An energy trade framework using smart contracts: Overview and challenges

© 1986-2012 IEEE. The increasing demand for clean, sustainable and reliable energy sources that are secure and stable requires the integration of renewable and edge energy products with the existing power grid. With the introduction of technological advancements and distributed resources, energy users (aka prosumers) can now generate, store and manage their energy requirements, and share their resources with others. BC is a promising technology that can provide secure and verifiable transactions for P2P energy trading, and promote energy conservation. This article recognizes the best practices for sustainable energy, and highlights the benefits of BC and smart contracts in the energy sector. A distributed trading framework and smart contracts are proposed for future versions of BC and integration with other energy products, and potential solutions are suggested

Resource Allocation in Moving Small Cell Network using Deep Learning based Interference Determination

© 2019 IEEE. Mobile cellular users traveling in city buses are experiencing poor quality of signals due to the interference and the large number of mobile devices. To enhance the Quality-of-Service (QoS), deployment of small cell networks in city buses is a promising solution. The deployment of small cells in vehicular environment makes the resource allocation more challenging because of the dynamic interference relationships experienced by them. Therefore, resource allocation in vehicular environment within moving small cells (MSCs) needs to be handled carefully. In this study, we investigate the problem of resource allocation in city bus transit system with multiple routes. Then, we propose a Percentage Threshold Interference Graph (PTIG) based allocation of resources to MSCs in a network. City buses of multiple routes travel with variable speed and may share some of the same road segments which make it difficult to extract the exact interference patterns between them. Therefore, Long Short Term Memory (LSTM) neural networks are used to predict the city buses locations. The predicted locations of city buses are then used to generate PTIG by finding the dynamic interference relationship between MSCs. Graph coloring algorithm is used to allocate the resources to PTIG. Numerical results are presented to show the comparison of resource allocation using PTIG and Time Interval based Interference Graph (TIIG) in terms of resource block utilization and time complexity

QoS enhancement with deep learning-based interference prediction in mobile IoT

© 2019 Elsevier B.V. With the acceleration in mobile broadband, wireless infrastructure plays a significant role in Internet-of-Things (IoT) to ensure ubiquitous connectivity in mobile environment, making mobile IoT (mIoT) as center of attraction. Usually intelligent systems are accomplished through mIoT which demands for the increased data traffic. To meet the ever-increasing demands of mobile users, integration of small cells is a promising solution. For mIoT, small cells provide enhanced Quality-of-Service (QoS) with improved data rates. In this paper, mIoT-small cell based network in vehicular environment focusing city bus transit system is presented. However, integrating small cells in vehicles for mIoT makes resource allocation challenging because of the dynamic interference present between small cells which may impact cellular coverage and capacity negatively. This article proposes Threshold Percentage Dependent Interference Graph (TPDIG) using Deep Learning-based resource allocation algorithm for city buses mounted with moving small cells (mSCs). Long–Short Term Memory (LSTM) based neural networks are considered to predict city buses locations for interference determination between mSCs. Comparative analysis of resource allocation using TPDIG, Time Interval Dependent Interference Graph (TIDIG), and Global Positioning System Dependent Interference Graph (GPSDIG) is presented in terms of Resource Block (RB) usage and average achievable data rate of mIoT-mSC network

Resource allocation in moving small cell network

The exponentially increasing capacity demand and ubiquitous coverage requirement of cellular users has shifted the cellular operators towards heterogeneity in the network. Heterogeneity promises to meet the capacity demand and coverage requirement by creating small cells on top of the existing macrocells. Small cells are coverage areas served by low powered base stations, called small cell access points (SAPs). SAPs can be deployed to provide good Quality-of-Service (QoS) to users in areas where macrocell antenna’s signal quality is low. Users in moving vehicles such as trains and buses are among those who have been suffering from low QoS. This paves the way for SAPs to be deployed in vehicles and we call them as Moving-SAPs (M-SAPs). Since SAPs are added on top of the existing macrocell, the co-channel interference makes the resource allocation a challenging problem. Furthermore, with the addition of M-SAPs, the dynamic interference relationship exacerbates the problem. Intelligent resource allocation schemes are required to fully utilize the benefits of deploying M-SAPs in the network. In this thesis, we studied the resource allocation problem in an uplink orthogonal frequency division multiple access (OFDMA) cellular network with M-SAPs deployed. We considered OFDMA due to its inherent robustness against frequency selective fading. The resources distributed in the network are OFDMA resource blocks (RB) and their assigned transmission powers. We first studied the allocation of RBs to moving small cell network with deterministic mobility (e.g. M-SAPs deployed in trains). The interference relationship is determined using the mobility model of the SAPs. We proposed to represent the interference relationship in the network as a time-interval-dependent-interference (TIDI) graph. Using the TIDI graph, a cluster-based resource allocation (CBRA) is proposed to allocate RBs to small cells such that the RBs are efficiently utilized in the network. We then investigated the resource allocation of RBs to a moving small cell network with non-random mobility (e.g. M-SAPs deployed in city buses). Exploiting the headway characteristics of the city buses, we captured the interference relationship between SAPS. We proposed a probabilistic graph-based resource allocation (PGRA) scheme to distribute RBs in the moving small cell networks to efficiently utilize the RBs. Next, we solved the problem of jointly allocating RBs and transmission power in a small cell network. Owing to the complexity of the problem, we decomposed the problem into the sub-problems to make it more tractable. The sub-problems are a) RB allocation for a fixed power allocation, and b) the power allocation for a fixed RB allocation. The sub-problems are solved separately. Using the results obtained from the sub-problems, we proposed an iterative resource allocation algorithm (IRAA) to compute the RBs and transmission powers assigned to the users in the moving small cell network. Finally, we studied the backhaul resource allocation in the downlink for the newly arrived SAPs in the network such that the service requirement of the macrocell users and the backhaul of the existing SAPs are satisfied.published_or_final_versionElectrical and Electronic EngineeringDoctoralDoctor of Philosoph

A Comparative Analysis of Wi-Fi Offloading and Cooperation in Small-Cell Network

Small cells deliver cost-effective capacity and coverage enhancement in a cellular network. In this work, we present the interplay of two technologies, namely Wi-Fi offloading and small-cell cooperation that help in achieving this goal. Both these technologies are also being considered for 5G and B5G (Beyond 5G). We simultaneously consider Wi-Fi offloading and small-cell cooperation to maximize average user throughput in the small-cell network. We propose two heuristic methods, namely Sequential Cooperative Rate Enhancement (SCRE) and Sequential Offloading Rate Enhancement (SORE) to demonstrate cooperation and Wi-Fi offloading, respectively. SCRE is based on cooperative communication in which a user data rate requirement is satisfied through association with multiple small-cell base stations (SBSs). However, SORE is based on Wi-Fi offloading, in which users are offloaded to the nearest Wi-Fi Access Point and use its leftover capacity when they are unable to satisfy their rate constraint from a single SBS. Moreover, we propose an algorithm to switch between the two schemes (cooperation and Wi-Fi offloading) to ensure maximum average user throughput in the network. This is called the Switching between Cooperation and Offloading (SCO) algorithm and it switches depending upon the network conditions. We analyze these algorithms under varying requirements of rate threshold, number of resource blocks and user density in the network. The results indicate that SCRE is more beneficial for a sparse network where it also delivers relatively higher average data rates to cell-edge users. On the other hand, SORE is more advantageous in a dense network provided sufficient leftover Wi-Fi capacity is available and more users are present in the Wi-Fi coverage area

Joint pairing and resource allocation for backhaul of small cells using NOMA

International audienc

High-Rate Secret Key Generation Using Physical Layer Security and Physical Unclonable Functions

Physical layer security (PLS) can be adopted for efficient key generation and sharing in secured wireless systems. The inherent random nature of the wireless channel and the associated channel reciprocity (CR) are the main pillars for realizing PLS techniques. However, for applications that involve air-to-air (A2A) transmission, such as unmanned aerial vehicle (UAV) applications, the channel does not generally have sufficient randomness to enable reliable key generation. Therefore, this work proposes a novel system design to mitigate the channel randomness constraint and enable a high-rate secret key generation process. The proposed system integrates physically unclonable functions (PUFs) and CR to generate and exchange secret keys between two nodes securely. Moreover, an adaptive and controllable artificial fading (AF) level with interleaving is used to mitigate the impact of low randomness variations in the wireless channel. Moreover, we propose a novel bit extraction scheme to reduce the number of overhead bits required to share the intermediate keys. The obtained Monte Carlo simulation results show that the proposed system can operate efficiently even when the channel is nearly flat or time-invariant. Consequently, the time required for generating and sharing a key is significantly shorter than conventional techniques. Furthermore, the results show that a key agreement can be reached at the first trial for moderate and high signal-to-noise ratios (SNRs) substantially faster than other PLS techniques. Adopting the AF into static channels managed to reduce the mismatch ratio between the generated secret sequences and degrade the eavesdropper’s capability to predict the secret keys