58,823 research outputs found
Energy-Delay Tradeoff and Dynamic Sleep Switching for Bluetooth-Like Body-Area Sensor Networks
Wireless technology enables novel approaches to healthcare, in particular the
remote monitoring of vital signs and other parameters indicative of people's
health. This paper considers a system scenario relevant to such applications,
where a smart-phone acts as a data-collecting hub, gathering data from a number
of wireless-capable body sensors, and relaying them to a healthcare provider
host through standard existing cellular networks. Delay of critical data and
sensors' energy efficiency are both relevant and conflicting issues. Therefore,
it is important to operate the wireless body-area sensor network at some
desired point close to the optimal energy-delay tradeoff curve. This tradeoff
curve is a function of the employed physical-layer protocol: in particular, it
depends on the multiple-access scheme and on the coding and modulation schemes
available. In this work, we consider a protocol closely inspired by the
widely-used Bluetooth standard. First, we consider the calculation of the
minimum energy function, i.e., the minimum sum energy per symbol that
guarantees the stability of all transmission queues in the network. Then, we
apply the general theory developed by Neely to develop a dynamic scheduling
policy that approaches the optimal energy-delay tradeoff for the network at
hand. Finally, we examine the queue dynamics and propose a novel policy that
adaptively switches between connected and disconnected (sleeping) modes. We
demonstrate that the proposed policy can achieve significant gains in the
realistic case where the control "NULL" packets necessary to maintain the
connection alive, have a non-zero energy cost, and the data arrival statistics
corresponding to the sensed physical process are bursty.Comment: Extended version (with proofs details in the Appendix) of a paper
accepted for publication on the IEEE Transactions on Communication
Dynamic Time-domain Duplexing for Self-backhauled Millimeter Wave Cellular Networks
Millimeter wave (mmW) bands between 30 and 300 GHz have attracted
considerable attention for next-generation cellular networks due to vast
quantities of available spectrum and the possibility of very high-dimensional
antenna ar-rays. However, a key issue in these systems is range: mmW signals
are extremely vulnerable to shadowing and poor high-frequency propagation.
Multi-hop relaying is therefore a natural technology for such systems to
improve cell range and cell edge rates without the addition of wired access
points. This paper studies the problem of scheduling for a simple
infrastructure cellular relay system where communication between wired base
stations and User Equipment follow a hierarchical tree structure through fixed
relay nodes. Such a systems builds naturally on existing cellular mmW backhaul
by adding mmW in the access links. A key feature of the proposed system is that
TDD duplexing selections can be made on a link-by-link basis due to directional
isolation from other links. We devise an efficient, greedy algorithm for
centralized scheduling that maximizes network utility by jointly optimizing the
duplexing schedule and resources allocation for dense, relay-enhanced OFDMA/TDD
mmW networks. The proposed algorithm can dynamically adapt to loading, channel
conditions and traffic demands. Significant throughput gains and improved
resource utilization offered by our algorithm over the static,
globally-synchronized TDD patterns are demonstrated through simulations based
on empirically-derived channel models at 28 GHz.Comment: IEEE Workshop on Next Generation Backhaul/Fronthaul Networks -
BackNets 201
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
A New Efficient Stochastic Energy Management Technique for Interconnected AC Microgrids
Cooperating interconnected microgrids with the Distribution System Operation
(DSO) can lead to an improvement in terms of operation and reliability. This
paper investigates the optimal operation and scheduling of interconnected
microgrids highly penetrated by renewable energy resources (DERs). Moreover, an
efficient stochastic framework based on the Unscented Transform (UT) method is
proposed to model uncertainties associated with the hourly market price, hourly
load demand and DERs output power. Prior to the energy management, a newly
developed linearization technique is employed to linearize nodal equations
extracted from the AC power flow. The proposed stochastic problem is formulated
as a single-objective optimization problem minimizing the interconnected AC MGs
cost function. In order to validate the proposed technique, a modified IEEE 69
bus network is studied as the test case
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