Internet Of Things Communication Research Articles

Security Threats to Internet of Things : A Survey

Internet of things (IoT) is an emerging technology in the present era. The term IoT refers to as an interconnection of several smart nodes through some heterogeneous link for the purpose of data communication. Some particular protocols control the entire communication in IoT. Due to plenitude of devices, it becomes a huge task to check the loyalty status of each node which is going to be a part of IoT environment. These nodes sometimes get involved in some malicious activities which may cause critical threats to this environment. These anonymous activities may include some attack on the working or security of IoT. In this uncongenial circumstance we need a strong security measurement to countermeasure these attacks. Innumerable efforts have been made to improve the security of IoT. This paper is an effort to make a glance of some of these security schemes

DSIT: A Dynamic Lightweight Cryptography Algorithm for Securing Image in IoT Communication

One of the most significant challenges appears to be securing the Internet of Things (IoT) communication network. As a corollary, information security has become the basis for establishing trustworthiness in IoT network communication. Cryptography is one of the ways for securing information in this case. However, the majority of current approaches are static, making them subject to security threats. As a consequence, a new concept, dynamic encryption, is growing rapidly in IoT communication. In this paper, a dynamic encryption algorithm (DSIT) has been proposed to secure IoT communication. This algorithm is based on Feistel and Substitution–Permutation Network. DSIT is a block cipher that takes the 64-bit block of plaintext, 64-bit secret key, and a secret dynamic box (D-box) as input. It produces a 64-bit ciphertext by performing eight rounds of the DSIT algorithm. For each round, the key and D-box are updated. This dynamic effect provides high security to a dynamic IoT network. The proposed algorithm has been executed in IoT environment using Raspberry Pi 3 Model B[Formula: see text] and 50% average Avalanche effect has been achieved. The proposed algorithm efficiently encrypts the image data to secure the communication and high resistant to cryptanalysis attacks.

Open network structure and smart network to sharing cybersecurity within the 5G network

The <span>next-generation communication system incorporates information technology (IT) and operations technology (OT) for generating, delivering, and collecting, and obtaining communication power. We plan to include a brief outline of internet of thing (IoT) communication and its context, along with security concerns that arise for IoT data on the network and some methods for detecting and avoiding cyber security threats. With the rise of the 5G networks, we introduce the smart network's emergent technology and its opportunities and more cybersecurity issues. Whereas, finding or responding to a power outage is an essential part of system security That is why we will discuss the innumerable advantages of 5G networks and we must also cover the inevitable problems that we will encounter in power delivery. The use of smart IoT communication technologies is becoming more common in the energy sector, particularly with the network (5G. The smart network and energy flow integration Real-time data on generation, electricity distribution, and energy consumption is measured using computers and cutting-edge technologies. This information aids utility companies in managing electricity supply and demand, as well as price. While enhanced communication and information technologies are unquestionably crucial to </span>the smart network.

Energy Optimization for Green Communication in IoT Using Harris Hawks Optimization

Internet of Things (IoT) has gained immense attention over the last decade. Even though IoT has great potential, several issues like security, energy optimization, data storage, real time data analytics, etc. have hindered the realization of true potential of IoT. As the sensors in the IoT network continuously monitor the environment, the longevity of the IoT networks is affected. Many researchers have tried to solve the issue of network longevity/energy optimization in IoT networks in the past few years. Optimizing the energy in IoT networks is still undergoing research. One of the methods used to optimize the energy in IoT networks is through selecting the optimal Cluster Head (CH) in the IoT networks. In this paper we have made an attempt to optimize the energy utilization in the IoT networks through an optimal CH selection using recently developed nature inspired algorithm, namely, Harris Hawks Optimization algorithm (HHO). The performance of the HHO-based CH model is analyzed through several metrics such as delay, load, number of alive nodes, residual energy, and temperature. The experimental results prove that the proposed HHO-based CH selection method performs better than the state-of-the-art CH selection models. The proposed model extends the network’s lifetime by keeping more alive nodes even after the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3000^{\textit {th}}$ </tex-math></inline-formula> round. The proposed model retains thirty nodes, genetic algorithm (GA) retains one node, artificial bee colony algorithm (ABC) retains three nodes, moth-flame optimization algorithm (MFO) retains seven nodes, and whale optimization algorithm (WOA) retains eleven nodes.

Ultra-Low-Power IoT Communications: A Novel Address Decoding Approach for Wake-Up Receivers

Providing energy-efficient Internet of Things (IoT) connectivity has attracted significant attention in fifth-generation (5G) wireless networks and beyond. A potential solution for realizing a long-lasting network of IoT devices is to equip each IoT device with a wake-up receiver (WuR) to have always-accessible devices instead of always-on devices. WuRs typically comprise a radio frequency demodulator, sequence decoder, and digital address decoder and are provided with a unique authentication address in the network. Although the literature on efficient demodulators is mature, it lacks research on fast, low-power, and reliable address decoders. As this module continuously monitors the received ambient energy for potential paging of the device, its contribution to WuR’s power consumption is crucial. Motivated by this need, a low-power, reliable address decoder is developed in this paper. We further investigate the integration of WuR in low-power uplink/downlink communications and, using system-level energy analysis; we characterize operation regions in which WuR can contribute significantly to energy saving. The device-level energy analysis confirms the superior performance of our decoder. The results show that the proposed decoder significantly outperforms the state-of-the-art with power consumption of 60 nW, at cost of compromising negligible increase in decoding delay.

EuDaimon: PUF-Based Robust and Lightweight Authenticated Session Key Establishment Protocol for IoT-Enabled Smart Society

The fast-growing technology has increased the demands of the Internet of Things (IoT) in different sectors, and the protection of security threats in IoT communication is a major challenge. This article presents a network diagram of the smart societies by enabling the sensor and camera technology. This article aims to provide and protect all kinds of security concerns during the transmission of sensed data to the cloud server. Therefore, a physically-unclonable-function-based authentication protocol has been proposed (called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">EuDaimon</i> ) to establish secure communication among the entities involved. The EuDaimon protocol not only achieves the lightweight property, but also assists remote users who can access the cloud server as well as any sensor or camera after performing only one-time registration. The informal security analysis shows that the EuDaimon protocol is free from all kinds of security threats. The Scyther simulation ensures that the EuDaimon protocol has no security vulnerabilities. The performance comparison claims that the EuDaimon protocol is providing better performance although more participants are involved in the architecture.

Blockchain on Security and Forensics Management in Edge Computing for IoT: A Comprehensive Survey

Security and forensics represent two key components for network management, especially to guarantee the trusted operation of massive access networks such as the Internet of Things (IoT). As a core technology to provide low latency and high communication for IoT, Mobile Edge Computing (MEC) pulls computing resources from remote cloud centers to devices. The process of MEC service involves three types of entities: devices, data generated by devices and digital evidence generated after the data interaction. These entities are fully distributed and difficult to protect through traditional, highly centralized security and authentication mechanisms. As a decentralized shared ledger and database, the emerging blockchain is considered to provide cooperative trust and collaborative action among multiple subjects while ensuring the integrity and confidentiality of data. Because of its anonymity, non-tampering and traceability, the blockchain arouses research on the combination of blockchain and edge computing for device security, data security and forensics in IoT. This survey analyzes the application of blockchain in MEC-IoT systems and mainly focuses on approaches and technologies to manage the security and forensics issues for IoT. Finally, we present open issues and prospects for future work and research directions.

Proactive Network Fault Management for Reliable Subscribed Network Slicing in Software-Defined Mobile Data IoT Services

Proactive network solutions (PNS) become the precise management and orchestration (MANO) in the applied artificial intelligence (AI) era. The PNS proposed to invent future mobile edge communications by predicting the fault networks for reliable slicing configurations. Furthermore, federated learning (FL) systems have been appealed to apply for critical mobile data privacy of the Internet of Things (IoT) services. Therefore, FL-based IoT communications need a precise PNS to pretend the network failures to maximize the model inference and improve end-to-end (E2E) quality of services (QoS). This paper proposed an adopted software-defined network slicing (NS) for IoT communications based on network failure prediction and resource allocations by utilizing a deep-Q-network approach (DQN). The proposed proactive reliable subscribed network slicing was based on software-defined DQN-based proactive dynamic resource allocations (SDQN-PDRA) for adaptive communication configurations. The experiment showed that the proposed approach enhanced the significant outcomes of stability, reliability, convergence time, and other communication QoS.

Green Communication in Internet of Things: A Hybrid Bio-Inspired Intelligent Approach.

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%.

The Method of Allocating Resources for Ideological and Political Education in Universities Based on IoT Technology

The traditional resource sharing method allocates resources according to their importance and node weight ranking, which leads to uneven distribution of node loads and resource allocation with less efficiency and high energy consumption. In order to solve the above problems, the method of resource allocation of college ideological and political education based on Internet of Things (IoT) technology is studied. The IoT technology is used for establishing the communication Internet of Things or for sharing college ideological and political education resources, and the MCTS algorithm is used to search for college ideological and political education resources. For the case of education resources in colleges and universities, according to the simple semantic reasoning for establishing the mapping relationship between education resources and distribution of nodes, we realise the allocation of resources for education. The test experiment results show that the researched resource allocation method has low allocation delay and reduces at least about 23.7% of energy consumption, which is more effective.

Research and Analysis of Evaluation and Expansion of Physical Education Teaching System Based on Internet of Things Communication

Under the influence of China’s education reform, the assessment and evaluation of physical education have gradually attracted more and more people’s attention. Teaching evaluation ignores the development of students’ sports lifestyle. The article uses principal component analysis to establish a teaching quality evaluation model and design an extensible teaching evaluation system. The article uses box plots to reflect the characteristics of data changes, which can clearly draw differences between samples. Three variables of experimental class, reference class, and standard class are set up to conduct controlled experiments, and statistics are made from the survey results of the 2018-2019 school year physical education evaluation student evaluation. The experimental results indicate that after using the system, students’ learning satisfaction and enthusiasm have been greatly increased, and students’ weekly exercise time and sports items have also increased. Before and after the use of the teaching evaluation system, the students’ satisfaction with the evaluation system and the degree of movement recognition were tested. The results show that the system has a positive effect on the students’ evaluation satisfaction and movement recognition.

Deep Reinforcement Learning-Based Multidimensional Resource Management for Energy Harvesting Cognitive NOMA Communications

The combination of energy harvesting (EH), cognitive radio (CR), and non-orthogonal multiple access (NOMA) is a promising solution to improve energy efficiency and spectral efficiency of the upcoming beyond fifth generation network (B5G), especially for support the wireless sensor communications in Internet of things (IoT) system. However, how to realize intelligent frequency, time, and energy resource allocation to support better performances is an important problem to be solved. In this paper, we study joint spectrum, energy, and time resource management for the EH-CR-NOMA IoT systems. Our goal is to minimize the number of data packets losses for all secondary sensing users (SSU), while satisfying the constraints on the maximum charging battery capacity, maximum transmitting power, maximum buffer capacity, and minimum data rate of primary users (PU) and SSUs. Due to the non-convexity of this optimization problem and the stochastic nature of the wireless environment, we propose a distributed multidimensional resource management algorithm based on deep reinforcement learning (DRL). Considering the continuity of the resources to be managed, the deep deterministic policy gradient (DDPG) algorithm is adopted, based on which each agent (SSU) can manage its own multidimensional resources without collaboration. In addition, a simplified but practical action adjuster (AA) is introduced for improving the training efficiency and battery performance protection. The provided results show that the convergence speed of the proposed algorithm is about 4 times faster than that of DDPG, and the average number of packet losses (ANPL) is about 8 times lower than that of the greedy algorithm.

Finite Block Length Analysis of RIS-Assisted UAV-Based Multiuser IoT Communication System With Non-Linear EH

Reconfigurable intelligent surface (RIS) has emerged as an important transmission technology for numerous applications in Internet of Things (IoT) systems. Thus, in this paper, we investigate the application of RIS in energy harvesting (EH) based unmanned aerial vehicle (UAV) communication network with finite block length (BL) codes, where a rotary wing type flying UAV communicates with the multiple single antenna IoT users with the aid of multiple RISs mounted on several skyscraper buildings. To transmit the signal to a particular IoT user, the UAV selects an RIS on the basis of either UAV-RIS (i.e., partial) or UAV-RIS-IoT (i.e., full) channel state information (CSI) and then transmits the signal through the selected RIS along with the direct link transmission. In particular, we derive (i) the expression for probability of RIS selection, (ii) the statistical distribution of instantaneously received information signal-to-noise ratio (SNR) at the IoT user. Based on the derived statistics, we analyze the performance of the considered system under finite BL codes in terms of the average outage probability, average block error rate (ABLER) and goodput averaged over entire flying duration. Moreover, the BLER performance with finite BL codes is also compared with the infinite BL codes scenario. Additionally, we also investigate the impact of various channel and system parameters like imperfect CSI, number of RISs and the number of reflecting elements at each RIS, location of IoT users, variable altitude of the UAV, and the severity of channel fading of UAV-RIS link on the system performance. Furthermore, we have obtained the optimum UAV location in each time slot which minimizes the ABLER per time slot over all the users in the network. The analytical results are corroborated with Monte Carlo simulations.

Heterogeneous online/offline signcryption for secure communication in Internet of Things

Internet of things (IoT) is a promising network that combines various information sensing devices with the Internet to realize the interconnection of pervasive things. Security is a pivotal issue to ensure widespread adoption of the IoT. To ensure the security of data transmission, in this paper, we construct a heterogeneous online and offline signcryption from a wireless sensor network (WSN) to an Internet server. In our construction, the WSN is in certificateless cryptosystem (CLC) which does not involve public key certificate management problem in public key infrastructure (PKI) and key escrow problem in identity based cryptosystem (IBC), while the server is in IBC which avoids public key certificate management problem. For signcryption process, the proposed scheme is split into two steps: offline step and online step. In the former step, a lot of time-consuming calculations are completed, but do not involve the message to be transmitted. In the latter step, only some lightweight calculations are involved, but message must be available. Confidentiality, authentication, non-repudiation, and integrity can be achieved in a logical single step. Meanwhile, a formal security proof is given in the random oracle model. In addition, an elaborate performance analysis demonstrates that our proposed scheme has advantages in terms of computational cost and private key size, offline storage, as well as communication overhead. Therefore, our construction can be well applied to the IoT.

DEroute: trust-aware data routing protocol based on encryption and fuzzy concept for MANET secure communication in Iot

ABSTRACT The Internet of Things in smart environments interrelates with Mobile Ad‐hoc Network and Wireless Sensor Network, which becomes more attractive and efficiently flourishing. Moreover, Mobile Ad‐hoc Network is applied in various fields including intelligent transportation, emergency communications after disaster, and Internet of Things. The incorporation of Internet of Things and Mobile Ad‐hoc Network become a rising theory for enabling opportunistic communication in Internet of Things. The security provisioning for secure communication is still the major challenge in Mobile Ad‐hoc Network – Internet of Things. Thus, in this paper, the DEroute algorithm is developed for secure communication Internet of Things structure, where Internet of Things nodes are located in mobile ad-hoc manner. In order to avoid the route list modification, nodes’ address is encrypted based on generated secret keys by Diffie–Hellman key-exchange algorithm. The fuzzy system with trust parameters, like historical, indirect and direct trust factors are considered for the identification of secure nodes. The bi-filtering process is also performed for filtering important nodes. Thus, the developed DEroute model achieved enhanced performance with respect to delay, packet delivery ratio and throughput of 0.1674 sec, 87.23% and 25.19 mbps, respectively.

Secure Communication Protocol for Arduino-based IoT Using Lightweight Cryptography

We witness massive implementations of the Internet of Things (IoT) in smart homes, smart buildings, smart vehicles, smart wearables as well as Industry 4.0 initiatives. Along with the massive adoption, IoT security has become more important and crucial in this case. Arduino, as IoT hardware platform, also requires enhancements on its security to ensure that data it transmits and receives is secured and has not been tampered in any way. Transmitting of IoT data and telecommand in plaintext is not secure. Securing transmission using traditional block cipher is computationally intensive for embedded-systems with low memory and computing power like Arduino. This research proposes a novel lightweight security communication protocol that is lightweight enough to run on the Arduino platform. The proposed protocol shall be utilizing a lightweight key agreement scheme, the SPECK lightweight block cipher, and BLAKE2s hash function. This protocol is designed to support telemetry and telecommand by using publisher-subscriber, which also is aimed to be extensible but straightforward for future enhancements. This research shows that a secure IoT communication protocol can be designed and implemented on Arduino devices and another IoT platform running Arduino core such as the ESP32. The performance evaluation of this protocol in Arduino Mega shows that the INIT phase's average execution time is 26.83 milliseconds. The key agreement is 13.50 milliseconds, and the encryption-decryption of telemetry and telecommand messages requires 25 milliseconds execution time. The protocol performance evaluation in ESP32 has an average execution time for INIT phase 44.63 milliseconds. The key agreement phase, 13.90 milliseconds, and the encryption and decryption of telemetry and telecommand messages requires an execution time of 17.10 milliseconds.

A Trusted IoT Communication Architecture Based on Blockchain and Named Data Network

The trusted cross-domain sharing capability of the Internet of Things (IoT) terminals needs to be improved urgently. Aiming at the technical issues of routing overhead and authentication security of data sharing of IoT devices, this paper proposes a trusted transmission architecture for network-chain collaboration. Introduce the mobile edge computing (MEC) platform in named data network (NDN) to deploy blockchain light nodes and improve the PBFT consensus algorithm. Besides, a cross-domain authentication algorithm based on elliptic curve encryption is proposed. Simulation results show that the improved PBFT and cross-domain authentication algorithm can effectively improve IoT cross-domain sharing capabilities.

An Efficient Secure Cryptography Scheme for New ML-based RPL Routing Protocol in Mobile IoT Environment

Internet of Things (IoT) offers reliable and seamless communication for the heterogeneous dynamic lowpower and lossy network (LLNs). To perform effective routing in IoT communication, LLN Routing Protocol (RPL) is developed for the tiny nodes to establish connection by using deflaut objective functions: OF0, MRHOF, for which resources are constraints like battery power, computation capacity, memory communication link impacts on varying traffic scenarios in terms of QoS metrics like packet delivery ratio, delay, secure communication channel. At present, conventional Internet of Things (IoT) are having secure communication channels issue for transmission of data between nodes. To withstand those issues, it is necessary to balance resource constraints of nodes in the network. In this paper, we developed a security algorithm for IoT networks with RPL routing. Initially, the constructed network in corporates optimizationbased deep learning (reinforcement learning) for route establishment in IoT. Upon the establishment of the route, the ClonQlearn based security algorithm is implemented for improving security which is based onaECC scheme for encryption and decryption of data. The proposed security technique incorporates reinforcement learning-based ClonQlearnintegrated with ECC (ClonQlearn+ECC) for random key generation. The proposed ClonQlearn+ECCexhibits secure data transmission with improved network performance when compared with the earlier works in simulation. The performance of network expressed that the proposed ClonQlearn+ECC increased the PDR of approximately 8% - 10%, throughput of 7% - 13%, end-to-end delay of 5% - 10% and power consumption variation of 3% - 7%.

Machine learning-based fast transcoding for low-power video communication in Internet of Things

Machine learning-based fast transcoding for low-power video communication in Internet of Things

QoS‐aware IoT networks and protocols: A comprehensive survey

SummaryThe vast number of smart cloud applications that communicate with the “things” over a variety of physical networks and communication protocols contribute to the rise of complexity in Internet of Things (IoT) systems. The diversity of end‐user requirements related to the volume of generated data, its variety, and the velocity of its transmission makes quality of service (QoS) provisioning even more crucial and challenging in IoT. This paper provides a comprehensive and up‐to‐date survey of QoS support in IoT networks and communication protocols. An analysis of the QoS‐aware IoT architectures, layer‐dependent QoS metrics, and network resource optimization methods utilized in IoT systems are given. The limitations of the current state‐of‐the‐art studies for efficient delivery of QoS metrics are discussed. The paper concludes with future research directions on end‐to‐end QoS provisioning in IoT.