Efficient energy, cost reduction, and QoS based routing protocol for wireless sensor networks

Recent developments and widespread in wireless sensor network have led to many routing protocols, many of these protocols consider the efficiency of energy as the ultimate factor to maximize the WSN lifetime. The quality of Service (QoS) requirements for different applications of wireless sensor networks has posed additional challenges. Imaging and data transmission needs both QoS aware routing and energy to ensure the efficient use of sensors. In this paper, we propose an Efficient, Energy-Aware, Least Cost, (ECQSR) quality of service routing protocol for sensor networks which can run efficiently with best-effort traffic processing. The protocol aims to maximize the lifetime of the network out of balancing energy consumption across multiple nodes, by using the concept of service differentiation, finding lower cost by finding the shortest path using nearest neighbor algorithm (NN), also put certain constraints on the delay of the path for real-time data from where link cost that captures energy nodes reserve, energy of the transmission, error rate and other parameters. The results show that the proposed protocol improves the network lifetime and low power consumption

A Reinforcement Learning-based Assignment Scheme for EVs to Charging Stations

Due to recent developments in electric mobility, public charging infrastructure will be essential for modern transportation systems. As the number of electric vehicles (EVs) increases, the public charging infrastructure needs to adopt efficient charging practices. A key challenge is the assignment of EVs to charging stations in an energy efficient manner. In this paper, a Reinforcement Learning (RL)-based EV Assignment Scheme (RL-EVAS) is proposed to solve the problem of assigning EV to the optimal charging station in urban environments, aiming at minimizing the total cost of charging EVs and reducing the overload on Electrical Grids (EGs). Travelling cost that is resulted from the movement of EV to CS, and the charging cost at CS are considered. Moreover, the EV’s Battery State of Charge (SoC) is taken into account in the proposed scheme. The proposed RL-EVAS approach will approximate the solution by finding an optimal policy function in the sense of maximizing the expected value of the total reward over all successive steps using Q-learning algorithm, based on the Temporal Difference (TD) learning and Bellman expectation equation. Finally, the numerous simulation results illustrate that the proposed scheme can significantly reduce the total energy cost of EVs compared to various case studies and greedy algorithm, and also demonstrate its behavioural adaptation to any environmental conditions

Electric Vehicle Charging Modes, Technologies and Applications of Smart Charging

The rise of the intelligent, local charging facilitation and environmentally friendly aspects of electric vehicles (EVs) has grabbed the attention of many end-users. However, there are still numerous challenges faced by researchers trying to put EVs into competition with internal combustion engine vehicles (ICEVs). The major challenge in EVs is quick recharging and the selection of an optimal charging station. In this paper, we present the most recent research on EV charging management systems and their role in smart cities. EV charging can be done either in parking mode or on-the-move mode. This review work is novel due to many factors, such as that it focuses on discussing centralized and distributed charging management techniques supported by a communication framework for the selection of an appropriate charging station (CS). Similarly, the selection of CS is evaluated on the basis of battery charging as well as battery swapping services. This review also covered plug-in charging technologies including residential, public and ultra-fast charging technologies and also discusses the major components and architecture of EVs involved in charging. In a comprehensive and detailed manner, the applications and challenges in different charging modes, CS selection, and future work have been discussed. This is the first attempt of its kind, we did not find a survey on the charging hierarchy of EVs, their architecture, or their applications in smart cities

A Novel Approach to Improve the Adaptive-Data-Rate Scheme for IoT LoRaWAN

The long-range wide-area network (LoRaWAN) uses the adaptive-data-rate (ADR) algorithm to control the data rate and transmission power. The LoRaWAN ADR algorithm adjusts the spreading factor (SF) to allocate the appropriate transmission rate and transmission power to reduce power consumption.The updating SF and transmission power of the standard ADR algorithm are based on the channel state, but it does not guarantee efficient energy consumption among all the nodes in complex environments with high-varying channel conditions. Therefore, this article proposes a new enhancement approach to the ADR+ algorithm at the network server, which only depends on the average signal-to-noise ratio (SNR). The enhancement ADR algorithm ADR++ introduces an energy-efficiency controller α related to the total energy consumption of all nodes, to use it for adjusting the average SNR of the last records. We implement our new enhanced ADR at the network server (NS) using the FLoRa module in OMNET++. The simulation results demonstrate that our proposed ADR++ algorithm leads to a significant improvement in terms of the network delivery ratio and energy efficiency that reduces the network energy consumption up to 17.5% and improves the packet success rate up to 31.55% over the existing ADR+ algorithm

LiNEV: Visible Light Networking for Connected Vehicles

DC-biased optical orthogonal frequency division multiplexing (DCO-OFDM) has been introduced to visible light networking framework for connected vehicles (LiNEV) systems as a modulation and multiplexing scheme. This is to overcome the light-emitting diode (LED) bandwidth limitation, as well as to reduce the inter-symbol interference caused by the multipath road fading. Due to the implementation of the inverse fast Fourier transform, DC-OFDM suffers from its large peak-to-average power ratio (PAPR), which degrades the performance in LiNEV systems, as the LEDs used in the vehicles’ headlights have a limited optical power-current linear range. To tackle this issue, discrete Fourier transform spread-optical pulse amplitude modulation (DFTS-OPAM) has been proposed as an alternative modulation scheme for LiNEV systems instead of DCO-OFDM. In this paper, we investigate the system performance of both schemes considering the light-emitting diode linear dynamic range and LED 3 dB modulation bandwidth limitations. The simulation results indicate that DCO-OFDM has a 9 dB higher PAPR value compared with DFTS-OPAM. Additionally, it is demonstrated that DCO-OFDM requires an LED with a linear range that is twice the one required by DFTS-OPAM for the same high quadrature amplitude modulation (QAM) order. Furthermore, the findings illustrate that when the signal bandwidth of both schemes significantly exceeds the LED modulation bandwidth, DCO-OFDM outperforms DFTS-OPAM, as it requires a lower signal-to-noise ratio at a high QAM order

Machine Learning Failure-Aware Scheme for Profit Maximization in the Cloud Market

A successful cloud trading system requires suitable financial incentives for all parties involved. Cloud providers in the cloud market provide computing services to clients in order to perform their tasks and earn extra money. Unfortunately, the applications in the cloud are prone to failure for several reasons. Cloud service providers are responsible for managing the availability of scheduled computing tasks in order to provide high-level quality of service for their customers. However, the cloud market is extremely heterogeneous and distributed, making resource management a challenging problem. Protecting tasks against failure is a challenging and non-trivial mission due to the dynamic, heterogeneous, and largely distributed structure of the cloud environment. The existing works in the literature focus on task failure prediction and neglect the remedial (post) actions. To address these challenges, this paper suggests a fault-tolerant resource management scheme for the cloud computing market in which the optimal amount of computing resources is extracted at each system epoch to replace failed machines. When a cloud service provider detects a malfunctioning machine, they transfer the associated work to new machinery

Efficient Route Planning Using Temporal Reliance of Link Quality for Highway IoV Traffic Environment

Intermittently connected vehicular networks, terrain of the highway, and high mobility of the vehicles are the main critical constraints of highway IoV (Internet of Vehicles) traffic environment. These cause GPS outage problem and the existence of short-lived wireless mobile links that reduce the performance of designed routing approaches. Nevertheless, geographic routing has attracted a lot of attention from researchers as a potential means of accurate and efficient information delivery. Various distance-based routing protocols have been proposed in the literature, with an emphasis on restricting the forwarding area to the next forwarding vehicle. Many of these protocols have issues with significant one-hop link disconnection, long end-to-end delays, and low throughput even at normal vehicle speeds in high-vehicular-density environments due to frequently interrupted wireless links. In this paper, an efficient geocast routing (EGR) approach for highway IoV–traffic environment considering the shadowing fading condition is proposed. In EGR, a geometrical localization for GPS outage problem and a temporal link quality estimation model considering underlying vehicular movement have been proposed. Geocast routing to select a next forwarding vehicle from forward region by utilizing temporal link quality is proposed for four different scenarios. To evaluate the effectiveness and scalability of EGR, a comparative performance evaluation based on simulations has been performed. It is clear from the analysis of the results that EGR performs better than state-of-the-art approaches in highway traffic environment in terms of handling the problem of wireless communication link breakage and throughput, as well as ensuring the faster delivery of the messages

Improving Recommender Systems by a Further Factorization of the Factor Matrices

Due to the availability of massive numbers of items for any product on the Web, the burden of selecting an item is borne by the user. A Recommender System (RS) is a useful tool that has been employed to save the user’s time by recommending preferred items for him/her efficiently. Collaborative-based RS’s use the latent factor or/and the neighbourhood techniques including matrix factorization (MF), which is an efficient approach utilizing latent factors. The idea of the approach is based on calculating similarities through between users and items simultaneously, to predict appropriate recommendations. MF can be seen as mathematical model capable to split an entity into multiple smaller entries through an ordered rectangular array of functions, to discover the features or information underlying the interactions between users and items. The technique factorizes the user-item rating matrix ( $R$ ) (first-level factorization) into users ( $P$ ) and items ( $Q$ ) matrices. A recent RS system, named NLM, combines the neighbourhood and the latent factor techniques to produce attractive results. This paper proposes to improve the NLM method by proposing a more effective technique, named NLM+, by further factorizing $P$ and $Q$ using MF (second-level factorization). To the best of our knowledge, current studies in this field have not considered the use of the aforementioned two levels of factorizing. To validate our method, CiaoDVD, MovieLens, and FilmTrust datasets have been used and incorporated to demonstrate that NLM+ improves NLM by 48% and 42% in the recall and the precision values, respectively