4,995 research outputs found
Reliable data delivery in low energy ad hoc sensor networks
Reliable delivery of data is a classical design goal for reliability-oriented collection routing protocols for ad hoc wireless sensor networks (WSNs). Guaranteed packet delivery performance can be ensured by careful selection of error free links, quick recovery from packet losses, and avoidance of overloaded relay sensor nodes. Due to limited resources of individual senor nodes, there is usually a trade-off between energy spending for packets transmissions and the appropriate level of reliability. Since link failures and packet losses are unavoidable, sensor networks may tolerate a certain level of reliability without significantly affecting packets delivery performance and data aggregation accuracy in favor of efficient energy consumption. However a certain degree of reliability is needed, especially when hop count increases between source sensor nodes and the base station as a single lost packet may result in loss of a large amount of aggregated data along longer hops. An effective solution is to jointly make a trade-off between energy, reliability, cost, and agility while improving packet delivery, maintaining low packet error ratio, minimizing unnecessary packets transmissions, and adaptively reducing control traffic in favor of high success reception ratios of representative data packets. Based on this approach, the proposed routing protocol can achieve moderate energy consumption and high packet delivery ratio even with high link failure rates. The proposed routing protocol was experimentally investigated on a testbed of Crossbow's TelosB motes and proven to be more robust and energy efficient than the current implementation of TinyOS2.x MultihopLQI
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
Channel estimation and transmit power control in wireless body area networks
Wireless body area networks have recently received much attention because of their application to assisted living and remote patient monitoring. For these applications, energy minimisation is a critical issue since, in many cases, batteries cannot be easily replaced or recharged. Reducing energy expenditure by avoiding unnecessary high transmission power and minimising frame retransmissions is therefore crucial. In this study, a transmit power control scheme suitable for IEEE 802.15.6 networks operating in beacon mode with superframe boundaries is proposed. The transmission power is modulated, frame-by-frame, according to a run-time estimation of the channel conditions. Power measurements using the beacon frames are made periodically, providing reverse channel gain and an opportunistic fade margin, set on the basis of prior power fluctuations, is added. This approach allows tracking of the highly variable on-body to on-body propagation channel without the need to transmit additional probe frames. An experimental study based on test cases demonstrates the effectiveness of the scheme and compares its performance with alternative solutions presented in the literature
A Review of the Energy Efficient and Secure Multicast Routing Protocols for Mobile Ad hoc Networks
This paper presents a thorough survey of recent work addressing energy
efficient multicast routing protocols and secure multicast routing protocols in
Mobile Ad hoc Networks (MANETs). There are so many issues and solutions which
witness the need of energy management and security in ad hoc wireless networks.
The objective of a multicast routing protocol for MANETs is to support the
propagation of data from a sender to all the receivers of a multicast group
while trying to use the available bandwidth efficiently in the presence of
frequent topology changes. Multicasting can improve the efficiency of the
wireless link when sending multiple copies of messages by exploiting the
inherent broadcast property of wireless transmission. Secure multicast routing
plays a significant role in MANETs. However, offering energy efficient and
secure multicast routing is a difficult and challenging task. In recent years,
various multicast routing protocols have been proposed for MANETs. These
protocols have distinguishing features and use different mechanismsComment: 15 page
A Search Strategy of Level-Based Flooding for the Internet of Things
This paper deals with the query problem in the Internet of Things (IoT).
Flooding is an important query strategy. However, original flooding is prone to
cause heavy network loads. To address this problem, we propose a variant of
flooding, called Level-Based Flooding (LBF). With LBF, the whole network is
divided into several levels according to the distances (i.e., hops) between the
sensor nodes and the sink node. The sink node knows the level information of
each node. Query packets are broadcast in the network according to the levels
of nodes. Upon receiving a query packet, sensor nodes decide how to process it
according to the percentage of neighbors that have processed it. When the
target node receives the query packet, it sends its data back to the sink node
via random walk. We show by extensive simulations that the performance of LBF
in terms of cost and latency is much better than that of original flooding, and
LBF can be used in IoT of different scales
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
Multihop clustering algorithm for load balancing in wireless sensor networks
The paper presents a new cluster based routing algorithm that exploits the redundancy properties of the sensor networks in order to address the traditional problem of load balancing and energy efficiency in the WSNs.The algorithm makes use of the nodes in a sensor network of which area coverage is covered by the neighbours of the nodes and mark them as temporary cluster heads. The algorithm then forms two layers of multi hop communication. The bottom layer which involves intra cluster communication and the top layer which involves inter cluster communication involving the temporary cluster heads. Performance studies indicate that the proposed algorithm solves effectively the problem of load balancing and is also more efficient in terms of energy consumption from Leach and the enhanced version of Leach
Wireless Data Acquisition for Edge Learning: Data-Importance Aware Retransmission
By deploying machine-learning algorithms at the network edge, edge learning
can leverage the enormous real-time data generated by billions of mobile
devices to train AI models, which enable intelligent mobile applications. In
this emerging research area, one key direction is to efficiently utilize radio
resources for wireless data acquisition to minimize the latency of executing a
learning task at an edge server. Along this direction, we consider the specific
problem of retransmission decision in each communication round to ensure both
reliability and quantity of those training data for accelerating model
convergence. To solve the problem, a new retransmission protocol called
data-importance aware automatic-repeat-request (importance ARQ) is proposed.
Unlike the classic ARQ focusing merely on reliability, importance ARQ
selectively retransmits a data sample based on its uncertainty which helps
learning and can be measured using the model under training. Underpinning the
proposed protocol is a derived elegant communication-learning relation between
two corresponding metrics, i.e., signal-to-noise ratio (SNR) and data
uncertainty. This relation facilitates the design of a simple threshold based
policy for importance ARQ. The policy is first derived based on the classic
classifier model of support vector machine (SVM), where the uncertainty of a
data sample is measured by its distance to the decision boundary. The policy is
then extended to the more complex model of convolutional neural networks (CNN)
where data uncertainty is measured by entropy. Extensive experiments have been
conducted for both the SVM and CNN using real datasets with balanced and
imbalanced distributions. Experimental results demonstrate that importance ARQ
effectively copes with channel fading and noise in wireless data acquisition to
achieve faster model convergence than the conventional channel-aware ARQ.Comment: This is an updated version: 1) extension to general classifiers; 2)
consideration of imbalanced classification in the experiments. Submitted to
IEEE Journal for possible publicatio
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