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With the advent of the Internet of Things (IoT) and 5G networks, edge computing is offering new opportunities for business model and use cases innovations. Service providers can now virtualize the cloud beyond the data center to meet the latency, data sovereignty, reliability, and interoperability requirements. Yet, many new applications (e.g., augmented reality, virtual reality, artificial intelligence) are computation-intensive and delay-sensitivity. These applications are invoked heavily with similar inputs that could lead to the same output. Compute-less networks aim to implement a network with a minimum amount of computation and communication. This can be realized by offloading prevalent services to the edge and thus minimizing communication in the core network and eliminating redundant computations using the computation reuse concept. In this paper, we present matching-based services offloading schemes for compute-less IoT networks. We adopt the matching theory to match service offloading to the appropriate edge server(s). Specifically, we design, WHISTLE, a vertical many-to-many offloading scheme that aims to offload the most invoked and highly reusable services to the appropriate edge servers. We further extend WHISTLE to provide horizontal one-to-many computation reuse sharing among edge servers which leads to bouncing less computation back to the cloud. We evaluate the efficiency and effectiveness of WHISTLE with a real-world dataset. The obtained findings show that WHISTLE is able to accelerate the tasks completion time by 20%, reduce the computation up to 77%, and decrease the communication up to 71%. Theoretical analyses also prove the stability of the designed schemes.

Federated Edge Learning (FEEL) is a promising distributed learning technique that aims to train a shared global model while reducing communication costs and promoting users' privacy. However, the training process might significantly occupy a long time due to the nature of the used data for training, which leads to higher energy consumption and therefore impacts the model convergence. To tackle this issue, we propose a data-driven federated edge learning scheme that tends to select suitable participating nodes based on quality data and energy. First, we design an unsupervised data-aware splitting scheme that partitions the node's local data into diverse samples used for training. We incorporate a similarity index to select quality data that enhances the training performance. Then, we propose a heuristic participating nodes selection scheme to minimize the communication and computation energy consumption, as well as the amount of communication rounds. The obtained results show that the proposed scheme substantially outperforms the vanilla FEEL in terms of energy consumption and the number of communication rounds., Comment: This paper has been accepted for publication by IEEE International Conference on Communications (ICC). The final version will be published by the IEEE

The promising vision of Information-Centric Networking (ICN) and of its realization, Named Data Networking (NDN), has attracted extensive attention in recent years in the context of the Internet of Things (IoT) and Wireless Sensor Networks (WSNs). However, a comprehensive NDN/ICN-based architectural design for WSNs, including specially tailored naming schemes and forwarding mechanisms, has yet to be explored. In this paper, we present single-Channel Cluster-based Information-Centric WSN (CCIC-WSN), an NDN/ICN-based framework to fulfill the requirements of cluster-based WSNs, such as communication between child nodes and cluster heads, association of new child nodes with cluster heads, discovery of the namespace of newly associated nodes, and child node mobility. Through an extensive simulation study, we demonstrate that CCIC-WSN achieves 71-90% lower energy consumption and 74-96% lower data retrieval delays than recently proposed frameworks for NDN/ICN-based WSNs under various evaluation settings., Comment: This paper has been accepted for publication by the IEEE Internet of Things Journal. The copyright is with IEEE and the final version will be published by IEEE

Internet of Things (IoT) and Network Softwarization are fast becoming core technologies of information systems and network management for next generation Internet. The deployment and applications of IoT ranges from smart cities to urban computing, and from ubiquitous healthcare to tactile Internet. For this reason the physical infrastructure of heterogeneous network systems has become more complicated, and thus requires efficient and dynamic solutions for management, configuration, and flow scheduling. Network softwarization in the form of Software Defined Networks (SDN) and Network Function Virtualization (NFV) has been extensively researched for IoT in recent past. In this article we present a systematic and comprehensive review of virtualization techniques explicitly designed for IoT networks. We have classified the literature into software defined networks designed for IoT, function virtualization for IoT networks, and software defined IoT networks. These categories are further divided into works which present architectural, security, and management solutions. In addition, the paper highlights a number of short term and long term research challenges and open issues related to adoption of software defined Internet of things., Initial version. An update is in progress