The sockets API has become the standard way that applications access the transport services offered by the IP stack. This article presents NEAT, a user space library that can provide an alternate transport API. NEAT allows applications to request the service they need using a new design that is agnostic to the specific choice of transport protocol underneath. This not only allows applications to take advantage of common protocol machinery, but also eases introduction of new network mechanisms and transport protocols. The article describes the components of the NEAT library and illustrates the important benefits that can be gained from this new approach. NEAT is a software platform for developing advanced network applications that was designed in accordance with the standardization efforts on transport services in the IETF, but its features exceed the envisioned functionality of a TAPS system.

This paper introduces SATS, an innovative online task scheduling strategy tailored for hierarchical Mobile Edge Computing (MEC) architectures. Leveraging a Simulated Annealing-based approach, SATS extends the applicability of this method beyond offline scheduling, demonstrating its efficacy in real-time task allocation. Crucially, the paper underscores the pivotal role of accurate service request predictions in enhancing SATS’ performance. Through a comparative analysis of three prediction models, neutral, conservative, and optimistic, the paper reveals that SATS achieves optimal outcomes when paired with a conservative predictor, which deliberately overestimates service request volumes. In fact, employing this predictor yields significantly higher acceptance rates and reduced processing times. Remarkably, SATS, coupled with a conservative predictor, achieves an acceptance ratio within a mere 5% deviation from an ideal scenario where the frequency of service request arrivals is known in advance, maintaining consistency across various experimental scenarios.

The Cellular Internet of Things (CIoT), a new paradigm, paves the way for a large-scale deployment of IoT devices. CIoT promises enhanced coverage and massive deployment of low-cost IoT devices with an expected battery life of up to 10 years. However, such a long battery life can only be achieved provided the CIoT device is configured with energy efficiency in mind. This paper conducts a comprehensive survey on energy-saving solutions in 3GPP-based CIoT networks. In comparison to current studies, the contribution of this paper is the classification and an extensive analysis of existing energy-saving solutions for CIoT, e.g., configuration of particular parameter values and software modifications of transport- or radio-layer protocols, while also stressing key parameters impacting the energy consumption such as the frequency of data reporting, discontinuous reception cycles (DRX), and Radio Resource Control (RRC) timers. In addition, we discuss shortcomings, limitations, and possible opportunities which can be investigated in the future to reduce the energy consumption of CIoT devices.

BBR is a promising new congestion control algorithm (CCA) that has been shown to result in significantly lower latency compared to conventional loss-based CCAs. However, in cellular networks, where there is a high variability in the available rate, BBR does not perform as well as expected. In such scenarios, BBR tends to overestimate the available capacity and create queues that cause longer packet delays. In this work, we propose Receiver-driven BBR (RBBR), a modified version of BBR that uses rate estimates made at the receiver side rather than at the sender side.We employ a Kalman filter to make a more accurate estimate of the available bandwidth, and we implement the algorithm in QUIC. An evaluation of the proposed CCA is done through extensive 4G trace-based emulations, real 4G network tests andmmWave trace-based emulations representing a 5G scenario. The results show that RBBR is able to achieve an RTT reduction of up to 80% with a worst-case throughput loss of about 30%. The results also show that in real 4G networks, RBBR flows experience a more predictable and consistent RTT than what BBR flows do.