Joint Beamforming Design for Secure RIS-Assisted IoT Networks

A novel framework of the uncrewed aerial vehicle (UAV)-mounted active simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) communications with the nonorthogonal multiple access (NOMA) is proposed for Internet of Things (IoT) networks. In particular, an active STAR-RIS is deployed onboard to enhance the communication link between the base station (BS) and the IoT devices, and NOMA is utilized for supporting the multidevice connectivity. Based on the proposed framework, a system sum rate maximization problem is formulated for the joint optimization of the active STAR-RIS beamforming, the UAV trajectory design, and the power allocation. To solve the nonconvex problem with highly coupled variables, an alternating optimization (AO) algorithm is proposed to decouple the original problem into three subproblems. Specifically, for the active STAR-RIS beamforming, the amplification coefficient, the power-splitting ratio, and the phase shift are incorporated into a combined variable to simplify the optimization process. Afterward, the penalty-based method is invoked for handling the nonconvex rank-one constraint. For the UAV trajectory design and the power allocation subproblems, the successive convex optimization method is applied for iteratively approximating the local-optimal solution. Numerical results demonstrate that: 1) the proposed algorithm achieves superior performance compared to the benchmarks in terms of the sum rate and 2) the UAV-mounted active STAR-RIS can effectively enhance the channel gain from the BS to the IoT devices by the high-quality channel construction and the power compensation.

In this work, we investigate the performance of a multi-active reconfigurable intelligent surface (ARIS)-aided full-duplex (FD) secure Internet of Things (IoT) network with imperfect Channel State Information (iCSI) in the presence of an eavesdropper (Eve). We formulate a power minimization problem while ensuring the minimum Quality of Service (QoS) of all the nodes within available resource constraints considering the norm-bounded iCSI. To tackle the nonconvex nature of the formulated problem, we adopt analytical methods, such as semidefinite programming, S-procedure, and general sign-definiteness, and propose an alternating optimization (AO)-based algorithm that jointly optimizes the receive and transmit beamforming at Alice, power allocation at each uplink user, and active beamforming at ARIS. The efficacy and convergence of the proposed algorithm are validated via extensive numerical simulation. The potential of ARISs, compared to its passive RIS (PRIS) counterpart, toward achieving a robust and secure FD system is demonstrated. Finally, we discuss the impact of key parameters, such as maximum amplification factor, RIS, and CSI error on the performance of the considered system.

Since the Internet of Things (IoT) will be entwined with everything we use in our daily life, the consequence of security flaws escalates. Smart objects will govern most of the home appliances and car engines yielding potential disaster scenarios. In this context, successful attacks could lead to chaos and scary scenarios (www.darkreading.com). Unprotected personal information may expose sensitive and embarrassing data to the public and attacks may threaten not only our computers and smart devices, but our intimacy and perhaps our lives too. Because persons and objects will be bonded with each other, user consent becomes critical. Therefore, thing, object, and user Identity will be the focus of future IoT security solutions, yielding a Trust, Security, and Privacy (TSP) paradigm, which may constitute the Achilles' heel of IoT. While security issues are quite straightforward, mainly from background knowledge, privacy issues are far more complex. Privacy constitutes a rather challenging task, even for the m...

This paper studies a cooperative cognitive radio network (CRN), where the primary system leases the licensed band to the secondary system for a fraction of time in exchange for the secondary user (SU) acting as a decode-and-forward relay. Our aim is to determine the cooperative resource allocation strategy among the primary user (PU) and SU so as to minimize both the total power consumption and the leased time. Considering the delay-sensitive applications in CRN, the proposed policy ensures delay-related quality-of-service (QoS) requirements of both PU and SU. To this end, a multi-objective optimization problem of power and time allocation is formulated based on the effective capacity. Via employing weighting method the optimization problem is transformed to a convex one and solved based on the lagrangian dual method. The proposed policy has been shown to be deterministic functions of delay QoS exponent and channel fading states. In particular, the power allocation is achieved by multi-level QoS based water-filling policy. Moreover, numerical results are provided to demonstrate the performance of the proposed resource allocation policy.

With the widespread penetration of the Internet of Things in various fields, its secure communication issues have become a key factor restricting the further development of the Internet of Things. This study deeply analyzes the many challenges faced by secure communications in the Internet of Things, including security risks of IoT cards , difficulties at the device and network levels, etc. The role of key technologies such as identity authentication, encryption, security analysis and threat prediction in ensuring secure communications in the Internet of Things is elaborated in detail. At the same time, the development trend of secure communications in the Internet of Things is discussed, such as the gradual improvement of standards and specifications and the application prospects of new technologies in security protection. Finally, strategies for dealing with secure communications issues in the Internet of Things from the technical level, management and cooperation level are proposed, aiming to provide comprehensive reference and guidance for research and practice in the field of secure communications in the Internet of Things, and promote the healthy and sustainable development of the Internet of Things industry.

The rapid advancement of Internet of Things (IoT) technology has brought convenience to people’s lives; however further development of IoT faces serious challenges, such as limited energy and shortage of network spectrum resources. To address the above challenges, this study proposes a simultaneous wireless information and power transfer IoT adaptive time slot resource allocation (SIATS) algorithm. First, an adaptive time slot consisting of periods for sensing, information transmission, and energy harvesting is designed to ensure that the minimum energy harvesting requirement is met while the maximum uplink and downlink throughputs are obtained. Second, the optimal transmit power and channel assignment of the system are obtained using the Lagrangian dual and gradient descent methods, and the optimal time slot assignment is determined for each IoT device such that the sum of the throughput of all devices is maximized. Simulation results show that the SIATS algorithm performs satisfactorily and provides an increase in the throughput by up to 14.4% compared with that of the fixed time slot allocation (FTS) algorithm. In the case of a large noise variance, the SIATS algorithm has good noise immunity, and the total throughput of the IoT devices obtained using the SIATS algorithm can be improved by up to 34.7% compared with that obtained using the FTS algorithm.

The internet of things (IoT) is now advancing at a tremendous pace. Devices in the IoT can connect and communicate in a fully autonomous mode. Because of its autonomy and numerous infractions of security policies, the IoT network is subject to various security risks. However, because of this autonomy, dependable security and storage mechanisms for authentication to share data across IoT devices are essential. Furthermore, since it has initially been popularized in Bitcoin, blockchain development has accelerated. Blockchain can be used to solve security challenges in the IoT. Making secure communication between IoT devices is one approach to accomplish this. To address this issue, we present an effective technique for intranetwork and internetwork device‐to‐device secure communication that enables IoT device identification and authentication while also providing secure communication in an open environment. The method also provides data integrity in addition to authentication and secure communication. We have presented informal security analysis, which confirms that there is no important security threats in our protocol. The performance of the protocol is also better in comparison with previously published papers.

Unmanned aerial vehicle (UAV) communication has become a prominent technology that can effectively assist in IoT systems. The inherent features of UAV such as mobility, flexibility, and fast deployment make it preferable for emergency Internet of Things (IoT) applications. In this paper, we consider a multi-UAV assisted wireless network to support uplink communication for IoT devices distributed over a disaster area. The network involves two types of UAVs: sector UAV (SU) and anchor UAV (AU). The SU hovers at a fixed height over the sector around the temporary base station (TBS), collects the information from the respective IoT devices and relays them to the TBS via AU. The AU revolves continuously around the TBS and relays the information between the SUs and TBS periodically. We aim to improve the uplink capacity of the system. To achieve this, we employ non-orthogonal multiple access (NOMA), where we jointly optimize the positions of SUs and the power control of IoT devices. We propose a two-step approach to solve this. First, we optimize the position of SU in each sector by minimizing the sum distances of SU from the respective IoT devices. Then, by considering the optimal SU location, we optimize the transmit power of IoT devices using Lagrange dual method. Finally, the experimental results show that the proposed scheme improves the system capacity by 22% compared to the state-of-the-art schemes.

With the rapid development of Internet of Things (IoT) services, technologies that leverage multimedia computer communication for information sharing in embedded systems have become a research focus. To address the challenges of low spectral efficiency and poor network flexibility in multimedia computer communications, this paper proposes a resource allocation scheme based on parallel Convolutional Neural Network (CNN). The scheme optimizes the base station beamforming vector and the Reconfigurable Intelligent Surface (RIS) phase shifts to maximize the secure transmission rate for cellular users (CUs), while ensuring normal and secure communication for device-to-device (D2D) users. First, to mitigate interference caused by D2D users reusing CU spectrum resources, the RIS phase shifts and beamforming vectors are optimized to suppress interference and enhance system secrecy rates. Second, to maximize the CU secrecy rate, the paper proposes a parallel CNN-based resource allocation model that considers base station transmission power, RIS reflection coefficients, and D2D communication rate constraints, incorporating multi-scale residual modules in the convolutional layers of the model. Simulation results demonstrate that the proposed CNN-based resource allocation scheme significantly improves the secrecy rate of embedded system communications, ensuring secure multimedia computing, and outperforms traditional methods.

The paper proposed a novel intelligent transmission surface (ITS) aided transmitter in an intelligent reflection surface (IRS) assisted multiuser multiple-input multiple-output (MIMO) network. The ITS deployed in the transmitter architecture can reduce the power consumption in signal beamforming at the base station (BS), and the IRS can help the information transfer from the ITS-aided transmitter to the users. We first maximize the weighted sum rate (WSR) of the users by jointly designing the beamforming vector at the BS and the phase shifts of ITS and IRS. To solve this non-convex optimization problem, we propose an effective algorithm in which the Lagrangian dual transform, the alternative optimization (AO) algorithm and the quadratic transform (QT) method are adopted to simplify the objective function. Then, the bisection search and the alternating direction method of multipliers (ADMM) algorithm are considered to design the optimal beamforming vector and phase shifts of ITS and IRS, respectively. Furthermore, the paper explores the energy efficiency (EE) maximization problem to emphasize the value of the ITS-assisted transmitter in terms of power savings. Finally, we compare the simulation results to various state-of-the-art techniques to see how much better the proposed algorithm is in terms of WSR and EE.

In this paper, energy efficiency (EE) is maximized for the reconfigurable intelligent surface (RIS) aided millimeter-Wave (mmWave) networks with non-orthogonal multiple access (NOMA) and multiple mobile devices. To this end, we first propose the EE optimization, under the constraints of maximum power, minimal rate of devices and constant modulus of beamforming (BF) vectors. Then, the joint resource allocation scheme of power allocation (PA) and BF is designed. Specifically, given PA, an effective iterative algorithm based on the majorizationminimization, concave-convex procedure and block coordinate descent (BCD) is presented to obtain closed-form solutions of suboptimal passive BF (PBF) and analog BF (ABF) for each iteration. Then, given PBF and ABF, an effective iterative algorithm based on the successive convex approximation, BCD and Dinkelbach methods is derived to achieve suboptimal closedform PA for each iteration. By incorporating these two algorithms into the BCD method, a joint optimization algorithm for EE maximization is presented. As a result, joint resource allocation of PA, PBF and ABF is attained. Besides, the convergence and complexity of the algorithms are analyzed. For comparison, the benchmark scheme based on the multidimensional search method and artificial bee colony algorithm is also presented. Simulation results show that the proposed joint scheme is effective and higher EE can be obtained with lower complexity.

Ubiquitous sensing enabled networks have transformed the living chores into a modern living standard in which each move is automated. The dependency of humans on IoT is increasing every day, so the IoT networks must be efficient and secure. Secure communication in the Internet of Things (IoT) networks is a hotly debated research issue. Cryptography is one of the solutions for secure data transmission. In IoT networks, designing a cryptography algorithm is a major challenge for resource constraint environment. So, when we talk about cryptography in IoT, this term transforms into “lightweight cryptography.” This paper presents a review of lightweight cryptography algorithms in the form of a stream cipher, block cipher, symmetric encryption, and asymmetric encryption.

The Internet of Things (IoT) devices is able to communicate with another IoT devices in heterogeneous environments. They are also able to create a network by their own using wireless capabilities without a centralized approach. This network is called wireless mobile ad hoc network (MANET). The MANET facilitate IoT devices to communicate with each other in an infrastructure less network of the Internet of Things environment. The IoT devices can dynamically join, create connection and exchange information among them. The role of cloud in this framework is to store and process the data by IoT devices. The communication Security has been raised as one of the most critical issues of cloud computing where resolving such an issue would result in a constant growth in the use and popularity of cloud. Our purpose of this study is to create a framework for providing the communication in an integrated framework of Cloud and MANET among IoT devices. Our main contribution links a new study that integrates cloud computing to the MANET and provides communication to the internet of things. This study can be used in the framework of the Internet of Things in future.

The Internet of Things (IoT) is evolving in various sectors such as industries, healthcare, smart homes, and societies. Billions and trillions of IoT devices are used in e-health systems, known as the Internet of Medical Things (IoMT), to improve communication processes in the network. Scientists and researchers have proposed various methods and schemes to ensure automatic monitoring, communication, diagnosis, and even operating on patients at a distance. Several researchers have proposed security schemes and approaches to identify the legitimacy of intelligent systems involved in maintaining records in the network. However, existing schemes have their own performance issues, including delay, storage efficiency, costs, and others. This paper proposes trusted schemes that combine mean and subjective logic aggregation methods to compute the trust of each communicating device in the network. Additionally, the network maintains a blockchain of legitimate devices to oversee the trusted devices in the network. The proposed mechanism is further verified and analyzed using various security metrics, such as reliability, trust, delay, beliefs, and disbeliefs, in comparison to existing schemes.

Machine-to-Machine (M2M) communications have received considerable attentions in recent years. By allowing machine type communications devices (MTCDs) to communicate with each other or with infrastructures under reduced human intervention, M2M communications are expected to offer a large variety of MTCD-related applications. While the vision of M2M communications is quite promising, the diverse quality of service (QoS) requirements and the complicated network architecture pose challenges and difficulties to resource allocation, cell selection and random access schemes, etc. In this paper, we take into account the impacts of random access on the transmission performance of the MTCDs and examine the energy efficiency of each MTCD. Applying the concept of utility function, we define network utility function and formulate the problem of joint cell selection and power allocation as a network utility function maximization problem. By applying iterative method and Lagrange dual method, the formulated optimization problem is solved and the optimal joint cell selection and power allocation strategy can be obtained. Simulation results demonstrate the effectiveness of the proposed algorithm.