Some Machine-to-Machine (M2M) communication links particularly those in a industrial automation plant have stringent latency requirements. In this paper, we study the delay-performance for the M2M uplink from the sensors to a Programmable Logic Controller (PLC) in a industrial automation scenario. The uplink traffic can be broadly classified as either Periodic Update (PU) and Event Driven (ED). The PU arrivals from different sensors are periodic, synchronized by the PLC and need to be processed by a prespecified firm latency deadline. On the other hand, the ED arrivals are random, have low-arrival rate, but may need to be processed quickly depending upon the criticality of the application. To accommodate these contrasting Quality-of-Service (QoS) requirements, we model the utility of PU and ED packets using step function and sigmoidal functions of latency respectively. Our goal is to maximize the overall system utility while being proportionally fair to both PU and ED data. To this end, we propose a novel online QoS-aware packet scheduler that gives priority to ED data as long as that results the latency deadline is met for PU data. However as the size of networks increases, we drop the PU packets that fail to meet latency deadline which reduces congestion and improves overall system utility. Using extensive simulations, we compare the performance of our scheme with various scheduling policies such as First-Come-First-Serve (FCFS), Earliest-Due-Date (EDD) and (preemptive) priority. We show that our scheme outperforms the existing schemes for various simulation scenarios.
Machine-to-machine (M2M) communications are bringing new challenges to congestion control in the Internet of Things. One key issue is to facilitate the proper functioning of a wide range of M2M applications with drastically different throughput demands. Traditional Internet congestion control algorithms aim at sharing bandwidth among traffic flows equally, limiting their suitability for M2M communications. To maintain comparable levels of QoS of heterogeneous M2M applications when congestion is present, we propose a distributed congestion control algorithm which allocates transmission rates to M2M flows in proportion to their demands, through the use of a simple technique which we call “proportional additive increase”. To ease M2M application development, we make a further attempt to stabilize the throughputs of M2M flows controlled by the algorithm. We present simulation results to illustrate the effectiveness of the algorithm in achieving the desired rate allocation, as well as the challenge we face in stabilizing throughputs. Providing LTE connectivity to emerging Machine-to-Machine (M2M) applications imposes several challenges to the operation and optimization of current and future 3GPP mobile broadband standard releases. Scheduling in an efficient way M2M traffic over the existing LTE MAC infrastructure is decisive for the smooth evolution towards an M2M-enabled LTE system. The large number of connecting devices compared to classical LTE terminals, and their vastly diverse quality-of-service requirements, call for the design of new packet scheduling schemes tailored to the M2M paradigm. To this end, we propose low complexity and signaling scheduling policies which periodically grant access to the M2M devices. In particular, we first propose an analytical model for predicting the QoS performance of M2M services when the fixed periodic scheduling algorithm is employed. Next we propose a modification to this scheme, which exploit queueing-dynamics and finally we examine QoS-differentiation issues when devices are grouped into clusters. Interesting performance-complexity trade-offs are exposed. The results of our study may aid the system designer in tuning and optimizing M2M traffic scheduling. Some M2M applications such as those found in a Smart Grid environment generate event driven and delay sensitive uplink traffic. Wide area cellular systems such as LTE are usually not optimized for such traffic. In this paper, we design an LTE scheduler with the main objective of maximizing the percentage of uplink packets that satisfy their individual delay budgets. In order to do this accurately, we allow devices to notify the eNodeB of the age of the oldest packet in their buffers via a new MAC control element in the uplink MPDU. This information is used by the eNodeB to calculate an absolute deadline for each packet request individually, and the eNodeB scheduler ranks requests according to an urgency metric that depends upon the time remaining to the deadline and other factors such as the volume of pending data in the device buffers. Using an OPNET simulation model of an LTE TDD system, we show that our proposed scheduler can satisfy the uplink delay budget for more than 99 of packets for bursty delay sensitive M2M traffic even when the system is fully loaded with regard to the data channel utilization.