Estimating traffic demand, or origin-destination (OD) matrices, is crucial for transport studies and smart city development. The main objective is to calculate an OD matrix based on available sources (e.g. link traffic counts obtained from traffic sensors) to accurately reproduce field data. A significant complication when using information obtained from traffic sensors, is that such sensors are subject to considerable disruptions impacting data quality and reliability. Despite the extensive study of efficient OD estimation, there is a considerable gap in detecting faulty measurements and identifying faulty sensors within the estimation procedure. This work presents a novel methodology for OD matrix estimation in the presence of faulty measurements. The path-based cell transmission model (CTM) is employed to capture traffic network dynamics within a specified time window, linking link densities with per-path densities and path demand. For the purposes of this work, traffic networks that operate under free-flow conditions are considered and the problem is formulated in an optimisation framework with two distinct variations: 1) no explicit formulation of potential faulty sensors and 2) explicit modelling of potential faulty sensors. Following, a fault-adaptive algorithm is constructed that identifies, isolates and corrects faults to achieve robust demand estimation. The methodology is tested on two realistic literature networks and shows great potential in terms of OD matrix estimation in the presence of faulty measurements. Simulation results underpin the advantage of the proposed approach in terms of performance in estimating quantities of interest as well as identifying the faulty sensors and their fault characteristics.

This work proposes a jointly optimized trajectory generation and camera control approach, enabling an autonomous agent, such as an unmanned aerial vehicle (UAV) operating in 3D environments, to plan and execute coverage trajectories that maximally cover the surface area of a 3D object of interest. Specifically, the UAV's kinematic and camera control inputs are jointly optimized over a rolling planning horizon to achieve complete 3D coverage of the object. The proposed controller incorporates ray-tracing into the planning process to simulate the propagation of light rays, thereby determining the visible parts of the object through the UAV's camera. This integration enables the generation of precise look-ahead coverage trajectories. The coverage planning problem is formulated as a rolling finite-horizon optimal control problem and solved using mixed-integer programming techniques. Extensive real-world and synthetic experiments validate the performance of the proposed approach.

Supervised online learning relies on the assumption that ground truth information is available for model updates at each time step.As this is not realistic in every setting, alternatives such as active online learning, or online learning with verification latency have been proposed.In this work, we assume that no label information is available after intitial training.We argue that provided we can characterize the expected concept drift as incremental drift, we can rely on a self-labeling strategy to keep updated models.We derive a k-NN-based self-labeling online learner implementing the presented self-supervised scheme and experimentally show that this is an option for learning from incrementally drifting data streams in the absence of label information.

In the contemporary digital landscape, the continuous generation of extensive streaming data across diverse domains has become pervasive. Yet, a significant portion of this data remains unlabeled, posing a challenge in identifying infrequent events such as anomalies. This challenge is further amplified in non-stationary environments, where the performance of models can degrade over time due to concept drift. To address these challenges, this paper introduces a new method referred to as VAE4AS (Variational Autoencoder for Anomalous Sequences). VAE4AS integrates incremental learning with dual drift detection mechanisms, employing both a statistical test and a distance-based test. The anomaly detection is facilitated by a Variational Autoencoder. To demonstrate the effectiveness of VAE4AS, a comprehensive experimental study is conducted using real-world and synthetic datasets characterized by anomalous rates below 10% and recurrent drift. The results show that the proposed method surpasses both robust baselines and state-of-the-art techniques, providing compelling evidence for their efficacy in effectively addressing some of the challenges associated with anomalous sequence detection in non-stationary streaming data.

Data stream mining aims at extracting meaningful knowledge from continually evolving data streams, addressing the challenges posed by nonstationary environments, particularly, concept drift which refers to a change in the underlying data distribution over time. Graph structures offer a powerful modelling tool to represent complex systems, such as, critical infrastructure systems and social networks. Learning from graph streams becomes a necessity to understand the dynamics of graph structures and to facilitate informed decision-making. This work introduces a novel method for graph stream classification which operates under the general setting where a data generating process produces graphs with varying nodes and edges over time. The method uses incremental learning for continual model adaptation, selecting representative graphs (prototypes) for each class, and creating graph embeddings. Additionally, it incorporates a loss-based concept drift detection mechanism to recalculate graph prototypes when drift is detected.

In the rapidly changing environments of disaster response, planning and decision-making for autonomous agents involve complex and interdependent choices. Although recent advancements have improved traditional artificial intelligence (AI) approaches, they often struggle in such settings, particularly when applied to agents operating outside their well-defined training parameters. To address these challenges, we propose an attention-based cognitive architecture inspired by Dual Process Theory (DPT). This framework integrates, in an online fashion, rapid yet heuristic (human-like) responses (System 1) with the slow but optimized planning capabilities of machine intelligence (System 2). We illustrate how a supervisory controller can dynamically determine in real-time the engagement of either system to optimize mission objectives by assessing their performance across a number of distinct attributes. Evaluated for trajectory planning in dynamic environments, our framework demonstrates that this synergistic integration effectively manages complex tasks by optimizing multiple mission objectives.

The origin-destination (OD) matrix is a crucial requirement for transportation management and planning. Efficient OD matrix estimation is important to enhance the advancement of intelligent transportation systems. We present a novel approach for the estimation of static OD matrices using within-day traffic flow dynamics. The signalised cell transmission model (CTM) is utilised to capture the dynamics of a specific network and associate road segment count observations with path demands. This model is extended to capture per-path densities, yielding a path-based OD matrix problem formulation that results in a nonlinear optimisation problem. Efficient solution methodologies, based on convex and nonconvex optimisation theory, are developed for free-flow and congested conditions, respectively. In contrast with the majority of research for the OD matrix estimation problem, this work offers the following advantages: 1) no prior or target OD matrices are needed to implement the approach outlasting the bias and dependency on such matrices, 2) no historical data are required for accurate estimations, 3) no route choice model or split ratios are needed, 4) no user equilibrium conditions are required for high-quality estimation, and 5) even low partial coverage of the network is sufficient to provide high-quality OD matrix estimation. We illustrate the efficiency of the proposed approach on three literature real-life arterial networks and show that the proposed approach yields accurate results under both free-flow and congested scenarios.

In this demonstration paper, we present FixCyprus, which is a cost-effective crowdsourcing service for road transportation authorities in Cyprus to gather information and manage defects and incidents on the road network and surrounding infrastructure (e.g., pavements, lighting, drinking water pipes, etc.). The production service, which includes a lightweight and user-friendly mobile application for sharing image-annotated incident reports, is already operating nationwide for six months providing significant budget savings by reducing field inspections and the use of expensive equipment. We will demonstrate FixCyprus using two modes: i) Interactive mode, where attendees will be able to create and submit their own dummy incident reports and see the end-to-end processing flow in a test environment and ii) Trace-driven mode, where attendees will be able to visualize a large number of synthetic reports, see the workload for manually managing them, and explore enhancements that are underway for automating some of the underlying tasks using machine learning.

Crowdsourcing is considered an efficient and promising paradigm for constructing large-scale signal fingerprint radio maps due to the proliferation of Wi-Fi-enabled devices. However, a crowdsourced indoor positioning system (IPS) has to handle diverse devices and the inherent heterogeneity in received signal strength (RSS) measurements. To address the device heterogeneity problem, differential fingerprinting methods have been explored, which mitigate the device characteristics that cause RSS from different commercial devices to report differently. In this article, we focus on mean differential fingerprinting (MDF) that produces the differential fingerprints by subtracting the mean RSS value of all access points from the original RSS fingerprints. We study the localization performance of the MDF method by means of the Cramér–Rao lower bound (CRLB) and show analytically that it outperforms another method that addresses device diversity. Furthermore, we evaluate the localization accuracy of existing solutions using real-life Wi-Fi RSS data sets collected by multiple consumer devices. The experimental results confirm our analytical findings and demonstrate the effectiveness of the MDF method to mitigate device diversity, as well as other factors that affect the RSS readings, including the device carrying mode and power control schemes of the Wi-Fi infrastructure, thus contributing to the wider adoption of crowdsourced IPS.

Traffic state estimation is a challenging task due to the collection of sparse and noisy measurements from specific points of the traffic network. The emergence of Connected and Automated Vehicles (CAVs) provides new capabilities for traffic state estimation using extended floating car data such as position, speed and spacing information. In this work we propose a Bayesian Traffic State Estimation (BTSE) methodology for estimating the traffic density based on extended floating car data. BTSE utilizes the Bayesian paradigm to express any prior information to derive probability distributions of the traffic density of different road segments of the traffic network. Two variations of the BTSE methodology are developed to handle the offline and online estimation problem. The BTSE methodology is evaluated both using realistic SUMO micro-simulations for M25 Highway, London, U.K., and a real-life vehicle-trajectory dataset from German highways, extracted from videos recorded by drones. The efficiency and accuracy of the BTSE methodology is compared to an existing methodology in the literature. We present results for the estimation performance of the methods showing that the Bayesian methodology consistently results in lower mean absolute percentage error than the compared literature method. The BTSE methodology yields high-quality estimation results even for a low penetration rate of CAVs (e.g. 5%).

In this work, a novel distributed search-planning framework is proposed, where a dynamically varying team of autonomous agents cooperate in order to search multiple objects of interest in three-dimension (3-D). It is assumed that the agents can enter and exit the mission space at any point in time, and as a result the number of agents that actively participate in the mission varies over time. The proposed distributed search-planning framework takes into account the agent dynamical and sensing model, and the dynamically varying number of agents, and utilizes model predictive control (MPC) to generate cooperative search trajectories over a finite rolling planning horizon. This enables the agents to adapt their decisions on-line while considering the plans of their peers, maximizing their search planning performance, and reducing the duplication of work.

Distribution system operators (DSOs) are currently deploying telecommunications infrastructures so as to enable the efficient operation of smart grids. A basic part of a DSO's telecommunications network covers the interconnection of electrical substations with the central management system. This work describes a geographic information system (GIS)-based tool for the design of a backbone optical network, deployed specifically to support the DSO's telecommunication requirements. The QGIS open-source software was chosen as a highly suitable platform for the development of all necessary geospatial tools, along with the use of efficient heuristic algorithms, for selecting cost-efficient routes that allow all electrical substations to be reachable from the central management system. This topological design of the DSO's telecommunications network is essential for the deployment of intelligent power distribution networks with increased functionalities currently being implemented by the DSOs.

In this work, we propose a joint route guidance and demand management strategy for multi-region networks with macroscopic traffic dynamics. Route guidance is used to identify the optimal transfer flows between neighbouring regions so that the trip completion rate across all regions is maximized. Demand management is utilized to control the traffic flows entering the network by forcing a portion of the traffic flows to wait at their origin. Towards this direction, we develop a Model Predictive Control (MPC) framework that aims to minimize the total time spent by all vehicles in the network (including the waiting time at the origin) by jointly optimizing the demand flows allowed in the network and the transfer flows between regions. To solve the resulting nonconvex and nonlinear optimization problem, by relaxing the nonconvex constraints, we develop a novel Linear Programming formulation that provides tight lower bounds on the optimal solution, as well as a feasible solution through the proposed MPC framework. Furthermore, (in another formulation) we restrict each region to only operate in the free-flow regime of the macroscopic fundamental diagram, which enables the transformation of the problem to a linear MPC formulation which can be solved in real-time using standard solvers and which provides a feasible solution to the original MPC problem. Extensive simulation results demonstrate that the linear MPC schemes execute in real-time and yield near-optimal results even under heavy traffic scenarios.

Contact Tracing (CT) is an effective mitigation measure against the Covid-19 pandemic that is typically performed manually. Its technological counterpart, i.e., Digital Contact Tracing (DCT), has emerged as potential, yet controversial due to privacy concerns, complementary tool to identify exposed individuals automatically. Among DCT solutions, privacy-preserving DCT mobile apps that rely on a Bluetooth-based protocol recently developed jointly by Google and Apple are becoming increasingly popular and have already been deployed in 67 territories around the world. Recent studies indicate that such apps are promising in controlling the spread of infections, yet their adoption is still low. This work presents CovTracer-EN, the national privacy-preserving DCT app in Cyprus that triggers Exposure Notifications (EN) to users that had contact with another infected user who shared this information anonymously with his/her consent. Performance evaluation results and lessons learned aim to offer useful insights and practical guidelines to public health authorities that consider deploying DCT apps for reducing the impact of Covid-19 and future pandemics.

Connected and Autonomous Vehicles (CAVs) are expected to become a reality on roads in the near future bringing significant social, economic, and environmental benefits. Cooperation and coordination among CAVs will be enabled through Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) wireless communications. Each vehicle knows its current location and in many cases will have to communicate this information to its associated Roadside Unit (RSU). With the proliferation of CAVs, the RSU is expected to receive and process a large amount of feedback information from its assigned CAVs. Thus, an event-triggered communication scheme is proposed instead of conventional approaches where communication takes place in a periodic manner. Further, the effect of age of information on the resulting accuracy of the vehicle tracking error is taken into account by considering the message queue wait time at the RSU. A family of optimization problems is proposed, used to determine the optimal accuracy threshold for the event-triggered algorithm, so as to minimize the effect of the tracking error caused by the queue wait time.

There is currently a rapid evolution of technologies enabling the “Internet-of-Things” paradigm, which leads to the design of systems with augmented sensing, analysis, and actuation capabilities. The intelligent control research community is beginning to recognize the challenge of designing flexible and adaptable control systems that will take advantage of the online evolution of the number and types of “things” in a large-scale system. Responding to this challenge, in a previous article, we designed and presented a semantically enhanced control system supervisor that integrates a knowledge graph and deductive inference capabilities, to achieve the online (re)configuration of feedback control schemes. In this article, we show that by exploiting the capabilities of this supervisor, we facilitate the automatic synthesis and plugging of model-based controllers for the space heating problem in multizone buildings. The supervisor retrieves stored knowledge about the building and the available control system components and uses it to configure online a scheme of distributed feedback controllers, one per building zone. The controllers are designed to utilize information online and optimally allocate the control signal to multiple heating devices in each building zone.

An enormous and ever-growing volume of data is nowadays becoming available in a sequential fashion in various real-world applications. Learning in nonstationary environments constitutes a major challenge, and this problem becomes orders of magnitude more complex in the presence of class imbalance. We provide new insights into learning from nonstationary and imbalanced data in online learning, a largely unexplored area. We propose the novel Adaptive REBAlancing (AREBA) algorithm that selectively includes in the training set a subset of the majority and minority examples that appeared so far, while at its heart lies an adaptive mechanism to continually maintain the class balance between the selected examples. We compare AREBA with strong baselines and other state-of-the-art algorithms and perform extensive experimental work in scenarios with various class imbalance rates and different concept drift types on both synthetic and real-world data. AREBA significantly outperforms the rest with respect to both learning speed and learning quality. Our code is made publicly available to the scientific community.

Severa1 logistic companies started utilizing electric vehicles (EVs) in their daily operations to reduce greenhouse gas pollution. However, the limited driving range of EVs may require visits to recharging stations during their operation. These potential visits have to be addressed, avoiding unnecessary long detours. We formulate the electric capacitated vehicle routing problem (E-CVRP), which incorporates the possibility of EVs visiting a recharging station while satisfying the delivery demands of customers. The energy consumption of the EVs is proportional to their cargo load which is an important constraint in real-world logistics applications. A new set of benchmark instances is proposed for the E-CVRP. As solution methods to these new benchmarks, we apply the ant colony optimization metaheuristic method and an exact method. Experimental results on the ECVRP demonstrate the high complexity of the problem and the efficiency of the applied metaheuristic solution method.

An ever increasing volume of data is nowadays becoming available in a streaming manner in many application areas, such as, in critical infrastructure systems, finance and banking, security and crime and web analytics. To meet this new demand, predictive models need to be built online where learning occurs on-the-fly. Online learning poses important challenges that affect the deployment of online classification systems to real-life problems. In this paper we investigate learning from limited labelled, nonstationary and imbalanced data in online classification. We propose a learning method that synergistically combines siamese neural networks and active learning. The proposed method uses a multi-sliding window approach to store data, and maintains separate and balanced queues for each class. Our study shows that the proposed method is robust to data nonstationarity and imbalance, and significantly outperforms baselines and state-of-the-art algorithms in terms of both learning speed and performance. Importantly, it is effective even when only 1% of the labels of the arriving instances are available.

This work investigates the problem of simultaneous tracking and jamming of a rogue drone in 3D space with a team of cooperative unmanned aerial vehicles (UAVs). We propose a decentralized estimation, decision and control framework in which a team of UAVs cooperate in order to a) optimally choose their mobility control actions that result in accurate target tracking and b) select the desired transmit power levels which cause uninterrupted radio jamming and thus ultimately disrupt the operation of the rogue drone. The proposed decision and control framework allows the UAVs to reconfigure themselves in 3D space such that the cooperative simultaneous tracking and jamming (CSTJ) objective is achieved; while at the same time ensures that the unwanted inter-UAV jamming interference caused during CSTJ is kept below a specified critical threshold. Finally, we formulate this problem under challenging conditions i.e., uncertain dynamics, noisy measurements and false alarms. Extensive simulation experiments illustrate the performance of the proposed approach.

Efficient traffic monitoring is playing a fundamental role in successfully tackling congestion in transportation networks. Congestion is strongly correlated with two measurable characteristics, the demand and the network density that impact the overall system behavior. At large, this system behavior is characterized through the fundamental diagram of a road segment, a region or the network. In this paper we devise an innovative way to obtain the fundamental diagram through aerial footage obtained from drone platforms. The derived methodology consists of 3 phases: vehicle detection, vehicle tracking and traffic state estimation. We elaborate on the algorithms developed for each of the 3 phases and demonstrate the applicability of the results in a real-world setting.

In this paper, we investigate the problem of joint searching and tracking of multiple mobile targets by a group of mobile agents. The targets appear and disappear at random times inside a surveillance region and their positions are random and unknown. The agents have limited sensing range and receive noisy measurements from the targets. A decision and control problem arises, where the mode of operation (i.e., search or track) as well as the mobility control action for each agent, at each time instance, must be determined so that the collective goal of searching and tracking is achieved. We build our approach upon the theory of random finite sets (RFS) and we use Bayesian multi-object stochastic filtering to simultaneously estimate the time-varying number of targets and their states from a sequence of noisy measurements. We formulate the above problem as a non-linear binary program (NLBP) and show that it can be approximated by a genetic algorithm. Finally, to study the effectiveness and performance of the proposed approach we have conducted extensive simulation experiments.

Secure logging is essential for the integrity and accountability of cyber-physical systems (CPS). To prevent modification of log files the integrity of data must be ensured. In this work, we propose a solution for secure event in cyber-physical systems logging based on the blockchain technology, by encapsulating event data in blocks. The proposed solution considers the real-time application constraints that are inherent in CPS monitoring and control functions by optimizing the heterogeneous resources governing blockchain computations. In doing so, the proposed blockchain mechanism manages to deliver events in hard-to-tamper ledger blocks that can be accessed and utilized by the various functions and components of the system. Performance analysis of the proposed solution is conducted through extensive simulation, demonstrating the effectiveness of the proposed approach in delivering blocks of events on time using the minimum computational resources.

Indoor location systems that rely on WiFi signal strength values from the existing building infrastructure deliver adequate accuracy with no installation cost at the expense of time and effort to populate the signal database. To this end, crowdsourcing has been widely explored to leverage on large volumes of user-collected data; however, mobile devices that report signal strength differently, known as device diversity, limit its applicability in practice. We consider crowdsourced location systems and present a qualitative (in terms of analytical results) and a quantitative (with respect to real-life experimental data) evaluation of several approaches that are robust to diverse devices while focusing on differential signal strength methods.

In today's “smart era” there is a growing ecosystem of Internet of Things (IoT)-enabled devices, which exploit (wireless) Internet connectivity and use standard communication protocols to interact with each other and the environment. As various IoT components are becoming widely available in the marketplace, a key challenge from a feedback control viewpoint is the ability to seamlessly integrate new IoT components or modify existing configurations in feedback control settings without having to halt the operation of the system and redesign the overall feedback control scheme. This paper exploits technologies from the semantic Web domain, for the design of a novel semantically enhanced IoT-enabled intelligent control system (SEMIoTICS) architecture. The proposed SEMIoTICS scheme incorporates a supervisor module able to facilitate the semantic modeling of IoT components and the subsequent online composition/reconfiguration of feedback control loops. We demonstrate the applicability of the SEMIoTICS architecture through illustrative scenarios from the smart buildings domain.

In this paper, we investigate a problem associated with transferring a set of containers from the storage to the loading area of a warehouse using autonomous robots. In addition to assigning robots to containers, the special topology considered in this paper requires coordinated planning of the robots' movement to avoid conflicts. We formulate the joint problem of robot assignment and movement coordination with the objective of minimizing the time required for all robots to carry their assigned containers to the destination, subject to conflict-free movement of all robots. We use the concept of abstract time windows (ATWs) to represent the movement of robots. The conditions for detecting conflicts in the ATW representation are introduced along with the necessary operations for resolving conflicts. For the solution of the problem, two approaches are developed. The first is a mathematical programming approach that formulates the problem as a mixed-integer linear program that allows optimal solution using appropriate solvers, while the second is a heuristic approach that allows fast, close-to-optimal solutions. Even though the proposed approaches focus on the case where the number of robots is equal to the number of containers, we also discuss how to solve the problem when having unequal number of robots and containers. Simulation results show that the heuristic approach provides a solution within 5% of the optimal solution with the minimum time of completion of all tasks being the performance metric, and executes six orders of magnitude faster than a state-of-the-art mathematical programming solver.

Networks of smart cameras, equipped with on-board processing and communication infrastructure, are increasingly being deployed in a variety of different application fields, such as security and surveillance, traffic monitoring, industrial monitoring, and critical infrastructure protection. The task(s) that a network of smart cameras executes in these applications, e.g., activity monitoring and object identification, can be severely degraded due to errors in the detection module. However, in most cases, higher level tasks and decision making processes in smart camera networks (SCNs) assume ideal detection capabilities for the cameras, which is often not the case due to the probabilistic nature of the detection process, especially for low-cost cameras with limited capabilities. Realizing that it is necessary to introduce robustness in the decision process, this paper presents results toward uncertainty-aware SCNs. Specifically, we introduce a flexible uncertainty model that can be used to characterize the detection behavior in a camera network. We also show how to utilize the model to formulate detection-aware optimization algorithms that can be used to reconfigure the network in order to improve the overall detection efficiency and thus increase the effective number of detected targets. We evaluate our proposed model and algorithms using a network of Raspberry-Pi-based smart cameras that reconfigure in order to improve the detection performance based on the position of targets in the area. The experimental results in the laboratory as well as in a human monitoring application and extensive simulation results indicate that the proposed solutions are able to improve the robustness and reliability of SCNs.

Recent progress toward the realization of the "Internet of Things" has improved the ability of physical and soft/cyber entities to operate effectively within large-scale, heterogeneous systems. It is important that such capacity be accompanied by feedback control capabilities sufficient to ensure that the overall systems behave according to their specifications and meet their functional objectives. To achieve this, such systems require new architectures that facilitate the online deployment, composition, interoperability, and scalability of control system components. Most current control systems lack scalability and interoperability because their design is based on a fixed configuration of specific components, with knowledge of their individual characteristics only implicitly passed through the design. This paper addresses the need for flexibility when replacing components or installing new components, which might occur when an existing component is upgraded or when a new application requires a new component, without the need to readjust or redesign the overall system. A semantically enhanced feedback control architecture is introduced for a class of systems, aimed at accommodating new components into a closed-loop control framework by exploiting the semantic inference capabilities of an ontology-based knowledge model. This architecture supports continuous operation of the control system, a crucial property for large-scale systems for which interruptions have negative impact on key performance metrics that may include human comfort and welfare or economy costs. A case-study example from the smart buildings domain is used to illustrate the proposed architecture and semantic inference mechanisms.

Intelligent buildings are responsible for ensuring the indoor air quality for their occupants under normal operation as well as under possibly harmful contaminant events due to accidental or malicious actions. An emerging environmental control application is monitoring the intelligent buildings against the presence of such events, by incorporating various sensing technologies and distributed detection and isolation algorithms. The needed simplicity, the improved scalability and fault tolerance are some of the main reasons for choosing distributed approaches over centralized ones. Hence, the effective partitioning of buildings into smaller sections for contaminant detection and isolation approaches is of great importance. In this paper, we present an exact Mixed Integer Linear Programming (MILP) formulation for partitioning the building into smaller sections. The building is transformed into a graph which is partitioned into subgraphs indicating the groups of zones in each section while ensuring (i) maximum decoupling between the various subgraphs, (ii) strong connectivity between the zones of a subgraph and (iii) control of the number of allocated zones in each subgraph. The main contribution of this work is the automatic partitioning of the building into sections, which enables the distributed simulation, modeling, analysis and management of the intelligent building in real time, while ensuring the effective detection and isolation of contaminants in the building interior.

This paper presents a novel architecture for the design of fault-detection schemes, aiming to automate the cognitive process performed by human experts when designing fault detection schemes for certain systems. The work starts with the identification of types of cyber-physical components participating in a fault-detection scheme. These are semantically characterized, adopting a model driven by previous efforts of the World Wide Web Consortium on the semantic composition of Web services. The semantic characterizations of the components are then exploited by a Cognitive Agent with semantic reasoning capabilities, to achieve the configuration of a fault-detection scheme, given a set of specifications and available components. The Cognitive Agent has access to a knowledge representation model and is able to interact with human operators and with the components to enrich its knowledge for making and enforcing decisions about the configuration. The applicability of the architecture and the reasoning steps are demonstrated through the configuration of a water contamination event-detection scheme with learning capabilities within a smart water distribution network.

Event-triggering (ET) is an up-and-coming technological paradigm for monitoring, optimization, and control in the Internet of Things (IoT) that achieves improved levels of operational efficiency. This paper first defines the envisioned ET architecture for the IoT domain. It then classifies and reviews the various different ET approaches obtained from the available literature for the three phases of ET, namely behavior modeling, event detection, and event handling. Thereafter, a novel data-driven technique is developed to address all three phases of ET in an efficient and reliable manner. Finally, the applicability of the proposed data-driven technique is showcased in a real-world public transport scenario, demonstrating a substantial improvement in energy and spectrum efficiency compared to existing periodic techniques.

Camera Sensor Networks (CSN) are becoming increasingly popular in a variety of security and safety-critical applications including public space surveillance, monitoring of attack-sensitive facilities, and critical infrastructure protection. Cameras in such networks are equipped with high-resolution visual sensors and on-board processors, while featuring wireless communication capabilities. These features enable the execution of various tasks, such as area coverage, activity recognition and target tracking, in a cooperative fashion. However, the performance of CSN may be compromised when faults occur, either due to unintentional software and hardware faults or as the result of a malicious attack. Paving the way for fault tolerance in CSN-based target tracking, we introduce a flexible fault model that can be used to generate different types of erroneous behaviour, thus simulating realistic faults in CSN. We also propose a fault-tolerant decentralized solution for tracking a target that passes through the area monitored by the CSN. Our simulation results indicate that the proposed solution is able to track the target reliably despite the presence of faults.

Vehicular connectivity is considered as one of the most highly anticipated emerging technologies since it promises to transform the automotive sector and have a significant impact on all related markets. Data gathered (and information generated) within and around vehicles will be used to improve road safety, travelling efficiency, and passenger comfort and convenience. However, delivering such data to the infrastructure (to process information and generate intelligence) is a challenging task, mainly due to the very large volume of data traffic produced. A promising approach to support these communication needs is to deliver data traffic opportunistically through the available WiFi APs. Evidently, the intermittent connectivity of these hotspots and the inherent mobility of the vehicles severely limit the volume of traffic sent at any one instance in time. The latter limitation is studied in this paper, where decision policies are derived for vehicle-to-vehicle-assisted offloading to maximize the transmission opportunities and thus expedite data traffic delivery. As illustrated in this paper, these policies are easy to implement in practice and offer significant improvement in vehicular data traffic offloading as compared with opportunistic offloading and basic relaying practices.

Traffic signal control is a key ingredient in intelligent transportation systems to increase the capacity of existing urban transportation infrastructure. However, to achieve optimal system-wide operation, it is essential to coordinate traffic signals at various intersections. In this paper, we model the multiple-intersection traffic signal control problem using the cell transmission model as a mixed-integer linear program. The solution of the problem is facilitated by its special structure, which allows both temporal and spatial decomposition. Temporal decomposition is employed to reduce the problem size by solving subproblems of a smaller time window compared to the original problem. Temporal subproblems can be further spatially decomposed into subproblems associated with different intersections, which are jointly solved by exchanging messages between neighboring intersections. The proposed distributed solution strategy is comprised of two phases. First, the relaxed linear problem is reformulated and distributedly solved via the alternating direction method of multipliers. Second, two distributed rounding schemes are developed to solve the original problem. Simulation results indicate that the proposed solution strategy is scalable to large transportation topologies, which is suitable for online execution, and provides close-to-optimal results.

In the realm of cooperative Intelligent Transportation Systems, vehicles are able to communicate with each other and with the available telecommunications infrastructure to support various safety-related services, traffic-efficiency solutions, and a wide variety of infotainment applications. As the number of mobile devices and information they need to exchange steadily increases, this will further strain mobile networks. It is for that reason that communication over wireless local area networks (WLANs) is becoming the primary means of transporting data to/from vehicles. However, due to their limit range WLANs only offer intermittent connectivity, and due to node mobility the established connections are only available for a short period of time. The present paper investigates how inter-vehicle communication can help minimize the upload/download delivery times in the context of delay tolerant traffic and intermittent communication. To do so, each vehicular station distributes part of its message to neighboring nodes prior to accessing the infrastructure. In turn, vehicles transmit their buffered messages when passing by the infrastructure terminals to complete the file transfer. This paper presents a thorough study of the aforementioned strategy providing a mathematical framework and optimized forwarding techniques. Performance results validate the applicability of the proposed solution.

In this work we present the problem of contamination event detection in drinking water distribution networks using real-time learning approaches and a model-based event detection scheme. By using chlorine concentration measurements a contamination event detection algorithm is developed with the aim of detecting the occurrence of contaminant injection into a water tank by monitoring the change of the chlorine concentration. The proposed methodology is comprised of two steps: a) learn in real-time the unknown chlorine reaction dynamics using a Radial-Basis Function network; b) activate a contamination-event detection methodology which uses an adaptive detection threshold. A contamination event detection alarm is activated when the magnitude of the estimation error exceeds the detection threshold. To demonstrate the proposed methodology realistic case study is evaluated with Monte-Carlo simulations of contamination events of different magnitudes occurrence times and environmental characteristics. The results demonstrate the improvement in the contamination event detection performance when using the real-time learning approach.

This paper presents the design of a methodology for diagnosing sensor faults in heating, ventilation and air-conditioning (HVAC) systems, and compensating their effects on the distributed control architecture. The proposed methodology is developed in a distributed framework, considering a multi-zone HVAC system as a set of interconnected, nonlinear subsystems. For each of the interconnected subsystems, we design a local virtual sensor agent that can detect and isolate faults in its monitored sensors and provide sensor fault estimations for correcting the faulty measurements. Adaptive estimation schemes are implemented in each local virtual sensor agent, using adaptive approximation models for learning the unknown fault function. Simulation results are used for illustrating the effectiveness of the proposed methodology applied to a two-zone HVAC system.

The provision of reliable location estimates in WiFi fingerprinting localization is challenging, mainly because users typically carry heterogeneous devices that report Received Signal Strength (RSS) measurements from surrounding Access Points (AP) very differently. This may render the user-carried device incompatible with the fingerprinting system, in case the RSS radiomap was collected with a different device, thus incurring high localization errors. To this end, we introduce a novel differential fingerprinting method that computes the difference between the RSS value of each AP and the mean RSS value across all APs in the original fingerprint. We show that the new fingerprints are robust to device heterogeneity, as opposed to traditional RSS fingerprints. In addition, we derive analytical results and demonstrate with simulations and experimental data that the proposed approach performs considerably better than existing differential fingerprinting solutions, in terms of localization accuracy and computational overhead.

Distance-based network localization is a very effective way to obtain the position of nodes in a network via range measuring; this, however, requires nodes with nontrivial sensing capabilities. Range-free techniques, conversely, use much simpler nodes but often provide just a rough estimate on the node's position. In this paper we develop a hybrid localization algorithm that combines range-free and distance-based sensors. A detailed simulation campaign aimed at corroborating the findings and at showing the potentialities of the approach concludes the paper.

This paper investigates the use of a Wireless Sensor Network for localizing and tracking multiple event sources (targets) using only binary data. Due to the simple nature of the sensor nodes, sensing can be tampered (accidentally or maliciously), resulting in a significant number of sensor nodes reporting erroneous observations. Therefore, it is essential that any event tracking algorithm used in Wireless Sensor Networks (WSNs) exhibits fault tolerant behavior in order to tolerate misbehaving nodes. The main contribution of this paper is the development and analysis of a low-complexity, distributed, real-time algorithm that uses the binary observations of the sensors for identifying, localizing, and tracking multiple targets in a fault tolerant way. Specifically, our results indicate that the proposed algorithm retains its performance in tracking accuracy in the presence of noise and faults, even when a large percentage of sensor nodes (25 percent) report erroneous observations.

This paper presents an adaptive approximation-based design methodology and analytical results for distributed detection and isolation of multiple sensor faults in a class of nonlinear uncertain systems. During the initial stage of the nonlinear system operation, adaptive approximation is used for online learning of the modeling uncertainty. Then, local sensor fault detection and isolation (SFDI) modules are designed using a dedicated nonlinear observer scheme. The multiple sensor fault isolation process is enhanced by deriving a combinatorial decision logic that integrates information from local SFDI modules. The performance of the proposed diagnostic scheme is analyzed in terms of conditions for ensuring fault detectability and isolability. A simulation example of a single-link robotic arm is used to illustrate the application of the adaptive approximation-based SFDI methodology and its effectiveness in detecting and isolating multiple sensor faults.

The provision of accurate and reliable localization and tracking information for a target moving inside a binary Wireless Sensor Network (WSN) is quite challenging, especially when sensor failures due to hardware and/or software malfunctions or adversary attacks are considered. Most tracking algorithms assume fault-free scenarios and exploit all binary sensor observations, thus their accuracy may degrade when faults are present in the field. Spatiotemporal information available while the target is traversing the sensor field can be used not only for tracking the target, but also for detecting certain types of faults that appear highly correlated both in time and space. Our main contribution is ftTRACK, a target tracking architecture that is resilient to sensor faults and consists of three main components, namely the sensor health-state estimator, a fault-tolerant localization algorithm, and a location smoothing component. The key idea in the ftTRACK architecture lies in the sensor health-state estimator that leverages spatiotemporal information from previous estimation steps to intelligently choose which sensors to employ in the localization and tracking tasks. Simulation results indicate that ftTRACK maintains a high level of tracking accuracy, even when a large number of sensors fail.

Tracking of a source (target) which is moving inside a binary Wireless Sensor Network (WSN) is a challenging problem particularly when sensors may fail either due to hardware and/or software malfunctions, energy depletion or adversary attacks. Using information from failed sensors to locate and track a target may lead to high estimation errors, therefore, there is a need to develop fault tolerant localization algorithms which perform well even when a percentage of the sensors report erroneous observations. Alternatively, one may fuse information from neighboring sensors in order to determine the health state of each sensor, and subsequently use only healthy sensors in the localization and tracking process. Our contribution is the development of an architecture which combines the sensor health state estimation together with fault tolerant localization algorithms that leads to more robust target tracking in binary WSNs. Simulation results indicate that the proposed approach is resilient to various types of faults.

We consider the problem of target (event source) tracking using a binary Wireless Sensor Network (WSN). For this problem, a WSN consisting of sensors that can detect the presence of a target in an area around them, should fuse the information received by the individual sensors in order to localize and track the target. This is a challenging problem particularly when sensors may fail either due to hardware and/or software malfunctions, energy depletion or adversary attacks. Using information from failed sensors during target tracking may lead to high estimation errors. Since failure of individual sensors is unavoidable, there is a need to estimate the health state of each sensor in order to ignore those sensors that are considered as faulty. The contribution of this work is the investigation of three different algorithms for estimating the sensors' health state simultaneously with target tracking.

Crowdsourcing is an emerging field that allows to tackle difficult problems by soliciting contributions from common people, rather than trained professionals. In the post-pc era, where smartphones dominate the personal computing market offering both constant mobility and large amounts of spatiotemporal sensory data, crowdsourcing is becoming increasingly popular. In this context, crowdsourcing stands as the only viable solution for collecting the large amount of location-related network data required to support location-based services, e.g., the signal strength radiomap of a fingerprinting localization system inside a multi-floor building. However, this benefit does not come for free, because crowdsourcing also poses new challenges in radiomap creation. We focus on the problem of device diversity that occurs frequently as the contributors usually carry heterogeneous mobile devices that report network measurements very differently. We demonstrate with simulations and experimental results that the traditional signal strength values from the surrounding network infrastructure are not suitable for crowdsourcing the radiomap. Moreover, we present an alternative approach, based on signal strength differences, that is far more robust to device variations and maintains the localization accuracy regardless of the number of contributing devices.

In this demo, we present an efficient hybrid indoor positioning solution that uses multi-sensory location-oriented observations, including WiFi, accelerometer, gyroscope and digital compass data, that are widely available on Android smartphones.

This paper presents a design methodology and some analytical results for distributed sensor fault detection and isolation (SFDI) of a class of nonlinear uncertain systems. During the initial stage of the nonlinear system operation, an adaptive approximation technique is used for learning online the modeling uncertainty. Then, local SFDI modules are designed using a dedicated nonlinear observer scheme. The fault isolation process is enhanced by deriving a combinatorial decision logic that integrates information from local SFDI modules. The performance of the proposed diagnostic scheme is analyzed in terms of its robustness with respect to the modeling uncertainties and conditions for ensuring fault detectability and isolability.

Traditionally, positioning with WLAN Received Signal Strength (RSS) fingerprints involves the laborious task of collecting a radiomap with a reference mobile device. Best accuracy can be guaranteed only in case the user carries the same device, while positioning with different devices requires a calibration step to make the new devices' RSS values compatible with the existing radiomap. We propose a novel device self-calibration method that uses histograms of RSS values. First, we use the existing radiomap to create the RSS histogram of the reference device. Subsequently, when the user enters a building and starts positioning, the observed RSS values are recorded simultaneously in the background to create and update the histogram of the user device. We use these RSS histograms to fit a linear mapping between the user and reference device. Our calibration method is running concurrently with positioning and does not require any user intervention. Experimental results with five smartphones in a real indoor environment indicate that soon after positioning is initiated the device is self-calibrated, thus improving the accuracy to be comparable with the case of using a radiomap created with the same device.

This work proposes the use of 3D Ray Tracing (RT) to construct radiomaps for WLAN Received Signal Strength (RSS) fingerprint-based positioning, in conjunction with calibration techniques to tackle with the problem using different devices. In addition to the fact that RSS data collection might be a tedious and time-consuming process, the measured radiomap accuracy and applicability is subject to potential changes in the wireless environment. Therefore, RT becomes a very suitable and efficient solution to tackle this problem. Moreover, in traditional fingerprint-based methods, the underlying radiomap is restricted to the mobile device for which the radiomap has been created. To overcome this limitation, we propose the use of linear data transformation to match the characteristics of various devices. We address both challenges by using 3D RT-generated radiomaps and highlight the efficiency of this approach in terms of the time spent to create the radiomap, the amount of data required to calibrate the radiomap for different devices and the positioning error which is compared against the case of using dedicated radiomaps collected with each device.

In this demonstration paper, we present an indoor positioning system developed for Android smartphones, coined Airplace. To infer the unknown user location we rely on ubiquitous WLANs and exploit Received Signal Strength (RSS) values from neighboring Access Points (AP) that are constantly monitored by the mobile devices under normal operation. Our system follows a mobile-based network-assisted architecture to eliminate the communication overhead and respect user privacy. In a typical scenario, when a user walks inside a building a smartphone client conducts a single communication with our Distribution Server to receive the RSS radiomap and is then able to position itself independently using the observed RSS values. Moreover, we have implemented an Android application to facilitate the collection of RSS values by users that may contribute their data to our system for constructing and updating the radiomap through crowdsourcing1. We will demonstrate the real-time positioning capabilities of the system during the conference by allowing attendees to carry an Android tablet in order to view their position on a floorplan map, while walking around inside the demo area (interactive scenario). Moreover, we will illustrate how to evaluate the performance of different positioning algorithms using profiled data in a trace-driven scenario. Our objective is to highlight the effectiveness and applicability of our system and at the same time the participants will be able to appreciate the potential of indoor location-oriented services and applications.

In this demo paper, we present the Airplace indoor positioning platform developed for Android smartphones [1]. Airplace relies on existing WLAN infrastructure and exploits Received Signal Strength (RSS) values from neighboring Access Points (AP) to infer the unknown user location. Our system utilizes a number of RSS fingerprints collected a priori to build the so-called radiomap. Location is then estimated by finding the best match between the currently measured fingerprint and fingerprints in the radiomap [2].

This paper investigates the use of a sensor network for localizing and tracking a moving target using only binary data. Due to the simple nature of the sensor nodes, sensing can be tampered (accidentally or maliciously), resulting in a significant number of sensor nodes reporting erroneous observations. Therefore, it is essential that any event tracking algorithm used in Wireless Sensor Networks (WSNs) exhibits fault tolerant behavior in order to tolerate a number of misbehaving nodes. SNAP (Subtract on Negative Add on Positive), is a simple event localization algorithm designed for WSNs applications that exhibits this fault tolerant behavior. The main contribution of this paper is to combine the decentralized implementation of SNAP with Kalman Filter techniques for tracking the movement of a target. This efficient tracking procedure provides fairly accurate results and turns out to be fault tolerant even when a large percentage of the sensor nodes report erroneous observations.

The increasing demand for indoor location-based services has motivated the development of positioning methods that exploit the existing wireless network infrastructure. Accuracy is an important requirement, however fault tolerance is also highly desirable in case of failures or malicious attacks. We investigate the fault tolerance of fingerprint-based methods under a variety of fault or attack scenarios. We study the Subtract on Negative Add on Positive (SNAP) algorithm and modify it appropriately for the WLAN setup. Our results indicate that SNAP achieves adequate accuracy with very low computational complexity and exhibits smoother performance degradation in the presence of faults compared to other methods.

The provision of reliable location estimates in case of unpredicted failures or malicious attacks, which inject faults and compromise the performance of the positioning system, is very important. Thus, our main interest is on the fault tolerance of WLAN fingerprint-based methods, rather than the absolute positioning error in the fault-free case. We study the Nearest Neighbor method and as a first step we develop a robust detection scheme to accurately detect faults. We incorporate this into a hybrid positioning method that switches to a modified distance metric, instead of the Euclidean, if faults are present. Experimental results indicate that the proposed approach exhibits higher resilience to faults compared to other positioning methods.

Considering a tandem network of queues (each representing the buffer in a router) our objective is to allow each individual queue to dynamically control its own parameters (in this paper the buffer size) using only information available locally and from neighboring nodes. For each node we adopt control approaches that are based on Infinitesimal Perturbation Analysis (IPA) estimates of certain performance measures. In this family of approaches we investigate collaboration schemes that can lead us to global optimal (or near optimal) solutions. The contribution of the paper is the design of a simple protocol that allows neighboring nodes to collaboratively exchange information in order to converge to a global optimal solution.

Accurate and reliable location estimates using wireless networks are important for enabling indoor location oriented services and applications, such as in-building guidance and asset tracking. Providing adequate level of accuracy in case of faults or attacks to the positioning system is equally significant, thus our main interest is on the fault tolerance of positioning methods, rather than the absolute accuracy in the fault-free case. We introduce several fault models to capture the effect of failures in the wireless infrastructure or malicious attacks and discuss how these models can simulate the corruption of signal strength values during positioning. The models are used to investigate the fault tolerance of positioning methods and evaluate them in terms of their performance degradation as the percentage of corrupted signal strength measurements increases. Experimental results using our fault models are also presented.

Fingerprinting localization techniques provide reliable location estimates and enable the development of location aware applications especially for indoor environments, where satellite based positioning is infeasible. In our approach we utilize received signal strength (RSS) fingerprints collected in known locations and employ a radial basis function (RBF) neural network to approximate the function that maps fingerprints to location coordinates. We present a clustering scheme to reduce the size and computational complexity of the RBF architecture and demonstrate the applicability of this approach in a real-world WLAN setup. Experimental results indicate that the RBF based method is an efficient approach to the location determination problem that outperforms existing techniques in terms of the positioning error.

One of the main applications of Wireless Sensor Networks (WSNs) is area monitoring (e.g., environmental monitoring). In such problems, it is desirable to maximize the area coverage which can be achieved by appropriately positioning the sensors (if possible) and/or by increasing the detection range of the sensors. This paper considers the latter. The emphasis is on pairs of closely spaced sensors that can collaborate in order to increase their collective area coverage. The main contribution of this work is to investigate collaborative detection schemes between a pair of sensor nodes and show that the area coverage achieved by each scheme depends on the distance between the two sensors. For closely spaced sensors, we propose the Enhanced Covariance Detector (ECD) that combines the energy and the covariance information from the two nodes by utilizing two different thresholds (one for the energy test statistic and another for the covariance).

Wireless sensor networks (WSNs) consist of small nodes with sensing, computation, and wireless communications capabilities. Even in predominantly static sensor networks, it is possible to have a few mobile nodes. Mobility of nodes in WSNs adds a significant challenge. In this article we present a survey of state-of-the-art routing techniques in wireless ad hoc and sensor networks and highlight the advantages/disadvantages and performance issues of each routing technique. The aim is to identify routing protocols that will be able to support the mobility of sensor nodes in WSNs consisting of both static and mobile (mixed WSN) nodes. The article concludes by presenting an approach for such a routing protocol.

This paper investigates Wireless Sensor Networks (WSNs) for achieving fault tolerant localization of an event using only binary information from the sensor nodes. In this context, faults occur due to various reasons and are manifested when a node outputs a wrong decision. The main contribution of this paper is to propose the Fault Tolerant Maximum Likelihood (FTML) estimator. FTML is compared against the Centroid (CE) and the classical maximum likelihood (ML) estimators and is shown to be significantly more fault tolerant. Moreover, this paper compares FTML against the SNAP (Subtract on Negative Add on Positive) algorithm and shows that in the presence of faults the two can achieve similar performance; FTML is slightly more accurate while SNAP is computationally less demanding and requires fewer parameters.

This paper investigates the use of wireless sensor networks for estimating the location of an event that emits a signal that propagates over a large region. In this context, we assume that the sensors make binary observations and report the event (positive observations) if the measured signal at their location is above a threshold; otherwise, they remain silent (negative observations). Based on the sensor binary beliefs, a likelihood matrix is constructed whose maximum value points to the event location. The main contribution of this work is Subtract on Negative Add on Positive (SNAP), an estimation algorithm that provides an efficient way of constructing the likelihood matrix by simply adding \pm 1 contributions from the sensor nodes depending on their alarm state (positive or negative). This simple estimation procedure provides very accurate results and turns out to be fault tolerant even when a large percentage of the sensor nodes report erroneous observations.

Statistical terminal assisted mobile positioning is a methodology that enhances the performance of existing localization techniques, by exploiting historical measurements collected at the terminal side. We propose a unified positioning framework in which different types of network related measurements may be employed by multiple techniques to derive coarse position estimates, while filtering is applied as a post processing step to further increase accuracy. The proposed architecture is applicable to user plane location architectures, while its open and modular design allows easy integration of new positioning algorithms and post processing techniques. The implementation of this concept in ubiquitous terminal assisted positioning prototype concentrates particularly on quality of position issues and compatibility with currently available standards and communication protocols.

Monitoring a large area with stationary sensor networks requires a very large number of nodes which with current technology implies a prohibitive cost. The motivation of this work is to develop an architecture where a set of mobile sensors will collaborate with the stationary sensors in order to reliably detect and locate an event. The main idea of this collaborative architecture is that the mobile sensors should sample the areas that are least covered (monitored) by the stationary sensors. Furthermore, when stationary sensors have a ldquosuspicionrdquo that an event may have occurred, they report it to a mobile sensor that can move closer to the suspected area and can confirm whether the event has occurred or not. An important component of the proposed architecture is that the mobile nodes autonomously decide their path based only on local information (their own beliefs and measurements as well as information collected from the stationary sensors in their communication range). We believe that this approach is appropriate in the context of wireless sensor networks since it is not feasible to have an accurate global view of the state of the environment.

This paper investigates the benefits of spatial correlation for the problem of coverage in Wireless Sensor Networks (WSN). Specifically, it studies two detectors, the Mean Detector (MD) and the Covariance Detector (CD) and compares their coverage performance for various scenarios. The main contribution of this paper is to show that one can exploit the possible correlation between measurements of neighboring sensor nodes in order to achieve significantly better coverage. Moreover, the results of this paper have direct implications on the topology of the network. Our results indicate that for the CD the best placement would be pairs of sensor nodes placed on a grid configuration.

Third generation (3G) wireless networks enable the deployment of real time applications that put extra pressure on network scarce resources (bandwidth and buffers) thus generating a need for effective resource allocation and management. This work studies the problem of dynamic control and optimization of buffer thresholds in wireless networks. Based on a stochastic fluid model (SFM), this paper derives infinitesimal perturbation analysis (IPA) estimators of the sensitivity of some performance measure of interest with respect to the control parameter. Subsequently these estimators are evaluated based on data observed from the sample path of the system and are used to dynamically control the threshold allowing the network to work continuously at an optimal or near optimal point.

The paper presents a new two-step cellular location determination (CLD) method based on signal strength and wave scattering models. The received signal level (RSL) method is first used in combination with maximum likelihood estimation (MLE) and triangulation to obtain an estimate of the location of the mobile. Due to non line of sight (NLOS) conditions and multipath propagation, this estimate lacks acceptable accuracy and consistency for demanding services, as numerical simulations reveal. Thus, the wave scattering 3D multipath channel model of Aulin is employed together with extended Kalman filtering (EKF) to obtain improved location estimates with high accuracy. The EKF is initialized at the MLE obtained from the RSL method, which is proved to be highly appropriate. Numerical simulations under urban, suburban and rural environments were utilized to evaluate the accuracy and consistency of the proposed two-step enhanced RSL method; the results of the worst-case rural environment are presented.

The Wireless environment requires new type of services and new ways for structuring the needed content. Personalization comes into aid via the creation of personalized portals that directly tones down factors that break up the functionally of the Internet/wireless services when viewed through wireless devices; factors like the “click count”, user response time (the “choice ” factor) and the size of the wireless network traffic. In this demo we present a flexible personalization system tuned for the wireless Internet taking into consideration user mobility and not only the user profile but the device profile as well. We demonstrate appropriate, to the wireless environment, metrics and our initial performance evaluation, using a real content provider’s content, indicates improvement ranging from 33 % to, for certain metrics, 60%. 1.

We address the problem of dynamically assigning the time-slots of a transmission frame to the various classes of transmitters of a packet radio network. We assume that packets are transmitted in a fixed-length frame and employ a discrete optimization scheme to construct the frame slot assignments in order to minimize the mean packet delay. Several simulation results are also included.