A source traffic model for machine-to-machine communications is presented in this paper. We consider a model in which devices operate in a regular mode until they are triggered into an alarm mode by an alarm event. The positions of devices and events are modeled by means of Poisson point processes, where the generated traffic by a given device depends on its position and event positions. We first consider the case where devices and events are static and devices generate traffic according to a Bernoulli process, where we derive the total rate from the devices at the base station. We then extend the model by defining a two-state Markov chain for each device, which allows for devices to stay in alarm mode for a geometrically distributed holding time. The temporal characteristics of this model are analyzed via the autocovariance function, where the effect of event density and mean holding time are shown.
Models for M2M traffic can broadly be categorized into two groups, source models and aggregated models @cite_6 . In a source model, each source has its own set of tunable parameters which control its individual traffic characteristics. In an aggregated traffic model all sources have a common set of parameters. A source model thus allows for more fine-grained control; however this comes at a higher cost in computational complexity because of a higher number of parameters.
@cite_6
2151771412 2048601342 2160995598 2093590637
Machine-to-machine (M2M) communications have many applications, such as in smart grid, health monitoring alerting, and intelligent transportation. Due to unique characteristics that are different from the traditional human-to-human communications, numerous design challenges have to be tackled in M2M. A critical issue is that of dealing with the massive access of machine-type communication (MTC) devices while guaranteeing the desired quality of service (QoS). In this paper, we group MTC devices into clusters based on their QoS requirement. By utilizing the time-controlled M2M feature, we formulate the problem of massive access with QoS guarantee as a queuing problem. We derive an overflow probability estimation model by applying a large deviation principle and then propose an online-measurement-based adaptive massive access management for deciding the amount of resources allocated for each cluster. The proposed method bases the decision on the observed traffic workload with no prior knowledge of its statistics. We conduct the simulations in multicluster scenarios with distinct maximum tolerable delay and corresponding acceptable probability in a long-term evolution system. The simulation results show the adaptability of the proposed massive access management regime to the variations in the traffic workload; thus, it is capable of accommodating dynamic propagation conditions and unknown traffic characteristics, particularly in burst-by-burst communications. 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. As the operation of our fiber-optic backbone networks migrates from interconnected SONET rings to arbitrary mesh topology, traffic grooming on wavelength-division multiplexing (WDM) mesh networks becomes an extremely important research problem. To address this problem, we propose a new generic graph model for traffic grooming in heterogeneous WDM mesh networks. The novelty of our model is that, by only manipulating the edges of the auxiliary graph created by our model and the weights of these edges, our model can achieve various objectives using different grooming policies, while taking into account various constraints such as transceivers, wavelengths, wavelength-conversion capabilities, and grooming capabilities. Based on the auxiliary graph, we develop an integrated traffic-grooming algorithm (IGABAG) and an integrated grooming procedure (INGPROC) which jointly solve several traffic-grooming subproblems by simply applying the shortest-path computation method. Different grooming policies can be represented by different weight-assignment functions, and the performance of these grooming policies are compared under both nonblocking scenario and blocking scenario. The IGABAG can be applied to both static and dynamic traffic grooming. In static grooming, the traffic-selection scheme is key to achieving good network performance. We propose several traffic-selection schemes based on this model and we evaluate their performance for different network topologies. An abstract model for aggregated connectionless traffic, based on the fractional Brownian motion, is presented. Insight into the parameters is obtained by relating the model to an equivalent burst model. Results on a corresponding storage process are presented. The buffer occupancy distribution is approximated by a Weibull distribution. The model is compared with publicly available samples of real Ethernet traffic. The degree of the short-term predictability of the traffic model is studied through an exact formula for the conditional variance of a future value given the past. The applicability and interpretation of the self-similar model are discussed extensively, and the notion of ideal free traffic is introduced. >