An acceleration simulation method for power law priority traffic

A method for accelerated simulation for simulated self-similar processes is proposed. This technique simplifies the simulation model and improves the efficiency by using excess packets instead of packet-by-packet source traffic for a FIFO and non-FIFO buffer scheduler. In this research is focusing on developing an equivalent model of the conventional packet buffer that can produce an output analysis (which in this case will be the steady state probability) much faster. This acceleration simulation method is a further development of the Traffic Aggregation technique, which had previously been applied to FIFO buffers only and applies the Generalized Ballot Theorem to calculate the waiting time for the low priority traffic (combined with prior work on traffic aggregation). This hybrid method is shown to provide a significant reduction in the process time, while maintaining queuing behavior in the buffer that is highly accurate when compared to results from a conventional simulatio

User relay assisted traffic shifting in LTE-advanced systems

In order to deal with uneven load distribution, mobility load balancing adjusts the handover region to shift edge users from a hot-spot cell to the less-loaded neighbouring cells. However, shifted users suffer the reduced signal power from neighbouring cells, which may result in link quality degradation. This paper employs a user relaying model and proposes a user relay assisted traffic shifting (URTS) scheme to deal with the above problem. In URTS, a shifted user selects a suitable non-active user as relay user to forward data, thus enhancing the link quality of the shifted user. Since the user relaying model consumes relay user’s energy, a utility function is designed in relay selection to reach a trade-off between the shifted user’s link quality improvement and the relay user’s energy consumption. Simulation results show that URTS scheme could improve SINR and throughput of shifted users. Also, URTS scheme keeps the cost of relay user’s energy consumption at an acceptable level

Energy-delay aware Restricted Access Window with novel retransmission for IEEE 802.11ah networks

Restricted Access Window (RAW) has been introduced to IEEE 802.11ah MAC layer to decrease collision probability. However, the inappropriate application of RAW duration for diverse groups of devices would increase uplink energy consumption, delay and lower down the data rate. In this paper, we study a RAW optimization problem with a novel retransmission scheme that utilizes the next empty slot for retransmission in the uplink. The problem is formulated based on overall energy efficiency and delay of each RAW by applying probability theory and Markov Chain. To jointly optimize energy efficiency and delay, an energy-delay aware window control algorithm is proposed to adapt RAW size by estimating the number of time slots and internal slot duration in one RAW for different groups. The optimal solution is derived by applying Gradient Descent approach. Simulation results show that our proposed algorithm improves up to 113.3% energy efficiency and reduces 53.4% delay compared to the existing RAW

QoS-aware Energy Efficient Cooperative Scheme for Cluster-based IoT Systems

The Internet of Things (IoT) technology with huge number power-constrained devices has been heralded to improve the operational efficiency of many industrial applications. It is vital to reduce the energy consumption of each device, however, this could also degrade the Quality of Service (QoS) provisioning. In this paper, we study the problem of how to achieve the tradeoff between the QoS provisioning and the energy efficiency for the industrial IoT systems. We first formulate the multi-objective optimization problem to achieve the objective of balancing the outage performance and the network lifetime. Then we propose to combine the Quantum Particle Swarm Optimization (QPSO) with the improved Non-dominated Sorting Genetic algorithm (NSGA-II) to obtain the Pareto optimal front. In particular, NSGA-II is applied to solve the formulated multi-objective optimization problem and QPSO algorithm is used to obtain the optimum cooperative coalition. The simulation results suggest that the proposed algorithm can achieve the tradeoff between the energy efficiency and QoS provisioning by sacrificing about 10% network lifetime but improving about 15% outage performance

QPSO-based energy-aware clustering scheme in the capillary networks for Internet of Things systems

Energy efficiency is a crucial challenge in cluster-based capillary networks for Internet of Things (IoT) systems, where the cluster heads (CHs) selection has great impact on the network performance. It is an optimization problem to find the optimum number of CHs as well as which devices are selected as CHs. In this paper, we formulate the clustering problem into the CHs selection procedure with the aim of maximizing the average network lifetime in every round. In particular, we propose a novel CHs selection scheme based on QPSO and investigate how effective it is to prolong network lifetime and reserve the overall battery capacity. The simulation results prove that the proposed QPSO outperforms other evolutionary algorithms and can improve the network lifetime by almost 10%

Energy-aware Restricted Access Window control with retransmission scheme for IEEE 802.11ah (Wi-Fi HaLow) based networks

Restricted Access Window (RAW) has been introduced to IEEE 802.11ah MAC layer to decrease collision probability. However, the inappropriate application of RAW for different groups of devices would increase uplink energy consumption and decrease data rate. In this paper, we study an energy efficient RAW optimization problem for IEEE 802.11ah based uplink communications. We first present a novel retransmission scheme that utilizes the next empty slot to retransmit for collided devices, and formulate the problem based on overall energy consumption and the data rate of each RAW by applying probability theory and Markov Chain. Then, we derive the energy efficiency of the uplink transmission. Last but not the least, an energy-aware window control algorithm to adapt the RAW size is proposed to optimize the energy efficiency by identifying the number of slots in each RAW for different group scales. Simulation results show that our proposed algorithm outperforms existing RAW on uplink energy efficiency and delivery ratio

Optimal design of measurements on queueing systems

We examine the optimal design of measurements on queues with particular reference to the M/M/1 queue. Using the statistical theory of design of experiments, we calculate numerically the Fisher information matrix for an estimator of the arrival rate and the service rate to find optimal times to measure the queue when the number of measurements are limited for both interfering and non-interfering measurements. We prove that in the non-interfering case, the optimal design is equally spaced. For the interfering case, optimal designs are not necessarily equally spaced. We compute optimal designs for a variety of queuing situations and give results obtained under the $D-$- and $D_s$-optimality criteria

Efficient accelerated simulation technique for packet switched networks : a buffer with two priority inputs

An Enhanced Traffic Aggregation (E_TA) technique for acceleration simulation of packet switched network is proposed. This technique simplifies the simulation model and improves the efficiency by using packet-train or packet rate source traffic with non FIFO scheduler in the buffer. The model employs power law traffic which recently proved to be able to capture both long-range dependence and the burstiness of aggregate broadband network traffic. Our results show that using E_TA with FIFO scheduler simulation times can be reduced by 39%, and using E_TA with non FIFO scheduler by 83 %