The Internet of Things (IoT) is an emerging paradigm where smart objects are seamlessly connected to the overall Internet and can potentially cooperate to achieve common objectives such as supporting innovative home automation services. With reference to such a scenario, this paper presents an Intrusion Detection System (IDS) framework for IoT empowered by IPv6 over low-power personal area network (6LoWPAN) devices. In fact, 6LoWPAN is an interesting protocol supporting the realization of IoT in a resource constrained environment. 6LoWPAN devices are vulnerable to attacks inherited from both the wireless sensor networks and the Internet protocols. The proposed IDS framework which includes a monitoring system and a detection engine has been integrated into the network framework developed within the EU FP7 project `ebbits'. A penetration testing (PenTest) system had been used to evaluate the performance of the implemented IDS framework. Preliminary tests revealed that the proposed framework represents a promising solution for ensuring better security in 6LoWPANs.

The Internet of Things (IoT) concept attracts considerable interest from the academia and industry. This paper provides a set of proof-of-concept prototype descriptions, based on such IoT exploitation, which aim at gathering real-time data along the manufacturing processes that enables a responsive production management and maintenance, including energy consumption and water usage monitoring at each stage of a production cycle. The presented work takes advantage of the ebbits platform, which provides a middleware infrastructure for integrating industrial sensors, devices and emerging wireless technology in the physical world, transforming them into web services that enable seamless integration into mainstream business system such as MES and ERP.

In this paper, we describe a SOA-based middleware to integrate Internet-of-Things technologies in industrial setups. The middleware allows a seamless horizontal integration among heterogeneous technologies and vertical integration with applications and business systems. Using the middleware, we evaluated an approach to improve the reliability of 6LoWPAN-based sensor networks with self-configuration and self-healing capabilities to support an innovative monitoring and control framework in a manufacturing line. The sensor networks were evaluated in a test bed consisting of various physical devices that emulates a welding station.

This paper presents a novel cognitive and cooperative tracking (CCT) approach based on extended Kalman filter (EKF) to localize mobile nodes in wireless networks. The proposed algorithm shows three important features: energy efficient, cognitive and cooperative. More specifically, the tracking algorithm adaptively adjusts the transmission power to optimize the energy consumption while meeting the required localization accuracy Paimposed by a generic application. Moreover, it adopts a self-learning scheme to track the time-variant environment's characteristics (e.g., range measurement noise) and use this knowledge to improve tracking performance. Finally, the algorithm exploits the cooperation among unknown nodes that leads to further improved performance and reduced power consumption. Simulation results show that the proposed CCT approach is able to improve positioning performance and meet the required accuracy Pawhile energy consumption is optimized.

This work proposes mechanisms enabling connectivity in multi-radio wireless sensor and actuator networks, namely: network discovery, interface/channel switching, and connection-loss recovery. The considered mechanisms are optimized with the aim to maximize the availability of system services (minimize network down-time) and achieve an increased overall robustness. The proposed approach offers dynamicity and adaptability to the constantly changing operating conditions of the different radio interfaces as well as to application-level requirements, and ensures self-organizing and autonomous operations.

The wide spread of smart embedded devices and the trend towards their inclusion in a unique Internet of Things are making urgent their full integration within the TCP/IP protocol stack. This paper considers ZigBee, a short-range radio communication standard for embedded devices, and compares two alternative protocol stacks, namely ZigBee Pro and ZigBee IP, developed for the integration of ZigBee in the so called Internet of Things. The performance evaluation is founded on experimental results achieved using two test-beds built to this purpose and is based on service reliability and latency as metrics.

Smart objects connected to the Internet, constituting the so called Internet of Things (IoT), are revolutionizing human beings' interaction with the world. As technology reaches everywhere, anyone can misuse it, and it is always essential to secure it. In this work we present a denial-of-service (DoS) detection architecture for 6LoWPAN, the standard protocol designed by IETF as an adaptation layer for low-power lossy networks enabling low-power devices to communicate with the Internet. The proposed architecture integrates an intrusion detection system (IDS) into the network framework developed within the EU FP7 project ebbits. The aim is to detect DoS attacks based on 6LoWPAN. In order to evaluate the performance of the proposed architecture, preliminary implementation was completed and tested against a real DoS attack using a penetration testing system. The paper concludes with the related results proving to be successful in detecting DoS attacks on 6LoWPAN. Further, extending the IDS could lead to detect more complex attacks on 6LoWPAN.

Before a wider adoption of the Internet of Things (IoT) vision occurs, many urgent technological and social challenging issues still need to be addressed, including device interoperability, systems autonomy, privacy and security concerns, which could have a significant impact on several aspects of everyday-life or potential end-user. Due to the very large number of technologies normally in place within the IoT paradigm, some type of middleware layer is employed to enforce seamless integration of devices and data within the same information network. Within such middleware, data must be exchanged respecting strict protection constraints. Both the networking and security issues have driven the design and the development of the VIRTUS Middleware, an IoT middleware relying on the open XMPP protocol to provide secure event- driven communications within an IoT scenario. Leveraging the standard security features provided by XMPP, the middleware offers a reliable and secure communication channel for distributed applications, protected with both authentication (through TLS protocol) and encryption (SASL protocol) mechanisms. The proposed architecture provides the possibility to isolate an instance of VIRTUS, allowing the exchange of data only within a private network. This paper presents an overview of VIRTUS, providing an overall platform description and details regarding its security features.

In Europe, in a context of growing population and decreasing resources, ageing related diseases represent one of the most relevant challenges in terms of healthcare organization complexity, plus care levels and system financing balancing. Nowadays there are several researches that are studying the application of the IoT (Internet of Things) paradigm in the e-health field. This paper explains how a solution, built on the top of the VIRTUS IoT middleware, provides a valid alternative to current IoT solutions, which are mainly based on SOA (Service Oriented Architecture). VIRTUS leverage an Instant Messaging protocol (XMPP) to guarantee a (near) real-time, secure and reliable communication channel among heterogeneous devices. The presented development has been exploited in a healthcare case study: an implementation of a cost-savvy remote body movement monitoring system, aimed at classify daily patients' activities, designed as a modular architecture and deployed in a large scale scenario. The paper analyzes the features offered by the VIRTUS middleware, if used within a e-health solution, providing a comparison with other well-known systems.

This work analyzes the impact of medium access control (MAC) layer on the performance of real-time cooperative positioning in ad-hoc mobile wireless networks. The probability mass function (pmf) of the latency introduced by the MAC layer is here derived to assess the influence of MAC parameters on the latency and then operate on those to improve the positioning performance while reducing the number of position estimation iterations.

This paper presents a novel ‘Energy Efficient Tracking’ (EET) algorithm that tries to meet the localization accuracy requirement, Pa, imposed by a generic location-based application while the energy consumption for ranging and communication is optimized. More specifically, given the set of range measurements performed by a mobile node with respect to its neighboring anchors (i.e., nodes whose exact positions are known a priori), the proposed EET algorithm selects, on the basis of the Cramér-Rao lower bound (CRLB), the closest set of anchors such that the resulting positioning error is lower than the required Pa. At the same time, the EET approach minimizes the average energy consumption by dynamically adapting the transmitted power, Pt, to be used in the next positioning estimation step. Based on time-of-arrival (TOA) estimation with ultra-wide bandwidth (UWB) nodes, the EET approach uses an Extended Kalman Filter (EKF) to track the mobile node's position. Simulation results show that the EET algorithm effectively reduces the energy consumption while the achieved positioning performance is almost identical to the one achieved by a classical EKF with fixed Pt, named ‘Energy Fixed Tracking’ (EFT) algorithm.

Wireless Sensor Network (WSN) technology is being increasingly adopted in a wide variety of applications ranging from home/building and industrial automation to more safety critical applications including e-health or infrastructure monitoring. Considering mobility in the above application scenarios actually introduces additional technological challenges, especially with respect to security. The resource constrained devices should be robust to diverse security attacks and communicate securely while they are moving in the considered environment. To this aim, proper authentication and key management schemes supporting node mobility should be used. This paper presents an effective mutual authentication and key establishment scheme for heterogeneous sensor networks consisting of numerous mobile sensor nodes and only a few more powerful fixed sensor nodes. Moreover, OMNET++ simulations are used to provide a comprehensive performance evaluation of the proposed scheme. The obtained results show that the proposed solution assures better network connectivity, consumes less memory, has low communication overhead during the authentication and key establishment phase and has better network resilience against mobile nodes attacks compared with existing approaches for authentication and key establishment.

We propose a novel hybrid GNSS-terrestrial localization algorithm based on particle filter that fuses ranging data from both satellites and terrestrial receivers. The proposed positioning approach, named hybrid-cooperative particle filter (HCPF), is fully distributed and allows both increased positioning availability and accuracy compared to GNSS-only localization in challenged scenarios. Moreover, simulation results based on a realistic indoor scenario show that the proposed solution outperforms several state of the art algorithms such as unscented Kalman filter and an approach based on belief propagation.

Social system networks with high data rates and limited storage will discard data if the system cannot connect and upload the data to a central server. We address the challenge of limited storage capacity in mobile health systems during network partitions with a heuristic that achieves efficiency in storage capacity by modifying the granularity of the medical data during long intercontact periods. Patterns in the connectivity, reception rate, distance, and location are extracted from the social system network and leveraged in the global algorithm and online heuristic. In the global algorithm, the stochastic nature of the data is modeled with maximum likelihood estimation based on the distribution of the reception rates. In the online heuristic, the correlation between system position and the reception rate is combined with patterns in human mobility to estimate the intracontact and intercontact time. The online heuristic performs well with a low data loss of 2.1%-6.1%.

This paper addresses the problem of spectrum sensing for cognitive radio, in the case of a primary signal characterized by a discontinuous channel occupation within the considered sensing window. Under such conditions, the performance of two different energy detectors is investigated. The first one (energy average detector) decides whether the channel is free or busy on the basis of the energy sample average; the second one (energy threshold detector) performs a soft estimation of the channel occupation probability by comparing the energy samples with a proper threshold. For both the detectors, an analytical model is derived and validated through simulation, and the optimal decision threshold is found as a function of the primary signal parameters.

This paper presents a wireless local area network (WLAN)-based real time system for indoors and outdoors vehicle localization. The proposed solution uses a neural network trained with a map of received power fingerprints from WLAN Access Points (APs) surrounding the vehicle. The practical implementation of the system is described and results from an outdoor experimental testbed are analyzed to address system performance in terms of calibration, estimation complexity, and location accuracy. Pre- and post-processing approaches aimed at improving system accuracy are also discussed.

This paper presents a traffic monitoring system implemented through Wireless Sensor Network (WSN) technology within SAFESPOT Project funded by the European Community. The aim of this project is to provide a flexible, robust, low-cost and low-maintenance wireless solution for obtaining traffic-related data that can be used for automatically generating safety warnings at black spots along the road network. The WSN consists of one Gateway Node (GN) and n Sensor Nodes (SNs) deployed along the roadside according to an approximately linear topology. Each SN is able to monitor a section of road, collecting parameters such as vehicle count, speed and direction. Data from the SNs is collected by the GN and delivered to a Road Side Unit (RSU) responsible for fusing it with traffic-related data generated by alternative sources. The system has been tested and performance evaluated under a number of real use-case scenarios.

As a consequence of the diffusion of wireless systems operating in the 2.4 GHz ISM band, harmful interference among heterogeneous networks is becoming a serious issue for their performance. This paper is focused on IEEE 802.15.4 WSNs undergoing the interference of co-located IEEE 802.11b/g WLANs or Bluetooth piconets. On the basis of energy measurements carried out using a standard-compliant testbed, the statistics of the interfering energy are characterized for an extensive set of traffic conditions. Then the impact of each interference pattern onto a practical WSN application is analyzed in terms of packet loss rate at the application layer. The last part of the paper introduces a channel selection algorithm based on the estimation of the information-theoretic capacity of the considered channels.

The increasing availability of ubiquitous, small, low-cost devices using wireless communications to build wireless sensor networks calls for autonomous solutions and algorithms capable of calculating the location where the information is gathered, processed, used. The requirements are particularly strict when sensor nodes are mobile; in fact, mobile applications demand more accurate locating and real time tracking, with limited impact on hardware complexity, network load and latency. Despite of remarkable research efforts put into this field by the scientific community, a common unique solution has not been adopted yet, due to the great variety of scenarios and application requirements. This paper focuses on the design and performance evaluation of Kalman filters for tracking a mobile target moving at low dynamics and for smoothing range estimations from noisy measurements. Some adaptive techniques to self-tune the filter and estimate the propagation model parameters are presented. One among the most promising adaptive algorithm presented is implemented in a real-life wireless sensor network test-bed including a mobile node. The resulting experimental results illustrate the benefits of this approach with respect to traditional multilateration based on least mean squares estimators.

Eigenvalue-based detection is an efficient signal detection technique, recently introduced in the Cognitive Radio context to guarantee a reliable identification of primary users. In this paper we contribute to the theoretical analysis of this detection scheme by deriving a mathematical expression for the probability of missed detection as a function of the number of cooperating receivers, the number of samples and the signal-to-noise ratio of the primary user. The analysis is referred to a detector using as test statistic the ratio between the largest and the smallest eigenvalue of the covariance matrix. Along with previous results on the probability of false alarm, this contribution completes the performance evaluation of this type of detector.

Moore at al. presented in [1] a cluster-based distributed algorithm for localizing wireless sensor network (WSN) nodes in presence of range measurement noise. They introduced the notion of "robust quadrilaterals" as a means of avoiding flip ambiguities that often corrupt the results of localization algorithm. This paper1proposes an enhancement of the algorithm presented in [1] that uses a new quadrilateral robustness test. A second novel distributed algorithm for anchor-less localization of WSN nodes in presence of range measurements noise is presented. This second algorithm reduces the total computational complexity with respect to the first two algorithms by a factor at least equal to the number of nodes in the WSN. It is made of two main steps: the first one, where a single cluster of nodes is localized, and a second one where the other unknown nodes are estimated by successive trilaterations. The original algorithm [1] and the two novel algorithms proposed in this paper are compared by means of simulations in terms of complexity, convergence and location accuracy performance.

This paper focuses on the location estimation of legacy GSM (Global System for Mobile communications) handsets. Three network-based software location methods using CI (cell identity), TA (timing advance), and RXLEV (received signal strength) information available in GSM are presented and their implementation is discussed. Experimental performance of the methods in a commercial GSM network is presented and a comparative analysis is carried out. It is shown that the location error of the most accurate method tends to scale according to the average cell size.