Blockchain-Assisted Authentication and Energy-Efficient Clustering Framework for Secure IoT Communication

The rapid proliferation of smart devices in Internet of Things (IoT) networks has amplified the security challenges associated with device communications. To address these challenges in 5G-enabled IoT networks, this paper proposes a multi-level blockchain security architecture that simplifies implementation while bolstering network security. The architecture leverages an adaptive clustering approach based on Evolutionary Adaptive Swarm Intelligent Sparrow Search (EASISS) for efficient organization of heterogeneous IoT networks. Cluster heads (CH) are selected to manage local authentication and permissions, reducing overhead and latency by minimizing communication distances between CHs and IoT devices. To implement network changes such as node addition, relocation, and deletion, the Network Efficient Whale Optimization (NEWO) algorithm is employed. A localized private blockchain structure facilitates communication between CHs and base stations, providing an authentication mechanism that enhances security and trustworthiness. Simulation results demonstrate the effectiveness of the proposed clustering algorithm compared to existing methodologies. Overall, the lightweight blockchain approach presented in this study strikes a superior balance between network latency and throughput when compared to conventional global blockchain systems. Further analysis of system under test (SUT) behavior was accomplished by running many benchmark rounds at varying transaction sending speeds. Maximum, median, and lowest transaction delays and throughput were measured by generating 1000 transactions for each benchmark. Transactions per second (TPS) rates varied between 20 and 500. Maximum delay rose when throughput reached 100 TPS, while minimum latency maintained a value below 1 s.

Wireless sensor networks (WSN) are consisted of several sensor nodes scattered in an area to gather data from their ambient environment and send it to base station (BS). The energy of nodes in WSNs is limited. One of the most significant issues in WSNs is reducing the energy consumption of nodes, which leads to increased network lifetime. One method to reduce energy consumption in WSNs is energy-efficient routing. In energy-efficient routing, gathered data is sent to the sink in a way to save the energy of nodes. This paper proposed a cluster-based two-level routing method. In the proposed method, we seek to improve packet delivery rate and reduce energy consumption through clustering, selecting backup cluster head (BCH), layering cluster heads (CH), and dividing each cluster into four sections. The method is consisted of two phases. In the first phase, CHs and BCHs are selected, and nodes are clustered based on their residual energy, distance to BS, and centrality. To perform intra-cluster routing, each cluster is divided into four sections so that nodes directly deliver their data to CH or through the most proper node in their sections. To perform inter-cluster routing, CHs are layered based on their distance to BS. Since CHs are layered, the source CH selects the next hop from CHs in the upper layer based on their residual energy and distance to BS. The proposed method has been simulated by NS-2 software and compared with CFPT (Yarinezhad and Hashemi in J Syst Softw 155:145–161, 2019), FBCFP (Thangaramya et al. in Comput Netw 151:211–223, 2019) and DFCR (Azharuddin et al. in Comput Electr Eng, 41:177–190, 2015) methods. The results reveal that the proposed method leads to reduced end-to-end delay, number of total hops, energy consumption as well as increased packet delivery rate and network lifetime.

The environmental impacts that are associated with the interconnected sensor devices in the Internet of Things (IoT) network are minimized with the utilization of green communication. This is because, when the IoT devices tend to grow, the IoT system becomes more prone to issues regarding the utilization of resources, sustainability, and energy consumption within the communication system. Tuning the communication protocols, lowering the IoT device’s energy usage rate, and establishing an effective technique for the transmission of data are performed. This is achieved by the green communication technique, which aids in the establishment of significant energy savings in the IoT network. For devices with limited power sources, the green communication technology offers an extended battery life. Despite the enormous potential of IoT technology, a number of obstacles need to be overcome, including those related to load balancing, security, storage, privacy, energy management, and device heterogeneity. In response to the challenges posed by conventional models, an innovative solution for the selection of Cluster Head (CH) using a hybrid approach is devised here. The energy consumption of sensor nodes is influenced by various factors, such asCH load, temperature, distance, residual energy, the number of alive nodes, delay, and so on. To address these challenges in IoT networks, a hybrid approach that combines the Squid Game Optimizer (SGO) and the Artificial Gorilla Troops Optimizer (AGTO) for the optimal selection of CH is suggested. The implemented approach is named Hybrid Squid Game with Artificial Gorilla Troops Optimizer (HSG-AGTO). This approach aims to optimize the above-mentioned factors and overcome energy consumption challenges in IoT networks. The effectiveness of the model is validated, and the results demonstrate the superior performance of the suggested model.

In this paper, we study unmanned aerial vehicle (UAV) aided internet of things (IoT) networks, where UAVs facilitate data transmission of IoT devices. We focus on uplink transmission from IoT devices to base station (BS). Multiple UAVs are employed as UAV relays between IoT devices and BS to enhance received signal strength at BS. Specifically, IoT devices periodically detect wireless channel quality between IoT devices and BS, as well as that among IoT devices. Based on the wireless channel quality, we propose a distributed user cluster (UC) algorithm to cluster IoT devices as multiple UCs. One IoT device in a UC, which is named cluster head (CH), is selected to connect to the BS and gather uplink signals of IoT devices. If the wireless channel quality between CH and BS is good, a direct connection between CH and the BS can be built. Otherwise, UAVs are divided into multiple UAV cooperative relay clusters (CRCs). The UAVs in a CRC are located between a specific CH and BS to relay uplink signals. We then formulate a system optimization model to minimize system power consumption, where UAV deployment and transmission power of UAV are jointly optimized. We solve this optimization problem by dual decomposition method. By extensive simulations, we demonstrate the effectiveness of the proposed algorithm. We also reveal several interesting insights for practical UAV aided IoT networks.

Internet of things (IoT) is all about the exciting era of smart devices exchanging data and information among themselves autonomously using the internet. The IoT devices are ubiquitous in nature and have grown in numbers rapidly. These devices are prone to vulnerabilities that jeopardize the security and privacy of the user. Incorporation of trust has become a critical issue in securing information exchange in IoT devices and networks. Device and network heterogeneity, scalability, context awareness and cost involved are some of the issues to be considered while implementing trust management in IoT networks. This paper presents an exhaustive overview on the need for trust mechanisms and trust management in data aggregation, routing, clustering, authentication and attack detection in IoT networks. It also includes a brief description of trust aware IoT-based applications like smart cities, social IoT, vehicular networks, industrial IoT and healthcare. The research challenges in incorporation of trust in IoT devices and networks have also been presented.

Clustering is a promising technique for optimizing energy consumption in sensor-enabled Internet of Things (IoT) networks. Uneven distribution of cluster heads (CHs) across the network, repeatedly choosing the same IoT nodes as CHs and identifying cluster heads in the communication range of other CHs are the major problems leading to higher energy consumption in IoT networks. In this paper, using fuzzy logic, bio-inspired chicken swarm optimization (CSO) and a genetic algorithm, an optimal cluster formation is presented as a Hybrid Intelligent Optimization Algorithm (HIOA) to minimize overall energy consumption in an IoT network. In HIOA, the key idea for formation of IoT nodes as clusters depends on finding chromosomes having a minimum value fitness function with relevant network parameters. The fitness function includes minimization of inter- and intra-cluster distance to reduce the interface and minimum energy consumption over communication per round. The hierarchical order classification of CSO utilizes the crossover and mutation operation of the genetic approach to increase the population diversity that ultimately solves the uneven distribution of CHs and turnout to be balanced network load. The proposed HIOA algorithm is simulated over MATLAB2019A and its performance over CSO parameters is analyzed, and it is found that the best fitness value of the proposed algorithm HIOA is obtained though setting up the parameters, number of rooster, number of hen’s and swarm updating frequency . Further, comparative results proved that HIOA is more effective than traditional bio-inspired algorithms in terms of node death percentage, average residual energy and network lifetime by 12%, 19% and 23%.

ABSTRACTThe Internet of Things (IoT) is the network of numerous smart sensors and physical devices connected via the internet. It can be used in many areas like agriculture, military, environmental and pollution control, smart cities, and health care (HC). Because IoT devices are resource‐constrained in nature, energy consumption (EC), battery lifetime, and frequent and reliable communication are challenging issues to handle when transmitting medical data from the patient to the hospital via the wireless channel. Clustering and routing techniques are considered better solutions to offer energy‐efficient communication for IoT applications with extended network lifetimes (NL). Most existing techniques treated clustering and routing as separate solutions in the IoT network. So there is a need for a framework to offer both clustering and routing optimally and efficiently. This paper proposes a novel clustering‐based optimal routing (CBEAOR) methodology for IoT‐HC systems to attain the network's maximum level of energy utilization and lifetime. Initially, a modified K‐means (MKM) clustering algorithm is proposed for the creation of clusters. Further, the optimal cluster heads (CHs) are chosen according to the enhanced arithmetic optimization algorithm (EAOA). After CHs selection, the highly disruptive polynomial mutation an adaptive inertia weighted grasshopper optimization algorithm (HAGOA) is applied for the optimum selection of routes to destinations. The simulations were carried out to evaluate the effectiveness of the proposed method. The performance is evaluated for different number of nodes; when number of nodes is 250, energy consumption, throughput and packet delivery rate (PDR) are 0.274 mj, 0.956% and 97.87%. The results proved that the proposed CBEAOR achieves superior performance than the existing routing models.

An Energy Balancing LEACH Algorithm for Wireless Sensor Network

The vast expansion of the Internet of Things (IoT) devices and related applications has bridged the gap between the physical and digital world. Unfortunately, security remains a major challenge and the lack of secure links have fueled the increased attacks on IoT devices and networks. Due to its inherent scalability, Public Key Infrastructure (PKI) is the well-known and classic approach to bring public-key certificate based security to IoT. Even though the standard X.509 explicit certificates can be viable solution, they are inefficient and too large for resource constrained IoT networks and therefore, smaller, faster and more efficient Elliptic Curve Qu Vanstone (ECQV) implicit certificates can be employed for establishing authenticated connections in IoT. Moreover, the existing certificate-based authentication proposals in standardized IoT networks have either been deployed at the transport or physical layers. Thus, these proposals fail to provide true end-to-end security to messages at the application layer in the presence of intermediate CoAP proxies. This challenging aspect is addressed in this proposal by focusing on the certificate-based authentication at the application layer to ensure true end-to-end security of messages. Additionally, IoT application layer security protocols like EDHOC lacks mechanism for authenticated distribution of public keys and thus, there is a need for lightweight authentication based cryptographic primitive for establishing secure key agreement in IoT. This paper introduces a design and implementation of a lightweight ECQV implicit certificate and use them for authenticated key exchange in EDHOC at the application layer. We also design a lightweight profile with a novel encoding mechanism for ECQV implicit certificate, called L-ECQV. To prove its viability, L-ECQV has been implemented and evaluated on Contiki operating system. Our evaluation results show that the proposed L-ECQV certificate approach reduces energy consumption by 27%, message overhead of EDHOC handshake by 52%, and shows improvements in certificate validation time. The security analysis demonstrates that proposed L-ECQV certificates for EDHOC protocol is secure against a number of attack vectors present in the IoT network. This novel combination of ECQV certificates with EDHOC key exchange leads to a secure and lightweight authenticated key agreement in IoT networks.

Stochastic Election of Appropriate Range Cluster Heads (SEARCH) represents a semi-centralized, cluster head selection method that provides significant number of cluster heads in each round in a cost effective manner. The algorithm has been further refined for reduced energy consumption, higher throughput, improved stable period and therefore improved network lifetime by proposing Power-aware Aggregated SEARCH. Balancing the power requirement in the network along with local aggregation of data at the level of sensor nodes, result in significant improvement in throughput, reduced energy consumption and prolongs the lifetime of sensor network. The proposed technique paves a way to attain spectrum efficient IoT networks based on data aggregation at level of collector sensor nodes.

In this paper, we consider Internet of Things (IoT) based mobile edge computing (MEC) system. IoT devices are controlled by access points (APs), which do not have access to any licensed spectrum. This puts limitations on IoT devices’ offloading capability and hence effectively reduces the number of computed bits. Under such scenario, we consider spectrum sharing, where IoT networks get access to licensed spectrum by helping a primary network (owns licensed spectrum) in cooperative relaying. We aim to maximize sum of the primary network’s communication rate and IoT networks’ total number of computed bits by jointly optimizing relay selection, licensed spectrum allocation, local computation, and offloading sequence selection. Moreover, we consider important constraints, e.g., the guaranteed rate for the primary network, IoT devices’ available energy, and available licensed spectrum resource blocks (RBs). We observe that the optimization problem is combinatoric in nature, which becomes more complicated when IoT devices’ scheduling order comes into the formulation. From our analysis, we devise an optimal and computationally efficient algorithm. In the result section, we compare with relevant benchmark systems and show the efficacy of our proposed model. Moreover, we also show how IoT and primary networks benefit from spectrum sharing.

Distributed Energy-Harvesting-Aware Routing Algorithms (DEHARAs) have emerged as a pivotal solution in addressing the energy challenges of modern Internet of Things (IoT) networks. With the proliferation of IoT devices powered by diverse energy sources, efficient energy management and communication are crucial for network sustainability. DEHARAs leverage the capabilities of energy-harvesting devices, aiming to optimize energy consumption, extend network lifetime, and ensure reliable communication. This paper provides a comprehensive survey of DEHARAs, encompassing their evolution, design principles, evaluation methodologies, and future prospects. We commence by elucidating the context of IoT networks and their energy challenges, paving the way for the necessity of DEHARAs. Emphasizing the importance of energy harvesting technologies, we explore their role in sustainable IoT deployments. Subsequently, we delve into the motivations behind DEHARAs, uncovering their design considerations and the factors driving their development. An exploration of various energy harvesting techniques, including solar, kinetic, and thermal methods, sheds light on the diverse energy sources harnessed by IoT devices. Our survey encompasses a detailed evaluation of energy-harvesting rates and constraints inherent in IoT devices, providing a foundation for understanding the energy dynamics at play. Traditional routing protocols for IoT networks are dissected, offering insights into their strengths and limitations. Addressing the challenges posed by heterogeneous energy-harvesting capabilities and varying energy demands, we discuss the intricacies of energy-aware routing algorithms and the approaches to tackle these complexities. Key factors to consider in formulating energy-aware routing algorithms are identified, outlining design considerations for DEHARAs. Metrics for evaluating their performance, including network lifetime, energy efficiency, and data transmission delays, are elucidated, enabling a comprehensive assessment of their effectiveness. An in-depth analysis of existing prominent DEHARAs offers insights into their design philosophies and capabilities. A comparative study of these algorithms' strengths and limitations provides a holistic view of their applicability in different scenarios. International Journal of Scientific Research in Engineering and Management (IJSREM) Volume: 07 Issue: 09 | September - 2023 SJIF Rating: 8.176 ISSN: 2582-3930 © 2023, IJSREM | www.ijsrem.com DOI: 10.55041/IJSREM25689 | Page 2 The categorization of DEHARAs based on their approaches, be it centralized, distributed, or hybrid, offers a taxonomy for understanding their architectural foundations. An overview of simulation environments employed for assessing DEHARA performance sheds light on the tools and methodologies used in research. Concluding the survey, we summarize the insights gained, highlight the challenges yet to be addressed, and present potential avenues for future research and improvements. In sum, this survey paper aims to provide a comprehensive understanding of the current state and future prospects of DEHARAs in the realm of heterogeneous IoT networks. By exploring their motivations, designs, and evaluations, we contribute to the advancement of energy-efficient and resilient routing solutions, fostering innovation and collaboration across disciplines.

Internet of Things (IoT) networks' wide range and heterogeneity make them prone to cyberattacks. Most IoT devices have limited resource capabilities (e.g., memory capacity, processing power, and energy consumption) to function as conventional intrusion detection systems (IDSs). Researchers have applied many approaches to lightweight IDSs, including energy-based IDSs, machine learning/deep learning (ML/DL)-based IDSs, and federated learning (FL)-based IDSs. FL has become a promising solution for IDSs in IoT networks because it reduces the overhead in the learning process by engaging IoT devices during the training process. Three FL architectures are used to tackle the IDSs in IoT networks, including centralized (client-server), decentralized (device-to-device), and semi-decentralized. However, none of them has solved the heterogeneity of IoT devices while considering lightweight-ness and performance at the same time. Therefore, we propose a semi-decentralized FL-based model for a lightweight IDS to fit the IoT device capabilities. The proposed model is based on clustering the IoT devices-FL clients-and assigning a cluster head to each cluster that acts on behalf of FL clients. Consequently, the number of IoT devices that communicate with the server is reduced, helping to reduce the communication overhead. Moreover, clustering helps in improving the aggregation process as each cluster sends the average model's weights to the server for aggregation in one FL round. The distributed denial-of-service (DDoS) attack is the main concern in our IDS model, since it easily occurs in IoT devices with limited resource capabilities. The proposed model is configured with three deep learning techniques-LSTM, BiLSTM, and WGAN-using the CICIoT2023 dataset. The experimental results show that the BiLSTM achieves better performance and is suitable for resource-constrained IoT devices based on model size. We test the pre-trained semi-decentralized FL-based model on three datasets-BoT-IoT, WUSTL-IIoT-2021, and Edge-IIoTset-and the results show that our model has the highest performance in most classes, particularly for DDoS attacks.

Distributed robust channel allocation for clustered cognitive radio-based IoT networks using graph theory

Reducing energy consumption in IoT networks remains a critical challenge in energy-efficient clustering routing protocols (EECR-IoT). Several routing protocols have been developed to address power consumption, with cluster- based protocols emerging as particularly effective. In these protocols, cluster heads are selected to aggregate data from root nodes and transmit it to the base station, optimizing energy usage. Efficient selection of cluster heads is essential to prolong network lifetime. Our proposed protocol employs static clustering for optimal cluster head selection, ensuring effective performance in both large and small areas. To enhance communication efficiency, we divide large sensor fields into rectangular clusters, which are then grouped into zones to facilitate communication between cluster heads and the base station. EECR-IoT involves a vast network of tiny sensor nodes capable of sensing, processing, and transmitting environmental data to the base station. Energy efficiency is crucial for sustaining these networks, and our study introduces an energy-efficient clustering protocol based on the Divide and Conquer Quad Tree dynamic multi-hop LEACH algorithm. This approach optimizes energy efficiency through balanced cluster creation, thereby distributing the load among cluster heads and extending network lifetime. In addition to Euclidean distance, the protocol considers residual energy for cluster head selection. Multi-hop communication between cluster heads and the base station depends on their distance, further improving energy efficiency. Simulations demonstrate that the proposed method outperforms existing heterogeneous protocols, including LEACH-B, BPK-means, Park’s approach, and Mk-means, by up to 50% in energy savings. The protocol’s effectiveness is evaluated using performance metrics such as throughput, End-to-End delay (EED), Packet Drop Rate (PDR), and node lifespan, showcasing its superiority in enhancing network longevity and reducing energy consumption across various IoT applications.