4,392 research outputs found
On battery recovery effect in wireless sensor nodes
With the perennial demand for longer runtime of battery-powered Wireless Sensor Nodes (WSNs), several techniques have been proposed to increase the battery runtime. One such class of techniques exploiting the battery recovery effect phenomenon claims that performing an intermittent discharge instead of a continuous discharge will increase the usable battery capacity. Several works in the areas of embedded systems and wireless sensor networks have assumed the existence of this recovery effect and proposed different power management techniques in the form of power supply architectures (multiple battery setup) and communication protocols (burst mode transmission) in order to exploit it. However, until now, a systematic experimental evaluation of the recovery effect has not been performed with real battery cells, using high accuracy battery testers to confirm the existence of this recovery phenomenon. In this paper, a systematic evaluation procedure is developed to verify the existence of this battery recovery effect. Using our evaluation procedure we investigated Alkaline, Nickel-Metal Hydride (NiMH) and Lithium-Ion (Li-Ion) battery chemistries, which are commonly used as power supplies for WSN applications. Our experimental results do not show any evidence of the aforementioned recovery effect in these battery chemistries. In particular, our results show a significant deviation from the stochastic battery models, which were used by many power management techniques. Therefore, the existing power management approaches that rely on this recovery effect do not hold in practice. Instead of a battery recovery effect, our experimental results show the existence of the rate capacity effect, which is the reduction of usable battery capacity with higher discharge power, to be the dominant electrochemical phenomenon that should be considered for maximizing the runtime of WSN applications. We outline power management techniques that minimize the rate capacity effect in order to obtain a higher energy output from the battery
Is There Light at the Ends of the Tunnel? Wireless Sensor Networks for Adaptive Lighting in Road Tunnels
Existing deployments of wireless sensor networks (WSNs) are often conceived as stand-alone monitoring tools. In this paper, we report instead on a deployment where the WSN is a key component of a closed-loop control system for adaptive lighting in operational road tunnels. WSN nodes along the tunnel walls report light readings to a control station, which closes the loop by setting the intensity of lamps to match a legislated curve. The ability to match dynamically the lighting levels to the actual environmental conditions improves the tunnel safety and reduces its power consumption. The use of WSNs in a closed-loop system, combined with the real-world, harsh setting of operational road tunnels, induces tighter requirements on the quality and timeliness of sensed data, as well as on the reliability and lifetime of the network. In this work, we test to what extent mainstream WSN technology meets these challenges, using a dedicated design that however relies on wellestablished techniques. The paper describes the hw/sw architecture we devised by focusing on the WSN component, and analyzes its performance through experiments in a real, operational tunnel
Adaptive Hierarchical Data Aggregation using Compressive Sensing (A-HDACS) for Non-smooth Data Field
Compressive Sensing (CS) has been applied successfully in a wide variety of
applications in recent years, including photography, shortwave infrared
cameras, optical system research, facial recognition, MRI, etc. In wireless
sensor networks (WSNs), significant research work has been pursued to
investigate the use of CS to reduce the amount of data communicated,
particularly in data aggregation applications and thereby improving energy
efficiency. However, most of the previous work in WSN has used CS under the
assumption that data field is smooth with negligible white Gaussian noise. In
these schemes signal sparsity is estimated globally based on the entire data
field, which is then used to determine the CS parameters. In more realistic
scenarios, where data field may have regional fluctuations or it is piecewise
smooth, existing CS based data aggregation schemes yield poor compression
efficiency. In order to take full advantage of CS in WSNs, we propose an
Adaptive Hierarchical Data Aggregation using Compressive Sensing (A-HDACS)
scheme. The proposed schemes dynamically chooses sparsity values based on
signal variations in local regions. We prove that A-HDACS enables more sensor
nodes to employ CS compared to the schemes that do not adapt to the changing
field. The simulation results also demonstrate the improvement in energy
efficiency as well as accurate signal recovery
Bioans: bio-inspired ambient intelligence protocol for wireless sensor networks
This paper describes the BioANS (Bio-inspired Autonomic Networked Services) protocol that uses a novel utility-based service selection mechanism to drive autonomicity in sensor networks. Due to the increase in complexity of sensor network applications, self-configuration abilities, in terms of service discovery and automatic negotiation, have become core requirements. Further, as such systems are highly dynamic due to mobility and/or unreliability; runtime self-optimisation and self-healing is required. However the mechanism to implement this must be lightweight due to the sensor nodes being low in resources, and scalable as some applications can require thousands of nodes. BioANS incorporates some characteristics of natural emergent systems and these contribute to its overall stability whilst it remains simple and efficient. We show that not only does the BioANS protocol implement autonomicity in allowing a dynamic network of sensors to continue to function under demanding circumstances, but that the overheads incurred are reasonable. Moreover, state-flapping between requester and provider, message loss and randomness are not only tolerated but utilised to advantage in the new protocol
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
Power Aware Routing for Sensor Databases
Wireless sensor networks offer the potential to span and monitor large
geographical areas inexpensively. Sensor network databases like TinyDB are the
dominant architectures to extract and manage data in such networks. Since
sensors have significant power constraints (battery life), and high
communication costs, design of energy efficient communication algorithms is of
great importance. The data flow in a sensor database is very different from
data flow in an ordinary network and poses novel challenges in designing
efficient routing algorithms. In this work we explore the problem of energy
efficient routing for various different types of database queries and show that
in general, this problem is NP-complete. We give a constant factor
approximation algorithm for one class of query, and for other queries give
heuristic algorithms. We evaluate the efficiency of the proposed algorithms by
simulation and demonstrate their near optimal performance for various network
sizes
- …