We propose iMoT, an innovative Transformer-based inertial odometry method that retrieves cross-modal information from motion and rotation modalities for accurate positional estimation. Unlike prior work, during the encoding of the motion context, we introduce Progressive Series Decoupler at the beginning of each encoder layer to stand out critical motion events inherent in acceleration and angular velocity signals. To better aggregate cross-modal interactions, we present Adaptive Positional Encoding, which dynamically modifies positional embeddings for temporal discrepancies between different modalities. During decoding, we introduce a small set of learnable query motion particles as priors to model motion uncertainties within velocity segments. Each query motion particle is intended to draw cross-modal features dedicated to a specific motion mode, all taken together allowing the model to refine its understanding of motion dynamics effectively. Lastly, we design a dynamic scoring mechanism to stabilize iMoT's optimization by considering all aligned motion particles at the final decoding step, ensuring robust and accurate velocity segment estimation. Extensive evaluations on various inertial datasets demonstrate that iMoT significantly outperforms state-of-the-art methods in delivering superior robustness and accuracy in trajectory reconstruction.

Despite remarkable progress in knowledge transfer across visual and textual domains, extending these achievements to indoor localization, particularly for learning transferable representations among Received Signal Strength (RSS) fingerprint datasets, remains a challenge. This is due to inherent discrepancies among these RSS datasets, largely including variations in building structure, the input number and disposition of WiFi anchors11An anchor describes a radio-emitting source. (e.g., WiFi access points, Bluetooth beacons.). Accordingly, specialized networks, which were deprived of the ability to discern transferable representations, readily incorporate environment-sensitive clues into the learning process, hence limiting their potential when applied to specific RSS datasets. In this work, we propose a plug-and-play (PnP) framework of knowledge transfer, facilitating the exploitation of transferable representations for specialized networks directly on target RSS datasets through two main phases. Initially, we design an Expert Training phase, which features multiple surrogate generative teachers, all serving as a global adapter that homogenizes the input disparities among independent source RSS datasets while preserving their unique characteristics. In a subsequent Expert Distilling phase, we continue introducing a triplet of underlying constraints that requires minimizing the differences in essential knowledge between the specialized network and surrogate teachers through refining its representation learning on the target dataset. This process implicitly fosters a representational alignment in such a way that is less sensitive to specific environmental dynamics. Extensive experiments conducted on three benchmark WiFi RSS fingerprint datasets underscore the effectiveness of the framework that significantly exerts the full potential of specialized networks in localization22Our code is available at https://github.com/Minh-Son-Nguyen/RSS_TL..

The article evaluates the effectiveness of coarsegrained channel quality prediction (CQP) for 5G-RedCap/5G NR-Light devices within industrial IoT (IIoT) networks. Finegrained predictions refine real-time communication, enhancing throughput and reducing resource utilization (RU), albeit with increased computation complexity. In contrast, coarse-grained CQP offers low computational overhead while optimizing longterm network characteristics, such as redundancy planning. Our study investigates the potential applications of coarsegrained CQP in real-time communication within the IIoT context, aiming to enhance the efficiency of simple devices (5G-RedCap) without adding the computational overhead. The varying traffic profiles and quality of service levels across diverse 5G use cases, including massive Machine Type Communication (mMTC), enhanced Mobile Broadband (eMBB), and Ultra-Reliable and Low Latency Communication (URLLC), present different challenges. For mMTC devices, coarse-grained CQP demonstrates comparable RU gains to fine-grained CQP (with up to a 50% reduction in RU), showcasing its effectiveness without added complexity. However, in the eMBB scenario, where throughput is paramount, it yields only marginal improvements. Similarly, RU gains for URLLC devices are negligible due to their stricter QoS requirements. The effectiveness of coarse-grained CQP is intricately linked to the variability in experienced channel quality across different scenarios within an indoor IIoT network. This research underscores the potential of AI applications for enhancing the performance of simple 5G-RedCap/5G NR-Light devices without compromising device complexity.

In this paper, we present all-embracing Transformers (AaTs) that are capable of deftly manipulating attention mechanism for Received Signal Strength (RSS) fingerprints in order to invigorate localizing performance. Since most machine learning models applied to the RSS modality do not possess any attention mechanism, they can merely capture superficial representations. Moreover, compared to textual and visual modalities, the RSS modality is inherently notorious for its sensitivity to environmental dynamics. Such adversities inhibit their access to subtle but distinct representations that characterize the corresponding location, ultimately resulting in significant degradation in the testing phase. In contrast, a major appeal of AaTs is the ability to focus exclusively on relevant anchors in RSS sequences, allowing full rein to the exploitation of subtle and distinct representations for specific locations. This also facilitates disregarding redundant clues formed by noisy ambient conditions, thus enhancing accuracy in localization. Apart from that, explicitly resolving the representation collapse (i.e., none-informative or homogeneous features, and gradient vanishing) can further invigorate the self-attention process in transformer blocks, by which subtle but distinct representations to specific locations are radically captured with ease. For that purpose, we first enhance our proposed model with two sub-constraints, namely covariance and variance losses at the Anchor2Vec. The proposed constraints are automatically mediated with the primary task towards a novel multi-task learning manner. In an advanced manner, we present further the ultimate in design with a few simple tweaks carefully crafted for transformer encoder blocks. This effort aims to promote representation augmentation via stabilizing the inflow of gradients to these blocks. Thus, the problems of representation collapse in regular Transformers can be tackled. To evaluate our AaTs, we compare the models with the state-of-the-art (SoTA) methods on three benchmark indoor localization datasets. The experimental results confirm our hypothesis and show that our proposed models could deliver much higher and more stable accuracy.

In this paper, we propose Anchor-agnostic Transformers (AaTs) that can exploit the attention mechanism for Received Signal Strength (RSS) based fingerprinting localization. In real-world applications, the RSS modality is inherently well-known for its extreme sensitivity to dynamic environments. Since most machine learning algorithms applied to the RSS modality do not possess any attention mechanism, they can only capture superficial representations, yet subtle but distinct ones characterizing specific locations, thereby leading to significant degradation in the testing phase. In contrast, AaTs are enabled to focus exclusively on relevant anchors at every Received Signal Strength (RSS) sequence for these subtle but distinct representations. This also facilitates the model to neglect redundant clues formed by noisy ambient conditions, thus achieving better accuracy in fingerprinting localization. Moreover, explicitly resolving collapse problems at the feature level (i.e., none-informative or homogeneous features) can further invigorate the self-attention process, by which subtle but distinct representations to specific locations are radically captured with ease. To this end, we enhance our proposed model with two sub-constraints, namely covariance and variance losses that are mediated with the main task within the representation learning stage towards a novel multi-task learning manner. To evaluate our AaTs, we compare the models with the state-of-the-art (SoTA) methods on three benchmark indoor localization datasets. The experimental results confirm our hypothesis and show that our proposed models could provide much higher accuracy.

A premature crack is a significant indicator for early failure detection for underground polyvinyl chloride (PVC) water pipes. Using an array of strain gauges mounted on a pipe surface to monitor the strain of adjacent areas of a premature crack is a novel technology that has not been explored. To reduce the risks and up-front investments, we need a testbed to investigate, verify, and validate the innovative technology. However, establishing such a pipe monitoring testbed that covers completely realistic underground situations is challenging. The main reason lies in three main challenges: (i) mimicking the natural changes of water flowing in pipes; (ii) identifying the proper placement of strain gauges and detectable crack sizes; (iii) simulating the crucial underground conditions such as temperature and external stress. To this end, in this paper, we present a testbed to get more insights into the effects of different crack types on the pressure-strain characteristic in realistic conditions. In particular, we use pressure meters and water pumps to control the water flow (for challenge (i)); deploy various strain gauges types and induce cracks with different sizes (for challenge (ii)); fill the pipe with water at various temperatures and underground-like external stress (for challenge (iii)); Analyzing experimental results reveals useful hints for designing a realistic testbed, including but not limited to, the required distance among strain gauges, the influence of temperature and pipe axial stress. The dataset and analytic results of this work would provide more insights into how to design a realistic testbed for underground PVC water pipe crack detection.

Accurate calculation of temporal stride parameters is essential in horse gait analysis. A prerequisite for calculating these parameters is identifying the exact timings of gait events, i.e., hoof-on and hoof-off moments. A hoof-mounted inertial measurement unit (IMU) can be used to identify these moments accurately, yet this approach is often impractical due to the vulnerability of IMU to the impacts during locomotion. In this study, we investigated the possibility of accurately estimating the gait events using the signals of an IMU mounted on a less vulnerable location, such as a limb or upper body. To achieve the goal, we equipped IMUs on horses limbs, withers, and sacrum and measured them during different gaits. Then, we estimated the gait events timings by training recurrent neural networks models on the output signals of each IMU. Finally, we evaluated the models by comparing their results to the gait events timings labeled from hoof-mounted IMUs. The best performing model represented the best location (between the limbs, withers, and sacrum) for gait event estimation. Compared to the previous studies, our models yielded higher accuracy and were more generic by supporting more gaits. In conclusion, accurate calculation of temporal stride parameters is feasible by estimating gait event timings using an IMU mounted on less vulnerable body locations.

The drinking water mains, mostly buried underground and stay there for decades, require proper maintenance to prevent failures. Among different kinds of material, polyvinyl chloride (PVC) has been widely used due to positive features, such as high durability, corrosion resistance, low price, and easy installation. To the best of our knowledge, this is the first article that reviews the inspection methods toward continuously monitoring the structural health of PVC drinking water mains. To understand which properties need inspecting, we first investigated the attributes that influence PVC pipe and joint failures. Then, we reviewed the methods that have already been applied or can inspect these influencing attributes. We categorized the prospects into five groups: 1) sound wave; 2) fiber optic sensing; 3) hydraulic monitoring; 4) multiple discrete sensors; and 5) other inline methods. Finally, we discussed the possibility and challenges in implementing these methods into a continuous monitoring system of PVC water mains for early failure warning. The result, which includes active sound wave, fiber optic sensing, hydraulic vibration, and multiple discrete sensors methods, can help future researchers select the appropriate methods to develop the continuous monitoring system for the PVC water mains.

With increasing network complexity, intelligent mechanisms to efficiently achieve the required quality of service of wireless-enabled applications are being developed, especially for industrial environments due to the onset of the fourth industrial revolution. In this paper, the potential benefits of wireless channel quality prediction for two of the three major use cases supported by 5G viz. enhanced Mobile BroadBand (eMBB) and Ultra-Reliable Low Latency Communication (URLLC) are quantified in an industrial indoor environment through simulations. Our analysis shows that the ability to perform perfect prediction improves the 10thuser throughput percentile by up to 125% for eMBB use case and decreases the 90thresource utilization percentile by up to 37% for URLLC use case. Furthermore, the maximum tolerable prediction inaccuracy is found to be up to 5 dB and 0.35 dB for eMBB and URLLC use cases, respectively.

Many researchers try to make a comparison between various Internet-of-Things (IoT) platforms based on specific requirements. However, none of the reviewed studies proposed a thorough analysis of the variety of comparative methods. Since there is a lack of comparison frameworks for IoT platforms, individuals or companies have difficulties when selecting a suitable IoT platform matching their associated business requirements. In order to support this selection process, a set of functional and nonfunctional requirements is identified. A framework containing methods in selecting an IoT platform is presented. The methodology is based on statistical and visualization techniques to recommend a suitable IoT platform. Five IoT platforms: 1) Azure; 2) AWS; 3) SaS; 4) ThingWorx; and 5) Kaa IoT are studied to evaluate the performance of the framework. Different comparison methods are proposed and a multicriteria decision analysis method was applied by using an analytical hierarchical process (AHP). One of the methods clusters the functional requirements and compares the IoT platforms based on their ability in supporting a specific requirement or not. The$K$-means clustering was applied to determine the clusters of functional requirements. The comparison was made based on the hierarchical level of requirements per main requirement. The other methods use the following statistical tests: error bar test, one-way Anova test, and Tukey’s honest significant difference test. Based on the selected requirements, an approach is suggested for which IoT platform can be used.

People counting techniques have been widely researched recently and many different types of sensors can be used in this context. In this paper, we propose a system based on a deep-learning model able to identify the number of people in the crowded scenarios through the speech sound. In a nutshell the system relies on two components: counting concurrent speakers in overlapping talking sound directly and clustering single-speaker sound by speaker-identity over time. Compared to previously proposed speaker-counting systems models that only cluster single-speaker sound, this system is more accurate and less vulnerable to the overlapping sound in the crowded environment. In addition, counting speakers in overlapping sound also gives the minimal number of speakers so that it also improves the counting accuracy in a quiet environment.Our methodology is inspired by the newly proposed SincNet deep neural network framework which proves to be outstanding and highly efficient in sound processing with raw signals. By transferring the bottleneck layer of SincNet model as features fed to our speaker clustering model we reached a noticeably better performance than previous models who rely on the use MFCC and other engineered features.

One of the most practical localization techniques is WLAN-based fingerprinting for location-based services because of the availability of WLAN Access Points (APs). This technique measures the Received Signal Strength (RSS) from APs at each indicated location to construct fingerprints. However, the collection of fingerprints is notoriously laborious and needs to be repeatedly updated due to the changes of environments. To reduce the workload of fingerprinting, we apply Deep Belief Networks to unlabeled RSS measurements to extract hidden features of the fingerprints, and thereby minimize the collection of fingerprints. These features are used as inputs for conventional regression techniques such as Support Vector Machine and K-Nearest Neighbors. The experiment results show that our feature representations learned from unlabeled fingerprints provide better performance for indoor localization than baseline approaches with a small fraction of labeled fingerprints traditionally used. In the experiment, our approach already improves the localization accuracy by 1.9 m when using only 10% of labeled fingerprints, compared to the closest baseline approach which used 100% of labeled fingerprints.

Bluetooth Low Energy (BLE) devices such as iBeacons have been popularly deployed for Location Based Services (LBS), including indoor infrastructure monitoring, positioning, and navigation. In these applications, the positions of iBeacons are assumed to be known. However, the location information is often unavailable or inaccurate as most iBeacons were deployed by different external parties. In addition, manual localizing the already-deployed iBeacons is costly and even impractical, especially in large-scale and complex indoor environments. Therefore, we propose a novel method, namely SomeBe, which can localize deployed iBeacons with a minimal effort and invasiveness to existing infrastructures. Specifically, our approach uses cooperative multilateration based on Received Signal Strength (RSS) of available smartphones and WiFi access points (APs) in the environment. Both Bluetooth signal strengths (between smartphones and iBeacons) and WiFi signal strengths (between smartphones and APs) are jointly employed in a single optimization cost function to surpass the local minima. Requiring that the positions of the APs are known only, the proposed cost function can also localize the iBeacons without knowing the positions of smartphones. To improve the localization accuracy, we employ a clustering method based on the RSS values for the coarse estimation of iBeacons' positions. SomBe also can be used to simplify iBeacon deployment as it can localize the iBeacons with a minimal effort. The performance evaluation results of our testbed experiments as well as realistic simulations show that SomBe outperforms non-cooperative approaches with 85% better in terms of accuracy.

In this paper, we present an effective and low- cost wireless communication system for extremely long and narrow pipes that can replay the extant wire system in underground sensor network applications such as soil sampling and testing with the Cone Penetration Test (CPT), the most widely used underground sensor device. Different from existing in-pipe wireless techniques, we consider real-world pipelines that are very narrow and long. In particular, in our design data are first modulated at a commercial frequency and then converted to high frequency, between 14-15 GHz, to be transmitted along of the pipelines under the circular waveguide mode TM01. Especially, we design a cone-shaped antenna to overcome the aligning problem of feeds between the transmitter and receiver. To evaluate the applicability and efficiency of our design, we conduct realistic simulations as well as experiments with real prototypes. The results of experiments are consistent with our theoretical design and simulations and show that our proposed wireless system can transfer sensory data up to 20 m in narrow CPT pipes with a diameter of 17 mm when using the LoRa modulation with a transmitting power of 1 W, whereas existing underground radio techniques can transfer data from a depth of 2 m at maximum in the same condition. In our approach, it is also possible to add repeaters to extend the communication range when needed.

Most of the studies on human activity recognition using smartphones and smartwatches are performed in an offline manner. In such studies, collected data is analyzed in machine learning tools with less focus on the resource consumption of these devices for running an activity recognition system. In this paper, we analyze the resource consumption of human activity recognition on both smartphones and smartwatches, considering six different classifiers, three different sensors, different sampling rates and window sizes. We study the CPU, memory and battery usage with different parameters, where the smartphone is used to recognize seven physical activities and the smartwatch is used to recognize smoking activity. As a result of this analysis, we report that classification function takes a very small amount of CPU time out of total app's CPU time while sensing and feature calculation consume most of it. When an additional sensor is used besides an accelerometer, such as gyroscope, CPU usage increases significantly. Analysis results also show that increasing the window size reduces the resource consumption more than reducing the sampling rate. As a final remark, we observe that a more complex model using only the accelerometer is a better option than using a simple model with both accelerometer and gyroscope when resource usage is to be reduced.

Self-organization is critical to enable novel indoor Location-Based Services (LBSs) for users and businesses in large, complex and unstructured buildings. Inspired by high densities of smartphones in public indoor spaces, in this paper we propose a self-organizing indoor localization approach that allows the use of available WiFi Access Points (APs) and iBeacons in the area to improve location accuracy and environment adaptability. Our approach is based on a semi-anchored localization that estimates the unknown location of smartphones, given known-location anchors (APs) and unknown-location anchors (iBeacons). We exploit the capabilities of Levenberg-Marquardt optimization algorithm to accurately estimate smartphone locations in realtime, in contrast to fingerprinting methods that require a tedious off-line training phase. Moreover, we use a clustering method based on the Received Signal Strength (RSS) values to obtain the initial estimated location for the optimization. We evaluate our approach using available APs and non-coordinated iBeacons in a large building to localize smartphones. The experimental results confirm that our self-organizing approach not only effortlessly estimates the position of mobile devices, but also provides a higher localization accuracy than other widely used approaches such as extant fingerprinting techniques for both scenarios, with and without iBeacons.

Ground transport infrastructures require in-situ monitoring to evaluate their condition and deterioration and to design appropriate preventive maintenance strategies. Current monitoring practices provide accurate and detailed spatial measurements but often lack the required temporal resolution. This is because the large scale of these infrastructures and the expensive equipments required for monitoring activities do not allow running very frequent measurement campaigns. In this paper, we present RoVi, a novel smartphone-based framework for continuous monitoring of a number of health and condition indicators for variety of ground infrastructures and assets. These indicators include railroad track geometry features such as Cant, Twist, Curvature, and Alignment for different segment lengths as well as road and bike path roughness index (i.e., an equivalent to the International Roughness Index, the so called IRI). RoVi uses an optimized processing algorithm technique on data acquired by smartphones' inertial sensors and relies on sensing, processing power, and networking capabilities of smartphones carried by car/bike drivers and train passengers to provide real time space-time information for fine-grained monitoring of infrastructures. It utilizes the crowd sensing concept to fill in the gap between current sparse consecutive inspections. RoVi provides a reliable and accurate analytic tool for engineers and maintenance planners by offering them features and indicators they require for asset management and maintenance planning. We extract these features and indicators from noisy smartphone data utilizing adaptive signal processing techniques followed by feature calculations and geo-location visualization. Our fast data aggregation algorithm based on Delaunay triangulation updates profiles with new measurements arriving in real time from smartphones. By doing so, it tackles the notorious problem of smartphone GPS accuracy. Performance evaluation of our framework has been performed on measurements collected by smartphones and compared with the ground truth measurements collected by the highend measurement vehicles (i.e., ARAN for roads and UMF120 measurement train for railroads).

Chronic care is an eminent application of implantable body sensor networks (IBSN). Performing physical activities such as walking, running, and sitting is unavoidable during the long-term monitoring of chronic-care patients. These physical activities cripple the radio frequency (RF) signal between the implanted sensor nodes. This is because various body postures shadow the RF signal. Although shadowing itself may be short, a prolonged activity will significantly increase the effect of the RF-shadowing. This effect dampens the communication between implantable sensor nodes and hence increases the chance of missing life-critical data. To overcome this problem, in this paper we propose a link quality-aware medium access control (MAC) protocol called HACMAC, which adapts the access mechanism during different human activities based on the wireless link-quality. Our simulation results show that compared with the access mechanism suggested by the IEEE 802.15.6 standard, the reliability of the wireless communication is increased using HACMAC even while transmitting at a strongly low transmission power of 25μW effective isotropic radiated power (EIRP) set by the IEEE 802.15.6 standard.

In the context of acoustic monitoring, the location of a sound source can be passively estimated by exploiting time-of-arrival and time-difference-of-arrival measurements. To evaluate the fundamental hardness of a location estimator, the Cramer-Rao bound (CRB) has been used by many researchers. The CRB is computed by inverting the Fisher Information Matrix (FIM), which measures the amount of information carried by given distance measurements. The measurements are commonly expressed as actual distances plus white noise. However, the measurements do include extra noise types caused by time synchronization, acoustic sensing latency, and signal-to-noise ratio. Such noise can significantly affect the performance and depend highly on the sensing platforms such as Android smartphones. In this paper, we first remodel the acoustic-based distance measurements considering such additive errors. Then, we derive a new FIM with the new statistical ranging error models. As a result, we obtain new CRBs for both non-cooperative and cooperative localization schemes that provide better insight into the causality of the uncertainties. Theoretical analysis also proves that the proposed CRBs for localization become the old CRBs when the additional errors are ignored, which gives a robust check for the new CRBs. Thus, the new CRBs can serve as a benchmark for localization estimators with both new and old measurement models. The new CRBs also indicate that there is room to improve current localization schemes, however, it is a daunting challenge.

Smoking is known to be one of the main causes for premature deaths. A reliable smoking detection method can enable applications for an insight into a user's smoking behaviour and for use in smoking cessation programs. However, it is difficult to accurately detect smoking because it can be performed in various postures or in combination with other activities, it is less-repetitive, and it may be confused with other similar activities, such as drinking and eating. In this paper, we propose to use a two-layer hierarchical smoking detection algorithm (HLSDA) that uses a classifier at the first layer, followed by a lazy context-rule-based correction method that utilizes neighbouring segments to improve the detection. We evaluated our algorithm on a dataset of 45 hours collected over a three month period where 11 participants performed 17 hours (230 cigarettes) of smoking while sitting, standing, walking, and in a group conversation. The rest of 28 hours consists of other similar activities, such as eating, and drinking. We show that our algorithm improves recall as well as precision for smoking compared to a single layer classification approach. For smoking activity, we achieve an F-measure of 90-97% in person-dependent evaluations and 83-94% in person-independent evaluations. In most cases, our algorithm corrects up to 50% of the misclassified smoking segments. Our algorithm also improves the detection of eating and drinking in a similar way. We make our dataset and data logger publicly available for the reproducibility of our work.

Localization, a process of determining the position of a blind node, can be used in various applications. Signal-strength localization provides a low-cost and low-power solution to positioning. Signal-strength positioning approaches using fingerprinting or calibrated approaches require a time-consuming calibration phase. Existing self-calibrating approaches, which do not require a priori calibration, use a least-squares fitting model to determine both the position of the blind node as well as the optimal environmental parameters. In this paper, we propose an approach using the Product-Moment correlation between the measured signal strength and the estimated signal strengths. Such approach does not require estimation of the environmental parameters or prior calibration and outperforms existing self-calibrating least-squares approaches. We compare our approach to existing least-squares calibration-free positioning approaches. Moreover, we look at the Cramer-Rao Bound (CRB) of signal-strength localization and using simulations we show that the product-moment correlation outperforms least-squares approaches and follows the CRB closely. Simulation and evaluation using a real-world experiment dataset show the product-moment approach significantly outperforms least-squares approaches. The product-moment approach follows the CRB much more closely and achieves up to twice more accurate positions in certain scenarios. When the error ratio increases and the number of reference positions stays fixed at 6, the product-moment approach scores 20% more accurate positions. In the cooperative localization scenario, the product-moment correlation performs 40% better.

Modern society is surrounded by an ample spectrum of smart mobile devices. This ubiquity forms a high potential for community-oriented opportunistic ad hoc networking applications. Nevertheless, today’s smart mobile devices such as smartphones, tablets, and wristbands are still onerous to automatically establish mobile ad hoc connections with our physical circle of friends and between occasional contact opportunities. Motivated by this, this study presents Cocoon as a lightweight middleware proposed for smart mobile platforms to support mobile opportunistic communications for general public use. Cocoon employs an adaptive context-aware service that can fairly coordinate a multitude of concurrently-running networking applications. Along with this service, the opportunistic networking service of Cocoon facilitates fast and reliable information sharing between participating devices according to our real-world applicability and validation experiments presented in this work. The routing protocols of Cocoon are designed above the universally-accepted Wi-Fi and Bluetooth standards. Without requiring any configuration or modification on top of the affiliated wireless interfaces, Cocoon is therefore suitable for direct use on any kind of smart mobile platform.

This study introduces an ad-hoc opportunistic data dissemination protocol, called VADISS, that facilitates participatory traffic monitoring applications with smartphones. As a ubiquitous alternative to existing vehicular networking methods, VADISS uses the default WiFi interfaces universally adopted by today's mobile devices. The routing is enabled with intermittent service advertisements and discoveries, and the data exchange is provided via built-in IEEE 802.11 beacon frames. VADISS orients neither association nor connection between devices, is not based on any road-side unit, and thus is readily available for vehicular end-use applications. Together with a smartphone-based online road monitoring application, VADISS has been tested in a real- traffic setup to assess the data dissemination performance. With the increasing coverage, quite promising efficiency has been reached, especially for routing of critical traffic information.

The emerging wave of technology in human-centric devices such as smart phones, tablets, and other small wearable sensor modules facilitates pervasive systems and applications to be economically deployed on a large scale with human participation. To exploit such environment, data gathering and dissemination based on opportunistic contact times among humans is a fundamental requirement. To tackle the lack of contemporaneous end-to-end connectivity in Delay-tolerant Networks (DTNs), most current algorithms assess the probability of the contact times to gradually convey a message towards its destination. These contact-based approaches do not perform well when historical locations of nodes have mixture distribution. In this paper, we formulate routing problems in spatial and spatiotemporal domains as an online unsupervised learning problem given location data. The key insight is that nodes frequently appearing nearer the message destinations are regarded as possessing higher delivery probability even if they have low contact times. We show how to solve the formulated problems with two basic algorithms, Location-Mean and Location-Cluster, by estimating the means of historical locations to calculate delivery probability of nodes. To our best knowledge, this is the first work to tackle DTN routing problem using online unsupervised learning on geographical locations. In the context of human mobility, simulation results of the Location-Mean algorithm show that the online unsupervised learning approach given node locations achieves better routing performances in term of delivery ratio, latency, transmission cost, and computation efficiency compared to the contact-based approach.

Current wireless technologies for industrial application, such as WirelessHART and ISA100.11a, are not designed to support harvester-powered input/output (I/O) devices, where energy availability varies in a non-deterministic manner. The centralized management approach of these standards makes it difficult and costly for harvester-powered I/O devices (sensor/actuators) to re-join in the network in case of power failure. The communication overhead and delay to cope with the dynamic environment of a large-scale industrial network are also very high for an I/O device. In this paper, we therefore propose a Distributed Management scheme for Hybrid networks to provide Real-time communication (D-MHR) based on the IEEE 802.15.4e and Routing Protocol for Low power and Lossy Networks (RPL) standards, which can address the requirements of energy constrained I/O devices. In D-MHR, the routers can dynamically reserve communication resources and manage the I/O devices in the local star sub-networks. We demonstrate that D-MHR achieves higher network management efficiency compared to IS100.11a standard, without compromising the latency and reliability requirements of industrial wireless networks.

Ubiquity of portable location-aware devices and popularity of online location-based services, have recently given rise to the collection of datasets with high spatial and temporal resolution. The subject of analyzing such data has consequently gained popularity due to numerous opportunities enabled by understanding objects' (people and animals, among others) mobility patterns. In this paper, we propose a hidden semi-Markov-based model to understand the behavior of mobile entities. The hierarchical state structure in our model allows capturing spatio-temporal associations in the locational history both at stay-points and on the paths connecting them. We compare the accuracy of our model with a number of other spatio-temporal models using two real datasets. Furthermore, we perform sensitivity analysis on our model to evaluate its robustness in presence of common issues in mobility datasets such as existence of noise and missing values. Results of our experiments show superiority of the proposed scheme compared with the other models.

Performing real world experiments with underwater communication is difficult and time-consuming. Input for evaluation of localization and time-synchronization derived from experiments is not readily available.

Current wireless technologies for industrial application, such as WirelessHART and ISA100.11a, use a centralized management approach which makes it difficult and costly for harvester-powered I/O devices to re-join the network in case of power failure. The communication overhead and delay to cope with the dynamic environment of a large-scale industrial network are also very high for an I/O device. In this paper, we therefore propose a distributed management scheme named D-MHR, which can address the requirements of energy constrained I/O devices. In D-MHR, the routers can dynamically reserve communication resources and manage the I/O devices in the local star sub-networks. We demonstrate that D-MHR achieves higher network management efficiency compared to IS100.11a standard, without compromising the latency and reliability requirements of industrial wireless networks.

Wireless standards developed for industrial applications such as ISA100.11a and WirelessHART, generally use centralized management approaches. However, such centralized approaches cannot cope with network dynamicity in real-time manner. They also incur high management overhead and latency. Consequently, the network becomes unsuitable for resource constraint devices, e.g I/O devices. The problems become exacerbated when the network scales up. ISA100.11a standard allows reduced functionality devices in the network and supports hybrid network topology. We propose an extension to ISA100.11a to better address the requirements of the energy constrained I/O devices. The proposed extension makes the management more decentralized by delegating a part of the management responsibility to the routers in the network. It also allows the I/O devices to choose their best routers according to the metric considered using local statistics and advertised routers' ranks. We show that the proposed extension can better address the realtime and reliability requirements of industrial wireless networks. It can achieve higher network management efficiency in terms of reducing the delay and overhead of I/O devices than the ISA100.11a standard.

Ever-increasing emergence of location-aware ubiquitous devices has facilitated collection of time-stamped mobility data. This large volume of data not only provides trajectory information but also information about social interaction between individuals. Unlike trajectory representation and discovery, discovery of social ties and interactions hidden in mobility data has not yet been fully explored. To identify such interaction, social network analysis has been recently used. However, compared with data from emails, phone calls, and messages, which are commonly used for social network analysis, mobility data convey less information about interaction between entities. Therefore, identifying the type of tie between two entities using only mobility data is a great challenge. In this paper, we propose a method for measuring the strength and type of social ties between people only based on their spatio-temporal correlations. Using mutual information metric, we propose utilization of two types of measures for identifying the purpose of being in a certain location. Our experimental results using a location-aware sensing device show that our method can identify different social ties between various entities successfully.

Wireless sensor networks (WSNs) have revolutionized the industrial networks by enabling wireless sensing and control to the machine parts where wiring is impossible. However, new challenges in terms of communication reliability and latency, appear with the advances in the industrial wireless control systems. Existing standards are found inadequate to support many of these demanding situations as most of those are based on IEEE 802.15.4 standard, which is unable to provide high communication reliability with low latency (milliseconds). Digital Enhanced Cordless Telecommunications (DECT), a communication standard developed by European Telecommunications Standards Institute (ETSI), seems to support the timing and reliability requirements of modern industrial wireless control networks. In this paper, we evaluate the performance of DECT in various industrial environments and found that it can maintain excellent communication reliability between sensors and control centre with low latency in such scenarios.

It is essential for any highly trained cyclist to optimize his pedalling movement in order to maximize the performance and minimize the risk of injuries. Current techniques rely on bicycle fitting and off-line laboratory measurements. These techniques do not allow the assessment of the kinematics of the cyclist during training and competition, when fatigue may alter the ability of the cyclist to apply forces to the pedals and thus induce maladaptive joint loading. We propose a radically different approach that focuses on determining the actual status of the cyclist’s lower limb segments in real-time and real-life conditions. Our solution is based on body area wireless motion sensor nodes that can collaboratively process the sensory information and provide the cyclists with immediate feedback about their pedalling movement. In this paper, we present a thorough study of the accuracy of our system with respect to the gold standard motion capture system. We measure the knee and ankle angles, which influence the performance as well as the risk of overuse injuries during cycling. The results obtained from a series of experiments with nine subjects show that the motion sensors are within 2.2° to 6.4° from the reference given by the motion capture system, with a correlation coefficient above 0.9. The wireless characteristics of our system, the energy expenditure, possible improvements and usability aspects are further analysed and discussed.

Current wireless standards and protocols for industrial applications, such as WirelessHART and ISA100.11a, typically use centralized network management for communication scheduling and route establishment. However, due to their centralized nature, these protocols have difficulty coping with dynamic large-scale networks. To address this problem, we propose D-SAR, a distributed resource reservation algorithm that allows source nodes to meet the Quality-of-Service requirements for peer-to-peer communication. D-SAR uses concepts derived from circuit switching and Asynchronous Transfer Mode (ATM) networks and applies them to wireless sensor and actuator networks. Simulations show that latency in connection setup is 93% less in D-SAR compared to WirelessHART and that 89% fewer messages are sent during connection setup in case the distance from source to destination is 12 hops.

One of the first standards in the wireless sensor networks domain, WirelessHART, was introduced to address industrial process automation and control requirements. The standard can be used as a reference point to evaluate other wireless protocols in the domain of industrial monitoring and control. This makes it worthwhile to set up a reliable WirelessHART simulator to achieve that reference point in a relatively easy way. This paper explains our implementation of WirelessHART in the NS-2 simulator. According to our knowledge, this is the first implementation that supports the WirelessHART network manager as well as the whole stack of the WirelessHART standard. We evaluated the performance of our implementation in terms of delay and communication load in the network. This implementation offers an alternative to expensive testbeds for testing WirelessHART.

In this paper we present the vision of the Go Green project on a smart home that is capable of decreasing energy consumption while at the same time increasing user comfort. To identify the main challenges we introduce a general model for intelligent homes that describes the current state, the target state and the transition. A key point in the project's vision is the concept of personas and entities to model groups of individuals and their preferences. A second key point is the strategy of coaching the users towards the global system goals by adapting the environment.

Wireless sensor network (WSN) applications require efficient, accurate and timely data analysis in order to facilitate (near) real-time critical decision-making and situation awareness. Accurate analysis and decision-making relies on the quality of WSN data as well as on the additional information and context. Raw observations collected from sensor nodes, however, may have low data quality and reliability due to limited WSN resources and harsh deployment environments. This article addresses the quality of WSN data focusing on outlier detection. These are defined as observations that do not conform to the expected behaviour of the data. The developed methodology is based on time-series analysis and geostatistics. Experiments with a real data set from the Swiss Alps showed that the developed methodology accurately detected outliers in WSN data taking advantage of their spatial and temporal correlations. It is concluded that the incorporation of tools for outlier detection in WSNs can be based on current statistical methodology. This provides a usable and important tool in a novel scientific field.

An essential component in the ubiquitous computing vision is the ability of detecting with which objects the user is interacting during his or her activities. We explore in this paper a solution to this problem based on wireless motion and orientation sensors (accelerometer and compass) worn by the user and attached to objects. We evaluate the performance in realistic conditions, characterized by limited hardware resources, measurement noise due to motion artifacts and unreliable wireless communication. We describe the complete solution, from the theoretical design, going through simulation and tuning, to the full implementation and testing on wireless sensor nodes. The implementation on sensor nodes is lightweight, with low communication bandwidth and processing needs. Compared to existing work, our approach achieves better performance (higher detection accuracy and faster response times), while being much more computationally efficient. The potential of the concept is further illustrated by means of an interactive multi-user game. We also provide a thorough discussion of the advantages, limitations and trade-offs of the proposed solution.

In this paper we describe a smart way to apply dynamic wireless sensor networks (WSN) in logistics. Especially in the temperature controlled supply chain (cold chain), perishable goods like fruits and pharmaceuticals greatly benefit from real-time quality monitoring during storage and transport in order to avoid quality degradation and spoilage. In our system, wireless sensor nodes called SmartPoints monitor the environmental conditions and generate alarms when specific events are detected. Additionally, they calculate the remaining shelf life of the perishable goods they travel with. When there is an Internet-connected WSN available during travel, the shelf-life prediction and associated alarms are directly sent to a back-end server. Alternatively they are logged on the SmartPoints and flushed upon arrival, such that the remaining shelf-life and alarms are immediately clear and a full history will be available later. Our dynamic WSN supports a number of protocols that enable support for the dynamic processes in logistic processes. The Ambient middleware supports real-time monitoring and remote maintenance across the Internet via wired and mobile wireless network access technologies. Additionally, the middleware offers easy integration with third-party applications. Ambient Studio utilizes the middleware for remote WSN configuration and monitoring.

This paper presents a novel Radio Interferometric Positioning System (RIPS), which we call Stochastic RIPS (SRIPS). Although RIPS provides centimeter accuracy, it is still not widely adopted due to (1) the limited set of suitable radio platforms and (2) the relatively long measurement and calibration times. SRIPS overcomes these practical limitations by (1) omitting the calibration phase of the existing RIPS and by (2) applying a novel positioning algorithm. SRIPS exploits the phenomenon of the small but stable difference between two transmitted frequencies that often exists when two radios are tuned to the same frequency. We obtain an experimental measure for this stability. This approach enables the implementation of RIPS on commonly available radio platforms, such as the CC2430, because fine-tuning in small steps relative to the beat frequencies for calibration is not required. In addition, we show that SRIPS calculates the position that provides the best fit to the set of measurements, given the underlying statistical and propagation models. Therefore, SRIPS converges more accurately to the true locations in a variety of situations of practical interest. Experiments in a 20x20m2 set-up verify this and show that our SRIPS CC2430 implementation reduces the number of required measurements by a factor of three, and it reduces the measurement time to less than 0.1 seconds, while providing accuracy similar to that of the existing RIPS implementation on the CC1000 platform, which requires seconds.

This paper contains a feasibility study of Radio Interferometric Positioning (RIP) implemented on a widely used 2.4 GHz radio (CC2430). RIP is a relatively new localization technique that uses signal strength measurements. Although RIP outperforms other RSS-based localization techniques, it imposes a set of unique requirements on the used radios. Therefore, it is not surprising that all existing RIP implementations use the same radio (CC1000), which operates below the 1 GHz range. This paper analyzes to what extent the CC2430 complies with these requirements. This analysis shows that the CC2430 platform introduces large and dynamic sources of errors. Measurements with a CC2430 test bed in a line-of-sight indoor environment verify this. The measurements indicate that the existing RIP algorithm cannot cope with these types of errors, and will incur a relatively low accuracy of 3.1 meter. Based on these results, we made an initial implementation of a new algorithm, which can cope with these errors, and decreases this positioning error by a factor of two to 1.5 meter accuracy.

In mission-critical applications such as battlefield reconnaissance or industrial safety and security, a large number of sensor nodes are deployed in a large area to detect and report event related information to the end-users. When a critical event in the monitoring region is detected by a node, alarm should be broadcast to all the other nodes in the neighborhood. This effect is shown in Figure 1.

A coordinated team of mobile wireless sensor and actuator nodes can bring numerous benefits for various applications in the field of cooperative surveillance, mapping unknown areas, disaster management, automated highway and space exploration. This article explores the idea of mobile nodes using vehicles on wheels, augmented with wireless, sensing, and control capabilities. One of the vehicles acts as aleader, being remotely driven by the user, the others represent thefollowers. Each vehicle has a low-power wireless sensor node attached, featuring a 3D accelerometer and a magnetic compass. Speed and orientation are computed in real time using inertial navigation techniques. The leader periodically transmits these measures to the followers, which implement a lightweight fuzzy logic controller for imitating the leader's movement pattern. We report in detail on all development phases, covering design, simulation, controller tuning, inertial sensor evaluation, calibration, scheduling, fixed-point computation, debugging, benchmarking, field experiments, and lessons learned.

One of the fundamental operations in sensor networks is convergecast which refers to the communication pattern in which data is collected from a set of sensor nodes and forwarded to a common end-point gateway, namely sink node, in the network. In case of multiple sinks within the network, the total load of the network has to be balanced among these sinks to minimize the problem of packet loss in the convergecast process in wireless sensor networks (WSNs) due to congestion and collisions near the sinks. In this paper, we present a novel cross-layered communication protocol for efficient data dissemination in multi-sink WSNs which is under consideration of SENSEI project. It basically combines network wide load balancing, clustering techniques and local routing optimizations with SENSEI architecture which make it efficient on both global and local level. The performance evaluation of the proposed technique shows how our routing protocol can balance the network load without additional control packets for routing tree maintenance.

Recently, Wireless Sensor Networks (WSN) community has witnessed an application focus shift. Although, monitoring was the initial application of wireless sensor networks, in-network data processing and (near) real-time actuation capability have made wireless sensor networks suitable candidate for ev

Autonomous vehicles are used in areas hazardous to humans, with significantly greater utility than the equivalent, manned vehicles. This paper explores the idea of a coordinated team of autonomous vehicles, with applications in cooperative surveillance, mapping unknown areas, disaster management or space exploration. Each vehicle is augmented with a wireless sensor node with movement sensing capabilities. One of the vehicles is the leader and is manually controlled by a remote controller. The rest of the vehicles are autonomous followers controlled by wireless actuator nodes. Speed and orientation are computed by the sensor nodes in real time using inertial navigation techniques. The leader periodically transmits these measures to the followers, which implement a lightweight fuzzy logic controller for imitating the leader's movement pattern. The solution is not restricted to vehicles on wheels, but supports any moving entities capable of determining their velocity and heading, thus opening promising perspectives for machine-to-machine and human-to-machine spontaneous interactions in the field. Visit [1] to see a video demonstration of the system.

Context processing in Body Area Networks (BANs) faces unique challenges due to the user and node mobility, the need of real-time adaptation to the dynamic topological and contextual changes, and heterogeneous processing capabilities and energy constraints present on the available devices. This paper proposes a service-oriented framework for the execution of context recognition algorithms. We describe and theoretically analyze the performance of the main framework components, including the sensor network organization, service discovery, service graph construction, service distribution and mapping. The theoretical results are followed by the simulation of the proposed framework as a whole, showing the overall cost of dynamically distributing applications on the network.

A typical wireless sensor node has little protection against radio jamming. The situation becomes worse if energy-efficient jamming can be achieved by exploiting knowledge of the data link layer. Encrypting the packets may help to prevent the jammer from taking actions based on the content of the packets, but the temporal arrangement of the packets induced by the nature of the protocol might unravel patterns that the jammer can take advantage of, even when the packets are encrypted. By looking at the packet interarrival times in three representative MAC protocols, S-MAC, LMAC, and B-MAC, we derive several jamming attacks that allow the jammer to jam S-MAC, LMAC, and B-MAC energy efficiently. The jamming attacks are based on realistic assumptions. The algorithms are described in detail and simulated. The effectiveness and efficiency of the attacks are examined. In addition, we validate our simulation model by comparing its results with measurements obtained from actual implementation on our sensor node prototypes. We show that it takes little effort to implement such effective jammers, making them a realistic threat. Careful analysis of other protocols belonging to the respective categories of S-MAC, LMAC, and B-MAC reveals that those protocols are, to some extent, also susceptible to our attacks. The result of this investigation provides new insights into the security considerations of MAC protocols.

Wireless sensor networks (WSNs) are increasingly being used to monitor various parameters in a wide range of environmental monitoring applications. In many instances, environmental scientists are interested in collecting raw data using long-running queries injected into a WSN for analyzing at a later stage, rather than injecting snap-shot queries containing data-reducing operators (e.g., MIN, MAX, AVG) that aggregate data. Collection of raw data poses a challenge to WSNs as very large amounts of data need to be transported through the network. This not only leads to high levels of energy consumption and thus diminished network lifetime but also results in poor data quality as much of the data may be lost due to the limited bandwidth of present-day sensor nodes. We alleviate this problem by allowing certain nodes in the network to aggregate data by taking advantage of spatial and temporal correlations of various physical parameters and thus eliminating the transmission of redundant data. In this article we present a distributed scheduling algorithm that decides when a particular node should perform this novel type of aggregation. The scheduling algorithm autonomously reassigns schedules when changes in network topology, due to failing or newly added nodes, are detected. Such changes in topology are detected using cross-layer information from the underlying MAC layer. We first present the theoretical performance bounds of our algorithm. We then present simulation results, which indicate a reduction in message transmissions of up to 85% and an increase in network lifetime of up to 92% when compared to collecting raw data. Our algorithm is also capable of completely eliminating dropped messages caused by buffer overflow.

The supply chain management business can benefit greatly from automation, as recent developments with RFID technology shows. The use of Wireless Sensor Network technology promises to bring the next leap in efficiency and quality of service. However, current WSN system software does not yet provide the required functionality, flexibility and safety. This paper discusses a scenario showing how WSN technology can benefit supply chain management, and presents SensorScheme, a platform for realizing the scenario. SensorScheme is a general purpose WSN platform, providing a safe execution environment for dynamically loaded programs. It uses high level programming primitives like marshalled communication, automatic memory management, and multiprocessing facilities. SensorScheme makes efficient use of the little available memory present in WSN nodes, to allow larger and more complex programs than the state of the art. We present a SensorScheme implementation and provide experimental results to show its compactness, speed of operation and energy efficiency.

Heterogeneous Wireless Sensor Networks (WSNs) are envisioned to provide different types of services in an open and dynamic environment. This paper presents the design, implementation and evaluation of a service discovery and usage solution for heterogeneous WSNs. The users have the possibility to discover and use the services available in the WSN, while sensor nodes can search for the existing gateways to the outside world in order to signal important events. The WSN self-organizes in a clustered structure that acts as a distributed directory of service registrations. The clustering offers the necessary support to achieve energy-efficient discovery within the WSN. We implement the combined solution on resource-constrained sensor nodes, and we analyse the performance characteristics. The results show that the clustering algorithm has low communication overhead and the service discovery protocol scales with the number of nodes and network density. In addition, we show that the solution is lightweight (both code and data memory footprint) and the the interaction user-WSN is straightforward and intuitive.

Supply chain management is an application area where wireless sensor networks can play an important role in improving process efficiency. This demonstration shows an implementation of a supply chain management monitoring application using standard WSN nodes. It makes use of an interpreted runtime system called SensorScheme to allow efficient reprogramming and safe execution of monitoring applications running on these nodes. The demo shows all aspects of programming nodes, monitoring and fault detection, and shows the use of the SensorScheme tool set to achieve this functionality.

Besides the currently realized applications, wireless sensor networks can be put to use in logistics processes. However, current WSN software platforms cannot provide the flexibility and safety needed. This paper presents SensorScheme, a runtime environment based on semantics of the Scheme programming language, used to realize a logistics scenario. SensorScheme is a general purpose WSN platform, providing dynamic reprogramming, memory safety (sandboxing), blocking I/O, marshalled communication and compact code transport. We illustrate the use of our platform and provide experimental results that show its speed of operation and energy efficiency.

Building wireless sensor network applications is a challenging task, and it has become apparent that it is crucial for many sensor networks to be able to load or update the application after deployment. Since communication is a scarce resource and costly in terms of energy, it is important to minimize code size when reprogramming WSNs in the field. This paper introduces SensorScheme as a novel interpreted WSN platform for dynamically loading sensor network applications. It is based on the semantics of the Scheme language and is equipped with high-level programming facilities such as higher-order functions, garbage collection, communication by automatic marshalling of data items, and co-routines to implement blocking I/O operations. SensorScheme makes efficient use of the little available memory in WSN nodes, uses a very compact program notation during wireless transmission, and provides a safe interpreted execution environment, preventing malfunctioning programs from crashing the device. We illustrate the use of SensorScheme and evaluate its code compactness and energy- efficiency.

We report our experiences with implementing a distributed and self-organizing scheduling algorithm designed for energy-efficient data gathering on a 25-node multihop wireless sensor network (WSN). The algorithm takes advantage of spatial correlations that exist in readings of adjacent sensor nodes and utilizes cross-layer information from the underlying MAC layer to minimize message transmissions. We describe how we modify our experiments in order to meet the assumptions made in the earlier theoretical analysis of the algorithm. The implementation results which are virtually identical to the preliminary simulation results, show that the algorithm achieves up to 80% energy savings when compared to conventional raw data collection.

Summary form only given. Wireless sensor networks are a hot issue worldwide, and significant progress has been achieved in the past few years. However, we are only beginning to find out about their real potential, and there are still major challenges that need to be solved. In this presentation an overview of the biggest challenges in wireless sensor networks are addressed, and some of the solutions will be highlighted. Then, some applications of sensor network technologies are presented, which go beyond traditional sensor network applications

Wireless sensor networks (WSNs) will be able to assist industrial and business processes and to render rich functionality in a dependable way. Two key elements that can make this real are: a simple and efficient way of expressing the business logic, and a reliable mechanism for selectively reconfiguring sensor nodes. We present a solution that combines both elements. The main objective is to guarantee the dissemination of business rules to multicast groups of sensor nodes, while striving for energy efficiency and low overhead. Simple cross-layer optimizations are used to achieve this. For scalability reasons, our solution demands only local knowledge, performs local retransmission of lost packets and uses aggregation of acknowledgements. The results of our evaluation indicate a good ability of recovering from serious errors, even under high error rates.

Factory and industrial automation systems gradually start to incorporate wireless networks of smart objects and sensor nodes. In this context, one fundamental problem is the reliability of data dissemination, particularly in the case of total or partial network reconfiguration. We propose RMD, a reliable data dissemination solution targeting multicast groups of collaborating objects. Compared to previous work, our approach focuses on guaranteeing the data transmission rather than improving the delivery ratio. In addition, RMD scales better by utilizing the multicast support and proves more energy efficient due to cross-layer optimizations. To achieve these, we combine end-to-end aggregated acknowledgements with local error detection and recovery, and apply a selective listening scheme for reducing unnecessary radio operation

We propose an energy-efficient service discovery protocol for wireless sensor networks. Our solution exploits a cluster overlay, where the clusterhead nodes form a distributed service registry. A service lookup results in visiting only the clusterhead nodes. We aim for minimizing the communication costs during discovery of services and maintenance of a functional distributed service registry. We compare theoretically and by simulation the impact of the chosen clustering algorithm on the service discovery protocol

The resource limitations of wireless sensor networks (WSN), especially in terms of energy, require novel and collaborative approach for the wireless communication. In this paper we focus on the dynamic aspects and present a new reliable costbased data-centric routing algorithm for such dynamic WSNs. Current research in this area generally assumes a rather static network, leading to a strong performance degradation in a dynamic environment. A network wide reflooding of messages is the common solution to network topology changes. The situation gets worse when the data sink moves, when a stable network is hardly able to form. In our research we try to maintain the communication when sensors move, such that less energy is used to re-setup the network. Moreover, event mobility poses a great pressure on the WSNs. We have designed the routing protocol to route message intelligently to reduce the effect of event mobility. Simulation results show that our approach to data-centric routing for WSNs benefits clearly from the globallocal paradigm we are able to use. In a static network, our approach increases the network reliability with at least 25%. And in a dynamic topology, the local recovery approaches of our protocol clearly outperforms the traditional protocol in reliability, while the energy consumption is less than 50%.

No abstract available.

Routing on a two-dimensional torus architecture by means of the wormhole routing algorithm is introduced and extended to an n-dimensional torus model. To prevent blocking deadlocks caused by this algorithm, a multiple virtual channel solution is introduced. An implementation of virtual channels is introduced that allows channels with higher labels to pre-empt 'lower' channels. This algorithm is tested with a simplified model of a HiperLAN/2 receiver. The model proves to be capable of running this application on the Chameleon architecture according to http://chameleon.ctit.utwente.nl/.

In wireless sensor networks (WSN) data produced by one or more sources usually has to be routed through several intermediate nodes to reach the destination. Problems arise when intermediate nodes to reach the destination. Problems arise when intermediate nodes fail to forward the incoming messages. The reliability of the system can be increased by providing several paths from source to destination and sending the same packet through each of them (the algorithm is known as multipath routing). Using this technique, the traffic increases significantly. In this paper, we analyze a new mechanism that enables the trade-off between the amount of traffic and the reliability. The data packet is split in k subpackets (k = number of disjoined paths from source to destination). If only E/sub k/ subpackets (E/sub k/ < k) are necessary to rebuild the original data packet (condition obtained by adding redundancy to each subpacket), then the trade-off between traffic and reliability can be controlled.

We present a study on the effects on a mobile system's energy efficiency of enhancing, with partial reliability, our energy efficient TCP variant (E/sup 2/TCP) (see Donckers, L. et al., Proc. 2nd Asian Int. Mobile Computing Conf. - AMOC2002, p.18-28, 2002). Partial reliability is beneficial for multimedia applications and is especially attractive in wireless communication environments. It allows applications to trade a controlled amount of data loss for a higher throughput, lower delays, and lower energy consumption. In the design of E/sup 2/TCP, we provide solutions to four problem areas in TCP that prevent it from reaching high levels of energy efficiency. We have implemented a simulation model of the protocol, and the results show that E/sup 2/TCP has a significant higher energy efficiency than TCP.

One of the two keywords for the next generation wireless communications is seamless. Being involved in the essential e-Japan Plan promoted by the Japanese Government, the MIRAI (Multimedia Integrated network by Radio Access Innovation) project is responsible for the research and development on the seamless integration of various wireless access systems for practical use by the year 2005. A heterogeneous network architecture including a common tool, a common platform, and a common access is proposed in this paper. Concretely, software-defined-radio technologies are used to develop a multi-service user terminal to be used for access to different wireless networks. The common platform for various wireless networks is based on a wireless supporting IPv6 network. A basic access network, separated from other wireless access networks, is used as a means for wireless system discovery, signaling and paging. A proof-of-concept experimental demonstration system is available from March 2002.

Dynamically reconfigurable systems offer the potential for realising efficient systems as well as providing adaptability to changing system requirements. Such systems are suitable for future mobile multimedia systems that have limited battery resources, must handle diverse data types, and must operate in dynamic application and communication environments. We propose an approach in which reconfiguration is applied dynamically at various levels of a mobile system, whereas traditionally, reconfigurable systems mainly focus at the gate level only. The research performed in the CHAMELEON project 1aims at designing such a heterogeneous reconfigurable mobile system. The two main motivations for the system are (1) to have an energy-efficient system, while (2) achieving an adequate Quality of Service for applications.

Abstract In this paper we identify the most prominent problems of wireless multimedia networking and present several state‐of‐the‐art solutions with a focus on energy efficiency. Three key problems in networked wireless multimedia systems are: (1) the need to maintain a minimum quality of service over time‐varying channels; (2) to operate with limited energy resources; and (3) to operate in a heterogeneous environment. We identify two main principles to solve these problems. The first principle is that energy efficiency should involve all layers of the system. Second, Quality of Service is an essential mechanism for mobile multimedia systems not only to give users an adequate level of service, but also as a tool to achieve an energy‐efficient system. Owing to the dynamic wireless environment, adaptability of the system will be a key issue in achieving this. Copyright © 2001 John Wiley & Sons, Ltd.

Energy efficiency is an important issue for mobile computers since they must rely on their batteries. We present an energy-efficient highly adaptive architecture of a network interface and novel data link layer protocol for wireless networks that provides quality of service (QoS) support for diverse traffic types. Due to the dynamic nature of wireless networks, adaptations are necessary to achieve energy efficiency and an acceptable quality of service. The paper provides a review of ideas and techniques relevant to the design of an energy efficient adaptive wireless network.

In the MOBY DICK project we develop and define the architecture of a new generation of mobile hand-held computers called Mobile Digital Companions. The Companions must meet several major requirements: high performance, energy efficient, a notion of Quality of Service (QoS), small size, and low design complexity. To address these requirements we need to revise the architecture of the hardware, the operating system, and applications. -The approach is based on dedicated functionality and the extensive use of energy reduction techniques at all levels of system design. The Mobile Digital Companion has an unconventional architecture that saves energy by using system decomposition at different levels of the architecture and exploits locality of reference with dedicated, optimised modules. A reconfigurable internal communication network switch called Octopus exploits locality of reference and eliminates wasteful data copies. The switch is implemented as a simplified ATM switch and provides Quality of Service guarantees and enough bandwidth for multimedia applications. We have built a testbed of the architecture, of which we will present performance and energy consumption characteristics.

Since high error rates are inevitable to the wireless environment, energy-efficient error-control is an important issue for mobile computing systems. We have studied the energy efficiency of two different error correction mechanisms and have measured the efficiency of an implementation in software. We show that it is not sufficient to concentrate on the energy efficiency of error control mechanisms only, but the required extra energy consumed by the wireless interface should be incorporated as well. A model is presented that can be used to determine an energy-efficient error correction scheme of a minimal system consisting of a general purpose processor and a wireless interface. As an example we have determined these error correction parameters on two systems with a WaveLAN interface.