secure the WSN and to regularise the multi-hop routing techniques, the present work has been designed and implemented. A Fuzzy Based Trust-Aware Routing Framework (FBTARF) is the proposed method for security improvisation in dynamic WSN. FBTARF provides energy-efficient routing and reliable trust using fuzzification methods. Also, FBTARF provides the effective solution against harmful attacks due to identity deception. The dynamic nature of FBTARF is analysed by means of detailed evaluation using simulation and empirical experimental procedures. This has been studied for large-scale WSN under various environments including mobile and harsh network conditions. To improvise the security parameters, the proposed work is developed using a Fuzzy Based Trust Model which simultaneously considers multiple constraints and provides better security and energy conservation. The secured FBTARF model also provides effective and efficient routing in the dynamic wireless sensor network environment. Then, the comparison analysis based on normal TARF and Secured Fuzzy based trust aware routing framework (FBTARF) model is developed and the results show that the secured fuzzy model provides better results in terms of security, packet delivery ratio and energy conservation.

In the heterogeneous network, MANETs are the collective gathering of diverse mobile devices with the ability to join and leave the network at any moment as the most prominent feature. As a consequence, mobile nodes in the decentralized network may link, interact, and transfer information to one another without the need of an intermediary router. Several academics have recently explored a variety of routing approaches to tackle issues such as packet data transmission delays and poor PDR. This paper aims to introduce a new trust-aware routing in MANET that ensures the trust level among the nodes. For this, a new trust rate estimation process is introduced based on energy and mobility of nodes exist. Thereby, a Self-Improved Particle Swarm optimization algorithm (SI-PSO) is proposed for choosing the optimal trust aware route for data transmission. The optimal route selection is performed by considering certain parameters like trust rate (security), Packet Drop Ratio (PDR) distance, congestion, energy, and as well. The performance of the adopted work is examined to the existing schemes regarding Energy, Delay, and Network Lifetime.

Trust aware routing in Wireless Sensor Network (WSN) is an important direction in designing routing protocols for WSN that are susceptible to malicious attacks. The common approach to provide trust aware routing is to implement an efficient reputation system. Reputation systems in WSN require a good rating approach that can model the information on the behavior of nodes in a way that represents different sources of this information. In some WSN applications, nodes need to be more cautious in rating other nodes since it may be in a very hostile environment or it may be very intolerant to malicious behavior. Moreover, to prove the creditability of a reputation system or its related rating components, a global and system-independent technique is required that can evaluate the proposed solution. In this paper, a new rating approach called Cautious RAting for Trust Enabled Routing (CRATER). CRATER is introduced which provides a rating model that takes into account the cautious aspect of WSN nodes. Further, a promising evaluation mechanism for reputation systems called REputation Systems-Independent Scale for Trust On Routing (RESISTOR). RESISTOR is presented which can be used to evaluate and compare reputation and rating systems in a global, simple, and independent manner.

Due to open network nature of wireless sensor networks make them highly vulnerable to a variety of security attacks and easy target for adversaries, which may capture these nodes, analyze and easily insert fake route information. Wireless sensor network is an emerging, cost effective and unsupervised solution for collecting this information from the physical world and sending this information back to centralized authority for further processing. Reliable data gathering and delivery is always a challenging task due to dynamic, unattended and unpredictable behavior of wireless sensor network and its broadcast nature of communication. To protect sensor network from routing attacks in the presence of malicious nodes is always a challenge. In this paper, we propose a trust aware distance vector routing protocol (T-AODV) to protect wireless sensor network from wormhole attacks. Through experimental results, our propose approach proved the network efficiency in terms of improved packet delivery ratio, end-to-end delay and number of node to the destination.