Neuromorphic sensors, a.k.a. dynamic vision sensors (DVS) or silicon retinas, do not capture full images (frames) at a fixed rate, but asynchronously capture spikes indicating changes of brightness in the scene, following the principles of biological vision and perception in mammals. DVS sensing and processing produces a data representation where the scene can be represented with a very high time resolution with a limited number of bits (an inherent data compression is performed at the time of acquisition). Such representation can be used locally to derive actionable responses and selected parts can be transmitted and then processed in another network location. Due to these features, such sensors represent an excellent choice as visual sensing technology for next-generation Internet-of-Things, e.g. in surveillance, drone technology, and robotics. It is in fact becoming evident that in this framework acquiring, processing, and transmitting frame-based video is inefficient in terms of energy consumption and reaction times, in particular in some scenarios. Hence, we explore here the feasibility of advanced Machine to Machine (M2M) communications systems that directly capture, compress and transmit spike-based visual information to cloud computing services in order to produce content classification or retrieval results with extremely low power and low latency.

In this paper, we consider joint antenna selection and optimal beamforming for energy efficient delay minimization. We assume multiple-input multi-output (MIMO) system with full duplex simultaneous wireless information and power transfer (FD-SWIPT) where each sensor is equipped with a power splitting (PS) system and can simultaneously receive both energy and information from the aggregator (AGG). We show that the antenna selection and beamforming power control policies are adaptive to the energy state information (ESI), the queue state information (QSI) and the channel state information (CSI). We develop an analytical framework for energy efficient delay-optimal control problem based on the theory of infinite horizon partially observable Markov decision process (POMDP). The infinite-horizon POMDP problem is transformed into an equivalent value Bellman program and solved by near-optimal point-based Heuristic Search Value Iteration (PB-HSVI) method under specific standard conditions. The proposed solution outcome is a set of sub-optimal antenna selection and beamforming control policies. Simulation results reveal an effective trade-off between the contradictory objectives (i.e. delay and power consumption) and show the enhancement in delay by using FD-SWIPT systems in comparison to Half Duplex (HD)-SWIPT systems.

We propose an optimal radio resource allocation (RRA) scheme for scalable H.264/SVC multi-user video transmission over downlink orthogonal frequency division multiple Access (OFDMA)-based cognitive radio (CR) networks. Our framework adopts a new probabilistic approach to mitigate the total imposed interference by cognitive users on the licensed spectrum. We consider two fundamental network service objectives, i.e., the number of satisfied users and the video quality. We devise the 3-dimensional scalable quality of the H.264/SVC video transmission for an OFDMA-based CR network and develop efficient suboptimal algorithms to solve the probabilistic constrained mixed discrete-continuous non-linear programming (MDCNLP) problem. Simulation results indicate that the proposed quality-aware scheme can achieve up to 1.3 dB increase in the average peak signal-to-noise ratio (PSNR) per user over the conventional non-quality-aware RRA algorithms.

A stochastic radio resource allocation (RRA) algorithm is designed to maximize the total transmission rate of orthogonal frequency-division multiple access (OFDMA) cognitive radios (CRs) with hybrid (i.e., joint underlay and overlay) spectrum access strategy. Our novel solution incorporates the probabilities of channel availability obtained through spectrum sensing for allocating power and subcarrier in a multi-user multi-band environment. In order to protect the licensed users from harmful intervention under imperfect sensing information, stochastic transmit and interference power constraints are imposed on the CRs. The performance of the proposed RRA algorithm and advantages over the conventional hard-decision-based approaches are demonstrated via simulation.

In this paper, a cross-layer resource allocation algorithm is proposed in the context of orthogonal frequency division multiple access (OFDMA)-based cognitive radio (CR) video application systems. User video quality and channel awareness are incorporated in the design, towards an optimal subcarrier and power allocation scheme, subject to minimum secondary receiver (SRx) video quality and primary receiver (PRx) interference threshold constraints. First, the relationship between PRx interference margin and power limits at the secondary transmitter (STx) is analytically derived. Then, to provide PRx with satisfactory quality of service (QoS), we propose a new probabilistic approach to mitigate the total imposed interference of the STx on PRx, considering imperfect STx to PRx channel state information (CSI). The effect of PRx interference limit violation probability, interference threshold, and error variance of imperfect CSI are evaluated through mathematical analysis and computer simulations. Results indicate that significant improvement in SRx total video quality is achieved through our proposed quality-aware (QA) algorithm over other state-of-the-art non-quality-aware (NQA) designs in the literature. The enhanced performance was obtained whilst guaranteeing SRx minimum quality and PRx prescribed QoS constraints.

Radio resource allocation (RRA) algorithms are developed to enhance the spectral efficiency of downlink adaptive modulation and coding (AMC)-based orthogonal frequency-division multiple access (OFDMA) cognitive radios (CRs) under average transmit and peak interference power constraints. We consider the practical case of noisy channel-state-information (CSI) between cognitive transmitter (CTx) and primary receiver (PRx), and design novel interference management techniques to model and control the CR-inflicted interference on licensed spectrum. An expression for the cumulative density function (cdf) of the CRs' received signal-to-interference-plus-noise ratio (SINR) is developed to evaluate the resultant average spectral efficiency. Simulation results reveal that by adopting the joint resource allocation and interference management framework a considerable performance gain in the AMC-based OFDMA CR network is achieved over the conventional optimization methods.