837 research outputs found
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
An Adaptive Algorithm to Optimize the Dynamics of IEEE 802.15.4 Networks
Presentado en ICST 2013IEEE 802.15.4 standard is becoming one of the most popular technologies for the deployment of low rate Wireless Personal Area Networks with strong power constraints. In order to reduce the energy consumption, beacon-enabled networks with long network inactive periods can be employed. However, the duration of these inactivity periods, as some other configuration parameters, are conventionally set to default values and remain fixed during the whole network operation. This implies that if they are misconfigured the network will not adapt to changes in the conditions of the environment, particularly to the most determining one, i.e. the traffic load. This paper proposes a simple procedure for the dynamic adaptation of several key parameters of IEEE 802.15.4 networks. Under this procedure, the 802.15.4 parameters are modified as a function of the existing traffic conditions.Spanish National Project No.TEC2009-13763-C02-01
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
Energy Efficient Downstream Communication in Wireless Sensor Networks
This dissertation studies the problem of energy efficient downstream communication in Wireless Sensor Networks (WSNs). First, we present the Opportunistic Source Routing (OSR), a scalable, reliable, and energy-efficient downward routing protocol for individual node actuation in data collection WSNs. OSR introduces opportunistic routing into traditional source routing based on the parent set of a node’s upward routing in data collection, significantly addressing the drastic link dynamics in low-power and lossy WSNs. We devise a novel adaptive Bloom filter mechanism to effectively and efficiently encode a downward source-route in OSR, which enables a significant reduction of the length of source-route field in the packet header. OSR is scalable to very large-size WSN deployments, since each resource-constrained node in the network stores only the set of its direct children. The probabilistic nature of the Bloom filter passively explores opportunistic routing. Upon a delivery failure at any hop along the downward path, OSR actively performs opportunistic routing to bypass the obsolete/bad link. The evaluations in both simulations and real-world testbed experiments demonstrate that OSR significantly outperforms the existing approaches in scalability, reliability, and energy efficiency. Secondly, we propose a mobile code dissemination tool for heterogeneous WSN deployments operating on low power links. The evaluation in lab experiment and a real world WSN testbed shows how our tool reduces the laborious work to reprogram nodes for updating the application. Finally, we present an empirical study of the network dynamics of an out-door heterogeneous WSN deployment and devise a benchmark data suite. The network dynamics analysis includes link level characteristics, topological characteristics, and temporal characteristics. The unique features of the benchmark data suite include the full path information and our approach to fill the missing paths based on the principle of the routing protocol
Availability and End-to-end Reliability in Low Duty Cycle Multihop Wireless Sensor Networks
A wireless sensor network (WSN) is an ad-hoc technology that may even consist of thousands of nodes, which necessitates autonomic, self-organizing and multihop operations. A typical WSN node is battery powered, which makes the network lifetime the primary concern. The highest energy efficiency is achieved with low duty cycle operation, however, this alone is not enough. WSNs are deployed for different uses, each requiring acceptable Quality of Service (QoS). Due to the unique characteristics of WSNs, such as dynamic wireless multihop routing and resource constraints, the legacy QoS metrics are not feasible as such. We give a new definition to measure and implement QoS in low duty cycle WSNs, namely availability and reliability. Then, we analyze the effect of duty cycling for reaching the availability and reliability. The results are obtained by simulations with ZigBee and proprietary TUTWSN protocols. Based on the results, we also propose a data forwarding algorithm suitable for resource constrained WSNs that guarantees end-to-end reliability while adding a small overhead that is relative to the packet error rate (PER). The forwarding algorithm guarantees reliability up to 30% PER
Delay-aware Backpressure Routing Using Graph Neural Networks
We propose a throughput-optimal biased backpressure (BP) algorithm for
routing, where the bias is learned through a graph neural network that seeks to
minimize end-to-end delay. Classical BP routing provides a simple yet powerful
distributed solution for resource allocation in wireless multi-hop networks but
has poor delay performance. A low-cost approach to improve this delay
performance is to favor shorter paths by incorporating pre-defined biases in
the BP computation, such as a bias based on the shortest path (hop) distance to
the destination. In this work, we improve upon the widely-used metric of hop
distance (and its variants) for the shortest path bias by introducing a bias
based on the link duty cycle, which we predict using a graph convolutional
neural network. Numerical results show that our approach can improve the delay
performance compared to classical BP and existing BP alternatives based on
pre-defined bias while being adaptive to interference density. In terms of
complexity, our distributed implementation only introduces a one-time overhead
(linear in the number of devices in the network) compared to classical BP, and
a constant overhead compared to the lowest-complexity existing bias-based BP
algorithms.Comment: 5 pages, 5 figures, submitted to IEEE ICASSP 202
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