Node Role Selection and Rotation Scheme for Energy Efficiency in Multi-Level IoT-Based Heterogeneous Wireless Sensor Networks (HWSNs)

The emergence of Internet of Things (IoT)-based heterogeneous wireless sensor network (HWSN) technology has become widespread, playing a significant role in the development of diverse human-centric applications. The role of efficient resource utilisation, particularly energy, becomes further critical in IoT-based HWSNs than it was in WSNs. Researchers have proposed numerous approaches to either increase the provisioned resources on network devices or to achieve efficient utilisation of these resources during network operations. The application of a vast proportion of such methods is either limited to homogeneous networks or to a single parameter and limited-level heterogeneity. In this work, we propose a multi-parameter and multi-level heterogeneity model along with a cluster-head rotation method that balances energy and maximizes lifetime. This method achieves up to a 57% increase in throughput to the base station, owing to improved intra-cluster communication in the IoT-based HWSN. Furthermore, for inter-cluster communication, a mathematical framework is proposed that first assesses whether the single-hop or multi-hop inter-cluster communication is more energy efficient, and then computes the region where the next energy-efficient hop should occur. Finally, a relay-role rotation method is proposed among the potential next-hop nodes. Results confirm that the proposed methods achieve 57.44%, 51.75%, and 17.63% increase in throughput of the IoT-based HWSN as compared to RLEACH, CRPFCM, and EERPMS, respectively.

Discrete and Balanced Spectral Clustering With Scalability

Spectral Clustering (SC) has been the main subject of intensive research due to its remarkable clustering performance. Despite its successes, most existing SC methods suffer from several critical issues. First, they typically involve two independent stages, i.e., learning the continuous relaxation matrix followed by the discretization of the cluster indicator matrix. This two-stage approach can result in suboptimal solutions that negatively impact the clustering performance. Second, these methods are hard to maintain the balance property of clusters inherent in many real-world data, which restricts their practical applicability. Finally, these methods are computationally expensive and hence unable to handle large-scale datasets. In light of these limitations, we present a novel Discrete and Balanced Spectral Clustering with Scalability (DBSC) model that integrates the learning the continuous relaxation matrix and the discrete cluster indicator matrix into a single step. Moreover, the proposed model also maintains the size of each cluster approximately equal, thereby achieving soft-balanced clustering. What's more, the DBSC model incorporates an anchor-based strategy to improve its scalability to large-scale datasets. The experimental results demonstrate that our proposed model outperforms existing methods in terms of both clustering performance and balance performance. Specifically, the clustering accuracy of DBSC on CMUPIE data achieved a 17.93% improvement compared with that of the SOTA methods (LABIN, EBSC, etc.).

Secure Key Management and Mutual Authentication Protocol for Wireless Sensor Network by Linking Edge Devices using Hybrid Approach

Wireless Sensor Networks (WSNs) play a crucial role in developing the Internet of Things (IoT) by collecting data from hostile environments like military and civil domains with limited resources. IoT devices need edge devices to perform real-time processing without compromising the security with the help of key management and authentication schemes. The above applications are prone to eavesdropper due to cryptographic algorithms' weaknesses for providing security in WSNs. The security protocols for WSNs are different from the traditional networks because of the limited resource of sensor nodes. Existing key management schemes require large key sizes to provide high-security levels, increasing the computational and communication cost for key establishment. This paper proposes a Hybrid Key Management Scheme for WSNs linking edge devices which use Elliptic Curve Cryptography (ECC) and a hash function to generate key pre-distribution keys. The Key establishment is carried out by merely broadcasting the node identity. The main reason for incorporating a hybrid approach in the key pre-distribution method is to achieve mutual authentication between the sensor nodes during the establishment phase. The proposed method reduces computational complexity with greater security and the proposed scheme can be competently applied into resource constraint sensor nodes.

Dynamic Load Balancing Techniques in the IoT: A Review

The Internet of things (IoT) extends the Internet space by allowing smart things to sense and/or interact with the physical environment and communicate with other physical objects (or things) around us. In IoT, sensors, actuators, smart devices, cameras, protocols, and cloud services are used to support many intelligent applications such as environmental monitoring, traffic monitoring, remote monitoring of patients, security surveillance, and smart home automation. To optimize the usage of an IoT network, certain challenges must be addressed such as energy constraints, scalability, reliability, heterogeneity, security, privacy, routing, quality of service (QoS), and congestion. To avoid congestion in IoT, efficient load balancing (LB) is needed for distributing traffic loads among different routes. To this end, this survey presents the IoT architectures and the networking paradigms (i.e., edge–fog–cloud paradigms) adopted in these architectures. Then, it analyzes and compares previous related surveys on LB in the IoT. It reviews and classifies dynamic LB techniques in the IoT for cloud and edge/fog networks. Lastly, it presents some lessons learned and open research issues.

A weighted energy consumption minimization-based multi-hop uneven clustering routing protocol for cognitive radio sensor networks

Aiming at solving the effective data delivery and energy hole problem in multi-hop cognitive radio sensor networks (CRSNs), a weighted energy consumption minimization-based uneven clustering (ECMUC) routing protocol is proposed in this paper. For the first time, the impact of control overhead on the network performance is taken into consideration, to be specific, the energy consumption of control overhead is integrated with that of data communication to model the network energy consumption. Through effective transformation and theoretical analysis, cluster radius of each ring is derived by minimizing the network energy consumption and balancing the residual energy among nodes in different rings. Distributed cluster heads (CHs) selection and cluster formation are carried out within this range to control the cluster size and the corresponding energy cost. Expected times for being CHs metric is defined to measure nodes' energy and spectral potential and help select powerful CHs. Simulation results show that ECMUC protocol is superior to most clustering protocols designed for CRSNs in terms of network surveillance capability and network lifetime, and it is also demonstrated that taking control overhead into consideration is beneficial for improving the network performance.

Fault Tolerance Structures in Wireless Sensor Networks (WSNs): Survey, Classification, and Future Directions

The Industrial Revolution 4.0 (IR 4.0) has drastically impacted how the world operates. The Internet of Things (IoT), encompassed significantly by the Wireless Sensor Networks (WSNs), is an important subsection component of the IR 4.0. WSNs are a good demonstration of an ambient intelligence vision, in which the environment becomes intelligent and aware of its surroundings. WSN has unique features which create its own distinct network attributes and is deployed widely for critical real-time applications that require stringent prerequisites when dealing with faults to ensure the avoidance and tolerance management of catastrophic outcomes. Thus, the respective underlying Fault Tolerance (FT) structure is a critical requirement that needs to be considered when designing any algorithm in WSNs. Moreover, with the exponential evolution of IoT systems, substantial enhancements of current FT mechanisms will ensure that the system constantly provides high network reliability and integrity. Fault tolerance structures contain three fundamental stages: error detection, error diagnosis, and error recovery. The emergence of analytics and the depth of harnessing it has led to the development of new fault-tolerant structures and strategies based on artificial intelligence and cloud-based. This survey provides an elaborate classification and analysis of fault tolerance structures and their essential components and categorizes errors from several perspectives. Subsequently, an extensive analysis of existing fault tolerance techniques based on eight constraints is presented. Many prior studies have provided classifications for fault tolerance systems. However, this research has enhanced these reviews by proposing an extensively enhanced categorization that depends on the new and additional metrics which include the number of sensor nodes engaged, the overall fault-tolerant approach performance, and the placement of the principal algorithm responsible for eliminating network errors. A new taxonomy of comparison that also extensively reviews previous surveys and state-of-the-art scientific articles based on different factors is discussed and provides the basis for the proposed open issues.

Context-Aware Edge-Based AI Models for Wireless Sensor Networks-An Overview

Recent advances in sensor technology are expected to lead to a greater use of wireless sensor networks (WSNs) in industry, logistics, healthcare, etc. On the other hand, advances in artificial intelligence (AI), machine learning (ML), and deep learning (DL) are becoming dominant solutions for processing large amounts of data from edge-synthesized heterogeneous sensors and drawing accurate conclusions with better understanding of the situation. Integration of the two areas WSN and AI has resulted in more accurate measurements, context-aware analysis and prediction useful for smart sensing applications. In this paper, a comprehensive overview of the latest developments in context-aware intelligent systems using sensor technology is provided. In addition, it also discusses the areas in which they are used, related challenges, motivations for adopting AI solutions, focusing on edge computing, i.e., sensor and AI techniques, along with analysis of existing research gaps. Another contribution of this study is the use of a semantic-aware approach to extract survey-relevant subjects. The latter specifically identifies eleven main research topics supported by the articles included in the work. These are analyzed from various angles to answer five main research questions. Finally, potential future research directions are also discussed.

Energy-Efficient Routing Protocols for Wireless Sensor Networks: Architectures, Strategies, and Performance

Recent developments in low-power communication and signal processing technologies have led to the extensive implementation of wireless sensor networks (WSNs). In a WSN environment, cluster formation and cluster head (CH) selection consume significant energy. Typically, the CH is chosen probabilistically, without considering the real-time factors such as the remaining energy, number of clusters, distance, location, and number of functional nodes to boost network lifetime. Based on the real-time issues, different strategies must be incorporated to design a generic protocol suited for applications such as environment and health monitoring, animal tracking, and home automation. Elementary protocols such as LEACH and centralized-LEACH are well proven, but gradually limitations evolved due to increasing desire and need for proper modification over time. Since the selection of CHs has always been an important criterion for clustered networks, this paper overviews the modifications in the threshold value of CH selection in the network. With the evolution of bio-inspired algorithms, the CH selection has also been enhanced considering the behavior of the network. This paper includes a brief description of LEACH-based and bio-inspired protocols, their pros and cons, assumptions, and the criteria of CH selection. Finally, the performance factors such as longevity, scalability, and packet delivery ratio of various protocols are compared and discussed.