A Latency and Coverage Optimized Data Collection Scheme for Smart Cities Based on Vehicular Ad-hoc Networks

Abstract

Using mobile vehicles as “data mules” to collect data generated by a huge number of sensing devices that are widely spread across smart city is considered to be an economical and effective way of obtaining data about smart cities. However, currently most research focuses on the feasibility of the proposed methods instead of their final performance. In this paper, a latency and coverage optimized data collection (LCODC) scheme is proposed to collect data on smart cities through opportunistic routing. Compared with other schemes, the efficiency of data collection is improved since the data flow in LCODC scheme consists of not only vehicle to device transmission (V2D), but also vehicle to vehicle transmission (V2V). Besides, through data mining on patterns hidden in the smart city, waste and redundancy in the utilization of public resources are mitigated, leading to the easy implementation of our scheme. In detail, no extra supporting device is needed in the LCODC scheme to facilitate data transmission. A large-scale and real-world dataset on Beijing is used to evaluate the LCODC scheme. Results indicate that with very limited costs, the LCODC scheme enables the average latency to decrease from several hours to around 12 min with respect to schemes where V2V transmission is disabled while the coverage rate is able to reach over 30%.

Improving cellular downlink throughput by multi-hop relay-assisted outband D2D communications

“…Therefore, we could estimate transmission power by using formulas (1) and (6). If the evaluated transmission power is not beyond the maximum transmission power of transmitter, the evaluated transmission power will be adopted.…”

“…Thus, more and more people are involved into an integration of physical, social, and mental space [4][5][6], which leads to an exponential rise in traffic demand and generates a significant burden on current cellular networks [7,8]. The fifth-generation (5G) cellular network, with larger data rate, higher spectrum and energy efficiency, and smaller delay, supports a large number of connected devices and provides flexible wireless interfaces.…”

Learning-based synchronous approach from forwarding nodes to reduce the delay for Industrial Internet of Things

“…In such kind of application scenarios, sensory data are valid only when they reach the sink within the specified time threshold. In particular, alarm information obtained by sensor nodes need to be delivered as soon as possible; otherwise, it will cause great damage and disaster to industrial manufacturing [4,6,24,29,32,36,[38][39][40][41]. Therefore, there are lots of research trying to reduce delay [2].…”

“…Therefore, it is important to quickly send the perceived information to the sink. So it is very important to study how to reduce the delay effectively for IIoTs, edge network, and society network as well as cloud computing [33][34][35], and there are also some related research work [4,6,24,29,32,[36][37][38].…”

Reliability Improved Cooperative Communication over Wireless Sensor Networks

“…The Internet of Things (IoT) [1][2][3][4] uses smart equipment such as sensor-based devices [5][6][7][8][9][10][11], smartphones [12] and vehicle sensor devices [13] to gathering information and give a different method for carrying out complex data-sensing-based tasks to satisfy the significant demands of critical infrastructures [14][15][16][17][18], such as environmental and weather monitoring systems [19][20][21][22][23]. Wireless Sensor Networks (WSNs) has large numbers of sensor nodes, it has huge potential to solve military and civilian domains [24][25][26][27][28][29].…”