166,166 research outputs found
Transmitted Energy as a Basic System Resource
Energy is a basic resource in digital transmission links.
Physically, radio channels correspond to passive circuits and
most of the transmitted energy is lost in the channel. Two
alternative approaches are used for performance measurements
in terms of energy. Either the average transmitted or received
energy per bit is used, both usually normalized by the receiver
noise spectral density. This leads to the average transmitted or
received signal-to-noise ratio (SNR) per bit, respectively.
However, the transmitted energy is the basic system resource.
The average energy gain of a channel depends on the transmitted
signal. For convenience, the transmitted SNR referred to the
receiver is defined to be the product of the transmitted SNR and
the representative energy gain, which is defined as the average
energy gain of a signal that is uniformly distributed in all
dimensions: time, frequency and space. An explicit relationship
between the transmitted and received SNRâs using the covariance
concept is derived. Limitations of the use of different SNR
definitions are summarized
Resource Aware Sensor Nodes in Wireless Sensor Networks
Wireless sensor networks are continuing to receive considerable research interest due, in part, to the range of possible applications. One of the greatest challenges facing researchers is in overcoming the limited network lifetime inherent in the small locally powered sensor nodes. In this paper, we propose IDEALS, a system to manage a wireless sensor network using a combination of information management, energy harvesting and energy monitoring, which we label resource awareness. Through this, IDEALS is able to extend the network lifetime for important messages, by controlling the degradation of the network to maximise information throughput
Facilitating the creation of IoT applications through conditional observations in CoAP
With the advent of IPv6, the world is getting ready to incorporate smart objects to the current Internet to realize the idea of Internet of Things. The biggest challenge faced is the resource constraint of the smart objects to directly utilize the existing standard protocols and applications. A number of initiatives are currently witnessed to resolve this situation. One of such initiatives is the introduction of Constrained Application Protocol. This protocol is developed to fit in the resource-constrained smart object with the ability to easily translate to the prominent representational state transfer implementation, hypertext transfer protocol (and vice versa). The protocol has several optional extensions, one of them being, resource observation. With resource observation, a client may ask a server to be notified every state change of the resource. However, in many applications, all state changes are not significant enough for the clients. Therefore, the client will have to decide whether to use a value sent by a server or not. This results in wastage of the already constrained resources (bandwidth, processing power,aEuro broken vertical bar). In this paper, we introduced an alternative to the normal resource observation function, named Conditional Observation, where clients tell the servers the criteria for notification. We evaluated the power consumption and number of packets transmitted between clients and servers by using different network sizes and number of servers. In all cases, we found out that the existing observe option results in excessive number of packets (most of them unimportant for the client) and higher power consumption. We also made an extensive theoretical evaluation of the two approaches which give consistent result with the results we got from experimentation
Autonomous monitoring framework for resource-constrained environments
Acknowledgments The research described here is supported by the award made by the RCUK Digital Economy programme to the dot.rural Digital Economy Hub, reference: EP/G066051/1. URL: http://www.dotrural.ac.uk/RemoteStream/Peer reviewedPublisher PD
Energy-saving Resource Allocation by Exploiting the Context Information
Improving energy efficiency of wireless systems by exploiting the context
information has received attention recently as the smart phone market keeps
expanding. In this paper, we devise energy-saving resource allocation policy
for multiple base stations serving non-real-time traffic by exploiting three
levels of context information, where the background traffic is assumed to
occupy partial resources. Based on the solution from a total energy
minimization problem with perfect future information,a context-aware BS
sleeping, scheduling and power allocation policy is proposed by estimating the
required future information with three levels of context information.
Simulation results show that our policy provides significant gains over those
without exploiting any context information. Moreover, it is seen that different
levels of context information play different roles in saving energy and
reducing outage in transmission.Comment: To be presented at IEEE PIMRC 2015, Hong Kong. This work was
supported by National Natural Science Foundation of China under Grant
61120106002 and National Basic Research Program of China under Grant
2012CB31600
Physical Layer Service Integration in 5G: Potentials and Challenges
High transmission rate and secure communication have been identified as the
key targets that need to be effectively addressed by fifth generation (5G)
wireless systems. In this context, the concept of physical-layer security
becomes attractive, as it can establish perfect security using only the
characteristics of wireless medium. Nonetheless, to further increase the
spectral efficiency, an emerging concept, termed physical-layer service
integration (PHY-SI), has been recognized as an effective means. Its basic idea
is to combine multiple coexisting services, i.e., multicast/broadcast service
and confidential service, into one integral service for one-time transmission
at the transmitter side. This article first provides a tutorial on typical
PHY-SI models. Furthermore, we propose some state-of-the-art solutions to
improve the overall performance of PHY-SI in certain important communication
scenarios. In particular, we highlight the extension of several concepts
borrowed from conventional single-service communications, such as artificial
noise (AN), eigenmode transmission etc., to the scenario of PHY-SI. These
techniques are shown to be effective in the design of reliable and robust
PHY-SI schemes. Finally, several potential research directions are identified
for future work.Comment: 12 pages, 7 figure
Energy-efficient wireless communication
In this chapter we present an energy-efficient highly adaptive network interface architecture and a novel data link layer protocol for wireless networks that provides Quality of Service (QoS) support for diverse traffic types. Due to the dynamic nature of wireless networks, adaptations in bandwidth scheduling and error control are necessary to achieve energy efficiency and an acceptable quality of service. In our approach we apply adaptability through all layers of the protocol stack, and provide feedback to the applications. In this way the applications can adapt the data streams, and the network protocols can adapt the communication parameters
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