Advances and emerging challenges in cognitive internet-of-things

The evolution of IoT devices and their adoption in new generation intelligent systems has generated a huge demand for wireless bandwidth. This bandwidth problem is further exacerbated by another characteristics of IoT applications, i.e. IoT devices are usually deployed in massive number, thus leading to an awkward scenario that many bandwidth-hungry devices are chasing after the very limited wireless bandwidth within a small geographic area. As such, cognitive radio has received much attention of there search community as an important means for addressing the bandwidth needs of IoT applications. When enabling IoT devices with cognitive functionalities including spectrum sensing, dynamic spectrum accessing, circumstantial perceiving and self-learning, one will also need to fully study other critical issues such as standardization, privacy protection and heterogeneous coexistence. In this paper, we investigate the structural frameworks and potential applications of cognitive IoT. We further discuss the spectrum-based functionalities and heterogeneity for cognitive IoT. Security and privacy issues involved in cognitive IoT are also investigated. Finally, we present the key challenges and future direction of research on cognitiveradio-based IoT networks

Technology Implications of UWB on Wireless Sensor Network-A detailed Survey

In today’s high tech “SMART” world sensor based networks are widely used. The main challenge with wireless-based sensor networks is the underneath physical layer. In this survey, we have identified core obstacles of wireless sensor network when UWB is used at PHY layer. This research was done using a systematic approach to assess UWB’s effectiveness (for WSN) based on information taken from various research papers, books, technical surveys and articles. Our aim is to measure the UWB’s effectiveness for WSN and analyze the different obstacles allied with its implementation. Starting from existing solutions to proposed theories. Here we have focused only on the core concerns, e.g. spectrum, interference, synchronization etc.Our research concludes that despite all the bottlenecks and challenges, UWB’s efficient capabilities makes it an attractive PHY layer scheme for the WSN, provided we can control interference and energy problems. This survey gives a fresh start to the researchers and prototype designers to understand the technological concerns associated with UWB’s implementatio

From Sensing to Predictions and Database Technique: A Review of TV White Space Information Acquisition in Cognitive Radio Networks

Strategies to acquire white space information is the single most significant functionality in cognitive radio networks (CRNs) and as such, it has gone some evolution to enhance information accuracy. The evolution trends are spectrum sensing, prediction algorithm and recently, geo-location database technique. Previously, spectrum sensing was the main technique for detecting the presence/absence of a primary user (PU) signal in a given radio frequency (RF) spectrum. However, this expectation could not materialized as a result of numerous technical challenges ranging from hardware imperfections to RF signal impairments. To convey the evolutionary trends in the development of white space information, we present a survey of the contemporary advancements in PU detection with emphasis on the practical deployment of CRNs i.e. Television white space (TVWS) networks. It is found that geo-location database is the most reliable technique to acquire TVWS information although, it is financially driven. Finally, using financially driven database model, this study compared the data-rate and spectral efficiency of FCC and Ofcom TV channelization. It was discovered that Ofcom TV channelization outperforms FCC TV channelization as a result of having higher spectrum bandwidth. We proposed the adoption of an all-inclusive TVWS information acquisition model as the future research direction for TVWS information acquisition techniques

Insights on Significant Implication on Research Approach for Enhancing 5G Network System

With the exponential growth of mobile users, there is a massive growth of data as well as novel services to support such data management. However, the existing 4G network is absolutely not meant for catering up such higher demands of bandwidth utilization as well as servicing massive users with similar Quality of service. Such problems are claimed to be effectively addressed by the adoption of 5G networking system. Although the characteristics of 5G networking are theoretically sound, still it is under the roof of the research. Therefore, this paper presents a discussion about the conventional approach as well as an approach using cognitive radio network towards addressing the frequently identified problems of energy, resource allocation, and spectral efficiency. The study collects the existing, recent researches in the domain of 5G communications from various publications. Different from existing review work, the paper also contributes towards identifying the core research findings as well as a significant research gap towards improving the communication in the 5G network system

Integrated cooperative spectrum sensing and access control for cognitive Industrial Internet of Things

Industrial Internet of Things (IIoT) usually utilizes 2.4-GHz unlicensed frequency band, which is also heavily used by many other communication systems, such as ZigBee, WiFi, Bluetooth, etc. Therefore, the lack of spectrum resources has become a key technical bottleneck to restrict the development of IIoT. Integrating cognitive radio (CR) into IIoT, Cognitive IIoT (CIIoT) can cope with the spectrum resource shortage by accessing the frequency bands licensed to primary user (PU). However, spectrum sensing and access control must be performed to avoid bringing severe interference to the PU. In this article, an integrated cooperative spectrum sensing (CSS) and access control model is proposed to improve the transmission performance of the CIIoT while guaranteeing the CSS’s detection probability and controlling the interference to the PU. This model is optimized to maximize the total throughput of IIoT in each frame by jointly optimizing sensing time, the number of sensing nodes and the transmit power for each node under the constraints of the minimum detection probability, the total power control, the interference control, and the minimum rate for each node. The optimization problem is solved by the joint optimization of spectrum sensing and access control. A simultaneous CSS and access control model is also proposed to increase the communication time by using one time slot to perform CSS and access control simultaneously. The simulation results show that there exist optimal sensing and control parameters to maximize the total throughput of CIIoT

Cognitive networking for next generation of cellular communication systems

This thesis presents a comprehensive study of cognitive networking for cellular networks with contributions that enable them to be more dynamic, agile, and efficient. To achieve this, machine learning (ML) algorithms, a subset of artificial intelligence, are employed to bring such cognition to cellular networks. More specifically, three major branches of ML, namely supervised, unsupervised, and reinforcement learning (RL), are utilised for various purposes: unsupervised learning is used for data clustering, while supervised learning is employed for predictions on future behaviours of networks/users. RL, on the other hand, is utilised for optimisation purposes due to its inherent characteristics of adaptability and requiring minimal knowledge of the environment. Energy optimisation, capacity enhancement, and spectrum access are identified as primary design challenges for cellular networks given that they are envisioned to play crucial roles for 5G and beyond due to the increased demand in the number of connected devices as well as data rates. Each design challenge and its corresponding proposed solution are discussed thoroughly in separate chapters. Regarding energy optimisation, a user-side energy consumption is investigated by considering Internet of things (IoT) networks. An RL based intelligent model, which jointly optimises the wireless connection type and data processing entity, is proposed. In particular, a Q-learning algorithm is developed, through which the energy consumption of an IoT device is minimised while keeping the requirement of the applications--in terms of response time and security--satisfied. The proposed methodology manages to result in 0% normalised joint cost--where all the considered metrics are combined--while the benchmarks performed 54.84% on average. Next, the energy consumption of radio access networks (RANs) is targeted, and a traffic-aware cell switching algorithm is designed to reduce the energy consumption of a RAN without compromising on the user quality-of-service (QoS). The proposed technique employs a SARSA algorithm with value function approximation, since the conventional RL methods struggle with solving problems with huge state spaces. The results reveal that up to 52% gain on the total energy consumption is achieved with the proposed technique, and the gain is observed to reduce when the scenario becomes more realistic. On the other hand, capacity enhancement is studied from two different perspectives, namely mobility management and unmanned aerial vehicle (UAV) assistance. Towards that end, a predictive handover (HO) mechanism is designed for mobility management in cellular networks by identifying two major issues of Markov chains based HO predictions. First, revisits--which are defined as a situation whereby a user visits the same cell more than once within the same day--are diagnosed as causing similar transition probabilities, which in turn increases the likelihood of making incorrect predictions. This problem is addressed with a structural change; i.e., rather than storing 2-D transition matrix, it is proposed to store 3-D one that also includes HO orders. The obtained results show that 3-D transition matrix is capable of reducing the HO signalling cost by up to 25.37%, which is observed to drop with increasing randomness level in the data set. Second, making a HO prediction with insufficient criteria is identified as another issue with the conventional Markov chains based predictors. Thus, a prediction confidence level is derived, such that there should be a lower bound to perform HO predictions, which are not always advantageous owing to the HO signalling cost incurred from incorrect predictions. The outcomes of the simulations confirm that the derived confidence level mechanism helps in improving the prediction accuracy by up to 8.23%. Furthermore, still considering capacity enhancement, a UAV assisted cellular networking is considered, and an unsupervised learning-based UAV positioning algorithm is presented. A comprehensive analysis is conducted on the impacts of the overlapping footprints of multiple UAVs, which are controlled by their altitudes. The developed k-means clustering based UAV positioning approach is shown to reduce the number of users in outage by up to 80.47% when compared to the benchmark symmetric deployment. Lastly, a QoS-aware dynamic spectrum access approach is developed in order to tackle challenges related to spectrum access, wherein all the aforementioned types of ML methods are employed. More specifically, by leveraging future traffic load predictions of radio access technologies (RATs) and Q-learning algorithm, a novel proactive spectrum sensing technique is introduced. As such, two different sensing strategies are developed; the first one focuses solely on sensing latency reduction, while the second one jointly optimises sensing latency and user requirements. In particular, the proposed Q-learning algorithm takes the future load predictions of the RATs and the requirements of secondary users--in terms of mobility and bandwidth--as inputs and directs the users to the spectrum of the optimum RAT to perform sensing. The strategy to be employed can be selected based on the needs of the applications, such that if the latency is the only concern, the first strategy should be selected due to the fact that the second strategy is computationally more demanding. However, by employing the second strategy, sensing latency is reduced while satisfying other user requirements. The simulation results demonstrate that, compared to random sensing, the first strategy decays the sensing latency by 85.25%, while the second strategy enhances the full-satisfaction rate, where both mobility and bandwidth requirements of the user are simultaneously satisfied, by 95.7%. Therefore, as it can be observed, three key design challenges of the next generation of cellular networks are identified and addressed via the concept of cognitive networking, providing a utilitarian tool for mobile network operators to plug into their systems. The proposed solutions can be generalised to various network scenarios owing to the sophisticated ML implementations, which renders the solutions both practical and sustainable