Internet Of Things Communication Research Articles

SecIoTComm: An Actor-Based Model and Framework for Secure IoT Communication.

Internet of Things (IoT) ecosystems are becoming increasingly ubiquitous and heterogeneous, adding extra layers of complexity to secure communication and resource allocation. IoT computing resources are often located at the network edge and distributed across many heterogeneous sensors, actuators, and controller devices. This makes it challenging to provide the proper security mechanisms to IoT ecosystems in terms of manageability and maintainability. In an IoT ecosystem, computational resources are naturally distributed and shareable among their constituency, which creates an opportunity to distribute heavy tasks to them. However, resource allocation in IoT requires secure and complex communication and coordination mechanisms, which existing ones do not adequately support. In this paper, we present Secure Actor-based Model for IoT Communication (SecIoTComm), a model for representing secure IoT communication. SecIoTComm aims to represent secure IoT communication properties and design and implement novel mechanisms to improve their programmability and performance. SecIoTComm separates the communication and computation concerns, achieving design modularity in building IoT ecosystems. First, this paper presents the syntax and operational semantics of SecIoTComm. Then, we present an IoT framework implementing the key concepts of the model. Finally, we evaluate the developed framework using various performance and scalability metrics.

Digital Keying Enabled by Reconfigurable 2D Modulators.

Energy, area, and bandwidth efficient communication primitives are essential to sustain the rapid increase in connectivity among internet-of-things (IoT) edge devices. While IoT edge-sensing, edge-computing, and edge-storage have witnessed innovation in materials and devices, IoT edge communication is yet to experience such transformation. The aging silicon (Si)-based complementary metal-oxide-semiconductor (CMOS) technology continues to remain the mainstay of communication devices where they are used to implement amplitude, frequency, and phase shift keying (amplitude-shift keying [ASK]/frequency-shift keying [FSK]/phase-shift keying [PSK]). Keying allows digital information to be communicated over a radio channel. While CMOS-based keying devices have evolved over the years, their hardware footprint and energy consumption are major concerns for resource constrained IoT communication. Furthermore, separate circuit designs and hardware elements are needed for each keying scheme and achieving multibit modulation to improve bandwidth efficiency remains a challenge. Here, a reconfigurable modulator is introduced that exploits unique ambipolar transport and programmable Dirac voltage in ultrathin MoTe2 field-effect transistors to achieve ASK, FSK, and PSK modulation. Furthermore, by integrating two programmed MoTe2 field-effect transistors, multibit data modulation is demonstrated, which improves the bandwidth efficiency by 200%. Finally, a frequency quadrupler is also realized exploiting the unique "double-well" transfer characteristic.

Cooperative self-scheduling routing protocol based IOT communication for improving life time duty cycled energy efficient protocol in SDN controlled embedded network

Internet of Things (IoT) is a developing interconnecting medium in a wireless network through internet of communication in network devices like sensors, smart devices and controllers. This interconnect the devices via the internet to make communication. Due to large sectors IoT devices and Software-Defined Networking (SDN) controllers contains energy constraints in improper routing strategies. Specifically in health care industries, the patient monitoring data's consumes higher energy to reduce the lifetime of network communication leads data losses during the IoT devices collecting the information become loss.The embedded communication are relatively controls through the Software defined network to maintain the energy consumption to improve the lifetime of energy efficient network. But leastpropagation energy losses arise communication losses because of traffic, packet losses, latency beyond with throughput limits. The main problem is Passive Infrared Sensor (PHR) sensor nodes and Relay nodes attain more energy because of non-uniform node displacement in routing in uneven energy drainage. So resolving these problems, in this system propose a cooperative self-scheduling routing protocol based IoT communication for improving energy efficient SDN controlled embedded network.Initially this construct the Neighbor Distance Discovery Protocol (NDDP) to identify the minimum depletion path by identifying Sensor closest route to the relay node. Then construct the Multipath Cooperative Self-Scheduling Protocol (MCSSP) to formalize the non-traffic route. By estimating the energy consumption on each sensor node on routing medium, this considers the prior routing communication rate by calculating average transmission delay, sensor absorption rate, power consumption, node response rate, and packet loss rate. UsingLife Time Duty Cycled Energy Efficient Protocol (LTDCEEP) to calculate the delimit of the feature limits along the network threshold latency energy limits. Based on the transition limit the weights of the nodes are estimated by energy consumption from each duty cycle by consuming more transmission energy, the route be scheduled to optimize the communication delay to reduce the power consumption in the sense of responsive nodes. This improves the power consumption rate, latency, power quality in transmission, throughput ratio in SDN network communication.

Full-duplex wireless deep ultraviolet light communication.

With recent advancements in deep ultraviolet (DUV) light-emitting diodes (LEDs) and solar-blind photodetectors, wireless DUV light communication is emerging as a novel technique, which can extend transmission ranges and avoid solar interference. Herein, a full-duplex, real-time wireless light communication system using 275 nm DUV LEDs is proposed. We adopted high-power DUV LEDs and designed a high-speed transmitter, a high-sensitivity receiver, and a main processing unit for the system. Furthermore, the DUV communication system, using a Reed-Solomon (RS) encoder and an on-off keying (OOK) modem with frequency control, achieves a 10 Mbit/s bidirectional data transmission rate within 5 m in free space, while a full-duplex video communication link is formed. The encapsulated DUV communication system described in this Letter provides a feasible scheme for confidential and anti-electromagnetic interference communication in Internet of Things (IoT) applications.

A High-Gain and Wideband MIMO Antenna for 5G mm-Wave-Based IoT Communication Networks

In this paper, an antenna with a multiple-input, multiple-output (MIMO) configuration is demonstrated for mm-wave 5G-based Internet of Things (IoT) applications. The two antenna elements are arranged next to each other to form a two-port antenna system such that significant field decorrelation is achieved. Moreover, a dielectric layer is backed by an eventual multiport system to amend and analyze the radiation characteristics. The overall size of the MIMO configuration is 14 mm × 20 mm, and the operation bandwidth achieves ranges from 16.7 to 25.4 GHz, considering the −10 dB criterion with a maximum isolation of more than −30 dB within the operating band. The peak gain offered by the antenna system is nearly 5.48 dB, and incorporating a dielectric layer provides an increase in the gain value to 8.47 dB. Within the operating band, more than 80% total efficiency is observed, and analysis shows several MIMO performance metrics with favorable characteristics. The compactness of the proposed design with high isolation, improved gain, and wideband features make it a suitable candidate for mm-wave-based 5G applications.

Joint Optimization of Trajectory and Resource Allocation in Secure UAV Relaying Communications for Internet of Things

As unmanned aerial vehicle (UAV) communication has been widely used in all walks of life, its secrecy issue has also received more and more attention. This article studies the physical-layer security of UAV relaying communication system in multiterminal Internet of Things (IoT) scenarios. Specifically, while receiving the information from the ground base station, the UAV safely forwards the information to one of a group of IoT terminals in the presence of an eavesdropper. Under the constraints of information causality and UAV mobility, our goal is to maximize the minimum average secrecy rate among all IoT terminals. Based on the nonconvex problem, this article proposes a high-efficiency algorithm for joint optimization of UAV trajectory and resource allocation. The simulation results show that the proposed algorithm not only effectively improves information secrecy of IoT terminals, but also enhances the fairness of communication between the IoT terminals.

MOSAIC: Multiobjective Optimization Strategy for AI-Aided Internet of Things Communications

Future Internet of Things (IoT) communication trends toward heterogeneous services and diverse quality-of-service requirements pose fundamental challenges for network management strategies. In particular, multiobjective optimization (MOO) is necessary in resolving the competition among different nodes sharing limited wireless network resources. A unified coordination mechanism is essential such that individual nodes conduct the opportunistic maximization of heterogeneous local objectives for efficient distributed resource allocation. To such a problem, this article proposes an artificial intelligence (AI)-based framework, which is termed as MOO strategy for AI-aided IoT communications (MOSAIC). This framework enables to tackle numerous MOO tasks in IoT network management with simple reconfiguration of learning rules. In this strategy, a component unit associated with an individual network node includes a pair of deep neural networks (DNNs) to learn optimal local functions responsible for calculation and distributed coordination, respectively. The resultant AI module swarm called DNN tiles realizes the node cooperation that collectively seeks distributed MOO calculation rules. The advantage of MOSAIC is characterized by Pareto tradeoffs among conflicting performance metrics in diverse wireless networking configurations subject to severe interference and distinct criteria for multiple targets.

Intelligent Sensor Network Using Internet of Things in Urban Community Network Governance

The purpose is to conform to the development of the times and study a new way of urban community network governance. First, the ways of governance, social governance, urban community governance, and urban community network governance are discussed. Next, the intelligent sensor function of Internet of things (IoT) technology is applied to realize the communication between IoT and urban community network governance. An urban community network governance system based on an intelligent sensor network is established. The system combines Kalman filtering to realize the dynamic monitoring of the mobile Application (APP) of the urban community residents and can update and process the collected information in real time. Finally, a survey is conducted on the satisfaction of residents of a community in Jiaozuo City, Henan Province, in 2019 on community network governance under the IoT. The efficiency and ability of urban community network governance under different network modes are compared and analyzed. The results show that the community has improved the needs of community residents through IoT network governance in multiple aspects, but the community service level of culture is not high. The community needs to strengthen the network publicity of community culture to improve the cultural level of the community. Meanwhile, in urban community governance, the comprehensive governance ability of the network governance model under network organization management is the highest, with an average of 50.93%. It shows that the established system is beneficial to the construction of urban community network governance. This exploration provides the direction for the IoT urban community network governance and constructs the urban community network governance system based on the intelligent sensor network.

Empowering secure transmission for downlink of multiple access system relying non-orthogonal signal multiplexing

The growth of internet-of-things (IoT) inspired use cases in different run of the mill environments such as cities, industries, healthcare, agriculture, and transportation, has led to a greater desire for safer IoT data gathering and storage. However, securing IoT is challenging due to form-factor, complexity, energy, and connectivity limitations. Conventional coding-based security techniques are unsuitable for ultra-reliable low-latency and energy-efficient communication in IoT. Numerous research studies on physical layer security (PLS) techniques for fifth generation (5G) have emerged recently, but not all of the solutions can be used in IoT networks due to complexity limitations. Non-orthogonal multiple access (NOMA) is billed as a possible technology to solve connectivity and latency requirements in IoT. In this study, we exploit the power allocation characteristics of NOMA to enhance security in a downlink deviceto-device (D2D) decode and forward (DF) IoT network infiltrated by an eavesdropper. Our performance metric of choice is the secrecy outage probability (SOP). We formulate exact SOP results for different users. Simulation results demonstrate the positive impact of NOMA on SOP in a D2D IoT-NOMA network.

Recent Advances in Artificial Intelligence for Wireless Internet of Things and Cyber–Physical Systems: A Comprehensive Survey

Advances in artificial intelligence (AI) and wireless technology are driving forward the large deployment of interconnected smart technologies that constitute cyber–physical systems (CPSs) and Internet of Things (IoT) for many commercial and military applications. CPS is characterized by communication, computing, and control engineering based on a large volume of data originating from various devices, plants, sensors, etc. Wireless technologies have enabled the ease of networking and communications for both CPS and IoT, by providing massive and critical connectivity and control mechanisms. However, they are prone to challenges, such as low latency, throughput, and scheduling. Recent research trends focus on how to intelligently use data from CPS units to enhance wireless connectivity in CPS. AI tools, particularly AI systems and machine learning (ML) algorithms, have been widely applied in the literature to develop efficient schemes for wireless CPS/IoT. This article presents a review on the role of AI in wireless networking for CPS and IoT. In particular, we focus on ML paradigms, such as transfer learning (TL), distributed learning, and federated learning, that have evolved as building blocks for the utilization of large data for learning, adaptation, and predictions in CPS and IoT systems that leverage wireless networking. Furthermore, we also highlight challenges faced by current and future wireless networks pertaining to CPS/IoT, which are yet to be addressed.

Global IoT Mobility: A Path Based Forwarding Approach

With the huge proliferation of mobile Internet of Things (IoT) devices such as connected vehicles, drones, and healthcare wearables, IoT networks are promising mobile connectivity capacity far beyond the conventional computing platforms. The success of this service provisioning is highly dependent on the flexibility offered by such enabling technologies to support IoT mobility using different devices and protocol stacks. Many of the connected mobile IoT devices are autonomous, and resource constrained, which poses additional challenges for mobile IoT communication. Therefore, given the unique mobility requirements of IoT devices and applications, many challenges are still to be addressed. This paper presents a global mobility management solution for IoT networks that can handle both micro and macro mobility scenarios. The solution exploits a path-based forwarding fabric together with mechanisms from Information-Centric Networking. The solution is equally suitable for legacy session-based mobile devices and emerging information-based IoT devices such as mobile sensors. Simulation evaluations have shown minimum overhead in terms of packet delivery and signalling costs to support macro mobility handover across different IoT domains.

RIS-Assisted UAV Communications for IoT With Wireless Power Transfer Using Deep Reinforcement Learning

Many of the devices used in Internet-of-Things (IoT) applications are energy-limited, and thus supplying energy while maintaining seamless connectivity for IoT devices is of considerable importance. In this context, we propose a simultaneous wireless power transfer and information transmission scheme for IoT devices with support from the reconfigurable intelligent surface (RIS)-aided unmanned aerial vehicle (UAV) communications. In particular, in the first phase, IoT devices harvest energy from the UAV through wireless power transfer; and then in the second phase, the UAV collects data from the IoT devices through information transmission. To characterise the agility of the UAV, we consider two scenarios: a hovering UAV and a mobile UAV. Aiming at maximising the total network sum-rate, we jointly optimise the trajectory and the power allocation of the UAV, the energy harvesting scheduling of IoT devices, and the phase-shift matrix of the RIS. We formulate a Markov decision process and propose two deep reinforcement learning algorithms to solve the optimisation problem of maximising the total network sum-rate. Numerical results illustrate the effectiveness of the UAV’s flying path optimisation and the network’s throughput of our proposed techniques compared with other benchmark schemes. Given the strict requirements of the RIS and UAV, the significant improvement in processing time and throughput performance demonstrates that our proposed scheme is well applicable for practical IoT applications.

Machine Learning in IoT Networking and Communications

The fast and wide spread of Internet of Things (IoT) applications offers new opportunities in multiple domains but also presents new challenges [...]

Perancangan dan Implementasi Encoder dan Decoder CRC-8 untuk Pendeteksian Error pada Transmisi Data antar Perangkat IoT

Cyclic Redundancy Check (CRC) is a widely used error detection code in digital communication systems to detect errors in data transmitted between transmitter and receiver. With the development of Internet-of-Things (IoT) technology, where many devices communicate wirelessly, the application of error detection codes such as CRC is essential to obtain reliable communication. The computation of CRC can be implemented in hardware using a linear feedback shift register (LFSR) or in software with shift and bitwise exclusive OR (XOR) operations for polynomial division. Due to the frequent use of CRC in wireless communication for IoT, research for the design and implementation of CRC continues to this day. This study aims to design and implement the encoder and decoder of CRC-8 with generator polynomial 0x07 on IoT devices. Two algorithms for computing the CRC, namely bitwise computation using LFSR and another one using lookup tables, are implemented on the Arduino Uno R3 board. Two Arduino boards connected in serial were used to verify the encoder and decoder implementation in the data transmission and error detection process. The experiment results showed that the encoder could correctly calculate the CRC value from the input data. Furthermore, the computation using lookup tables takes about four times faster than the bitwise method but requires more memory. In contrast, the bitwise computation method requires less memory but slower computation time. When testing using input data of 128 characters, the bitwise-based encoder requires a computation time of 2.37 milliseconds, while the lookup table-based encoder utilizing a lookup table requires a computation time of 0.5 milliseconds. In the error detection test, the results demonstrated that the receiver could detect errors in the transmitted data with a percentage of 100%.

Secured Protocol with Collaborative IoT-Enabled Sustainable Communication Using Artificial Intelligence Technique

In recent years, 5G and the Internet of Things (IoT) have been integrated into a variety of applications to support sustainable communication systems. In the presence of intermediate hardware, IoT devices collect the network data and transfer them to cloud technologies. The interconnect machines provide essential information to the connected devices over the Internet. Many solutions have been proposed to address the dynamic and unexpected characteristics of IoT-based networks and to support smart developments. However, more work needs to explore efficient quality-aware data routing for distributed processing. Additionally, to handle the massive amount of data created by smart cities and achieve the transportation objectives for resource restrictions, artificial intelligence (AI)-oriented approaches are necessary. This research proposes a secured protocol with collaborative learning for IoT-enabled sustainable communication using AI techniques. This approach increases systems’ reaction times in critical conditions and also controls the smart functionalities for inter-device communication. Furthermore, fitness computing can help in balancing the contribution of quality-aware metrics to achieve load balancing and efficient energy consumption. To deal with security, IoT communication is broken down into stages, resulting in a more dependable network for unpredictable environments. The simulation results of the proposed protocol have been compared to existing approaches and improved the performance of response time by 17%, energy consumption by 14%, number of re-transmissions by 16%, and computing overhead by 16%, under a varying number of nodes and data packets.

IEEE 802.15.4g/4x-Based Orthogonal Frequency-Division Multiplexing Transmission Scheme for Wide-Area and Mobile IoT Communication Systems

Radio communication systems using the frequency-shift keying (FSK) scheme standardized by the IEEE 802.15.4g standard can realize low-power consumption and highly reliable communications and are mainly adopted for smart metering as part of the Internet of Things (IoT). However, smart metering has recently broadened its application expectations, with broadband and long-distance communications now required. To fulfill these requirements, orthogonal frequency-division multiplexing (OFDM) has been incorporated into the IEEE 802.15.4g and 802.15.4x standards. However, comprehensive transmission performance analysis of OFDM under static and multipath fading environments has not been conducted. In this article, we first evaluated the packet error rate (PER) of OFDM assuming fixed communications using computer simulations. We then evaluated the transmission distance of OFDM and showed that OFDM can communicate at transmission distances up to 3.0 times longer than those specified in IEEE 802.15.4g FSK. Next, the PER in a mobile communication environment was evaluated using computer simulations. When only the long training field (LTF) was used to estimate multipath radio channels, the required PER of 10% was not achieved, even in a mobile communication environment at a speed of several km/h. To solve this problem, we proposed a receiving scheme which included a new channel estimation scheme. This scheme successively utilized not only the LTF but also pilot signals inserted into the transmitter to estimate radio propagation characteristics. Computer simulation results showed that the required PER was achieved even in a mobile communication environment at 30–40 km/h by using the proposed scheme.

Designing green IoT communication by adaptive spotted hyena tunicate swarm optimization‐based cluster head selection

AbstractThe Internet of Things (IoT) has completely transformed the digital as well as virtual worlds of interconnected objects. In today's environment, the IoT describes a critical enabler for a broader variety of applications. Energy is at the center of the smart IoT application that allows the sensors to function. The sensors' ability to perform efficiently is hampered by rapid energy loss. To avoid the rapid loss of energy from sensors in the IoT, and energy‐effective method is necessary. Because heuristic approaches are not ideal for these issues and may turn into NP‐hard problems, meta‐heuristic techniques are mainly successful in solving various problems with near‐optimal solutions. This paper intends to develop a novel clustering protocol in Green IoT using a well‐performing novel hybrid adaptive meta‐heuristic algorithm. Here, an energy‐efficient clustering is performed by the hybrid spotted hyena optimization (SHO) and tunicate swarm algorithm (TSA) referred to as adaptive spotted hyena tunicate swarm optimization (ASHTSO) by deriving a multi‐objective function with “distance, energy, delay, security, and QoS”. The simulation findings show that the suggested technique outperforms existing algorithms significantly.

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.