Energy-efficient scheduling algorithms for real-time data reporting in 4G LTE machine-to-machine communication networks

This paper describes a novel uplink scheduling (ULSC) algorithm for the Galileo system. The algorithm is designed to meet the stringent dissemination requirements that are currently foreseen for the provision of the Galileo Commercial Service (CS). In particular, these include “link availability” (LA) requirements that call for a minimum percentage of time (“availability”) when a minimum number of satellites (“space vehicles,” SVs), connected with an uplink station antenna, shall be in view with a generic user at a minimum elevation angle. LA requirements for CS were not fully considered in the design phase of the ULSC algorithm, which is currently implemented at the Mission and Uplink Control Facility (MUCF) of the Galileo Ground Segment. Similarly, this type of requirements is not considered in the ULSC algorithms available in the scientific literature. In particular, current solutions take into account only specifications about 1) the maximum “gap” duration between two subsequent contacts and 2) the minimum contact duration (MCD). In the new ULSC algorithm, LA specifications are effectively dealt with by a proper selection of the subset of the SVs to be allocated. In particular, the proposed selection scheme takes into account the specifications about both maximum gap duration and MCD while maximizing the “spatial diversity” of the solution. Such a diversity is defined as the union of the coverage areas of the allocated satellites. The performance of the new ULSC algorithm is evaluated based on the final deployment stage (full operational capability) of the Galileo system. The performance is also compared with that of the ULSC algorithm currently implemented at the Galileo MUCF (“reference” algorithm). Numerical results show that, compared to the reference algorithm, the average disconnection duration is reduced by more than 50%. In addition, the availability of the links needed for the provision of CS is significantly improved (up to 7%). As a result, preliminary LA requirements for CS are met using the new ULSC algorithm, while the reference algorithm has limitations to meet the new stringent LA requirements.

In Long Term Evolution-Advanced (LTE-A) network, Machine Type Communication (MTC) provides random access-based communication for automation applications. In MTC, one of the most critical issues is the emergence of large number of MTC devices which may send requests to the base station at the same time, such that severe collision occurs. When collision occurs, the message delay accumulates, and the data delivery fails. In this paper, we propose a two-stage radio access scheme to alleviate collision for MTC. MTC devices are divided into several groups, where random access in each group (as the first stage) is controlled by a device called MTC gateway. Collision of random access to the base station (as the second stage) is then effectively alleviated. To observe the effect of numbers and sizes of groups, we propose analytical model and simulation model to investigate the performance in terms of utilization of random access slots, access success probability and average message delay. Simulation results show that, by sacrificing a little message delay, the proposed scheme significantly improves access success probability.

Power conservation is crucial in designing wireless sensor networks (WSNs) with long network lifetime. An energy and delay efficient TDMA scheduling algorithm for a cluster based Wireless Sensor Network is proposed. The proposed energy efficient dynamic scheduling (EEDS) algorithm in this paper uses the information about node traffic load, the number of member nodes and its QoS (Quality of Service) requirement. Our EEDS algorithm could reduce energy consumption and transmission delay. Through mathematical analysis we evaluate the performance of our algorithm and compare to the related works.

Enabling machine-type communication (MTC) over large scale cellular networks is a promising solution to handling the emerging MTC traffic. To enable a massive number of machines to connect to the base station, random access mechanisms and data aggregation have been largely studied separately in the literature. In this paper, we use stochastic geometry to investigate MTC over cellular with access class barring enhanced random access and data aggregation. We present an approximate yet accurate and tractable analytical framework for characterizing the MTC performance in terms of the machine type device (MTD) success probability, average number of successful MTDs and probability of successful preamble utilization. We validate the proposed model by comparison with simulations. Our results show that while the provision of more resources for the relaying phase benefits MTC, the provision of more preambles in the random access is not always beneficial to MTC. Thus, system parameters need to be chosen carefully to benefit the MTC traffic.

The next generation cellular networks are expected to provide massive connectivity with support for high data-rate applications. Machine type communication (MTC) is one of the emerging technologies envisaged to facilitate large-scale provisioning of diverse services with varying quality of service (QoS) requirements. Spectrum aggregation (SA) is one of the promising techniques to increase capacity for high data rate applications and services. In this paper, cognitive radio (CR) MTC (CR-MTC) framework with spectrum aggregation (SA) in cellular networks is considered. The CR-MTC comprises MTC gateway (MTCG) through which data from MTC devices (MTCDs) is relayed to the base-station (BS). In order to support high data-rates, the MTCG employs SA for concurrent transmissions through multiple channels. Performance of SA over fading wireless channels is therefore presented, based on the developed analytical models for ergodic capacity and outage probability. The presented simulation and analytical results reveal that performance of SA in wireless channels is subject to fading, wherefore it can be deduced that SA is mainly beneficial in high signal-to-noise (SNR) ratio regime.

Machine Type Communication (MTC) device for cellular communication is being defined by 3GPP based on LTE in Rel-12 and Rel-13. Low cost has been the major objective of the definition for Rel-12 based specification. In Rel-13, coverage enhancement of MTC devices has been the major focus along with reduced power consumption, complexity and narrow bandwidth operation. Coverage enhancement (CE) of ∼15dB is expected to be achieved by using multiple power aggregation techniques, e.g. boosting Power Spectral Density (PSD), repetitions and relaxed requirements in few other cases. In CE scenarios, many of the receiver algorithms designed for normal coverage (NC) do not perform well. Requirement to operate at very low SINR changes the problem setup for few receiver algorithms. In this paper we introduce multiple UE receiver algorithms spanning various receiver functions, for coverage enhancement with reduced complexity. Specifically, practical cell search algorithm with complete procedure with joint time and frequency uncertainty hypothesis, novel timing estimation algorithm using PBCH, improved Channel Quality Index (CQI) estimation and novel method for opportunistic early decoding criterion based on mutual information metric. Algorithms proposed in this paper are first of its kind addressing coverage enhancement requirement for 3GPP LTE Rel-13 MTC devices. We show the performance of different algorithms proposed at very low SNRs as required for CE case.

Machine type communication (MTC), as one of the most promising technologies in the future wireless communication, has brought mobile communication network into a new level. The breakthrough of cutting-edge technology and broad coverage of cellular networks in long term evolution advanced network constitute an ideal platform for ubiquitous MTC service provisioning on a large scale. However, under the traditional attach approach, the massive MTC devices always select the evolved NodeB (eNB) with the best signal quality for the attachment, thereby causing the network congestion and overload. As a result, it is necessary to design an efficient eNB selection scheme to avoid overload issue. In this paper, by modeling MTC arrivals using nonhomogeneous poisson process with the memoryless property, we formulate the eNB selection problem as a Markov decision process (MDP) and try to compute an optimal solution. Since the network parameters for MDP are not obtained easily, a learning-based algorithm with value-difference based exploration (VDBE) policy who could leverage both present conditions and expected future demands is further proposed. The performances of the proposed reinforcement learning (RL) VDBE, other RL-based, the legacy best-signal-quality, and MDP without RL eNB-selection schemes are analyzed in terms of blocking probability, transmission rate, and load balancing. The simulation results show that our scheme has the best performance on the blocking probability and loading balancing while an acceptable result on the transmission rate. It is suitable for the MTC environment with a highly changed number of MTC arrivals while without high requirement on the transmission rate.

It is predicted that there will be billions of machine type communication (MTC) devices to be deployed in near future. This will certainly cause severe access congestion and system overload which is one of the major challenges for the proper operation of 5G networks. Adopting device-to-device (D2D) communications into massive multiple-input multiple-output (MIMO) systems has been considered as a potential solution to alleviate the overload of MTC devices by offloading the MTC traffic onto D2D links. This work proposes a novel pilot reuse (PR) and power control (PC) for energy efficiency (EE) optimization of the uplink D2D underlaying massive MIMO cellular systems. Although the use of large scale antenna array at the base station (BS) can eliminate most of the D2D-to-Cellular interference, the Cellular-to-D2D interference and the channel estimation error caused by PR will remain significant. Motivated by this, and in order to reduce the channel estimation error, in this paper a novel heuristic PR optimum pilot reuse scheme is proposed for D2D transmitters (D2DTs) selection. By taking into account the interference among users as well as the overall power consumption, the overall system EE is maximized through power optimization while maintaining the quality-of-service (QoS) provisions for both cellular users (CUEs) and D2D pairs. The power optimization problem is modeled as a non-cooperative game and, as such, a distributed iterative power control algorithm which optimizes users power sequentially is proposed. Various performance evaluation results obtained by means of computer simulations have shown that the proposed PR scheme and PC algorithm can significantly increase the overall system EE.

In recent years, people have paid more and more attention to traditional manufacturing's environmental impact, especially in terms of energy consumption and related emissions of carbon dioxide. Except for adopting new equipment, production scheduling could play an important role in reducing the total energy consumption of a manufacturing plant. Machine tools waste a considerable amount of energy because of their underutilization. Consequently, energy saving can be achieved by switching machines to standby or off when they lay idle for a comparatively long period. Herein, we first introduce the objectives of minimizing non-processing energy consumption, total weighted tardiness and earliness, and makespan into a typical production scheduling model-the job shop scheduling problem, based on a machine status switching framework. The multi-objective genetic algorithm U-NSGA-III combined with MME (a heuristic algorithm combined with the MinMax (MM) and Nawaz-Enscore-Ham (NEH) algorithms) population initialization method is used to solve the problem. The multi-objective optimization algorithm can generate a Pareto set of solutions so that production managers can flexibly select a schedule from these non-dominated schedules based on their priorities. Three sets of numerical experiments have been carried out on the extended Taillard benchmark to verify this three-objective model's effectiveness and the multi-objective optimization algorithm. The results show that U-NSGA-III has obtained better Pareto solutions in most test problem instances than NSGA-II and NSGA-III. Furthermore, the non-processing energy consumption is reduced by 46%-69%, which is 13-83% of the total energy consumption.

The MTC (Machine Type Communications) system is one of the most promising technologies to provide IoT (Internet of Things) applications. The MTC system suffers from congestion due to limited data transmission capacity and burst traffic. The congestion disturbs data delivery, results in an increase of energy consumption due to data retransmission, and finally poses a threat to the providers of IoT applications. In this paper, we focus on the congestion problem and present an efficient data forwarding mechanism to regulate the burst channel access from a large number of MTC devices. To regulate the communication channel access from the devices, we predict the number of devices by separating traffic load into the prediction of the number of devices that newly attempt to have access and the prediction of the number of devices that retry to access the channel. Through simulations, we show that our mechanism improves access success probability and reduces collision probability and access delay.

In today’s Wireless Communication World MMIMO (Massive Multiple Input and Multiple Output) is the sounding word and capable system for high data transmission rates in Wireless Systems and Networks (WSN). Higher Data Transmission Rates are achieved by posing many more antennas at base station, as result the power consumption should be more, and the bandwidth requirement also enhanced. To address this issue, in this article an innovative method of selecting the Transmit Antenna by making active and passive combination a novel method is proposed. This novel method not only optimizes the antenna selection but also provides the mechanism of reducing power utilization and energy consumption. Initially we consider the GSA (Gravitational Search Algorithm) with velocity parameter and proposed an alternative velocity, treated as Enhanced Velocity Gravitational Search Algorithm (EV-GAS). Moreover EV-GASA on conventional models of Greedy Algorithm, Antenna Bee Colony, Particle Swarm Optimization, Fierce fly for antenna optimization, Energy and Power consumption. Obtained outcomes are proved the EV-GAS is the prominent algorithm for high data transmission rates.

Future cellular networks are expected to support new communication paradigms such as machine-type communication (MTC) services along with human-type communication (HTC) services. This requires base stations to serve a large number of devices in relatively short channel coherence intervals which renders allocation of orthogonal pilot sequence per-device approaches impractical. Furthermore, the stringent power constraints, place-and-play type connectivity and various data rate requirements of MTC devices make it impossible for the traditional cellular architecture to accommodate MTC and HTC services together. Massive multiple-input-multiple-output (MaMIMO) technology has the potential to allow the coexistence of HTC and MTC services, thanks to its inherent spatial multiplexing properties and low transmission power requirements. In this work, we investigate the performance of a single cell under a shared physical channel assumption for MTC and HTC services and propose a novel scheme for sharing the time-frequency resources. The analysis reveals that MaMIMO can significantly enhance the performance of such a setup and allow the inclusion of MTC services into the cellular networks without requiring additional resources.

Traditional cellular networks require the downlink (DL) and uplink (UL) of mobile users (MUs) to be associated with a single base station (BS). However, the power gap between BSs and MUs in different transmission environments results in the BS with the strongest downlink differing from the BS with the strongest uplink. In addition, the significant increase in the number of wireless machine type communication (MTC) devices accessing cellular networks has created a DL/UL traffic imbalance with higher traffic volume on the uplink. In this paper, a joint user association and resource partition framework for downlink-uplink decoupling (DUDe) is developed for a tiered heterogeneous cellular network (HCN). Different from the traditional association rules such as maximal received power and range extension, a coalition game based scheme is proposed for the optimal user association with DUDe. The stability and convergence of this scheme are proven and shown to converge to a Nash equilibrium at a geometric rate. Moreover, the DL and UL optimal bandwidth partition for BSs is derived based on user association considering fairness. Extensive simulation results demonstrate the effectiveness of the proposed scheme, which enhances the sum rate compared with other user association strategies.

Vehicular communications is an important enabler for enhancing the safety on roads by supporting mutual awareness of vehicles as well as for improving the efficiency of transportation through smart traffic management by intelligent transport systems (ITSs). Governments around the world have set ambitious goals for road fatality reduction in the near future; e.g., the European Union targets a 50% reduction of road fatalities by 2020 as compared to the year 2010. Furthermore, traffic telematic systems aim to minimize the environmental impact of transportation and maximize the utilization of available road infrastructure by adaptive traffic management. To realize these challenging targets, autonomous wireless information exchange among vehicles-vehicle to vehicle (V2V)-and with roadside infrastructure-vehicle to infrastructure (V2I)-are central ingredients. In addition to traffic efficiency and safetyrelated issues, vehicular communications is increasingly recognized as an important revenue driver by car manufacturing companies since it enables wirelessly connected in-vehicle entertainment systems that support on-demand video streaming and online Internet access for passengers. Also, in the future, machine-type communication is expected to play a major role in vehicular environments, with more sensors that monitor the internal state of vehicles and autonomously exchange service and maintenance information with cloud servers of manufacturers. Depending on the considered use-case, distinct quality of service (QoS) requirements come into play [1]: infotainment applications for in-car users require high bandwidth and network capacity, active road safety relies on delay- and outage-critical data transmission, whereas information exchange for road traffic efficiency management typically comes without strict QoS requirements and exhibits graceful degradation of performance with increasing latency.

Energy consumption has become a key metric for evaluating how good an embedded system is, alongside more performance metrics like respecting operation deadlines and speed of execution. Schedulability improvement is no longer the only metric by which optimality is judged. In fact, energy efficiency is becoming a preferred choice with a fundamental objective to optimize the system’s lifetime. In this work, we propose an optimal energy efficient scheduling algorithm for aperiodic real-time jobs to reduce CPU energy consumption. Specifically, we apply the concept of real-time process scheduling to a dynamic voltage and frequency scaling (DVFS) technique. We address a variant of earliest deadline first (EDF) scheduling algorithm called energy saving-dynamic voltage and frequency scaling (ES-DVFS) algorithm that is suited to unpredictable future energy production and irregular job arrivals. We prove that ES-DVFS cannot attain a total value greater than C/000000, where 000000 is the minimum speed of any job and C is the available energy capacity. We also investigate the implications of having in advance, information about the largest job size and the minimum speed used for the competitive factor of ES-DVFS. We show that such advance knowledge makes possible the design of semi-on-line algorithm, ES-DVFS**, that achieved a constant competitive factor of 0.5 which is proved as an optimal competitive factor. The experimental study demonstrates that substantial energy savings and highest percentage of feasible job sets can be obtained through our solution that combines EDF and DVFS optimally under the given aperiodic jobs and energy models.