In last two decades, various monitoring systems have been designed and deployed in urban environments, toward the realization of the so called smart cities. Such systems are based on both dedicated sensor nodes, and ubiquitous but not dedicated devices such as smart phones and vehicles’ sensors. When we design sensor network monitoring systems for smart cities, we have two essential problems: node deployment and sensing management. These design problems are challenging, due to large urban areas to monitor, constrained locations for deployments, and heterogeneous type of sensing devices. There is a vast body of literature from different disciplines that have addressed these challenges. However, we do not have yet a comprehensive understanding and sound design guidelines. This paper addresses such a research gap and provides an overview of the theoretical problems we face, and what possible approaches we may use to solve these problems. Specifically, this paper focuses on the problems on both the deployment of the devices (which is the system design/configuration part) and the sensing management of the devices (which is the system running part). We also discuss how to choose the existing algorithms in different type of monitoring applications in smart cities, such as structural health monitoring, water pipeline networks, traffic monitoring. We finally discuss future research opportunities and open challenges for smart city monitoring.

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The Sensable City: A Survey on the Deployment and Management for Smart City Monitoring

One of the major problems in wireless multihop networks is the scheduling of transmissions in a fair and efficient manner Time Division Multiple Access (TDMA) seems to be one of the dominant solutions to achieve this goal since it is a simple scheme and can prolong the devices’ lifetime by allowing them to transmit only a portion of the time during conversation For that reason, several TDMA scheduling algorithms may be found in the literature The scope of this article is to classify the existing TDMA scheduling algorithms based on several factors, such as the entity that is scheduled, the network topology information that is needed to produce or maintain the schedule, and the entity or entities that perform the computation that produces and maintains the schedules, and to discuss the advantages and disadvantages of each category

A Survey of TDMA Scheduling Schemes in Wireless Multihop Networks

Vehicular infotainment systems have attracted great interests from both academia and industry. A vehicular infotainment system includes two parts: 1) the information part and 2) the entertainment part, which are integrated together to provide a unified platform to drivers and passengers. Although some excellent works have been done on vehicular infotainment systems, there are still some significant challenges that need to be addressed. Recently, compressed sensing (CS) has emerged as a new technique, which could be used to tackle the challenges in vehicular infotainment systems. In this paper, we present a comprehensive survey and research challenges on the applications of CS in vehicular infotainment systems, including object tracking for driving safety, privacy protection and security, vehicular crowdsensing, vehicular communications, road traffic estimation, video streaming, and object recognition. In addition, we compare the traditional methods and CS methods in solving the main problems in vehicular infotainment systems. Moreover, research challenges of applying CS in vehicular infotainment systems are discussed.

A Survey on Compressed Sensing in Vehicular Infotainment Systems

This paper deals with city-wide air quality estimation with limited air quality monitoring stations which are geographically sparse. Since air pollution is influenced by urban dynamics (e.g., meteorology and traffic) which are available throughout the city, we can infer the air quality in regions without monitoring stations based on such spatial-temporal (ST) heterogeneous urban big data. However, big data-enabled estimation poses three challenges. The first challenge is data diversity, i.e., there are many different categories of urban data, some of which may be useless for the estimation. To overcome this, we extend Granger causality to the ST space to analyze all the causality relations in a consistent manner. The second challenge is the computational complexity due to processing the massive volume of data. To overcome this, we introduce the non-causality test to rule out urban dynamics that do not “Granger” cause air pollution, and the region of influence (ROI), which enables us to only analyze data with the highest causality levels. The third challenge is to adapt our grid-based algorithm to non-grid-based applications. By developing a flexible grid-based estimation algorithm, we can decrease the inaccuracies due to grid-based algorithm while maintaining computation efficiency.

An Extended Spatio-Temporal Granger Causality Model for Air Quality Estimation with Heterogeneous Urban Big Data

A deep learning and image-based model for air quality estimation.

Vehicular sensor network (VSN) using vehicle-based sensors is an emerging technology that can provide an inexpensive solution for surveillance and urban monitoring applications. For the constantly moving vehicles, resulting in unpredictable network topology, data transmission in VSN is vulnerable to packet losses, thus deteriorating the surveillance quality. To resolve this problem, a cooperative data sampling and compression approach is proposed. Based on compressive sensing, this approach does not require inter-sensor communication and adopts sparse random projections to remove redundancy in spatially neighbouring measurements. It is experimentally shown that the proposed algorithm provides fairly accurate reconstruction of the field under surveillance, and incurs much less communication traffic load compared to conventional sampling strategies. Practical data sets, including the temperature distribution in Beijing and the global position system (GPS) tracking data of over 6000 taxis in the city, are used in our experiments to verify the reconstruction accuracy and energy efficiency of the scheme. Different vehicular mobility models are also employed to study the impact of movement behavior. Simulation results show that our proposed approach outperforms the conventional sampling and interpolation strategy, which propagates data in uncompressed format, by 5 dB in reconstruction quality and by 50% in communication complexity reduction for the same sampling rate.

Efficient sampling and compressive sensing for urban monitoring vehicular sensor networks

Recent advances in the physical layer have enabled wireless devices to have multiple packet reception (MPR) capability, which is the capability of decoding more than one packet, simultaneously, when concurrent transmissions occur. In this paper, we focus on the interaction between the MPR physical layer and the medium access control (MAC) layer. Some random access MAC protocols have been proposed to improve the network performance by exploiting the powerful MPR capability. However, there are very few investigations on the schedule-based MAC protocols. We propose a novel m-MPR-l-code topology-transparent scheduling ((m, l)-TTS) algorithm for mobile ad hoc networks with MPR, where m indicates the maximum number of concurrent transmissions being decoded, and l is the number of codes assigned to each user. Our algorithm can take full advantage of the MPR capability to improve the network performance. The minimum guaranteed throughput and average throughput of our algorithm are studied analytically. The improvement of our (m, l)-TTS algorithm over the conventional topology-transparent scheduling algorithms with the collision-based reception model is linear with m. The simulation results show that our proposed algorithm performs better than slotted ALOHA as well.

Topology-Transparent Scheduling in Mobile Ad Hoc Networks With Multiple Packet Reception Capability

Broadcasting is a key function in mobile ad hoc networks. Topology-dependent scheduling algorithms depend on the detailed network connectivity information and cannot adapt to dynamic topological changes in mobile networks. To overcome this limitation, topology-transparent broadcast scheduling algorithms have been proposed. Due to the large overhead of implement- ing the acknowledgement mechanism in broadcast communica- tions and to guarantee that there is at least one collision-free transmission in each frame, most existing topology-transparent broadcast scheduling algorithms require a node to transmit the same packet repeatedly at multiple slots during one frame. This is very inefficient. In this paper, we propose an efficient topology- transparent broadcast scheduling algorithm. First, instead of transmitting the same packet repeatedly during one frame, each node collects transmission codes of its two-hop neighbors and transmits several different packets during one frame. Second, noting that many unassigned slots are not utilized, we propose several methods to utilize the unassigned slots efficiently in a collision-free and traffic-adaptive manner. Thus, our algorithm provides a guaranteed throughput and achieves a much better average throughput. The analytical and simulation results show that our proposed algorithm outperforms other existing topology- transparent broadcast algorithms dramatically.

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Performance Improvement of Topology-Transparent Broadcast Scheduling in Mobile Ad Hoc Networks

Transmission scheduling is a key problem in mobile ad hoc networks. Many transmission scheduling algorithms have been proposed to maximize the spatial reuse and minimize the time-division multiple-access (TDMA) frame length in mobile ad hoc networks. Most algorithms are dependent on the exact network topology and cannot adapt to the dynamic topology in a mobile wireless network. To overcome this limitation, several topology-transparent scheduling algorithms have been proposed. The slots are assigned to guarantee that there is at least one collision-free time slot in each frame. In this paper, we consider multicast and broadcast, and propose a novel topology-transparent distributed scheduling algorithm. Instead of guaranteeing at least one collision-free transmission, the proposed algorithm guarantees one successful transmission exceeding a given probability, and achieves a much better average throughput. The simulation results show that the performance of our proposed algorithm is much better than the conventional TDMA and other existing algorithms in most cases.

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Topology-Transparent Distributed Multicast and Broadcast Scheduling in Mobile Ad Hoc Networks

Topology-transparent scheduling algorithms are oblivious to the network topology changes and can provide throughput and delay guarantees in mobile multihop networks. However, it has been argued that topology-transparent scheduling algorithms are inefficient when the network is static, compared to topology-dependent scheduling algorithms. In this paper, we propose to utilize both assigned and unassigned slots efficiently to boost the performance of topology-transparent scheduling algorithms. We conclude that, in certain cases, the performance of the proposed topology-transparent scheduling algorithm can be comparable to or better than that of some topology-dependent algorithms even when the network topology remains unchanged. Yet, the proposed algorithm also works even when the network topology is dynamic.

Yiming Liu

Papers

Efficient sampling and compressive sensing for urban monitoring vehicular sensor networks

Topology-Transparent Scheduling in Mobile Ad Hoc Networks With Multiple Packet Reception Capability

Performance Improvement of Topology-Transparent Broadcast Scheduling in Mobile Ad Hoc Networks

Topology-Transparent Distributed Multicast and Broadcast Scheduling in Mobile Ad Hoc Networks

Is Topology-Transparent Scheduling Really Inefficient in Static Multihop Networks?