Road Traffic Monitoring System Based on Mobile Devices and Bluetooth Low Energy Beacons

The paper proposes a method, which utilizes mobile devices (smartphones) and Bluetooth beacons, to detect passing vehicles and recognize their classes. The traffic monitoring tasks are performed by analyzing strength of radio signal received by mobile devices from beacons that are placed on opposite sides of a road. This approach is suitable for crowd sourcing applications aimed at reducing travel time, congestion, and emissions. Advantages of the introduced method were demonstrated during experimental evaluation in real-traffic conditions. Results of the experimental evaluation confirm that the proposed solution is effective in detecting three classes of vehicles (personal cars, semitrucks, and trucks). Extensive experiments were conducted to test different classification approaches and data aggregation methods. In comparison with state-of-the-art RSSI-based vehicle detection methods, higher accuracy was achieved by introducing a dedicated ensemble of random forest classifiers with majority voting

An Overview of Moving Object Trajectory Compression Algorithms

Compression technology is an efficient way to reserve useful and valuable data as well as remove redundant and inessential data from datasets. With the development of RFID and GPS devices, more and more moving objects can be traced and their trajectories can be recorded. However, the exponential increase in the amount of such trajectory data has caused a series of problems in the storage, processing, and analysis of data. Therefore, moving object trajectory compression undoubtedly becomes one of the hotspots in moving object data mining. To provide an overview, we survey and summarize the development and trend of moving object compression and analyze typical moving object compression algorithms presented in recent years. In this paper, we firstly summarize the strategies and implementation processes of classical moving object compression algorithms. Secondly, the related definitions about moving objects and their trajectories are discussed. Thirdly, the validation criteria are introduced for evaluating the performance and efficiency of compression algorithms. Finally, some application scenarios are also summarized to point out the potential application in the future. It is hoped that this research will serve as the steppingstone for those interested in advancing moving objects mining

Antenna integration for wireless and sensing applications

As integrated circuits become smaller in size, antenna design has become the size limiting factor for RF front ends. The size reduction of an antenna is limited due to tradeoffs between its size and its performance. Thus, combining antenna designs with other system components can reutilize parts of the system and significantly reduce its overall size. The biggest challenge is in minimizing the interference between the antenna and other components so that the radiation performance is not compromised. This is especially true for antenna arrays where the radiation pattern is important. Antenna size reduction is also desired for wireless sensors where the devices need to be unnoticeable to the subjects being monitored. In addition to reducing the interference between components, the environmental effect on the antenna needs to be considered based on sensors' deployment. This dissertation focuses on solving the two challenges: 1) designing compact multi-frequency arrays that maintain directive radiation across their operating bands and 2) developing integrated antennas for sensors that are protected against hazardous environmental conditions. The first part of the dissertation addresses various multi-frequency directive antennas arrays that can be used for base stations, aerospace/satellite applications. A cognitive radio base station antenna that maintains a consistent radiation pattern across the operating frequencies is introduced. This is followed by multi-frequency phased array designs that emphasize light-weight and compactness for aerospace applications. The size and weight of the antenna element is reduced by using paper-based electronics and internal cavity structures. The second part of the dissertation addresses antenna designs for sensor systems such as wireless sensor networks and RFID-based sensors. Solar cell integrated antennas for wireless sensor nodes are introduced to overcome the mechanical weakness posed by conventional monopole designs. This can significantly improve the sturdiness of the sensor from environmental hazards. The dissertation also introduces RFID-based strain sensors as a low-cost solution to massive sensor deployments. With an antenna acting as both the sensing device as well as the communication medium, the cost of an RFID sensor is dramatically reduced. Sensors' strain sensitivities are measured and theoretically derived. Their environmental sensitivities are also investigated to calibrate them for real world applications.Ph.D.Committee Chair: Tentzeris, Emmanouil; Committee Member: Akyildiz, Ian; Committee Member: Allen, Mark; Committee Member: Naishadham, Krishna; Committee Member: Peterson, Andrew; Committee Member: Wang, Yan

A review of the role of sensors in mobile context-aware recommendation systems

Recommendation systems are specialized in offering suggestions about specific items of different types (e.g., books, movies, restaurants, and hotels) that could be interesting for the user. They have attracted considerable research attention due to their benefits and also their commercial interest. Particularly, in recent years, the concept of context-aware recommendation system has appeared to emphasize the importance of considering the context of the situations in which the user is involved in order to provide more accurate recommendations. The detection of the context requires the use of sensors of different types, which measure different context variables. Despite the relevant role played by sensors in the development of context-aware recommendation systems, sensors and recommendation approaches are two fields usually studied independently. In this paper, we provide a survey on the use of sensors for recommendation systems. Our contribution can be seen from a double perspective. On the one hand, we overview existing techniques used to detect context factors that could be relevant for recommendation. On the other hand, we illustrate the interest of sensors by considering different recommendation use cases and scenarios

Exploratory Analysis of Connected Fully Autonomous Vehicles on the Safety and Efficiency of Road Networks using Microsimulation

The research had set out to explore the effects of the widespread introduction of driverless technology by using publicly available data and assessing the changes it brings to the efficiency and safety of the road network. ConFAVs were slowly introduced to the network and average vehicle delays and the level of service (LOS) of links observed, followed by a surrogate safety assessment. Two published behaviour models (Atkins and CoEXist), and a third model (Tested Logic) was created, which accounted for a change in ConFAV behaviour while following another ConFAV. A comparison of the change in the average vehicle delay and the total number of serious conflicts recorded, highlighted that the CoEXist behavioural model had performed the best in three types of junctions and was used to further analyse the case study. The case study involved 2 small, isolated networks within the Queen Elizabeth Olympic Park Area of London (‘Site A’ was residential and ‘Site B’ was commercial). ‘Site A’ performed well with delays but performed poorly when comparing the number of recorded conflicts against the increasing numbers of ConFAVs. ‘Site B’ showed limited improvement in LOS and performed poorly in the safety analysis as the number of recorded conflicts increased fourfold in some scenarios. The results of the case study led to a conclusion that increased numbers of ConFAVs driving in platoons within the network could reduce delays and as a result either maintained the LOS of the chosen route or made it better. The lead vehicle in the platoon was able to anticipate changes in signals and communicate this with the trailing vehicles, allowing them to perform better at signalised junctions. Platoons also increased network capacity on congested links allowing better performance in the average delays, as observed in Case Study B. However, greater numbers of platoons resulted in larger numbers of rear-end conflicts when a surrogate safety analysis was performed using Time to Collision (TTC) as a parameter. Thus, it was recommended that another method is used to investigate potential conflicts that could recognise and account for platoons

Numerical methods for queues with shared service

A queueing system is a mathematical abstraction of a situation where elements, called customers, arrive in a system and wait until they receive some kind of service. Queueing systems are omnipresent in real life. Prime examples include people waiting at a counter to be served, airplanes waiting to take off, traffic jams during rush hour etc. Queueing theory is the mathematical study of queueing phenomena. As often neither the arrival instants of the customers nor their service times are known in advance, queueing theory most often assumes that these processes are random variables. The queueing process itself is then a stochastic process and most often also a Markov process, provided a proper description of the state of the queueing process is introduced. This dissertation investigates numerical methods for a particular type of Markovian queueing systems, namely queueing systems with shared service. These queueing systems differ from traditional queueing systems in that there is simultaneous service of the head-of-line customers of all queues and in that there is no service if there are no customers in one of the queues. The absence of service whenever one of the queues is empty yields particular dynamics which are not found in traditional queueing systems. These queueing systems with shared service are not only beautiful mathematical objects in their own right, but are also motivated by an extensive range of applications. The original motivation for studying queueing systems with shared service came from a particular process in inventory management called kitting. A kitting process collects the necessary parts for an end product in a box prior to sending it to the assembly area. The parts and their inventories being the customers and queues, we get ``shared service'' as kitting cannot proceed if some parts are absent. Still in the area of inventory management, the decoupling inventory of a hybrid make-to-stock/make-to-order system exhibits shared service. The production process prior to the decoupling inventory is make-to-stock and driven by demand forecasts. In contrast, the production process after the decoupling inventory is make-to-order and driven by actual demand as items from the decoupling inventory are customised according to customer specifications. At the decoupling point, the decoupling inventory is complemented with a queue of outstanding orders. As customisation only starts when the decoupling inventory is nonempty and there is at least one order, there is again shared service. Moving to applications in telecommunications, shared service applies to energy harvesting sensor nodes. Such a sensor node scavenges energy from its environment to meet its energy expenditure or to prolong its lifetime. A rechargeable battery operates very much like a queue, customers being discretised as chunks of energy. As a sensor node requires both sensed data and energy for transmission, shared service can again be identified. In the Markovian framework, "solving" a queueing system corresponds to finding the steady-state solution of the Markov process that describes the queueing system at hand. Indeed, most performance measures of interest of the queueing system can be expressed in terms of the steady-state solution of the underlying Markov process. For a finite ergodic Markov process, the steady-state solution is the unique solution of $N-1$ balance equations complemented with the normalisation condition, $N$ being the size of the state space. For the queueing systems with shared service, the size of the state space of the Markov processes grows exponentially with the number of queues involved. Hence, even if only a moderate number of queues are considered, the size of the state space is huge. This is the state-space explosion problem. As direct solution methods for such Markov processes are computationally infeasible, this dissertation aims at exploiting structural properties of the Markov processes, as to speed up computation of the steady-state solution. The first property that can be exploited is sparsity of the generator matrix of the Markov process. Indeed, the number of events that can occur in any state --- or equivalently, the number of transitions to other states --- is far smaller than the size of the state space. This means that the generator matrix of the Markov process is mainly filled with zeroes. Iterative methods for sparse linear systems --- in particular the Krylov subspace solver GMRES --- were found to be computationally efficient for studying kitting processes only if the number of queues is limited. For more queues (or a larger state space), the methods cannot calculate the steady-state performance measures sufficiently fast. The applications related to the decoupling inventory and the energy harvesting sensor node involve only two queues. In this case, the generator matrix exhibits a homogene block-tridiagonal structure. Such Markov processes can be solved efficiently by means of matrix-geometric methods, both in the case that the process has finite size and --- even more efficiently --- in the case that it has an infinite size and a finite block size. Neither of the former exact solution methods allows for investigating systems with many queues. Therefore we developed an approximate numerical solution method, based on Maclaurin series expansions. Rather than focussing on structural properties of the Markov process for any parameter setting, the series expansion technique exploits structural properties of the Markov process when some parameter is sent to zero. For the queues with shared exponential service and the service rate sent to zero, the resulting process has a single absorbing state and the states can be ordered such that the generator matrix is upper-diagonal. In this case, the solution at zero is trivial and the calculation of the higher order terms in the series expansion around zero has a computational complexity proportional to the size of the state space. This is a case of regular perturbation of the parameter and contrasts to singular perturbation which is applied when the service times of the kitting process are phase-type distributed. For singular perturbation, the Markov process has no unique steady-state solution when the parameter is sent to zero. However, similar techniques still apply, albeit at a higher computational cost. Finally we note that the numerical series expansion technique is not limited to evaluating queues with shared service. Resembling shared queueing systems in that a Markov process with multidimensional state space is considered, it is shown that the regular series expansion technique can be applied on an epidemic model for opinion propagation in a social network. Interestingly, we find that the series expansion technique complements the usual fluid approach of the epidemic literature

Politiche di scheduling ottime per sensori radio alimentati da fonti di energia rinnovabili

L’utilizzo di fonti di energia rinnovabili per alimentare i sensori radio di una WSN (Wireless Sensor Network) è una soluzione molto interessante per risolvere il problema della limitata autonomia dei nodi della rete. In questa tesi si propone un modello probabilistico in grado di descrivere in modo formale e rigoroso un generico sensore radio alimentato da una fonte di energia rinnovabile. In particolare, il dispositivo viene rappresentato mediante un processo decisionale di Markov. A quest’ultimo viene applicato, poi, un algoritmo di ottimizzazione, opportunamente individuato, attraverso il quale determinare le politiche di scheduling ottime che consentano al sensore radio di sfruttare nel modo più efficiente possibile la fonte di energia a disposizione. Si realizza, poi, un ottimizzatore ottenuto implementando in C++ sia il modello che il relativo algoritmo di ottimizzazion

An Algorithmic View on Sensor Networks - Surveillance, Localization, and Communication

This thesis focuses on scalability issues of diverse problems on sensor networks and presents efficient solutions. First, we show that it is NP-hard to find optimal activation schedules for monitoring areas and provide an EPTAS algorithm. Second, we present a distributed algorithm for the detection of network boundaries that only requires local connectivity information. Finally, we introduce an FPTAS for computing shortest paths and describe an algorithm for determining alternative routes