In many real-world wireless IoT networks, the application dictates the location of the nodes and therefore the link characteristics are inhomogeneous. Furthermore, nodes may in many scenarios only communicate with the Internet-attached gateway via multiple hops. If an energy-efficient short-range modulation scheme is used, nodes that are reachable only via high-path-loss links cannot communicate. Using a more energy-demanding long-range modulation allows connecting more nodes but would be inefficient for nodes that are easily reachable via low-path-loss links. Combining multiple modulations is challenging, as low-power radios usually only support the use of a single modulation at a time. In this article, we present the Long-Short-Range (LSR) protocol which supports low-power multi-hop communication using multiple modulations and is suited for networks with inhomogeneous link characteristics. To reduce the inherent redundancy of long-range modulations, we present a method to determine the connectivity graph of the network during regular data communication without adding significant overhead. In simulations, we show that LSR allows for reducing power consumption significantly for many scenarios when compared to a state-of-the-art multi-hop communication protocol using a single long-range modulation. We demonstrate the applicability of LSR with an implementation on real hardware and a testbed with long-range links.

Testbeds for wireless IoT devices facilitate testing and validation of distributed target nodes. A testbed usually provides methods to control, observe, and log the execution of the software. However, most of the methods used for tracing the execution require code instrumentation and change essential properties of the observed system. Methods that are non-intrusive are typically not applicable in a distributed fashion due to a lack of time synchronization or necessary hardware/software support. In this article, we present a tracing system for validating time-critical software running on multiple distributed wireless devices that does not require code instrumentation, is non-intrusive and is designed to trace the distributed state of an entire network. For this purpose, we make use of the on-chip debug and trace hardware that is part of most modern microcontrollers. We introduce a testbed architecture as well as models and methods that accurately synchronize the timestamps of observations collected by distributed observers. In a case study, we demonstrate how the tracing system can be applied to observe the distributed state of a flooding-based low-power communication protocol for wireless sensor networks. The presented non-intrusive tracing system is implemented as a service of the publicly accessible open source FlockLab 2 testbed.

Low-power wireless sensor networks have demonstrated their potential for the detection of rare events such as rockfalls and wildfires, where rapid reporting as well as long-term energy-efficient operation is vital. However, current systems require periodic synchronization to maintain network coordination, heavily rely on node placement or use costly long-range links to infrastructure. We present STeC, a novel wireless communication design that directly exploits the spatial and temporal correlation of signals from the sensed phenomenon to orchestrate event-based communication. We leverage the locality of a co-detection, where a physical event triggers multiple sensors quasi-simultaneously, to efficiently collect, characterize and report sensor data. This eliminates the overhead of periodic network activity and centralized control, resulting in more energy-efficient communication with a lower, more consistent detection latency. In doing so, we propose a fundamentally new approach to avoid the elementary conflict between duty cycle and latency requirements immanent to synchronous protocols by exploiting correlated sensor signals for networking. Experiments using real-world traces of a natural hazard detection application show that STeC reduces the detection latency by up to 87 % compared to standard single-hop communication and outperforms traditional schedule-based methods by up to 58.4 x in energy efficiency.

Low-power wireless communication is a central building block of Cyber-physical Systems and the Internet of Things. Conventional low-power wireless protocols make avoiding packet collisions a cornerstone design choice. The concept of synchronous transmissions challenges this view. As collisions are not necessarily destructive, under specific circumstances, commodity low-power wireless radios are often able to receive useful information even in the presence of superimposed signals from different transmitters. We survey the growing number of protocols that exploit synchronous transmissions for higher robustness and efficiency as well as unprecedented functionality and versatility compared to conventional designs. The illustration of protocols based on synchronous transmissions is cast in a conceptional framework we establish, with the goal of highlighting differences and similarities among the proposed solutions. We conclude the paper with a discussion on open research questions in this field.

In this demo abstract we present a custom-built low-power geo-phone sensor node which features on-device mountaineer classification using a convolutional neural network. The execution of such a processing-heavy algorithm on an embedded platform is enabled by optimizing the memory requirement of the neural network through advanced quantization and pipelining techniques. As a result, real-time classification with low energy consumption can be achieved.

In natural hazard warning systems fast decision making is vital to avoid catastrophes. Decision making at the edge of a wireless sensor network promises fast response times but is limited by the availability of energy, data transfer speed, processing and memory constraints. In this work we present a realization of a wireless sensor network for hazard monitoring based on an array of event-triggered single-channel micro-seismic sensors with advanced signal processing and characterization capabilities based on a novel co-detection technique. On the one hand we leverage an ultra-low power, threshold-triggering circuit paired with on-demand digital signal acquisition capable of extracting relevant information exactly and efficiently at times when it matters most and consequentially not wasting precious resources when nothing can be observed. On the other hand we utilize machine-learning-based classification implemented on low-power, off-the-shelf microcontrollers to avoid false positive warnings and to actively identify humans in hazard zones. The sensors' response time and memory requirement is substantially improved by quantizing and pipelining the inference of a convolutional neural network. In this way, convolutional neural networks that would not run unmodified on a memory constrained device can be executed in real-time and at scale on low-power embedded devices. A field study with our system is running on the rockfall scarp of the Matterhorn Hörnligrat at 3500 m a.s.l. since 08/2018.

The Dual Processor Platform (DPP) is a novel architecture template for networked embedded systems based on a strictly asynchronous processor interconnect that allows to minimize interference with a proven predictable behavior [7]. Contrary to traditional platforms [1, 5] DPP tries to mitigate interference by isolating different tasks and mapping them onto dedicated hardware resources. Typically, two different task sets - (i) sensing/actuation/data processing and (ii) communication - are mapped onto two different physically separated processing elements (usually low-power microcontrollers), allowing each to be optimized according to their individual requirements. Such hardware partitioning is a standard approach, frequently found in more complex sensor system implementations [2]. While the communication processor handles wireless packet transmission and reception, the application processor is dedicated to the sensor data acquisition, processing and actuation. But this strict separation of resources and function also requires a processor interconnect: BOLT [7], a stateful processor interconnect specifically designed based on this paradigm allows a strict decoupling of the power, clock and time domains of the two processing elements by allowing only asynchronous message passing between the two. The strict limitation to an asynchronous interface allows for predictable run-time behavior that, in addition to typical behavior observed from experiments, has been formally verified [7]. The most notable advantages of this approach are:

Designing and testing low-power wireless communication protocols often requires experimental deployments on real hardware in realistic settings. Infrastructure testbeds have the advantage that they allow reproducible results using different network configurations. However, most testbeds are either in- or outdoor only and do not span long and short ranges at the same time. In this work, we present an extension to the popular FlockLab testbed on a campus-scale in order to better support testing of long-range communciation, for example using the LoRa modulation. Different to existing LoRa test networks where specific protocol layers are fixed, we support custom modification above the physical hardware (above PHY) which allows the development and testing of alternative full custom MAC layers that are not based on LoRaWAN.

Event-triggered wireless sensing systems are an important class of wireless sensor network, where the detection of non-deterministic events enables the monitoring and control of processes in industries such as manufacturing, healthcare, and agriculture. The system properties of low latency, energy efficiency, and adaptability make event-triggered wireless sensing systems a key technological enabler for the Industrial Internet of Things. <?tight?>Wireless sensing systems based on periodic multi-hop communication exhibit a fundamental trade-off between latency and energy efficiency, which is unfavorable for event-triggered application scenarios. To address this technological gap, we present B litz , the first communication architecture that combines asynchronous and synchronous flooding primitives to facilitate low latency and energy-efficient multi-hop communication of non-deterministic events. B litz also incorporates a novel scheme for mitigating erroneous wake-ups, which is shown analytically and experimentally to further reduce energy consumption. We present a prototype implementation of B litz and evaluate its performance in an indoor testbed deployment. Experiments show that BLITZ supports a mean latency as low as 108.9ms for an 8-bit event packet and its associated data packet of 32 bytes through a 4-hop network, and a power dissipation of 16μW during periods of inactivity.

Wired fieldbuses have long been proven effective in supporting Cyber-Physical Systems (CPS). However, various domains are now striving for wireless solutions due to ease of deployment or novel functionality requiring the ability to support mobile devices. Low-power wireless protocols have been proposed in response to this need, but requirements of a large class of CPS applications can still not be satisfied. We thus propose Time-Triggered Wireless (TTW), a distributed low-power wireless system design that minimizes communication energy consumption and offers end-to-end timing predictability, runtime adaptability, reliability, and low latency. Evaluation shows a 2× reduction in communication latency and 33-40% lower radio-on time compared with DRP, the closest related work, validating the suitability of TTW for new exciting wireless CPS applications.

We propose a novel method for mitigating erroneous wake-ups that are commonly associated with ultra-low power wake-up receivers. Recent research in low-power protocols has demonstrated significant improvements in energy-efficiency by employing ultra-low power wake-up receivers. However, due to the low-complexity receiver structures adopted, wake-up receivers are susceptible to external interference, which can cause the detection of non-existent wake-ups. The occurrence of these erroneous wake-ups wastes precious energy resources, thereby negating the potential energy savings in employing wake-up receivers. We address this challenging problem by extracting time-domain features from the output of the wake-up receiver, and construct a classifier to distinguish between correct and erroneous wake-ups. We describe the design of the proposed wake-up classifier and present preliminary results.

We present a heterogeneous system architecture for event-triggered wireless sensing capable of supporting high spatial resolution. The key differentiator between the proposed architecture and alternative state-of-the-art approaches is the ability to simultaneously maximize operational lifetime and minimize end-to-end latency of detected events. Our novel architecture takes advantage of heterogeneity with respect to the operation of the wireless communication protocol and the construction of the sensing platform. We present a two-hop proof of concept implementation, exhibiting end-to-end latencies on the order of tenths of a second, while dissipating on the order of tens of microwatts during periods of inactivity.

If visions and forecasts of industry come true then we will be soon surrounded by billions of interconnected embedded devices. We will interact with them in a cyber-human symbiosis, they will not only observe us but also our environment, and they will be part of many visible and ubiquitous objects around us. The information that is collectively gathered and analyzed is supposed to help us in our daily live, in making faithful decisions, but it will also directly be used for actuation and it will cause changes by means of local and global control loops.

In cyber-physical systems (CPS), the communication among the sensing, actuating, and computing elements is often subject to hard real-time constraints. Real-time communication among wireless network interfaces and real-time scheduling for complex, dynamic applications have been intensively studied. Despite these major efforts, there is still a significant gap to fill. In particular, the integration of several real-time components to provide end-to-end real-time guarantees between interfaces of distributed applications in wireless CPS is an unsolved problem. We thus present a distributed protocol that considers the complete transmission chain including peripheral busses, memory accesses, networking interfaces, and the wireless real-time protocol. Our protocol provably guarantees that message buffers along this chain do not overflow and that all messages received at the destination application interface meet their end-to-end deadlines. To achieve this while being adaptive to unpredictable changes in the system and the real-time traffic requirements, our protocol establishes at run-time a set of contracts among all major elements of the transmission chain based on a worst-case delay and buffer analysis of the overall system. Using simulations, we validate that our analytic bounds are both safe and tight.

Experimental research in low-power wireless networking lacks a reference benchmark. While other communities such as databases or machine learning have standardized benchmarks, our community still uses ad-hoc setups for its experiments and struggles to provide a fair comparison between communication protocols. Reasons for this include the diversity of network scenarios and the stochastic nature of wireless experiments. Leveraging on the excellent testbeds and tools that have been built to support experimental validation, we make the case for a reference benchmark to promote a fair comparison and reproducibility of results. This abstract describes early design elements and a benchmarking methodology with the goal to gather feedback from the community rather than propose a definite solution.

Resource interference is a fundamental barrier to realizing predictable wireless embedded systems. We address this problem by (i) partitioning application and communication tasks onto dedicated platforms, and (ii) designing a platform interconnect to facilitate asynchronous message exchange with predictable run-time behavior. We motivate the need for this platform interconnect, termed Bolt, and describe a prototype implementation. Evaluation results indicate that the developed platform interconnect exhibits tightly bounded run-time execution with low jitter, and a negligible resource overhead with respect to state-of-the-art application and communication platforms.

In this paper, we tackle the challenge of rapidly disseminating rare events through a multi-hop network, while achieving unprecedented energy-efficiency. Contrary to state-of-the-art approaches, we circumvent the undesirable trade-offs associated with low-power duty-cycled protocols and backscatter technologies, and demonstrate a paradigm shift in low-power protocol design. We present Zippy, an on-demand flooding technique that provides robust asynchronous network wake-up, fine-grained per-hop synchronization and efficient data dissemination by leveraging low-complexity transmitter and receiver hardware. We are the first to demonstrate the on-demand flooding of rare events through a multi-hop network with end-to-end latencies of tens of milliseconds, while dissipating less than 10 microwatts during periods of inactivity. We present a prototype implementation of our proposed approach using a wireless sensor platform constructed from commercially available components. We extensively evaluate Zippy's performance in a laboratory setting and in an indoor testbed.

The wireless sensor network community is currently undergoing a platform paradigm shift, moving away from classical single-processor motes toward heterogeneous multi-processor architectures. These emerging platforms promise efficient concurrent processing with energy-proportional system performance. The use of shared interconnects and shared memory for inter-processor communication, however, causes interference in the time, power, and clock domains, which prevents designers from fully harnessing these benefits. We thus designed Bolt, the first ultra-low-power processor interconnect for the compositional construction of heterogeneous wireless embedded platforms. This paper presents the architectural blueprint for interconnecting two independent processors, while enabling asynchronous inter-processor communication with predictable run-time behavior. We detail a prototype implementation of Bolt, and apply formal methods to analytically derive bounds on the execution time of its message passing operations. Experiments with a custom-built dual-processor platform show that our Bolt prototype incurs a negligible power overhead relative to state-of-the-art platforms, offers predictable message passing with empirical bounds that match the analytical ones to within a few clock cycles, and achieves a high throughput of up to 3.3 Mbps.

We demonstrate the capabilities of Bolt, an ultra-low-power processor interconnect for the composable construction of new multi-processor wireless embedded platforms. Bolt provides asynchronous bidirectional communication between two processors with predictable message transfer times. In this way, Bolt solves the resource interference problem inherent in today's wireless embedded platforms, enabling simpler and more robust system designs with minimal resource overhead. Using our Bolt prototype implemented on a state-of-the-art microcontroller, we demonstrate Bolt's composability and decoupling in time, power, and clock domains.

In this work we consider a real-world environmental monitoring scenario that requires uninterrupted system operation over time periods on the order of multiple years. To achieve this goal, we propose a novel approach to dynamically adjust the system's performance level such that energy neutral operation, and thus long-term uninterrupted operation can be achieved. We first consider the annual dynamics of the energy source to design an appropriate power subsystem (i.e., solar panel size and energy store capacity), and then dynamically compute the long-term sustainable performance level at runtime. We show through trace-driven simulations using eleven years of real-world data that our approach outperforms existing predictive, e.g., EWMA, WCMA, and reactive, e.g., ENO-MAX, approaches in terms of average performance level by up to 177%, while reducing duty-cycle variance by up to three orders of magnitude. We further demonstrate the benefits of the dynamic power management scheme using a wireless sensor system deployed for environmental monitoring in a remote, high-alpine environment as a case study. A performance evaluation over two years reveals that the dynamic power management scheme achieves a two-fold improvement in system utility when compared to only applying appropriate capacity planning.

Health monitoring is an integral service for the long-term operation of wireless sensor networks. Instead of actively adding probe or status traffic, recently proposed passive health monitoring systems infer system health solely from existing application traffic. Our results from extensive testbed experiments prove that the detection rate of an exemplary monitoring application is significantly improved, i.e., by up to 28% to overall 94%, when a passive health estimation method is complemented with a minimally active component. This paper presents Hybrid Monitoring, a novel health monitoring system that combines the advantages of passive and active health monitoring systems. Results from the analysis of passively reconstructed, inexact per-hop timing information are significantly improved when one bit of extra information is added to every packet. The resulting system is able to estimate a significant health metric, i.e., the number of occurred failure events on a node, with a high confidence. The temporal resolution of the estimated signal is equal to the packet sampling interval, the performance of our system is not affected by large packet delays, e.g., when a sensor node is disconnected for a long time.

Testbeds are indispensable for debugging and evaluating wireless embedded systems. While existing testbeds provide ample opportunities for realistic, large-scale experiments, they are limited in their ability to closely observe and control the distributed operation of resource-constrained nodes - access to the nodes is restricted to the serial port. This paper presents FlockLab, a testbed that overcomes this limitation by allowing multiple services to run simultaneously and synchronously against all nodes under test in addition to the traditional serial port service: tracing of GPIO pins to record logical events occurring on a node, actuation of GPIO pins to trigger actions on a node, and high-resolution power profiling. FlockLab's accurate timing information in the low microsecond range enables logical events to be correlated with power samples, thus providing a previously unattained level of visibility into the distributed behavior of wireless embedded systems. In this paper, we describe FlockLab's design, benchmark its performance, and demonstrate its capabilities through several real-world test cases.

Monitoring and managing urban air pollution is a significant challenge for the sustainability of our environment. We quickly survey the air pollution modeling problem,introduce a new dataset of mobile air quality measurements in Zurich, and discuss the challenges of making sense of these data.

As the application of WSNs for long-term monitoring purposes becomes real, the issue of WSN system health monitoring grows increasingly important. Manually understanding the root causes of an observed behavior is time-consuming and difficult, often knowledge of prior behavior is necessary for understanding the potential risk on the long-term system performance. The challenges lie in the balance between the amount of system data collected and the level of detail in which state can be inferred from this data. In this paper, we propose a lightweight runtime logging and corresponding network state inference mechanism that enables scalable WSN health monitoring. Concretely, we propose that nodes only report their internal state on the occurrence of important events. Having a very low computational complexity and message overhead within the sensor network, reported events are analyzed at a less constrained network sink.

In the context of wireless data collection, a common application class in wireless sensor networks, this paper presents a novel, non-intrusive algorithm for the precise reconstruction of the packet path, the per-hop arrival order and the per-hop arrival times of individual packets from partial in-band information at runtime. Information is reconstructed outside the network immediately after a packet is received at the sink. After establishing the correctness of our proposed algorithm, we evaluate its performance in testbed experiments using CTP and Dozer, two well-known data collection protocols. Foremost interested in obtaining a better understanding of the performance of long-term real-world deployments, Multi-hop Network Tomography (MNT) is applied to in total more than 140 million packets that have been obtained from three multi-year WSN deployments of the PermaSense project. The capabilities of the performance analysis of deployed systems using the proposed algorithm and methodology are demonstrated in a case study.

Developing, testing, debugging, and evaluating communication protocols for low-power wireless networks is a long and cumbersome task. Simulators can be helpful in the early stages of development, but their models of hardware components and the wireless channel are often rather simplistic and hence cannot substitute experiments on real sensor node platforms. The resources available on common platforms are however very limited, and so are the possibilities for non-intrusive debugging and testing. With most existing testbeds it is only possible to collect information from the serial port, which requires adding highly intrusive logging statements that alter the timing behavior of the software running on the nodes. This is particularly detrimental to the operation of time-critical components, such as radio drivers, media access control (MAC) protocols, and certain flooding protocols [2], hindering their testbed-assisted development.

In the context of low-power wireless sensor networks, this paper presents multi-hop network tomography (MNT), a novel, non-intrusive algorithm for reconstructing the path, the per-hop arrival order, and the per-hop arrival time of individual packets at runtime. While explicitly transmitting this information over the radio would negatively impact the performance of the system under investigation, information is instead reconstructed after packets have been received at the sink.

The field of Wireless Sensor Networks (WSNs) is now in a stage where serious applications of societal and economical importance are in reach. For example, it is well known that the global climate change dramatically influences the visual appearance of mountain areas like the European Alps. Very destructive geological processes may be triggered or intensified, impacting the stability of slopes, possibly inducing landslides. Unfortunately, the interactions between these complex processes is poorly understood. Therefore, one needs to develop wireless sensing technology as a new scientific instrument for environmental sensing under extreme conditions. Large variations in temperature, humidity, mechanical forces, snow coverage, and unattended operation play a crucial role in long-term deployments. We argue that, in order to significantly advance the application domain, it is inevitable that sensor networks be created as a quality scientific instrument with known and predictable properties, and not as a research toy delivering average observations at best. In this paper, key techniques for achieving highly reliable, yet resource efficient wireless sensor networks are discussed on the basis of productive wireless sensor networks measuring permafrost processes in the Swiss Alps.

In this paper, we present a performance analysis of the communication stack of the PermaDozer application. Our offline analysis is based on long-term performance data from three diverse real-world wireless sensor network deployments. Two of the deployments considered are located in the Swiss mountains at 3.500 meters a.s.l., the third deployment is located along a river in the Swiss midlands. Apart from climatic differences, the three deployments also vary in network size, network density, node placement, and type and quantity of RF interference. From September 2008 to May 2011 more than 99.6 million WSN packets have been collected serving as the dataset. All three deployments are based on the same software and hardware. This allows us to comparatively study the performance of the PermaDozer protocol under different deployment settings and environmental conditions. For the period between June 2010 and May 2011, our networks achieved a data yield of >; 99.5%. But, we can also clearly notice that achieving this performance requires varying effort in terms of radio duty cycle resulting in different power consumption and network lifetime.

Commercial, coin-sized iButton temperature logger devices are well-suited for densely instrumenting large outdoor areas. An efficient workflow for deploying and maintaining those devices is necessary when striving to deploy and operate several hundreds of data logger devices. Additionally, a sophisticated data management is required for handling the emerging, large amounts of meta and measurement data. Therefore, we developed iAssist, a solution that integrates the handling of iButton data logger devices together with a GPS receiver and a digital camera for gathering accurate location information. iAssist efficiently supports the whole workflow consisting of deploying, relocating and reading tiny sensing systems. iAssist is especially tailored for outdoor operation asking for as little user interaction as possible.

Wireless sensor networks (WSNs) are used for long-term observation and monitoring. Such long-lasting deployments require different maintenance tasks, such as the replacement of nodes and the most critical initial installation of the sensor nodes. During maintenance, the actual node placement is modified resulting in temporary topology fluctuations, which are very expensive in terms of energy. We propose the NoSE protocol stack enhancement for WSNs to target maintenance tasks. NoSE provides the functionality for switching the network between an operational state and a deep sleep state. The deep sleep state allows for switching the network to energy savings, while performing maintenance. The network may be woken up at any given time. During the time bounded start-up, a comprehensive neighborhood assessment provides a solid basis for the subsequent network topology setup. Thus the success of a maintenance task, e.g., the initial deployment of the nodes, can be instantly validated. We present NoSE on a case study focusing on the initialization of a fire-detector WSN validated on a testbed and in simulation.

A vital factor for a successful deployment of sensor nodes is testing of all system aspects in a realistic setup. This work presents a testbed architecture which allows for detailed monitoring and stimulation of a wireless sensor node. In particular, time-accurate state extraction and power measurements are provided in a distributed, yet synchronized context. The FlockLab testbed architecture provides a distributed lab instrument, where detailed observations of every sensor node enable thorough testing. Software services allow for formulating testcases and reliable test data collection.

Within the PermaSense project, two wireless sensor networks have been deployed for a long-term operation in the Swiss Alps. For enabling state-of-the-art permafrost research based on the collected data, highest possible data quality and yield have to be ensured. But, the operation of wireless sensors networks remains a hard research problem. Firstly, deployed wireless sensors networks are subject to continuous changes. Second, there are scenarios that can only be tested in the field as the capabilities of testbeds are too limited. Basically, it is not possible to test for many months before deploying in the field. In this poster, we present an analysis of our data that has been collected over nine months. In addition to describing our system design and methods, we also share our experiences from discovered severe incidences.

Summary form only given. Embedded systems driven by future applications will be tightly coupled with the increasing complexity of the real world. Consisting of myriads of wireless networked devices, of heterogeneous architectures, distributed and interacting in a number of ways and serving a multitude of purposes systems have to adapt and take advantage of conditions unpredictable at design time. In their realisation software both on a system and on an application level is playing an increasingly important role that cannot be designed independently. Dominant design factors are the severe resource constraints, the unreliability of the wireless medium and the dynamics of both the applications and the environment. Selected challenges in the area of wireless sensor networks are addressed.

There are numerous possibilities to assemble a very resource-efficient and power-aware distributed sensor network tailored to a specific application. However, the task of initializing the network has not yet attracted much attention. This paper presents the NoSE (Neighbor Search and link Estimation) initialization scheme. NoSE provides an exhaustive neighbor search including a thorough link assessment, leveraging both high reactivity and greatly minimized energy consumption. Based on the information obtained in the initial link assessment phase, a routing protocol can subsequently set up and use an optimized network topology.

We present S-XTC, a topology control algorithm that uses the received signal strength indicator (RSSI) of the radio on wireless sensor nodes. The algorithm is based on XTC, a practical topology control algorithm that constructs a relative neighborhood graph. In contrast to the pure XTC our extensions add to the robustness and resilience against fluctuation in the RSSI values. While guaranteeing connectivity and a bounded node degree, network topologies are able to adapt to gradual changes in the network and it's environment. In this paper, we discuss the shortcomings of the previous algorithm and evaluate S-XTC by analytical proofs and simulation results. Furthermore, we have successfully implemented and tested S-XTC on the BTnode platform of which we discuss a case study and performance evaluation

Low power has become a primary concern in the field of ad–hoc and personal area networks. As manufacturers start endowing their designs with scatternet support, Bluetooth is emerging as a key enabling technology. Although this is driving research on Bluetooth power optimization, most proposals are based on over–simplified, fully theoretical, or old and inadequate power models. We present a real–world power model of a Bluetooth device supporting scatternets and sniff–mode, and validate it experimentally on a real implementation. Whilst guaranteeing a low computational complexity, the model achieves a 4 % RMS error and can be a precious aid in the design and implementation of power–critical Bluetooth applications. 1.

The BTnode platform is a versatile and flexible platform for functional prototyping of ad hoc and sensor networks. Based on an Atmel microcontroller, a Bluetooth radio and a low-power ISM hand radio it offers ample resources to implement and test a broad range of algorithms and applications ranging from pure technology studies to complete application demonstrators. Accompanying the hardware is a suite of system software, application examples and tutorials as well as support for debugging, test, deployment and validation of wireless sensor network applications. We discuss aspects of system design, development and deployment based on our experience with real wireless sensor network experiments. We further discuss our approach of a deployment-support network that tries to close the gap between current proof-of-concept experiments to sustainable real-world sensor network solutions

Deployment-support networks (DSNs) have been proposed as a novel tool for the development, test, deployment, and validation of wireless sensor networks. They are expected to enhance scalability and flexibility in deployment by eliminating cable connections. In this paper, we describe our implementation of a DSN, giving details on the algorithms for topology control and maintenance. We provide various measurements of DSNs spanning up to 71 BTnode rev3 devices, featuring the largest Bluetooth scatternet reported to date. We also discuss our experience gained in the development and experimentation. Our results strongly suggest that our implementation scales well to a large number of nodes.

Large-scale deployment of sensor networks is more and more becoming an issue to researchers and industry alike. The recently revised BTnode architecture provides two wireless radios and facilitates the interconnection of heterogeneous devices. Apart from offering interesting new opportunities in using multi-frontend devices in sensor-network research, this architecture is optimally suited for deployment-support networks as introduced in the following.

Wearable computing has recently gained much popularity as an ambitious vision for future personalized mobile systems. Its aim is intelligent, environment aware systems unobtrusively embedded into the mobile environments of their users. With the combination of complex processing requirements, the necessity of placing sensors and input/output modules at different locations on the user's body, and stringent limits on size, weight, and battery capacity, the design of such systems is an inherently challenging problem. We demonstrate how systematic design and quantitative analysis can be applied to wearable architectures. We first present a model that allows various factors influencing the design of a wearable system to be incorporated into formal cost metrics. In particular, we show how to consistently incorporate specific wearable factors such as device placement requirements, ergonomics, and dynamic workload profiles into the model. We then discuss how efficient estimation algorithms can be extended and applied to the evaluation of different architectures with respect to our cost metrics. Finally, we discuss quantitative results from a proof-of-concept case study showing the trade offs between different architectures for a given wearable scenario. Summarized, we demonstrate how the description and the design of wearable systems can be put on a systematic, formal basis allowing us to treat them similar as conventional embedded systems.

We motivate a prototyping platform for ad hoc networking research showing some requirements and constraints. The architecture of the BTnodes, each of which can store information, compute and communicate, is explained in conjunction with some demo applications that have been implemented. Important requirements and design trade-offs to be able to support multiple, compatible communication interfaces, to handle limited resources, for power-aware operation and for efficient testbed deployment are discussed.

Evolving networks of ad-hoc wireless sensing nodes rely heavily on the ability to establish position information. The algorithms presented herein rely on range measurements between pairs of nodes and the a priori coordinates of sparsely located anchor nodes. Clusters of nodes surrounding anchor nodes cooperatively establish confident position estimates through assumptions, checks, and iterative refinements. Once established, these positions are propagated to more distant nodes, allowing the entire network to create an accurate map of itself. Major obstacles include overcoming inaccuracies in range measurements as great as /spl plusmn/50%, as well as the development of initial guesses for node locations in clusters with few or no anchor nodes. Solutions to these problems are presented and discussed, using position error as the primary metric. Algorithms are compared according to position error, scalability, and communication and computational requirements. Early simulations yield average position errors of 5% in the presence of both range and initial position inaccuracies.