Since the inception of IEEE 802.11 wireless local area networks (WLANs) in 1997, wireless networking technologies have tremendously grown in the last few decades. The fundamental IEEE 802.11 physical (PHY) and medium access control (MAC) protocols have continuously been enriched with new technologies to provide the last mile wireless broadband connectivity to end users. Consequently, several new amendments of the basic IEEE 802.11 gradually came up in the forms of IEEE 802.11a, IEEE 802.11b, and IEEE 802.11g. More recently, IEEE 802.11n, IEEE 802.11ac, and IEEE 802.11ad are introduced with enhanced PHY and MAC layers that boost up physical data rates to the order of Gigabit per second. So, these amendments are generally known as high throughput WLANs (HT-WLANs). In HT-WLANs, PHY layer is enhanced with multiple-input multiple-output antenna technologies, channel bonding, short guard intervals, enhanced modulation and coding schemes. The MAC sublayer overhead is reduced by introducing frame aggregation and block acknowledgement technologies. However, several existing studies reveal that, many a time, the aforesaid PHY and MAC enhancements yield negative impact on various upper layer protocols, that is end-to-end transport and application layer protocols. As a consequence, a large number of researchers have focused on improving the coordination among PHY/MAC and upper layer protocols. In this survey, we discuss impact of HT-WLAN PHY and MAC layer enhancements on various transport and application layer protocols. This paper also summarizes several research works that use aforesaid enhancements effectively to boost up data rate of end-to-end protocols. We also point out limitations of the existing researches and list down different open challenges that can be meaningfully explored for the development of the next generation HT-WLAN technologies.

The heterogeneous network (HetNet) is an important concept for next-generation wireless Internet architecture and Internet of Things, where several wireless technologies can coexist, and the users should have the flexibility to select the connectivity based on the environmental condition and application demands. IEEE 802.11 (or WiFi) and IEEE 802.16 (or WiMAX) are the two primary building block technologies for HetNets because of their sustainability, cost effectiveness, well-deployed architectures, and ability to support high-data-rate wireless communications. The existing literature has extensively studied the interoperability between these two technologies, and seamless handover schemes are designed to support WiFi-WiMAX integration in a HetNet environment. However, effective utilization of these two technologies from the end users' point of view is another important research area. We have the following question: When should a user migrate between these two technologies in a HetNet environment? This paper proposes a handover decision mechanism in a WiFi-WiMAX integrated HetNet environment, which supports the “quality of service” and “quality of experience” requirements of the end users. The effectiveness of the proposed scheme is analyzed using simulation results.

Traditional human-in-the-loop-based annotation for time-series data like inertial data often requires access to alternate modalities like video or audio from the environment. These alternate sources provide the necessary information to the human annotator, as the raw numeric data are often too obfuscated even for an expert. However, this traditional approach has many concerns surrounding overall cost, efficiency, storage of additional modalities, time, scalability, and privacy. Interestingly, recent large language models (LLMs) are also trained with vast amounts of publicly available alphanumeric data, which allows them to comprehend and perform well on tasks beyond natural language processing. Naturally, this opens up a potential avenue to explore the opportunities in using these LLMs as virtual annotators where the LLMs will be directly provided the raw sensor data for annotation instead of relying on any alternate modality. Naturally, this could mitigate the problems of the traditional human-in-the-loop approach. Motivated by this observation, we perform a detailed study in this article to assess whether the state-of-the-art (SOTA) LLMs can be used as virtual annotators for labeling time-series physical sensing data. To perform this in a principled manner, we segregate the study into two major phases. In the first phase, we investigate the challenges an LLM like GPT-4 faces in comprehending raw sensor data. Considering the observations from Phase 1, in the next phase, we investigate the possibility of encoding the raw sensor data using SOTA SSL approaches and utilizing the projected time-series data to get annotations from the LLM. Detailed evaluation with four benchmark HAR datasets shows that SSL-based encoding and metric-based guidance allow the LLM to make more reasonable decisions and provide accurate annotations without requiring computationally expensive fine-tuning or sophisticated prompt engineering.

Applications hosted in commercial clouds are typically multi-tier and comprise multiple tightly coupled virtual machines (VMs). Service providers (SPs) cater to the users using VM instances with different configurations and pricing depending on the location of the data center (DC) hosting the VMs. However, selecting VMs to host multi-tier applications is challenging due to the trade-off between cost and quality of service (QoS) depending on the placement of VMs. This paper proposes a multi-cloud broker model called CoMCLOUD to select a sub-optimal VM coalition for multi-tier applications from an SP with minimum coalition pricing and maximum QoS. To strike a trade-off between the cost and QoS, we use an ant-colony-based optimization technique. The overall service selection game is modeled as a first-price sealed-bid auction aimed at maximizing the overall revenue of SPs. Further, as the hosted VMs often face demand spikes, we present a parallel migration strategy to migrate VMs with minimum disruption time. Detailed experiments show that our approach can improve the federation profit up to 23% at the expense of increased latency of approximately 15%, compared to the baselines.

Accurate modeling of network traffic has a wide variety of applications. In this paper, we propose Network Transmission Point Process (NTPP), a probabilistic deep machinery that models the traffic characteristics of hosts on a network and effectively forecasts the network traffic patterns, such as load spikes. Existing stochastic models relied on the network traffic being self-similar in nature, thus failing to account for traffic anomalies. These anomalies, such as short-term traffic bursts, are very prevalent in certain modern-day traffic conditions, e.g. datacenter traffic, thus refuting the assumption of self-similarity. Our model is robust to such anomalies since it effectively leverages the self-exciting nature of the bursty network traffic using a temporal point process model.On seven diverse datasets collected from the fields of cyberdefense exercises (CDX), website access logs, datacenter traffic, and P2P traffic, NTPP offers a substantial performance boost in predicting network traffic characteristics against several baselines, ranging from forecasting the network traffic volume to detecting traffic spikes. We also demonstrate an application of our model to a caching scenario, showing that it can be used to effectively lower the cache miss rate.