A firefly-inspired scheme for energy-efficient transmission scheduling using a self-organizing method in a wireless sensor network

Various types of natural phenomena are regarded as primary sources of information for artificial occurrences that involve spontaneous synchronization. Among the artificial occurrences that mimic natural phenomena are Wireless Sensor Networks (WSNs) and the Pulse Coupled Oscillator (PCO), which utilizes firefly synchronization for attracting mating partners. However, the PCO model was not appropriate for wireless sensor networks because sensor nodes are typically not capable to collect sensor data packets during transmission (because of packet collision and deafness). To avert these limitations, this study proposed a self-organizing time synchronization algorithm that was adapted from the traditional PCO model of fireflies flashing synchronization. Energy consumption and transmission delay will be reduced by using this method. Using the proposed model, a simulation exercise was performed and a significant improvement in energy efficiency was observed, as reflected by an improved transmission scheduling and a coordinated duty cycling and data gathering ratio. Therefore, the energy-efficient data gathering is enhanced in the proposed model than in the original PCO-based wave-traveling model. The battery lifetime of the Sensor Nodes (SNs) was also extended by using the proposed model

A Survey and Future Directions on Clustering: From WSNs to IoT and Modern Networking Paradigms

Many Internet of Things (IoT) networks are created as an overlay over traditional ad-hoc networks such as Zigbee. Moreover, IoT networks can resemble ad-hoc networks over networks that support device-to-device (D2D) communication, e.g., D2D-enabled cellular networks and WiFi-Direct. In these ad-hoc types of IoT networks, efficient topology management is a crucial requirement, and in particular in massive scale deployments. Traditionally, clustering has been recognized as a common approach for topology management in ad-hoc networks, e.g., in Wireless Sensor Networks (WSNs). Topology management in WSNs and ad-hoc IoT networks has many design commonalities as both need to transfer data to the destination hop by hop. Thus, WSN clustering techniques can presumably be applied for topology management in ad-hoc IoT networks. This requires a comprehensive study on WSN clustering techniques and investigating their applicability to ad-hoc IoT networks. In this article, we conduct a survey of this field based on the objectives for clustering, such as reducing energy consumption and load balancing, as well as the network properties relevant for efficient clustering in IoT, such as network heterogeneity and mobility. Beyond that, we investigate the advantages and challenges of clustering when IoT is integrated with modern computing and communication technologies such as Blockchain, Fog/Edge computing, and 5G. This survey provides useful insights into research on IoT clustering, allows broader understanding of its design challenges for IoT networks, and sheds light on its future applications in modern technologies integrated with IoT.acceptedVersio

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

Internet of Vehicles and Real-Time Optimization Algorithms: Concepts for Vehicle Networking in Smart Cities

Achieving sustainable freight transport and citizens’ mobility operations in modern cities are becoming critical issues for many governments. By analyzing big data streams generated through IoT devices, city planners now have the possibility to optimize traffic and mobility patterns. IoT combined with innovative transport concepts as well as emerging mobility modes (e.g., ridesharing and carsharing) constitute a new paradigm in sustainable and optimized traffic operations in smart cities. Still, these are highly dynamic scenarios, which are also subject to a high uncertainty degree. Hence, factors such as real-time optimization and re-optimization of routes, stochastic travel times, and evolving customers’ requirements and traffic status also have to be considered. This paper discusses the main challenges associated with Internet of Vehicles (IoV) and vehicle networking scenarios, identifies the underlying optimization problems that need to be solved in real time, and proposes an approach to combine the use of IoV with parallelization approaches. To this aim, agile optimization and distributed machine learning are envisaged as the best candidate algorithms to develop efficient transport and mobility systems