sponsorship: VLAIO|HBC.2024.0406

Swarm robotics focuses on designing and coordinating large groups of relatively simple robots to perform tasks in a decentralised and collective manner. The swarm provides a resilient and flexible solution for many applications. However, contemporary swarm robots have a significant power problem in that secondary (i.e. rechargeable) batteries are slow to charge and offer lifetimes of only a few years, increasing maintenance costs and pollution due to battery replacement. We imagine a different future, wherein battery-free robots powered by supercapacitors can be recharged in seconds, offer long-life autonomous operation and can rapidly pass charge between one another using trophallaxis. In pursuit of this vision, we contribute the CapBot, a battery-free swarm robot equipped with Mecanum wheels, a Cortex M4F application processor and Bluetooth Low Energy networking. The CapBot fully recharges in 16 s, offers 51 min of autonomous operation at top speed, and can transfer up to 50 % of its available charge to a peer via trophallaxis in under 20 s. The CapBot is fully open source and all software and hardware source is available online.

Battery-Powered Internet of Things (IoT) devices are widely deployed in applications like smart cities, industrial automation, healthcare, and agriculture. Concurrently, expanded cellular coverage provides ubiquitous connectivity, enabling transmission of diverse sensor data. However, conventional 4G and 5G devices designed for general use consume high power, limiting battery life to just a few days. Although cellular IoT networks such as LTE-M and NB-IoT offer lower power consumption, generic and reusable platforms remain scarce. This paper addresses this gap by introducing the Real-Time Actuator and Sensor Platform (RTASP), a software/hardware platform for large-scale cellular sensing supporting low-latency sensor and actuator streams, edge intelligence, and low-power operation. We provide an open-source hardware and software implementation and evaluate RTASP in a real-world industrial scenario, demonstrating low-latency multimedia streaming (initiating remote streams within seconds) and significantly longer battery life compared to conventional platforms (standby time of several months). These features enable the deployment of long-life sensors that uniquely support complex multimedia sensing and processing workflows.

Battery-powered IoT devices incur significant maintenance cost and complexity in remote and large-scale environmental monitoring deployments due to regular battery replacement. Energy harvesting in combination with supercapacitor charge storage offers a promising solution, but practical deployments are few and far between. This paper contributes to application research in this space by presenting an experience report on deploying a battery-free sensing system for wireless monitoring of forest microclimates. Our approach combines adaptive power management hardware and intelligent scheduling algorithms, which are compared against a baseline rechargeable battery-based solar alternative. In both cases, the wireless devices are used to drive a sensing system that reports atmospheric pressure, temperature, humidity, air quality and light levels over the LoRaWAN network. This data is used by environmental scientists to study and better understand the forest ecosystem. We evaluate our battery-free approach against the battery-powered baseline in a two-month forest monitoring deployment, demonstrating reliable operation using only harvested energy, while highlighting important lessons learned for researchers and practitioners deploying battery-free sensing systems.

Most Internet of Things (IoT) devices rely on batteries with operational lifetimes of only a few years, resulting in high maintenance costs and significant environmental impact from battery waste. Energy harvesting (EH) combined with supercapacitor-based energy storage has emerged as a promising alternative, enabling longer-lived and more sustainable IoT systems. However, the design and deployment of such battery-free systems remain challenging due to their complexity and perceived unreliability. To overcome these barriers, we present Flute, a plug-and-play (PnP), battery-free IoT platform designed for remote sensing applications. Flute integrates energy harvesting, supercapacitor storage, and NB-IoT connectivity to enable autonomous long-term operation. In this paper, we demonstrate Flute’s capabilities through a contactless waterlevel sensing application, highlighting its potential to simplify the development and deployment of sustainable IoT systems.

The developers of critical networked applications currently relied on kernel-level protections to ensure the timely delivery of critical messages like Wireless Emergency Alert (WEA) or multi-factor authentication (MFA) notifications. However, the increasing complexity of mobile operating system kernels and network stacks increases the attack surface for adversaries to exploit. In this paper, we introduce a system which safeguards network availability for critical mobile applications against powerful attackers. We achieve this by using the Trusted Execution Environment (TEE) found in most mobile devices to host minimal network drivers. Further, we utilize Trusted I/O to ensure that critical messages reach the end-user even if the device’s kernel is compromised. To demonstrate the feasibility of our approach, we provide a PoC implementation that mimics multi-factor authentication. Our Evaluation demonstrates that latency for all applications is reduced by around $21 \%$ on a representative mobile platform (ARM Cortex A9), though a significant throughput performance is observed.

Body area networking is an important segment of the wireless landscape, connecting a growing range of devices such as fitness trackers and smart watches using short range wireless links. However, conventional wireless technologies such as BLE and ANT provide poor support for low-latency applications such as haptic feedback due to their dependence upon duty-cycling to maintain acceptable power consumption. Recent physical-layer design efforts introduce a low-power alternative by establishing capacitively coupled communication through the human body. This mitigates duty-cycling requirements, but results in channel access times similar to those found in more conventional wireless networks, failing to provide adequate latency guarantees even for modestly sized networks. We address this problem by introducing ENOCH, an ultra-low power 12 MHz receiver that relies on capacitive coupling to establish a fieldbus-like through-body network. ENOCH delivers a sub-μW receiver, 10 kbps link-layer throughput, traffic prioritisation, 97% baseline reliability and 7 ms latency in a 7-node network, regardless of contention for the channel. Our design is simple and uses only commercial off-the-shelf components. As it requires neither large antennas nor crystal oscillators, we believe that further miniaturization beyond the current 1 cm2hardware/software prototype is possible.

Acoustic Anomaly Detection (AAD) has gained significant attention for the detection of suspicious activities or faults. Contrastive learning-based unsupervised AAD has outperformed traditional models on academic datasets, however, its model training is predominantly based on datasets containing only normal samples. In real industrial settings, a dataset of normal samples can still be corrupted by abnormal samples. Handling such noisy data is a crucial challenge, yet it remains largely unsolved. To address this issue, this paper proposes a Selective Contrastive learning framework Against Noisy data (SCAN) to mitigate the adverse effects of training the AAD model with anomaly-corrupted data. Specifically, SCAN progressively constructs confidence sample pairs based on the Mahalanobis distance, which is derived from the geometric median. These selected pairs are then integrated into the contrastive learning framework to enhance representation learning and model robustness. Extensive experiments under varying levels of label noise (i.e., the proportion of mislabeled abnormal samples in training data) demonstrate that SCAN outperforms state-of-the-art (SOTA) AAD methods on the real-world industrial datasets DCASE2022 and DCASE2024 Task2.

Today’s wireless sensor networks focus on achieving reliable data transfer over a lossy medium at the expense of latency. However, sensor data are often noisy and thus only lossily characterise real-world phenomena, rendering their exact transfer wasteful. Furthermore, many next-generation privacy-sensitive applications, such as smart grid control, real-time distributed object tracking, and inter-vehicle federated learning face latency and traffic bottlenecks due to the sheer amount of data collection required to overcome noise. We tackle this problem by introducing Twofer, a communication approach which reduces latency and traffic in high-noise or high-privacy settings by abandoning the focus on reliable networking. Twofer empowers developers to tune networks for latency-bound rather than reliability-bound performance; the system coordinates ambiguous transmissions, which are used to estimate the network-wide distribution of data, rather than to reliably communicate exact data from individual nodes. Twofer’s full-stack design maintains black-box compatibility with existing application code, but advocates for, and shows the value of, uncommon physical-layer features such as symbol-synchronous communication. The system is therefore implemented and evaluated on a prototype low-latency wireless mesh network called Zero-Wire. Experiments using state-of-the-art local differential privacy protocols show 25–75% latency reductions relative to conventional approaches. The results are also future-proof, with performance advantages increasing with the strength of the privacy guarantees that are offered.

ERATOSTHENES is an EC, co-funded, research project strongly considering modern security challenges in the domain of Internet of Things in mind of their huge penetration into our day to day lives. There are a series of recent challenges that recently have been converted into obstacles or risk points that could block the secure operation of IoT networks in all day to day activities, from home to office, to leisure and security. These include examples such as the highly increased number of connected devices (at all network levels) that are on top forming inhomogeneous networks and systems of systems. Different vendor characteristics further increase the attack surface that is expected to further rise in the upcoming years. Such, highly critical, characteristics, dramatically increase the needs for confidentiality access control, user and things’ privacy, devices’ trustworthiness and compliance that require lifecycle considerations. The ERATOSTHENES project orchestrates a novel distributed, automated, auditable, yet privacy-respectful, Trust and Identity Management Framework and Reference Architecture with the ultimate scope to dynamically and holistically manage IoT devices in a lifecycle approach, strengthening trust, identities, and resilience in the entire IoT ecosystem while supporting the enforcement of the NIS directive, GDPR and Cybersecurity Act. This publication describes the ERATOSTHENES technical concept and reference architecture as well as design considerations, architecture characteristics, connectivity and interoperability.

A growing range of networked embedded devices are moving away from batteries and towards super-capacitor charge storage. However, mobile robots remain largely dependent upon batteries with slow recharge cycles and limited lifetimes. In this demonstration paper, we introduce a novel battery-free platform for swarm robotics which features: 24 minutes of operation running at its top speed of 1.24 km/h, a carrying capacity of over 2.5kg, full recharge cycles of under 12 seconds and rapid peer-to-peer charge transfer or trophallaxis in the field. This is supported by an nRF52840 Cortex-M4F equipped with BLE/ANT/802.15.4 transceiver. Notably, while the autonomy of FreeBots is limited compared to battery-powered robots, their operational vs charging duty-cycle is significantly higher at over 99%.

Low-power wide-area networks enable large-scale deployments of low-power wireless devices. LoRaWAN is a long-range wireless technology that has emerged as a low-power and low data rate solution to support Internet of Things applications. Although LoRaWAN provides a low-power and cost-efficient networking solution, recent literature shows that it performs poorly in terms of reliability and security in dense deployments due to the uncoordinated (ALOHA-based) nature of the MAC (medium access control) protocol. Furthermore, LoRaWAN is not robust against selective jamming attacks. This article proposes CRAM: a time-synchronized cryptographic frequency hopping MAC protocol designed for the LoRa physical layer. CRAM reduces the contention by fairly exploiting the available frequency space and maximizes the entropy of the channel hopping algorithm. We develop a large physical testbed and a simulator to thoroughly evaluate the proposed protocol. Our evaluations show that CRAM significantly improves reliability and scalability and increases channel utilization while making selective jamming difficult to perform compared to the standard LoRaWAN protocol.

Intelligent Environments (IEs) enrich the physical world by connecting it to software applications in order to increase user comfort, safety and efficiency. IEs are often supported by wireless networks of smart sensors and actuators, which offer multi-year battery life within small packages. However, existing radio mesh networks suffer from high latency, which precludes their use in many user interface systems such as real-time speech, touch or positioning. While recent advances in optical networks promise low end-to-end latency through symbol-synchronous transmission, current approaches are power hungry and therefore cannot be battery powered. We tackle this problem by introducing BoboLink, a mesh network that delivers low-power and low-latency optical networking through a combination of symbol-synchronous transmission and a novel wake-up technology. BoboLink delivers mesh-wide wake-up in 1.13ms, with a quiescent power consumption of 237µW. This enables building-wide human computer interfaces to be seamlessly delivered using wireless mesh networks for the first time.

Faults in industrial equipment lead to significant costs due to down-time and unplanned maintenance interventions. Acoustic Internet of Things (IoT) sensors combined with machine learning offers the possibility of early fault detection to mitigate these costs. How-ever, prior approaches to Acoustic Anomaly Detection (AAD) are poorly suited to operating within the resource constraints of IoT systems as they require the acquisition of large volumes of data to train models from scratch for different machine types or oper-ating environments. To overcome the limitations, we introduce a system utilizing pretrained low-dimensional features and Gaussian mixture models. Preliminary results on real-world datasets show that our proposed approach outperforms state-of-the-art solutions based on the area under the curve (AUC) score with an average of 4 different machine types. Furthermore, our approach requires far less training data, making it more suitable to operate within the power and network constraints of IoT devices.

Sensor networks gather data at one or more gateway devices by sequentially receiving data frames from individual sensor nodes. In cases where applications rely on data that is distributed across sev-eral nodes, the latency of such node-by-node data collection scales with the number of nodes and the frame sizes involved, impeding fast decision-making. This paper seeks to overcome that limitation for latency-sensitive classification problems by introducing One-Take, a cross-layer aggregation protocol that aims to decouple the process of data gathering from network size and frame length. The protocol leverages dominance properties between concurrently transmitted symbols to convey socalled collages, messages that de-scribe relationships between data held by several nodes, rather than local information from any single node. Collages thus transmit a hash of distributed data that is computed within the physical layer, thereby communicating more informative features than regular frames of the same length. Experiments on a recent wireless mesh network platform, called Zero-wire, demonstrate that two-byte collages are delivered correctly 99% of the time and, using public sensor network data sets, show that One-take achieves a latency reduction of circa 42% relative to node-by-node transmissions.

With the emergence of mobile devices having enough resources to execute real-time ML inference, deployment opportunities arise on mobile devices while keeping privacy-sensitive data close to the source and reducing server load. Moreover, offloading inference to a cloud server facilitates deployment of neural network-based applications on resource-constrained devices. Depending on the application goals and execution context of the application, the optimal deployment on either cloud server or mobile device varies during the lifetime of an application. In this paper, we propose a context-aware middleware that enables optimization of deployed application software to satisfy the application’s functional goals in accordance with changing execution context and environmental conditions. We facilitate system design through the abstraction of deployed software components as states and make use of finite state machines and contextual triggers to model the reconfiguration of the system. We evaluate our framework using a real-world nutritional monitoring application via food image recognition deployed in a two-tier mobile and cloud architecture. We compare the proposed solution with various static deployments of the application and show that our approach can react to changing application goals at run-time in order to reduce server load and thereby increase scalability.

This paper starts from the observation that mobile and edge devices are powerful enough to execute Machine Learning (ML) application components, which in turn creates opportunities to keep privacy-sensitive data close to its source. Composing and deploying a distributed ML application is far from trivial because the optimal configuration depends on the application’s goals and execution context, both of which may change throughout its lifetime. Prior research on context-aware reconfigurations in ML based applications offer limited capabilities for dynamically migrating software components between mobile, edge and cloud devices. In this paper, we propose a context-aware middleware that enables automated optimizations of the application deployment in order to satisfy the application’s functional goals while the execution context changes in terms of available computation, memory and network resources. We use finite state machines to model the reconfiguration of the application based on contextual triggers and facilitate system design through the abstraction of system states. We illustrate the benefits of our approach with an image recognition application with well-defined performance goals that is deployed in a three-tier mobile-edge-cloud architecture.

Vibration monitoring uses data gathered from accelerometers to study kinetic phenomena in applications such as: structural health monitoring and predictive maintenance. The Internet of Things (IoT) has the potential to greatly expand the range and scope of vibration monitoring applications by delivering long-life wireless sensors that can be cost-effectively embedded in hard to reach places such as; within machines, infrastructure or the built environment. However, achieving this vision is difficult due to the stringent resource constraints of contemporary IoT devices and networks. This has led the research community to develop a creative range of application-specific near-sensor processing firmware. However, systematic support for generic vibration monitoring on resource-poor IoT networks remains an open problem. We tackle this challenge by introducing ReFrAEN, a software framework that efficiently enables a wide range of vibration monitoring applications on IoT networks. ReFrAEN achieves this through a deeply configurable combination of compression techniques and data processing algorithms. These features allow end-users to effectively trade-off between resource consumption and data resolution in order to meet battery life constraints while preserving sufficient data quality to support the target application. Our evaluation shows that ReFrAEN is capable of identifying bearing faults, while dramatically improving battery lifetime and reducing latency in comparison to prior approaches.

Recent innovations in energy harvesting promise extended operational life and reduced maintenance costs for the next generation of Internet of Things (IoT) platforms. However, energy management in these platforms remains problematic due to dynamism in energy supply and demand, inefficiency in storing and converting energy and a lack of per-task charge isolation. This paper tackles this problem by proposing a software defined charge storage module called Morphy, which combines a polymorphic capacitor array with intelligent power management software. Morphy delivers energy to application tasks in a flexible, efficient, and isolated manner. Morphy provides two software extensions to the Operating System scheduler: the energy semaphore blocks the execution of tasks until sufficient charge is available to safely run them, and the energy watchdog monitors and mitigates energy management bugs. We have realized a prototype of Morphy with the hardware form factor of a standard 9V (PP3) battery package and a software library that integrates with the FreeRTOS scheduler. Our evaluation shows that, in comparison to standard energy storage and management approaches, our prototype reaches an operational voltage more quickly, sustains operation longer in the case of power failure and effectively isolates charge storage for dedicated tasks with minimal compute, memory and energy overhead.

Accurate indoor positioning remains an open research question. Existing solutions are either expensive, short-range, or inaccurate. A new approach is therefore required to cost-effectively and accurately support localization in large indoor environments. We tackle this problem by introducing a practical optical localization scheme, called OSLo, that cost-effectively scales to support large buildings. OSLo uses a meshed network of low-cost cameras as localization anchors and smart LEDs as tags, which transmit their IDs and context sensor data over the meshed cameras using optical communication. OSLo is capable of localizing dense deployments of tags with an accuracy of under 1 meter at a distance of 35 meters from the localization anchor. Furthermore, smart LED tags can be manufactured for less than $1. We systematically evaluate the performance of OSLo in the context of a real-world car localization use-case at Ford Motor Company in Germany and demonstrate promising results in terms of detection distance, and localization accuracy.

Low power wide area networks (LPWANs) are being applied in many Internet of Things applications around the globe. These technologies offer economic coverage of wide areas, while retaining low power operation. LoRaWAN is a key technology in this space, with a world-wide presence and millions of devices deployed in the field. Despite this early success, recent research has shown that LoRa performs poorly in dense deployments with a high degree of contention. Furthermore, LoRa is not robust against selective jamming attacks. In this paper, we propose CRAM: a cryptographic frequency hopping MAC protocol designed for the LoRa physical layer that reduces contention by fairly exploiting all available frequency space, while making it significantly more difficult to perform selective jamming. Our evaluation shows that CRAM significantly reduces contention, thereby dramatically increasing scalability and reliability in comparison to the standard LoRa protocol.

The performance dichotomy between wired and wireless networks for the Internet of Things primarily arises from the inherent complexity and inefficiency of networking abstractions such as routing, medium access control and store-and-forward packet switching. This paper aims to enable a new class of latency-sensitive applications by breaking all three of these abstractions to deliver a performance envelope that resembles that of a wired bus in terms of deterministic latency and throughput. The essence of this approach is a novel networking paradigm for optical wireless communication, referred to as a symbol-synchronous bus, wherein a mesh of nodes concurrently transmit LED-based signals. This paper realises the paradigm within a platform called Zero-Wire and evaluates it on a 25-node testbed under laboratory conditions. Key end-to-end performance measurements on this physical prototype include 19 kbps of contention-agnostic goodput, interface-level latency under 1 ms for two-byte frames across four hops, jitter on the order of 10s of μs, and a base reliability of 99%. These first results indicate a bright future for the under-explored area of optical wireless mesh networks in delivering ubiquitous connectivity through a simple and low-cost physical layer.

Despite the ubiquitous deployment and development of wireless technology for the Internet of Things (IoT), contemporary radio frequency (RF)-based solutions still cannot match the performance of a "wire" in terms of latency and throughput. This abstract presents a demonstration of Zero-Wire, a novel optical wireless approach that addresses this gap to enable latency-sensitive IoT applications. The essence of this approach is a new networking paradigm, referred to as a symbol-synchronous bus, wherein a mesh of nodes concurrently transmits optical signals. The demonstration setup is composed of 25 Zero-Wire nodes, forming a mesh network, and the demo showcases the network's behavior during a series of transmissions. End-to-end performance measurements include 19 kbps of contention-agnostic goodput, latency under 1 ms for two-byte frames, jitter on the order of 10s of μs, and a base reliability of 99%.

Application development for Cyber Physical Systems (CPS) is challenging, because the wireless network and the devices introduce latencies that vary continuously along with the load, status, or environmental conditions of the infrastructure. Reactive programming is well suited for the development of event-driven applications, yet current reactive programming frameworks require developers to predict event arrival time-boundaries at compile time, which is impractical, if not impossible, for CPS. Thus, there is a tradeoff between timeliness and completeness of complex event computations, e.g., operational efficiency in a manufacturing plant: Waiting too long until all individual events arrive can fail to produce a useful result, while not waiting long enough may lead to faults due to incomplete status information. In this article, we propose (a) a set of extensions to state-of-the-art reactive programming frameworks, which remove the burden of specifying timeouts at compile time by utilizing (b) Khronos, a middleware that automatically determines timeouts by taking into account variations in event arrival times due to the underlying infrastructure. Evaluation on a physical testbed shows that the extensions significantly decrease developer effort and that Khronos considerably improves timeliness under varying network configurations and conditions, while still satisfying the application’s tolerance to missed events.

Recommender systems have gained prominence in bringing users tailor-made content from the web aiding their decision making process. However, this personalization comes at a cost of privacy of sharing personal information with the recommendation provider. Moreover, with growing sizes of datasets and models, centralized processing of such data has become challenging. To this end, we propose a federated matrix factorization algorithm to enable personal data to be stored and used on-device for training while sending updates to train a centralized model. We illustrate preliminary results from our algorithm applied to recommendation of articles to users of a mobile application based on their reading history. We compare our performance with centralized matrix factorization applied on the same dataset.

Early gestational weight gain prediction can help expecting women overcome several associated risks. However, training the model requires access to centrally stored privacy sensitive weight and other meta-data. In this demo, we present a privacy preserving federated learning approach where we train a global weight gain prediction model by aggregating client models trained locally on their personal data. We showcase a software data-exploration tool that exhibits local model generation, sharing and updating across users and server for proposed collaborative learning. Our proposed model predicts the final weight category with 61.3% accuracy on day 140, with a 8.8% compromise on the centralized training accuracy.

The widespread use of mobile devices has allowed the development of participatory sensing systems that capture various types of data using the existing sensors on mobile devices in order to upload the data to cloud based services for later use. Gathering data from such sources requires a mechanism to establish trust on the sensor data. For example an application may require a proof of authenticity of sensor readings originating from anonymous sources. Establishment of trust on the sensor data has been addressed in the literature. However, in many cases this sensor data needs to be preprocessed on the device itself before being uploaded to the target server. This processing could include resizing of images, hiding identifiable faces and sensitive data in images, anonymization of GPS data etc. while ensuring the chain of trust from data capture to the delivery of data to the consumer. There is a need for a framework that provides a means to implement arbitrary operations to be performed on trusted sensor data while guaranteeing the authenticity of the data. This paper presents the design and implementation of a framework that allows the capture of trusted sensor data, the development of trusted operations on sensor data, and provides a mechanism for performing predefined trusted operations on the sensor data such that the chain of trust is maintained. Evaluation shows that the performance of the proposed system is reasonable and that the trust guarantees are strong.

Internet of Things (IoT) applications require diverse sensors and actuators. However, contemporary IoT devices provide limited support for the integration of third-party peripherals. To tackle this problem, we introduce μPnP: a hardware and software solution for plug-and-play integration of embedded peripherals with IoT devices. μPnP provides support for: driver development, automatic integration of third-party peripherals, discovery and remote access to peripheral services. This is achieved through a low-cost hardware identification approach, a lightweight driver language and a multicast network architecture. Evaluation shows that μPnP has a minimal memory footprint, reduces development effort and provides true plug-and-play integration at orders of magnitude less energy than USB.

Developing reliable application for the Internet-of-Things (IoT) is challenging due to heterogenous sensors and the resource-constraints of IoT platforms. Multimedia sensors such as cameras and microphones require significant memory and bandwidth. Typically, multimedia content results in bursty traffic. It is shown in the literature that the bursty traffic degrades the performance of the network. Hence, the regulation of bursty transmission is important for achieving high reliability. In this paper, we propose Dawn, a dependable networking framework for time-synchronised IoT platforms, which uses application meta-data to derive bandwidth requirements and uses this information to optimally allocate bandwidth for the network. Dawn regulates the transmission of bursty traffic generated by multimedia sensors, while performing admission control for dynamically added sensors. Dawn guarantees end-to-end reliability for heterogenous IoT applications with minimal energy consumption. Our preliminary evaluation results show that dawn is dependable and provides 100% reliability for dynamic multimedia-enabled IoT applications while increasing the network lifetime by up to 4 years.

This paper discusses the merits and challenges of adopting software product line engineering (SPLE) as the main development process for an automotive Hall Effect sensor. This versatile component is integrated into a number of automotive applications with varying safety requirements (e.g., windshield wipers and brake pedals).

Smart applications enable pervasive and informed interactions between the physical and digital worlds. These applications are deployed on resource constrained wireless sensor networks and are commonly implemented using software modularization schemes such as components and services. Composing smart applications at the abstraction level offered by embedded software modularization schemes is complex and time consuming. The complexity of composing them is derived from the need to understand many low-level issues, e.g. embedded programming languages, coordination mechanisms, software tool chains. We present an application composition service that reduces composition effort by offering a declarative specification of sense, process and consume flows. We demonstrate reduction in composition effort for three real-world smart applications deployed on a smart office environment.

Contemporary Wireless Sensor Networks like Smart Offices and Smart Cities are evolving to become multi-purpose application hosting platforms. These WSN platforms can simultaneously support multiple applications which may be managed by multiple actors. Reconfigurable component models have been shown to be viable solutions to reducing the complexity of managing and developing these applications while promoting software re-use. However, implicit parameter dependencies between components make reconfiguration complex and error-prone. Our approach achieves automatic composition-safe re-parametrization of distributed component compositions. To achieve this, we propose the use of language annotations that allow component developers to make these dependencies explicit and constraint-aware network protocols to ensure constraint propagation and enforcement.

Wireless Sensor Networks (WSN) are evolving beyond research prototypes towards real world deployments in various application domains. While prior research has resulted in a range of operating systems and associated programming languages, a comprehensive empirical analysis of WSN operating systems is missing from the literature. We address this problem through an empirical study of all actively maintained WSN operating systems for the popular Tmote Sky / TelosB platform: TinyOS, Contiki and Lorien. Our analysis considers overhead at each stage of the software life cycle. During the development phase, we measure developer effort in terms of lines of application code. During the execution phase we measure energy consumption, flash footprint and RAM usage. During the reconfiguration phase we measure artefact size and developer effort in terms of number of configuration commands. Our results indicate distinct trade-offs in terms of development effort, application performance and reconfiguration performance. We find that TinyOS performs best for static applications with tight RAM constraints, while Contiki offers the lowest development effort and Lorien performs best in dynamic applications which require reconfiguration.

Wireless Sensor Networks (WSNs) are subject to high levels of dynamism arising from changing environmental conditions and application requirements. Reconfiguration allows software functionality to be optimized for current environmental conditions and supports software evolution to meet variable application requirements. Contemporary software modularization approaches for WSNs allow for software evolution at various granularities; from monolithic re-flashing of OS and application functionality, through replacement of complete applications, to the reconfiguration of individual software components. As the nodes that compose a WSN must typically operate for long periods on a single battery charge, estimating the energy cost of software evolution is critical. This paper contributes a generic model for calculating the energy cost of the reconfiguration in WSN. We have embedded this model in the LooCI middleware, resulting in the first energy aware reconfigurable component model for sensor networks. We evaluate our approach using two real-world WSN applications and find that (i.) our model accurately predicts the energy cost of reconfiguration and (ii.) component-based reconfiguration has a high initial cost, but provides energy savings during software evolution.

Creating and managing applications for Wireless Sensor Networks (WSNs) is complicated by large scale, resource constraints and network dynamics. Reconfigurable component models minimize these complexities throughout the application lifecycle. However, contemporary component based middleware for WSNs is limited by its poor support for distribution. This paper introduces the Loosely-coupled Component Infrastructure (LooCI), a middleware for building distributed component-based WSN applications. LooCI advances the state-of-the-art by cleanly separating distributed concerns from component implementation, supporting application-level interoperability between heterogeneous WSN platforms and providing compatibility testing of bindings at runtime. Together, these features promote the safe and efficient composition and reconfiguration of distributed WSN applications. We evaluate the performance of LooCI on three classes of sensor nodes and demonstrate that these features can be provided with minimal overhead in terms of computation, memory and message passing.

Nowadays, public as well as private communication infrastructures are all contending for the same limited amount of bandwidth. To optimally share network resources, symbiotic networks have been proposed, which cross logical and physical boundaries to improve the reliability, scalability, and energy efficiency of the network as a whole as well as its constituents. This paper focuses on software services in such symbiotic networks. We propose a platform for the intelligent composition of services provided by symbiotically connected parties, resulting in novel cooperation opportunities. The platform harvests Semantic Web technology to describe services in a highly expressive manner, and constructs service compositions using SeCoA, our tunable best-first search algorithm. The resulting compositions are then enacted via CaPI, a reconfigurable middleware infrastructure. By means of an illustrative scenario, we provide further insight into the platform's functioning.

SUMMARY Achieving a clean separation of concerns is a well known approach to improving system adaptability and evolution. We propose to apply this principle to run‐time reconfigurable component models for networked embedded systems. By separating configuration properties from runtime component instances, we achieve: (i) improved support for concurrent component use, (ii) optimized resource use, and (iii) reduced effort in runtime configuration management. We demonstrate how this approach can be seamlessly implemented on existing component models for wireless sensor networks without imposing additional constraints and without changes to their API or coordination model. Furthermore, significant memory savings are achieved in concurrent scenarios. Copyright © 2012 John Wiley & Sons, Ltd.

Wireless ad-hoc sensor networks in industrial settings often consist of multiple independent parties, each owning a subset of the nodes. In order to reduce costs, minimize time to market and increase coverage and functionality, these parties must share the capabilities of their individual sensor nodes; this creates a multi-owner and multi-application environment that requires advanced and secure mechanisms to control the deployment and operation of sensor applications. Although there is clear demand like in transport & logistics, state-of-the-art on secure application deployment in WSNs is lacking support for this sharing of sensor nodes. This paper presents SASHA: a proof-of-concept protocol that enables lightweight and secure deployment of multiple applications on heterogeneously owned sensor nodes.

Contemporary vehicles offer an advanced telematics platform with multiple applications available such as electronic road tolling, emergency call, breakdown call, or route planning. Given the sensitivity of collected driver and vehicle data and the potential use of this data by third party applications, security mechanisms are needed to protect services as well as data. Although security has been investigated in recent telematics studies, they do not consider potentially malicious or erroneous third party applications. This paper presents a secure multi-application platform that is designed as a modular software architecture with security features to support availability, confidentiality and integrity; a proof-of-concept prototype was developed on state-of-the-art hardware and software.

Wireless Sensor Networks are becoming federated and mobile environments. These new capabilities pose a lot of new possibilities and challenges. One of these challenges is to create a secure environment to allow multiple trusted companies to share and merge their sensor network infrastructure. The most basic need for a secure environment is the deployment of key material. However, most current day research assumes pre-shared secrets between the sensor nodes of most, if not all, companies in a federation. These solutions are often not scalable nor mobile enough to meet realistic business requirements. Additionally, most key deployment protocols totally omit any connectivity with back-end infrastructure. This paper proposes a novel deployment protocol for the MAnagement of Secret keYs (MASY). MASY allows secure deployment of a key to a sensor node when it enters a previously unknown network. By off-loading the trust creation process to the resource-rich back-end infrastructure, the burden on the sensor nodes remains very limited.

Considerable research has been performed in applying run-time reconfigurable component models to the domain of wireless sensor networks. The ability to dynamically deploy and reconfigure software components has clear advantages in sensor network deployments, which are typically large in scale and expected to operate for long periods in the face of node mobility, dynamic environmental conditions and changing application requirements. To date, research on component and binding models for sensor networks has primarily focused on the development of specialized component models that are optimized for use in resource-constrained environments. However, current approaches impose significant overhead upon developers and tend to use inflexible binding models based on remote procedure calls. To address these concerns, we introduce a novel component and binding model for networked embedded systems (LooCI). LooCI components are designed to impose minimal additional overhead on developers. Furthermore, LooCI components use a novel event-based binding model that allows developers to model rich component interactions, while providing support for easy interception, re-wiring and re-use. A prototype implementation of our component and binding model has been realised for the SunSPOT platform. Our preliminary evaluation shows that LooCI has an acceptable memory footprint and imposes minimal overhead on developers.

Abstract — Although various publications confirm the need for a generic DRM software architecture, we observe that current efforts to define a DRM architecture do not always provide sufficient support to enable the creation and management of DRM systems and content distribution applications. This is a considerable problem that implies a crucial challenge for the evolution of DRM, given the impact of a software architecture on the functional and non-functional qualities of the implemen-tation. This paper (1) presents a generic DRM architecture, (2) evaluates it in the context of interoperability, extendability, and modifiability, and (3) compares it to related work in the Digital Media Project (DMP). To the best of our knowledge, the proposed architecture is more detailed than related work published so far. I.

The domain of digital rights management (DRM) is currently lacking a generic architecture that supports interoperability and reuse of specific DRM technologies. This lack of architectural support is a serious drawback in light of the rapid evolution of a complex domain like DRM. It is highly unlikely that a single DRM technology or standard will be able to support the diversity of devices, users, platforms, and media, or the wide variety of system requirements concerning security, flexibility, and efficiency. This paper analyses state-of-the-art DRM technologies and extracts from them high level usage scenarios according to content consumers, producers, and publishers. In addition, the key services are identified both from a functional and security perspective. Identifying key DRM services and locating them in an overall structure brings us one step closer to a software architecture for DRM. Having available a software architecture should help the DRM community in reasoning about DRM systems, and in achieving reuse and interoperability of multiple domain-specific DRM technologies and standards.

Unanticipated software adaptations are becoming increasingly important in the domain of distributed systems. Due to performance and availability requirements, these adaptations need to be enforced at run-time. However for dynamic changes to yield valid systems, a safe state for reconfiguration of the involved software modules must be enforced. This paper proposes a solution to modularize support for achieving safe reconfiguration and separate it from basic application functionality. In addition, we describe a mechanism to obtain a safe state for unanticipated reconfiguration of producer/consumer based systems, rather than being limited to updates of independent software components. The presented method imposes such a safe state with minimal interference to the rest of the system, and with minimal contribution from the programmer. We believe that a wide range of producer/consumer based systems can take advantage of the presented solution.

Research domains such as active networks, ad-hoc networks, ubiquitous computing, pervasive computing, grid computing, and sensor networks, clearly show that computer networks will become more complex and heterogeneous. In many cases, central management and control of the network are far from trivial since both the topology and the connected devices change rapidly in such highly dynamic environments, while load circumstances may vary arbitrarily. The software architecture in a node needs to support flexibility. We have developed an architecture tailored to protocol stack software that allows customizing internal resource management in order to handle overload conditions gracefully. We show that the investment in explicit support for modularity and architectural constraints pays off: the paper elaborates on a case study in which dynamic adaptation of access control behavior leads to significant performance improvements.

In this position paper we unify three essential features for flexible system software: a component oriented approach, self-adaptation and separation of concerns. We propose DiPS (Distrinet Protocol Stack), a component framework, which offers components, an anonymous interaction model and connectors to handle non-functional aspects such as concurrency.