The availability of location information has become a key factor in today's communications systems allowing location based services. In outdoor scenarios, the mobile terminal position is obtained with high accuracy thanks to the global positioning system (GPS) or to the standalone cellular systems. However, the main problem of GPS and cellular systems resides in the indoor environment and in scenarios with deep shadowing effects where the satellite or cellular signals are broken. In this paper, we survey different technologies and methodologies for indoor and outdoor localization with an emphasis on indoor methodologies and concepts. Additionally, we discuss in this review different localization-based applications, where the location information is critical to estimate. Finally, a comprehensive discussion of the challenges in terms of accuracy, cost, complexity, security, scalability, etc. is given. The aim of this survey is to provide a comprehensive overview of existing efforts as well as auspicious and anticipated dimensions for future work in indoor localization techniques and applications.

Recent Advances in Indoor Localization: A Survey on Theoretical Approaches and Applications

Wireless sensor networks (WSNs) monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002–2013 of machine learning methods that were used to address common issues in WSNs. The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.

https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=3963&context=sis_research

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Currently, the network traffic control systems are mainly composed of the Internet core and wired/wireless heterogeneous backbone networks. Recently, these packet-switched systems are experiencing an explosive network traffic growth due to the rapid development of communication technologies. The existing network policies are not sophisticated enough to cope with the continually varying network conditions arising from the tremendous traffic growth. Deep learning, with the recent breakthrough in the machine learning/intelligence area, appears to be a viable approach for the network operators to configure and manage their networks in a more intelligent and autonomous fashion. While deep learning has received a significant research attention in a number of other domains such as computer vision, speech recognition, robotics, and so forth, its applications in network traffic control systems are relatively recent and garnered rather little attention. In this paper, we address this point and indicate the necessity of surveying the scattered works on deep learning applications for various network traffic control aspects. In this vein, we provide an overview of the state-of-the-art deep learning architectures and algorithms relevant to the network traffic control systems. Also, we discuss the deep learning enablers for network systems. In addition, we discuss, in detail, a new use case, i.e., deep learning based intelligent routing. We demonstrate the effectiveness of the deep learning-based routing approach in contrast with the conventional routing strategy. Furthermore, we discuss a number of open research issues, which researchers may find useful in the future.

State-of-the-Art Deep Learning: Evolving Machine Intelligence Toward Tomorrow’s Intelligent Network Traffic Control Systems

The main characteristic of Reconfigurable Computing is the presence of hardware that can be reconfigured to implement specific functionality more suitable for specially tailored hardware than on a simple uniprocessor. Reconfigurable computing systems join microprocessors and programmable hardware in order to take advantage of the combined strengths of hardware and software and have been used in applications ranging from embedded systems to high performance computing. Many of the fundamental theories have been identified and used by the Hardware/Software Co-Design research field. Although the same background ideas are shared in both areas, they have different goals and use different approaches.This book is intended as an introduction to the entire range of issues important to reconfigurable computing, using FPGAs as the context, or "computing vehicles" to implement this powerful technology. It will take a reader with a background in the basics of digital design and software programming and provide them with the knowledge needed to be an effective designer or researcher in this rapidly evolving field.

· Treatment of FPGAs as computing vehicles rather than glue-logic or ASIC substitutes
· Views of FPGA programming beyond Verilog/VHDL
· Broad set of case studies demonstrating how to use FPGAs in novel and efficient ways

Reconfigurable Computing: The Theory and Practice of FPGA-Based Computation

To make transportation safer, more efficient, and less harmful to the environment, traffic telematics services are currently being intensely investigated and developed. Such services require dependable wireless vehicle-to-infrastructure and vehicle-to-vehicle communications providing robust connectivity at moderate data rates. The development of such dependable vehicular communication systems and standards requires accurate models of the propagation channel in all relevant environments and scenarios. Key characteristics of vehicular channels are shadowing by other vehicles, high Doppler shifts, and inherent nonstationarity. All have major impact on the data packet transmission reliability and latency. This paper provides an overview of the existing vehicular channel measurements in a variety of important environments, and the observed channel characteristics (such as delay spreads and Doppler spreads) therein. We briefly discuss the available vehicular channel models and their respective merits and deficiencies. Finally, we discuss the implications for wireless system design with a strong focus on IEEE 802.11p. On the road towards a dependable vehicular network, room for improvements in coverage, reliability, scalability, and delay are highlighted, calling for evolutionary improvements in the IEEE 802.11p standard. Multiple antennas at the onboard units and roadside units are recommended to exploit spatial diversity for increased diversity and reliability. Evolutionary improvements in the physical (PHY) and medium access control (MAC) layers are required to yield dependable systems. Extensive references are provided.

/pdf/vehicular-channel-characterization-and-its-implications-for-3l69uw88ht.pdf

Vehicular Channel Characterization and Its Implications for Wireless System Design and Performance

Future intelligent transportation systems (ITS) will necessitate wireless vehicle-to-infrastructure (V2I) communications. This wireless link can be implemented by several technologies, such as digital broadcasting, cellular communication, or dedicated short-range communication (DSRC) systems. Analyses of the coverage and capacity requirements are presented when each of the three systems is used to implement the V2I link. We show that digital broadcasting systems are inherently capacity limited and do not appropriately scale. Furthermore, we show that the Universal Mobile Telecommunications System (UMTS) can implement the V2I link using either a dedicated channel (DCH) or a multimedia broadcast/multicast service (MBMS), as well as a hybrid approach. In every case, such V2I systems scale well and are capacity limited. We also show that wireless access in vehicular environment (WAVE) systems scale well, provide ample capacity, and are coverage limited. Finally, a direct quantitative comparison of the presented systems is given to show their scaling behavior with the number of users and the geographical coverage.

/pdf/on-wireless-links-for-vehicle-to-infrastructure-2itxomp8lf.pdf

On Wireless Links for Vehicle-to-Infrastructure Communications

We present a parameterized floating-point library for use with reconfigurable hardware. Our format is both general and flexible. All IEEE formats are a subset of our format, as are all previously published floating-point formats for reconfigurable hardware. We have developed a library of fully parameterized hardware modules for format control, arithmetic operations and conversion to and from any fixed-point format. The format converters allow for hybrid implementations that combine both fixed and floating-point calculations. This permits the designer to choose between the increased range of floating-point and the increased precision of fixed-point within the same application. We illustrate the use of this library with a hybrid implementation of the K-means clustering algorithm applied to multispectral satellite images.

A Library of Parameterized Floating-Point Modules and Their Use

Principal component analysis is a powerful technique for data analysis and compression, with a wide range of potential applications in wireless sensor networks. However, its centralized implementation, with a fusion center collecting all the samples, is inefficient in terms of energy consumption, scalability, and fault tolerance. Previous distributed approaches reduce the communication cost, but not the lack of flexibility, as they require multi-hop communications if the network is not fully connected. We present two fully distributed consensus-based algorithms that are guaranteed to converge to the global results, using only local communications among neighbors, regardless of the data distribution or the sparsity of the network: CB-DPCA is based on finding the eigenvectors of local covariance matrices, while CB-EM-DPCA is a distributed version of the expectation maximization algorithm. Both offer a flexible trade-off between the tightness of the achieved approximation and the associated communication cost.

Consensus-based distributed principal component analysis in wireless sensor networks

Conversion from floating-point to fixed-point formats is a necessary step in the design process of embedded systems and is traditionally performed manually. Automating this conversion process brings significant and much needed improvement in the efficiency of the design process. The fixify environment presented here fully automates the conversion process and comprises three optimization methods. The restricted-set full search algorithm is suited to designs that will be implemented on DSP cores and is, for such designs, guaranteed to find globally optimal solutions. On the other hand, the greedy search algorithm finds solution in the continuous search space and produces nearly optimal results, with the shortest required runtime. The branch-and-bound algorithm also works in the continuous search space and finds optimal solutions, but requires relatively long runtimes.

/pdf/automated-floating-point-to-fixed-point-conversion-with-the-2zlgh7emnn.pdf

Automated floating-point to fixed-point conversion with the fixify environment

Traffic safety can be improved by using a vehicular dedicated communication protocol. The standard IEEE 802.11p is being developed for this purpose. The physical layer properties of this draft are based on the already widely used IEEE 802.11a standard. Nevertheless, the propagation conditions in vehicular communications are different to the ones considered for 802.11a, which is focusing on nomadic indoor usage, and well studied until now. In this paper we present the simulation results obtained from an implemented physical layer model for this standard. The used channel model describes the very peculiar characteristics of the vehicular radio channel, specially the nonstationarity. Several channel estimators are tested based on the pilot structure defined in the standard focusing on low complexity implementations. The results show that diffuse components, present in vehicular channels on highways, have a very significant impact on the system performance. Furthermore, in situations of poor line-of-sight contribution, an acceptable frame error rate is not achievable even at high signal-to-noise ratio values. Therefore, more complex channel estimation and equalization techniques based on the current standard pilot pattern have to be developed that are able to cope with the properties of the vehicular radio channel.

/pdf/physical-layer-simulation-results-for-ieee-802-11p-using-17dzh5nosy.pdf

Pavle Belanovic

Papers

On Wireless Links for Vehicle-to-Infrastructure Communications

A Library of Parameterized Floating-Point Modules and Their Use

Consensus-based distributed principal component analysis in wireless sensor networks

Automated floating-point to fixed-point conversion with the fixify environment

Physical Layer Simulation Results for IEEE 802.11p Using Vehicular Non-Stationary Channel Model