4,773 research outputs found
Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey
Wireless sensor networks (WSNs) consist of autonomous and resource-limited
devices. The devices cooperate to monitor one or more physical phenomena within
an area of interest. WSNs operate as stochastic systems because of randomness
in the monitored environments. For long service time and low maintenance cost,
WSNs require adaptive and robust methods to address data exchange, topology
formulation, resource and power optimization, sensing coverage and object
detection, and security challenges. In these problems, sensor nodes are to make
optimized decisions from a set of accessible strategies to achieve design
goals. This survey reviews numerous applications of the Markov decision process
(MDP) framework, a powerful decision-making tool to develop adaptive algorithms
and protocols for WSNs. Furthermore, various solution methods are discussed and
compared to serve as a guide for using MDPs in WSNs
Cross-layer design of multi-hop wireless networks
MULTI -hop wireless networks are usually defined as a collection of nodes
equipped with radio transmitters, which not only have the capability to
communicate each other in a multi-hop fashion, but also to route each others’ data
packets. The distributed nature of such networks makes them suitable for a variety of
applications where there are no assumed reliable central entities, or controllers, and
may significantly improve the scalability issues of conventional single-hop wireless
networks.
This Ph.D. dissertation mainly investigates two aspects of the research issues
related to the efficient multi-hop wireless networks design, namely: (a) network
protocols and (b) network management, both in cross-layer design paradigms to
ensure the notion of service quality, such as quality of service (QoS) in wireless mesh
networks (WMNs) for backhaul applications and quality of information (QoI) in
wireless sensor networks (WSNs) for sensing tasks. Throughout the presentation of
this Ph.D. dissertation, different network settings are used as illustrative examples,
however the proposed algorithms, methodologies, protocols, and models are not
restricted in the considered networks, but rather have wide applicability.
First, this dissertation proposes a cross-layer design framework integrating
a distributed proportional-fair scheduler and a QoS routing algorithm, while using
WMNs as an illustrative example. The proposed approach has significant performance
gain compared with other network protocols. Second, this dissertation proposes
a generic admission control methodology for any packet network, wired and
wireless, by modeling the network as a black box, and using a generic mathematical
0. Abstract 3
function and Taylor expansion to capture the admission impact. Third, this dissertation
further enhances the previous designs by proposing a negotiation process,
to bridge the applications’ service quality demands and the resource management,
while using WSNs as an illustrative example. This approach allows the negotiation
among different service classes and WSN resource allocations to reach the optimal
operational status. Finally, the guarantees of the service quality are extended to
the environment of multiple, disconnected, mobile subnetworks, where the question
of how to maintain communications using dynamically controlled, unmanned data
ferries is investigated
Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications
Wireless sensor networks monitor dynamic environments that change rapidly
over time. This dynamic behavior is either caused by external factors or
initiated by the system designers themselves. To adapt to such conditions,
sensor networks often adopt machine learning techniques to eliminate the need
for unnecessary redesign. Machine learning also inspires many practical
solutions that maximize resource utilization and prolong the lifespan of the
network. In this paper, we present an extensive literature review over the
period 2002-2013 of machine learning methods that were used to address common
issues in wireless sensor networks (WSNs). The advantages and disadvantages of
each proposed algorithm are evaluated against the corresponding problem. We
also provide a comparative guide to aid WSN designers in developing suitable
machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial
Secure Routing in Wireless Mesh Networks
Wireless mesh networks (WMNs) have emerged as a promising concept to meet the
challenges in next-generation networks such as providing flexible, adaptive,
and reconfigurable architecture while offering cost-effective solutions to the
service providers. Unlike traditional Wi-Fi networks, with each access point
(AP) connected to the wired network, in WMNs only a subset of the APs are
required to be connected to the wired network. The APs that are connected to
the wired network are called the Internet gateways (IGWs), while the APs that
do not have wired connections are called the mesh routers (MRs). The MRs are
connected to the IGWs using multi-hop communication. The IGWs provide access to
conventional clients and interconnect ad hoc, sensor, cellular, and other
networks to the Internet. However, most of the existing routing protocols for
WMNs are extensions of protocols originally designed for mobile ad hoc networks
(MANETs) and thus they perform sub-optimally. Moreover, most routing protocols
for WMNs are designed without security issues in mind, where the nodes are all
assumed to be honest. In practical deployment scenarios, this assumption does
not hold. This chapter provides a comprehensive overview of security issues in
WMNs and then particularly focuses on secure routing in these networks. First,
it identifies security vulnerabilities in the medium access control (MAC) and
the network layers. Various possibilities of compromising data confidentiality,
data integrity, replay attacks and offline cryptanalysis are also discussed.
Then various types of attacks in the MAC and the network layers are discussed.
After enumerating the various types of attacks on the MAC and the network
layer, the chapter briefly discusses on some of the preventive mechanisms for
these attacks.Comment: 44 pages, 17 figures, 5 table
Throughput Optimal Flow Allocation on Multiple Paths for Random Access Wireless Multi-hop Networks
In this paper we consider random access wireless multi-hop mesh networks with
multi-packet reception capabilities where multiple flows are forwarded to the
gateways through node disjoint paths. We address the issue of aggregate
throughput-optimal flow rate allocation with bounded delay guarantees. We
propose a distributed flow rate allocation scheme that formulates flow rate
allocation as an optimization problem and derive the conditions for
non-convexity for an illustrative topology. We also employ a simple model for
the average aggregate throughput achieved by all flows that captures both
intra- and inter-path interference. The proposed scheme is evaluated through
NS-2 simulations. Our preliminary results are derived from a grid topology and
show that the proposed flow allocation scheme slightly underestimates the
average aggregate throughput observed in two simulated scenarios with two and
three flows respectively. Moreover it achieves significantly higher average
aggregate throughput than single path utilization in two different traffic
scenarios examined.Comment: Accepted for publication at the 9th IEEE BROADBAND WIRELESS ACCESS
WORKSHOP (BWA2013), IEEE Globecom 2013 Workshop
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