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Wireless Sensor Networks (WSN) are part of the technical fundament enabling the Internet of Things (IoT), where sensing and actuator nodes instantaneously interact with the environment at large. As such they become part of everyday life and drive applications as diverse as medical monitoring, smart homes, smart environment, and smart factories, to name but a few. To acquire data, individual sensors interact with the physical environment by sensing physical phenomena in proximity. The wireless network connectivity is leveraged to collect the raw data or pre-processed events, and to disseminate code, queries or commands. Actuating capabilities facilitate instant interactions with the environment or application processes. Experience on how to operate large scale heterogeneous WSNs in (critical) real-world applications is still scarce, and operational considerations are often an afterthought to WSN deployment. A principled look into the metrics, i.e., a standard or best practice of measurement of the vital parameters in WSNs is still missing. In this article, we contribute a survey on the most important metrics to characterize the performance of WSNs. We define an abstract system model for WSNs, take a look on what the WSN community considers metrics that matter, and categorize the metrics into scopes of relevance. We discuss the properties of the metrics as well as practical aspects on how to obtain and process them. Our survey can serve as a manual for implementors and operators of WSNs in the IoT.

An AWSN composed of bird-sized Unmanned Aerial Vehicles (UAVs) equipped with sensors and wireless radio, enables low cost high granularity three-dimensional sensing of the physical world. The sensed data is relayed in real-time over a multi-hop wireless communication network to ground stations. In this paper, we investigate the use of a hybrid antenna to accomplish efficient neighbor discovery and reliable communication in AWSNs. We propose the design of a hybrid Omni Bidirectional ESPAR (O-BESPAR) antenna, which combines the complimentary features of an isotropic omni radio (360°źcoverage) and directional ESPAR antennas (beamforming and reduced interference). Control and data messages are transmitted separately over the omni and directional modules of the antenna, respectively. Moreover, a communication protocol is presented to perform neighbor UAVs discovery and beam steering. We present results from an extensive set of simulations. We consider three different real-world AWSN application scenarios and employ empirical aerial link characterization to demonstrate that the proposed antenna design and protocol reduces the packet loss rate and end to end delay by up to 54% and 49% respectively, and increases the goodput by up to 33%, as compared to a single omni or ESPAR antenna.

A noise map facilitates the monitoring of environmental noise pollution in urban areas. It can raise citizen awareness of noise pollution levels, and aid in the development of mitigation strategies to cope with the adverse effects. However, state-of-the-art techniques for rendering noise maps in urban areas are expensive and rarely updated (for months or even years), as they rely on population and traffic models rather than on real data. Smart phone based urban sensing can be leveraged to create an open and inexpensive platform for rendering up-to-date noise maps. In this paper, we present the design, implementation and performance evaluation of an end-to-end, context-aware, noise mapping system called Ear-Phone. Ear-Phone investigates the use of different interpolation and regularization methods to address the fundamental problem of recovering the noise map from incomplete and random samples obtained by crowdsourcing data collection. Ear-Phone, implemented on Nokia N95, N97 and HP iPAQ, HTC One mobile devices, also addresses the challenge of collecting accurate noise pollution readings at a mobile device. A major challenge of using smart phones as sensors is that even at the same location, the sensor reading may vary depending on the phone orientation and user context (for example, whether the user is carrying the phone in a bag or holding it in her hand). To address this problem, Ear-Phone leverages context-aware sensing. We develop classifiers to accurately determine the phone sensing context. Upon context discovery, Ear-Phone automatically decides whether to sense or not. Ear-Phone also implements in-situ calibration which performs simple calibration that can be carried out by general public. Extensive simulations and outdoor experiments demonstrate that Ear-Phone is a feasible platform to assess noise pollution, incurring reasonable system resource consumption at mobile devices and providing high reconstruction accuracy of the noise map.

In this paper, we present a novel non-intrusive second-factor user authentication mechanism for smart spaces called Cardiopulmonary-ID (alluded as CP-ID), that does not require any special hardware to conform the second factor of authentication. CP-ID uses the unique perturbations in the ambient WiFi signals caused by the cardiopulmonary activity of an individual (referred to as CP-imprint) to confirm the inherence (i.e., something that the user is) as a second-factor of authentication. We show that the CP-imprint of Alice is uniquely manifested in the Channel State Information (CSI) of ubiquitous WiFi signals. We also demonstrate the distinctiveness between the CP-imprints of Alice and Bob. CP-ID proposes a comprehensive pipeline that assists in obtaining a noise-free smooth CP-imprint that is manifested in the CSI of commercial WiFi devices. For feature engineering, CP-ID employs PCA and then formulates a comprehensive set of features extracted from the selected principal components. A multiclass SVM is proposed to identify a target individual from a set of N people. We implement CP-ID using off-the-shelf 802.11n devices and evaluate its performance. Our tests reveal that CP-ID can identify an individual with an average accuracy of 84% to 65% from a group of size 2 to 5 individuals, respectively.

Reputation systems rate the contributions to participatory sensing campaigns from each user by associating a reputation score. The reputation scores are used to weed out incorrect sensor readings. However, an adversary can deanonmyize the users even when they use pseudonyms by linking the reputation scores associated with multiple contributions. Since the contributed readings are usually annotated with spatiotemporal information, this poses a serious breach of privacy for the users. In this paper, we address this privacy threat by proposing a framework called IncogniSense. Our system utilizes periodic pseudonyms generated using blind signature and relies on reputation transfer between these pseudonyms. The reputation transfer process has an inherent trade-off between anonymity protection and loss in reputation. We investigate by means of extensive simulations several reputation cloaking schemes that address this tradeoff in different ways. Our system is robust against reputation corruption and a prototype implementation demonstrates that the associated overheads are minimal.