Voice-based Speaker Identification (SI) can be framed as the problem of Closed-Set Speaker Identification (CSSI), recognizing a speaker from a known set, or OSSI, additionally recognizing unknown speakers. Precise and accurate Open-Set Speaker Identification (SI) can enable a variety of applications, ranging from human presence detection to authentication. Existing SI solutions are typically driven by deep learning approaches, which involve computationally demanding models often running in cloud back-ends. Enabling local SI models running directly on resource-constrained embedded devices can enable new use cases while preserving speaker privacy. In this work, we fill this gap and present SPIDER, a lightweight, on-device CSSI and OSSI solution capable of running on the off-the-shelf Nordic nRF52840 and nRF5340 system-on-chip microcontrollers, which feature as little as 256 and 512 kB of RAM, respectively, and 1 MB of flash memory. SPIDER is 16x-67x smaller than currently-available SI models, and yet, the 16x smaller version achieves a comparable accuracy of 94.33 % for CSSI and 91.8% for OSSI. Our evaluation across multiple datasets confirms the viability of performing accurate SI directly on resource-constrained embedded devices using only low-cost microphones. To foster further research and development, we open source our implementation of SPIDER, empowering the community to explore new SI use cases where cloud connectivity or backhaul infrastructure is impractical or undesirable.

Precise clock synchronization underpins deterministic operation in wireless systems spanning industrial automation, vehicular networks, distributed extended reality (XR), smart infrastructure, and wide-area precision agriculture. Wireless links introduce variable propagation delays, channel asymmetry, interference, clock drift, and scalability constraints that make sub-microsecond alignment difficult. This article provides a comprehensive survey and tutorial on wireless clock synchronization. We introduce a five-dimension taxonomy covering: system architecture, synchronization mechanism, correction strategy, delay and uncertainty modeling, and resource and deployment class, and apply it to eight canonical protocol families and to synchronization as realized across IEEE 802.15.4, ZigBee, Bluetooth low energy (BLE), LoRa, Wi-Fi, Ultrawide band (UWB), and 4G/5G/6G systems. We examine solutions across five application domains: industrial automation and Industrial Internet of Things (IIoT), vehicular V2X, distributed XR and metaverse, infrastructure monitoring, and wide-area Internet of Things (IoT) and precision agriculture; alongside tools, testbeds, and datasets supporting evaluation. Open challenges addressed include scalability and mobility, ultralow-jitter determinism, robust clock parameter estimation under non-Gaussian delay distributions, distributed and consensus-based synchronization for infrastructure-free networks, secure and resilient synchronization against wireless-specific threats, and cross-domain convergence encompassing time-sensitive networking (TSN)–5G/6G interoperability and joint communication, sensing, and timing as an emerging 6G design paradigm. Together, these contributions provide the first unified cross-technology framework connecting fundamentals, protocol families, application domains, and open research challenges in wireless clock synchronization.

In the context of large-scale data collection like in the Internet of Things, Data Confidence Fabrics are expected to play an essential role in verifying and authenticating sensor data. To this end, metadata is generated at each network node and stored on the blockchain. However, storing metadata on the blockchain introduces significant privacy risks, as it can be exploited to reveal sensitive information, such as network structures and communication paths. This paper addresses these challenges by proposing two novel schemes to protect network structures: Hostname Mapping and Hostname Encryption . Our work demonstrates that the Hostname Mapping approach effectively conceals network patterns but introduces inefficiencies due to additional table storage and computational overhead. In contrast, the Hostname Encryption method eliminates the need for additional table management, offering a more efficient and secure alternative. Despite these advancements, the timestamp field in metadata could still allow attackers to infer patterns using machine learning, highlighting the need for further research to fully secure metadata. By combining encryption with some enhancements to timestamp obfuscation, our approach lays the foundation for additional privacy protection against metadata exploitation.

Time-Sensitive Networking (TSN) is increasingly adopted in industrial systems to meet strict latency, jitter, and reliability requirements. However, evaluating TSN’s fault tolerance under realistic failure conditions remains challenging. This paper presents IN2C, a modular OMNeT++/INET-based simulation framework that models two synchronized production cells connected to centralized infrastructure. IN2C integrates core TSN features—including time synchronization, traffic shaping, per-stream filtering, and Frame Replication and Elimination for Redundancy (FRER)—alongside XML-driven fault injection for link and node failures. Four fault scenarios are evaluated to compare TSN performance with and without redundancy. Results show that FRER eliminates packet loss and achieves sub-millisecond recovery, though with 2–3× higher link utilization. These findings offer practical guidance for deploying TSN in bandwidth-constrained industrial environments.

Private 5G networks are emerging as key enablers for smart factories, where a single device often handles multiple concurrent traffic flows with distinct Quality of Service (QoS) requirements. Existing simulation frameworks, however, lack the fidelity to model such multi-flow behavior at the QoS Flow Identifier (QFI) level. This paper addresses this gap by extending Simu5G to support per-QFI modeling and by introducing a novel QoS-aware Proportional Fairness (QoS-PF) scheduler. The scheduler dynamically balances delay, Guaranteed Bit Rate (GBR), and priority metrics to optimize resource allocation across heterogeneous flows. We evaluate the proposed approach in a realistic smart factory scenario featuring edge-hosted machine vision, real-time control loops, and bulk data transfer. Results show that QoS-PF improves deadline adherence and fairness without compromising throughput. All extensions are implemented in a modular and open-source manner to support future research. Our work provides both a methodological and architectural foundation for simulating and analyzing advanced QoS policies in industrial 5G deployments.

Data Confidence Fabrics (DCFs) are emerging as a mechanism to obtain measurable trust in decentralized smart computing environments, while remote attestation (RA) is being established as a key mechanism for verifying security in distributed systems. However, both of these approaches remain underutilized in container orchestration platforms, resulting in inadequate trust guarantees, increased attack surface areas, and insufficient mechanisms for verifying the integrity of devices and applications. In this paper, we propose leveraging DCFs to integrate RA with software security practices, and utilizing this integration to securely onboard devices and containerized applications by tracing their provenance and analyzing vulner-abilities. This dual-level protection bridges device attestation measurements with measurements of application security to provide measurable and transparent confidence scores for both. The proposed approach brings trustworthiness into a distributed environment where devices are continuously monitored for malicious tampering, and applications are assessed before being run. We verified this approach on a setup representing real environments. Additionally, a machine learning model was put under test and trained on data weighted with confidence scores produced by our proposed approach. The model saw improved performance and accuracy, showing that this approach can increase the reliability of systems.

Time-Sensitive Networking (TSN) standards have enabled deterministic Ethernet solutions, providing low-latency, reliable communication critical for Industry 4.0 applications. With industrial environments increasingly incorporating wireless solutions, the integration of TSN over 5G and Wi-Fi has emerged as a key research focus. This paper provides a comparative analysis of the integration of 5 G and Wi-Fi with TSN, evaluating latency, reliability, scalability, synchronization, mobility support, and implementation complexity. The simulation results identify the conditions under which 5G+TSN or Wi-Fi+TSN offers superior performance, offering clear recommendations for the selection of technology adapted to industrial use cases.

The need to trust data has become a key requirement in modern distributed systems. To facilitate measurable trust and confidence in data and applications spanning heterogeneous systems, the emerging concept of a Data Confidence Fabric (DCF) offers a compelling solution. Data producers and processors provide metadata, known as annotations, recording trust insertion along the data delivery chain. Thus, it is possible to asses the trustworthiness of data before processing it. While a DCF, such as the Alvarium framework, enables this management of trust, there is a cost in terms of the overheads associated with security annotations themselves. To improve efficiency of a DCF, we therefore propose a set of techniques for making annotations more compact and to reduce the number of DCF transactions. Our work shows that transaction efficiency gains of up to 93% in our considered use cases can be achieved.

The growing heterogeneity and decentralization in the modern computing paradigm of edge-cloud continuum introduces new constraints on storage systems, such as storage type, associated processors, privacy, scarce resources, compliance, GDPR and geographical restrictions. While existing distributed data and object stores can ensure data availability and fault-tolerance, they are not flexible or dynamic enough to address these diverse set of constraints. In this paper, we introduce a modular policy-driven data placement framework, CATER, designed to seamlessly integrate with existing storage systems and overcome the aforementioned limitations. CATER formulates the data placement problem as an optimization model, incorporating data collocation and hardware constraints. We integrated a pro-totype of CATER with Apache Ozone and conducted experiments and simulations. Results show a 23% improvement in data placement while respecting 100 % of the constraints.

Voice assistants overhear conversations, and a consent management mechanism is required. Consent management can be implemented using speaker recognition. Users that do not give consent enroll their voice, and all their further recordings are discarded. Building speaker recognition-based consent management is challenging as dynamic registration, removal, and reregistration of speakers must be efficiently handled. This work proposes a consent management system addressing the aforementioned challenges. A contrastive-based training is applied to learn the underlying speaker equivariance inductive bias. The contrastive features for buckets of speakers are trained a few steps into each iteration and act as replay buffers. These features are progressively selected using a multi-strided random sampler for classification. Moreover, new methods for dynamic registration using a portion of old utterances, removal, and reregistration of speakers are proposed. The results verify memory efficiency and dynamic capabilities of the proposed methods and outperform the existing approaches from the literature in terms of convergence rate and number of required parameters.

In the study of software security many factors must be considered. Once venturing beyond the simplest of laboratory experiments, the researcher is obliged to contend with exponentially complex conditions. Software security has been shown to be affected by priming, tool usability, library documentation, organisational security culture, the content and format of internet resources, IT team and developer interaction, Internet search engine ordering, developer personality, security warning placement, mentoring, developer experience and more. In a systematic review of software security papers published since 2016, we have identified a number of unhelpful assumptions that are commonly made by software security researchers. In this paper we list these assumptions, describe why they sometimes do not reflect reality, and suggest implications for researchers.

In distributed environments, data for Machine Learning (ML) applications may be generated from numerous sources and devices, and traverse a cloud-edge continuum via a variety of protocols, using multiple security schemes and equipment types. While ML models typically benefit from using large training sets, not all data can be equally trusted. In this work, we examine data trust as a factor in creating ML models, and explore an approach using annotated trust metadata to contribute to data weighting in generating ML models. We assess the feasibility of this approach using well-known datasets for both linear regression and classification problems, demonstrating the benefit of including trust as a factor when using heterogeneous datasets. We discuss the potential benefits of this approach, and the opportunity it presents for improved data utilisation and processing.

Software security research has a core problem: it is impossible to prove the security of complex software. A low number of known defects may simply indicate that the software has not been attacked yet, or that successful attacks have not been detected. A high defect count may be the result of white-hat hacker targeting, or of a successful bug bounty program which prevented insecurities from persisting in the wild. This makes it difficult to measure the security of non-trivial software. Researchers instead usually measure effort directed towards ensuring software security. However, different researchers use their own tailored measures, usually devised from industry secure coding guidelines. Not only is there no agreed way to measure effort, there is also no agreement on what effort entails. Qualitative studies emphasise the importance of security culture in an organisation. Where software security practices are introduced solely to ensure compliance with legislative or industry standards, a box-ticking attitude to security may result. The security culture may be weak or non-existent, making it likely that precautions not explicitly mentioned in the standards will be missed. Thus, researchers need both a way to assess software security practice and a way to measure software security culture. To assess security practice, we converted the empirically-established 12 most common software security activities into questions. To assess security culture, we devised a number of questions grounded in prior literature. We ran a secure development survey with both sets of questions, obtaining organic responses from 1,100 software coders in 59 countries. We used proven common activities to assess security practice, and made a first attempt to quantitatively assess aspects of security culture in the broad developer population. Our results show that some coders still work in environments where there is little to no attempt to ensure code security. Security practice and culture do not always correlate, and some organisations with strong secure coding practice have weak secure coding culture. This may lead to problems in defect prevention and sustained software security effort.

Voice is a convenient and popular way to interact with our digital world. Besides translating speech to text, it is also possible to identify speakers based on their voice profile. To date, speaker identification has predominantly been limited to high-performance computational platforms owing to the intricate nature of the underlying algorithms. In this work, we demonstrate that it is possible to reduce model complexity by the required factor of ~10, such that speaker identification can be made feasible for embedded devices with limited resources. We further describe and discuss novel use cases, such as voice-based presence detection and authentication, that become feasible on these class of devices.

Personal voice assistants (PVAs) are increasingly used as interfaces to digital environments. Voice commands are used to interact with phones, smart homes, or cars. In the United States alone, the number of smart speakers, such as Amazon’s Echo and Google Home, has grown by 78% to 118.5 million, and 21% of the U.S. population own at least one device. Given the increasing dependency of society on PVAs, security and privacy of these have become a major concern of users, manufacturers, and policy makers. Consequently, a steep increase in research efforts addressing security and privacy of PVAs can be observed in recent years. While some security and privacy research applicable to the PVA domain predates their recent increase in popularity, many new research strands have emerged. This article provides a survey of the state of the art in PVA security and privacy. The focus of this work is on the security and privacy challenges arising from the use of the acoustic channel. Work that describes both attacks and countermeasures is discussed. We highlight established areas such as voice authentication (VA) and new areas such as acoustic Denial of Service (DoS) that deserve more attention. This survey describes research areas where the threat is relatively well understood but where countermeasures are lacking, for example, in the area of hidden voice commands. We also discuss work that looks at privacy implications; for example, work on management of recording consent. This survey is intended to provide a comprehensive research map for PVA security and privacy.

A Unique Object (UNO) is a physical object with unique characteristics that can be measured externally. The usually analogue measurement can be converted into a digital representation - a fingerprint - which uniquely identifies the object. For practical applications it is necessary that measurements can be performed without the need of specialist equipment or complex measurement setup. Furthermore, a UNO should be able to defeat simulation attacks; an attacker may replace the UNO with a device or system that produces the expected measurement. Recently a novel type of UNOs based on Quantum Dots (QDs) and exhibiting unique photo-luminescence properties has been proposed. The uniqueness of these UNOs is based on quantum effects that can be interrogated using a light source and a camera. The so called Quantum Confinement UNO (QCUNO) responds uniquely to different light excitation levels which is exploited for simulation attack protection, as opposed to focusing on features too small to reproduce and therefore difficult to measure. In this paper we describe methods for extraction of fingerprints from the QCUNO. We evaluate our proposed methods using 46 UNOs in a controlled setup. Focus of the evaluation are entropy, error resilience and the ability to detect simulation attacks.

As we advance towards smart cities, autonomous vehicles and the avalanche of IoT devices proposed for the future, we need to give careful consideration to how easily compromised devices can impact network state. Current proposals for devices typically use cellular networks as the backhaul or final hop. These devices will leverage existing trust-based client-side channel metrics, such as Channel Quality Indicator (CQI), when the base-station determines scheduling decisions. In this paper, we investigate the scheduling impact of a malicious device when it changes its channel metrics, so as to improve its download rate or even to negate the download rate of others. We utilise real-time 4K ultra-high definition video delivery as an example of high throughput demand application and compare the delivery rates of multiple devices in an open-source 5G simulated NS-3 network. Our results illustrate that when a malicious client deceives the scheduler, the other clients in the network have a noticeable decrease in both viewable quality and underlying delivery rate (25% decrease in the average video quality across the non malicious clients).

Clear Channel Assessment (CCA) is a core element of Wireless Sensor Network (WSN) Medium Access Control (MAC) protocols which is used on the transmitter and receiver sides. Current CCA implementations cannot determine the device type occupying the media, leaving nodes unable to differentiate between WSN traffic and interference. However, this would be valuable as MAC protocols benefit from reacting differently depending on the channel occupier. In this article, we describe a method called Power Differentiating Clear Channel Assessment (P-DCCA). Transmitters vary the output power of the radio while the packet is being sent. Receivers are able to identify signals with this characteristic, enabling a Differentiating Clear Channel Assessment (DCCA) check to reveal if the medium is currently occupied by WSN traffic or other interference. We present an implementation and thorough evaluation of Power P-DCCA. Using ContikiMAC as an example, we describe how P-DCCA can be integrated within MAC protocols. We show via large-scale testbed experiments and deployments that P-DCCA enabled networks have a significant improved performance. For example, we show that a P-DCCA enabled network can improve Packet Reception Rate (PRR) by up to a factor of 10 while reducing energy usage by more than 80% under heavy interference.

Low-Power Wide-Area Network (LPWAN) technologies such as Long Range (LoRa) are emerging that enable power efficient wireless communication over very long distances. LPWAN devices typically communicate directly to a sink node which removes the need of constructing and maintaining a complex multi-hop network. However, to ensure efficient and reliable communication LPWAN devices often provide a large number of transmission parameters. For example, a LoRa device can be configured to use different spreading factors, bandwidth settings, coding rates and transmission powers, resulting in over 6720 possible settings. It is a challenge to determine the setting that minimises transmission energy cost while meeting the required communication performance. This paper is the first to present a thorough analysis of the impact of LoRa transmission parameter selection on communication performance. We study in detail the impact of parameter settings on energy consumption and communication reliability. Using this study we develop a link probing regime which enables us to quickly determine transmission settings that satisfy performance requirements. The presented work is a first step towards an automated mechanism for LoRa transmission parameter selection that a deployed LoRa network requires, but is not yet specified within the Long Range Wide Area Network (LoRaWAN) framework.

Virtualisation encourages users to procure, relin-quish and scale resources frequently. Such fluid deployments require continuous performance assessment to inform resource allocation. In current systems Tick Accounting, Load Average, and Memory Usage are used to judge system performance and to trigger resource scaling. We argue that the readily available Context Switch (CS) counter is also an effective performance indicator. We demonstrate the efficacy of CS and show that it can also be used as base for scaling decisions.

New Internet of Things (IoT) technologies such as Long Range (LoRa) are emerging which enable power efficient wireless communication over very long distances. Devices typically communicate directly to a sink node which removes the need of constructing and maintaining a complex multi-hop network. Given the fact that a wide area is covered and that all devices communicate directly to a few sink nodes a large number of nodes have to share the communication medium. LoRa provides for this reason a range of communication options (centre frequency, spreading factor, bandwidth, coding rates) from which a transmitter can choose. Many combination settings are orthogonal and provide simultaneous collision free communications. Nevertheless, there is a limit regarding the number of transmitters a LoRa system can support. In this paper we investigate the capacity limits of LoRa networks. Using experiments we develop models describing LoRa communication behaviour. We use these models to parameterise a LoRa simulation to study scalability. Our experiments show that a typical smart city deployment can support 120 nodes per 3.8 ha, which is not sufficient for future IoT deployments. LoRa networks can scale quite well, however, if they use dynamic communication parameter selection and/or multiple sinks.

Abstract Comprehensive security mechanisms are required for a successful implementation of the Internet of Things (IoT). Existing solutions focus mainly on securing the communication links between Internet hosts and IoT devices. However, as most IoT devices nowadays provide vast amounts of flash storage space, it is as well required to consider storage security within a comprehensive security framework. Instead of developing independent security solutions for storage and communication, we propose Fusion, a framework that provides coalesced confidential storage and communication. Fusion uses existing secure communication protocols for the IoT such as Internet protocol security (IPsec) and datagram transport layer security (DTLS) and re‐uses the defined communication security mechanisms within the storage component. Thus, trusted mechanisms developed for communication security are extended into the storage space. Notably, this mechanism allows us to transmit requested data directly from the file system without decrypting read data blocks and then re‐encrypting these for transmission. Thus, Fusion provides benefits in terms of processing speed and energy efficiency, which are important aspects for resource‐constrained IoT devices. This paper describes the Fusion architecture and its instantiation for IPsec‐based and DTLS‐based systems. We describe Fusion's implementation and evaluate its storage overheads, communication performance, and energy consumption. Copyright © 2015 John Wiley & Sons, Ltd.

The Internet of Things (IoT) is increasingly used for critical applications and securing the IoT has become a major concern. Among other issues it is important to ensure that tampering with IoT devices is detected. Many IoT devices use WiFi for communication and Channel State Information (CSI) based tamper detection is a valid option. Each 802.11n WiFi frame contains a preamble which allows a receiver to estimate the impact of the wireless channel, the transmitter and the receiver on the signal. The estimation result - the CSI - is used by a receiver to extract the transmitted information. However, as the CSI depends on the communication environment and the transmitter hardware, it can be used as well for security purposes. If an attacker tampers with a transmitter it will have an effect on the CSI measured at a receiver. Unfortunately not only tamper events lead to CSI fluctuations; movement of people in the communication environment has an impact too. We propose to analyse CSI values of a transmission simultaneously at multiple receivers to improve distinction of tamper and movement events. A moving person is expected to have an impact on some but not all communication links between transmitter and the receivers. A tamper event impacts on all links between transmitter and the receivers. The paper describes the necessary algorithms for the proposed tamper detection method. In particular we analyse the tamper detection capability in practical deployments with varying intensity of people movement. In our experiments the proposed system deployed in a busy office environment was capable to detect 53% of tamper events (TPR = 53%) while creating zero false alarms (FPR = 0%).

The interaction between devices sharing the same frequency domain, but using different communication protocols, is an important issue prevalent to the Internet of Things (IoT). IEEE 802.11 and IEEE 802.15.4, protocols that overlap in applications such as home automation, health care and factory automation, is one such example. In these situations 802.11 networks are known to detrimentally interfere with 802.15.4 networks, degrading performance significantly. We show that the response to unsuccessful Clear Channel Assessments (CCA) is a large contributor to this degradation. Current responses do not distinguish between different transmission sources, employing the same response independent of the interferer type. However, as we show in this paper, it is beneficial to discriminate interferer types when performing CCA as this allows us to employ different strategies in handling occupied media. Our work considers in particular the low-power Medium Access Control (MAC) protocols based on the 802.15.4 standard. In such protocols, CCA is applied at the transmitter to arbitrate channel access, and also at the receiver for power efficient transmission detection. We present a protocol building block called Differentiating Clear Channel Assessment (DCCA) which allows us to tailor the reaction to a busy channel depending on the nature of the interfering network type. We evaluate an implementation of DCCA for the ContikiMAC protocol on the Maxfor MTM-CM5000MSP platform. The experimental evaluation shows that DCCA improves throughput of 802.15.4 networks in the presence of 802.11 networks significantly, ten-fold in some settings.

Wireless devices are often used in application scenarios with strict security requirements. Examples are physical intrusion detection systems commonly used to protect factories, airports or government buildings. In such scenarios, additional security features such as tamper detection are highly desirable to complement traditional cryptographic mechanisms. In this paper we use channel state information (CSI), extracted from off-the-shelf 802.11n Wi-Fi cards, to calculate a tamper-evidence value for transmitters. This value enables detection of tampering due to device movement or replacement. We describe algorithms for tamper-evidence value computation, discuss the interpretation of this value and evaluate its effectiveness.

ABSTRACT The future Internet is an IPv6 network interconnecting traditional computers and a large number of smart objects. This Internet of Things (IoT) will be the foundation of many services and our daily life will depend on its availability and reliable operation. Therefore, among many other issues, the challenge of implementing secure communication in the IoT must be addressed. In the traditional Internet, IPsec is the established and tested way of securing networks. It is therefore reasonable to explore the option of using IPsec as a security mechanism for the IoT. Smart objects are generally added to the Internet using IPv6 over Low‐power Wireless Personal Area Networks (6LoWPAN), which defines IP communication for resource‐constrained networks. Thus, to provide security for the IoT based on the trusted and tested IPsec mechanism, it is necessary to define an IPsec extension of 6LoWPAN. In this paper, we present such a 6LoWPAN/IPsec extension and show the viability of this approach. We describe our 6LoWPAN/IPsec implementation, which we evaluate and compare with our implementation of IEEE 802.15.4 link‐layer security. We also show that it is possible to reuse crypto hardware within existing IEEE 802.15.4 transceivers for 6LoWPAN/IPsec. The evaluation results show that IPsec is a feasible option for securing the IoT in terms of packet size, energy consumption, memory usage, and processing time. Furthermore, we demonstrate that in contrast to common belief, IPsec scales better than link‐layer security as the data size and the number of hops grow, resulting in time and energy savings. Copyright © 2012 John Wiley & Sons, Ltd.

Wireless sensor networks for industrial process monitoring and control require highly reliable and timely data delivery. To match performance requirements, specialised schedule based medium access control (MAC) protocols are employed. In order to construct an efficient system, it is necessary to find a schedule that can support the given application requirements in terms of data delivery latency and reliability. Furthermore, additional requirements such as transmission power may have to be taken into account when constructing the schedule. In this article, we show how such schedule can be constructed. We describe methods and tools to collect the data necessary as input for schedule calculation. Moreover, due to the high complexity of schedule calculation, we also introduce a heuristic. We evaluate the proposed methods in a real-world process automation and control application deployed in an oil refinery and further present a long-term experiment in an office environment. Additionally, we discuss a framework for schedule life-cycle management.

Network coding (NC) has frequently been promoted as an approach for improving throughput in wireless networks. Existing work has mostly focused on the fundamental aspects of NC, while constraints arising in real-world network deployments have not received much attention. In particular, NC requires network nodes to overhear each other's packets, which oftentimes contradicts many security standards that attempt to provide link-layer confidentiality, e.g., by utilizing pairwise encryption keys as is the case IEEE 802.11i and ZigBee. There is an inherent trade-off between gains from NC and link-layer security: if many nodes share the secret link-layer key, NC will improve throughput, yet a leakage of the key will affect many nodes. On the other hand, having distinct secret keys will increase resilience against key compromise, but will also minimize the coding gain. We formulate this security vs. performance trade-off as an optimization problem and evaluate the effectiveness of NC under different sizes of key-sharing groups and network topologies. Our results show that increasing the key-sharing group by a single node can result in a maximum coding gain between 1.3% and 13.7%.

The future Internet of Things (IoT) may be based on the existing and established Internet Protocol (IP). Many IoT application scenarios will handle sensitive data. However, as security requirements for storage and communication are addressed separately, work such as key management or cryp-tographic processing is duplicated. In this paper we present a framework that allows us to combine secure storage and secure communication in the IP-based IoT. We show how data can be stored securely such that it can be delivered securely upon request without further cryptographic processing. Our prototype implementation shows that combined secure storage and communication can reduce the security-related processing on nodes by up to 71% and energy consumption by up to 32.1%.

Today's industrial facilities, such as oil refineries, chemical plants, and factories, rely on wired sensor systems to monitor and control the production processes. The deployment and maintenance of such cabled systems is expensive and inflexible. It is, therefore, desirable to replace or augment these systems using wireless technology, which requires us to overcome significant technical challenges. Process automation and control applications are mission-critical and require timely and reliable data delivery, which is difficult to provide in industrial environments with harsh radio environments. In this article, we present the GINSENG system which implements performance control to allow us to use wireless sensor networks for mission-critical applications in industrial environments. GINSENG is a complete system solution that comprises on-node system software, network protocols, and back-end systems with sophisticated data processing capability. GINSENG assumes that a deployment can be carefully planned. A TDMA-based MAC protocol, tailored to the deployment environment, is employed to provide reliable and timely data delivery. Performance debugging components are used to unintrusively monitor the system performance and identify problems as they occur. The article reports on a real-world deployment of GINSENG in an especially challenging environment of an operational oil refinery in Sines, Portugal. We provide experimental results from this deployment and share the experiences gained. These results demonstate the use of GINSENG for sensing and actuation and allow an assessment of its ability to operate within the required performance bounds. We also identify shortcomings that manifested during the evaluation phase, thus giving a useful perspective on the challenges that have to be overcome in these harsh application settings.

Wireless sensor and actuator networks (WSANs) will increasingly require support for managed software evolution: that is, systematic, ongoing, efficient and nondisruptive means of updating the software running on the nodes of a WSAN. While aspects of this requirement have been examined in the literature, the big picture remains largely untouched, resulting in the generally static WSAN deployments we see today. In this article, we propose a comprehensive approach to managed software evolution. Our approach has the following key features: (i) it supports divergent evolution of the WSAN's software, such that different nodes can evolve along different lines (e.g., to meet the needs of different stakeholders, or to address localized adaptations) and (ii) it supports both instructed and autonomous evolution such that nodes can be instructed to change their software configuration or can evolve their own configuration (e.g., to manage rapidly-changing environmental conditions where remote micromanagement would be infeasible due to the high latency of the WSAN environment). We present the four intra-WSAN protocols that comprise our solution, along with an accompanying server-side infrastructure, and evaluate our approach at scale.

A new generation of Wireless Sensor Network (WSN) communication transceivers are now available that support time-of-flight distance measurement. This measurement can be inseparably integrated with message transmission making it possible to authenticate messages based on distance. In this paper we present a practical implementation of Distance Based Message Authentication (DBMA) for WSNs using Nanotron NA5TR1 transceivers. We show that DBMA can be used to reject messages sent from outside a secure (trusted) area. With DBMA, messages can be authenticated without involving costly cryptographic algorithms. The DBMA implementation is evaluated in two different deployment scenarios. Distance measurement errors and their impact on the size of the required secure area are evaluated. Furthermore, we present methods to reduce the size of the required secure area.

The goal of the GINSENG project is a performance-controlled sensor network suitable for applications with specific network performance requirements, such as plant automation and health monitoring. Any such sensor network must include a deterministic MAC protocol that can meet the application's targets in areas like delivery delays and reliability. We present a description and preliminary evaluation results of GinMAC, designed to be such a MAC protocol. Also included are details of a deployment to the GALP oil refinery in Sines, Portugal.

In this demo we show how the Lorien operating system [5] supports lightweight, efficient and safe online channges to any aspect of the software running on sensor nodes - and how this promotes reuse of deployed sensor networks through run-time software evolution.

Almost all modern cars can be controlled remotely using a personal communicator (keyfob). However, the degree of interaction between currently available personal communicators and cars is very limited. The communication link is unidirectional and the communication range is limited to a few dozen meters. However, there are many interesting applications that could be supported if a keyfob would be able to support energy efficient bidirectional longer range communication. In this paper we investigate off-the-shelf transceivers in terms of their usability for bidirectional longer range communication. Our evaluation results show that existing transceivers can generally support the required communication ranges but that links tend to be very unreliable. This high unreliability must be handled in an energy efficient way by the keyfob to car communication protocol in order to make off-the-shelf transceivers a viable solution.

Wireless sensor networks are being considered for use in industrial process and control environments. Unlike traditional deployment scenarios for sensor networks, in which energy preservation is the main design principle, industrial environments stress worker safety and uninterrupted production. To fulfill these requirements, sensor networks must be able to provide performance guarantees for radio communication. In this paper, we consider as a case study the deployment of a sensornet in an oil refinery in Portugal, where sensor nodes are deployed outdoors and might experience high temperature fluctuations. We investigate how the variations of ambient temperature influence data delivery performance and link quality in low-power radio communications. We also study the impact that specific implementation requirements, such as the ATEX fire-safety regulations, can have on the design of the overall network. Our experiments show that temperature directly affects the communication between sensor nodes, and that significantly less transmission power is required at low temperatures. We further illustrate that it is possible to save up to 16% energy during nights and cold periods of the year, while still ensuring reliable communication among sensor nodes. In view of these experimental results, we elaborate on how the temperature influences both the design and the deployment of wireless sensor networks in industrial environments.

To implement duty cycles with packet based transceivers, a sender transmits a trail of identical packets (which we call framelets) of which the receiver is able to catch one in its active listening phase. This communication concept is used in the standard low power listening (LPL) protocol shipped with TinyOS 2.x. This existing solution has many shortcomings which result in a very limited network performance. In this paper, we firstly present an alternative framelet based low power listening implementation called Framelet Communications (FrameComm) that eliminates these shortcomings. Secondly, we present a novel additional improvement to FrameComm - Interception and Aggregation of Framelet Communications (i-FrameComm) - that further improves network performance by opportunistically aggregating packets over the radio channel. A prototype implementation of the proposed FrameComm mechanism in TinyOS 2.02 on TelosB nodes is used for evaluation and comparison. The experiments show that the interception and aggregation method increases network throughput and lifetime as communication resources are used more efficiently.

FrameComm is a contention based, duty cycled, MAC protocol that ensures a message will be transmitted during the receiverpsilas listen phase by sending a packet, followed by a short gap, repeatedly for a precalculated number of times or until an acknowledgment is received. While introducing duty cycled communications can yield large power savings it does so at the cost of increased delay and decreased throughput. Many WSNs may incorporate several distinct message types of varying priority. A node with a high priority message to send may find the channel to be busy with a lesser priority message from another node and must therefore dasiaback-offpsila leading to further delays. In a multi-hop environment, these delays are compounded and may become unacceptably large. This paper proposes adding a high priority interrupt message to FrameComm that allows a node with important data to send to interrupt another nodepsilas lesser priority transmission giving immediate access to the channel. The priority interrupt mechanism is evaluated using an implementation in TinyOS 2 on a small laboratory testbed.

Real-world deployments of wireless sensor networks require secure communication. In many application cases it is sufficient to provide message authentication at the sink. To implement this requirement using symmetric ciphers, keys shared between each sensor node and the sink have to be established and kept fresh during network operation. This paper presents a key distribution scheme based on the well known elliptic curve Diffie-Hellman key exchange mechanism that allows us to fulfil the previously outlined requirements efficiently. The DHB-KEY scheme requires only the distribution of a single sink-initiated broadcast message to set individual keys on all sensor nodes. Thus, DHB-KEY has a low complexity and preserves scarce resources such as bandwidth and energy. In the paper we present a protocol specification based on the DHB-KEY scheme and its implementation for the well known TinyOS platform. A physical intrusion detection system in an office building is used to evaluate the protocol implementation. The evaluation shows that DHB-KEY is practical in real-world deployments.

Data aggregation is a method used in sensor networks to reduce the amount of messages transported. By aggregating, the data contained in several messages is fused into one single message. If such a message, containing the equivalent of many individual messages, is lost due to transmission errors then this has a detrimental effect on the application quality experienced. In many sensor network applications a constant supply of data is needed and therefore application quality is severely effected by excessive data loss. This paper proposes and evaluates the use of an in-network control mechanism to offset this disadvantageous effect. The control mechanism analytically calculates the correct reliability that an aggregate of given size must be forwarded at in order to meet application specific goals.

Abstract—Wireless sensor networks are collections of autonomous devices with computational, sensing and wireless communication capabilities. Research in this area has been growing in the past few years given the wide range of applications that can benefit from such a technology. This paper reports on a joint project between The Tyndall National Institute and the Computer Science Department at University College Cork, Ireland in developing a novel miniaturised modular platform for wireless sensor networks. The system architecture, hardware and software will be discussed as well as details of the deployment scenario chosen for the project – a car park management system. Results and problems encountered during deployment will be presented Keywords-component Wireless Sensor Networks, deployment, car park monitoring

Data collected in a sensor network is transported hop-by-hop to a sink for further analysis. The quality of the analysis depends on the amount of data reaching the sink. Hence, data transport reliability influences the quality of the analysis. Data aggregation is a common method used in sensor networks to reduce the amount of messages transported. By aggregating, the data contained in several messages is fused into one single message. Therefore, data aggregation significantly influences the overall data transport reliability observed at the sink. This influence is analyzed and described analytically and by experiment within this paper. Furthermore it is shown how the influence of data aggregation on data transport reliability can be controlled for a particular class of data gathering application

A complete wireless sensor network solution for carpark management is presented in this paper. The system architecture and design are first detailed, followed by a description of the current working implementation, which is based on our DSYS25z sensing nodes. Results of a series of real experimental tests regarding connectivity, sensing and network performance are then discussed. The analysis of link characteristics in the car-park scenario shows unexpected reliability patterns which have a strong influence on MAC and routing protocol design. Two unexpected link reliability patterns are identified and documented. First, the presence of the objects (cars) being sensed can cause significant interference and degradation in communication performance. Second, link quality has a high temporal correlation but a low spatial correlation. From these observations we conclude that a) the construction and maintenance of a fixed topology is not useful and b) spatial rather than temporal message replicates can improve transport reliability

Wireless sensor networks are collections of autonomous devices with computational, sensing and wireless communication capabilities. Research in this area has been growing in the past few years given the wide range of applications that can benefit from such a technology. In this paper, the development of a highly modular and miniaturized wireless platform for sensor networks is described. The system incorporates a radio transceiver (in the 2.4 GHz ISM Band) with embedded protocol software to minimize power consumption and maximize data throughput. Additional input capability for sensor and actuator integration can be incorporated seamlessly due to the modular nature of the system. The total system is packaged in a modular 25 mm cubed form factor. A smaller, (10 mm cubed), prototype is currently under development. Ongoing development of highly miniaturized nodes is discussed.

A basic problem introduced by the use of radio duty cycles as an energy saving technique is needed to establish rendezvous between transmitter and receiver. Since communication can only take place when the receiver's radio is active, the transmission of frames needs to somehow overlap with this active period. This paper investigates the use of framelets - small, fixed sized frames - to achieve transmitter-receiver rendezvous and contrasts this technique with the use of long frames. The benefits of applying framelets is assessed analytically and an implementation of the concept for the DSYS25 sensor platform is presented and evaluated. The results show that substantial energy savings can be achieved with framelets as well as an increase in communication throughput

In this demonstration, a new sensor platform named DSYS25 is presented. The platform has a unique hardware design and runs a customized version of the TinyOS operating system. Transceiver hardware and packaging distinguish the D-Systems platform from other available designs.

Within a global networked environment, security aspects have become more and more important and access control at network borders is considered essential. For this purpose firewall systems are used which provide a well-established security mechanism to restrict the exchanged traffic to a certain subset of users and applications. In order to cope with the increasing demand for new applications, a firewall must be flexible and extensible to support such new applications and their protocols. RSVP is a dynamic signalling protocol, which has been invented to negotiate resource requirements between end systems and a packet-based communication network. We investigate the interoperation of RSVP with a firewall system in order to support new applications in a generic way. We show how the resulting system flexibility allows for a variety of employment scenarios and incremental deployment of such a technology. We back up our claims by describing a prototype that we have implemented.