Internet of Things has a profound effect on everyday life and critical vertical services including healthcare, factories of the future and intelligent transport systems. The highly distributed nature of such networks and the heterogeneity of the devices, which constitute them, necessitates that their users should be able to trust them at all times. A method to determine the device's service trustworthiness is Trust Management (TM), which assigns scores to devices according to their trustworthiness level, based on evaluations from other entities that interacted with it. Often Internet of Things devices that just joined the network, have not interacted with any other entity of this network before, hence there is no way to determine its trustworthiness. Such an event is referred to as the cold start trust score or initial trust score problem. The majority of the trust management approaches address this problem by setting an arbitrary initial trust score, while others will ignore it. Assigning arbitrary trust scores for devices connected to the network for the first time has the potential to disrupt the operation of the entire system, when a high trust score is assigned to a non-trusted malicious device, or lead to unfair policies, when trusted devices are assumed as potential intruders, which also deteriorates the performance of the system. This paper proposes a mechanism, which combines the blockchain based BARRETT remote attestation protocol with a set of device's properties and communication and operational context parameters, in order to determine accurately and assign the initial trust score to each device. Through a set of extensive simulations over different experimental setups, the proposed scheme is achieving to safely distribute initial trust scores to one thousand devices over less than 6ms, while minimising the risk of computational denial of service attacks due to the inherent characteristics of the BARRETT remote attestation protocol.

Detection of anomalous trajectories is an important problem with potential applications to various domains, such as video surveillance, risk assessment, vessel monitoring and high-energy physics. Modeling the distribution of trajectories with statistical approaches has been a challenging task due to the fact that such time series are usually non stationary and highly dimensional. However, modern machine learning techniques provide robust approaches for data-driven modeling and critical information extraction. In this paper, we propose a Sequence to Sequence architecture for real-time detection of anomalies in human trajectories, in the context of risk-based security. Our detection scheme is tested on a synthetic dataset of diverse and realistic trajectories generated by the ISL iCrowd simulator. The experimental results indicate that our scheme accurately detects motion patterns that deviate from normal behaviors and is promising for future real-world applications.

A new range-free solution based on simple RSSI measurements is presented herein. The proposed method includes a two-stage algorithm and is able to incorporate fingerprints of specific locations, when available, in order to enhance localization accuracy. The proposed algorithm requires a strategic placement of nodes in each room while being easily scalable to a large number of rooms and adaptable to rooms with complex geometry. Simulation results are derived in order to evaluate the proposed algorithm and compared to relative research results in the literature.

Location information is a cornerstone for the development of the so-called location-based services. A method for the enhancement of a localization system in terms of achieved accuracy is proposed herein, which can be applied to new as well as existing systems regardless of the underlying localization technique. The method is based on modelling the position measurement error introduced by the localization algorithm using a polynomial approximation approach. Measurements results demonstrate the applicability of the proposed technique in enhancing the achieved accuracy in a mixed indoor and outdoor environment.

Localization of elderly people is considered to be a key task in ambient assisted living platforms. The problem of timely and valid emergency medical services notifications may be addressed in the context of a location-aware, event-triggered system provided an accurate, robust and reliable underlying localization system exists. Furthermore, first responders' reaction quality can improve considerably in the case where location-related information is available. In this paper, a system capable of providing localization data to an overlying ambient assisted living and event detection platform is presented, together with initial measurements results. Furthermore, the proposed localization technique offers enhanced accuracy and reliability with respect to a typical Ultra Wideband (UWB) system.

Switched-beam arrays (SBAs) are considered to be attractive alternatives to fully adaptive arrays (mainly due to deployment and maintenance cost) and can be used in order to increase system capacity and spectral efficiency. In this paper, dual-band operation and array failure correction of switched beam dipole arrays are considered. Dual-band operation is needed in a growing number of telecommunications systems (e.g. GSM900/1800, or WiFi/WiMAX), while array failure needs to be corrected remotely in cases of base station or satellite antennas. A genetic-algorithm based technique is proposed, and the design of a dual-band antenna array, with the ability to remain fully functional after one element failure, is presented. To the best of our knowledge, a joint design technique for dual-band operation and element failure correction is not present in open literature. The suitability of the proposed array is demonstrated via numerical results.

In this paper we present the results of the Taxi on Demand (ToD) system Test Trial which was conducted on 25-29 June, 2007. The ToD system, featuring a location-based and traffic-sensitive methodology, incorporates automated taxi booking, dispatching and monitoring functionalities. The ToD solution effectively addresses current taxi sector needs, and may be expanded to emerging car-pooling/sharing models, allowing integration between modes of transport in urban or densely populated environments.

Present paper focuses on context awareness in service provisioning and proposes a framework that enables efficient management of contextual profiles. Furthermore, a more flexible and innovative model for user profiling is introduced. The innovation is based on the enrichment of common user profiling architectures to include location and other contextual attributes, so that enhanced adaptability and personalization can be achieved. For each context instance an associated user profile instance is created and hence, service provisioning is adapted to the user profile instance that better apply to the current context. The generic model, the structure and the content of that context-sensitive user profile, along with some related implementation issues, are discussed

Modern surveillance systems often utilize multiple physically distributed sensors of different types to provide complementary and overlapping coverage on targets. In order to generate target tracks and estimates, the sensor data need to be fused. While a centralized processing approach is theoretically optimal, there are significant advantages in distributing the fusion operations over multiple processing nodes. This paper discusses architectures for distributed fusion, whereby each node processes the data from its own set of sensors and communicates with other nodes to improve on the estimates, The information graph is introduced as a way of modeling information flow in distributed fusion systems and for developing algorithms. Fusion for target tracking involves two main operations: estimation and association. Distributed estimation algorithms based on the information graph are presented for arbitrary fusion architectures and related to linear and nonlinear distributed estimation results. The distributed data association problem is discussed in terms of track-to-track association likelihoods. Distributed versions of two popular tracking approaches (joint probabilistic data association and multiple hypothesis tracking) are then presented, and examples of applications are given.

An effective method for visual pattern recognition using morphological techniques is presented. It is shown that it can be successfully used for the recognition of deformed letters. The method extracts morphological information by the successive dilation of an idealized letter set. At each stage a properly defined similarity index is computed. The maximum values of the similarity index for each stored letter are compared and are used for the classification decision. Classification results are presented for a set of deformed capital English letters with a realistic level of deformation. Skeletonization of the deformed pattern is shown to improve the performance of the classification method. The described method can be easily implemented using a parallel architecture.>

A common framework for model reference adaptive identification and control (MRAI and MRAC) is established. The key idea for this common framework is to maintain the same set of equations for describing the parameterizations of the plant and the model and to solve the control equation properly for each case (identification or control) for the corresponding tuned system, i.e. the model (identification) or the plant (control). Within this framework, two methods, the MOEM (modified output error method) and the IEM (input error method), for the MRAI and MRAC problems are studied, and global asymptotic stability properties are established.>

A modified output error method (MOEM) for model reference adaptive control (MRAC) and identification (MRAI) is introduced. The regressors are properly chosen so that the open-loop system can be compactly expressed as a stable system with an input linear with respect to the unknown plant parameters. The output error satisfies a constructible stable filtered equation. The method does not require any strictly positive real or arbitrary stable filterings, and the uncertainty on the magnitude of the high-frequency gain of the plant does not result in an overparameterization of the identifier. Sufficient conditions on the reference input under which the parameter and output errors converge exponentially to zero are also given.>

A simple, decentralized control for reducing the delays and stabilizing random-access channels is presented. The control, which is based on a computationally efficient recursive implementation of the minimum mean-squared error (MMSE) predictor of the channel backlog, applies to slotted ALOHA, to reservation ALOHA, and to local area networks (LANs) with carrier-sense multiple access (CSMA) or CSMA with collision detection (CSMA/CD) protocols. The MMSE predictor controller (MMSE-PC) can stabilize the slotted ALOHA for all traffic rates not exceeding e/sup -1/, and it can achieve stable throughput arbitrarily close to one with finite delays in the reservation ALOHA and in LANs. Extensive simulation has shown that the MMSE-PC performs extremely well in all three random-access environments. For the implementation of the MMSE-PC in LANs, synchronization of transmissions is not required but it is required in slotted ALOHA and reservation ALOHA. The MMSE-PC has been implemented in hardware and tested in asynchronous LANs.>

The minimum error entropy (MEE) estimation problem on discrete, finite, or countably infinite ensembles of arbitrary statistical nature is considered. A combinatorial approach is taken to solve the minimization problem. The solution is obtained in algorithmic form. The numerical complexity of the MEE algorithm is analyzed. An entropic prediction filtering (EPF) coding scheme that utilizes the MEE algorithm to minimize the per symbol error entropy is introduced. Simulation has shown that the EPF can effectively be used to reduce the average codeword length required to encode data. Other applications of the MEE algorithm in signal interpolation and extrapolation are also discussed.