2,842 research outputs found
Traffic eavesdropping based scheme to deliver time-sensitive data in sensor networks
Due to the broadcast nature of wireless channels, neighbouring sensor nodes may overhear packets transmissions from each other even if they are not the intended recipients of these transmissions. This redundant packet reception leads to unnecessary expenditure of battery energy of the recipients. Particularly in highly dense sensor networks, overhearing or eavesdropping overheads can constitute a significant fraction of the total energy consumption. Since overhearing of wireless traffic is unavoidable and sometimes essential, a new distributed energy efficient scheme is proposed in this paper. This new scheme exploits the inevitable overhearing effect as an effective approach in order to collect the required information to perform energy efficient delivery for data aggregation. Based on this approach, the proposed scheme achieves moderate energy consumption and high packet delivery rate notwithstanding the occurrence of high link failure rates. The performance of the proposed scheme is experimentally investigated a testbed of TelosB motes in addition to ns-2 simulations to validate the performed experiments on large-scale network
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
Reliable routing scheme for indoor sensor networks
Indoor Wireless sensor networks require a highly dynamic, adaptive routing scheme to deal with the high rate of topology changes due to fading of indoor wireless channels. Besides that, energy consumption rate needs to be consistently distributed among sensor nodes and efficient utilization of battery power is essential. If only the link reliability metric is considered in the routing scheme, it may create long hops routes, and the high quality paths will be frequently used. This leads to shorter lifetime of such paths; thereby the entire network's lifetime will be significantly minimized. This paper briefly presents a reliable load-balanced routing (RLBR) scheme for indoor ad hoc wireless sensor networks, which integrates routing information from different layers. The proposed scheme aims to redistribute the relaying workload and the energy usage among relay sensor nodes to achieve balanced energy dissipation; thereby maximizing the functional network lifetime. RLBR scheme was tested and benchmarked against the TinyOS-2.x implementation of MintRoute on an indoor testbed comprising 20 Mica2 motes and low power listening (LPL) link layer provided by CC1000 radio. RLBR scheme consumes less energy for communications while reducing topology repair latency and achieves better connectivity and communication reliability in terms of end-to-end packets delivery performance
An Adaptive Algorithm to Optimize the Dynamics of IEEE 802.15.4 Networks
Presentado en ICST 2013IEEE 802.15.4 standard is becoming one of the most popular technologies for the deployment of low rate Wireless Personal Area Networks with strong power constraints. In order to reduce the energy consumption, beacon-enabled networks with long network inactive periods can be employed. However, the duration of these inactivity periods, as some other configuration parameters, are conventionally set to default values and remain fixed during the whole network operation. This implies that if they are misconfigured the network will not adapt to changes in the conditions of the environment, particularly to the most determining one, i.e. the traffic load. This paper proposes a simple procedure for the dynamic adaptation of several key parameters of IEEE 802.15.4 networks. Under this procedure, the 802.15.4 parameters are modified as a function of the existing traffic conditions.Spanish National Project No.TEC2009-13763-C02-01
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
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
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