This paper investigates the feasibility of a novel primitive for concurrent ranging in ultra-wideband (UWB) radios. Conventional ranging protocols schedule the packet transmissions used to estimate distance to be separate in time. In contrast, our concurrent ranging primitive relies on the overlapping of these transmissions; when a ranging request is issued by an initiator node, all nodes in range immediately reply back. These concurrent signals are “fused” in the communication channel, whose channel impulse response (CIR) is made available by the DecaWave DW1000 transceiver we use in this paper. Combined with the fact that UWB transmissions rely on very short (
Due to the inherent nature of their heterogeneity, resource scarcity and dynamism, the provision of middleware for future networked embedded environments is a challenging task. In this paper we present a middleware approach that addresses these key challenges; we also discuss its application in a realistic networked embedded environment. Our application scenario involves fire management in a road tunnel that is instrumented with networked sensor and actuator devices. These devices are able to reconfigure their behaviour and their information dissemination strategies as they become damaged under emergency conditions, and firefighters are able to coordinate their operations and manage sensors and actuators through dynamic reprogramming. Our supporting middleware is based on a two-level architecture: the foundation is a language-independent, component-based programming model that is sufficiently minimal to run on any of the devices typically found in networked embedded environments. Above this is a layer of software components that offer the necessary middleware functionality. Rather than providing a monolithic middleware 'layer', we separate orthogonal areas of middleware functionality into self-contained components that can be selectively and individually deployed according to current resource constraints and application needs. Crucially, the set of such components can be updated at runtime to provide the basis of a highly dynamic and reconfigurable system
Recent trends in wireless sensor network (WSN) applications exhibit increasing degrees of decentralization. This is particularly true of scenarios where the data reported by sensors is used to control actuators affecting the environment. Implementing this control loop in a decentralized fashion is much more complex than in mainstream, single-sink, sense-only applications.In this paper we describe virtual nodes, a programming abstraction simplifying the development of decentralized WSN applications. The data acquired by a set of sensors can be collected, processed according to an application-provided aggregation function, and then perceived as the reading of a single virtual sensor. Dually, a virtual actuator provides a single entry point for distributing commands to a set of real actuator nodes. The set of physical nodes to be abstracted into a virtual one is specified using logical neighborhoods [11, 12]. Using virtual nodes, the programmer focuses on the application logic, rather than on low-level implementation details. We present the programming language constructs supporting virtual nodes, exemplify their use, and show that they can be implemented by making efficient use of communication resources.
In distributed computing, global information is rarely available and most actions are carried out locally. However, when proving system properties we frequently turn to defining abstractions of the global state, and when programming we often find it convenient to think of a distributed system as a global centralized resource. In this paper we build upon this observation and propose the notion of global virtual data structures as a model for building a new generation coordination models and middleware that allows programmers to think of local actions as having a global impact and places upon the underlying system the burden of preserving this appearance. The model itself is inherently peer-to-peer, lending itself toward applications which are largely decentralized, and built out of autonomous components.
Abstract Automated contact detection by means of proximity loggers permits the measurement of encounters between individuals (animal‐animal contacts) and the time spent by individuals in the proximity of a focal resource of interest (animal‐fixed logger contacts). The ecological inference derived from contact detection is intrinsically associated with the distance at which the contact occurred. But no proximity loggers currently exist that record this distance and therefore all distance estimations are associated with error. Here we applied a probabilistic approach to model the relationship between contact detection and inter‐logger distance, and quantify the associated error, on free‐ranging animals in semi‐controlled settings. The probability of recording a contact declined with the distance between loggers, and this decline was steeper for weaker radio transmission powers. Even when proximity loggers were adjacent, contact detection was not guaranteed, irrespective of the radio transmission power. Accordingly, the precision and sensitivity of the system varied as a function of inter‐logger distance, radio transmission power, and experimental setting (e.g., depending on animal body mass and fine‐scale movements). By accounting for these relationships, we were able to estimate the probability that a detected contact occurred at a certain distance, and the probability that contacts were missed (i.e., false negatives). These calibration exercises have the potential to improve the predictability of the study and enhance the applicability of proximity loggers to key wildlife management issues such as disease transmission rates or wildlife use of landscape features and resources.
Recent developments in wireless sensor networks (WSNs) are pushing scenarios where application intelligence is no longer relegated to the fringes of the system (i.e., on a data sink running on a powerful node) rather it is distributed within the WSN itself.To support this scenario, we propose TeenyLIME, a tuple space model and middleware supporting applications where sensing and acting devices themselves drive the network behavior. In other words, the application core is not confined to the powerful sinks, rather it is deployed on the devices embedded within the physical world. Tuple space operations are used both for data collection as well as to effect coordination among sensing and acting devices. This paper describes the TeenyLIME model and corresponding middleware implementation.
