Abstract
The IEEE 802.11s standard specifies a mesh technology to be supported by IEEE 802.11 devices. Among the limitations caused by this heritage, we focus on the low scalability of the current IEEE 802.11s standard mechanisms. As a main contribution to the related research field, this article presents the DHT-based Cluster Routing Protocol (DCRP). DCRP addresses IEEE 802.11s' scalability issues by exploiting both Distributed Hash Tables and Clustering schemes, which have proved to be effective in scaling wireless (e.g., MANETs) and wired (e.g., P2P) networking systems. DCRP was implemented and compared with the HWMP through simulation for different scalability parameters and performance metrics. Simulation results indicate that the proposed scheme improves performance in terms of packet delivery ratio, end-to-end delay, throughput, and overhead.

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The use of a mobile sink in wireless sensor networks has been a game changing to enable the development of smart cities applications. The mobility feature allows more effective data gathering and energy saving in the network, since the sink can be closer to source nodes, which could activate their radios only when the sink approximates. Doing so, more efficient settings can be achieved when configuring and deploying sensor nodes for a myriad of applications. However, this mobility-centric strategy can generate applications scenarios with large delays when sensor networks are monitoring the environment, which may result in considerable data losses in critical applications. To cope with that, this paper proposes an algorithm to dynamically plan the repositioning of a single mobile sink within distributed sensing applications. The algorithm considers dependability requirements associated with network connectivity, operability, and energy consumption, implicitly minimizing the energy-hole problem and connectivity issues. Simulation results are presented to demonstrate how the can be applied to move the sink through the network meeting dependability requirements. © 2022 IEEE.

Abstract
Among the innovative services provided by smart cities initiatives, emergencies management systems have stood out as a mean to prevent the occurrence of disasters in urban areas, detecting emergencies as soon as possible and triggering response actions. For that, such systems may rely on multiple emergencies detection units spread over a city, which will be used to detect abnormal situations and report them for further processing. Although the use of multi-sensors hardware units seems to be reasonable to detect a lot of emergency-related variables such as temperature, humidity, smoke, and toxic gases, cities may have different geographical zones concerning the potential negative impacts (risk) that an emergency may have until it is properly mitigated. Therefore, such risk associated to those zones should guide the deployment of emergencies detection units, but their computation is not straightforward and it may depend on different parameters. In this context, this paper proposes a mathematical model to compute mitigation zones in any city, taking as reference the availability of response centers retrieved from open geospatial databases, notably hospitals, fire departments, and police stations. An algorithm is defined to compute a critical index to each zone, which will be exploited to indicate the proportional number of detection units that should be allocated according to the total number of available units. Initial results for the city of Porto, Portugal, are presented, which are discussed when concerning the construction of practical emergencies management systems. © 2022 IEEE.