Wireless sensor networks are increasingly being deployed in areas where several types of information need to be harvested. Monitoring a given area is one of the main goals of this technology. This consists in deploying sensor nodes in the Area of Interest (AoI) in order to detect any event occurring in this area, collect information and send them to the base station. However, in this type of configuration, the quantity and the quality of data collected are important factors in making better decisions by the end user. It therefore becomes crucial to deploy sensors in the AoI so that the latters can cover as much as possible the AoI, and propose mechanism to collect and send data to the base station while minimizing the energy consumption of the sensors. In this paper, we bring into focus a solution (A2CDC) to resolve this problem which performs in two main stages: in the first stage, we propose an algorithm that guarantees a maximal coverage of the AoI after a random deployment of static sensors and mobile sensors; and in the second stage, we propose a node activity scheduling that minimizes energy consumption of both static and mobile nodes while sending collected data to the base station. Compared to many other algorithms in the literature, our solution is better in term of coverage percentage of the AoI, data received by the base station and in term of energy minimization.

Edge computing (EC) environments are increasingly essential in ensuring low latency and high throughput for modern applications and in smart cities. Scheduling applications in EC environments should be designed to address challenges such as uneven workload distribution, high latency, and frequent request retransmissions, which are not well addressed by current state-of-the-art solutions. In this work, we propose a smart Kubernetes scheduling solution that embeds a machine learning model into the Kube-scheduler for more effective application deployment. By analyzing application keywords and edge server metrics, such as geographic location, workload, and keyword-based request frequency, the proposed solution dynamically selects the optimal edge server for application deployment. The simulation results depict substantial improvements, including reduced latency, better workload equilibrium, increased achievable throughput, and minimized retransmissions between servers. Compared to existing approaches, the proposed model provides a more robust solution to the complex and dynamic requirements of edge computing environments.

The Internet of Things (IoT) and Edge Computing (EC) are now pervasive. IoT networks are made up of several objects, deployed in an area of interest (AoI), that can communicate with each other and with a remote computing centre for decision-making. EC reduces latency and data congestion by bringing data processing closer to the source. In this paper, we address the problems of network coverage and data collection in IoT-based EC networks. Several solutions exist designed to solve these problems unfortunately, they are either not energy-efficient or do not consider connectivity and they do not cover AoI. The proposed routing mechanisms are often not suited for AoI coverage schemes and lead to poor data routing delay or high packet losses. To address these shortcomings, we propose ATENA, a periodic, lightweight and energy-efficient protocol that aims to improve network coverage based on the two new schemes used to define a few number of objects to be kept awake at each period it also uses an adaptive routing scheme to send the collected data to the computing centre. This protocol is designed to take into account the limited resources of objects and ensures a longer network lifetime. A comparison of ATENA’s simulation results with recent existing protocols shows that it significantly improves network coverage, network lifetime and end-to-end delay to the computing centre.

Efficient emergency response systems are vital for smart cities, facing unique challenges such as those in Chad, where infrastructure limitations compound the need for innovative solutions. The proliferation and maturation of numerous advanced computing technologies that can be used to improve emergency response efficiency is a sign that innovative solutions can actually be designed and implemented. This paper proposes a novel system architecture integrating Field Programmable Gate Arrays (FPGAs), Dijkstra's algorithm, and Edge Computing; the architecture aims to set out the context for the optimization of emergency response by accelerating route planning and resource allocation using FPGA-based computations and decentralized Edge Computing. The key components of this architecture and their main algorithms are described and studied in the paper. Provided analysis clearly outlines possibilities for improved response times and resource allocation, addressing specific challenges faced in Chadian-like contexts.