Given the significant amount of time that people spend indoors, public spaces are reinventing themselves to deliver immersive experiences. One approach is to provide interactive gaming, such as the spaces created in big international airports like Singapore. The problem is that these gaming areas rely on either (i) wearable devices, which can easily spread germs, particularly in highly transited areas, such as those in airports, or (ii) cameras, which are increasingly raising privacy concerns and are being forbidden in some public areas. To provide an immersive gaming experience that is device-free and privacy-aware, we propose a system based on mmWave radar. In particular, our work provides three contributions. (1) For the first time, we compare various tracking and human pose estimation (HPE) models in the SoA under a unified framework. This approach allows us to identify the best methods in terms of position accuracy, latency, and smoothness–which are critical gaming metrics. (2) We build an integrated system with tracking and HPE capabilities. Our system includes the design of four games with different levels of complexity and a dataset collected specifically to train models for gaming applications. (3) Our evaluation, which includes a subjective users’ survey and an objective comparison with a Kinect console, shows that today’s mmWave is suitable for games where only the user’s location is required or when coarse-grained gestures are needed. There are two key areas where mmWave research needs to improve to match camera-based consoles: sensors need a higher sampling rate, since a high frame rate is critical for seamless gaming, and HPE models need to master fine-grained gestures, in particular when arms end up in front of the body. All the code and datasets will be made available as a stepping stone for future work.

In recent years, the number of wireless applications has increased significantly, resulting in the radio bands becoming expensive and prone to interference. There is a new research area aiming at mitigating these issues by creating communication links using ambient light. This area, called passive-VLC, not only exploits the visible light frequencies, but does so with low-power transmitters. All the previous work in passive-VLC, however, forget about individual wavelength bands of light, and do not exploit its wide spectrum, reducing the potential channel capacity. In this paper, we propose a novel method to transmit and decode data, using liquid crystal cells that modulate and consider the full spectrum, and put it to the test by prototyping a multi-symbol communication link. The main contribution of our work is to show that passive-VLC can move from spectrum-agnostic to spectrum-aware modulation. We explore this new domain by making use of a novel type of receiver (i.e., a spectrometer) and uncovering the advantages and caveats of this spectrum-aware approach.

To take advantage of Visible Light Communication (VLC) for low-power applications, such as IoT tags, researchers have been developing systems to modulate (backscatter) ambient light using LC shutters. Various approaches have been explored for single-pixel transmitters, but without following a principled approach. This has resulted in either relatively low data rates, short ranges, or the need for powerful artificial light sources. This paper takes a step back and proposes a more theoretical framework: ChromaLux. By considering the fundamental characteristics of liquid crystals (birefringence and thickness), we demonstrate that the design space is way larger than previously explored, allowing for much better systems. In particular, we uncover the existence of a transient state where the switching time can be reduced by an order of magnitude without lowering the contrast significantly, improving both range and data rate. Using a prototype, we demonstrate that our framework is applicable to different LCs. Our results show significant improvements over state-of-the-art single-pixel systems, achieving ranges of 50 meters at 1 kbps and with bit-error-rates below 1%.

The main obstacles to achieve truly ubiquitous sensing are (i) the limitations of battery technology - batteries are short-lived, hazardous, bulky, and costly - and (ii) the unpredictability of ambient power. The latter causes sensors to operate intermittently, violating the availability requirements of many real-world applications. In this paper, we present the Coalesced Intermittent Sensor (CIS), an intermittently-powered "sensor" that senses continuously! Although a single node will frequently be off charging, a group of nodes can –in principle– sense 24/7 provided that their awake times are spread apart. As communication is too expensive, we rely on inherent component variations that induce small differences in power cycles. This basic assumption has been verified through measurements of different nodes and power sources. However, desynchronizing nodes is not enough. An important finding is that a CIS designed for certain (minimal) energy conditions will become synchronized when the available energy exceeds the design point. Nodes employing a sleep mode (to extend their availability) do wake up collectively at some event, process it, and return to charging as the remaining energy is typically too low to handle another event. This results in multiple responses (bad) and missing subsequent events (worse) due to the synchronized charging. To counter this undesired behavior we designed an algorithm to estimate the number of active neighbors and respond proportionally to an event. We show that when intermittent nodes randomize their responses to events, in favorable energy conditions, the CIS reduces the duplicated captured events by 50% and increases the percentage of capturing entire bursts above 85%.

Typical Wireless Sensor Networks (WSN) deployments use more nodes than needed to accurately sense the phenomena of interest. This redundancy can be leveraged by switching-on only a subset of nodes at any time instant (node-scheduling) and putting the remaining nodes sleep. This effectively extends the network lifetime. In addition to sensing coverage, node-scheduling schemes must also ensure that (i) the network stays connected, and (ii) the time needed to wake-up the complete protocol stack after sleeping is minimized. We present Sleeping Beauty, a highly-efficient data collection protocol that aids node-scheduling schemes in both aspects. Sleeping Beauty uses a slotted and tightly synchronized communication primitive, where a node keeps its radio off for most of the time, except in the slots when it needs to participate for successful communication. Further, an efficient neighbor-discovery mechanism is included that provides partial, but sufficient topology information (potential parents) to avoid network partitions. Furthermore, Sleeping Beauty employs a novel, yet simple clock-offset estimation technique that maintains highly-accurate time synchronization over long radio-off periods (i.e., less than 500 us deviation even after 45, min of sleeping). This minimizes time wasted in resynchronizing the network in between data collection rounds. Through experiments on two different testbeds, we verified that Sleeping Beauty decreases the duty cycle up to a factor of 3 compared to state-of-the-art techniques, while achieving similar delivery ratios.

Opportunistic routing protocols tackle the problem of efficient data collection in dynamic wireless sensor networks, where the radio is duty-cycled to save energy and the topology changes unpredictably due to node mobility and/or link dynamics. Unlike protocols that maintain a routing structure, in opportunistic protocols nodes forward packets to any neighbor that wakes up first, reducing latency and energy costs and increasing the resilience to network dynamics.

In data gathering wireless sensor network applications, data correlation among the sensor nodes have been utilized to extend network lifetimes. It has been shown that the data correlation also exists between nodes that are far away, contrary to the assumption that correlation decreases as a function of distance. Therefore, it is possible to group the nodes based on the correlation among their data regardless of their location. Given that data from one active node per group is sufficient to reconstruct the sensed data for the remaining sensor nodes, most of the nodes can be kept in low-power sleep mode. However, only few active nodes will usually create a disconnected network, and hence failing the purpose of the deployment. In this paper we formalize this problem, referred to as Sleep-route, of selecting the minimum number of connected active nodes that are sufficient to predict the sensed data for remaining sleeping nodes with high accuracy. We prove that the problem is NP-hard. Thus, we develop a greedy algorithm, Sleep-route heuristic that provides near-optimal solutions. Using Contiki-based simulations, we show that our scheme can extend network lifetime up to 42% as compared to the state-of-the-art solutions.

Wi-Fi based localization has proven to be a compelling alternative to GPS for mobile devices. But Wi-Fi scanning consumes a large amount of energy on smartphones because they perform full scans, i.e. all the channels in their band(s) are visited. This inefficient behavior greatly reduces battery life, raising the threshold for user acceptance. We propose a novel, incremental approach that reduces the energy consumption of Wi-Fi localization by scanning just a few selected channels. We evaluate our incremental scanning approach on eight Android devices using traces from five test subjects. Our results show that, compared to full scans, incremental scanning can reduce the energy consumption between 20.64% and 57.79%. The modern smartphones included in our study all show an energy reduction of at least 40%.

Analyzing the power consumption of smartphones is difficult because of the complex interplay between soft- and hardware. Currently, researchers rely on mainly two options: external measurement tools, which are precise but constrain the mobility of the device and require the annotation of power traces; or modelling methods, which allow mobility and consider explicitly the state of events, but have less accuracy and lower sampling rates than external tools.

In large-scale resource-constrained systems, such as wireless sensor networks, global objectives should be ideally achieved through inexpensive local interactions. A technique satisfying these requirements is information potentials, in which distributed functions disseminate information about the process monitored by the network. Information potentials are usually computed through local aggregation or gossiping. These methods however, do not consider the topological properties of the network, such as node density, which could be exploited to enhance the performance of the system. This paper proposes a novel aggregation method with which a potential becomes sensitive to the network topology. Our method introduces the notion of affinity spaces, which allow us to uncover the deep connections between the aggregation scope (the radius of the extended neighborhood whose information is aggregated) and the network's Laplacian (which captures the topology of the connectivity graph). Our study provides two additional contributions: (i) It characterizes the convergence of information potentials for static and dynamic networks. Our analysis captures the impact of key parameters, such as node density, time-varying information, as well as of the addition (or removal) of links and nodes. (ii) It shows that information potentials are decomposed into wave-like eigenfunctions that depend on the aggregation scope. This result has important implications, for example it prevents greedy routing techniques from getting stuck by eliminating local-maxima. Simulations and experimental evaluation show that our main findings hold under realistic conditions, with unstable links and message loss.

The high popularity of wireless sensor networks has led to novel applications with diverse, and sometimes demanding, data communication requirements, for example, streaming camera images in surveillance applications. In response bulk-data transfer protocols were proposed that provide low latency and high throughput communication over multiple hops. However, due to typical hardware platforms only providing a single radio, which implies that forwarding nodes need to serialize send and receive actions, the maximum end-to-end throughput is limited to 1/2 the radio capacity. To bridge this performance gap we present Fast Forward, a connection-oriented multi-hop bulk-data transfer protocol optimized for dual-radio platforms, data packets are sent across a path of alternating radio and frequency channels to exploit parallel transfers and avoid intra-path interference. We implemented Fast Forward in TinyOS to run on the Opal platform equipped with two IEEE 802.15.4 radios. In this paper we show that, with some minor tweaking of the original protocol stack to streamline internal access to the SPI bus, Fast Forward is capable of operating both radios in parallel so packets can be forwarded at full speed. We have evaluated Fast Forward on a 12-node testbed in an office environment. The sustained throughput peaks around 23.7 kBps, or 76 % of the radio capacity while the best single-radio protocol flattens out at 19 %. When introducing artificial packet loss the built-in link-level acknowledgements ensure that Fast Forward manages to deliver packets with high yield (close to 100 %) at the sink across 11 hops.

Fair rate allocation deals with the fundamental problem of sharing the channel efficiently and fairly. In wireless networks, several notable works have proposed optimal solutions to this problem. These approaches work well for static networks, but rely on an assumption that renders them sub-optimal when nodes are mobile: at each computation step, nodes must collect the state of all their neighbors (1-hop knowledge assumption). In large-scale mobile networks, nodes need to continuously adapt to changing network conditions. Under these circumstances, it is hard to gather complete 1-hop information accurately and promptly. The key to any efficient solution in mobile networks is fast convergence with limited information. In this paper, we propose a simple decentralized algorithm for fair rate allocation that works well even with partial 1-hop information. The algorithm converges linearly and can be tuned to approach a wide range of trade-offs (from proportional to harmonic fairness). Our evaluation, using real-world mobility traces from 400 taxi cabs, shows that even in the challenging case of highly dynamic and dense networks, the algorithm assigns rate efficiently (mean error of 2.5% from the optimum), while using on average 37% of the 1-hop information.

In spite of using unreliable resource-constrained devices, sensor networks can nowadays deliver 99.9% of their data with duty cycles well below 1%. This remarkable performance is, however, dependent on one or more of the following assumptions: low traffic rates, medium size densities and static nodes.

Low power listening (LPL) has been widely adopted to save energy in wireless sensor networks. However, LPL is ineffective in adapting to dynamic networks with asymmetric traffic patterns, as it sets a network-wide check interval. As a result, nodes with low data traffic waste significant energy resources doing idle listening. This problem is particularly exacerbated in multi-radio networks where majority of data comes through the most reliable radio and the duty cycles of other radios could be reduced. We address this issue in AutoSync, a protocol that combines synchronous LPL with automatic selection of check intervals to reduce energy consumption in both single and multi-radio networks. We first present the justification for AutoSync's design, and we then discuss our implementation of AutoSync in TinyOS. We compare AutoSync against existing protocols in both simulations and empirical experiments. Results show that AutoSync attains a substantial increase in the operational lifetime and mean power consumption over existing protocols in single radio networks and even more in dual radio networks.

In this paper we propose a pose estimation algorithm based on Particle filtering which uses LED sightings gathered from wireless sensor nodes (WSN) to estimate the pose of the camera. The LEDs act as (visual) markers for our pose estimation algorithm. We also compare the performance of our pose estimation algorithm against two reference algorithms - (i) Extended Kalman filtering (EKF) and (ii) Discrete Linear Transform (DLT) based approaches. The performance of all the three algorithms are evaluated for different camera frame rates, varying level of measurement noise and for different marker distribution. Our results (small-scale experimental and room-level simulation studies) show that the particle filtering algorithm gives an accuracy of a few millimetres in position and a few degrees in orientation.

When a wireless sensor network is deployed indoors, sensor nodes often need to compete for channel usage with other, more powerful devices such as 802.11 access points. To mitigate the effects of such external interference we propose Chrysso, a protocol extension specifically designed for data collection applications that leverages the channel diversity of sensor node radios. In the face of external interference Chrysso switches only the directly affected set of nodes onto a new channel. In this paper, we present the design of Chrysso as well as its implementation in Contiki. Evaluation of Chrysso on two different testbeds, and its comparison with a state-of-the-art channel hopping protocol stack show that Chrysso is effective in maintaining a stable data flow at the sink, even under extreme interference.

Camera pose estimation (i.e. determining the position and orientation of a camera) found its applications, traditionally, in the field of virtual/augmented reality, gaming and robotics. In this paper we propose an inside-out system that uses LED sightings gathered from wireless sensor nodes (WSN) to estimate the pose of the camera. The LEDs act as (visual) markers for our pose estimation algorithm, which is based on Extended Kaiman filtering (EKF). We compare the performance of our EKF algorithm against an algorithm based on Discrete Linear Transform (DLT). We also consider the effectiveness of the presented algorithm for different camera frame rates, varying noise levels and varying LED visibility conditions using a mix of simulated and experimental data. Our initial results are promising and show that the EKF algorithm gives an accuracy of a few millimetres in position and few degrees in orientation, even under sparse LED conditions, low frame rates and high noise levels.

The fundamental WSN requirement to be energy-efficient has produced a whole range of specialized Medium Access Control (MAC) protocols. They differ in how performance (latency, throughput) is traded off for a reduction in energy consumption. The question “which protocol is best?” is difficult to answer because (i) this depends on specific details of the application requirements and hardware characteristics involved, and (ii) protocols have mainly been assessed individually with each outperforming the canonical S-MAC protocol, but with different simulators, hardware platforms, and workloads. This article addresses that void for low data-rate applications where collisions are of little concern, making an analytical approach tractable in which latency and energy consumption are modeled as a function of key protocol parameters (duty cycle, slot length, number of slots, etc.). By exhaustive search we determine the Pareto-optimal protocol settings for a given workload (data rate, network topology). Of the protocols compared we find that WiseMAC strikes the best latency vs. energy-consumption trade-off across the range of workloads considered. In particular, its random access scheme in combination with local synchronization does not only minimize protocol overhead, but also maximizes the available channel bandwidth.

The fundamental WSN requirement to be energy-efficient has produced a whole range of specialized Medium Access Control (MAC) protocols. They differ in how performance (latency, throughput) is traded off for a reduction in energy consumption. The question “which protocol is best?” is difficult to answer because (i) this depends on specific details of the application requirements and hardware characteristics involved, and (ii) protocols have mainly been assessed individually with each outperforming the canonical S-MAC protocol, but with different simulators, hardware platforms, and workloads. This article addresses that void for low data-rate applications where collisions are of little concern, making an analytical approach tractable in which latency and energy consumption are modeled as a function of key protocol parameters (duty cycle, slot length, number of slots, etc.). By exhaustive search we determine the Pareto-optimal protocol settings for a given workload (data rate, network topology). Of the protocols compared we find that WiseMAC strikes the best latency vs. energy-consumption trade-off across the range of workloads considered. In particular, its random access scheme in combination with local synchronization does not only minimize protocol overhead, but also maximizes the available channel bandwidth.

Most current WSN MAC protocol implementations have multiple tasks to perform—deciding on correct timing, sending of packets, sending of acknowledgements, etc. However, as much of this is common to all MAC protocols, there is duplication of functionality, which leads to larger MAC protocol code size and therefore increasing numbers of bugs. Additionally, extensions to the basic functionality must be separately implemented in each MAC protocol. In this paper, we look at a different way to design a MAC protocol, focusing on the providing of interfaces which can be used to implement the common functionality separately. This leaves the core of the MAC protocol, determining only when to send, which is substantially different for each protocol. We also look at some examples of MAC extensions that this approach enables. We demonstrate a working implementation of these principles as an implementation of B-MAC for TinyOS, and compare it with the standard TinyOS B-MAC implementation. We show a 35% smaller code size, with the same overall functionality but increased extensibility, and while maintaining similar performance. We also present results and experiences from using the same framework to implement T-MAC, LMAC, and Crankshaft. All are demonstrated with data from real-world experience using our 24 node testbed.

The programming and retasking of sensor nodes could benefit greatly from the use of a virtual machine (VM) since byte code is compact, can be loaded on demand, and interpreted on a heterogeneous set of devices. The challenge is to ensure good programming tools and a small footprint for the virtual machine to meet the memory constraints of typical WSN platforms. To this end we propose Darjeeling, a virtual machine modelled after the Java VM and capable of executing a substantial subset of the Java language, but designed specifically to run on 8- and 16-bit microcontrollers with 2--10 KB of RAM.

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.

The Java programming language has potentially significant advantages for wireless sensor nodes but there is currently no feature-rich, open source virtual machine available. In this paper we present Darjeeling, a system comprising offline tools and a memory efficient run-time. The offline post-compiler tool analyzes, links and consolidates Java class files into loadable modules. The runtime implements a modified Java VM that supports multithreading and is designed specifically to operate in constrained execution environments such as wireless sensor network nodes and supports inheritance, threads, garbage collection, and loadable modules. We have demonstrated Java running on AVR128 and MSP430 microcontrollers at speeds of up to 70,000 JVM instructions per second.

We report on preliminary experiences with deploying a large-scale sensor network (about 100 nodes) for a pilot in precision agriculture. The pilot did not answer the initial research questions, but instead revealed many engineering problems typically overlooked by (computer) scientists evaluating their work by means of simulation. The deployment prompted us to rethink our development process and includes important lessons for the WSN research community as a whole

Research into wireless sensor networks is rapidly moving from simulations to realistic testbeds. The widely varying characteristics (e.g., radio hardware, #nodes, and topology) of various testbeds raise concerns about the validity of results across different testbeds. This paper presents empirical data of an experiment involving one application (Surge), two routing protocols (multihop and mintroute), and two testbeds (MoteLab and MistLab). The outcome is somewhat mixed. When increasing the data rate, congestion causes goodput to fall off in a similar fashion on both testbeds, which is good, but only when ignoring mintroute ill-behaving for low rates on MoteLab, which is bad. Accounting for differences in communication hardware is necessary, but even then results should be taken with a grain of salt. This certainly holds for TOSSIM simulation results, since we found that they generally do not match those of physical testbeds

Guesswork is an adaptive, probabilistic routing algorithm for wireless ad-hoc sensor networks, using local knowledge of best guess next-hop nodes to efficiently implement source-to-sink routing. Guesswork is partially based on the existing ETX and ExOR routing methodologies, but extends both in new ways to create a unified robust method for sink information distribution and source data transmission. It is designed to work efficiently in a wide variety of application scenarios, being able to cope with low quality links as well as both static and mobile networks

Abstract Streaming multimedia content in real‐time over a wireless link is a challenging task because of the rapid fluctuations in link conditions that can occur due to movement, interference, and so on. The popular IEEE 802.11 standard includes low‐level tuning parameters like the transmission rate. Standard device drivers for today's wireless products are based on gathering statistics and consequently, adapt rather slowly to changes in conditions. To meet the strict latency requirements of streaming applications, we designed and implemented an advanced hybrid control algorithm that uses signal‐strength (SNR) information to achieve fast responses. Since SNR readings are quite noisy, we do not use that information to directly control the rate setting, but rather as a safeguard limiting the range of feasible settings to choose from. We report on real‐time experiments involving two laptops equipped with IEEE 802.11a wireless interface cards. The results show that using SNR information greatly enhances responsiveness in comparison to statistics‐based rate controllers. Finally, we will present the results of an experiment with real‐time video streaming to a moving laptop in an office‐like environment. Our hybrid control algorithm effectively prevented many packet losses, thereby achieving a much higher video quality than the statistics‐based algorithm. Copyright © 2005 John Wiley & Sons, Ltd.

Key issues in wireless sensor networks such as data aggregation, localisation, MAC protocols and routing, all have to do with communication at some level. At a low level, these are influenced by the link layer performance between two nodes. The lack of accurate sensor network specific radio models, and the limited experimental data on actual link behaviour warrant additional investigation in this area. We present the results from extensive experiments, exploring several factors that are relevant for the link layer performance. These include (i) the effect of interference from simultaneous transmissions, which has not been looked into before, (ii) the degree of symmetry in the links between nodes, and (iii) the use of calibrated RSSI measurements. Finally, we present some guidelines on how to use the results for effective protocol design.

In this paper we present the initial results of experiments where encoded video data is transmitted over an IEEE 802.11a wireless link. The changing link state and the perhaps changing constraints imposed by mobile device or user, moves the optimal settings of both the video encoder and the IEEE 802.11 network layer. The video encoder and the network layer negotiates their options facilitated by a quality of service negotiation scheme called "adaptive resource contracts", thereby jointly optimizing their parameters. When time is a constraint, this optimization has to be fast and simple, maybe even at the cost of finding suboptimal solutions, as long as the found settings gives a better quality of service than fixed parameter settings.

In this paper we describe T-MAC, a contention-based Medium Access Control protocol for wireless sensor networks. Applications for these networks have some characteristics (low message rate, insensitivity to latency) that can be exploited to reduce energy consumption by introducing an activesleep duty cycle. To handle load variations in time and location T-MAC introduces an adaptive duty cycle in a novel way: by dynamically ending the active part of it. This reduces the amount of energy wasted on idle listening, in which nodes wait for potentially incoming messages, while still maintaining a reasonable throughput.We discuss the design of T-MAC, and provide a head-to-head comparison with classic CSMA (no duty cycle) and S-MAC (fixed duty cycle) through extensive simulations. Under homogeneous load, T-MAC and S-MAC achieve similar reductions in energy consumption (up to 98%) compared to CSMA. In a sample scenario with variable load, however, T-MAC outperforms S-MAC by a factor of 5. Preliminary energy-consumption measurements provide insight into the internal workings of the T-MAC protocol.

Clock (and voltage) scheduling is an important technique to reduce the energy consumption of processors that support voltage scaling. It is difficult, however, to achieve good results using only statistics from the operating system level when applications show bursty (unpredictable) behavior. We take the approach that such applications must be made power-aware and specify their average execution time (AET) and the deadline to the scheduler controlling the clock speed and processor voltage. This paper describes our energy priority scheduling (EPS) algorithm supporting power-aware applications. EPS orders tasks according to how tight their deadlines are and how often tasks overlap. Low-priority tasks are scheduled first, since they can be easily preempted to accommodate for high-priority tasks later. The EPS algorithm does not always yield the optimal schedule, but has a low complexity. We have implemented EPS on a StrongARM-based variable-voltage platform. We conducted experiments with a modified video decoder that estimates the AET of each frame. Measurements show that application-directed voltage scaling reduces processor power consumption with 50% for the bursty video decoder without missing any frame deadlines.

Clock (and voltage) scheduling is an important technique to reduce energy consumption of variable-voltage processors. It is dicult, however, to achieve good results at the OS and hardware level when applications show bursty behavior. We take the approach that such applications must be made power aware and specify their future demands to a central scheduler controlling the clock speed and processor voltage. This paper describes our energy priority scheduling (EPS) heuristic that orders tasks according to how tight their deadlines are and how often tasks overlap. We schedule low-priority tasks rst, since they can be easily preempted to accommodate for high-priority tasks later. The EPS heuristic does not always yield the optimal schedule, but has low complexity and can be used as an incremental on-line algorithm. We implemented EPS on a StrongARM-based variable-voltage platform. Measurements show that EPS reduces energy consumption with 50% for a bursty video decoding application without missing any frame deadlines.

Power consumption is the limiting factor for the functionality of future wearable devices. Since interactive applications like wireless information access generate bursts of activities, it is important to match the performance of the wearable device accordingly. This paper describes a system with a microprocessor whose speed can be varied (frequency scaling) as well as its supply voltage. Voltage scaling is important for reducing power consumption to very low values when operating at low speeds. Measurements show that the energy per instruction at minimal speed is 1/5 of the energy required at full speed. The frequency and voltage can be scaled dynamically from user space in only 140 μs. This allows power-aware applications to quickly adjust the performance level of the processor whenever the workload changes. Experiments with an H.263 video benchmark show that the power-aware decoder outperforms a static fixed-frequency policy as well as a dynamic interval-based scheduler.

Parallel computing on clusters of workstations is attractive because of the low costs in comparison to MPPs, but the speed of the local area network limits the class of applications that can be run efficiently. Fortunately, faster network technology is becoming available for the next generation of workstation clusters. This paper studies the effect of running challenging applications that communicate heavily on three types of modern interconnects: 100 Mbit/s Fast Ethernet, 155 Mbit/s ATM, and 1.28 Gbit/s Myrinet. Experimental results show that even challenging communication-intensive applications can achieve acceptable performance on workstation clusters, but only if the communication software has been designed and tuned for high performance.

Orca is a portable, object-based distributed shared memory (DSM) system. This article studies and evaluates the design choices made in the Orca system and compares Orca with other DSMs. The article gives a quantitative analysis of Orca's coherence protocol (based on write-updates with function shipping), the totally ordered group communication protocol, the strategy for object placement, and the all-software, user-space architecture. Performance measurements for 10 parallel applications illustrate the trade-offs made in the design of Orca and show that essentially the right design decisions have been made. A write-update protocol with function shipping is effective for Orca, especially since it is used in combination with techniques that avoid replicating objects that have a low read/write ratio. The overhead of totally ordered group communication on application performance is low. The Orca system is able to make near-optimal decisions for object placement and replication. In addition, the article compares the performance of Orca with that of a page-based DSM (TreadMarks) and another object-based DSM (CRL). It also analyzes the communication overhead of the DSMs for several applications. All performance measurements are done on a 32-node Pentium Pro cluster with Myrinet and Fast Ethernet networks. The results show that Orca programs send fewer messages and less data than the TreadMarks and CRL programs and obtain better speedups.

Most distributed systems contain protocols for reliable communication, which are implemented either in the microkernel or in user space. In the latter case, the microkernel provides only low-level, unreliable primitives and the higher-level protocols are implemented as a library in user space. This approach is more flexible but potentially less efficient. We study the impact on performance of this choice for RPC and group communication protocols on Amoeba. An important goal in this paper is to look at overall system performance. For this purpose, we use several (communication-intensive) parallel applications written in Orca. We look at two implementations of Orca on Amoeba, one using Amoeba's kernel-space protocols and one using user-space protocols built on top of Amoeba's low-level FLIP protocol. The results show that comparable performance can be obtained with user-space protocols.

Abstract Our toolkit for the design and implementation of parallel functional programs supports the stepwise development of parallel programs from a high level sequential specification to an optimised parallel implementation. The toolkit is used as follows: The algorithm to be implemented is specified in a functional language. The program is debugged and tested using an interpreter. The program is compiled for a sequential machine. Its performance is analysed and improved. Annotation‐driven transformations are applied to the program to indicate parallel tasks. Simulations at task level, basic block level and bus transaction level make it possible to analyse the parallel performance of the program at three levels of detail. When the performance is optimised using the simulators, the program is executed on a genuine parallel machine. Several programs have been developed with the toolkit. A program that simulates tidal flow in an estuary of the North sea is presented as a case study to demonstrate the merits of the toolkit when developing complex parallel programs. The toolkit not only supports the design of parallel applications, it also allows the study of important concepts in parallel computer architecture. These include the behaviour of cached memory systems, bus protocols, scheduling algorithms and memory management algorithms.