Applications of wireless sensor networks for urban areas

We present the Low-Power Wireless Bus (LWB), a communication protocol that supports several traffic patterns and mobile nodes immersed in static infrastructures. LWB turns a multi-hop low-power wireless network into an infrastructure similar to a shared bus, where all nodes are potential receivers of all data. It achieves this by mapping all traffic demands on fast network floods, and by globally scheduling every flood. As a result, LWB inherently supports one-to-many, many-to-one, and many-to-many traffic. LWB also keeps no topology-dependent state, making it more resilient to link changes due to interference, node failures, and mobility than prior approaches. We compare the same LWB prototype on four testbeds with seven state-of-the-art protocols and show that: (i) LWB performs comparably or significantly better in many-to-one scenarios, and adapts efficiently to varying traffic loads; (ii) LWB outperforms our baselines in many-to-many scenarios, at times by orders of magnitude; (iii) external interference and node failures affect LWB's performance only marginally; (iv) LWB supports mobile nodes acting as sources, sinks, or both without performance loss.

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Low-power wireless bus

Advances in hardware technology have lead to an ability to collect data with the use of a variety of sensor technologies. In particular sensor notes have become cheaper and more efficient, and have even been integrated into day-to-day devices of use, such as mobile phones.This has lead to a much larger scale of applicability and mining of sensor data sets. The human-centric aspect of sensor data hascreated tremendous opportunities in integrating social aspects of sensor data collection into the mining process. Managing and Mining Sensor Data is a contributed volume by prominent leaders in this field, targeting advanced-level students in computer science as a secondary text book or reference. Practitioners and researchers working in this field will also find this book useful.

Managing and Mining Sensor Data

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 percent 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.

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Practical Data Prediction for Real-World Wireless Sensor Networks

We present pTunes, a framework for runtime adaptation of low-power MAC protocol parameters. The MAC operating parameters bear great influence on the system performance, yet their optimal choice is a function of the current network state. Based on application requirements expressed as network lifetime, end-to-end latency, and end-to-end reliability, pTunes automatically determines optimized parameter values to adapt to link, topology, and traffic dynamics. To this end, we introduce a flexible modeling approach, separating protocol-dependent from protocol-independent aspects, which facilitates using pTunes with different MAC protocols, and design an efficient system support that integrates smoothly with the application. To demonstrate its effectiveness, we apply pTunes to X-MAC and LPP. In a 44-node testbed, pTunes achieves up to three-fold lifetime gains over static MAC parameters optimized for peak traffic, the latter being current - and almost unavoidable - practice in real deployments. pTunes promptly reacts to changes in traffic load and link quality, reducing packet loss by 80% during periods of controlled wireless interference. Moreover, pTunes helps the routing protocol recover quickly from critical network changes, reducing packet loss by 70% in a scenario where multiple core routing nodes fail.

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pTunes: runtime parameter adaptation for low-power MAC protocols

Existing deployments of wireless sensor networks (WSNs) are often conceived as stand-alone monitoring tools. In this paper, we report instead on a deployment where the WSN is a key component of a closed-loop control system for adaptive lighting in operational road tunnels. WSN nodes along the tunnel walls report light readings to a control station, which closes the loop by setting the intensity of lamps to match a legislated curve. The ability to match dynamically the lighting levels to the actual environmental conditions improves the tunnel safety and reduces its power consumption. The use of WSNs in a closed-loop system, combined with the real-world, harsh setting of operational road tunnels, induces tighter requirements on the quality and timeliness of sensed data, as well as on the reliability and lifetime of the network. In this work, we test to what extent mainstream WSN technology meets these challenges, using a dedicated design that however relies on well-established techniques. The paper describes the hw/sw architecture we devised by focusing on the WSN component, and analyzes its performance through experiments in a real, operational tunnel.

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Sc Tefan Guna

Papers

Is there light at the ends of the tunnel? Wireless sensor networks for adaptive lighting in road tunnels