229 research outputs found
Coalitional Game Theoretic Approach for Cooperative Transmission in Vehicular Networks
Cooperative transmission in vehicular networks is studied by using
coalitional game and pricing in this paper. There are several vehicles and
roadside units (RSUs) in the networks. Each vehicle has a desire to transmit
with a certain probability, which represents its data burtiness. The RSUs can
enhance the vehicles' transmissions by cooperatively relaying the vehicles'
data. We consider two kinds of cooperations: cooperation among the vehicles and
cooperation between the vehicle and RSU. First, vehicles cooperate to avoid
interfering transmissions by scheduling the transmissions of the vehicles in
each coalition. Second, a RSU can join some coalition to cooperate the
transmissions of the vehicles in that coalition. Moreover, due to the mobility
of the vehicles, we introduce the notion of encounter between the vehicle and
RSU to indicate the availability of the relay in space. To stimulate the RSU's
cooperative relaying for the vehicles, the pricing mechanism is applied. A
non-transferable utility (NTU) game is developed to analyze the behaviors of
the vehicles and RSUs. The stability of the formulated game is studied.
Finally, we present and discuss the numerical results for the 2-vehicle and
2-RSU scenario, and the numerical results verify the theoretical analysis.Comment: accepted by IEEE ICC'1
Diversity Maximized Scheduling in RoadSide Units for Traffic Monitoring Applications
This paper develops an optimal data aggregation policy for learning-based
traffic control systems based on imagery collected from Road Side Units (RSUs)
under imperfect communications. Our focus is optimizing semantic information
flow from RSUs to a nearby edge server or cloud-based processing units by
maximizing data diversity based on the target machine learning application
while taking into account heterogeneous channel conditions (e.g., delay, error
rate) and constrained total transmission rate. As a proof-of-concept, we
enforce fairness among class labels to increase data diversity for
classification problems. The developed constrained optimization problem is
non-convex. Hence it does not admit a closed-form solution, and the exhaustive
search is NP-hard in the number of RSUs. To this end, we propose an approximate
algorithm that applies a greedy interval-by-interval scheduling policy by
selecting RSUs to transmit. We use coalition game formulation to maximize the
overall added fairness by the selected RSUs in each transmission interval.
Once, RSUs are selected, we employ a maximum uncertainty method to handpick
data samples that contribute the most to the learning performance. Our method
outperforms random selection, uniform selection, and pure network-based
optimization methods (e.g., FedCS) in terms of the ultimate accuracy of the
target learning application
V2X Meets NOMA: Non-Orthogonal Multiple Access for 5G Enabled Vehicular Networks
Benefited from the widely deployed infrastructure, the LTE network has
recently been considered as a promising candidate to support the
vehicle-to-everything (V2X) services. However, with a massive number of devices
accessing the V2X network in the future, the conventional OFDM-based LTE
network faces the congestion issues due to its low efficiency of orthogonal
access, resulting in significant access delay and posing a great challenge
especially to safety-critical applications. The non-orthogonal multiple access
(NOMA) technique has been well recognized as an effective solution for the
future 5G cellular networks to provide broadband communications and massive
connectivity. In this article, we investigate the applicability of NOMA in
supporting cellular V2X services to achieve low latency and high reliability.
Starting with a basic V2X unicast system, a novel NOMA-based scheme is proposed
to tackle the technical hurdles in designing high spectral efficient scheduling
and resource allocation schemes in the ultra dense topology. We then extend it
to a more general V2X broadcasting system. Other NOMA-based extended V2X
applications and some open issues are also discussed.Comment: Accepted by IEEE Wireless Communications Magazin
MIFaaS: A Mobile-IoT-Federation-as-a-Service Model for dynamic cooperation of IoT Cloud Providers
In the Internet of Things (IoT) arena, a constant evolution is observed towards the deployment of integrated environments, wherein heterogeneous devices pool their capacities to match wide-ranging user requirements. Solutions for efficient and synergistic cooperation among objects are, therefore, required. This paper suggests a novel paradigm to support dynamic cooperation among private/public local clouds of IoT devices. Differently from . device-oriented approaches typical of Mobile Cloud Computing, the proposed paradigm envisages an . IoT Cloud Provider (ICP)-oriented cooperation, which allows all devices belonging to the same private/public owner to participate in the federation process. Expected result from dynamic federations among ICPs is a remarkable increase in the amount of service requests being satisfied. Different from the Fog Computing vision, the network edge provides only management support and supervision to the proposed Mobile-IoT-Federation-as-a-Service (MIFaaS), thus reducing the deployment cost of peripheral micro data centers. The paper proposes a coalition formation game to account for the interest of rational cooperative ICPs in their own payoff. A proof-of-concept performance evaluation confirms that obtained coalition structures not only guarantee the satisfaction of the players' requirements according to their utility function, but also these introduce significant benefits for the cooperating ICPs in terms of number of tasks being successfully assigned
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