2,046 research outputs found
Distributed Optimal Rate-Reliability-Lifetime Tradeoff in Wireless Sensor Networks
The transmission rate, delivery reliability and network lifetime are three
fundamental but conflicting design objectives in energy-constrained wireless
sensor networks. In this paper, we address the optimal
rate-reliability-lifetime tradeoff with link capacity constraint, reliability
constraint and energy constraint. By introducing the weight parameters, we
combine the objectives at rate, reliability, and lifetime into a single
objective to characterize the tradeoff among them. However, the optimization
formulation of the rate-reliability-reliability tradeoff is neither separable
nor convex. Through a series of transformations, a separable and convex problem
is derived, and an efficient distributed Subgradient Dual Decomposition
algorithm (SDD) is proposed. Numerical examples confirm its convergence. Also,
numerical examples investigate the impact of weight parameters on the rate
utility, reliability utility and network lifetime, which provide a guidance to
properly set the value of weight parameters for a desired performance of WSNs
according to the realistic application's requirements.Comment: 27 pages, 10 figure
Low Power, Low Delay: Opportunistic Routing meets Duty Cycling
Traditionally, routing in wireless sensor networks consists of
two steps: First, the routing protocol selects a next hop,
and, second, the MAC protocol waits for the intended destination
to wake up and receive the data. This design makes
it difficult to adapt to link dynamics and introduces delays
while waiting for the next hop to wake up.
In this paper we introduce ORW, a practical opportunistic
routing scheme for wireless sensor networks. In a dutycycled
setting, packets are addressed to sets of potential receivers
and forwarded by the neighbor that wakes up first
and successfully receives the packet. This reduces delay and
energy consumption by utilizing all neighbors as potential
forwarders. Furthermore, this increases resilience to wireless
link dynamics by exploiting spatial diversity. Our results
show that ORW reduces radio duty-cycles on average
by 50% (up to 90% on individual nodes) and delays by 30%
to 90% when compared to the state of the art
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
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
Review of Optimization Problems in Wireless Sensor Networks
International audienc
Cross-layer design for network performance optimization in wireless networks
In this dissertation, I use mathematical optimization approach to solve the complex network problems. Paper l and paper 2 first show that ignoring the bandwidth constraint can lead to infeasible routing solutions. A sufficient condition on link bandwidth is proposed that makes a routing solution feasible, and then a mathematical optimization model based on this sufficient condition is provided. Simulation results show that joint optimization models can provide more feasible routing solutions and provide significant improvement on throughput and lifetime. In paper 3 and paper 4, an interference model is proposed and a transmission scheduling scheme is presented to minimize the end-to-end delay. This scheduling scheme is designed based on integer linear programming and involves interference modeling. Using this schedule, there are no conflicting transmissions at any time. Through simulation, it shows that the proposed link scheduling scheme can significantly reduce end-to-end latency. Since to compute the maximum throughput is an NP-hard problem, efficient heuristics are presented in Paper 5 that use sufficient conditions instead of the computationally-expensive-to-get optimal condition to capture the mutual conflict relation in a collision domain. Both one-way transmission and two-way transmission are considered. Simulation results show that the proposed algorithms improve network throughput and reduce energy consumption, with significant improvement over previous work on both aspects. Paper 6 studies the complicated tradeoff relation among multiple factors that affect the sensor network lifetime and proposes an adaptive multi-hop clustering algorithm. It realizes the best tradeoff among multiple factors and outperforms others that do not. It is adaptive in the sense the clustering topology changes over time in order to have the maximum lifetime --Abstract, page iv
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