3,269 research outputs found
Coalition Formation Games for Collaborative Spectrum Sensing
Collaborative Spectrum Sensing (CSS) between secondary users (SUs) in
cognitive networks exhibits an inherent tradeoff between minimizing the
probability of missing the detection of the primary user (PU) and maintaining a
reasonable false alarm probability (e.g., for maintaining a good spectrum
utilization). In this paper, we study the impact of this tradeoff on the
network structure and the cooperative incentives of the SUs that seek to
cooperate for improving their detection performance. We model the CSS problem
as a non-transferable coalitional game, and we propose distributed algorithms
for coalition formation. First, we construct a distributed coalition formation
(CF) algorithm that allows the SUs to self-organize into disjoint coalitions
while accounting for the CSS tradeoff. Then, the CF algorithm is complemented
with a coalitional voting game for enabling distributed coalition formation
with detection probability guarantees (CF-PD) when required by the PU. The
CF-PD algorithm allows the SUs to form minimal winning coalitions (MWCs), i.e.,
coalitions that achieve the target detection probability with minimal costs.
For both algorithms, we study and prove various properties pertaining to
network structure, adaptation to mobility and stability. Simulation results
show that CF reduces the average probability of miss per SU up to 88.45%
relative to the non-cooperative case, while maintaining a desired false alarm.
For CF-PD, the results show that up to 87.25% of the SUs achieve the required
detection probability through MWCComment: IEEE Transactions on Vehicular Technology, to appea
Performance evaluation of cooperation strategies for m-health services and applications
Health telematics are becoming a major improvement for patients’ lives, especially for
disabled, elderly, and chronically ill people. Information and communication technologies have
rapidly grown along with the mobile Internet concept of anywhere and anytime connection.
In this context, Mobile Health (m-Health) proposes healthcare services delivering, overcoming
geographical, temporal and even organizational barriers. Pervasive and m-Health services aim
to respond several emerging problems in health services, including the increasing number of
chronic diseases related to lifestyle, high costs in existing national health services, the need
to empower patients and families to self-care and manage their own healthcare, and the need
to provide direct access to health services, regardless the time and place. Mobile Health (m-
Health) systems include the use of mobile devices and applications that interact with patients
and caretakers. However, mobile devices have several constraints (such as, processor, energy,
and storage resource limitations), affecting the quality of service and user experience. Architectures
based on mobile devices and wireless communications presents several challenged issues
and constraints, such as, battery and storage capacity, broadcast constraints, interferences, disconnections,
noises, limited bandwidths, and network delays. In this sense, cooperation-based
approaches are presented as a solution to solve such limitations, focusing on increasing network
connectivity, communication rates, and reliability. Cooperation is an important research topic
that has been growing in recent years. With the advent of wireless networks, several recent
studies present cooperation mechanisms and algorithms as a solution to improve wireless networks
performance. In the absence of a stable network infrastructure, mobile nodes cooperate
with each other performing all networking functionalities. For example, it can support intermediate
nodes forwarding packets between two distant nodes.
This Thesis proposes a novel cooperation strategy for m-Health services and applications.
This reputation-based scheme uses a Web-service to handle all the nodes reputation and networking
permissions. Its main goal is to provide Internet services to mobile devices without
network connectivity through cooperation with neighbor devices. Therefore resolving the above
mentioned network problems and resulting in a major improvement for m-Health network architectures
performances. A performance evaluation of this proposal through a real network
scenario demonstrating and validating this cooperative scheme using a real m-Health application
is presented. A cryptography solution for m-Health applications under cooperative environments,
called DE4MHA, is also proposed and evaluated using the same real network scenario and
the same m-Health application. Finally, this work proposes, a generalized cooperative application
framework, called MobiCoop, that extends the incentive-based cooperative scheme for
m-Health applications for all mobile applications. Its performance evaluation is also presented
through a real network scenario demonstrating and validating MobiCoop using different mobile
applications
An LSPI based reinforcement learning approach to enable network cooperation in cognitive wireless sensor networks
The number of wirelessly communicating devices increases every day, along with the number of communication standards and technologies that they use to exchange data. A relatively new form of research is trying to find a way to make all these co-located devices not only capable of detecting each other's presence, but to go one step further - to make them cooperate. One recently proposed way to tackle this problem is to engage into cooperation by activating 'network services' (such as internet sharing, interference avoidance, etc.) that offer benefits for other co-located networks. This approach reduces the problem to the following research topic: how to determine which network services would be beneficial for all the cooperating networks. In this paper we analyze and propose a conceptual solution for this problem using the reinforcement learning technique known as the Least Square Policy Iteration (LSPI). The proposes solution uses a self-learning entity that negotiates between different independent and co-located networks. First, the reasoning entity uses self-learning techniques to determine which service configuration should be used to optimize the network performance of each single network. Afterwards, this performance is used as a reference point and LSPI is used to deduce if cooperating with other co-located networks can lead to even further performance improvements
Impact of malicious node on secure incentive based advertisement distribution (SIBAD) in VANET
Last decade has seen an increasing demand for vehicle aided data delivery. This data delivery has proven to be beneficial for vehicular communication. The vehicular network provisions safety, warning and infotainment applications. Infotainment applications have attracted drivers and passengers as it provides location based entertainment services, a value add to the traveling experience. These infotainment messages are delivered to the nearby vehicles in the form of advertisements. For every advertisement disseminated to its neighboring vehicle, an incentive is awarded to the forwarder. The incentive based earning foresee a security threat in the form of a malicious node as it hoards the incentives, thus are greedy for earning incentives. The malicious behavior of the insider has an adverse effect on the incentive based advertisement distribution approach. In this paper, we have identified the malicious nodes and analyzed its effect on incentive based earning for drivers in vehicular networks. © 2017 IEEE
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