Mobile Vision Analytics (MVA) is essential for advancing applications, which analyze visual data using deep neural network (DNN) models to extract contextual insights. While cloud based approach is common, the high latency incurred in transmission leading to a shift toward edge computing. However, the limited resource on edge leads to the challenge of concurrent DNN execution on shared GPU. This paper proposes a novel mathematical framework that models MVA workflows over an edge infrastructure, including the partitioning of GPU resources for concurrent DNNs. Our model integrates local and remote processing steps, accounting for wireless transmission and GPU sharing, and provides a structured approach to estimating per-step time consumption. A detailed numerical evaluation demonstrates the latency and resource utilization of each part of the system under varied scenarios, offering insights for the design of latency-sensitive MVA solutions with concurrent models on shared edge GPU infrastructure.

Computer vision is becoming a building block in many intelligent systems. Accordingly, video frames are timely analyzed, and computationally generated information can be overlaid on users’ visual perception of the physical world to augment their experiences or produce commands to control autonomous mobile robots. Traditionally, computer vision is performed at cloud and edge servers, where deep learning models are deployed to process video frames. However, accelerators for embedded devices have been proposed, and video frame inference at embedded devices might reduce latency, overhead, and costs in computer vision-based systems. This paper proposes a novel architecture to enable distributed and collaborative vision analytics in networked embedded devices. The proposed architecture encompasses the different building blocks to enable video frame offloading among the network-embedded devices, video frame inference, and devices’ telemetry for data collection. A preliminary analysis of the proposed architecture is conducted, and its potential for enabling distributed video analytics at networked embedded devices is highlighted.

Computer vision for mobile systems is challenging. Video frame processing locally on mobile devices is impractical due to the device’s limited resources, whereas processing at remote cloud and edge servers is time-consuming due to the communication issues. In this paper, we propose an analytical approach to cope with the local and remote processing tradeoff in mobile computer vision systems. We devise a stochastic model for a mobile computer vision systems and derive closedform expressions for key performance metrics, namely, processing latency, offloading latency, and energy cost. We propose two heuristics to obtain useful insights regarding distributed computer vision in smart mobile systems. We illustrate the model’s application through a case study involving collaborative video analytics at mobile users and edge servers under different conditions. Numerical results show the distinctions in the processing and offloading energy costs at mobile devices and the high latency when a high offloading rate is used.

Mobile augmented reality (MAR) has gained increased attention thanks to its potential to transform applications in different domains. One of the challenges to realizing MAR systems is the processing of video frames efficiently. MAR user devices are often resource-constrained and unsuitable for real-time object detection and recognition from video streams. Edge computing has tremendous potential to enable MAR systems, where processing instances (e.g., serverless functions, containers, or virtual machines) can implement and manage the execution of convolutional neural networks (CNNs) for processing MAR offloaded video frames. One of the challenges is how to balance the video frames across the edge servers and processing instances. In this article, we proposed the LAOS orchestrator for resource management and load balancing of distributed edge servers for MAR systems. The LAOS orchestrator balances incoming video frames among processing instances at the edge servers that process video frames. It also determines when to spawn new instances of the CNN functions aimed at ensuring a predefined latency threshold for the processing of video frames. Besides, we devised a novel queuing-based framework for modeling the resource management problem of distributed edge servers for MAR systems. The obtained numerical results show that the proposed LAOS orchestrator reduces the latency and efficiently manages the edge computing resources when dynamic workload peaks are considered.

The reliability analysis has been focused on research in many domains. Reliability in Internet of Things (IoT) has been extensively addressed since IoT is one of the building blocks of many critical systems. Nevertheless, reliability analysis has not gained significant attention within the context of Internet of Underwater Things (IoUTs). This is critical since IoUT infrastructures are costly due to the high cost of underwater nodes and operations for node repair and replacement are challenging. Recent works have designed low-cost underwater sensor nodes for the experiments and validation of networking protocols for IoUTs. Such low-power, low-cost nodes can be attractive for IoUT heterogeneous deployments. Therefore, it is advantageous to perform a reliability analysis of low-cost underwater nodes designed from off-the-shelf components. This paper presents a model for the reliability analysis of low-cost underwater nodes. The proposed approach models the node’s reliability from the reliability of its individual components. As a case study, we conduct a reliability analysis of two low-cost node designs provided in the literature. The obtained results show that the reliability of low-cost underwater nodes can be diminished by the use of a series of many off-the-shelf components.

Internet of Things (IoT) has been used to empower smart environments and applications in different domains. In some IoT systems, IoT cameras live stream video frames to edge or cloud servers to be processed by machine learning (ML) models. The processing of the video frames will aim to detect and recognize objects or other entities of interest. However, the processing of IoT video frames at cloud servers often relies on high latency due to network congestion and high load at the cloud servers. In this paper, we proposed the SDNQ controller, a software-defined networking (SDN) controller that uses a reinforcement learning (RL) agent to decide how to route and where to process video frames from IoT flows. The proposed solution considers the network and servers' status, the latency experienced by admitted video flows, and the video flows' latency requirements when deciding the routing path and the destination edge or cloud server to process a newly admitted IoT video flow. Numerical results show that the proposed solution outperforms related works at the cost of blocking a low fraction of IoT flows.

Internet of things (IoT)-based cameras have been proposed for surveillance and mixed reality applications in different domains. IoT cameras stream video frames produced from the scenery within their coverage area. Video frames are then processed to determine, identify, classify, and track objects of interest. Traditionally, IoT video frames are offloaded to distant cloud servers and processed by computation-intensive algorithms for video analytics. However, this approach incurs increased latency and network overhead. Recent studies proposed edge computing for resource provisioning for IoT applications. Nevertheless, edge computing presents many daunting challenges, such as the efficient management and allocation of edge server resources to IoT cameras. We propose the collaborative work of IoT cameras for video analytics. Accordingly, idle resources on an IoT camera can be used to process video frames produced by neighboring cameras. In this regard, we devised a mathematical framework for collaborative cross-camera IoT video analytics, which allows engineers and practitioners to obtain useful insides for the further design of architectures for collaborative IoT video analytics. We conduct extensive numerical evaluations and the obtained results show that the resources' utilization, offloading latency, and offloading costs are sensitive to the neighborhood resources demand.

Augmented reality (AR) is gaining increasing attention thanks to its potential for enhancing applications in different domains. However, AR systems will reply to different computation-intensive tasks (e.g., object detection, object classification, and content rendering), which are demanding in terms of energy and latency. The use of multi-access edge computing (MEC) technology can significantly reduce the latency and energy cost of AR systems by providing computational resources closer to mobile AR users. In this paper, we proposed a reinforcement learning-based orchestrator for the management and allocation of networked edge servers’ computing resources for AR mobile users. The proposed Migration Enabled Task Allocation (META) orchestrator takes into consideration the AR tasks and edge servers characteristics when deciding if an incoming AR task will be admitted or not, and in which edge server it will be executed in case it is admitted. Moreover, the proposed orchestrator migrates tasks among edge servers if needed to free resources during the incoming of a new AR task. We also design the deep META-DQN and META-PPO algorithms to be used by the META orchestrator for predicting AR tasks’ arrival and learning the optimal policy in terms of allocating edge computing resources. Obtained results show that our proposed META-PPO model decreased the task blocking rate by up to 180% when compared to related work.

Edge computing has been designed to deploy resources in the proximity of IoT devices, which reduces latency and network overhead. Nevertheless, resources on edge servers are limited and must efficiently be managed. In this paper, we propose a novel software-defined networking (SDN)-based scheme to balance the computation resource requests among a network of edge servers aimed at supporting IoT video analytics streaming applications. In the proposed solution, programmable switches periodically report the IoT video streaming workload forwarded to each edge server. This information is then used at the SDN controller to estimate the incoming and outgoing traffic load at edge servers and balance IoT video streaming among them, by updating routing tables at the programmable switches. The performance of the proposed solution is evaluated and compared to related schemes through extensive simulations using the Mininet emulator. Obtained results show that the proposed solution can reduce up to 21 % of average latency with 20 % load saving in each edge server, compared to deterministic and random-based related solutions.

Internet of things (IoT) and 5G network are fundamental building blocks for industrial IoT (IIoT). IoT has enabled real-time monitoring and actuation in industrial floors and machinery, aimed at improving the efficiency and safety of industrial activities and processes. On the other hand, 5G networks will provide ultra-reliable and low-latency communication for the wireless integration of autonomous industrial machinery, mobile vehicles, and robots, and management systems, aimed at the real-time control and management of industrial machinery toward smart factories. In IIoT, machine learning (ML) will also play a fundamental role in handling complex tasks at industrial machinery and 5G networks management, configuration, and control. However, ML suffers from the cold-start problem and needs a large amount of highly accurate data samples for model training, which is costly and difficult to obtain in IIoT applications. In this article, we shed light on the design of transfer learning (TL)-based systems for IIoT. We discuss how TL can overcome the demand for high-quality large data samples required to training ML models in IIoT. We also highlight the work principles and daunting challenges faced during the TL systems for IIoT. Furthermore, we categorize the TL systems for IIoT into TL for IIoT machinery level and for IIoT networking level and provide an in-depth discussion of the design building blocks and challenges of TL systems in each proposed class. Finally, we point out some future research directions for the design of novel TL-based systems for envisioned 5G-enabled IIoT applications.

The advancements in Internet of Things (IoT) and embedded systems, 5G networks and next-generation wireless systems, and embedded and distributed artificial intelligence are empowering Industry 4.0. In this new industrial era, smart autonomous and connected systems will improve efficiency, reduce cost and pollution, and increase productivity and safety in industrial 4.0 based applications. Nevertheless, the new generation technologies mentioned above have not been explored to improve safety in multiplant and industrial zones. In this article, we shed light on the design of unmanned aerial vehicles (UAVs)-assisted systems for emergence response in multiplant and industrial zones. We discuss the important contributions of UAV-mounted cloudlet systems to support SAR missions by providing computation, communication, and storage resources to first responders, surveillance of the affected area, real-time monitoring of SAR members and victims, and caching nodes to improve content delivery among SAR teams. Besides, we design an envisioned UAV-cloudlet reference system for emergency response in industrial areas. Moreover, we highlight the current challenges that are being addressed in the literature aimed at making possible the efficient use of the designed UAV-mounted cloudlet systems for emergency response. Finally, we point out some future research directions that require further investigation.

Underwater wireless sensor networks (UWSNs) have the potential to empower smart ocean applications. However, the widespread use of UWSN applications has been limited due to the many daunting challenges incurred for underwater wireless acoustic communication. Moreover, underwater wireless communication is energy-hungry, which confines UWSN deployment to small-scale due to the risks and costs of missions for at sea replacement of the nodes' battery. The energy harvesting capability of underwater sensor nodes is an important characteristic that has been overlooked in the literature. In this paper, we study the data routing process in UWSNs with energy harvesting capabilities. We proposed a novel opportunistic routing protocol, named RELOR, that is the first in the literature to consider the energy harvesting capability of underwater sensor nodes during routing decisions. RELOR implements a learning framework for the best selection of the forwarder nodes based on the observed environment conditions. We conduct extensive simulations to compare the performance of the proposed protocol to the state-of-the-art solution. Obtained results show that RELOR outperforms the related work in terms of packet delivery ratio, end-to-end latency, and nodes' energy consumption.

Underwater wireless sensor networks (UWSNs) have emerged as an enabling technology for aquatic monitoring. However, data delivery in UWSNs is challenging, due to the harsh aquatic environment and characteristics of the underwater acoustic channel. In recent years, underwater nodes with multi-modal communication capabilities have been proposed to create communication diversity and improve data delivery in UWSNs. Nevertheless, less attention has been devoted to the design of networking protocols leveraging multi-modal communication capabilities of underwater nodes. In this paper, we propose a novel stochastic model for the study of opportunistic routing (OR) in multi-modal UWSNs. We also design two candidate set selection heuristics, named OMUS-E and OMUS-D, for the joint selection of the most suitable acoustic modem for data transmission and next-hop forwarder candidate nodes at each hop, aimed to reduce the energy consumption and improve the network data delivery ratio in multi-modal UWSNs, respectively. Numerical results showed that both proposed heuristics reduced the energy consumption by 65%, 70%, and 75% as compared to the DBR, HydroCast, and GEDAR classical related work protocols, while maintaining a similar data delivery ratio. Furthermore, the proposed solutions outperformed the CAPTAIN routing protocol in terms of data delivery ratio, while maintaining comparable energy consumption.

Internet of Underwater Things (IoUT) has gained increased attention as an envisioned technology for supporting smart ocean applications. However, the harsh aquatic environment and challenges of underwater acoustic communication still severely limit data collection in underwater networks and IoUT applications. In recent years, programmable physical layer and multi-modal communication for IoUT have been proposed to improve the performance of underwater networks. However, several fundamental challenges need yet to be investigated and tackled, in order to achieve efficient data collection in IoUT. One of the daunting fundamental challenge to be solved is the design of innovative routing protocols for multi-modal IoUT. In this paper, we propose a mathematical model for the study of opportunistic routing (OR) in multi-modal IoUT. The devised mathematical framework models the unique characteristics of OR in multi-modal IoUT scenarios, while considering the peculiar characteristics of the underwater environment and acoustic communication. Moreover, we propose a candidate set selection procedure of OR, which jointly selects the acoustic modem and next-hop forwarder candidate nodes at each hop, to increase data delivery. Numerical results showed the potential of multimodal communication for improving data delivery in the harsh environment of underwater acoustic communication.

The next revolution of industrial applications, known as smart industry or Industry 4.0, will rely on Internet of Things (IoT) to automate the monitoring, inspection, and control of industrial equipment and processes. In Industry 4.0, efficient content delivery is one of the fundamental challenges to be addressed. Nowadays, the promising solution for content delivery in smart industrial applications is the use of hierarchical caching systems at the network edge (5G small cells). This approach reduces the delay for content delivery and helps improve the performance of smart industrial applications. However, the caching management is a challenging and complex task, especially in those scenarios of shared storage resources on edge devices to support multiple concurrent applications (e.g., industrial, mobile users, and connected cars applications). In this article, we study the performance of a shared edge caching system for content delivery in smart industry and connected cars applications. To do so, we propose a mathematical framework to model the performance of a hierarchical shared edge caching system. The proposed mathematical framework considers the distinct content catalogs of the different applications (e.g., industrial and connected cars applications) and content request characteristics from industrial IoT devices and vehicles. Numerical results show that the performance of the shared edge caching system is sensitive to vehicular mobility (i.e., vehicular speed).

Recently, to improve the efficiency and safety of the transportation system that is severely affected by the increasing traffic demand, the Internet-of-Vehicles (IoVs)/Vehicular Networks (VNets) have received more and more attention because it can effectively improve the ability of the participants in the transportation system to perceive the traffic environment around them through Vehicle-to-everything (V2X) technique. Moreover, V2X also makes it possible to share computing and storage power between vehicles, which further promotes the development of vehicular edge computing (VEC). However, due to the highly dynamic nature of the VNet's topology, based on the instant traffic flow condition, how to determine whether the vehicles on a given road can form a relatively stable cloudlet with certain computing or data storage capabilities to support certain VEC applications becomes a crucial task that needs to be solved. Therefore, in this paper, we proposed a Cloudlet-Dwell-time (CDT) estimation method to theoretically derive some essential parameters for implementing VEC applications, i.e., the vehicular cloudlet existence probability and its corresponding dwell-time. We further demonstrate the results of the proposed work based on traffic flow data chosen from the England Highways data set.

Freeways have been known over decades as high-speed multi-lane roads, where the opposite traffic directions are completely separated. No intersections, pedestrians, or bicycles are expected on these road scenarios, besides specific ramps are designed to facilitate the entrance and exit of vehicles. Although freeway driving is considered safer and faster for experienced drivers, it can be more demanding and difficult for fresh or exhausted ones. Entering and existing the freeway road are considered the most critical situations where the driver needs to achieve high synchronizations with the surrounding traffic there. In this work, we introduce a freeway driving assistance protocol for drivers aiming to reduce the difficulties for fresh drivers and enhance the safety and efficiency conditions over the freeway road scenarios. This proposed protocol can also be used by the autonomous vehicles where no drivers control the vehicle. We evaluate the performance of the proposed protocol using several simulated driving scenarios. It shows a good performance in terms of increasing the safety of traffic and smoothing the traffic speed of vehicles.

In recent years, the opportunistic routing (OR) paradigm has been shown as one of the most viable solutions at the network layer for efficient data delivery under the nosily underwater acoustic channel. Since then, several OR protocols for underwater sensor networks, with major and minor variations, were proposed in the literature. While the performance of these protocols, in terms of data delivery rate, delay, and energy consumption, has been extensively studied in the literature, there is a lack of studies devoted for the evaluation of the topological properties OR will incur. In this paper, we evaluate the performance of the candidate set selection procedures of opportunistic routing protocols designed for underwater sensor networks. We discuss the principles that have been extensively considered for the design of OR protocols for underwater sensor networks. Moreover, we conduct a simulation-based performance evaluation to study topological properties (e.g., number of hops, number of paths and number of candidates), which will impact the performance of underwater wireless sensor network applications.

We are witnessing the development of the new era of the vehicles' evolution history: the era of connected and autonomous vehicles. In this new era, vehicles will be empowered with processing, sensing, actuators and wireless communication capabilities, which will support a wide range of vehicular applications to improving safety, efficiency and enjoyableness of transportation. In this revolution, processing and exchange of multimedia and big data will be fundamental. Therefore, content distribution is one of the critical challenges in connected cars and intelligent vehicular networks. In this tutorial, we will discuss the recent advancements and research directions towards the development of solutions for content distribution in connected cars and vehicular networks. We will highlight the current state-of-the-art and identify research opportunities that could interest researchers willing to contribute in different areas, such as vehicles' mobility characterization and modeling, modeling and performance evaluation of information-centric networking in vehicular networks, broadcast storm avoidance of Interest and Data packets, content placement and cache policies, as well as networking protocols and architectures for service-oriented information-centric multimedia content distribution for vehicular networking and connected vehicles' applications [1]-[12].

Internet of Underwater Things (IoUTs) has emerged as an evolution of traditional underwater wireless sensor networks, with programmable nodes interconnected to the Internet. Despite the advancements, IoUTs will still face critical challenges imposed by the use of the lossy and energy-hungry underwater acoustic channel. Two major critical challenges faced by IoUT applications are the low reliable data delivery due to the poor quality of underwater acoustic links, and the high energy cost for underwater wireless communication. In this paper, we tackle both challenges by proposing the PCon protocol. The PCon is a power-controlled opportunistic routing protocol for data routing in duty-cycled IoUTs. At each hop, the PCon protocol selects the most suitable transmission power, from a set of discrete transmission power levels, to maintain a reasonable data delivery ratio while reducing the energy consumption of duty-cycled IoUTs. To do so, the PCon takes into consideration the energy cost for delivering the data packet, calculated as a function if the probability of having the next-hop node awake during the transmission. Simulation results showed that the PCon protocol, even in a harsh scenario of duty-cycling of 50%, ensures a packet delivery rate of 40% while decreases the energy cost in 78%.

Recent advancements in underwater wireless sensor networks (UWSNs) are enabling day-one underwater monitoring applications. However, energy efficient and reliable UWSNs must be developed for achieving large scale and sustainable underwater monitoring and exploration applications. In this regard, the use of the underwater acoustic channel poses several daunting challenges. The acoustic channel is energy hungry and has low reliability, which shortens the UWSN lifetime and diminishes the performance of underwater monitoring applications. In the literature, both challenges have been addressed separately by means of duty-cycling and opportunistic routing, respectively. In this paper, we shed light on the symbiotic design of anypath routing and duty-cycling for sustainable UWSNs. We propose novel strobed preamble low power listening (LPL) and low power probing (LPP) methodologies for the design of asynchronous duty-cycling protocols, which symbiotically consider anypath routing for data delivery. Moreover, we develop an analytical framework for the performance evaluation of such a symbiotic design. Numerical results highlight potentials and drawbacks of this proposed approach in different classes of UWSN applications, and provide useful insights for the future symbiotic design of opportunistic routing and duty-cycling protocols for UWSNs.

Every year, natural and human-induced disasters result in infrastructural damages, monetary costs, distresses, injuries and deaths. Unfortunately, climate change is strengthening the destructive power of natural disasters. In this context, Internet-of-Things (IoT)-based disaster detection and response systems have been proposed to cope with disasters and emergencies by improving the disaster detection and search and rescue missions during disaster response. Accordingly, IoT devices are used to collect data and help to identify hazards after disasters and to localize injured people. However, a solely IoT-based detection and response system will not be totally suitable for emergency response in smart cities, as the lack of connectivity with IoT devices might occur, due to breakages in communication infrastructures or network congestions. Therefore, we propose a novel architecture for smart disaster detection and response system for smart cities. We discuss the main building blocks of our envisioned smart system, as well as the critical challenges that will be faced ahead to implement our smart system.

The peculiar characteristics of vehicular networks (e.g., high vehicular mobility, poor wireless link quality and short-lived and intermittent connectivity among vehicles) challenge host-centric content search and distribution in vehicular networking applications. In this regard, recent studies have proposed information-centric protocols to improve content distribution in vehicular networks. However, they are still severely impaired by the highly dynamic nature of vehicular network topologies and the broadcast storm problem due to uncontrolled Interest packet flooding for content discovery. In this paper, we tackle the broadcast storm problem of Interest packet transmissions for content discovery in vehicular named-data networks. We propose the location-based content distribution protocol (LOCOS) for oriented Interest packet transmissions towards the proximity area of a recently discovered content source vehicle. The LOCOS protocol leverages the recently discovered location of a vehicle content source to controlled transmit Interest packets to the area where the content source is located. Simulation results show that the LOCOS protocol improves content delivery rate in 10% and 28% when compared with related work, while it reduces the content delivery delay in 80%.

Multimedia data traffic will explosively increase in vehicular networking scenarios as the current advances in vehicular communication technologies and connected cars penetrate on the market. However, current data dissemination protocols and even the host-centric content delivery paradigm will not support the anticipated traffic load without degrading vehicular applications QoS/QoE. Recent research efforts are proposing the information-centric networking paradigm as a viable solution for handling multimedia content distribution in vehicular networks and connected and autonomous vehicles [1-10].

Oceans are a great unknown. To change this worryingly reality, underwater wireless sensor networks (UWSNs) have been proposed for the automated and real-time data collection from ocean, including the life and events beneath them. Currently, the underwater acoustic channel is the most viable technology for long-range underwater wireless communication, but its use impairs the data collection in UWSNs. It presents strong signal absorption and is severely affected by human-made and natural noise in the aquatic environment. Therefore, data collection in UWSNs is unreliable. In the recent years, opportunistic routing has been proposed to improve UWSN communication's reliability and, consequently, data delivery. However, not always the proposed opportunistic routing protocols will perform well, as the neighborhood configuration of a node might not be dense enough or at a maximum distance that would favor data communication. In this paper, we proposed the power control-based opportunistic routing protocol, named PCR, for reliable and energy-efficient data delivery in UWSNs. The proposed PCR protocol selects the most suitable transmission power level at each underwater sensor node, aimed at improving the packet delivery probability at each hop. To avoid the selection of high power transmission and the uncontrolled inclusion of neighboring nodes in the next-hop candidate set, which would drastically increase the energy consumption, the PCR protocol considers the energy waste that will occur in each neighboring underwater sensor node. Numerical results showed that PCR improves the packet delivery probability and reduces the energy waste for data delivery by adjusting the proper transmission power and selecting the suitable candidate set, leading to energy conservation when compared with related proposals presented in the literature.

Underwater wireless sensor networks (UWSNs) have been proposed for autonomous monitoring of underwater environments. Despite the recent advances on underwater acoustic modems, unreliable data delivery and high energy cost are two critical research problems that remain in UWSNs. In this context, energy-efficient and reliable data collection protocols must be proposed for UWSNs. In this paper, we propose to combine power control and anypath routing in order to simultaneously address both problems in UWSNs. We propose an analytical that considers the characteristics of power control, multiple next-hop forward nodes of anypath routing, underwater acoustic channel and aquatic environment. The model devised in this work is a mathematical tool to enable researchers and practitioners to study important performance metrics of UWSN applications, when power control and anypath routing are jointly explored. Moreover, it is helpful to provide insights for the future design of joint power control and anypath routing protocols for UWSNs. Numerical results show that for a reduced number of next-hop candidate nodes, data delivery can still be improved by increasing the transmission power of the sender. Conversely, for a high number of next-hop candidates nodes, a high transmission power leads to energy waste.

Opportunistic routing (OR) has been showed efficient for the harsh and challenging scenarios of underwater wireless sensor networks (UWSNs). This routing paradigm leverages the broadcast nature of wireless communication, for improving data delivery. In contrast to traditional multi-hop routing, OR selects a subset of the neighboring nodes to be the next-hop candidate nodes, in which will participate forwarding data packets towards the destination. Hence, at each hop, a transmitted data packet is lost only if none of the next-hop candidate nodes receives it. Therefore, OR not only improves packet delivery rate, but also reduces network energy consumption since fewer retransmissions will be needed. However, the design of OR protocols for UWSNs is challenging, due to the characteristics of the underwater acoustic channel. For instance, the high and variable delay, multipath propagation, low bandwidth, and high energy consumption render impractical the use of the up to date protocols developed for wireless sensor and mesh networks. In this context, this tutorial gives a comprehensive review of the potentials and challenges of opportunistic routing in underwater sensor networks. In addition, based on an in-deep literature review, this tutorial will provide important guidelines for the design of novel protocols for different scenarios of UWSNs.

Opportunistic routing (OR) has emerged as a promising paradigm to the design of routing protocols for underwater sensor networks (UWSNs). However, despite of its advantages, it introduces a critical problem that has been neglected until now: the immutable transmission priority level of the next-hop forwarding nodes. This characteristic can lead to an overuse of a unique node (or a few of them), quickly depleting its battery, creating network partitions, shortening the network lifetime and, consequently, degrading the application's performance. In this paper, we shed light on the need for mechanisms for rotating the forwarding priority level between candidate nodes. We propose a baseline new lightweight energy-aware opportunistic routing (EnOR) protocol, leading to a balanced energy consumption and prolonged UWSN network lifetime. EnOR rotates the transmission priority level of the forwarding candidate nodes by considering the remaining energy, link reliability and packet advancement of them. Simulation results reveal that EnOR effectively extends the network lifetime as compared with other underwater sensor network opportunistic routing protocols.

Efficient content distribution is a critical challenge in vehicular networks (VANETs). This is due to the characteristics of vehicular networks, such as high mobility, dynamic topologies, short-lived links and intermittent connectivity between vehicles. Recently, information-centric networking (ICN) has been proposed to VANET scenarios for improving content delivery of infotainmentapplications. However, ICN in VANETs suffers from the Interest transmission broadcast problem, which results in a waste of resources and diminishes the performance of VANETs' applications. In this paper, we propose the link stability-based Interest forwarding for content request (LISIC) protocol, in order to tackle the Interest broadcast storm problem during a content search in information-centric VANETs. The proposed protocol controls Interesttransmission by prioritizing neighboring vehicles with more stable links with the current sender. Simulation results show that the proposed protocol improves the content delivery rate by 40% while decreases the Interest packet transmissions by 26%, in scenario of a low number of content producers in the network.

Underwater wireless sensor networks (UWSNs) emerge as an enabling technology for the monitoring of vast areas of aquatic environments. This technology will pave the way for future large-scale applications of ocean monitoring, which will help to change the worryingly current reality where oceans are completely unknown. However, due to the harsh nature of aquatic environments and the underwater wireless communication features, efficient data collection in UWSN is still a daunting task. This tutorial will provide a comprehensive review of the research challenges and potential approaches for efficient data collection in UWSNs. It will highlight the characteristics of UWSNs and of the underwater acoustic channel, which diminish the performance of networking protocols. It will analyze the benefits of geographic and opportunistic routing for reliable data delivery and the potentials of duty-cycling for energy conservation in UWSN. Finally, based on an in-deep literature review, this tutorial will provide useful insights for the further design of networking protocols for data routing in UWSNs.

Recently, information-centric networking has been proposed to VANETs scenarios for improving content delivery of infotainment applications. Using the ICN paradigm, content-oriented search and in-network caching have the potential to improve content delivery in spatial- and time-dependent applications for VANETs and smart transportation. However, uncontrolled Interest packet transmissions for content search will result in a waste of resources and diminish the performance of VANETs' applications. In this paper, we propose a lightweight protocol to tackle the Interest broadcast storm problem during a content search in information-centric VANETs. The proposed protocol considers the distance between a current forwarder and its neighboring vehicles to opportunistically control redundant Interest packet transmissions in vehicular named data networking. Simulation results show that the proposed protocol improves the content delivery rate by 60% while decreases the Interest packet transmissions by 40%, in the scenario of a low number of content producers in the network.

Lately, there has been a growing interest in connecting opportunistic routing (OR) and low duty-cycling methodologies in underwater sensor network (UWSNs) applications. This connection improves the data collection reliability and prolongs the network lifetime. When sensor nodes operate in a duty-cycling manner, the properly sleep interval selection and its on-the-fly adjustment should be addressed. Both tasks are challenging when opportunistic routing protocols are used at the network layer, as they should consider the presence of the next-hop candidates set. In this paper, we propose a modeling framework to evaluate the effects of the sleep interval on the energy consumption of duty-cycled UWSNs, which employ opportunistic routing protocol at the network layer. We investigate the sleep interval control problem in the OR scenarios, formulating it as an optimization problem with the goal of extending the network lifetime. Our simulation results show that different fixed sleep interval duty-cycles do not impact on the average energy consumption whereas the sleep interval control can prolong the UWSN lifetime.

In underwater sensor networks, the design of energy efficient and reliable data collection protocols is a daunting challenge. In this context, topology control and opportunistic routing are promising techniques for improving reliability and conserve energy. However, due to the challenges of the underwater acoustic channel, the vast knowledge acquired and the solution proposed so far in the context of terrestrial wireless ad hoc sensor networks cannot be applied directly to underwater acoustic sensor networks. In this work, we shed light on network topology modeling from a routing viewpoint. We model the probabilistic multipath routing behavior driven by opportunistic routing protocols in underwater sensor networks. Afterward, we propose the PCen centrality metric to measure the importance of underwater sensor nodes to the data delivery task through opportunistic routing protocols. PCen is aimed to identify critical nodes that can be used to guide topology control solutions. Our simulation results consider different network densities and reveal the presence of a few number of nodes with high PCen centrality value that will have a high rate of carried traffic, being critical for the network performance.

Underwater wireless sensor networks (UWSNs) have been showed as a promising technology to monitor and explore the oceans in lieu of traditional undersea wireline instruments. Nevertheless, the data gathering of UWSNs is still severely limited because of the acoustic channel communication characteristics. One way to improve the data collection in UWSNs is through the design of routing protocols considering the unique characteristics of the underwater acoustic communication and the highly dynamic network topology. In this paper, we propose the GEDAR routing protocol for UWSNs. GEDAR is an anycast, geographic and opportunistic routing protocol that routes data packets from sensor nodes to multiple sonobuoys (sinks) at the sea's surface. When the node is in a communication void region, GEDAR switches to the recovery mode procedure which is based on topology control through the depth adjustment of the void nodes, instead of the traditional approaches using control messages to discover and maintain routing paths along void regions. Simulation results show that GEDAR significantly improves the network performance when compared with the baseline solutions, even in hard and difficult mobile scenarios of very sparse and very dense networks and for high network traffic loads.

The problem of reliable data delivery at low energy cost arises as one of the most challenging research topics in underwater wireless sensor networks (UWSNs). Reliable data delivery is demanding because of the acoustic channel impairments and channel fading. Moreover, it is energy hungry given the high cost of acoustic communication. In wireless ad hoc & sensor networks, separately, opportunistic routing has been employed to improve data delivery whereas duty cycled operation mode has been adopted to achieve energy efficiency and prolonging network lifetime. In this paper, we investigate the benefits and drawbacks of collision between opportunistic routing paradigm and duty cycle techniques in UWSNs. We propose an analytical model to study and evaluate the performance of opportunistic routing protocols under duty cycled settings designed from three mainly paradigms: simple asynchronous, strobed preamble and receiver initiated; and different network densities and traffic loads. The results show that while duty cycle reduces the energy consumption, it affects negatively in the opportunistic routing performance, increasing the delay and the expected number of transmissions to deliver a packet. The simple duty cycled approach is shown to be suitable for applications that require long-lived network and can tolerate some degree of packet losses. Our results indicate that strobed preamble-based duty cycle is the most effective approach to be integrated with opportunistic routing, when high fidelity monitoring is required, even having not the best performance in terms of energy savings.

Efficient protocols for data packet delivery in mobile underwater sensor networks (UWSNs) are crucial to the effective use of this new powerful technology for monitoring lakes, rivers, seas, and oceans. However, communication in UWSNs is a challenging task because of the characteristics of the acoustic channel. In this work, we present a feasible solution for improving the data packet delivery ratio in mobile UWSN. The GEographic and opportunistic routing with Depth Adjustment-based topology control for communication Recovery (GEDAR) over void regions uses the greedy opportunistic forwarding to route packets and to move void nodes to new depths to adjust the topology. Simulation results shown that GEDAR outperforms the baseline solutions in terms of packet delivery ratio, latency and energy per message.

Local maximum problem, where the node fails in determine the next-hop neighbor to continue forwarding the packet towards the destination, severely degrades the performance of geographic routing protocols. This degradation is more substantial in underwater sensor networks, that intrinsically have harsh environments and high energy consumption due to the underwater acoustic communication characteristics. In this work, we focus on the performance of the most commonly three methodologies used in design of local maximum recovery procedures of geographic routing protocols for underwater sensor networks: power control, bypassing void regions, and mobility controlled. Taking into consideration the underwater acoustic communication characteristics, we further develop a network energy consumption model for representative solutions of local maximum recovery procedure designed from these methodologies and then we evaluate their performance in terms of the improvements on routing task and the energy consumption. Simulation results show that all three methodologies improve the routing even in hard scenarios of lower network density whereas particularities of each methodology impacts on the network energy consumption.

Energy efficient and reliable communication are two very important and conflicting requirements in the design of large-scale, self-organizing wireless sensor networks (WSNs). By reducing the transmission power level of the nodes, energy conservation is achieved whereas the communication reliability is degraded. We propose a novel opportunistic routing protocol to reduce the energy consumption while keep the communication reliability in acceptable levels. Transmission power Control-based Opportunistic Routing (TCOR) saves energy by reducing the transmission power of the nodes while maintains the communication reliability by employing the opportunistic forwarding paradigm, leveraging the broadcast nature of wireless transmission medium. We propose an expected energy cost function for next-hop forwarder set selection, which considers the multiple available transmission power levels and the impact of each one on the next-hop packet reception probability.

Underwater sensor networks have recently been proposed as a way to observe and explore the lakes, rives, seas, and oceans. However, due to characteristics of the acoustic medium, efficient protocols for delivering data must exist. In this work, we propose a novel geographic routing protocol with network topology control for underwater sensor networks, that adjusts the depth of the nodes in order to organize the network topology for improving the network connectivity and forward data where the greedy geographic routing fail. The proposed protocol is the first geographic routing protocol for underwater sensor networks that considers the sensor node vertical movement ability to move it for topology control purpose. The simulation results show that, with the topology control, the fraction of disconnected nodes and nodes located into communication void regions, are drastically reduced and consequently the delivered data rate is improved. It achieves more than 90% of data delivered even in hard and difficult scenarios of very sparse or very dense networks.

Underwater sensor networks have recently been proposed as a way to observe and to explore the lakes, rivers, seas, and oceans. A challenging issue in these networks is the communication, mainly due to the impairments of the acoustic transmission. Thus, efficient mechanisms to improve the data delivery must be proposed. In this work we present a novel anycast greedy geographic forwarding protocol and two topology control mechanisms. The proposed geo-routing protocol considers the anycast network architecture in the data forwarding process. The proposed centralized topology control (CTC) and distributed topology control (DTC) mechanisms organize the network via depth adjustment of some nodes. The simulation results show that with these mechanisms, the data packet delivery ratio achieves more than 90% even in hard and difficult scenarios of very sparse or very dense networks, the end-to-end delay and energy consumption per delivered packet is reduced.

In this paper, we propose an optimal Joint Call Admission Control (JCAC) in next generation wireless networks, where different radio access technologies (RAT) coexist in a co-located way. Moreover, we study the impact of the different RAT's radius coverage area in the system performance and optimal policy structure. The Semi-Markov Decision Process (SMDP) framework is used for modeling of the JCAC proposed and the value iteration algorithm is used to compute the optimal policy. Numerical results show that variation in the proportionality of the radius of RAT coverage area impact on system performance.