Collaborative spectrum sensing optimisation algorithms for cognitive radio networks

The main challenge for a cognitive radio is to detect the existence of primary users reliably in order to minimise the interference to licensed communications. Hence, spectrum sensing is a most important requirement of a cognitive radio. However, due to the channel uncertainties, local observations are not reliable and collaboration among users is required. Selection of fusion rule at a common receiver has a direct impact on the overall spectrum sensing performance. In this paper, optimisation of collaborative spectrum sensing in terms of optimum decision fusion is studied for hard and soft decision combining. It is concluded that for optimum fusion, the fusion centre must incorporate signal-to-noise ratio values of cognitive users and the channel conditions. A genetic algorithm-based weighted optimisation strategy is presented for the case of soft decision combining. Numerical results show that the proposed optimised collaborative spectrum sensing schemes give better spectrum sensing performance

Collaborative spectrum sensing: optimising the number of collaborating users

In the IEEE 802.22 standard, the spectrum sensing mechanism is identified as a key functionality of a cognitive radio. Due to the channel uncertainty, a single cognitive user, in most cases, can not make a reliable decision and hence collaboration or cooperation of and among multiple users is required. However, when large number of cognitive users are collaborating with each other, the bandwidth requirements for sending their result to the fusion centre tends to be very large. In this paper, a metric for spectrum efficiency is defined and used for the optimisation of collaborative spectrum sensing. An optimisation algorithm is presented to calculate the optimal number of collaborating cognitive users with the aim to maximise overall spectrum efficiency by satisfying certain constraints in terms of global probability of detection and probability of false alarm. Numerical results show that for maximum spectrum efficiency collaboration of only a subset of the available cognitive users is required

Rule-based Reasoning Mechanism for Context-aware Service Presentation

With universal usability geared towards user focused customisation, a context reasoning engine can derive meaning from the various context elements and facilitate decision-taking for applications and context delivery mechanisms. The heterogeneity of available device capabilities means that the recommendation algorithm must be in a formal, effective and extensible form. Moreover, user preferences, capability context and media metadata must be considered simultaneously to determine appropriate presentation format. Towards this aim, this paper presents a reasoning mechanism that supports service presentation through a rule-based mechanism. The validation of the approach is presented through application use cases

Nodes Number Estimation based on ML for Multi-operator Unlicensed Band Sharing to Extend Indoor Connectivity

© 2023 IEEE. This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.1109/wcnc55385.2023.10118807Due to ever-increasing data and resource-hungry applications, the needs of new spectrum by mobile networks keep increasing. Unlicensed spectrum is still expected to play a crucial part in meeting the capacity demand for future mobile networks. But if this will be a reality, fair coexistence attained via practical and efficient channel access procedures would be necessary. In designing such channel access schemes, awareness of the number of nodes contending for the channel resource can be strategic. This paper investigates a node number estimation approach using machine learning (ML) techniques. When multiple nodes access the same unlicensed channel, varying idle-time can be associated to a statistical distribution. In this paper, a statistical distribution of the Idle-time slots over the channel are used to characterise and analyse the channel contention based on the number of nodes. Three ML model based approaches are evaluated and the results confirm that the proposed solution’s viability but also reveal the best performing ML technique for the task of node number estimations

Practical Spectrum Aggregation for Secondary Networks with Imperfect Sensing

We investigate a collision-sensitive secondary network that intends to opportunistically aggregate and utilize spectrum of a primary network to achieve higher data rates. In opportunistic spectrum access with imperfect sensing of idle primary spectrum, secondary transmission can collide with primary transmission. When the secondary network aggregates more channels in the presence of the imperfect sensing, collisions could occur more often, limiting the performance obtained by spectrum aggregation. In this context, we aim to address a fundamental query, that is, how much spectrum aggregation is worthy with imperfect sensing. For collision occurrence, we focus on two different types of collision: one is imposed by asynchronous transmission; and the other by imperfect spectrum sensing. The collision probability expression has been derived in closed-form with various secondary network parameters: primary traffic load, secondary user transmission parameters, spectrum sensing errors, and the number of aggregated sub-channels. In addition, the impact of spectrum aggregation on data rate is analysed under the constraint of collision probability. Then, we solve an optimal spectrum aggregation problem and propose the dynamic spectrum aggregation approach to increase the data rate subject to practical collision constraints. Our simulation results show clearly that the proposed approach outperforms the benchmark that passively aggregates sub-channels with lack of collision awareness

MakeSense: An IoT Testbed for Social Research of Indoor Activities

There has been increasing interest in deploying IoT devices to study human behaviour in locations such as homes and offices. Such devices can be deployed in a laboratory or `in the wild' in natural environments. The latter allows one to collect behavioural data that is not contaminated by the artificiality of a laboratory experiment. Using IoT devices in ordinary environments also brings the benefits of reduced cost, as compared with lab experiments, and less disturbance to the participants' daily routines which in turn helps with recruiting them into the research. However, in this case, it is essential to have an IoT infrastructure that can be easily and swiftly installed and from which real-time data can be securely and straightforwardly collected. In this paper, we present MakeSense, an IoT testbed that enables real-world experimentation for large scale social research on indoor activities through real-time monitoring and/or situation-aware applications. The testbed features quick setup, flexibility in deployment, the integration of a range of IoT devices, resilience, and scalability. We also present two case studies to demonstrate the use of the testbed, one in homes and one in offices.Comment: 20 pages, 11 figure

Practical Spectrum Aggregation for Secondary Networks with Imperfect Sensing

We investigate a collision-sensitive secondary network that intends to opportunistically aggregate and utilize spectrum of a primary network to achieve higher data rates. In opportunistic spectrum access with imperfect sensing of idle primary spectrum, secondary transmission can collide with primary transmission. When the secondary network aggregates more channels in the presence of the imperfect sensing, collisions could occur more often, limiting the performance obtained by spectrum aggregation. In this context, we aim to address a fundamental query, that is, how much spectrum aggregation is worthy with imperfect sensing. For collision occurrence, we focus on two different types of collision: one is imposed by asynchronous transmission; and the other by imperfect spectrum sensing. The collision probability expression has been derived in closed-form with various secondary network parameters: primary traffic load, secondary user transmission parameters, spectrum sensing errors, and the number of aggregated sub-channels. In addition, the impact of spectrum aggregation on data rate is analysed under the constraint of collision probability. Then, we solve an optimal spectrum aggregation problem and propose the dynamic spectrum aggregation approach to increase the data rate subject to practical collision constraints. Our simulation results show clearly that the proposed approach outperforms the benchmark that passively aggregates sub-channels with lack of collision awareness

Cyber–Physical–Social Frameworks for Urban Big Data Systems: A Survey

The integration of things’ data on the Web and Web linking for things’ description and discovery is leading the way towards smart Cyber–Physical Systems (CPS). The data generated in CPS represents observations gathered by sensor devices about the ambient environment that can be manipulated by computational processes of the cyber world. Alongside this, the growing use of social networks offers near real-time citizen sensing capabilities as a complementary information source. The resulting Cyber–Physical–Social System (CPSS) can help to understand the real world and provide proactive services to users. The nature of CPSS data brings new requirements and challenges to different stages of data manipulation, including identification of data sources, processing and fusion of different types and scales of data. To gain an understanding of the existing methods and techniques which can be useful for a data-oriented CPSS implementation, this paper presents a survey of the existing research and commercial solutions. We define a conceptual framework for a data-oriented CPSS and detail the various solutions for building human–machine intelligence

A Communication Middleware for Ubiquitous Multimedia Adaptation Services

Ubiquitous services have gained increasing attention in the area of mobile communication aiming to allow service access anywhere, anytime and anyhow while keeping complexity to a minimum for both users and service providers. Ubiquitous environment features a wide range and an increasing number of access devices and network technologies. Context-aware content/service adaptation is deemed necessary to ensure best user experience. We developed an Adaptation Management Framework (AMF) Web Service which manages the complexity of dynamic and autonomous content adaptation and serves as an invisible enabler for ubiquitous service delivery. It remains challenging to manage the tasks involved in the communication between the AMF Web Service and the user's environment, typically represented by various types of intelligent agents. This work presents a middleware which manages those tasks and serves not only as a protocol gateway, but also as a message translator, a service broker, a complexity shield etc., between AMF Web Services and User Agents