Ocean climate connectivity and the future range shifts of marine biodiversity.

Shifting distribution to track suitable climate is a potential strategy for marine species to cope with ocean warming. Yet, the ability of species to successfully reach future climate analogs largely depends on the length of the paths that connect them, and on the exposure of these paths to extreme climates during this transition. Here, we evaluate marine climate connectivity for trajectories between climatic analogs on a global scale. We find that while movement between climatic analogs is more intense in the northern seas of the planet, they require longer trajectories to reach climatic analogs, with high climatic exposure to extreme conditions. On the contrary, the southern seas host areas that have closer climatic analogs, further subjected to a lower exposure to dissimilar climates. These patterns are mirrored in the connectivity properties of the global marine protected areas, highlighting sites which might fail to facilitate connectivity to future climates. Our results suggest that potential shifts between climatic analogs might be subjected to more limitations than those suggested by previous studies, with marine connectivity offering novel insights for the establishment of climate-wise conservation future networks.

An Efficient Probe-Based Routing for Content-Centric Networking

With the development of new technologies and applications, such as the Internet of Things, smart cities, 5G, and edge computing, traditional Internet Protocol-based (IP-based) networks have been exposed as having many problems. Information-Centric Networking (ICN), Named Data Networking (NDN), and Content-Centric Networking (CCN) are therefore proposed as an alternative for future networks. However, unlike IP-based networks, CCN routing is non-deterministic and difficult to optimize due to frequent in-network caching replacement. This paper presents a novel probe-based routing algorithm that explores real-time in-network caching to ensure the routing table storing the optimal paths to the nearest content provider is up to date. Effective probe-selections, Pending Interest Table (PIT) probe, and Forwarding Information Base (FIB) probe are discussed and analyzed by simulation with different performance measurements. Compared with the basic CCN, in terms of qualitative analysis, the additional computational overhead of our approach is O(NCS + Nrt + NFIB ∗ NSPT) and O(NFIB) on processing interest packets and data packets, respectively. However, in terms of quantitative analysis, our approach reduces the number of timeout interests by 6% and the average response time by 0.6 s. Furthermore, although basic CCN and our approach belong to the same Quality of Service (QoS) category, our approach outperforms basic CCN in terms of real values. Additionally, our probe-based approach performs better than RECIF+PIF and EEGPR. Owing to speedup FIB updating by probes, our approach provides more reliable interest packet routing when accounting for router failures. In summary, the results demonstrate that compared to basic CCN, our probe-based routing approach raises FIB accuracy and reduces network congestion and response time, resulting in efficient routing.

A Comparative Study of Services Placement Algorithms in Future Networks: دراسة مقارنة لخوارزميّات توضيع الخدمات في الشّبكات المستقبليّة

The study aimed to analyze and compare several algorithms in the context of networks services placement, and then proposed a self-organized dynamic heuristic algorithm adaptable to continually changing network conditions in order to achieve the ideal placement of services replicas in future networks. It is known that future networks demand a high degree of self-organization to keep pace with ongoing changes while maintaining performance optimized. One of the important challenges in this context is the services placement problem. Service placement issue refers to the selection of the most appropriate network node for hosting a service. The ideal placement of services replicas reduces the cost of serving customers, improves connectivity between clients and servers as well as the use of available resources. The study summarized the results of qualitative comparison between several placement algorithms and refers to the most important requirements to be taken into account when implementing the placement algorithm. Generally, each service has its own placement technique, and the action taken by a specific service may affect other services decisions and force them to adapt. There is an urgent need to a management service for managing services replicas to make the optimal placement decision. This service should work in a distributed manner and does not require comprehensive knowledge about the network. It is also characterized by its ability to adapt to changing network conditions in terms of load and topology. Other services coordinate with the management service about replicating or migrating actions, thus services will be offered at a minimized cost.

Edge Computing in IoT: A 6G Perspective

Edge computing is one of the key driving forces to enable Beyond 5G (B5G) and 6G networks. Due to the unprecedented increase in traffic volumes and computation demands of future networks, Multi-access Edge Computing (MEC) is considered as a promising solution to provide cloud-computing capabilities within the radio access network (RAN) closer to the end users. There has been a huge amount of research on MEC and its potential applications; however, very little has been said about the key factors of MEC deployment to meet the diverse demands of future applications. In this article, we present key considerations for edge deployments in B5G/6G networks including edge architecture, server location and capacity, user density, security etc. We further provide state-of-the-art edge-centric services in future B5G/6G networks. Lastly, we present some interesting insights and open research problems in edge computing for 6G networks.

Edge Computing in IoT: A 6G Perspective

Edge computing is one of the key driving forces to enable Beyond 5G (B5G) and 6G networks. Due to the unprecedented increase in traffic volumes and computation demands of future networks, Multi-access Edge Computing (MEC) is considered as a promising solution to provide cloud-computing capabilities within the radio access network (RAN) closer to the end users. There has been a huge amount of research on MEC and its potential applications; however, very little has been said about the key factors of MEC deployment to meet the diverse demands of future applications. In this article, we present key considerations for edge deployments in B5G/6G networks including edge architecture, server location and capacity, user density, security etc. We further provide state-of-the-art edge-centric services in future B5G/6G networks. Lastly, we present some interesting insights and open research problems in edge computing for 6G networks.