Street lighting is an important resource; it has been shown to reduce crime, improve road safety, and increase economic activity. These benefits, however, come with a cost: an annual emission of 64 million tonnes of CO2. Solar-powered street lighting is attractive for its use of renewable energy and its ease of installation (particularly in off-grid applications), but sizing and control is a non-trivial task. This paper describes TALiSMaN-Green, a traffic-aware street lighting scheme which takes account of road users as well as the available energy to dynamically adjust lighting levels. Simulations using real traffic and sunlight data illustrate that solar-powered streetlights can be managed to deliver consistent usefulness throughout the night.

This paper presents a comprehensive overview of an investigation into the use of otoacoustic emissions (OAE) as an identity verification biometric. OAE could be important as a biometric identifier in applications where users wear headsets since it is discrete and difficult to spoof. OAE are very low level [ ~ 17 dB sound pressure level (SPL)] sounds emitted from the human ear as part of the normal hearing process. They can occur spontaneously or be invoked by a suitable stimulus, these being known as transient evoked otoacoustic emissions (TEOAE) and distortion product otoacoustic emission (DPOAE). An initial visual comparison shows that otoacoustic emissions are clearly distinctive and are stable over a six month period. A biometric analysis based on the Euclidean distance measurement of TEOAE recordings in the temporal domain was performed on prerecorded datasets captured for medical purposes and data were collected specifically for this study. For a database of 23 subjects, the equal error rate (EER) was 1.24% for a 90% confidence interval. DPOAEs also demonstrated biometric potential but the level of discrimination is inferior to TEOAE. The combination of DPOAE and TEOAE into a multimodal analysis was demonstrated to be feasible although the potential improvement in performance is yet to be quantified. Finally, the use of maximum length sequencing (MLS) was investigated to reduce capture time without decreasing performance. This demonstrated a reduction in capture time for a TEOAE from 1 min to 5 s with a visual analysis of a fourth-order MLS showing good stability and reproducibility. OAEs can potentially be used as a biometric and benefit from their small template size (512 data points in our TEOAE biometric) and simple analysis. The level of background noise is the most significant practical factor that affects biometric performance.

In this work, we present the design, development, and evaluation of a real-time target tracking system for wireless embedded nodes, capable of effectively tracking manoeuvring targets. The proposed tracking system is designed to operate solely on range measurements obtained with the use of a two-way time-of-flight method without the need for additional hardware being incorporated in the nodes. To address the challenge of coping with manoeuvring targets, the tracking problem is formulated as a dynamical estimation problem where an adaptive multiple-model approach is employed to represent the motion pattern of manoeuvring targets. The ranging observations are produced in real time and used as inputs to a particle filter algorithm that produces the estimates of the target's kinematic variables. Simulations are provided to assess the effect of several factors on the system's performance. Ultimately, the entire system is implemented on commercially available hardware and tested in an outdoor deployment. A total of 25 experiments demonstrate an average RMS accuracy of 2.6 m for position and 1.9 m/s for velocity, in a 15 m × 15 m area. Such performance, which is additionally confirmed from simulation results, reveals the potential of the proposed range-only system in application scenarios where real-time tracking of mobile targets is needed.

One of the main advantages of clustering algorithms is the ability to allow networks to be physically divided into smaller regions, thereby increasing the theoretical maximum number of nodes connected in the network. However, the physical limitations in bandwidth and number of nodes are drawbacks to this approach. While reducing the number of clusters can reduce the network complexity, it will increase the intra-cluster management. This paper analyses the different medium access schemes that can be used and discusses what influence they bring to the overall performance in terms of bandwidth usage, energy consumption and latency. Two novel approaches are also proposed. The first one refers to the limitation in the transmission period and the request for each node within the cluster to transmit once within that period.The second innovation compromises two of the access mechanisms described: the first divides the period in two different parts, while the second takes different bias during the slot selection process, so it can have theoretical advantage over the re-transmission procedure. The results show that there is a significant advantages in listening to the channel prior to the transmission, as well as in re-trying to transmit only once.

In this paper we propose a tracking system for wireless sensor networks, which operates on accumulated ranging data from a number of anchor sensor nodes in order to infer the trace and other kinematic characteristics of a mobile target. The nonlinear nature of the “tracking with range-only measureme

Energy-aware sensor nodes are usually tightly energy-constrained, execute energy-efficient algorithms, have the ability to interrogate and control the devices used for storing and consuming energy, and often feature one or more sources of energy harvesting. Due to the cost, time and expertise required to deploy a wireless sensor network (WSN), simulation is currently the most widely adopted evaluation method. Network simulation is well established for mobile ad hoc networks, using simulators such as the popular ns2. However, the differing characteristics and performance criteria of WSNs introduce additional simulation requirements, and this has resulted in a number of simulators and simulator extensions developed specifically for this purpose. This paper investigates the suitability of a number of state-of-the-art simulators for evaluating energy-aware WSNs, and subsequently proposes a novel structure for simulating energy-aware WSNs. The proposed structure provides diverse, flexible and extensible hardware and environment models, and integrates a structured architecture for embedded software to enhance the design of energy-aware sensor nodes. To illustrate an implementation of the structure, details of - and observations obtained using - an in-house simulator (WSNsim) are presented.

Wireless sensors are normally powered by non- rechargeable batteries, but these must be replaced when depleted. Recent developments in energy harvesting technology allow sensors to be powered by environmental energy where it is present, but the wide range of situations where sensors are deployed means that it is desirable for the energy components of a sensor node (i.e. batteries, supercapacitors, and power generation devices) to be selected and configured at the time of node deployment. Previous energy harvesting-powered systems have been designed for specific energy hardware and been difficult to adapt for different resources. Energy-awareness is useful for state-of-the-art network algorithms, but present systems do not provide a standardized or straightforward way for nodes to monitor and manage their energy hardware. The developments reported in this paper deliver a reconfigurable energy subsystem for wireless autonomous sensors. The new system permits energy modules to be selected and fitted to the sensor node in-situ, in a plug-and-play manner, without the need for reprogramming or the modification of hardware. The node can monitor and intelligently manage its energy resources and assess its overall energy status by analyzing its level of stored energy and rate of power generation. These activities are facilitated by a proposed common hardware interface (which allows multiple energy modules to be connected) and an electronic datasheet structure for the energy modules. The system has been verified through the development and testing of a prototype wireless sensor node which operates from a mix of energy sources.

Owing to the limited requirement for sensor processing in early networked sensor nodes, embedded software was generally built around the communication stack. Modern sensor nodes have evolved to contain significant on-board functionality in addition to communications, including sensor processing, energy management, actuation and locationing. The embedded software for this functionality, however, is often implemented in the application layer of the communications stack, resulting in an unstructured, top-heavy and complex stack. In this paper, we propose an embedded system architecture to formally specify multiple interfaces on a sensor node. This architecture differs from existing solutions by providing a sensor node with multiple stacks (each stack implements a separate node function), all linked by a shared application layer. This establishes a structured platform for the formal design, specification and implementation of modern sensor and wireless sensor nodes. We describe a practical prototype of an intelligent sensing, energy-aware, sensor node that has been developed using this architecture, implementing stacks for communications, sensing and energy management. The structure and operation of the intelligent sensing and energy management stacks are described in detail. The proposed architecture promotes structured and modular design, allowing for efficient code reuse and being suitable for future generations of sensor nodes featuring interchangeable components.

The modeling of energy components in wireless sensor network (WSN) simulation is important for obtaining realistic lifetime predictions and ensuring the faithful operation of energy-aware algorithms. The use of supercapacitors as energy stores on WSN nodes is increasing, but their behavior differs from that of batteries. This paper proposes a model for a supercapacitor energy store based upon experimental results, and compares obtained simulation results to those using an 'ideal' energy store model. The proposed model also considers the variety and behavior of energy consumers, and finds that contrary to many existing models, the energy consumed depends on the store voltage (which varies considerably during supercapacitor discharge). Furthermore, energy models in a node's embedded firmware are shown to be paramount for providing energy-aware operation.

Wireless sensor networks offer unique opportunities for applications in the human and natural environments. In this paper, the types of application, which utilize satellites, are explored. Also, a proportional differentiation model is proposed in order to provide the quality of service over satellite networks. We implement and evaluate a new dropper, called satellite proportional dropper, to successfully apply this model on satellite links.