No abstract

Abstract-Applications involving the dissemination of information directly relevant to humans (e.g., service advertising, news spreading, environmental alerts) often rely on publish-subscribe, in which the network delivers a published message only to the nodes whose subscribed interests match it. In principle, publishsubscribe is particularly useful in mobile environments, since it minimizes the coupling among communication parties.However, to the best of our knowledge, none of the (few) works that tackled publish-subscribe in mobile environments has yet addressed intermittently-connected human networks. Socially-related people tend to be co-located quite regularly. This characteristic can be exploited to drive forwarding decisions in the interest-based routing layer supporting the publish-subscribe network, yielding not only improved performance but also the ability to overcome high rates of mobility and long-lasting disconnections.In this paper we propose SocialCast, a routing framework for publish-subscribe that exploits predictions based on metrics of social interaction (e.g., patterns of movements among communities) to identify the best information carriers. We highlight the principles underlying our protocol, illustrate its operation, and evaluate its performance using a mobility model based on a social network validated with real human mobility traces. The evaluation shows that prediction of colocation and node mobility allow for maintaining a very high and steady event delivery with low overhead and latency, despite the variation in density, number of replicas per message or speed.

Abstract

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.

Large scale distributed systems are becoming of paramount importance, due to the evolution of technology and to the interest of market. Their development, however, is not yet supported by a sound teclmological and methodological background, as the results developed for small size distributed systems often do not scale up. Recently, mobile code languages (MCLs) have been proposed as a technological answer to the problem. In this work, -we abstract away from the details of these languages by deriving design paradigms exploiting code mobility that are independent of any particular technology. We present such design paradigms, together with a discussion of their features, their application domain, and some hints about the selection of the correct paradigm for a given distributed application.

Many research and industrial communities are betting on LoRa to provide reliable, long-range communication for the Internet of Things. This new radio technology, however, provides widely heterogeneous coverage; a LoRa link may span hundreds of meters or tens of kilometers, depending on the surrounding environment. This high variability is not captured by popular channel models for LoRa, and on-site measurements-a common alternative-are impractical due to the large geographical areas involved. We propose a novel, automated approach to estimate the coverage of LoRa gateways prior to deployment and without on-site measurements. We achieve this goal by combining free, readilyavailable multispectral images from remote sensing with the right channel model. Our processing toolchain automatically classifies the type of environment (e.g., buildings, trees, or open fields) traversed by a link, with high accuracy (>90%) and spatial resolution (10×10m 2). We use this information to explain the attenuation observed in experiments. As signal attenuation is not well captured by popular channel models, we focus on the Okumura-Hata empirical model, hitherto largely unexplored for LoRa, and show that i) it yields estimates very close to our observations, and ii) we can use our toolchain to automatically select and configure its parameters. A validation on 8,000+ samples from a real dataset shows that our automated approach predicts the expected signal power within a ∼10dBm error, against the 20-40dBm of popular channel models.

Abstract-Data prediction is proposed in wireless sensor networks (WSNs) to extend the system lifetime by enabling the sink to determine the data sampled, within some accuracy bounds, with only minimal communication from source nodes. Several theoretical studies clearly demonstrate the tremendous potential of this approach, able to suppress the vast majority of data reports at the source nodes. Nevertheless, the techniques employed are relatively complex, and their feasibility on resource-scarce WSN devices is often not ascertained. More generally, the literature lacks reports from real-world deployments, quantifying the overall system-wide lifetime improvements determined by the interplay of data prediction with the underlying network. These two aspects, feasibility and system-wide gains, are key in determining the practical usefulness of data prediction in real-world WSN applications. In this paper, we describe Derivative-Based Prediction (DBP), a novel data prediction technique much simpler than those found in the literature. Evaluation with real data sets from diverse WSN deployments shows that DBP often performs better than the competition, with data suppression rates up to 99% and good prediction accuracy. However, experiments with a real WSN in a road tunnel show that, when the network stack is taken into consideration, DBP only triples lifetime-a remarkable result per se, but a far cry from the data suppression rates above. To fully achieve the energy savings enabled by data prediction, the data and network layers must be jointly optimized. In our testbed experiments, a simple tuning of the MAC and routing stack, taking into account the operation of DBP, yields a remarkable seven-fold lifetime improvement w.r.t. the mainstream periodic reporting.

Abstract. Wireless sensor networks (WSNs) typically exploit a single base station for collecting data and coordinating activities. However, decentralized architectures are rapidly emerging, as witnessed by wireless sensor and actuator networks (WSANs), and in general by solutions involving multiple data sinks, heterogeneous nodes, and in-network coordination. These settings demand new programming abstractions to tame complexity without sacrificing efficiency. In this work we introduce the notion of logical neighborhood, which replaces the physical neighborhood provided by wireless broadcast with a higher-level, applicationdefined notion of proximity. The span of a logical neighborhood is specified declaratively based on the characteristics of nodes, along with requirements about communication costs. This paper presents the SPIDEY programming language for defining logical neighborhoods, and a routing strategy that efficiently supports the communication enabled by its programming constructs.