IoT-enabled Sensor Networks: Architecture, Methodologies, Security, and Futuristic Applications

Internet-of-Things is the future of connectivity that has turned the physical world into smart objects. The practical feature of Internet-of-Things is to combine all objects, rendering them dependent on a shared infrastructure, in such a manner that humans can regulate them as well as monitor their status. Internet-of-Things is a physical object network that is embedded with hardware, software, sensors, and networking to allow objects to share data with the connected devices. This chapter details the Internet of Things, vision challenges, and various intelligent applications in sensor-enabled networks. The wide-scale application of the Internet would significantly affect how computers and objects engage in real-life scenarios. This chapter aims to highlight the perspective of some novel technologies and innovative implementations for the protection, welfare, and privacy concerns due to the Internet of Things. Some critical sensor networks, which represent the most used sensor networks in many domains, such as Smart Applications, are included in this introduction section. A literature study on Internet-of-Things has been conducted for different aspects, such as infrastructure, implementation problems, etc. The authors offer several other applications that are significant. Future research directions for Internet-of-Things have been outlined in the study to equip novel researchers with the assessment of current status and to build upon them with creative ideas.

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.

This chapter proposes the implementation of an automatic accident detection and alerting system utilizing the Internet of Things (IoT). This system aims to swiftly and efficiently locate accident sites and notify emergency services, there by expediting the transfer of victims to medical centers. Road accidents are one of the leading causes of death annually, primarily due to delays in reporting the incidents. The proposed system operates in two main parts. First, when a vehicle is impacted, sensors installed in the vehicle activate and capture the location via the Global System for Mobile Communication (GSM) module. Subsequently, information about the accident site and the victim’s condition is sent to a registered phone number through the GSM module. Essentially, when an accident occurs, the installed piezoelectric sensor immediately detects the impact, relaying this information to the microcontroller. The microcontroller then sends an alert message, including location and other pertinent details, to the registered unit to ensure timely medical assistance. This system aims to enhance the efficiency of medical services in reaching accident victims promptly, potentially saving lives that might otherwise be lost due to delayed accident reporting.

In today's technological landscape, IoT-enabled Wireless Sensor Networks (WSNs) offer significant advantages over traditional networks, particularly when it is used under critical applications. However, network devices are typically limited in terms of their energy source; energy optimization has become a major concern in recent years. As a result, energy-efficient protocols are increasingly being prioritized to extend the network's functionality for a long period. In this chapter, we introduce a clustering routing protocol that operates on an unequal clustering basis. The protocol selects the best route for transmitting data to the sink based on various factors, such as the average residual energy of path sensor nodes, the average distance between nodes, the maximal distance nodes in the current path, and the number of hops. Our simulation results show that the proposed Optimal Energy Unequal Clustering Routing (OEUCR) protocol provides a significant improvement over the existing Energy Efficient Routing Protocol (EERP). Furthermore, we propose an optimal election clustering protocol that provides a new trade-off function based on near density factor and elect metric. Our simulation outcomes demonstrate that this protocol increases the network's functional duration by 6 rounds, reduces energy consumption by 0.727 J per round, and allows the base station to receive 975 more messages. Specifically, the packets received by the base station (BS) increased by 23%, while energy consumption decreased by 21% when using OEUCR instead of EERP. 

The effect of sink mobility on the improved dual-hop routing protocol (IDHR) and multiple data sink-based energy-efficient cluster-based routing protocol (MEEC) is taken into consideration. Sink mobility can be introduced into the network to prevent the creation of hotspots. The data sinks receive data from cluster heads which further collect data from the member nodes of the respective clusters. The cluster head (CH) performs data aggregation and sends the orchestrated data to the sink. The CH selection in IDHR and MEEC is done by taking into account the node density parameter along with other parameters, such as energy and distance between the node and the sink. In MEEC, multiple data sinks are used to resolve the burden on the relaying nodes involved in data transmission as well as to curb the hotspot problem. The movement of sinks is controlled and managed through the proposed approach, i.e., Sink Mobility based on CH Energy (SMCHE). The node density factor proves to be good for the energy preservation of nodes as it takes into account the average communication distance between the nodes and respective CH. The simulation results show that the network lifetime of the proposed approach is increased by 268%, 191%, 27%, and 17% when compared to MEEC, IDHR, DRESEP and TSEP, respectively.

 Internet of Things (IoT)-enabled intelligent systems are proliferating rapidly, providing the capability to connect virtually any device to the Internet. Consequently, this concept can be effectively utilized in home security applications. In this paper, we have introduced an IoT-enabled system designed to send security alerts to users via email upon detecting human intrusion. The system comprises a PIR sensor, Pi camera, Raspberry Pi-3, and an Internet connection. There are two operational modes in the proposed security system. In the first mode, movement by an intruder is detected, and simultaneously, every time someone rings the doorbell, the Pi camera captures an image. The system then accesses a stored database to ascertain whether the individual is recognized. If the person is unfamiliar, the user receives an email notification, including the captured image of the individual. On the other hand, if the person is recognized, the system stores the captured image. In the second mode, when someone exhibits suspicious behaviour in front of the door, the system sends an alert email to the user, prompting them to activate the security alert system installed at the entrance. For face detection, we employ the Haar cascade technique. Face recognition involves two steps: feature extraction and classification. In the feature extraction phase, we compare various algorithms, and a comparative study of these provides a methodology that achieves 99.56% accuracy, outperforming other existing models. The developed system leverages the IoT platform to fortify security against intruders, thereby fostering a safe and secure environment.

In today’s era of Information Technology, we have encountered drastic changes in computing methodologies due to a tremendous increase in ONLINE communication traffic both in terms of the number of users and data communication. The COVID–19 pandemic has brought the entire world online. Cyber Security plays an important role in the field of information technology. In order to secure information, one can face many challenges. Nowadays, governments and other organisations are following various measures to prevent vivid cybercrimes. In this chapter, we have raised concern over the drastic increment in an ONLINE communication system, which urges the need for the development and deployment of Cyber Security in a business environment.

In this chapter, we have discussed smart cities, their applications, and the associated security and privacy issues. We will begin with a brief introduction to smart cities, followed by a focus on the major and essential applications required to transform a city into a smart city. We will cover topics such as smart education, healthcare, governance, transportation, and services. Each of these applications plays a crucial and efficient role in realizing the objectives of a smart city. Furthermore, it is imperative to address the security and privacy concerns related to these applications, particularly concerning data access and protection, and to identify the necessary security requirements for these applications.

Given that dislodged working conditions are in play, system administrators are tasked with handling security solutions that, in turn, impact most of the working layers of the OSI model. This comprehensive approach depicts a situation in which the originator perceives that their data is traversing through a specified encryption process at every stage/layer, starting from the top layer (i.e., Application Layer) and gradually proceeding down to the last one (i.e., Physical Layer). Similarly, the decryption process takes place at every stage/layer at the destination end.