No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
Scalable Priority-based Resource Allocation Scheme for M2M Communication in LTE/LTE-A Network
Abstract
Machine-to-Machine (M2M) communication in the Long Term Evolution (LTE) network has recently grown exponentially as the volume of connected devices has increased rapidly in the last decade. M2M traffic can be understood via certain parameters in terms of packet length, packet generation frequency, delay, and data rate requirements, and it typically flows in the uplink direction. Primarily, the LTE network design is optimized for Human-to-Human (H2H) communication. As a result, designing uplink scheduling in LTE networks is fraught with difficulties which restrict the use of potential capacity. In response to the preceding methodologies, focusing on the QCI priority degrades resource utilization and cell throughput. Therefore, a scheduling mechanism needs to optimize the system performance with priority support to use LTE in M2M communication. This paper highlights existing flaws in the optimisation process and proposes a scalable priority-based resource allocation scheme for M2M communication in the LTE/LTE-Advance network. The proposed scheme for resource allocation strikes a balance between resource utilization and application priority support. According to the results, the proposed scheduling algorithm outperforms the standard algorithms concerning resource sharing fairness, average resource utilization, QCI priority support, and delay budget violation.
... Some M2M applications, like eHealth, have strict time and latency requirements. Denial of service (DoS) attacks and other stalling attacks can have dire repercussions [137]. These circumstances preclude deploying security solutions that cause delays, necessitating precise security measures to reduce any adverse effects on business. ...
The Internet of Things (IoT) is becoming increasingly popular, and machine-to-machine (M2M) technology is one of its major components. Reducing human interaction and tasks is one of the most important justifications for research in M2M communication. Also, M2M technology decreases network traffic, improving network effectiveness. In contrast, multiple networks are combined into M2M networks, which leads to security-related design concerns. However, a significant barrier to its growth is security. According to statistics, new gadgets may come under attack five minutes after they connect to the Internet. If these issues are resolved, it will be easier for people to trust this worldview. Security issues with possible solutions have gotten little attention despite extensive M2M studies. This article provides a comprehensive security analysis of M2M communication technologies, exploring the risks and solutions to better comprehend M2M communication and its security implications.
... The presence of ineffective protective elements in the scheme accounts for this. The research [32] identifies known optimization issues and provides a scalable priority-based resource allocation system for M2M communication in the LTE/LTE-Advance network. The resource allocation system provided finds a compromise between resource usage and application priority support. ...
Artificial intelligence and the introduction of Internet of Things technologies have benefited from technological advances and new automated computer system technologies. Eventually, it is now possible to integrate them into a single offline industrial system. This is accomplished through machine-to-machine communication, which eliminates the human factor. The purpose of this article is to examine security systems for machine-to-machine communication systems that rely on identification and authentication algorithms for real-time monitoring. The article investigates security methods for quickly resolving data processing issues by using the Security operations Center's main machine to identify and authenticate devices from 19 different machines. The results indicate that when machines are running offline and performing various tasks, they can be exposed to data leaks and malware attacks by both the individual machine and the system as a whole. The study looks at the operation of 19 computers, 7 of which were subjected to data leakage and malware attacks. AnyLogic software is used to create visual representations of the results using wireless networks and algorithms based on previously processed methods. The W76S is used as a protective element within intelligent sensors due to its built-in memory protection. For 4 machines, the data leakage time with malware attacks was 70 s. For 10 machines, the duration was 150 s with 3 attacks. Machine 15 had the longest attack duration, lasting 190 s and involving 6 malware attacks, while machine 19 had the shortest attack duration, lasting 200 s and involving 7 malware attacks. The highest numbers indicated that attempting to hack a system increased the risk of damaging a device, potentially resulting in the entire system with connected devices failing. Thus, illegal attacks by attackers using malware may be identified over time, and data processing effects can be prevented by intelligent control. The results reveal that applying identification and authentication methods using a protocol increases cyber-physical system security while also allowing real-time monitoring of offline system security.
... Over the past two decades, we have seen the introduction of various mobile standards, from 2 G to 5 G via 3 G/4 G and the trend will be towards 6 G. Observing the maximum speeds allowed by these technologies, the LTE standard (3GPP version 8) is specified to provide a maximum data rate of 300 Mbps. The advanced LTE 3GPP support a data rate more than 1 Gbps [1,2]. This remarkable increase has been achieved thanks to the development of new technologies introduced in a period of about 20 years. ...
This paper focuses on the evolution of cellular networks as part of their adaptation to the IoT, allowing their new belonging to the category of LPWAN networks. We will be more particularly interested in coverage extension mechanisms. The transmission chain used in this work based on GMSK scheme with maximum likelihood criterion for phase detection. During the theoretical study on the IQ-Prefilter mixed recombination mechanism, we determined an approximation expression of the mean SINR of the frame after recombination. This study allowed us to observe the impact of the temporal correlation of the propagation channel on the performance of the proposed mechanism. A TU6 (Typical Urban 6 paths) channel model will be used in this work. This model is adapted to the urban environment, in accordance with our study on the IoT and the smart city. The effect of the parameters introduced in the theoretical part such as recombination ratio, number of repetitions and duration between each repetition will be evaluated particularly regarding SINR and BER. Based on simulation, the performance of the proposed IQ-Prefilter mechanism is better than those of the IQ and Prefilter recombination for equivalent SNRs. Moreover, the performance of the IQ-Prefilter mechanism is better for low IQ-Prefilter ratios, that is to say when recombination by Prefilter is preferred to recombination by IQ. All the theoretical results and those obtained by simulation make it possible to better evaluate the performance of the suggested mechanisms in the IoT framework.
The exponential growth in data traffic from smart devices has led to a need for highly capable wireless networks with faster data transmission rates and improved spectral efficiency. Allocating resources efficiently in a 5G communication system with a huge number of machine type communication (MTC) devices is essential to ensure optimal performance and meet the diverse requirements of different applications. The LTE-A network offers high-speed mobile data services and caters to MTC devices and has relatively low data service requirements compared to human-to-human (H2H) communications. LTE-A networks require advanced scheduling schemes to manage the limited spectrum and ensure efficient transmissions. This necessitates effective resource allocation schemes to minimize interference between cells in future networks. To address this issue, a joint delay and energy aware Levy flight Brownian movement-based dragonfly optimization (DELFBDO)-based uplink resource allocation scheme for LTE-A Networks is proposed in this work to optimize energy efficiency, maximize the throughput and reduce the latency. The DELFDO algorithm efficiently organizes packets in both time and frequency domains for H2H and MTC devices, resulting in improved quality of service while minimizing energy consumption. The Simulation results demonstrate that the proposed method increases the energy efficiency by producing the appropriate channel and power assignment for UEs and MTC devices.
Mobile networks of the advanced fourth generation and fifth generation are among the most important leaps of modern technology in the last decade as Long-Term Evolution-Advance (LTE-A) network. Perhaps the most important feature that distinguishes it is the ability to receive communication from several service providers by taking advantage of Carrier Aggregation (CA) technology. However, the obstacle will be the limitations of this technology if one of the service providers works within the time domain and the other works within the frequency domain, or/and if the service providers work in two different phases. This article proposes a new type of carrier aggregation technique called Hybrid Phase Downlink Carrier Aggregation (HPDCA) that makes all service providers work in one downlink domain by using Fast Fourier Transform (FFT). The phase is also reversed by using the in-phase and quadrature IQ components phase convention to unify all the phases of the service providers to make them work in one phase for the purpose of assembling them when applying the assembling technique. Good performance and throughput results were obtained and the efficiency of the system remained at the required level. Furthermore, the performance calculation has achieved based on Energy Efficiency (EE), the phase response property, Error Vector Magnitude (EVM).
Machine Type Communication (MTC) becomes one of enablers of the internet of things, it faces many challenges in its integration with human-to-human (H2H) communication methods. To this aim, the Long Term Evolution (LTE) needs some adaptation in the scheduling algorithms that assign resources efficiently to both MTC devices (MTCDs) and H2H users. The minimum amount of LTE resources that can be assigned to one user is much larger than the requirements of a single MTCD. In this paper, a QoS-enabled algorithm is proposed to aggregate MTCD traffic coming from many sources at the Relay Node (RN) that classifies and aggregates the MTCD traffic based on the source type and delay requirements. In this study, three types of MTCD and one H2H sources will be considered. Each type of MTCD traffic will be grouped into a separate queue, and will be served with the appropriate priority. Resources are then assigned to the aggregated MTC traffic instead of an individual assignment for each MTCD, while the H2H users will be directly connected to the LTE. Two schemes of resource partitioning and sharing between the MTCDs and the H2H users will be considered: one proportional and the other moving-boundary. Simulation models will be built to evaluate the proposed algorithms. While the obtained results for the first scheme showed a clear improvement in LTE resource utilization for the MTCDs, a negative effect was noticed in the performance of the H2H users. The second scheme achieved a positive improvement for both MTCDs and H2H users.
Machine-to-Machine (M2M) communication refers to autonomous communication among devices that aims for a massive number of connected devices. M2M communication can support ubiquitous communication and full mechanical automation, and it will change everything from industry to ourselves. Recent developments in communication technology make Long Term Evolution (LTE)/Long Term Evolution-Advance (LTE-A) a promising technology for supporting M2M communication. LTE can support the diverse characteristic of M2M communication due to its IP connectivity, coverage area, and scalability. Therefore, the LTE schedulers should satisfy the need for M2M communication. Motivated by these facts, in this paper, we present a survey on the classification of LTE / LTE-A scheduling methodologies from the perspective of M2M communication. We classify the schedulers based on their objectives, such as energy efficiency, spectrum efficiency, group-based, and Quality-of-Service (QoS) support for Machine Type Communication Devices (MTCDs). We also highlight the scope of future research direction for the scheduling work.
Machine-to-machine (M2M) communications, which aims to enable autonomous communication between machine devices with minimum human intervention, have experienced remarkable growth over the past few years. In massive M2M communications, random access (RA) collision is one of critical problems at the network entry stage. To control the collision rate during the network access stage, most existing RA solutions for M2M communications need the base station (BS) to determine the optimal backoff parameters, which are hard to obtain and also would incur substantial signaling overhead, especially in dense scenarios. To address this issue, a distributive approach is highly desirable. In this paper, a new method for distributive estimation of optimal backoff parameters is proposed. Specifically, each MTD only needs to observe their own successful and total transmissions of access requests during an estimation interval, after which each MTD can obtain an estimation of the optimal backoff parameters according to an explicit expression of the observed statistics. It is found that the estimation interval determines the performance of the proposed scheme. When the number of MTDs in the network does not change or varies slowly, the network throughput can be maximized if the estimation interval is chosen properly. Compared to existing centralized methods, the proposed method can achieve similar performance with lower signaling overhead. The proposed scheme can be applied for both homogeneous and heterogeneous scenarios and provide a practical access design method for M2M communications.
Machine-to-machine (M2M) communication consists of the communication between intelligent devices without human intervention. Long term evolution (LTE) and Long-term evolution-advanced (LTE-A) cellular networks technologies are excellent candidates to support M2M communication as they offer high data rates, low latencies, high capacities and more flexibility. However, M2M communication over LTE/LTE-A networks faces some challenges. One of these challenges is the management of resource radios especially on the uplink. LTE schedulers should be able to meet the needs of M2M devices, such as power management and the support of large number of devices, in addition to handling both human-to-human (H2H) and M2M communications. Motivated by the fundamental requirement of extending the battery lives of M2M devices and managing an LTE network system, including both M2M devices and H2H users, in this paper, two channel-aware scheduling algorithms on the uplink are proposed. Both of them consider the coexistence of H2H and M2M communications and aim to reduce energy consumption in M2M devices. The first algorithm, called FDPS-carrier-by-carrier modified (CBC-M), takes into account channel quality and power consumption while allocating radio resources. Our second algorithm, recursive maximum expansion modified (RME-M), offers a balance between delay requirement and energy consumption. Depending on the system requirements, RME-M considers both channel quality and system deadlines in an adjustable manner according to M2M devices needs. Simulation results show that the proposed schedulers outperform the round-robin scheduler in terms of energy efficiency and have better cell spectral efficiency.
Machine‐to‐machine (M2M) communication is a challenging topic in the Internet‐of‐Things era. The increasing growth of machines and high rate of packet production have result in massive access to the network. Therefore, one of the media access control (MAC) challenges in 5G cellular networks is the management of massive access to the wireless media regarding quality‐of‐service (QoS) requirements and battery restrictions of the machines. Delay is one of the QoS requirements that should be guaranteed in most applications. To the best of our knowledge, previous studies on scheduled MAC mechanisms have not addressed energy efficiency and delay requirements appropriately. In this paper, the problem of scheduled massive access management has been considered with the aim of simultaneously meeting delay requirements of machines and increasing energy efficiency. The proposed solution exploits gateways to reuse the spectrum dedicated to M2M communications and introduces a clustering algorithm to allocate radio resources to the machines appropriately. Moreover, an optimization problem is defined and solved using genetic algorithms (GA) to adjust the optimal transmission power of machines, aiming at maximizing the energy efficiency. To preserve delay constraints, the proposed method exploits an existing scheduling algorithm. Simulation results demonstrate more than 50% reduction in probability of packet delay violation for various classes of service and in mean packet delay compared to the baseline method. The results have also shown more than 20% improvement in energy efficiency of proposed method compared to the baseline.
span>Single Carrier Frequency Division Multiple Access (SC-FDMA) is chosen because of the lower peak-to-average power ratio (PAPR) value in uplink transmission. However, the contiguity constraint is one of the major constraint presents in uplink packet scheduling, where all RBs allocated to a single UE must be contiguous in the frequency-domain within each time slot to maintain its single carrier. This paper proposed an uplink-scheduling algorithm namely the Maximum Expansion with Contiguity Constraints (MECC) algorithm, which supports both the RT and NRT services. The MECC algorithm is deployed in two stages. In the first stage, the RBs are allocated fairly among the UEs. The second stage allocates the RBs with the highest metric value and expands the allocation on both sides of the matrix, M with respect to the contiguity constraint. The performance of the MECC algorithm was observed in terms of throughput, fairness, delay, and Packet Loss Ratio (PLR) for VoIP, video and best effort flows. The MECC scheduling algorithm is compared to other algorithms namely the Round Robin (RR), Channel-Dependent First Maximum Expansion (CD-FME), and Proportional Fairness First Maximum Expansion (PF-FME). From here, it can be concluded that the MECC algorithm shows the best results among other algorithms by delivering the highest throughput which is up to 81.29% and 90.04% than CD-FME and RR scheduler for RT and NRT traffic respectively, having low PLR and delay which is up to 93.92% and 56.22% of improvement than CD-FME for the RT traffic flow. The MECC also has a satisfactory level of fairness for the cell-edge users in a vehicular environment of LTE network.</span
The dramatic growth of machine-to-machine (M2M) communication in cellular networks brings the challenge of satisfying the Quality of Service (QoS) requirements of a large number of M2M devices with limited radio resources. In this paper, we propose an optimization framework for the semi-persistent scheduling of M2M transmissions based on the exploitation of their periodicity with the goal of reducing the overhead of the signaling required for connection initiation and scheduling. The goal of the optimization problem is to minimize the number of frequency bands used by M2M devices to allow fair resource allocation of newly joining M2M and human-to-human communications. The constraints of the problem are delay and periodicity requirements of M2M devices. We first prove that the optimization problem is NP-hard, then propose a polynomial-time heuristic algorithm employing a fixed priority assignment according to the QoS characteristics of devices. We show that this heuristic algorithm provides an asymptotic approximation ratio of 2.33 to the optimal solution for the case where the delay tolerances of devices are equal to their periods. Through extensive simulations, we demonstrate that the proposed algorithm performs better than the existing algorithms in terms of frequency band usage and schedulability.
With the development of wireless communication systems, it is particularly essential to maximize the quality of experience (QoE) of machine-to-machine (M2M) communication. In this paper, we propose a new QoE-oriented uplink rate control and resource allocation scheme for the Internet of Things (IoT) network, by introducing an evaluation model based on Mean Opinion Score (MOS) for different machine-type communication (MTC) devices. The existing works are only dedicated to solve the shortterm resource allocation problems by considering the current transmission time slots, which cannot handle long-standing problems. To this end, based on the recently developed Lyapunov optimization, we convert the original long-term optimization problem into the admission rate control subproblem and the resource allocation subproblem in each time slot. To solve the joint power optimization and sub-channel selection subproblems, Gale-Shapley algorithm is utilized to formulate it as a two-dimensional matching problem and the preference lists are established by the transmission rate and signal to interference plus noise ratio (SINR). In the proposed algorithms, a priority mechanism is employed to ensure fairness. Simulation results demonstrate that without prior knowledge of the data arrivals and sub-channel statistics, the proposed algorithms can significantly improve the overall perceived quality from the users’ perspective.
We present an approach to schedule Long Term Evolution (LTE) uplink (UL) Machine-to-Machine (M2M) traffic in a densely deployed heterogeneous network, over the street lights of a big boulevard for smart city applications. The small cells operate with frequency reuse 1, and inter-cell interference (ICI) is a critical issue to manage. We consider a 3rd Generation Partnership Project (3GPP) compliant scenario, where single-carrier frequency-division multiple access (SC-FDMA) is selected as the multiple access scheme, which requires that all resource blocks (RBs) allocated to a single user have to be contiguous in the frequency within each time slot. This adjacency constraint limits the flexibility of the frequency-domain packet scheduling (FDPS) and inter-cell interference coordination (ICIC), when trying to maximize the scheduling objectives, and this makes the problem NP-hard. We aim to solve a multi-objective optimization problem, to maximize the overall throughput, maximize the radio resource usage and minimize the ICI. This can be modelled through a mixed-integer linear programming (MILP) and solved through a heuristic implementable in the standards. We propose two models. The first one allocates resources based on the three optimization criteria, while the second model is more compact and is demonstrated through numerical evaluation in CPLEX, to be equivalent in the complexity, while it performs better and executes faster. We present simulation results in a 3GPP compliant network simulator, implementing the overall protocol stack, which support the effectiveness of our algorithm, for different M2M applications, with respect to the state-of-the-art approaches.
In this paper, a weighted proportional fair (PF) scheduling method is proposed in the context of non-orthogonal multiple access (NOMA) with successive interference cancellation (SIC) at the receiver side. The new scheme introduces weights that adapt the classical PF metric to the NOMA scenario, improving performance indicators and enabling new services. The distinguishing value of the proposal resides in its ability to improve long term fairness and total system throughput while achieving a high level of fairness in every scheduling slot. Finally, it is shown that the additional complexity caused by the weight calculation has only a limited impact on the overall scheduler complexity while simulation results confirm the claimed improvements making the proposal an appealing alternative for resource allocation in a cellular downlink system.
Resource allocation, or scheduling, is one of the main challenges that face supporting Machine-to-Machine (M2M) communications on Long Term Evolution (LTE) networks. M2M traffic has unique characteristics. It generally consists of a large number of small data packets, with specific deadlines, generated by a potentially massive number of devices contending over the scarce radio resources. In this paper, we introduce a novel M2M scheduling metric that we term the “statistical priority”. Statistical priority is a term that indicates the uniqueness of the information carried by certain data packets sent by Machine-type Communications Devices (MTCDs). If an MTCD data unit is significantly dissimilar to the previously sent data, it is considered to carry non-redundant information. Consequently, it would be assigned higher statistical priority and this MTCD should then be given higher priority in the scheduling process. Using this proposed metric in scheduling, the scarce radio resources would be used for transmitting statistically important information rather than repetitive data, which is a common situation in M2M communications. Simulation results show that our proposed statistical priority-based scheduler outperforms the other baseline schedulers in terms of having the least number of deadline misses (less than 4%) for critical data packets. In addition, our scheduler outperforms the other baseline schedulers in non-redundant data transmission as it achieves a success ratio of at least 70%.
To achieve 1000-fold capacity increase in 5G wireless communications, ultra-dense network (UDN) is believed to be one of the key enabling technologies. Most of the previous research activities on UDNs were based very much on human-to-human (H2H) communications. However, to provide ubiquitous Internet of Things (IoT) services, machine-to-machine (M2M) communications will play a critical role in 5G systems. As the number of machine-oriented connections increases, it is expected that supporting M2M communications is an essential requirement in all future UDNs. In this paper, we aim to bridge the gaps between M2M communications and UDNs, which were commonly considered as two separate issues in the literature. The paper begins with a brief introduction on M2M communications and UDNs, and then will discuss the issues on the roles of M2M communications in future UDNs. We will identify different ways to implement M2M communications in the UDNs from the perspectives of layered architecture, including physical (PHY), media access control (MAC), network, and application layers. Other two important issues, i.e., security and network virtualization, will also be addressed. Before the end of this paper, we will give a summary on identified research topics for future studies.
Fairness means to maintain the property of equity or equivalence. It is the distribution, sharing, allocation, and supply of different working metrics fairly such as bandwidth, throughput, power, utilization, resources, frequency, rate, time slot, and spectrum in any wireless network. For every network including Cognitive Radio Networks (CRNs), fairness plays a significant role. In fact, CRNs provides an intelligent, autonomous and dynamic sensing environment performing different operations, through which unlicensed users get the benefit to use licensed spectrum. In CRNs, the operations performed on spectrum includes sensing, mobility, sharing and management. However, the existence of fairness maintains the equilibrium in these different operations of CRNs. Similarly, the stability of Cognitive Radio (CR) system or network rely on fairness. So, it has a great importance in the performance of CRNs. The performance of CRNs depends on parameters like throughput, efficiency, utilization, power consumption, bandwidth, Quality of Service (QoS), scheduling and some other aspects related to channel and spectrum in CRNs. In this article, fairness is discussed in the context of CRNs. We provide a comprehensive survey of fairness including measuring parameters, fairness models, fairness issues and discussion on different schemes proposed in the literature. We furthermore present common issues, challenges and future research directions for CRNs in fairness perspective.
Cellular networks have been engineered and optimized to carrying
ever-increasing amounts of mobile data, but over the last few years, a new
class of applications based on machine-centric communications has begun to
emerge. Automated devices such as sensors, tracking devices, and meters - often
referred to as machine-to-machine (M2M) or machine-type communications (MTC) -
introduce an attractive revenue stream for mobile network operators, if a
massive number of them can be efficiently supported. The novel technical
challenges posed by MTC applications include increased overhead and control
signaling as well as diverse application-specific constraints such as ultra-low
complexity, extreme energy efficiency, critical timing, and continuous data
intensive uploading. This paper explains the new requirements and challenges
that large-scale MTC applications introduce, and provides a survey on key
techniques for overcoming them. We focus on the potential of 4.5G and 5G
networks to serve both the high data rate needs of conventional human-type
communications (HTC) subscribers and the forecasted billions of new MTC
devices. We also opine on attractive economic models that will enable this new
class of cellular subscribers to grow to its full potential.
he Long Term Evolution (LTE) standard plays an important role in the development of Machine-to-Machine (M2M) communication. However, the M2M communication has several different characteristics regarding to Human-to-Human (H2H) communication. Therefore, the shortage of radio resources, satisfaction of the Quality of Service (QoS) requirements and the reduction of the H2H traffic performance are important issues to be addressed when introducing the M2M communication in the network. In this article, we present a scheduler that may dynamically adjust to the level of congestion of the network based on the current traffic information of each device. The main goals of our approach are (i) satisfy the QoS requirements (ii) ensure fair allocation of resources and (iii) control the impact of H2H traffic performance. The simulation results demonstrated that the proposed scheduling has good performance according to the three objectives aforementioned.
In this work we consider the problem of downlink re- source allocation for proportional fairness of long term received rates of data users and quality of service for real time sessions in an OFDMA-based wireless system. The base station allocates avail- able power and subchannels to individual users based on long term average received rates, quality of service (QoS) based rate con- straints and channel conditions. We formulate and solve a joint bandwidth and power optimization problem, solving which pro- vides a performance improvement with respect to existing resource allocation algorithms. We propose schemes for flat as well as fre- quency selective fading cases. Numerical evaluation results show that the proposed method provides better QoS to voice and video sessions while providing more and fair rates to data users in com- parison with existing schemes.
AbstractM2M communication in the LTE network is gaining attention with a growing number of connected devices and adaptation of new emerging technologies. As the traffic generated from M2M devices is heterogeneous in terms of packet size, intensity, strict delay, and throughput requirements and the M2M traffic generally flows in the uplink direction, it imposes several challenges in designing scheduling for uplink in LTE network. In this paper, a Quality-of-Service aware uplink packet scheduling scheme is proposed using a combinatorial game. The packet scheduling problem is modeled as an Auction game that handles the scheduling in both Time-Domain and Frequency-Domain. Packet scheduler uses the QoS requirement as an allocation metric, and a power control mechanism is applied to control the strategic behavior of the devices. Simulation is performed in Network Simulator 3 (NS3). The performance of proposed schemes is evaluated based on the total system utility in context of QoS satisfaction and compared to standard schedulers like Round Robin, and Proportional Fair schedulers.
The uplink data arriving at the Machine-to-Machine (M2M) Application Server (AS) via M2M Aggregators (MAs) is fairly heterogeneous along several dimensions such as maximum permissible packet delay, arrival rate, and payload size, thus necessitating the design of Quality-of-Service (QoS) aware packet schedulers. In this paper, we classify the M2M uplink data into multiple QoS classes and use sigmoidal function to map the delay requirements of each class onto utility functions. We propose a delay-optimal multiclass packet scheduler at AS obtained by iteratively searching for the optimal fraction of time-sharing between all preemptive priority scheduling policies, such that it maximizes a proportionally fair system utility metric. The iterative optimization process leads to reduced complexity, thus facilitating its online implementation. We then extend this work to determine a jointly optimal packet scheduler at the MAs and AS. We significantly reduce its computational complexity by iteratively solving a distributed optimization problem, independently at MAs and AS. Using Monte Carlo simulations, we show that the proposed joint packet scheduler outperforms other state-of-the-art packet schedulers and results in (desirable) near-minimal delay-jitter for delay-sensitive traffic at the expense of (tolerable) higher delay-jitter for delay-tolerant traffic.
With the exponential growth of technologies such as IoT, edge computing, and 5G, a tremendous amount of structured and unstructured data is being generated from different applications in the smart citiy environment in recent years. Thus, there is a need to develop sophisticated techniques that can efficiently process such huge volumes of data. One of the important components of smart cities, ITS, has led to many applications, including surveillance, infotainment, real-time traffic monitoring, and so on. However, its security, performance, and availability are major concerns facing the research community. The existing solutions, such as cellular networks, RSUs, and mobile cloud computing, are far from perfect because these are highly dependent on centralized architecture and bear the cost of additional infrastructure deployment. Also, the conventional methods of data processing are not capable of handling dynamic and scalable data efficiently. To mitigate these issues, this article proposes an advanced vehicular communication technique where RSUs are proposed to be replaced by edge computing platforms. Then secure V2V and V2E communication is designed using the Quotient filter, a probabilistic data structure. In summary, a smart security framework for VANETs equipped with edge computing nodes and 5G technology has been designed to enhance the capabilities of communication and computation in the modern smart city environment. It has been experimentally demonstrated that use of edge nodes as an intermediate interface between vehicle and cloud reduces access latency and avoids congestion in the backbone network, which allows quick decisions to be made based on the traffic scenario in the geographical location of the vehicles. The proposed scheme outperforms the conventional vehicular models by providing an energy-efficient secure system with minimum delay.
As a result of the increasing number of machine-type devices connected through the Internet, several challenges remain to date to support machine-to-machine (M2M) communications over long term evolution (LTE) cellular networks. In this paper, we tackle one important challenge of scheduling M2M traffic in uplink over LTE-M mobile networks. We propose a novel cross-layer scheme that considers packet scheduling in time and frequency domain using a memetic-based algorithm and aims to optimize resource allocation of M2M devices by considering their Quality of Service (QoS) needs while minimizing at the same time their energy consumption. After integrating an energy module for LTE in NS3 simulator, we perform simulations in a realistic M2M scenario and we evaluate the results which show how our proposed scheduling scheme outperforms other existing scheduling methods from the literature such as Round Robin (RR) and Proportional-Fair (PF) algorithms in terms of throughput, energy consumption and the percentage of satisfied devices with regard to their throughput and delay requirements.
Low power and long range machine-to-machine (M2M) communication techniques are expected to provide ubiquitous connections for the wireless devices. In this paper, three major low power and long range M2M solutions are surveyed. The first type of solutions is referred to as the low power wide area (LPWA) network. The design of the LPWA techniques features low cost, low data rate, long communication range, and low power consumption. The second type of solutions is the IEEE 802.11ah which features higher data rates using a wider bandwidth than the LPWA-based solutions. The third type of solutions is operated under the cellular network infrastructure. Based on the analysis of the pros and cons of the enabling technologies of the surveyed M2M solutions, as well as the corresponding deployment strategies, the gaps in knowledge are identified. The paper also presents a summary of the research directions for improving the performance of the surveyed low power and long range M2M communication technologies.
In LTE (Long Term Evolution) systems, the PRB (Physical Resource Block) usage ratio is considered importantly to manage the QoS (Quality of Service) of LTE traffic. As the PRB usage ratio increases, the resource may not be allocated in a timely and reliable manner to the users of the cell. It may cause the degradation of the QoS, particularly to the cell edge users. In this paper, we share the output from the discussion with the cellular operator and government, and also present how the PRB usage ratio can affect the QoS in the aspect of data rate of LTE users near the base station or the cell edge.
Providing diverse and strict quality of service (QoS) guarantees is one of the most important requirements in M2M communications, which particularly need for appropriate resource allocation for a large number of M2M devices. To efficiently allocate resource blocks (RBs) for M2M devices while satisfying QoS requirements, we propose group based M2M communications, in which M2M devices are clustered based on their wireless transmission protocols, their QoS characteristics and requirements. To perform joint RB and power allocation in SCFDMA based LTE-A networks, we formulate a sum-throughput maximization problem, while respecting all the constraints associated with SC-FDMA scheme as well as QoS requirements in M2M devices. The constraints in uplink SC-FDMA air interface in LTE-A networks complicate the resource allocation problem. We solve the resource allocation problem by first transforming it into a binary integer programming (BIP) problem, and then formulate a dual problem using the Lagrange duality theory. Numerical results show that the proposed algorithm outperforms traditional Greedy algorithm in terms of throughput maximization while satisfying QoS requirements, and its performance is close to the optimal design.
The Long Term Evolution (LTE) standard plays an important role in the development of Machine-to-Machine (M2M) communication. However, the M2M communication has several different characteristics regarding to Human-to-Human (H2H) communication. Therefore, the shortage of radio resources, satisfaction of the Quality of Service (QoS) requirements and the reduction of the H2H traffic performance are important issues to be addressed when introducing the M2M communication in the network. In this article, we present a scheduler that may dynamically adjust to the level of congestion of the network based on the current traffic information of each device. The main goals of our approach are (i) satisfy the QoS requirements (ii) ensure fair allocation of resources and (iii) control the impact of H2H traffic performance. The simulation results demonstrated that the proposed scheduling has good performance according to the three objectives aforementioned.
Machine-To-machine (M2M) communication, also referred to as Internet of Things (IoT), is a global network of devices such as sensors, actuators, and smart appliances which collect information, and can be controlled and managed in real time over the Internet. Due to their universal coverage, cellular networks and the Internet together offer the most promising foundation for the implementation of M2M communication. With the worldwide deployment of the fourth generation (4G) of cellular networks, the long-Term evolution (LTE) and LTE-Advanced standards have defined several quality-of-service classes to accommodate the M2M traffic. However, cellular networks are mainly optimized for human-To-human (H2H) communication. The characteristics of M2M traffic are different from the human-generated traffic and consequently create sever problems in both radio access and the core networks (CNs). This survey on M2M communication in LTE/LTE-A explores the issues, solutions, and the remaining challenges to enable and improve M2M communication over cellular networks. We first present an overview of the LTE networks and discuss the issues related to M2M applications on LTE. We investigate the traffic issues of M2M communications and the challenges they impose on both access channel and traffic channel of a radio access network and the congestion problems they create in the CN. We present a comprehensive review of the solutions for these problems which have been proposed in the literature in recent years and discuss the advantages and disadvantages of each method. The remaining challenges are also discussed in detail.
Scheduling for flows has been studied before. However, applying the previous schemes directly for LTE networks may not achieve good performance. To have good performance, both frequency domain allocations and time domain allocations for LTE resource blocks are suggested. Our method is suitable for real-time services and it consists of three phases. In frequency domain we design our method to utilize the RBs effectively. In time domain we first manage queues for different applications and propose a mechanism for predicting the packet delays. We introduce the concept of virtual queue to predict the behavior of future incoming packets based on the packets in the current queue. Then based on the calculated results, we introduce a cut-in process to rearrange the transmission order and discard those packets which cannot meet their delay requirements. We compare our scheduling mechanism with maximum throughput, proportional fair, modified largest delay first and exponential proportional fair. Simulation results show our scheduling method can achieve better performance than other schemes.
He is pursuing a PhD as a full-time scholar at BITS, Pilani. His primary area of interest is Computer Networks
- T H Cormen
- C E Leiserson
- R L Rivest
- C Stein
T. H. Cormen, C. E. Leiserson, R. L. Rivest, and C. Stein, Introduction to algorithms. MIT press, 2009.
AUTHORS BIOGRAPHY
Upendra Singh received his B.E. degree in Computer Science Engineering from the University of
Rajasthan, Jaipur, India, in 2009. He received his M. Tech. in Computer Science from Jagannath University,
Jaipur, India, in 2013. He is pursuing a PhD as a full-time scholar at BITS, Pilani. His primary area of
interest is Computer Networks, M2M Communication, and 4G-5G Cellular Network.
Proportional fair scheduling algorithm in OFDMA-based wireless systems with QoS constraints
- Girici