175,678 research outputs found
Symmetric Allocations for Distributed Storage
We consider the problem of optimally allocating a given total storage budget
in a distributed storage system. A source has a data object which it can code
and store over a set of storage nodes; it is allowed to store any amount of
coded data in each node, as long as the total amount of storage used does not
exceed the given budget. A data collector subsequently attempts to recover the
original data object by accessing each of the nodes independently with some
constant probability. By using an appropriate code, successful recovery occurs
when the total amount of data in the accessed nodes is at least the size of the
original data object. The goal is to find an optimal storage allocation that
maximizes the probability of successful recovery. This optimization problem is
challenging because of its discrete nature and nonconvexity, despite its simple
formulation. Symmetric allocations (in which all nonempty nodes store the same
amount of data), though intuitive, may be suboptimal; the problem is nontrivial
even if we optimize over only symmetric allocations. Our main result shows that
the symmetric allocation that spreads the budget maximally over all nodes is
asymptotically optimal in a regime of interest. Specifically, we derive an
upper bound for the suboptimality of this allocation and show that the
performance gap vanishes asymptotically in the specified regime. Further, we
explicitly find the optimal symmetric allocation for a variety of cases. Our
results can be applied to distributed storage systems and other problems
dealing with reliability under uncertainty, including delay tolerant networks
(DTNs) and content delivery networks (CDNs).Comment: 7 pages, 3 figures, extended version of an IEEE GLOBECOM 2010 pape
A Modified Real-Time Fault-Tolerant Task Allocation Scheme for Wireless Sensor Networks
In WSNs, the sensor nodes are at risk of failure and malicious attacks (selective forwarding). This may have a profound negative effect when you consider real-time WSNs, making them challenging to deploy. When there is a delay in tasks allocation execution processes in real-time WSNs because of sensor nodes failures, this will cause disastrous consequences if the systems are safety-critical, e.g. aircraft, nuclear power plant, forest fire detection, battlefield monitoring, thus the need to developed a real-time system that is fault-tolerable. This paper developed a modified real-time fault-tolerant task allocation scheme (mRFTAS) for WSNs (wireless sensor networks), using active replication techniques. mRFTAS and RFTAS performance were compared using time of execution of the task, network lifetime and reliability cost. The mRFTAS performance showed an improvement over that of RFTAS when it comes to reducing the time it takes for task execution by 45.56% and reliability cost of 7.99% while prolonging the network lifetime by 36.35%
All-terminal reliability evaluation through a Monte Carlo simulation based on an MPI implementation
All-terminal reliability (ATR), defined as the probability that every node in a network can communicate with every other node, is an important problem in research areas such as mobile ad-hoc wireless networks, grid computing systems, and telecommunications. The assessment of ATR has also been part of related problems like the reliability allocation problem. However, the exact calculation of ATR is a NP-hard problem. To obtain this probability, there are approaches based on analytic methods for small networks or estimation through Monte Carlo simulation (MCS). In this paper, a parallel MCS implementation, based on the Message Passing Interface (MPI) standard is presented. The implementation can take advantage of the existence of multiprocessor thus reducing the time required for the ATR assessment. Three examples related to real network illustrate the benefits.Peer ReviewedPostprint (author’s final draft
Heterogeneous Dynamic Spectrum Access in Cognitive Radio enabled Vehicular Networks Using Network Softwarization
Dynamic spectrum access (DSA) in cognitive radio networks (CRNs) is regarded as an emerging technology to solve the spectrum scarcity problem created by static spectrum allocation. In DSA, unlicensed users access idle channels opportunistically, without creating any harmful interference to licensed users. This method will also help to incorporate billions of wireless devices for different applications such as Internet-of-Things, cyber-physical systems, smart grids, etc. Vehicular networks for intelligent transportation cyber-physical systems is emerging concept to improve transportation security and reliability. IEEE 802.11p standard comprising of 7 channels is dedicated for vehicular communications. These channels could be highly congested and may not be able to provide reliable communications in urban areas. Thus, vehicular networks are expected to utilize heterogeneous wireless channels for reliable communications. In this thesis, real-time opportunistic spectrum access in cloud based cognitive radio network (ROAR) architecture is used for energy efficiency and dynamic spectrum access in vehicular networks where geolocation of vehicles is used to find idle channels. Furthermore, a three step mechanism to detect geolocation falsification attacks is presented. Performance is evaluated using simulation results
Adaptive relay techniques for OFDM-based cooperative communication systems
Cooperative communication has been considered as a cost-effective manner to exploit the spatial diversity, improve the quality-of-service and extend transmission coverage. However, there are many challenges faced by cooperative systems which use relays to forward signals to the destination, such as the accumulation of multipath channels, complex resource allocation with the bidirectional asymmetric traffic and reduction of transmission efficiency caused by additional relay overhead. In this thesis, we aim to address the above challenges of cooperative communications, and design the efficient relay systems.
Starting with the channel accumulation problem in the amplify-and-forward relay system, we proposed two adaptive schemes for single/multiple-relay networks respectively. These schemes exploit an adaptive guard interval (GI) technique to cover the accumulated delay spread and enhance the transmission efficiency by limiting the overhead. The proposed GI scheme can be implemented without any extra control signal. Extending the adaptive GI scheme to multiple-relay systems, we propose a relay selection strategy which achieves the trade-off between the transmission reliability and overhead by considering both the channel gain and the accumulated delay spread. We then consider resource allocation problem in the two-way decode-and-forward relay system with asymmetric traffic loads. Two allocation algorithms are respectively investigated for time-division and frequency-division relay systems to maximize the end-to-end capacity of the two-way system under a capacity ratio constraint. For the frequency-division systems, a balanced end-to-end capacity is defined as the objective function which combines the requirements of maximizing the end-to-end capacity and achieving the capacity ratio. A suboptimal algorithm is proposed for the frequency-division systems which separates subcarrier allocation and time/power allocation. It can achieve the similar performance with the optimal one with reduced complexity. In order to further enhance the transmission reliability and maintaining low processing delay, we propose an equalize-and-forward (EF) relay scheme. The EF relay equalizes the channel between source and relay to eliminate the channel accumulation without signal regeneration. To reduce the processing time, an efficient parallel structure is applied in the EF relay. Numerical results show that the EF relay exhibits low outage probability at the same data rate as compared to AF and DF schemes
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