Distributed on-demand address assignment in wireless sensornetworks

Sensor networks consist of autonomous wireless sensor nodes that are networked together in an ad hoc fashion. The tiny nodes are equipped with substantial processing capabilities, enabling them to combine and compress their sensor data. The aim is to limit the amount of network traffic, and as such conserve the nodes' limited battery energy. However, due to the small packet payload, the MAC header is a significant, and energy-costly, overhead. To remedy this, we propose a novel scheme for a MAC address assignment. The two key features which make our approach unique are the exploitation of spatial address reuse and an encoded representation of the addresses in data packets. To assign the addresses, we develop a purely distributed algorithm that relies solely on local message exchanges. Other salient features of our approach are the ability to handle unidirectional links and the excellent scalability of both the assignment algorithm and address representation. In typical scenarios, the MAC overhead is reduced by a factor of three compared to existing approaches     

Comparison of menaquinone-4 and menaquinone-7 bioavailability in healthy women

Background

Vitamin K₂ contributes to bone and cardiovascular health. Therefore, two vitamin K₂ homologues, menaquinone-4 (MK-4) and menaquinone-7 (MK-7), have been used as nutrients by the food industry and as nutritional supplements to support bone and cardiovascular health. However, little is known about the bioavailability of nutritional MK-4. To investigate MK-4 and MK-7 bioavailability, nutritional doses were administered to healthy Japanese women.

Findings

Single dose administration of MK-4 (420 ??g; 945 nmol) or MK-7 (420 ??g; 647 nmol) was given in the morning together with standardized breakfast. MK-7 was well absorbed and reached maximal serum level at 6 h after intake and was detected up to 48 h after intake. MK-4 was not detectable in the serum of all subjects at any time point. Consecutive administration of MK-4 (60 ??g; 135 nmol) or MK-7 (60 ??g; 92 nmol) for 7 days demonstrated that MK-4 supplementation did not increase serum MK-4 levels. However, consecutive administration of MK-7 increased serum MK-7 levels significantly in all subjects.

Conclusions

We conclude that MK-4 present in food does not contribute to the vitamin K status as measured by serum vitamin K levels. MK-7, however significantly increases serum MK-7 levels and therefore may be of particular importance for extrahepatic tissues.     

Efficient Sensing Topology Management for Spatial Monitoring with Sensor Networks

We focus on exploiting redundancy for sensor networks in the context of spatial interpolation. The network acts as a distributed sampling system, where sensors periodically sample a physical phenomenon of interest, e.g. temperature. Samples are then used to construct a continuous spatial estimate of the phenomenon over time through interpolation. In this regime, the notion of sensing range typically utilized to characterize redundancy in event detection applications is meaningless and sensor selection schemes based on it become unsuitable. Instead, this paper presents pragmatic approaches for exploiting redundancy in such applications. Their underlying characteristic is that no a-priori assumptions need to be made on the statistical properties of the physical phenomenon. These are instead learned by the network after deployment. Our approaches are evaluated through real as well as synthetic sensor network data showing that significant reductions in the number of active sensors are indeed possible.     

Energy-aware wireless microsensor networks

This article describes architectural and algorithmic approaches that designers can use to enhance the energy awareness of wireless sensor networks. The article starts off with an analysis of the power consumption characteristics of typical sensor node architectures and identifies the various factors that affect system lifetime. We then present a suite of techniques that perform aggressive energy optimization while targeting all stages of sensor network design, from individual nodes to the entire network. Maximizing network lifetime requires the use of a well-structured design methodology, which enables energy-aware design and operation of all aspects of the sensor network, from the underlying hardware platform to the application software and network protocols. Adopting such a holistic approach ensures that energy awareness is incorporated not only into individual sensor nodes but also into groups of communicating nodes and the entire sensor network. By following an energy-aware design methodology based on techniques such as in this article, designers can enhance network lifetime by orders of magnitude     

Traceback-Based Optimizations for Maximum a Posteriori Decoding Algorithms

Maximum A Posteriori (MAP) decoding is a crucial enabler of turbo coding and other powerful feedback-based algorithms. To allow pervasive use of these techniques in resources constrained systems, it is important to limit their implementation complexity, without sacrificing the superior performance they are known for. We show that introducing traceback information into the MAP algorithm, thereby leveraging components that are also part of Soft-Output Viterbi Algorithms (SOVA), offers two unique possibilities to simplify the computational requirements. Our proposed enhancements are effective at each individual decoding iteration and therefore provide gains on top of existing techniques such as early termination and memory optimizations. Based on these enhancements, we will present three new architectural variants for the decoder. Each one of these may be preferable depending on the decoder memory hardware requirements and number of trellis states. Computational complexity is reduced significantly, without incurring significant performance penalty.

Optimizing sensor networks in the energy-latency-density design space

In wireless sensor networks, energy efficiency is crucial to achieving satisfactory network lifetime. To reduce the energy consumption significantly, a node should turn off its radio most of the time, except when it has to participate in data forwarding. We propose a new technique, called sparse topology and energy management (STEM), which efficiently wakes up nodes from a deep sleep state without the need for an ultra low-power radio. The designer can trade the energy efficiency of this sleep state for the latency associated with waking up the node. In addition, we integrate STEM with approaches that also leverage excess network density. We show that our hybrid wakeup scheme results in energy savings of over two orders of magnitude compared to sensor networks without topology management. Furthermore, the network designer is offered full flexibility in exploiting the energy-latency-density design space by selecting the appropriate parameter settings of our protocol.     

Model-Based Techniques for Data Reliability in Wireless Sensor Networks

Wireless Sensor Networks are a fast-growing class of systems. They offer many new design challenges, due to stringent requirements like tight energy budgets, low-cost components, limited processing resources, and small footprint devices. Such strict design goals call for technologies like nanometer-scale semiconductor design and low-power wireless communication to be used. But using them would also make the sensor data more vulnerable to errors, within both the sensor nodes' hardware and the wireless communication links. Assuring the reliability of the data is going to be one of the major design challenges of future sensor networks. Traditional methods for reliability cannot always be used, because they introduce overheads at different levels, from hardware complexity to amount of data transmitted. This paper presents a new method that makes use of the properties of sensor data to enable reliable data collection. The approach consists of creating predictive models based on the temporal correlation in the data and using them for real-time error correction. This method handles multiple sources of errors together without imposing additional complexity or resource overhead at the sensor nodes. We demonstrate the ability to correct transient errors arising in sensor node hardware and wireless communication channels through simulation results on real sensor data.     

Memory optimization of MAP turbo decoder algorithms

Turbo codes are the most recent breakthrough in coding theory. However, the decoder's implementation cost limits their incorporation in commercial systems. Although the decoding algorithm is highly data dominated, no true memory optimization study has been performed yet. We have extensively and systematically investigated different memory optimizations for the maximum a posteriori (MAP) class of decoding algorithms. It turns out that it is not possible to present one decoder structure as being optimal. In fact, there are several tradeoffs, which depend on the specific turbo code, the implementation target (hardware or software), and the selected cost function. We therefore end up with a parametric family of new optimized algorithms out of which the designer can choose. The impact of our optimizations is illustrated by a representative example, which shows a significant decrease in both decoding energy (factor 2.5) and delay (factor 1.7)