Resource Allocation in Wireless Networks with RF Energy Harvesting and Transfer

Radio frequency (RF) energy harvesting and transfer techniques have recently become alternative methods to power the next generation of wireless networks. As this emerging technology enables proactive replenishment of wireless devices, it is advantageous in supporting applications with quality-of-service (QoS) requirement. This article focuses on the resource allocation issues in wireless networks with RF energy harvesting capability, referred to as RF energy harvesting networks (RF-EHNs). First, we present an overview of the RF-EHNs, followed by a review of a variety of issues regarding resource allocation. Then, we present a case study of designing in the receiver operation policy, which is of paramount importance in the RF-EHNs. We focus on QoS support and service differentiation, which have not been addressed by previous literatures. Furthermore, we outline some open research directions.Comment: To appear in IEEE Networ

Vehicle as a Service (VaaS): Leverage Vehicles to Build Service Networks and Capabilities for Smart Cities

Smart cities demand resources for rich immersive sensing, ubiquitous communications, powerful computing, large storage, and high intelligence (SCCSI) to support various kinds of applications, such as public safety, connected and autonomous driving, smart and connected health, and smart living. At the same time, it is widely recognized that vehicles such as autonomous cars, equipped with significantly powerful SCCSI capabilities, will become ubiquitous in future smart cities. By observing the convergence of these two trends, this article advocates the use of vehicles to build a cost-effective service network, called the Vehicle as a Service (VaaS) paradigm, where vehicles empowered with SCCSI capability form a web of mobile servers and communicators to provide SCCSI services in smart cities. Towards this direction, we first examine the potential use cases in smart cities and possible upgrades required for the transition from traditional vehicular ad hoc networks (VANETs) to VaaS. Then, we will introduce the system architecture of the VaaS paradigm and discuss how it can provide SCCSI services in future smart cities, respectively. At last, we identify the open problems of this paradigm and future research directions, including architectural design, service provisioning, incentive design, and security & privacy. We expect that this paper paves the way towards developing a cost-effective and sustainable approach for building smart cities.Comment: 32 pages, 11 figure

From MANET to people-centric networking: Milestones and open research challenges

In this paper, we discuss the state of the art of (mobile) multi-hop ad hoc networking with the aim to present the current status of the research activities and identify the consolidated research areas, with limited research opportunities, and the hot and emerging research areas for which further research is required. We start by briefly discussing the MANET paradigm, and why the research on MANET protocols is now a cold research topic. Then we analyze the active research areas. Specifically, after discussing the wireless-network technologies, we analyze four successful ad hoc networking paradigms, mesh networks, opportunistic networks, vehicular networks, and sensor networks that emerged from the MANET world. We also present an emerging research direction in the multi-hop ad hoc networking field: people centric networking, triggered by the increasing penetration of the smartphones in everyday life, which is generating a people-centric revolution in computing and communications

Reducing Operation Cost of LPWAN Roadside Sensors Using Cross Technology Communication

Low-Power Wide-Area Network (LPWAN) is an emerging communication standard for Internet of Things (IoT) that has strong potential to support connectivity of a large number of roadside sensors with an extremely long communication range. However, the high operation cost to manage such a large-scale roadside sensor network remains as a significant challenge. In this paper, we propose LOC-LPWAN, a novel optimization framework that is designed to reduce the operation cost using the cross technology communication (CTC). LOC-LPWAN allows roadside sensors to offload sensor data to passing vehicles that in turn forward the data to a LPWAN server using CTC aiming to reduce the data subscription cost. LOC-LPWAN finds the optimal communication schedule between sensors and vehicles to maximize the throughput given an available budget of the user. Furthermore, LOC-LPWAN optimizes the fairness among sensors by allowing sensors to transmit similar amounts of data and preventing certain sensors from dominating the opportunity for data transmissions. LOC-LPWAN also provides an option that allows all sensor to transmit data within a specific delay bound. Extensive numerical analysis performed with real-world taxi data consisting of 40 vehicles with 24-hour trajectories demonstrate that LOC-LPWAN improves the throughput by 72.6%, enhances the fairness by 65.7%, and reduces the delay by 28.8% compared with a greedy algorithm given the same budget

Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs

The Next Generation Intelligent Transportation System: Connected, Safe and Green

Modern Intelligent Transportation Systems (ITSs) employ communication technologies in order to ameliorate the passenger's commuting experience. Vehicular Networking lies at the core of inaugurating an efficient transportation system and aims at transforming vehicles into smart mobile entities that are able to sense their surroundings, collect information about the environment and communicate with each other as well as with Roadside Units (RSUs) deployed alongside roadways. As such, the novel communication paradigm of vehicular networking gave birth to an ITS that embraces a wide variety of applications including but not limited to: traffic management, passenger and road safety, environment monitoring and road surveillance, hot-spot guidance, Drive Thru Internet access, remote region connectivity, and so forth. Furthermore, with the rapid development of computation and communication technologies, the Internet of Vehicles (IoV) promises huge commercial interest and research value, thereby attracting a significant industrial and academic attention. This thesis studies and analyses fundamentally challenging problems in the context of vehicular environments and proposes new techniques targeting the improvement of the performance of ITSs envisioned to play a remarkable role in the IoV era. Unlike existing wireless mobile networks, vehicular networks possess unique characteristics, including high node mobility and a rapidly-changing topology, which should be carefully accounted for. Four major problems from the pool of existing vehicular networking persisting challenges will be addressed in this thesis, namely: a) establishing a connectivity path in a highly dynamic Vehicular Ad Hoc Network, b) examining the performance of Vehicle-to-Infrastructure communication Medium Access Control schemes, c) addressing the scheduling problem of a vehicular networking scenario encompassing an energy-limited RSU by exploiting machine learning techniques, particularly reinforcement learning, to train an agent to make appropriate decisions and develop a scheduling policy that prolongs the network's operational status and allows for acceptable Quality-of-Service levels and d) overcoming the limitations of reinforcement learning techniques in high-dimensional input scenarios by exploiting recent advances in deep learning in an effort to satisfy the driver's well-being as well as his demand for continuous connectivity in a green, balanced, connected and efficient vehicular network. These problems will be extensively studied throughout this thesis, followed by discussions that highlight open research directions worth further investigations

SEE-TREND: SEcurE Traffic-Related EveNt Detection in Smart Communities

It has been widely recognized that one of the critical services provided by Smart Cities and Smart Communities is Smart Mobility. This paper lays the theoretical foundations of SEE-TREND, a system for Secure Early Traffic-Related EveNt Detection in Smart Cities and Smart Communities. SEE-TREND promotes Smart Mobility by implementing an anonymous, probabilistic collection of traffic-related data from passing vehicles. The collected data are then aggregated and used by its inference engine to build beliefs about the state of the traffic, to detect traffic trends, and to disseminate relevant traffic-related information along the roadway to help the driving public make informed decisions about their travel plans, thereby preventing congestion altogether or mitigating its nefarious effects