7,941 research outputs found
Hybrid performance modelling of opportunistic networks
We demonstrate the modelling of opportunistic networks using the process
algebra stochastic HYPE. Network traffic is modelled as continuous flows,
contact between nodes in the network is modelled stochastically, and
instantaneous decisions are modelled as discrete events. Our model describes a
network of stationary video sensors with a mobile ferry which collects data
from the sensors and delivers it to the base station. We consider different
mobility models and different buffer sizes for the ferries. This case study
illustrates the flexibility and expressive power of stochastic HYPE. We also
discuss the software that enables us to describe stochastic HYPE models and
simulate them.Comment: In Proceedings QAPL 2012, arXiv:1207.055
SDDV: scalable data dissemination in vehicular ad hoc networks
An important challenge in the domain of vehicular ad hoc networks (VANET) is the scalability of data dissemination. Under dense traffic conditions, the large number of communicating vehicles can easily result in a congested wireless channel. In that situation, delays and packet losses increase to a level where the VANET cannot be applied for road safety applications anymore. This paper introduces scalable data dissemination in vehicular ad hoc networks (SDDV), a holistic solution to this problem. It is composed of several techniques spread across the different layers of the protocol stack. Simulation results are presented that illustrate the severity of the scalability problem when applying common state-of-the-art techniques and parameters. Starting from such a baseline solution, optimization techniques are gradually added to SDDV until the scalability problem is entirely solved. Besides the performance evaluation based on simulations, the paper ends with an evaluation of the final SDDV configuration on real hardware. Experiments including 110 nodes are performed on the iMinds w-iLab.t wireless lab. The results of these experiments confirm the results obtained in the corresponding simulations
Service Provisioning through Opportunistic Computing in Mobile Clouds
Mobile clouds are a new paradigm enabling mobile users to access the heterogeneous services present in a pervasive mobile environment together with the rich service offers of the cloud infrastructures. In mobile computing environments mobile devices can also act as service providers, using approaches conceptually similar to service-oriented models. Many approaches implement service provisioning between mobile devices with the intervention of cloud-based handlers, with mobility playing a disruptive role to the functionality offered by of the system. In our approach, we exploit the opportunistic computing model, whereby mobile devices exploit direct contacts to provide services to each other, without necessarily go through conventional cloud services residing in the Internet. Conventional cloud services are therefore complemented by a mobile cloud formed directly by the mobile devices. This paper exploits an algorithm for service selection and composition in this type of mobile cloud environments able to estimate the execution time of a service composition. The model enables the system to produce an estimate of the execution time of the alternative compositions that can be exploited to solve a user's request and then choose the best one among them. We compare the performance of our algorithm with alternative strategies, showing its superior performance from a number of standpoints. In particular, we show how our algorithm can manage a higher load of requests without causing instability in the system conversely to the other strategies. When the load of requests is manageable for all strategies, our algorithm can achieve up to 75% less time spent in average to solve requests
Autonomous Algorithms for Centralized and Distributed Interference Coordination: A Virtual Layer Based Approach
Interference mitigation techniques are essential for improving the
performance of interference limited wireless networks. In this paper, we
introduce novel interference mitigation schemes for wireless cellular networks
with space division multiple access (SDMA). The schemes are based on a virtual
layer that captures and simplifies the complicated interference situation in
the network and that is used for power control. We show how optimization in
this virtual layer generates gradually adapting power control settings that
lead to autonomous interference minimization. Thereby, the granularity of
control ranges from controlling frequency sub-band power via controlling the
power on a per-beam basis, to a granularity of only enforcing average power
constraints per beam. In conjunction with suitable short-term scheduling, our
algorithms gradually steer the network towards a higher utility. We use
extensive system-level simulations to compare three distributed algorithms and
evaluate their applicability for different user mobility assumptions. In
particular, it turns out that larger gains can be achieved by imposing average
power constraints and allowing opportunistic scheduling instantaneously, rather
than controlling the power in a strict way. Furthermore, we introduce a
centralized algorithm, which directly solves the underlying optimization and
shows fast convergence, as a performance benchmark for the distributed
solutions. Moreover, we investigate the deviation from global optimality by
comparing to a branch-and-bound-based solution.Comment: revised versio
SCAMPI: Service platform for soCial Aware Mobile and Pervasive computIng
Allowing mobile users to find and access resources available in the surrounding environment opportunistically via their smart devices could enable them to create and use a rich set of services. Such services can go well beyond what is possible for a mobile phone acting alone. In essense, access to diverse resources such as raw computational power, social networking relationships, or sensor readings across a set of different devices calls for distributed task execution. In this paper, we discuss the SCAMPI architecture designed to support distributed task execution in opportunistic pervasive networks. The key elements of the architecture include leveraging human social behavior for efficient opportunistic interaction between a variety of sensors, personal communication devices and resources embedded in the local environment. The SCAMPI architecture abstracts resources asservice components following a service-oriented model. This enables composing rich applications that utilize a collection of service components available in the environment
Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey
Wireless sensor networks (WSNs) consist of autonomous and resource-limited
devices. The devices cooperate to monitor one or more physical phenomena within
an area of interest. WSNs operate as stochastic systems because of randomness
in the monitored environments. For long service time and low maintenance cost,
WSNs require adaptive and robust methods to address data exchange, topology
formulation, resource and power optimization, sensing coverage and object
detection, and security challenges. In these problems, sensor nodes are to make
optimized decisions from a set of accessible strategies to achieve design
goals. This survey reviews numerous applications of the Markov decision process
(MDP) framework, a powerful decision-making tool to develop adaptive algorithms
and protocols for WSNs. Furthermore, various solution methods are discussed and
compared to serve as a guide for using MDPs in WSNs
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