16,479 research outputs found
An Algorithmic Solution for Computing Circle Intersection Areas and its Applications to Wireless Communications
A novel iterative algorithm for the efficient computation of the intersection
areas of an arbitrary number of circles is presented. The algorithm, based on a
trellis-structure, hinges on two geometric results which allow the
existence-check and the computation of the area of the intersection regions
generated by more than three circles by simple algebraic manipulations of the
intersection areas of a smaller number of circles. The presented algorithm is a
powerful tool for the performance analysis of wireless networks, and finds many
applications, ranging from sensor to cellular networks. As an example of
practical application, an insightful study of the uplink outage probability of
in a wireless network with cooperative access points as a function of the
transmission power and access point density is presented
An Intrusion Detection Architecture for Clustered Wireless Ad Hoc Networks
Intrusion detection in wireless ad hoc networks is a challenging task because
these networks change their topologies dynamically, lack concentration points
where aggregated traffic can be analyzed, utilize infrastructure protocols that
are susceptible to manipulation, and rely on noisy, intermittent wireless
communications. Security remains a major challenge for these networks due their
features of open medium, dynamically changing topologies, reliance on
co-operative algorithms, absence of centralized monitoring points, and lack of
clear lines of defense. In this paper, we present a cooperative, distributed
intrusion detection architecture based on clustering of the nodes that
addresses the security vulnerabilities of the network and facilitates accurate
detection of attacks. The architecture is organized as a dynamic hierarchy in
which the intrusion data is acquired by the nodes and is incrementally
aggregated, reduced in volume and analyzed as it flows upwards to the
cluster-head. The cluster-heads of adjacent clusters communicate with each
other in case of cooperative intrusion detection. For intrusion related message
communication, mobile agents are used for their efficiency in lightweight
computation and suitability in cooperative intrusion detection. Simulation
results show effectiveness and efficiency of the proposed architecture.Comment: 6 pages, 2 Figures, 2 tables. Second International Conference on
Computational Intelligence, Communication Systems and Networks (CICSYSN
2010), Liverpool, UK, July 28 - 30, 201
Aqua Computing: Coupling Computing and Communications
The authors introduce a new vision for providing computing services for
connected devices. It is based on the key concept that future computing
resources will be coupled with communication resources, for enhancing user
experience of the connected users, and also for optimising resources in the
providers' infrastructures. Such coupling is achieved by Joint/Cooperative
resource allocation algorithms, by integrating computing and communication
services and by integrating hardware in networks. Such type of computing, by
which computing services are not delivered independently but dependent of
networking services, is named Aqua Computing. The authors see Aqua Computing as
a novel approach for delivering computing resources to end devices, where
computing power of the devices are enhanced automatically once they are
connected to an Aqua Computing enabled network. The process of resource
coupling is named computation dissolving. Then, an Aqua Computing architecture
is proposed for mobile edge networks, in which computing and wireless
networking resources are allocated jointly or cooperatively by a Mobile Cloud
Controller, for the benefit of the end-users and/or for the benefit of the
service providers. Finally, a working prototype of the system is shown and the
gathered results show the performance of the Aqua Computing prototype.Comment: A shorter version of this paper will be submitted to an IEEE magazin
AACT: Application-Aware Cooperative Time Allocation for Internet of Things
As the number of Internet of Things (IoT) devices keeps increasing, data is
required to be communicated and processed by these devices at unprecedented
rates. Cooperation among wireless devices by exploiting Device-to-Device (D2D)
connections is promising, where aggregated resources in a cooperative setup can
be utilized by all devices, which would increase the total utility of the
setup. In this paper, we focus on the resource allocation problem for
cooperating IoT devices with multiple heterogeneous applications. In
particular, we develop Application-Aware Cooperative Time allocation (AACT)
framework, which optimizes the time that each application utilizes the
aggregated system resources by taking into account heterogeneous device
constraints and application requirements. AACT is grounded on the concept of
Rolling Horizon Control (RHC) where decisions are made by iteratively solving a
convex optimization problem over a moving control window of estimated system
parameters. The simulation results demonstrate significant performance gains
Exploiting Non-Causal CPU-State Information for Energy-Efficient Mobile Cooperative Computing
Scavenging the idling computation resources at the enormous number of mobile
devices can provide a powerful platform for local mobile cloud computing. The
vision can be realized by peer-to-peer cooperative computing between edge
devices, referred to as co-computing. This paper considers a co-computing
system where a user offloads computation of input-data to a helper. The helper
controls the offloading process for the objective of minimizing the user's
energy consumption based on a predicted helper's CPU-idling profile that
specifies the amount of available computation resource for co-computing.
Consider the scenario that the user has one-shot input-data arrival and the
helper buffers offloaded bits. The problem for energy-efficient co-computing is
formulated as two sub-problems: the slave problem corresponding to adaptive
offloading and the master one to data partitioning. Given a fixed offloaded
data size, the adaptive offloading aims at minimizing the energy consumption
for offloading by controlling the offloading rate under the deadline and buffer
constraints. By deriving the necessary and sufficient conditions for the
optimal solution, we characterize the structure of the optimal policies and
propose algorithms for computing the policies. Furthermore, we show that the
problem of optimal data partitioning for offloading and local computing at the
user is convex, admitting a simple solution using the sub-gradient method.
Last, the developed design approach for co-computing is extended to the
scenario of bursty data arrivals at the user accounting for data causality
constraints. Simulation results verify the effectiveness of the proposed
algorithms.Comment: Submitted to possible journa
Mimicking Full-Duplex Secure Communications for Buffer-Aided Multi-Relay Systems
This paper considers secure communication in buffer-aided cooperative
wireless networks in the presence of one eavesdropper, which can intercept the
data transmission from both the source and relay nodes. A new max-ratio
relaying protocol is proposed, in which different relays are chosen for
reception and transmission according to the ratio of the legitimate channels to
the eavesdropper channels, so that the relay selected for reception and the
relay selected for transmission can receive and transmit at the same time. It
is worth noting that the relay employs a randomize-and-forward (RF) strategy
such that the eavesdropper can only decode the signals received in the two hops
independently. Theoretical analysis of the secrecy throughput of the proposed
scheme is provided and the approximate closed-form expressions are derived,
which are verified by simulations. Through numerical results, it is shown that
the proposed scheme achieves a significant improvement in secrecy throughput
compared with existing relay selection policies.Comment: 8 pages, 4 figure
Mobile Edge Computation Offloading Using Game Theory and Reinforcement Learning
Due to the ever-increasing popularity of resource-hungry and
delay-constrained mobile applications, the computation and storage capabilities
of remote cloud has partially migrated towards the mobile edge, giving rise to
the concept known as Mobile Edge Computing (MEC). While MEC servers enjoy the
close proximity to the end-users to provide services at reduced latency and
lower energy costs, they suffer from limitations in computational and radio
resources, which calls for fair efficient resource management in the MEC
servers. The problem is however challenging due to the ultra-high density,
distributed nature, and intrinsic randomness of next generation wireless
networks. In this article, we focus on the application of game theory and
reinforcement learning for efficient distributed resource management in MEC, in
particular, for computation offloading. We briefly review the cutting-edge
research and discuss future challenges. Furthermore, we develop a
game-theoretical model for energy-efficient distributed edge server activation
and study several learning techniques. Numerical results are provided to
illustrate the performance of these distributed learning techniques. Also, open
research issues in the context of resource management in MEC servers are
discussed
Compression and Combining Based on Channel Shortening and Rank Reduction Techniques for Cooperative Wireless Sensor Networks
This paper investigates and compares the performance of wireless sensor
networks where sensors operate on the principles of cooperative communications.
We consider a scenario where the source transmits signals to the destination
with the help of sensors. As the destination has the capacity of processing
only out of these signals, the strongest signals are selected while
the remaining signals are suppressed. A preprocessing block similar to
channel-shortening is proposed in this contribution. However, this
preprocessing block employs a rank-reduction technique instead of
channel-shortening. By employing this preprocessing, we are able to decrease
the computational complexity of the system without affecting the bit error rate
(BER) performance. From our simulations, it can be shown that these schemes
outperform the channel-shortening schemes in terms of computational complexity.
In addition, the proposed schemes have a superior BER performance as compared
to channel-shortening schemes when sensors employ fixed gain amplification.
However, for sensors which employ variable gain amplification, a tradeoff
exists in terms of BER performance between the channel-shortening and these
schemes. These schemes outperform channel-shortening scheme for lower
signal-to-noise ratio.Comment: In IEEE Transactions on Vehicular Technology, 201
A Bayesian algorithm for distributed network localization using distance and direction data
A reliable, accurate, and affordable positioning service is highly required
in wireless networks. In this paper, the novel Message Passing Hybrid
Localization (MPHL) algorithm is proposed to solve the problem of cooperative
distributed localization using distance and direction estimates. This hybrid
approach combines two sensing modalities to reduce the uncertainty in
localizing the network nodes. A statistical model is formulated for the
problem, and approximate minimum mean square error (MMSE) estimates of the node
locations are computed. The proposed MPHL is a distributed algorithm based on
belief propagation (BP) and Markov chain Monte Carlo (MCMC) sampling. It
improves the identifiability of the localization problem and reduces its
sensitivity to the anchor node geometry, compared to distance-only or
direction-only localization techniques. For example, the unknown location of a
node can be found if it has only a single neighbor; and a whole network can be
localized using only a single anchor node. Numerical results are presented
showing that the average localization error is significantly reduced in almost
every simulation scenario, about 50% in most cases, compared to the competing
algorithms.Comment: Notice: This work has been submitted to the IEEE for possible
publication. Copyright may be transferred without notice, after which this
version may no longer be accessibl
Joint Power Adjustment and Interference Mitigation Techniques for Cooperative Spread Spectrum Systems
This paper presents joint power allocation and interference mitigation
techniques for the downlink of spread spectrum systems which employ multiple
relays and the amplify and forward cooperation strategy. We propose a joint
constrained optimization framework that considers the allocation of power
levels across the relays subject to an individual power constraint and the
design of linear receivers for interference suppression. We derive constrained
minimum mean-squared error (MMSE) expressions for the parameter vectors that
determine the optimal power levels across the relays and the linear receivers.
In order to solve the proposed optimization problem efficiently, we develop
joint adaptive power allocation and interference suppression algorithms that
can be implemented in a distributed fashion. The proposed stochastic gradient
(SG) and recursive least squares (RLS) algorithms mitigate the interference by
adjusting the power levels across the relays and estimating the parameters of
the linear receiver. SG and RLS channel estimation algorithms are also derived
to determine the coefficients of the channels across the base station, the
relays and the destination terminal. The results of simulations show that the
proposed techniques obtain significant gains in performance and capacity over
non-cooperative systems and cooperative schemes with equal power allocation.Comment: 6 figures. arXiv admin note: text overlap with arXiv:1301.009
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