Energy and spectral efficient solutions are indispensable to the success of Internet of Things (IoT). The design and development of energy and spectral efficient solutions for IoT are very challenging mainly because of the large-scale deployment of a massive number of sensors and devices. Energy harvesting and cognitive radios are considered as promising technologies for energy and spectral efficiency, respectively. In this paper, we propose an energy and spectrum efficient scheme for cognitive radio sensor networks (CRSN). We present an architecture of CRSNs for IoT, in which sensor nodes can access the spectrum opportunistically and harvest energy from ambient RF sources. We then propose an energy management scheme that consists of 1) energy-aware mode switching strategy which allows sensor nodes to perform dedicated energy harvesting based on their current energy level and 2) cluster head selection algorithm which considers current and average of past energy levels of sensor nodes to achieve a balance between network performance and lifetime. Furthermore, for reliable intra-cluster reporting, we propose a channel management strategy to assign the best quality channel to the sensor nodes in terms of stability and reliability. Extensive simulation results demonstrate the effectiveness of the proposed energy and spectrum efficient scheme and show superiority over existing schemes.

Internet-of-Things based sensor networks have gained unprecedented popularity in recent years and they become crucial for supporting high data rate real-time applications. For efficient data transmission within IoT networks, it is necessary that each IoT node learns and adapts itself to recent time/ spectral characteristics of channels to maximize the throughput and perform channel swapping wherever required. Many researchers have proposed channel allocation and channel quality measurement protocols within multichannel sensor networks. However, to the best of our knowledge, there is no literature available that proposes an automated and adaptive protocol that can learn and adapt according to changing channel characteristics in IoT network for achieving maximum data transmission and throughput. Therefore, this paper proposes a fully automated self-learning and adaptive protocol which can automatically transmit multiuser data by efficiently utilizing channel time/spectral characteristics. The proposed protocol is unique as it learns and adapts itself to the increasing network density based upon the network metrics. It also allows each node within IoT network to automatically detect the neighboring channel attributes so that they can swap channels to achieve maximum data transfer. This is accomplished by continuously extracting distinct features from the network topology. After extracting these features, the proposed protocol efficiently selects the best channel for an incoming node, provides the best channel utilization based upon its time/spectral attributes, detects and allocates the unused spectrum of neighboring channels through multistage Gaussian radial basis function and multilayer perceptron-based non-linear support vector machines (SVM) classification model. Simulation results demonstrate the supremacy of the proposed protocol in terms of throughput, successful reporting probability, average blocking probability, fairness, and classification accuracy.

This paper highlights three critical aspects of the internet of things (IoTs), namely (1) energy efficiency, (2) energy balancing and (3) quality of service (QoS) and presents three novel schemes for addressing these aspects. For energy efficiency, a novel radio frequency (RF) energy-harvesting scheme is presented in which each IoT device is associated with the best possible RF source in order to maximize the overall energy that the IoT devices harvest. For energy balancing, the IoT devices in close proximity are clustered together and then an IoT device with the highest residual energy is selected as a cluster head (CH) on a rotational basis. Once the CH is selected, it assigns channels to the IoT devices to report their data using a novel integer linear program (ILP)-based channel allocation scheme by satisfying their desired QoS. To evaluate the presented schemes, exhaustive simulations are carried out by varying different parameters, including the number of IoT devices, the number of harvesting sources, the distance between RF sources and IoT devices and the primary user (PU) activity of different channels. The simulation results demonstrate that our proposed schemes perform better than the existing ones.

Device to device (D2D) communication has emerged as a potential candidate for next-generation communication networks to support higher data rates and minimize power consumption. The spectral and energy efficiency of D2D communication can further be improved using cognitive radio (CR) and radio frequency energy harvesting (RF-EH) technologies. Therefore, in this paper, a sum throughput maximization problem for a CR assisted D2D network is modeled considering the RF-EH mechanism. A joint optimization problem is formulated for sum-rate maximization of cellular and D2D users by considering power allocation, channel assignment, user pairing and transmission time ratio allocation. Then, the problem is transformed into a standard convex optimization problem subject to power constraints at individual nodes, interference constraint, and the individual rate. Furthermore, the secrecy capacity requirements of cellular and D2D nodes are also considered. The duality theory is used to decompose the problem into multiple sub-problems and Karush-Kuhn-Tucker conditions are exploited to provide the solution of the sub-problems. The simulation results are provided for the validation of our proposed schemes.

The Internet of Things (IoT) has gained an incredible importance in the communication and networking industry due to its innovative solutions and advantages in diverse domains. The IoT’ network is a network of smart physical objects: devices, vehicles, buildings, etc. The IoT has a number of applications ranging from smart home, smart surveillance to smart healthcare systems. Since IoT consists of various heterogeneous devices that exhibit different traffic patterns and expect different quality of service (QoS) in terms of data rate, bit error rate and the stability index of the channel, therefore, in this paper, we formulated an optimization problem to assign channels to heterogeneous IoT devices within a smart building for the provisioning of their desired QoS. To solve this problem, a novel particle swarm optimization-based algorithm is proposed. Then, exhaustive simulations are carried out to evaluate the performance of the proposed algorithm. Simulation results demonstrate the supremacy of our proposed algorithm over the existing ones in terms of throughput, bit error rate and the stability index of the channel.

Device-to-device (D2D) communication has emerged as a potential candidate for 5G and beyond networks to optimize spectrum utilization, reduce power consumption, and support higher data rates. The energy and spectral efficiency of D2D communication can be enhanced further by adapting cognitive radio (CR), non-orthogonal multiple access (NOMA), and radio-frequency energy harvesting (RF-EH) technologies. Therefore, this paper proposes a sum-rate maximization problem for a D2D assisted CR network with NOMA, considering the RF-EH mechanism. A joint optimization problem is modelled to maximize the sum throughput of cellular and D2D nodes by considering power assignments, radio resource allocation, user pairing, and transmission time ratio assignment. The problem is then converted into a standard convex optimization problem subject to power allocations at individual nodes, interference temperature limits, individual data rate, and secrecy capacity. The duality theory is adopted to decompose the problem into multiple sub-problems, and Karush-Kuhn-Tucker (KKT) conditions are applied to provide the solution. Finally, the proposed schemes are validated through simulation results. The proposed schemes outperform the existing schemes in terms of sum throughput and energy harvesting under different quality of service requirements.