The study of multi-agent systems such as drone swarms has been intensified due to their cooperative behavior. Nonetheless, automating the control of a swarm is challenging as each drone operates under fluctuating wireless, networking and environment constraints. To tackle these challenges, we consider drone swarms as Networked Control Systems (NCS), where the control of the overall system is done enclosed within a wireless communication network. This is based on a tight interconnection between the networking and computational systems, aiming to efficiently support the basic control functionality, namely data collection and exchanging, decision-making, and the distribution of actuation commands. Based on a literature analysis, we do not find revision papers about design of drone swarms as NCS. In this review, we introduce an overview of how to develop self-organized drone swarms as NCS via the integration of a networking system and a computational system. In this sense, we describe the properties of the proposed components of a drone swarm as an NCS in terms of networking and computational systems. We also analyze their integration to increase the performance of a drone swarm. Finally, we identify a potential design choice, and a set of open research challenges for the integration of network and computing in a drone swarm as an NCS.

Flying Ad Hoc NETworks (FANETs) are expected to have a significant impact in several use-cases, from smart agriculture and cities, to mission critical scenarios. The recent surge in the use of FANETs is motivated by their adaptable and flexible behaviours in different scenarios (e.g. disaster-hit locations) allowing the usage of services that require information from remote locations, such as for assessment of damages, checking for survivors, or providing onsite views to assist rescue teams. While FANETs have been developed to provide such critical services, disseminating data with proper performance faces challenges due to inherent properties of FANETs, namely frequent wireless disconnections, intermittent available nodes, and dynamic topologies, mostly when facing an increasing number of deployed unmanned aerial vehicles. Aiming to tackle these challenges, we propose a new Dynamic Clustering Mechanism with Load-Balancing able to support efficient dissemination of data packets in FANETs while ensuring good reliability and scalability factors. The proposed solution is based on the combination of a new meta-heuristic optimization scheme, known as Political Optimizer, used to perform clustering while addressing limitations caused by topology changes, and a new Shannon entropy function implemented to address cluster fault tolerance and traffic overloads. Simulation results show that by combining our proposed model with standard position-based routing protocols, a higher number of end-to-end transmissions are ensured, while supporting an average packet delivery ratio of 97%, an average end-to-end delay of 0.225 seconds, and an average power consumption 37% lower than other state-of-the-art clustering protocols.

This work evaluates three Fog Computing data placement algorithms via experiments carried out with the iFogSim simulator. The paper describes the three algorithms (Cloud-only, Mapping, Edge-ward) in the context of an Internet of Things scenario, which has been based on an e-Health system with variations in applications and network topology. Results achieved show that edge placement strategies are beneficial to assist cloud computing in lowering latency and cloud energy expenditure.

The Internet-of-Things represent a future trend of the Internet, in which massive numbers of networked objects are assumed to have the capability of sharing data collected through sensing activities. It is envisioned that such objects are not dedicated hardware modules embedded specific machines communicating via proprietary wired or wireless networks to dedicated software applications. On contrary, Internet-of-Things may have a more autonomic deployment, in which objects are autonomous and communicate via existing IP based networks. Due to its distributed nature, there is the need to rely on autonomic networking to improve the efficiency while reducing the operational costs of Internet-of-Things frameworks. This paper aims to provide a study about the best data plane to support Internet-of-Things, and an analysis about the best model to support its distributed autonomic behavior.

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Purpose: The research reviews the implementation of Ghana’s Digital Addressing System. Nearly six years into implementation and without an outward appearance of the meeting of the announced goals of the project, the research sets out to find out the likelihood of success or failure of the project and suggest remedial measures. Relevance: The implementation of digital addressing in Ghana holds and demonstrates promise for benefits relative to transportation, courier services, taxation, property ownership and title, among others. For policy makers, the findings of this work give direct pointers to what needs to be done to keep the concept useful. For researchers, the work has only opened the doors for further academic and technical introspection into digital addressing and its nuances especially in Ghana. Methods: Using a variety of quantitative and qualitative techniques the study assessed the implementation of the policy employing interviews, surveys, workshops and group discussion. It hinged on the Design-Reality Gap (DRG) model to identify the issues of interest with Ghana's digital addressing systems. Findings: We found out that the attempts, although well intended, fall short in several best practice aspects. The research found it difficult to confidently showcase any immediate successes arising out of the implementation of the Ghana Post GPS App and System and proceeds to point out specific areas in need of attention if Ghana’s Digital Addressing efforts are to meet the presumptive goals. Conclusion: The Digital Addressing System might be a partial failure unless action is taken to close design-reality gaps.