882 research outputs found
Applications of Repeated Games in Wireless Networks: A Survey
A repeated game is an effective tool to model interactions and conflicts for
players aiming to achieve their objectives in a long-term basis. Contrary to
static noncooperative games that model an interaction among players in only one
period, in repeated games, interactions of players repeat for multiple periods;
and thus the players become aware of other players' past behaviors and their
future benefits, and will adapt their behavior accordingly. In wireless
networks, conflicts among wireless nodes can lead to selfish behaviors,
resulting in poor network performances and detrimental individual payoffs. In
this paper, we survey the applications of repeated games in different wireless
networks. The main goal is to demonstrate the use of repeated games to
encourage wireless nodes to cooperate, thereby improving network performances
and avoiding network disruption due to selfish behaviors. Furthermore, various
problems in wireless networks and variations of repeated game models together
with the corresponding solutions are discussed in this survey. Finally, we
outline some open issues and future research directions.Comment: 32 pages, 15 figures, 5 tables, 168 reference
Efficient Data Collection in Multimedia Vehicular Sensing Platforms
Vehicles provide an ideal platform for urban sensing applications, as they
can be equipped with all kinds of sensing devices that can continuously monitor
the environment around the travelling vehicle. In this work we are particularly
concerned with the use of vehicles as building blocks of a multimedia mobile
sensor system able to capture camera snapshots of the streets to support
traffic monitoring and urban surveillance tasks. However, cameras are high
data-rate sensors while wireless infrastructures used for vehicular
communications may face performance constraints. Thus, data redundancy
mitigation is of paramount importance in such systems. To address this issue in
this paper we exploit sub-modular optimisation techniques to design efficient
and robust data collection schemes for multimedia vehicular sensor networks. We
also explore an alternative approach for data collection that operates on
longer time scales and relies only on localised decisions rather than
centralised computations. We use network simulations with realistic vehicular
mobility patterns to verify the performance gains of our proposed schemes
compared to a baseline solution that ignores data redundancy. Simulation
results show that our data collection techniques can ensure a more accurate
coverage of the road network while significantly reducing the amount of
transferred data
Anticipatory Mobile Computing: A Survey of the State of the Art and Research Challenges
Today's mobile phones are far from mere communication devices they were ten
years ago. Equipped with sophisticated sensors and advanced computing hardware,
phones can be used to infer users' location, activity, social setting and more.
As devices become increasingly intelligent, their capabilities evolve beyond
inferring context to predicting it, and then reasoning and acting upon the
predicted context. This article provides an overview of the current state of
the art in mobile sensing and context prediction paving the way for
full-fledged anticipatory mobile computing. We present a survey of phenomena
that mobile phones can infer and predict, and offer a description of machine
learning techniques used for such predictions. We then discuss proactive
decision making and decision delivery via the user-device feedback loop.
Finally, we discuss the challenges and opportunities of anticipatory mobile
computing.Comment: 29 pages, 5 figure
On Achieving Diversity in the Presence of Outliers in Participatory Camera Sensor Networks
This paper addresses the problem of collection and
delivery of a representative subset of pictures, in participatory camera networks, to maximize coverage when a significant portion of the pictures may be redundant or irrelevant. Consider, for example, a rescue mission where volunteers and survivors of a large-scale disaster scout a wide area to capture pictures of
damage in distressed neighborhoods, using handheld cameras, and report them to a rescue station. In this participatory camera network, a significant amount of pictures may be redundant (i.e., similar pictures may be reported by many) or irrelevant (i.e., may
not document an event of interest). Given this pool of pictures, we aim to build a protocol to store and deliver a smaller subset of pictures, among all those taken, that minimizes redundancy and eliminates irrelevant objects and outliers. While previous work addressed removal of redundancy alone, doing so in the presence of outliers is tricky, because outliers, by their very nature, are different from other objects, causing redundancy minimizing algorithms to favor their inclusion, which is at odds with the goal of finding a representative subset. To eliminate both outliers and redundancy at the same time, two seemingly opposite objectives must be met together. The contribution of this
paper lies in a new prioritization technique (and its in-network
implementation) that minimizes redundancy among delivered
pictures, while also reducing outliers.unpublishedis peer reviewe
Opportunistic Sensing: Security Challenges for the New Paradigm
We study the security challenges that arise in Opportunistic people-centric sensing, a new sensing paradigm leveraging humans as part of the sensing infrastructure. Most prior sensor-network research has focused on collecting and processing environmental data using a static topology and an application-aware infrastructure, whereas opportunistic sensing involves collecting, storing, processing and fusing large volumes of data related to everyday human activities. This highly dynamic and mobile setting, where humans are the central focus, presents new challenges for information security, because data originates from sensors carried by people— not tiny sensors thrown in the forest or attached to animals. In this paper we aim to instigate discussion of this critical issue, because opportunistic people-centric sensing will never succeed without adequate provisions for security and privacy. To that end, we outline several important challenges and suggest general solutions that hold promise in this new sensing paradigm
A City-Scale ITS-G5 Network for Next-Generation Intelligent Transportation Systems: Design Insights and Challenges
As we move towards autonomous vehicles, a reliable Vehicle-to-Everything
(V2X) communication framework becomes of paramount importance. In this paper we
present the development and the performance evaluation of a real-world
vehicular networking testbed. Our testbed, deployed in the heart of the City of
Bristol, UK, is able to exchange sensor data in a V2X manner. We will describe
the testbed architecture and its operational modes. Then, we will provide some
insight pertaining the firmware operating on the network devices. The system
performance has been evaluated under a series of large-scale field trials,
which have proven how our solution represents a low-cost high-quality framework
for V2X communications. Our system managed to achieve high packet delivery
ratios under different scenarios (urban, rural, highway) and for different
locations around the city. We have also identified the instability of the
packet transmission rate while using single-core devices, and we present some
future directions that will address that.Comment: Accepted for publication to AdHoc-Now 201
- …