When telecommunication infrastructure is damaged by natural disasters, creating a network that can handle voice channels can be vital for search and rescue missions. Unmanned Aerial Vehicles (UAV) equipped with WiFi access points could be rapidly deployed to provide wireless coverage to ground users. This WiFi access network can in turn be used to provide a reliable communication service to be used in search and rescue missions. We formulate a new problem for UAVs optimal deployment which considers not only WiFi coverage but also the mac sublayer (i.e., quality of service). Our goal is to dispatch the minimum number of UAVs for provisioning a WiFi network that enables reliable VoIP communications in disaster scenarios. Among valid solutions, we choose the one that minimizes energy expenditure at the user's WiFi interface card in order to extend ground user's smartphone battery life as much as possible. Solutions are found using well-known heuristics such as K-means clusterization and genetic algorithms. Via numerical results, we show that the IEEE 802.11 standard revision has a decisive impact on the number of UAVs required to cover large areas, and that the user's average energy expenditure (attributable to communications) can be reduced by limiting the maximum altitude for drones or by increasing the VoIP speech quality.
This paper formulates a new problem for the optimal placement of Unmanned Aerial Vehicles (UAVs) geared towards wireless coverage provision for Voice over WiFi (VoWiFi) service to a set of ground users confined in an open area. Our objective function is constrained by coverage and by VoIP speech quality and minimizes the ratio between the number of UAVs deployed and energy efficiency in UAVs, hence providing the layout that requires fewer UAVs per hour of service. Solutions provide the number and position of UAVs to be deployed, and are found using well-known heuristic search methods such as genetic algorithms (used for the initial deployment of UAVs), or particle swarm optimization (used for the periodical update of the positions). We examine two communication services: (a) one bidirectional VoWiFi channel per user; (b) single broadcast VoWiFi channel for announcements. For these services, we study the results obtained for an increasing number of users confined in a small area of 100 m2 as well as in a large area of 10,000 m2. Results show that the drone turnover rate is related to both users’ sparsity and the number of users served by each UAV. For the unicast service, the ratio of UAVs per hour of service tends to increase with user sparsity and the power of radio communication represents 14–16% of the total UAV energy consumption depending on ground user density. In large areas, solutions tend to locate UAVs at higher altitudes seeking increased coverage, which increases energy consumption due to hovering. However, in the VoWiFi broadcast communication service, the traffic is scarce, and solutions are mostly constrained only by coverage. This results in fewer UAVs deployed, less total power consumption (between 20% and 75%), and less sensitivity to the number of served users.
We propose a novel cross-layer scheme to reduce energy consumption in wireless sensor networks composed of IEEE 802.15.4 IoT devices with adjustable transmit power. Our approach is based on the IETF's Routing Protocol for Low power and lossy networks (RPL). Nodes discover neighbors and keep fresh link statistics for each available transmit power level. Using the product of ETX and local transmit power level as a single metric, each node selects both the parent that minimizes the energy for packet transmission along the path to the root and the optimal local transmit power to be used. We have implemented our cross-layer scheme in NG-Contiki using the Z1 mote and two transmit power levels (55mW and 31mW). Simulations of a network of 15 motes show that (on average) 66% of nodes selected the low-power setting in a 25 m × 25 m area. As a result, we obtained an average reduction of 25% of the energy spent on transmission and reception of packets compared to the standard RPL settings where all nodes use the same transmit power level. In large scenarios (e.g., 150 m × 150 m and 40-100 motes), our approach provides better results in dense networks where reducing the transmit power of nodes does not translate into longer paths to the root nor degraded quality of service.
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