Fast Adaptive S-ALOHA Scheme for Event-driven Machine-to-Machine Communications

Machine-to-Machine (M2M) communication is now playing a market-changing role in a wide range of business world. However, in event-driven M2M communications, a large number of devices activate within a short period of time, which in turn causes high radio congestions and severe access delay. To address this issue, we propose a Fast Adaptive S-ALOHA (FASA) scheme for M2M communication systems with bursty traffic. The statistics of consecutive idle and collision slots, rather than the observation in a single slot, are used in FASA to accelerate the tracking process of network status. Furthermore, the fast convergence property of FASA is guaranteed by using drift analysis. Simulation results demonstrate that the proposed FASA scheme achieves near-optimal performance in reducing access delay, which outperforms that of traditional additive schemes such as PB-ALOHA. Moreover, compared to multiplicative schemes, FASA shows its robustness even under heavy traffic load in addition to better delay performance.Comment: 5 pages, 3 figures, accepted to IEEE VTC2012-Fal

Mobile Unmanned Aerial Vehicles (UAVs) for Energy-Efficient Internet of Things Communications

In this paper, the efficient deployment and mobility of multiple unmanned aerial vehicles (UAVs), used as aerial base stations to collect data from ground Internet of Things (IoT) devices, is investigated. In particular, to enable reliable uplink communications for IoT devices with a minimum total transmit power, a novel framework is proposed for jointly optimizing the three-dimensional (3D) placement and mobility of the UAVs, device-UAV association, and uplink power control. First, given the locations of active IoT devices at each time instant, the optimal UAVs' locations and associations are determined. Next, to dynamically serve the IoT devices in a time-varying network, the optimal mobility patterns of the UAVs are analyzed. To this end, based on the activation process of the IoT devices, the time instances at which the UAVs must update their locations are derived. Moreover, the optimal 3D trajectory of each UAV is obtained in a way that the total energy used for the mobility of the UAVs is minimized while serving the IoT devices. Simulation results show that, using the proposed approach, the total transmit power of the IoT devices is reduced by 45% compared to a case in which stationary aerial base stations are deployed. In addition, the proposed approach can yield a maximum of 28% enhanced system reliability compared to the stationary case. The results also reveal an inherent tradeoff between the number of update times, the mobility of the UAVs, and the transmit power of the IoT devices. In essence, a higher number of updates can lead to lower transmit powers for the IoT devices at the cost of an increased mobility for the UAVs.Comment: Accepted in IEEE Transactions on Wireless Communications, Sept. 201

Network-aware Evaluation Environment for Reputation Systems

Parties of reputation systems rate each other and use ratings to compute reputation scores that drive their interactions. When deciding which reputation model to deploy in a network environment, it is important to find the most suitable model and to determine its right initial configuration. This calls for an engineering approach for describing, implementing and evaluating reputation systems while taking into account specific aspects of both the reputation systems and the networked environment where they will run. We present a software tool (NEVER) for network-aware evaluation of reputation systems and their rapid prototyping through experiments performed according to user-specified parameters. To demonstrate effectiveness of NEVER, we analyse reputation models based on the beta distribution and the maximum likelihood estimation

Time series classification based on fractal properties

The article considers classification task of fractal time series by the meta algorithms based on decision trees. Binomial multiplicative stochastic cascades are used as input time series. Comparative analysis of the classification approaches based on different features is carried out. The results indicate the advantage of the machine learning methods over the traditional estimating the degree of self-similarity.Comment: 4 pages, 2 figures, 3 equations, 1 tabl

The Glasgow Parallel Reduction Machine: Programming Shared-memory Many-core Systems using Parallel Task Composition

We present the Glasgow Parallel Reduction Machine (GPRM), a novel, flexible framework for parallel task-composition based many-core programming. We allow the programmer to structure programs into task code, written as C++ classes, and communication code, written in a restricted subset of C++ with functional semantics and parallel evaluation. In this paper we discuss the GPRM, the virtual machine framework that enables the parallel task composition approach. We focus the discussion on GPIR, the functional language used as the intermediate representation of the bytecode running on the GPRM. Using examples in this language we show the flexibility and power of our task composition framework. We demonstrate the potential using an implementation of a merge sort algorithm on a 64-core Tilera processor, as well as on a conventional Intel quad-core processor and an AMD 48-core processor system. We also compare our framework with OpenMP tasks in a parallel pointer chasing algorithm running on the Tilera processor. Our results show that the GPRM programs outperform the corresponding OpenMP codes on all test platforms, and can greatly facilitate writing of parallel programs, in particular non-data parallel algorithms such as reductions.Comment: In Proceedings PLACES 2013, arXiv:1312.221