Energy-Efficient and Blockchain-Enabled Model for Internet of Things (IoT) in Smart Cities

Wireless sensor networks (WSNs) and Internet of Things (IoT) have gained more popularity in recent years as an underlying infrastructure for connected devices and sensors in smart cities. The data generated from these sensors are used by smart cities to strengthen their infrastructure, utilities, and public services. WSNs are suitable for long periods of data acquisition in smart cities. To make the networks of smart cities more reliable for sensitive information, the blockchain mechanism has been proposed. The key issues and challenges of WSNs in smart cities is efficiently scheduling the resources; leading to extending the network lifetime of sensors. In this paper, a linear network coding (LNC) for WSNs with blockchain-enabled IoT devices has been proposed. The consumption of energy is reduced for each node by applying LNC. The efficiency and the reliability of the proposed model are evaluated and compared to those of the existing models. Results from the simulation demonstrate that the proposed model increases the efficiency in terms of the number of live nodes, packet delivery ratio, throughput, and the optimized residual energy compared to other current techniques.     

Secure Multifactor Remote Access User Authentication Framework for IoT Networks

The term IoT refers to the interconnection and exchange of data among devices/sensors. IoT devices are often small, low cost, and have limited resources. The IoT issues and challenges are growing increasingly. Security and privacy issues are among the most important concerns in IoT applications, such as smart buildings. Remote cybersecurity attacks are the attacks which do not require physical access to the IoT networks, where the attacker can remotely access and communicate with the IoT devices through a wireless communication channel. Thus, remote cybersecurity attacks are a significant threat. Emerging applications in smart environments such as smart buildings require remote access for both users and resources. Since the user/building communication channel is insecure, a lightweight and secure authentication protocol is required. In this paper, we propose a new secure remote user mutual authentication protocol based on transitory identities and multi-factor authentication for IoT smart building environment. The protocol ensures that only legitimate users can authenticate with smart building controllers in an anonymous, unlinkable, and untraceable manner. The protocol also avoids clock synchronization problem and can resist quantum computing attacks. The security of the protocol is evaluated using two different methods: (1) informal analysis; (2) model check using the automated validation of internet security protocols and applications (AVISPA) toolkit. The communication overhead and computational cost of the proposed are analyzed. The security and performance analysis show that our protocol is secure and efficient.     

Collision Observation-Based Optimization of Low-Power and Lossy IoT Network Using Reinforcement Learning

The Internet of Things (IoT) has numerous applications in every domain, e.g., smart cities to provide intelligent services to sustainable cities. The next-generation of IoT networks is expected to be densely deployed in a resource-constrained and lossy environment. The densely deployed nodes producing radically heterogeneous traffic pattern causes congestion and collision in the network. At the medium access control (MAC) layer, mitigating channel collision is still one of the main challenges of future IoT networks. Similarly, the standardized network layer uses a ranking mechanism based on hop-counts and expected transmission counts (ETX), which often does not adapt to the dynamic and lossy environment and impact performance. The ranking mechanism also requires large control overheads to update rank information. The resource-constrained IoT devices operating in a low-power and lossy network (LLN) environment need an efficient solution to handle these problems. Reinforcement learning (RL) algorithms like Q-learning are recently utilized to solve learning problems in LLNs devices like sensors. Thus, in this paper, an RL-based optimization of dense LLN IoT devices with heavy heterogeneous traffic is devised. The proposed protocol learns the collision information from the MAC layer and makes an intelligent decision at the network layer. The proposed protocol also enhances the operation of the trickle timer algorithm. A Q-learning model is employed to adaptively learn the channel collision probability and network layer ranking states with accumulated reward function. Based on a simulation using Contiki 3.0 Cooja, the proposed intelligent scheme achieves a lower packet loss ratio, improves throughput, produces lower control overheads, and consumes less energy than other state-of-the-art mechanisms.     

自组网中一种基于跨层负载感知的蚁群优化路由协议

将蚁群优化和跨层优化方法结合起来,提出了一种基于跨层负载感知和蚁群优化的路由协议(CLAOR)。协议将整个路径中各节点MAC层的总平均估计时延和节点队列缓存的占用情况结合起来,共同作为路由选择和路由调整的重要度量标准进行按需路由发现和维护,通过拥塞节点丢弃蚂蚁分组以及借助部分兼具蚂蚁功能的数据分组实现正常路由表的维护等方法,减少了控制开销,增加了算法的可扩展性,较好地解决了自组网中现有基于蚁群优化的路由协议中普遍存在的拥塞问题、捷径问题和引入的路由开销问题。仿真结果表明,CLAOR在分组成功递交率、路由开销以及端到端平均时延等方面具有优良性能,能很好地实现网络中的业务流负载均衡。     

LOA-RPL: Novel Energy-Efficient Routing Protocol for the Internet of Things Using Lion Optimization Algorithm to Maximize Network Lifetime

Energy conservation is a significant task in the Internet of Things (IoT) because IoT involves highly resource-constrained devices. Clustering is an effective technique for saving energy by reducing duplicate data. In a clustering protocol, the selection of a cluster head (CH) plays a key role in prolonging the lifetime of a network. However, most cluster-based protocols, including routing protocols for low-power and lossy networks (RPLs), have used fuzzy logic and probabilistic approaches to select the CH node. Consequently, early battery depletion is produced near the sink. To overcome this issue, a lion optimization algorithm (LOA) for selecting CH in RPL is proposed in this study. LOA-RPL comprises three processes: cluster formation, CH selection, and route establishment. A cluster is formed using the Euclidean distance. CH selection is performed using LOA. Route establishment is implemented using residual energy information. An extensive simulation is conducted in the network simulator ns-3 on various parameters, such as network lifetime, power consumption, packet delivery ratio (PDR), and throughput. The performance of LOA-RPL is also compared with those of RPL, fuzzy rule-based energy-efficient clustering and immune-inspired routing (FEEC-IIR), and the routing scheme for IoT that uses shuffled frog-leaping optimization algorithm (RISA-RPL). The performance evaluation metrics used in this study are network lifetime, power consumption, PDR, and throughput. The proposed LOA-RPL increases network lifetime by 20% and PDR by 5%–10% compared with RPL, FEEC-IIR, and RISA-RPL. LOA-RPL is also highly energy-efficient compared with other similar routing protocols.     

5G Smart Mobility Management Based Fuzzy Logic Controller Unit

In the paper, we propose a fuzzy logic controller system to be implemented for smart mobility management in the 5G wireless communication network. Mobility management is considered as a main issue for all-IP mobile networks future generation. As a network-based mobility management protocol, Internet Engineering Task Force developed the Proxy Mobile IPv6 (PMIPv6) in order to support the mobility of IP devices, and many other results were presented to reduce latency handover and the amount of PMIPv6 signaling, but it is not enough for the application needs in real-time. The present paper describes an approach based on the IEEE 802.21 Media Independent Handover (MIH) standard and PMIPv6, so we present a new vertical handover algorithm for anticipating handover process efficiently. Our object is to propose a smart mobility management that contribute in 5G wireless communication system network operating functions. Two proposed dynamic thresholds were successfully made to guaranty process triggering, and a new primitive MIH is proposed for signaling a needed handover to be done. Simulation results demonstrate a significant reduction of the handover delay, packet loss, handover blocking probability and signaling overhead. Simulation results and tests are accomplished.     

Energy-Efficient Transmission Range Optimization Model for WSN-Based Internet of Things

With the explosive advancements in wireless communications and digital electronics, some tiny devices, sensors, became a part of our daily life in numerous fields. Wireless sensor networks (WSNs) is composed of tiny sensor devices. WSNs have emerged as a key technology enabling the realization of the Internet of Things (IoT). In particular, the sensor-based revolution of WSN-based IoT has led to considerable technological growth in nearly all circles of our life such as smart cities, smart homes, smart healthcare, security applications, environmental monitoring, etc. However, the limitations of energy, communication range, and computational resources are bottlenecks to the widespread applications of this technology. In order to tackle these issues, in this paper, we propose an Energy-efficient Transmission Range Optimized Model for IoT (ETROMI), which can optimize the transmission range of the sensor nodes to curb the hot-spot problem occurring in multi-hop communication. In particular, we maximize the transmission range by employing linear programming to alleviate the sensor nodes’ energy consumption and considerably enhance the network longevity compared to that achievable using state-of-the-art algorithms. Through extensive simulation results, we demonstrate the superiority of the proposed model. ETROMI is expected to be extensively used for various smart city, smart home, and smart healthcare applications in which the transmission range of the sensor nodes is a key concern.     

HELP-WSN-A Novel Adaptive Multi-Tier Hybrid Intelligent Framework for QoS Aware WSN-IoT Networks

Wireless Sensor Network is considered as the intermediate layer in the paradigm of Internet of things (IoT) and its effectiveness depends on the mode of deployment without sacrificing the performance and energy efficiency. WSN provides ubiquitous access to location, the status of different entities of the environment and data acquisition for long term IoT monitoring. Achieving the high performance of the WSN-IoT network remains to be a real challenge since the deployment of these networks in the large area consumes more power which in turn degrades the performance of the networks. So, developing the robust and QoS (quality of services) aware energy-efficient routing protocol for WSN assisted IoT devices needs its brighter light of research to enhance the network lifetime. This paper proposed a Hybrid Energy Efficient Learning Protocol (HELP). The proposed protocol leverages the multi-tier adaptive framework to minimize energy consumption. HELP works in a two-tier mechanism in which it integrates the powerful Extreme Learning Machines for clustering framework and employs the zonal based optimization technique which works on hybrid Whale-dragonfly algorithms to achieve high QoS parameters. The proposed framework uses the sub-area division algorithm to divide the network area into different zones. Extreme learning machines (ELM) which are employed in this framework categories the Zone's Cluster Head (ZCH) based on distance and energy. After categorizing the zone's cluster head, the optimal routing path for an energy-efficient data transfer will be selected based on the new hybrid whale-swarm algorithms. The extensive simulations were carried out using OMNET++-Python user-defined plugins by injecting the dynamic mobility models in networks to make it a more realistic environment. Furthermore, the effectiveness of the proposed HELP is examined against the existing protocols such as LEACH, M-LEACH, SEP, EACRP and SEEP and results show the proposed framework has outperformed other techniques in terms of QoS parameters such as network lifetime, energy, latency.     

Hyper Elliptic Curve Based Certificateless Signcryption Scheme for Secure IIoT Communications

Industrial internet of things (IIoT) is the usage of internet of things (IoT) devices and applications for the purpose of sensing, processing and communicating real-time events in the industrial system to reduce the unnecessary operational cost and enhance manufacturing and other industrial-related processes to attain more profits. However, such IoT based smart industries need internet connectivity and interoperability which makes them susceptible to numerous cyber-attacks due to the scarcity of computational resources of IoT devices and communication over insecure wireless channels. Therefore, this necessitates the design of an efficient security mechanism for IIoT environment. In this paper, we propose a hyperelliptic curve cryptography (HECC) based IIoT Certificateless Signcryption (IIoT-CS) scheme, with the aim of improving security while lowering computational and communication overhead in IIoT environment. HECC with 80-bit smaller key and parameters sizes offers similar security as elliptic curve cryptography (ECC) with 160-bit long key and parameters sizes. We assessed the IIoT-CS scheme security by applying formal and informal security evaluation techniques. We used Real or Random (RoR) model and the widely used automated validation of internet security protocols and applications (AVISPA) simulation tool for formal security analysis and proved that the IIoT-CS scheme provides resistance to various attacks. Our proposed IIoT-CS scheme is relatively less expensive compared to the current state-of-the-art in terms of computational cost and communication overhead. Furthermore, the IIoT-CS scheme is 31.25% and 51.31% more efficient in computational cost and communication overhead, respectively, compared to the most recent protocol.     

Controller Placement in Software Defined Internet of Things Using Optimization Algorithm

The current and future status of the internet is represented by the upcoming Internet of Things (IoT). The internet can connect the huge amount of data, which contains lot of processing operations and efforts to transfer the pieces of information. The emerging IoT technology in which the smart ecosystem is enabled by the physical object fixed with software electronics, sensors and network connectivity. Nowadays, there are two trending technologies that take the platform i.e., Software Defined Network (SDN) and IoT (SD-IoT). The main aim of the IoT network is to connect and organize different objects with the internet, which is managed with the control panel and data panel in the SD network. The main issue and the challenging factors in this network are the increase in the delay and latency problem between the controllers. It is more significant for wide area networks, because of the large packet propagation latency and the controller placement problem is more important in every network. In the proposed work, IoT is implementing with adaptive fuzzy controller placement using the enhanced sunflower optimization (ESFO) algorithm and Pareto Optimal Controller placement tool (POCO) for the placement problem of the controller. In order to prove the efficiency of the proposed system, it is compared with other existing methods like PASIN, hybrid SD and PSO in terms of load balance, reduced number of controllers and average latency and delay. With 2 controllers, the proposed method obtains 400 miles as average latency, which is 22.2% smaller than PSO, 76.9% lesser than hybrid SD and 91.89% lesser than PASIN.     

A DLT-based framework for secure IoT infrastructure in smart communities

In the field of smart communities, significant progress has been made in recent years. The objective of constructing smart communities is to improve the quality of life of their inhabitants. To accomplish this objective, technologies such as Internet of Things (IoT) and Artificial Intelligence (AI) were deployed. The data gathered and processed by IoT devices, particularly those with centralized control, are however susceptible to availability, integrity, and privacy risks. Due to its inherent properties of transparency, immutability, and underlying secure-by-design architecture, Distributed Ledger Technology (DLT) and Smart Contracts enable distributed, decentralized, automated workflows that can be incorporated to automate the management of the next generation of IoT networks. Using a potential use case, a conceptual architecture for securing smart communities with DLT is developed and explained. In this paper, a framework for IoT eco-systems is proposed that provides seamless integration between IoT and DLT to create a decentralized trusted architecture that ensures the trustworthiness of IoT eco-systems at design time and a trust reputation model based on the architecture to protect it at run-time. In addition, the initial implementation steps are described for this framework.     

区域性海洋观测水声通信网数据链路层研究

简要介绍了水声通信网的研究现状以及常用的组网方式,比较了几种可实际应用的数据链路层协议,在此基础上提出了一种适用于区域性海洋观测的混合型网络协议,进行了仿真研究。该协议通过记录网络中数据包的发送频率来动态地调整所使用的MAC（Media Access Control）协议,当频率较高时选择查询方式的MAC协议,减少数据包的冲突;当频率较低时选择MACAW（Media Access with Collision Avoidance for Wireless）,避免查询信号带来的额外开销。仿真结果表明,对于区域性海洋观测,这种包产生速率低,但可能出现短时间通信量大的应用情况,该协议可以有效地降低每bit的能耗,适用于长时间海洋观测。     

Energy Efficient Cluster Based Clinical Decision Support System in IoT Environment

Internet of Things (IoT) has become a major technological development which offers smart infrastructure for the cloud-edge services by the interconnection of physical devices and virtual things among mobile applications and embedded devices. The e-healthcare application solely depends on the IoT and cloud computing environment, has provided several characteristics and applications. Prior research works reported that the energy consumption for transmission process is significantly higher compared to sensing and processing, which led to quick exhaustion of energy. In this view, this paper introduces a new energy efficient cluster enabled clinical decision support system (EEC-CDSS) for embedded IoT environment. The presented EEC-CDSS model aims to effectively transmit the medical data from IoT devices and perform accurate diagnostic process. The EEC-CDSS model incorporates particle swarm optimization with levy distribution (PSO-L) based clustering technique, which clusters the set of IoT devices and reduces the amount of data transmission. In addition, the IoT devices forward the data to the cloud where the actual classification procedure is performed. For classification process, variational autoencoder (VAE) is used to determine the existence of disease or not. In order to investigate the proficient results analysis of the EEC-CDSS model, a wide range of simulations was carried out on heart disease and diabetes dataset. The obtained simulation values pointed out the supremacy of the EEC-CDSS model interms of energy efficiency and classification accuracy.     

A Double-Compensation-Based Federated Learning Scheme for Data Privacy Protection in a Social IoT Scenario

Nowadays, smart wearable devices are used widely in the Social Internet of Things (IoT), which record human physiological data in real time. To protect the data privacy of smart devices, researchers pay more attention to federated learning. Although the data leakage problem is somewhat solved, a new challenge has emerged. Asynchronous federated learning shortens the convergence time, while it has time delay and data heterogeneity problems. Both of the two problems harm the accuracy. To overcome these issues, we propose an asynchronous federated learning scheme based on double compensation to solve the problem of time delay and data heterogeneity problems. The scheme improves the Delay Compensated Asynchronous Stochastic Gradient Descent (DC-ASGD) algorithm based on the second-order Taylor expansion as the delay compensation. It adds the FedProx operator to the objective function as the heterogeneity compensation. Besides, the proposed scheme motivates the federated learning process by adjusting the importance of the participants and the central server. We conduct multiple sets of experiments in both conventional and heterogeneous scenarios. The experimental results show that our scheme improves the accuracy by about 5% while keeping the complexity constant. We can find that our scheme converges more smoothly during training and adapts better in heterogeneous environments through numerical experiments. The proposed double-compensation-based federated learning scheme is highly accurate, flexible in terms of participants and smooth the training process. Hence it is deemed suitable for data privacy protection of smart wearable devices.     

A Novel Ensemble Learning Algorithm Based on D-S Evidence Theory for IoT Security

In the last decade, IoT has been widely used in smart cities, autonomous driving and Industry 4.0, which lead to improve efficiency, reliability, security and economic benefits. However, with the rapid development of new technologies, such as cognitive communication, cloud computing, quantum computing and big data, the IoT security is being confronted with a series of new threats and challenges. IoT device identification via Radio Frequency Fingerprinting (RFF) extracting from radio signals is a physical-layer method for IoT security. In physical-layer, RFF is a unique characteristic of IoT device themselves, which can difficultly be tampered. Just as people’s unique fingerprinting, different IoT devices exhibit different RFF which can be used for identification and authentication. In this paper, the structure of IoT device identification is proposed, the key technologies such as signal detection, RFF extraction, and classification model is discussed. Especially, based on the random forest and Dempster-Shafer evidence algorithm, a novel ensemble learning algorithm is proposed. Through theoretical modeling and experimental verification, the reliability and differentiability of RFF are extracted and verified, the classification result is shown under the real IoT device environments.     

Adaptation of Vehicular Ad hoc Network Clustering Protocol for Smart Transportation

Clustering algorithms optimization can minimize topology maintenance overhead in large scale vehicular Ad hoc networks (VANETs) for smart transportation that results from dynamic topology, limited resources and non-centralized architecture. The performance of a clustering algorithm varies with the underlying mobility model to address the topology maintenance overhead issue in VANETs for smart transportation. To design a robust clustering algorithm, careful attention must be paid to components like mobility models and performance objectives. A clustering algorithm may not perform well with every mobility pattern. Therefore, we propose a supervisory protocol (SP) that observes the mobility pattern of vehicles and identifies the realistic Mobility model through microscopic features. An analytical model can be used to determine an efficient clustering algorithm for a specific mobility model (MM). SP selects the best clustering scheme according to the mobility model and guarantees a consistent performance throughout VANET operations. The simulation has performed in three parts that is the central part simulation for setting up the clustering environment, In the second part the clustering algorithms are tested for efficiency in a constrained atmosphere for some time and the third part represents the proposed scheme. The simulation results show that the proposed scheme outperforms clustering algorithms such as honey bee algorithm-based clustering and memetic clustering in terms of cluster count, re-affiliation rate, control overhead and cluster lifetime.     

一种新的基于移动预测的MANET路由协议

移动自组网是一种由一系列可以自由移动的节点主机聚集而成的一个临时性动态网络。它没有固定基站，也没有作为控制管理中心的节点主机，由于其拓扑的动态性，其路由协议与其他传统网络有很大的区别。为了能更有效地移动自组网中实现数据的传送，本文提出了一种基于移动预测下的MANET路由协议，该协议综合考虑了传输路径的延时和稳定性，选择在延时限制下平均拥有最稳特性的传输路径，模拟结果表明，这种方法比基于移动预测量稳路径路由协议的传输时延要短，传送成功概率要高，而控制开销却相关不大。     

Cogent and Energy Efficient Authentication Protocol for WSN in IoT

Given the accelerating development of Internet of things (IoT), a secure and robust authentication mechanism is urgently required as a critical architectural component. The IoT has improved the quality of everyday life for numerous people in many ways. Owing to the predominantly wireless nature of the IoT, connected devices are more vulnerable to security threats compared to wired networks. User authentication is thus of utmost importance in terms of security on the IoT. Several authentication protocols have been proposed in recent years, but most prior schemes do not provide sufficient security for these wireless networks. To overcome the limitations of previous schemes, we propose an efficient and lightweight authentication scheme called the Cogent Biometric-Based Authentication Scheme (COBBAS). The proposed scheme is based on biometric data, and uses lightweight operations to enhance the efficiency of the network in terms of time, storage, and battery consumption. A formal security analysis of COBBAS using Burrows–Abadi–Needham logic proves that the proposed protocol provides secure mutual authentication. Formal security verification using the Automated Validation of Internet Security Protocols and Applications tool shows that the proposed protocol is safe against man-in-the-middle and replay attacks. Informal security analysis further shows that COBBAS protects wireless sensor networks against several security attacks such as password guessing, impersonation, stolen verifier attacks, denial-of-service attacks, and errors in biometric recognition. This protocol also provides user anonymity, confidentiality, integrity, and biometric recovery in acceptable time with reasonable computational cost.     

A Drones Optimal Path Planning Based on Swarm Intelligence Algorithms

The smart city comprises various interlinked elements which communicate data and offers urban life to citizen. Unmanned Aerial Vehicles (UAV) or drones were commonly employed in different application areas like agriculture, logistics, and surveillance. For improving the drone flying safety and quality of services, a significant solution is for designing the Internet of Drones (IoD) where the drones are utilized to gather data and people communicate to the drones of a specific flying region using the mobile devices is for constructing the Internet-of-Drones, where the drones were utilized for collecting the data, and communicate with others. In addition, the SIRSS-CIoD technique derives a tuna swarm algorithm-based clustering (TSA-C) technique to choose cluster heads (CHs) and organize clusters in IoV networks. Besides, the SIRSS-CIoD technique involves the design of a biogeography-based optimization (BBO) technique to an optimum route selection (RS) process. The design of clustering and routing techniques for IoD networks in smart cities shows the novelty of the study. A wide range of experimental analyses is carried out and the comparative study highlighted the improved performance of the SIRSS-CIoD technique over the other approaches.     

Performance study on a new reservation dual‐bus token passing protocol

Abstract

The major advantage of token‐passing‐bus local area networks (LANs) is its collision‐free characteristic but its drawback is the overhead in passing the token to idle users when the channel load is light. It is commonly known that the system performance can be increased considerably by increasing the capacity of the bus. One way of increasing the bus capacity, and also the system reliability, is to increase the number of buses. In this paper, we propose adding a second bus to provide high bandwidth as well as reliable service for token‐passing‐bus LANs. The second bus proposed here normally is used for reservations so that the token on the data bus is dynamically passed to ready users for packet transmission. Furthermore, it will take the place of the data bus when the data bus fails. The protocol discussed here is a reservation token‐passing protocol which not only is collision‐free but also minimizes the token‐passing delay. The analysis of this dynamic token‐passing protocol is provided with both analytical results as well as simulation results.