Cooperation among Wirelessly Connected Static and Mobile Sensor Nodes for Surveillance Applications

Freitas, Edison Pignaton de; Heimfarth, Tales; Vinel, Alexey; Wagner, Flávio Rech; Pereira, Carlos Eduardo; Larsson, Tony

doi:10.3390/s131012903

Cited by 32 publications

(26 citation statements)

References 35 publications

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“…Thus, we plan to evaluate our mechanism under real-world scenarios and improve it by learning from the challenges that may arise from the experimental procedure. In the long term, we would like to also investigate solutions that detect arrivals of mobile nodes in a new wireless sensor network, in order to anticipate the interaction of mobile and static sensor nodes is a promising approach that will allow real-world deployments, such as advanced surveillance systems [24].…”

Section: Discussionmentioning

confidence: 99%

Wireless Medium Access Control under Mobility and Bursty Traffic Assumptions in WSNs

et al. 2015

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In Wireless Sensor Networks (WSNs) the nodes can be either static or mobile depending on the requirements of each application. During the design of Medium Access Control (MAC) protocols, mobility may pose many communication challenges. These difficulties require first a link establishment between mobile and static nodes, and then an energy efficient and low delay burst handling mechanism. In this study, we investigate preamble-sampling solutions that allow asynchronous operation. We first introduce anycast transmission to ContikiMAC where a mobile node emits an anycast data packet whose first acknowledging node will serve as responsible to forward it towards the sink. Once this link is established, burst transmission can start, according to the respective burst handling mechanism of ContikiMAC. Although it is considered as negligible in the literature, such an anycast-based on-the-fly operation Georgios Z. Papadopoulos actually results in high packet duplication at the sink. Hence, we demonstrate that even a basic anycast-based MContikiMAC would fail to handle bursty traffic from mobile nodes mainly due to increased unnecessary traffic and channel occupancy. We then propose Mobility-Enhanced ContikiMAC (ME-ContikiMAC), a protocol that reduces packet duplications in the network by more than 90 % comparing to M-ContikiMAC. Moreover, our results in a 48-node scenario show that ME-ContikiMAC outperforms a number of state-of-the-art solutions (including MoX-MAC and MOBINET), by terms of reducing both delay and energy consumption.

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Section: Discussionmentioning

confidence: 99%

Wireless Medium Access Control under Mobility and Bursty Traffic Assumptions in WSNs

et al. 2015

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“…To balance energy consumption, nodes located near the sink trajectory are grouped in small-sized clusters while nodes located farther away are grouped into clusters of larger size ( Figure 2 ). Compared to random mobility and fixed mobility, handling the mobility of sinks in a controlled manner is much more challenging [ 22 ]. In this mode, the MDC needs to continuously analyze and optimize its movement path according to the actual situation of the network.…”

Section: Related Workmentioning

confidence: 99%

A High-Efficiency Data Collection Method Based on Maximum Recharging Benefit in Sensor Networks

Sha

Wang

Zhang

et al. 2018

Sensors

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To reduce time delays during data collection and prolong the network lifetime in Wireless Rechargeable Sensor Networks (WRSNs), a type of high-efficiency data collection method based on Maximum Recharging Benefit (DCMRB) is proposed in this paper. According to the minimum number of the Mobile Data Collectors (MDCs), the network is firstly divided into several regions with the help of the Virtual Scan Line (VSL). Then, the MDCs and the Wireless Charging Vehicles (WCVs) are employed in each region for high efficient data collection and energy replenishment. In order to ensure the integrity of data collection and reduce the rate of packet loss, a speed adjustment scheme for MDC is also proposed. In addition, by calculating the adaptive threshold of the recharging request, those nodes with different energy consumption rates are recharged in a timely way that avoids their premature death. Finally, the limited battery capacity of WCVs and their energy consumption while moving are also taken into account, and an adaptive recharging scheme based on maximum benefit is proposed. Experimental results show that the energy consumption is effectively balanced in DCMRB. Furthermore, this can not only enhance the efficiency of data collection, but also prolong the network lifetime compared with the Energy Starvation Avoidance Online Charging scheme (ESAOC), Greedy Mobile Scheme based on Maximum Recharging Benefit (GMS-MRB) and First-Come First-Served (FCFS) methods.

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“…In particular, unmanned aerial vehicles (UAVs) can act as flying base stations to enhance the coverage and rate performance of wireless networks in different scenarios such as temporary hotspots and emergency situations [1]. In addition, mobile UAVs can establish efficient communications with ground sensors for alarm messages delivery scenarios [2]. Indeed, using UAVs as aerial base stations provides several advantages compared to the terrestrial base stations.…”

Section: Introductionmentioning

confidence: 99%

Efficient Deployment of Multiple Unmanned Aerial Vehicles for Optimal Wireless Coverage

et al. 2016

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In this paper, the efficient deployment of multiple unmanned aerial vehicles (UAVs) with directional antennas acting as wireless base stations that provide coverage for ground users is analyzed. First, the downlink coverage probability for UAVs as a function of the altitude and the antenna gain is derived. Next, using circle packing theory, the three-dimensional locations of the UAVs is determined in a way that the total coverage area is maximized while maximizing the coverage lifetime of the UAVs. Our results show that, in order to mitigate interference, the altitude of the UAVs must be properly adjusted based on the beamwidth of the directional antenna as well as coverage requirements. Furthermore, the minimum number of UAVs needed to guarantee a target coverage probability for a given geographical area is determined. Numerical results evaluate the various tradeoffs involved in various UAV deployment scenarios.

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Cooperation among Wirelessly Connected Static and Mobile Sensor Nodes for Surveillance Applications

Cited by 32 publications

References 35 publications

Wireless Medium Access Control under Mobility and Bursty Traffic Assumptions in WSNs

Wireless Medium Access Control under Mobility and Bursty Traffic Assumptions in WSNs

A High-Efficiency Data Collection Method Based on Maximum Recharging Benefit in Sensor Networks

Efficient Deployment of Multiple Unmanned Aerial Vehicles for Optimal Wireless Coverage

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