18,327 research outputs found
A Cross-Layer Approach for Minimizing Interference and Latency of Medium Access in Wireless Sensor Networks
In low power wireless sensor networks, MAC protocols usually employ periodic
sleep/wake schedule to reduce idle listening time. Even though this mechanism
is simple and efficient, it results in high end-to-end latency and low
throughput. On the other hand, the previously proposed CSMA/CA-based MAC
protocols have tried to reduce inter-node interference at the cost of increased
latency and lower network capacity. In this paper we propose IAMAC, a CSMA/CA
sleep/wake MAC protocol that minimizes inter-node interference, while also
reduces per-hop delay through cross-layer interactions with the network layer.
Furthermore, we show that IAMAC can be integrated into the SP architecture to
perform its inter-layer interactions. Through simulation, we have extensively
evaluated the performance of IAMAC in terms of different performance metrics.
Simulation results confirm that IAMAC reduces energy consumption per node and
leads to higher network lifetime compared to S-MAC and Adaptive S-MAC, while it
also provides lower latency than S-MAC. Throughout our evaluations we have
considered IAMAC in conjunction with two error recovery methods, i.e., ARQ and
Seda. It is shown that using Seda as the error recovery mechanism of IAMAC
results in higher throughput and lifetime compared to ARQ.Comment: 17 pages, 16 figure
A Cross-Layer Design Based on Geographic Information for Cooperative Wireless Networks
Most of geographic routing approaches in wireless ad hoc and sensor networks
do not take into consideration the medium access control (MAC) and physical
layers when designing a routing protocol. In this paper, we focus on a
cross-layer framework design that exploits the synergies between network, MAC,
and physical layers. In the proposed CoopGeo, we use a beaconless forwarding
scheme where the next hop is selected through a contention process based on the
geographic position of nodes. We optimize this Network-MAC layer interaction
using a cooperative relaying technique with a relay selection scheme also based
on geographic information in order to improve the system performance in terms
of reliability.Comment: in 2010 IEEE 71st Vehicular Technology Conference, 201
Toward a Robust Sparse Data Representation for Wireless Sensor Networks
Compressive sensing has been successfully used for optimized operations in
wireless sensor networks. However, raw data collected by sensors may be neither
originally sparse nor easily transformed into a sparse data representation.
This paper addresses the problem of transforming source data collected by
sensor nodes into a sparse representation with a few nonzero elements. Our
contributions that address three major issues include: 1) an effective method
that extracts population sparsity of the data, 2) a sparsity ratio guarantee
scheme, and 3) a customized learning algorithm of the sparsifying dictionary.
We introduce an unsupervised neural network to extract an intrinsic sparse
coding of the data. The sparse codes are generated at the activation of the
hidden layer using a sparsity nomination constraint and a shrinking mechanism.
Our analysis using real data samples shows that the proposed method outperforms
conventional sparsity-inducing methods.Comment: 8 page
Rate-distortion Balanced Data Compression for Wireless Sensor Networks
This paper presents a data compression algorithm with error bound guarantee
for wireless sensor networks (WSNs) using compressing neural networks. The
proposed algorithm minimizes data congestion and reduces energy consumption by
exploring spatio-temporal correlations among data samples. The adaptive
rate-distortion feature balances the compressed data size (data rate) with the
required error bound guarantee (distortion level). This compression relieves
the strain on energy and bandwidth resources while collecting WSN data within
tolerable error margins, thereby increasing the scale of WSNs. The algorithm is
evaluated using real-world datasets and compared with conventional methods for
temporal and spatial data compression. The experimental validation reveals that
the proposed algorithm outperforms several existing WSN data compression
methods in terms of compression efficiency and signal reconstruction. Moreover,
an energy analysis shows that compressing the data can reduce the energy
expenditure, and hence expand the service lifespan by several folds.Comment: arXiv admin note: text overlap with arXiv:1408.294
QoS-Aware Error Recovery in Wireless Body Sensor Networks Using Adaptive Network Coding
Wireless body sensor networks (WBSNs) for healthcare and medical applications are real-time and life-critical infrastructures, which require a strict guarantee of quality of service (QoS), in terms of latency, error rate and reliability. Considering the criticality of healthcare and medical applications, WBSNs need to fulfill users/applications and the corresponding network’s QoS requirements. For instance, for a real-time application to support on-time data delivery, a WBSN needs to guarantee a constrained delay at the network level. A network coding-based error recovery mechanism is an emerging mechanism that can be used in these systems to support QoS at very low energy, memory and hardware cost. However, in dynamic network environments and user requirements, the original non-adaptive version of network coding fails to support some of the network and user QoS requirements. This work explores the QoS requirements of WBSNs in both perspectives of QoS. Based on these requirements, this paper proposes an adaptive network coding-based, QoS-aware error recovery mechanism for WBSNs. It utilizes network-level and user-/application-level information to make it adaptive in both contexts. Thus, it provides improved QoS support adaptively in terms of reliability, energy efficiency and delay. Simulation results show the potential of the proposed mechanism in terms of adaptability, reliability, real-time data delivery and network lifetime compared to its counterparts
A novel cooperative opportunistic routing scheme for underwater sensor networks
Increasing attention has recently been devoted to underwater sensor networks (UWSNs) because of their capabilities in the ocean monitoring and resource discovery. UWSNs are faced with different challenges, the most notable of which is perhaps how to efficiently deliver packets taking into account all of the constraints of the available acoustic communication channel. The opportunistic routing provides a reliable solution with the aid of intermediate nodes’ collaboration to relay a packet toward the destination. In this paper, we propose a new routing protocol, called opportunistic void avoidance routing (OVAR), to address the void problem and also the energy-reliability trade-off in the forwarding set selection. OVAR takes advantage of distributed beaconing, constructs the adjacency graph at each hop and selects a forwarding set that holds the best trade-off between reliability and energy efficiency. The unique features of OVAR in selecting the candidate nodes in the vicinity of each other leads to the resolution of the hidden node problem. OVAR is also able to select the forwarding set in any direction from the sender, which increases its flexibility to bypass any kind of void area with the minimum deviation from the optimal path. The results of our extensive simulation study show that OVAR outperforms other protocols in terms of the packet delivery ratio, energy consumption, end-to-end delay, hop count and traversed distance
Towards Energy Neutrality in Energy Harvesting Wireless Sensor Networks: A Case for Distributed Compressive Sensing?
This paper advocates the use of the emerging distributed compressive sensing
(DCS) paradigm in order to deploy energy harvesting (EH) wireless sensor
networks (WSN) with practical network lifetime and data gathering rates that
are substantially higher than the state-of-the-art. In particular, we argue
that there are two fundamental mechanisms in an EH WSN: i) the energy diversity
associated with the EH process that entails that the harvested energy can vary
from sensor node to sensor node, and ii) the sensing diversity associated with
the DCS process that entails that the energy consumption can also vary across
the sensor nodes without compromising data recovery. We also argue that such
mechanisms offer the means to match closely the energy demand to the energy
supply in order to unlock the possibility for energy-neutral WSNs that leverage
EH capability. A number of analytic and simulation results are presented in
order to illustrate the potential of the approach.Comment: 6 pages. This work will be presented at the 2013 IEEE Global
Communications Conference (GLOBECOM), Atlanta, US, December 201
Developing an Efficient DMCIS with Next-Generation Wireless Networks
The impact of extreme events across the globe is extraordinary which
continues to handicap the advancement of the struggling developing societies
and threatens most of the industrialized countries in the globe. Various fields
of Information and Communication Technology have widely been used for efficient
disaster management; but only to a limited extent though, there is a tremendous
potential for increasing efficiency and effectiveness in coping with disasters
with the utilization of emerging wireless network technologies. Early warning,
response to the particular situation and proper recovery are among the main
focuses of an efficient disaster management system today. Considering these
aspects, in this paper we propose a framework for developing an efficient
Disaster Management Communications and Information System (DMCIS) which is
basically benefited by the exploitation of the emerging wireless network
technologies combined with other networking and data processing technologies.Comment: 6 page
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