Energy-efficiency and reliability are vital metrics of the robustness Wireless Sensor Networks (WSNs). Various data reduction techniques used to improve them, among them compressive sensing (CS) is a technique recover extensive from fewer samples in case sparse representation sensor-readings. Unfortunately, energy-efficiency accuracy contradictory metrics, as increased requires large number measurements, transmissions. Therefore, this paper, CS-based algorithm proposed for efficient transfer...

In this paper, novel energy-aware and reliable routing protocols are proposed. The aim is to maximize the lifespan of wireless sensor networks (WSNs) subject predefined reliability constraints by using multi-hop schemes, in which source node forwards packet base station (BS) via other nodes as relays. first proposed protocol, energy efficiency achieved maximizing minimum residual path fulfilling a constraints. second protocol an optimized version one with respect complexity. optimal...

Battery-operated medical implants—such as pacemakers or cardioverter-defibrillators—have already been widely used in practical telemedicine and telecare applications. However, no solution has yet found to mitigate the effect of fading that in-body off-body communication channel is subject to. In this paper, we reveal assess potential cooperative diversity combat fading—hence improve system performance—in implant systems. particular scenario consider, multiple cooperating receiver units are...

We study the problem of finding sparse, mean reverting portfolios based on multivariate historical time series. After mapping optimal portfolio selection into a generalized eigenvalue problem, we propose new optimization approach use simulated annealing. This method ensures that cardinality constraint is automatically satisfied in each step by embedding iterative neighbor function. empirically demonstrate produces better reversion coefficients than other heuristic methods, but also show this...

Energy efficiency is one of the key aspects IoT and Wireless Sensor Networks (WSNs) since nodes network are running on battery power lifespan system an important mission-critical parameter. In WSNs, where energy consumption mainly depends radio interface transmission protocols, reliable packet forwarding from source node to Base Station (BS) crucial. this paper, we focus developing new routing algorithms which extend WSNs by achieving optimal balancing subject criterion that packets must...

In this paper, we develop optimal scheduling mechanisms for packet forwarding in Wireless Sensor Network, where clusterheads are gathering information with a predefined Quality of Service. The objective is to ensure balanced energy consumption and minimize the loss probability, subject time constraints (i.e. different nodes must send all their packets within given interval). Novel solutions developed by combinatorial optimization, quadratic programming methods. our approach, broken down...

Power consuming users and buildings with different power consumption patterns may be treated conditions can taken into consideration parameters during capacity planning distribution. Thus the automated, unsupervised categorization of consumers is a very important task smart transmission systems. Knowing behavioral categories better models created which used for behavior forecast an load balancing. One existing best solutions consumer classification based scheme applies nonlinear techniques...

JSON Web Tokens provide a scalable solution with significant performance benefits for user access control in decentralized, large-scale distributed systems. Such examples would entail cloud-based, micro-services styled systems or typical Internet of Things solutions. One the obstacles still preventing wide-spread use Token–based is problem invalidating issued tokens upon clients leaving system. Token invalidation presently takes considerable processing overhead drastically increased...

In this paper we investigate trading with optimal mean reverting portfolios subject to cardinality constraints. First, identify the parameters of underlying VAR(1) model asset prices and then quantities corresponding Ornstein-Uhlenbeck (OU) process are estimated by pattern matching techniques. Portfolio optimization is performed according two approaches: (i) maximizing predictability solving generalized eigenvalue problem or (ii) return. The itself carried out stochastic search algorithms...

In this paper a Rayleigh fading model based reliability-centric routing algorithm is proposed for Wireless Sensor Networks (WSNs). The scheme optimized with respect to minimal power consumption improve longevity as well ensure reliable packet transmission the Base Station (BS). Reliability guaranteed by selecting path over which probability of correct reception transmitted will exceed predefined threshold at BS. It be pointed out that and efficient forwarding WSN can mapped into constrained...

Efficient data collection is the core concept of implementing Industry4.0 on IoT platforms. This requires energy aware communication protocols for Wireless Sensor Networks (WSNs) where different functions, like sensing and processing nodes must be supported by local battery power. Thus, network protocols, such as routing, became one fundamental challenges in schemes.In our research, we have developed novel routing algorithms which guarantee minimum consumption transfer achieved subject to...

In this paper, we study the problem of finding sparse, mean reverting portfolios in multivariate time series. This can be applied to developing profitable convergence trading strategies by identifying which traded advantageously when their prices differ from identified longterm mean. Assuming that underlying assets follow a VAR(1) process, propose simplified, dense parameter estimation techniques also provide goodness model fit measure based on historical data. Using these estimated parameters,...

We examine the problem of finding sparse, mean reverting portfolios based on multivariate historical time series. After mapping optimal portfolio selection into a generalized eigenvalue problem, two different heuristic algorithms are referenced for solution in subspace which satisfies cardinality constraint. Having identified portfolio, we outline known methods long-term and introduce novel approach pattern matching. Furthermore, present simple convergence trading algorithm with decision...

In this paper we optimize mean reverting portfolios subject to cardinality constraints. First, the parameters of corresponding Ornstein-Uhlenbeck (OU) process are estimated by auto-regressive Hidden Markov Models (AR-HMM) in order capture underlying characteristics financial time series. Portfolio optimization is then performed according maximizing return means introduced AR-HMM prediction algorithm. The itself carried out stochastic search algorithms. presented solutions satisfy constraint...

In this paper, novel call admission control (CAC) algorithms are developed based on cellular neural networks. These can achieve high network utilization by performing CAC in real-time, which is imperative supporting quality of service (QoS) communication over packet-switched The proposed solutions basic significance access technology where a subscriber population (connected to the Internet via an module) needs receive services. case, QoS only be preserved admitting those user configurations...

In this article, a novel algorithm is developed for electronic trading on financial time series. The new method uses quantization and volatility information together with feedforward neural networks achieving high-frequency (HFT). proposed procedures are based estimating the Forward Conditional Probability Distribution (FCPD) of quantized return values. From past samples, conditional expected value can be learned, from which FCPD obtained by using special encoding scheme. Based estimation,...