Received Signal Strength for Randomly Distributed Molecular Nanonodes

We consider nanonodes randomly distributed in a circular area and characterize the received signal strength when a pair of these nodes employ molecular communication. Two communication methods are investigated, namely free diffusion and diffusion with drift. Since the nodes are randomly distributed, the distance between them can be represented as a random variable, which results in a stochastic process representation of the received signal strength. We derive the probability density function of this process for both molecular communication methods. Specifically for the case of free diffusion we also derive the cumulative distribution function, which can be used to derive transmission success probabilities. The presented work constitutes a first step towards the characterization of the signal to noise ratio in the considered setting for a number of molecular communication methods.Comment: 6 pages, 6 figures, Nanocom 2017 conferenc

Network Coding for Distributed Antenna Systems

The mushroom growth of devices that require connectivity has led to an increase in the demand for spectrum resources as well as high data rates. 5G has introduced numerous solutions to counter both problems, which are inherently interconnected. Distributed antenna systems (DASs) help in expanding the coverage area of the network by reducing the distance between radio access unit (RAU) and the user equipment. DASs that use multiple-input multiple-output (MIMO) technology allow devices to operate using multiple antennas, which lead to spectrum efficiency. Recently, the concept of virtual MIMO (VMIMO) has gained popularity. VMIMO allows single antenna nodes to cooperate and form a cluster resulting in a transmission flow that corresponds to MIMO technology. In this chapter, we discuss MIMO-assisted DAS and its utility in forming a cooperative network between devices in proximity to enhance spectral efficiency. We further amalgamate VMIMO-assisted DAS and network coding (NC) to quantify end-to-end transmission success. NC is deemed to be particularly helpful in energy constrained environments, where the devices are powered by battery. We conclude by highlighting the utility of NC-based DAS for several applications that involve single antenna empowered sensors or devices

An Efficient Adaptive Noise Cancellation Scheme Using ALE and NLMS Filters

The basic theme of our paper is to implement a new idea of noise reduction in the real time applications using the concepts of adaptive filters.  Our model which is presented as one of the solutions is based on two stages of operation with the first stage based on the ALE (Adaptive Line Enhancer) filters and the second stage on NLMS (Normalized Least Mean Square) filter. The first stage reduces the sinusoidal noise from the input signal and the second stage reduces the wideband noise. Two input sources of voice are used; one for the normal speech and the other for the noise input, using separate microphones for both signals. The first signal is of the corrupted speech signal and the second signal is of only the noise containing both wideband and narrowband noise. In the first stage the narrowband noise is reduced by using the ALE technique. The second stage gets a signal with ideally only the wideband noise which is reduced using the NLMS technique.  In both the stages the concerned algorithms are used to update the filter coefficients in such a way that the noise is cancelled out from the signal and a clean speech signal is heard at the output.DOI:http://dx.doi.org/10.11591/ijece.v2i3.24

Spectrum on demand : a competitive open market model for spectrum sharing for UAV-assisted communications

Unmanned aerial vehicles (UAVs)-assisted communication has gathered significant interest of the industry, especially with regards to the vision of providing ubiquitous connectivity for beyond 5G (B5G) networks. In this article, we motivate the need for utilizing licensed spectrum for UAV-assisted communication and discuss its advantages such as reliability and security. Moreover, we explore a new dimension to spectrum sharing by proposing a decentralized competitive open market approach based model, where the different mobile network operators (MNOs) have the opportunity to lease the spectrum to UAV base stations (UAV-BSs), leading to new revenue generation opportunities. The proposed spectrum sharing mechanism is based on the logarithmic utility function and willingness to pay of each UAV-BS. We provide a tradeoff analysis between spectrum sharing and price offered by the MNOs, highlighting the impact of the willingness to pay on the spectrum sharing. The results also highlight the behaviour of price and spectrum shared w.r.t. time, thereby providing an insight into different performance regions until the algorithm converges to it’s optimal value. In addition, we also present future directions that could lead to interesting analyses, especially with regards to incentive-based spectrum sharing and security

Energy-aware AI-driven Framework for Edge Computing-based IoT Applications

The significant growth in the number of Internetof- things (IoT) devices has given impetus to the idea of edge computing for several applications. In addition, energy harvestable or wireless-powered wearable devices are envisioned to empower the edge intelligence in IoT applications. However, the intermittent energy supply and network connectivity of such devices in scenarios including remote areas and hard-to-reach regions such as in-body applications can limit the performance of edge computing-based IoT applications. Hence, deploying stateof-the-art convolutional neural networks (CNNs) on such energy constrained devices is not feasible due to their computational cost. Existing model compression methods such as network pruning and quantization can reduce complexity, but these methods only work for fixed computational or energy requirements, which is not the case for edge devices with an intermittent energy source. In this work, we propose a pruning scheme based on deep reinforcement learning (DRL), which can compress the CNN model adaptively according to the energy dictated by the energy management policy and accuracy requirements for IoT applications. The proposed energy policy uses predictions of energy to be harvested and dictates the amount of energy that can be used by the edge device for deep learning inference. We compare the performance of our proposed approach with existing state-of-the-art CNNs and datasets using different filter-ranking criteria and pruning ratios.We observe that by using DRL driven pruning, the convolutional layers that consume relatively higher energy are pruned more as compared to their counterparts. Thereby, our approach outperforms existing approaches by reducing energy consumption and maintaining accuracy

Towards On-Device AI and Blockchain for 6G enabled Agricultural Supply-chain Management

6G envisions artificial intelligence (AI) powered solutions for enhancing the quality-of-service (QoS) in the network and to ensure optimal utilization of resources. In this work, we propose an architecture based on the combination of unmanned aerial vehicles (UAVs), AI and blockchain for agricultural supply-chain management with the purpose of ensuring traceability, transparency, tracking inventories and contracts. We propose a solution to facilitate on-device AI by generating a roadmap of models with various resource-accuracy trade-offs. A fully convolutional neural network (FCN) model is used for biomass estimation through images captured by the UAV. Instead of a single compressed FCN model for deployment on UAV, we motivate the idea of iterative pruning to provide multiple task-specific models with various complexities and accuracy. To alleviate the impact of flight failure in a 6G enabled dynamic UAV network, the proposed model selection strategy will assist UAVs to update the model based on the runtime resource requirements.Comment: 8 pages, 5 figures, 1 table. Accepted to IEEE Internet of Things Magazin

Design and analysis of Maxwell fisheye lens based beamformer

Antenna arrays and multi-antenna systems are essential in beyond 5G wireless networks for providing wireless connectivity, especially in the context of Internet-of-Everything. To facilitate this requirement, beamforming technology is emerging as a key enabling solution for adaptive on-demand wireless coverage. Despite digital beamforming being the primary choice for adaptive wireless coverage, a set of applications rely on pure analogue beamforming approaches, e.g., in point-to-multi point and physical-layer secure communication links. In this work, we present a novel scalable analogue beamforming hardware architecture that is capable of adaptive 2.5-dimensional beam steering and beam shaping to fulfil the coverage requirements. Beamformer hardware comprises of a finite size Maxwell fisheye lens used as a scalable feed network solution for a semi-circular array of monopole antennas. This unique hardware architecture enables a flexibility of using 2 to 8 antenna elements. Beamformer development stages are presented while experimental beam steering and beam shaping results show good agreement with the estimated performance

Unlocking Unlicensed Band Potential to Enable URLLC in Cloud Robotics for Ubiquitous IoT

Cloud robotics (CR) support extremely high reliability and low-latency communications in ubiquitous Internet of Things applications. However, many of those applications currently rely on wired connection, limiting their use within the confines of Ethernet/optical links. Some wireless solutions such as Wi-Fi have been considered, but failed to meet the stringent criteria for latency and outage. On the other hand, cellular technology possesses expensive licensing. Thus, the Third Generation Partnership Project (3GPP) is actively working on New Radio in the unlicensed band for incorporating ultra-reliable low-latency communications (URLLC) into fifth generation and beyond communication networks. In this article, we aim to study the feasibility of URLLC in an unlicensed band specifically for CR applications. We open up various use cases and opportunities offered by the unlicensed band in achieving latency and reliability constraints for robotics applications. We then review the regulatory requirements of unlicensed band operation imposed by 3GPP and explore its medium access challenges for CR due to the shared use of unstable wireless channels. Finally, we discuss the potential technology enablers to achieve URLLC using the unlicensed band for the ubiquitous CR applications

Toward On-Device AI and Blockchain for 6G-Enabled Agricultural Supply Chain Management

6G envisions artificial intelligence (AI) powered solutions for enhancing the quality of service (QoS) in the network and to ensure optimal utilization of resources. In this work, we propose an architecture based on the combination of unmanned aerial vehicles (UAVs), AI, and blockchain for agricultural supply chain management with the purpose of ensuring traceability and transparency, tracking inventories, and contracts. We propose a solution to facilitate on-device AI by generating a roadmap of models with various resource-accuracy trade-offs. A fully convolutional neural network (FCN) model is used for biomass estimation through images captured by the UAV. Instead of a single compressed FCN model for deployment on UAVs, we motivate the idea of iterative pruning to provide multiple task-specific models with various complexities and accuracy. To alleviate the impact of flight failure in a 6G-enabled dynamic UAV network, the proposed model selection strategy will assist UAVs to update the model based on the runtime resource requirements