Swarm Intelligence-Based Bio-Inspired Framework for 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

Security Mechanisms in Unattended Wireless Sensor Networks

Wireless Sensor Networks (WSNs) consisting of a large number of sensor nodes are being deployed in potentially hostile environments for applications such as forest fire detection, battlefield surveillance, habitat monitoring, traffic management, etc. One common assumption in traditional WSNs is that a trusted third party, i.e., a sink, is assumed to be always available to collect sensed data in a real time or near real time fashion. Although many WSNs operate in such an on-site mode, there are WSN applications that do not fit into the real time data collection mode. For example, data collection in Unattended WSNs (UWSNs) relies on the periodical appearance of a mobile sink. As most existing security solutions developed for traditional WSNs rely on the presence of a trusted third party, it makes them not applicable to UWSNs directly. This motivates the research on security mechanisms for UWSNs. This dissertation contributes to security mechanisms in UWSNs from three important aspects, as, confidentiality and reliability, trust management, and capture resistance. The first aspect addresses data confidentiality and data reliability in UWSNs. We propose a data distribution scheme to provide forward secrecy, probabilistic backward secrecy and data reliability. Moreover, we demonstrate that backward secrecy of the historical data can be achieved through homomorphic encryption and key evolution. Furthermore, we propose a constrained optimization algorithm to further improve the above two data distribution schemes. The second study introduces trust management in UWSNs. We propose a set of efficient and robust trust management schemes for the case of UWSNs. The Advanced Scheme utilizes distributed trust data storage to provide trust data reliability and takes the advantages of both Geographic Hash Table (GHT) and Greedy Perimeter Stateless Routing (GPSR) to find storage nodes and to route trust data to them. In this way, it significantly reduces storage cost caused by distributed trust data storage and provides resilience to node compromise and node invalidation. The third study investigates how to detect a captured node and to resist node capture attack in UWSNs. We propose a node capture resistance and key refreshing scheme for UWSNs based on the Chinese remainder theorem. The scheme is able to provide forward secrecy, backward secrecy and collusion resistance for diminishing the effects of capture attacks