Internet of Things (IoT) has been growing rapidly due to recent advancements in communications and sensor technologies. Meanwhile, with this revolutionary transformation, researchers, implementers, deployers, and users are faced with many challenges. IoT is a complicated, crowded, and complex field; there are various types of devices, protocols, communication channels, architectures, middleware, and more. Standardization efforts are plenty, and this chaos will continue for quite some time. What is clear, on the other hand, is that IoT deployments are increasing with accelerating speed, and this trend will not stop in the near future. As the field grows in numbers and heterogeneity, “intelligence” becomes a focal point in IoT. Since data now becomes “big data,” understanding, learning, and reasoning with big data is paramount for the future success of IoT. One of the major problems in the path to intelligent IoT is understanding “context,” or making sense of the environment, situation, or status using data from sensors, and then acting accordingly in autonomous ways. This is called “context-aware computing,” and it now requires both sensing and, increasingly, learning, as IoT systems get more data and better learning from this big data. In this survey, we review the field, first, from a historical perspective, covering ubiquitous and pervasive computing, ambient intelligence, and wireless sensor networks, and then, move to context-aware computing studies. Finally, we review learning and big data studies related to IoT. We also identify the open issues and provide an insight for future study areas for IoT researchers.

Context-Aware Computing, Learning, and Big Data in Internet of Things: A Survey

This paper describes an algorithm which can achieve a constant bit rate when coding multiple video objects. The implementation is a nontrivial extension of the MPEG-4 rate control algorithm for single video objects which employs a quadratic rate quantizer model. The algorithm is organized into two stages: a pre- and a post-encoding stage. In the pre-encoding stage, an initial target estimate is made for each object. Based on the buffer fullness, the total target is adjusted and then distributed proportional to the relative size, motion, and variance of each object. Based on the new individual targets and rate-quantizer relation for texture, appropriate quantization parameters are calculated. After each object is encoded, the model parameters for each object are updated, and if necessary, frames are skipped to ensure that the buffer does not overflow. A preframeskip control is exercised to avoid buffer overflow when the motion and shape information occupies a significant portion of the bit budget. The rate control algorithm switches between two operation modes so that the coder can reduce the spatial coding accuracy for an improved temporal resolution. A shape-coding control mechanism is also proposed, which provides a tradeoff between texture and shape coding accuracy. Overall, the algorithm is able to successfully achieve the target bit rate, effectively code arbitrarily shaped objects, and maintain a stable buffer level. These techniques have been adopted by the MPEG committee in July 1997 as part of the video verification model (VM8).

MPEG-4 rate control for multiple video objects

Underwater wireless communications can be carried out through acoustic, radio frequency (RF), and optical waves. Compared to its bandwidth limited acoustic and RF counterparts, underwater optical wireless communications (UOWCs) can support higher data rates at low latency levels. However, the severe aquatic channel conditions (e.g., absorption, scattering, turbulence, etc.) pose great challenges for UOWCs and significantly reduce the attainable communication ranges, which necessitates efficient networking and localization solutions. Therefore, we provide a comprehensive survey on the challenges, advances, and prospects of underwater optical wireless networks (UOWNs) from a layer by layer perspective which includes: (1) Physical layer issues including propagation characteristics, channel modeling, and modulation techniques (2) Data link layer problems covering link configurations, link budgets, performance metrics, and multiple access schemes; (3) Network layer topics containing relaying techniques and potential routing algorithms; (4) Transport layer subjects such as connectivity, reliability, flow and congestion control; (5) Application layer goals, and (6) Localization and its impacts on UOWN layers. Finally, we outline the open research challenges and point out the prospective directions for underwater optical wireless communications, networking, and localization studies.

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Underwater Optical Wireless Communications, Networking, and Localization: A Survey

The Internet of Things (IoT) has become a reality. As the IoT is now becoming a far more common field, the demand for IoT technologies to manage the communication of devices with the rest of the world has increased. The IoT is connecting various individual devices called things and wireless sensor networks is also playing an important role. A thing can be defined as an embedded device based on a micro controller that can transmit and receive information. These devices are extremely low in power, memory, and resources. Therefore, the research community has recognized the importance of IoT device operating systems (OSs). An adequate OS with a kernel, networking, real-time capability, and more can make these devices flexible. This review provides a detailed comparison of the OSs designed for IoT devices on the basis of their architecture, scheduling methods, networking technologies, programming models, power and memory management methods, together with other features required for IoT applications. In addition, various applications, challenges, and case studies in the field of IoT research is discussed.

Internet of Things (IoT) Operating Systems Support, Networking Technologies, Applications, and Challenges: A Comparative Review

Underwater Wireless Sensor Networks (UWSNs) contain several components such as vehicles and sensors that are deployed in a specific acoustic area to perform collaborative monitoring and data collection tasks. These networks are used interactively between different nodes and ground-based stations. Presently, UWSNs face issues and challenges regarding limited bandwidth, high propagation delay, 3D topology, media access control, routing, resource utilization, and power constraints. In the last few decades, research community provided different methodologies to overcome these issues and challenges; however, some of them are still open for research due to variable characteristics of underwater environment. In this paper, a survey of UWSN regarding underwater communication channel, environmental factors, localization, media access control, routing protocols, and effect of packet size on communication is conducted. We compared presently available methodologies and discussed their pros and cons to highlight new directions of research for further improvement in underwater sensor networks.

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Underwater Wireless Sensor Networks: A Review of Recent Issues and Challenges

Underwater acoustic sensor networks (UASNs) have become more and more important in ocean exploration applications, such as ocean monitoring, pollution detection, ocean resource management, underwater device maintenance, etc. In underwater acoustic sensor networks, since the routing protocol guarantees reliable and effective data transmission from the source node to the destination node, routing protocol design is an attractive topic for researchers. There are many routing algorithms have been proposed in recent years. To present the current state of development of UASN routing protocols, we review herein the UASN routing protocol designs reported in recent years. In this paper, all the routing protocols have been classified into different groups according to their characteristics and routing algorithms, such as the non-cross-layer design routing protocol, the traditional cross-layer design routing protocol, and the intelligent algorithm based routing protocol. This is also the first paper that introduces intelligent algorithm-based UASN routing protocols. In addition, in this paper, we investigate the development trends of UASN routing protocols, which can provide researchers with clear and direct insights for further research.

https://www.mdpi.com/1424-8220/16/3/414/pdf

A Survey on Underwater Acoustic Sensor Network Routing Protocols

Context aware applications, which can adapt their behaviors to changing environments, are attracting more and more attention. To simplify the complexity of developing applications, context aware middleware, which introduces context awareness into the traditional middleware, is highlighted to provide a homogeneous interface involving generic context management solutions. This paper provides a survey of state-of-the-art context aware middleware architectures proposed during the period from 2009 through 2015. First, a preliminary background, such as the principles of context, context awareness, context modelling, and context reasoning, is provided for a comprehensive understanding of context aware middleware. On this basis, an overview of eleven carefully selected middleware architectures is presented and their main features explained. Then, thorough comparisons and analysis of the presented middleware architectures are performed based on technical parameters including architectural style, context abstraction, context reasoning, scalability, fault tolerance, interoperability, service discovery, storage, security a privacy, context awareness level, and cloud-based big data analytics. The analysis shows that there is actually no context aware middleware architecture that complies with all requirements. Finally, challenges are pointed out as open issues for future work.

https://www.mdpi.com/1424-8220/15/8/20570/pdf

Context Aware Middleware Architectures: Survey and Challenges

In previous years, an interest has developed in the coded representations of video signals allowing independent manipulation of semantically independent elements (objects). Along these lines, the ISO standard MPEG-4 enhances the traditional concept of the video sequence to convert it into a synchronized set of visual objects organized in a flexible way. The real-time generation of a bitstream according to this new paradigm, and suitable for its transmission through either fixed- or variable-rate channels, results in a challenging new bit allocation and rate control problem, which has to satisfy complex application requirements. This paper formalizes this new issue by focusing on the design of rate control systems for real-time applications. The proposed approach relies on the modelization of the source and the optimization of a cost criterion based on signal quality parameters. Different cost criteria are provided, corresponding to a set of relevant definitions of the object priority concept. Algorithms are introduced to minimize the average distortion of the objects, to guarantee desired qualities to the most relevant ones, and to keep constant ratios among the object qualities. The techniques have been applied to a coder implementing the MPEG-4 video verification model, showing good properties in terms of achievement of the control objectives.

Rate control and bit allocation for MPEG-4

This article proposes a novel method for detecting forest fires, through the use of a new color index, called the Forest Fire Detection Index (FFDI), developed by the authors. The index is based on methods for vegetation classification and has been adapted to detect the tonalities of flames and smoke; the latter could be included adaptively into the Regions of Interest (RoIs) with the help of a variable factor. Multiple tests have been performed upon database imagery and present promising results: a detection precision of 96.82% has been achieved for image sizes of 960 × 540 pixels at a processing time of 0.0447 seconds. This achievement would lead to a performance of 22 f/s, for smaller images, while up to 54 f/s could be reached by maintaining a similar detection precision. Additional tests have been performed on fires in their early stages, achieving a precision rate of p = 96.62%. The method could be used in real-time in Unmanned Aerial Systems (UASs), with the aim of monitoring a wider area than through fixed surveillance systems. Thus, it would result in more cost-effective outcomes than conventional systems implemented in helicopters or satellites. UASs could also reach inaccessible locations without jeopardizing people’s safety. On-going work includes implementation into a commercially available drone.

https://www.mdpi.com/1424-8220/16/6/893/pdf

Efficient Forest Fire Detection Index for Application in Unmanned Aerial Systems (UASs)

The main focus of this paper is on extracting features with SOund Navigation And Ranging (SONAR) sensing for further underwater landmark-based Simultaneous Localization and Mapping (SLAM). According to the characteristics of sonar images, in this paper, an improved Otsu threshold segmentation method (TSM) has been developed for feature detection. In combination with a contour detection algorithm, the foreground objects, although presenting different feature shapes, are separated much faster and more precisely than by other segmentation methods. Tests have been made with side-scan sonar (SSS) and forward-looking sonar (FLS) images in comparison with other four TSMs, namely the traditional Otsu method, the local TSM, the iterative TSM and the maximum entropy TSM. For all the sonar images presented in this work, the computational time of the improved Otsu TSM is much lower than that of the maximum entropy TSM, which achieves the highest segmentation precision among the four above mentioned TSMs. As a result of the segmentations, the centroids of the main extracted regions have been computed to represent point landmarks which can be used for navigation, e.g., with the help of an Augmented Extended Kalman Filter (AEKF)-based SLAM algorithm. The AEKF-SLAM approach is a recursive and iterative estimation-update process, which besides a prediction and an update stage (as in classical Extended Kalman Filter (EKF)), includes an augmentation stage. During navigation, the robot localizes the centroids of different segments of features in sonar images, which are detected by our improved Otsu TSM, as point landmarks. Using them with the AEKF achieves more accurate and robust estimations of the robot pose and the landmark positions, than with those detected by the maximum entropy TSM. Together with the landmarks identified by the proposed segmentation algorithm, the AEKF-SLAM has achieved reliable detection of cycles in the map and consistent map update on loop closure, which is shown in simulated experiments.

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Martina Eckert

Papers

A Survey on Underwater Acoustic Sensor Network Routing Protocols

Context Aware Middleware Architectures: Survey and Challenges

Rate control and bit allocation for MPEG-4

Efficient Forest Fire Detection Index for Application in Unmanned Aerial Systems (UASs)

An Improved Otsu Threshold Segmentation Method for Underwater Simultaneous Localization and Mapping-Based Navigation